EPSRC-NIHR HTC Partnership Award 'Plus': NewMind - Partnership with the MindTech HTC.
Lead Research Organisation:
University of Manchester
Department Name: School of Health Sciences
Abstract
This is a proposal to extend an existing partnership between engineering and physical science (EPS) researchers from a range of universities and the NIHR MindTech Healthcare Technology Cooperative. The aim is to explore the potential for technology to transform the management and treatment of mental health conditions, identifying underpinning EPS research challenges, and working together to address them. Mental health already accounts for 13% of the NHS budget (the highest proportion for any disease area, and growing rapidly) and is a major cause of reduced quality of life. Most care is in the community, but most of the cost is associated with unplanned hospital admissions resulting from inadequate or ineffective care. There is great potential for technology to transform care in the community - improving diagnosis, enabling stratification (identifying different sub-groups), supporting self-care, involving family and friends more effectively, and providing timely prompts and alerts for healthcare professionals. If this potential is to be realised, there are, however, significant EPS challenges to be addressed. We have established a multidisciplinary network (NewMind), involving engineering and physical science researchers, healthcare professionals, users of mental health services, representatives of mental health charities, and industry, which has grown steadily over the past 18 months to a membership of 220. We have also developed a Health Outcomes Framework and Engineering and Physical Science Research Roadmap, to inform future research. We now seek to extend the lifetime of the network, and obtain funding to support exploratory and feasibility projects.
The NewMind network focuses on four broad clinical areas of major societal importance, aligned with the NIHR MindTech HTC agenda: serious mental illness, mood and affective disorder, dementia, and developmental disorders - each with clinical leadership - drawing on mental health expertise in both Nottingham (the NIHR MindTech HTC) and Manchester.
We currently identify four areas of challenging EPS research required to underpin the development of effective technologies for managed self-care of mental health conditions: sensing systems for acquiring rich, 'real-time' longitudinal data; information management methods for integrating and linking heterogeneous information and data; data analytics for extracting predictive models, particularly from temporal data; and human-centric systems methods for the
managed self-care setting, providing collaborative decision-support.
The NewMind network focuses on four broad clinical areas of major societal importance, aligned with the NIHR MindTech HTC agenda: serious mental illness, mood and affective disorder, dementia, and developmental disorders - each with clinical leadership - drawing on mental health expertise in both Nottingham (the NIHR MindTech HTC) and Manchester.
We currently identify four areas of challenging EPS research required to underpin the development of effective technologies for managed self-care of mental health conditions: sensing systems for acquiring rich, 'real-time' longitudinal data; information management methods for integrating and linking heterogeneous information and data; data analytics for extracting predictive models, particularly from temporal data; and human-centric systems methods for the
managed self-care setting, providing collaborative decision-support.
Planned Impact
Academic: engineering and physical science researchers will understand the clinical need and service delivery context of concrete scenarios for technology interventions, allowing them to frame EPS research with the potential for significant healthcare benefit.
Economic: Mental health technology is expected to become a multi-billion pound business over the next few decades. A number of the companies who will engage with the network via the Greater Manchester Connected Health Ecosystem and the NIHR MindTech HTC network already have a strong and growing interest in mental health. The network will give them the opportunity to understand the healthcare need, engage with leading researchers in both EPS and healthcare in a setting focussed on mental health technology, influencing the research agenda and collaborating in projects, providing competitive advantage.
Societal: the burden of mental health care is a major economic and quality of life cost to the community. The ultimate aim of the research the network will spawn is to provide
better, cheaper care.
Economic: Mental health technology is expected to become a multi-billion pound business over the next few decades. A number of the companies who will engage with the network via the Greater Manchester Connected Health Ecosystem and the NIHR MindTech HTC network already have a strong and growing interest in mental health. The network will give them the opportunity to understand the healthcare need, engage with leading researchers in both EPS and healthcare in a setting focussed on mental health technology, influencing the research agenda and collaborating in projects, providing competitive advantage.
Societal: the burden of mental health care is a major economic and quality of life cost to the community. The ultimate aim of the research the network will spawn is to provide
better, cheaper care.
Organisations
- University of Manchester (Collaboration, Lead Research Organisation)
- Anna Freud Centre (Collaboration)
- LOUGHBOROUGH UNIVERSITY (Collaboration)
- YORK ST JOHN UNIVERSITY (Collaboration)
- Nottinghamshire Healthcare NHS Trust (Collaboration)
- University of Sheffield (Collaboration)
- Nottingham Trent University (Collaboration)
- Cambridgeshire Community Services NHS Trust (Collaboration)
- Salford Fibromyalgia Support Group (Collaboration)
- LIVERPOOL JOHN MOORES UNIVERSITY (Collaboration)
- DeepCognito (Collaboration)
- UNIVERSITY OF LIVERPOOL (Collaboration)
- UNIVERSITY OF SOUTHAMPTON (Collaboration)
- University of Bristol (Collaboration)
- London School of Economics and Political Science (University of London) (Collaboration)
- UNIVERSITY OF NOTTINGHAM (Collaboration)
- Lancaster University (Collaboration)
- MANCHESTER UNIVERSITY NHS FOUNDATION TRUST (Collaboration)
- UNIVERSITY OF EAST LONDON (Collaboration)
- OPENCLINICAL CIC (Collaboration)
- Alzheimer's Research UK (Collaboration)
- University of Chester (Collaboration)
- UNIVERSITY OF OXFORD (Collaboration)
- XenZone (Collaboration)
- PENNINE CARE NHS FOUNDATION TRUST (Collaboration)
- UNIVERSITY OF GLASGOW (Collaboration)
Publications
Locke HN
(2020)
Acceptability and usability of smartphone-based brainwave entrainment technology used by individuals with chronic pain in a home setting.
in British journal of pain
Lopez-Diaz K
(2021)
Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients: a proof-of-concept controlled study
in NeuroReport
Patel K
(2020)
Effects of neurofeedback in the management of chronic pain: A systematic review and meta-analysis of clinical trials.
in European journal of pain (London, England)
Arendsen LJ
(2020)
Entraining Alpha Activity Using Visual Stimulation in Patients With Chronic Musculoskeletal Pain: A Feasibility Study.
in Frontiers in neuroscience
Velcescu A
(2019)
Flexible 3D-Printed EEG Electrodes.
in Sensors (Basel, Switzerland)
Vaci N
(2020)
Natural language processing for structuring clinical text data on depression using UK-CRIS.
in Evidence-based mental health
Herring AMR
(2020)
Potential of using visual imagery to revolutionise measurement of emotional health.
in Archives of disease in childhood
| Description | The project developed a Technology for Mental Health Research Roadmap http://www.newmindnetwork.org.uk/research-roadmap/ including: a Health Outcomes Framework that provides a structured approach to describing the purposes of intervention; a summary of EPS Research Challenges, identifyinh key advances that will be necessary to deliver a transformation in mental health care; and an outline Ethical and Responsible Innovation Framework that sets out core principles which should guide researchers working in this field. The project also funded a set of multidisciplinary feasibility studies codeveloped by scientists/engineers, clinicians and service users. These explored new approaches to using technology in preventing, managing and living with mental illness. Details of the feasibility studies can be found at http://www.newmindnetwork.org.uk/newmind-plus-funded-feasibility-studies/. The project funded 17 studies in total. STAGE 1 STUDIES (all ~£15k @ 80% FEC) 'Unlocking the evidence from electronic patient records for smart intervention of mental health disorders - a case study in Alzheimer's Disease' Dr Chi-Hun Kim (Oxford) 'Towards a next generation platform for personalised neuro-therapeutic interventions in chronic pain' Dr Chris Brown (Liverpool) & Dr Alex Casson (Manchester) 'Unobtrusive behavioural monitoring via the interactions of daily living' Prof. John Crowe (Nottingham) 'Managing Mental Health in a school environment' Naomi Mwasambili (Liverpool & Chanua Health) 'AdaptivePlanning and raPid LEarning in mental health' Prof. John Fox (Oxford & Warwick, and OpenClinical CIC) 'Eating disorders in the digital age: Considering the safety measures required to prevent the abuse of healthy eating and fitness applications amongst young people' Dr Roisin McNaney (Lancaster, now Bristol) 'Dynamic well-being visualisation toolbox for digital products' Dr Paula Waddingham (Cambridgeshire Community Services NHS Trust) 'Tangible toys (TATO) with sensors and biofeedback mechanism: explorative work to assess its suitability and feasibility as a tool for treating anxious children' Dr Maki Rooksby (Glasgow) 'Digital biomarkers for dementia research' Francine Jury (Manchester) 'Understanding, Predicting and Preventing Non-Suicidal Self-Injury and thoughts amongst adolescents' Dr Mahsa Honary (Lancaster, now Cambridge) 'Developing early detection methods to assess the risk of pressure ulcers in individuals with mental illness' Prof. Dan Bader (Southampton/MDVSN Plus) 'Assessing the feasibility of using Ambient Conversational Interfaces in Support of Mental Illness' Prof. Simon Harper (Manchester) 'Developing an AI Empathy Agent' Dr Fuschia Sirois (Sheffield) STAGE 2 STUDIES (all ~£45k @ 80% FEC except where noted):- 'Detecting Mental health behaviours from Mobile Interactions: a focus on eating disorder and self-harm' Dr Roisin McNaney (Bristol) 'Unlocking evidence from electronic patient records for smart intervention of mental health disorders; towards systematic extension' Dr Alejo J Nevado-Holgado (Oxford) 'Real-time brain modelling for personalised neuro-therapeutic interventions in chronic pain' Dr Alex Casson (Manchester) 'Building and Testing a Demonstrator System for the AI Empathy Agent' Dr Fuschia Sirois (Sheffield) (£30k) |
| Exploitation Route | Inform funders and policy makers (the roadmap fed into the cross-council mental health agenda and network plus call). The funded feasibility studies have enabled new collaborations and partnerships in a number of different areas, and have provided the initial support and impetus to drive new research and funding submissions e.g. Crowe et al with NIHR Nottingham BRC & NIHR Maudsley BRC to develop an early warning signs system that could be applied to a range of conditions; Mwasamabili presenting at Games For Change in Eindhoven & New York; Bostan (Soton) presenting at European Pressure Ulcer Advisory Panel Meeting (Lyon, Sept 2019); Honary & McNaney presenting at NordCHI 2018 & CHI 2019 (Glasgow) |
| Sectors | Digital/Communication/Information Technologies (including Software),Education,Electronics,Healthcare |
| URL | http://www.newmindnetwork.org.uk/ |
| Description | The network has grown to over 465 members (62 NHS/clinical, 263 non-clinical researchers, 39 mental health service-users, 27 charity/patient representative, 59 industry, 14 funder/support) from 137 organisations. It has exposed researchers to new problems and has created a community of practice. The roadmap developed by the community has had impact beyond academia - it has, for instance, been used as input to the Cross-Council Mental Health initiative, and through that route may lead to targeted funding in the area of technology for mental health. It funded 13 stage 1 feasibility studies and 4 larger follow-on studies co-developed by researchers, practitioners and service users, creating new partnerships many of which have persisted beyond the funded activity. |
| First Year Of Impact | 2017 |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare |
| Impact Types | Policy & public services |
| Description | Membership of UKRI Cross Council Mental Health Networks Governance Group |
| Geographic Reach | National |
| Policy Influence Type | Membership of a guideline committee |
| URL | https://www.ukri.org/research/themes-and-programmes/mental-health-networks/ |
| Description | Feasibility Study (Stage 1) - AdaptivePlanning and raPid LEarning in mental health |
| Organisation | OpenClinical CIC |
| Sector | Public |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Clinical Need - Qualitative analyses have shown that current mental health treatment plans are not patient-centred or engaging. Service users are only given a paper copy, which becomes outdated, and are unable to access live versions. Paper audit trails prevent inter-agency communication and delay intervention. There is a clear need for an interactive, care planning platform to improve involvement and engagement mental with health care service delivery. Open Clinical - The need for a collaborative care planning tool can be addressed by providing decision support at key points in a patient's journey (diagnosis, treatment etc.) and updating care plans in light of carer or patient decisions. The platform for this will be OpenClinical, an innovative process management infrastructure and decision support platform [6] [7] (www.openclinical.net) which is described in a non-technical overview in Appendix 2. The vision of APPLEmh is to demonstrate: • Decision support, which handles multiple perspectives, including those of the service-user and carer. • Planning and monitoring of care, which responds dynamically to changes and takes the concerns of service users into account. • Human-centred methods for empowering service users through collaborative and accountable care and decision-making. The feasibility phase of the project will focus on establishing medical, functional and technical requirements for achieving clinical strength services. Unmet need - lack of patient ownership and collaboration in treatment plans. Lack of transparency and accountability in decision-making. Plans are inflexible (paper-based) and may not align with recent evidence. Technology vision: (a) Stakeholder-specific services (apps). Stakeholders include clinicians, patients and carers. We envisage the following services for each stakeholder: • Change requests: request treatment plan change; for example, indicate an emerging problem or concern; • Queries - to provide transparency and accountability. For example: what data is currently held? How were decisions made (showing options that were considered along with arguments for and against)? What is the current treatment plan status, and what options for change are available? • Communications between stakeholders: alerts, messages and requests. These might involve patient or carer concerns, automated sensor alerts, or clinician notifications to patients. (b) Rapid learning: we envisage that OpenClinical can be integrated into existing infrastructures for health learning. The process can be envisaged as follows: • Use of OpenClinical decision support and workflow management will generate data, including treatment outcomes, decisions made and plans executed. This will enrich existing population health data, particularly relating to mental health. • Data analysis and machine learning of the enriched data will lead to predictive models, which can be used to update OpenClinical pathways. Research challenges: 1. Semi-automated plan adaptation: Currently, OpenClinical automates execution of pre-existing plans, but does not change them. The plans and options are not automatically generated or revised. The next step is to automate some aspects of plan revision. This might involve the re-organisation of existing tasks (e.g. scheduling) or switching between alternative plans in response to new events, trends or instructions. 2. Multiple perspectives in argumentation: Multiple stakeholders are involved in decision-making; therefore, we need to provide different views of the argumentation which are most relevant to a particular user (e.g. patient emphasis on values and preferences, professional view based on efficacy, risks and evidence). Similarly, the language needs to be adjusted (e.g. lay or clinical) as well as the supporting background knowledge (e.g. links to patient leaflets and web pages, or links to detailed guidelines and research trials). 3. Interoperability and infrastructure: currently, OpenClinical uses data definitions that are not connected with existing medical ontologies or linked data. Such interoperability is necessary for rapid learning as well as for real-time monitoring and alerts. |
| Impact | See website for output report Disciplines - Psychology; Engineering Science; Clinical decison making; Computer Science; Health Services |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - AdaptivePlanning and raPid LEarning in mental health |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Clinical Need - Qualitative analyses have shown that current mental health treatment plans are not patient-centred or engaging. Service users are only given a paper copy, which becomes outdated, and are unable to access live versions. Paper audit trails prevent inter-agency communication and delay intervention. There is a clear need for an interactive, care planning platform to improve involvement and engagement mental with health care service delivery. Open Clinical - The need for a collaborative care planning tool can be addressed by providing decision support at key points in a patient's journey (diagnosis, treatment etc.) and updating care plans in light of carer or patient decisions. The platform for this will be OpenClinical, an innovative process management infrastructure and decision support platform [6] [7] (www.openclinical.net) which is described in a non-technical overview in Appendix 2. The vision of APPLEmh is to demonstrate: • Decision support, which handles multiple perspectives, including those of the service-user and carer. • Planning and monitoring of care, which responds dynamically to changes and takes the concerns of service users into account. • Human-centred methods for empowering service users through collaborative and accountable care and decision-making. The feasibility phase of the project will focus on establishing medical, functional and technical requirements for achieving clinical strength services. Unmet need - lack of patient ownership and collaboration in treatment plans. Lack of transparency and accountability in decision-making. Plans are inflexible (paper-based) and may not align with recent evidence. Technology vision: (a) Stakeholder-specific services (apps). Stakeholders include clinicians, patients and carers. We envisage the following services for each stakeholder: • Change requests: request treatment plan change; for example, indicate an emerging problem or concern; • Queries - to provide transparency and accountability. For example: what data is currently held? How were decisions made (showing options that were considered along with arguments for and against)? What is the current treatment plan status, and what options for change are available? • Communications between stakeholders: alerts, messages and requests. These might involve patient or carer concerns, automated sensor alerts, or clinician notifications to patients. (b) Rapid learning: we envisage that OpenClinical can be integrated into existing infrastructures for health learning. The process can be envisaged as follows: • Use of OpenClinical decision support and workflow management will generate data, including treatment outcomes, decisions made and plans executed. This will enrich existing population health data, particularly relating to mental health. • Data analysis and machine learning of the enriched data will lead to predictive models, which can be used to update OpenClinical pathways. Research challenges: 1. Semi-automated plan adaptation: Currently, OpenClinical automates execution of pre-existing plans, but does not change them. The plans and options are not automatically generated or revised. The next step is to automate some aspects of plan revision. This might involve the re-organisation of existing tasks (e.g. scheduling) or switching between alternative plans in response to new events, trends or instructions. 2. Multiple perspectives in argumentation: Multiple stakeholders are involved in decision-making; therefore, we need to provide different views of the argumentation which are most relevant to a particular user (e.g. patient emphasis on values and preferences, professional view based on efficacy, risks and evidence). Similarly, the language needs to be adjusted (e.g. lay or clinical) as well as the supporting background knowledge (e.g. links to patient leaflets and web pages, or links to detailed guidelines and research trials). 3. Interoperability and infrastructure: currently, OpenClinical uses data definitions that are not connected with existing medical ontologies or linked data. Such interoperability is necessary for rapid learning as well as for real-time monitoring and alerts. |
| Impact | See website for output report Disciplines - Psychology; Engineering Science; Clinical decison making; Computer Science; Health Services |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Assessing the feasibility of using Ambient Conversational Interfaces in Support of Mental Illness |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester led delivery of the study. |
| Collaborator Contribution | ZeroUI systems (those without a screen or visual interface are here and are increasing. Primary among these new interfaces are conversational voice assistants such as Alexa, OK Google, and Siri, and their use is expanding across third-party devices. Prominent characteristics of mental Illness can be a failure to understand reality and hearing voices that others do not. One might, therefore, see how a rise in Ambient Conversational Interfaces (ACI) could become disorientating. One might also see how there may well be benefits in using friendly auditory prompts to make suggestions and offer reassurance which could change behaviour or reduce symptoms. However, work does not yet exist which seeks to understand the problems, suggest solutions, or investigate how we might capitalise on the benefits. The possible problems associated with ACI and their solution are, however, most important to address as without this understanding leveraging the potential benefits will not be possible. This proposal seeks to contribute an understanding of these problems (and acceptable solutions). It proposes co-creation by using three patient workshops which will interact with a 'Wizard-of-Oz' Echo-Dot / Alexa system; whereby different sounds, utterances and vocalisation will be developed and 'tuned' in real-time as the group decides. It will develop an initial platform app for this co-creation activity, each group will be independent and will build on the work of the previous. Low, between group inter-rater reliability, will indicate acceptance. The overall vision is to apply for funding from the Amazon 'Alexa Fund' ($100 million in investments to fuel voice technology innovation) to continue this line of work integrating the ACI prototype into the Alexa Platform Technologies. This prototype will join a programme of work already underway in real-time sensing of user behaviour and real-time sensing of user interests so that tailored nudges can be made via the ACI. We might think of the whole system as a Recommender System for Behaviour Change. |
| Impact | Disciplines - Computer Science; Psychiatry; |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 1) - Developing an AI Empathy Agent |
| Organisation | Nottinghamshire Healthcare NHS Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | Current e-therapies lack empathy; they are often experienced as cold and not personally relevant. This project will test the feasibility of developing an Empathy Agent (EA) that will address these issues, delivering empathic support via smartphones, powered by a Peer Support Community of service users (SUs). The feasibility study will test how SUs respond to and interact with a mock EA across three phases. The first phase will involve generating a bank of supportive responses from the SU community via an online survey. In the second phase, workshops and focus groups will simulate how the EA functions as an intermediary between the SU community and individual SUs using the crowd-sourced response bank. In the third phase, focus group feedback will be analysed to gain an understanding of both the feasibility of crowdsourcing the EA's responses, and the SU experience of the process. User feedback is vital to evaluating the viability of delivering empathic support through the AI. The results will also inform a report that will be shared with key networks and stakeholders in the areas of peer support and mental health technology. This will create new possibilities for collaboration for two major areas of development in mental health The full research project will deliver the Empathy Agent AI (EA), which will provide tailored peer support, based on the 'live' response bank of empathic responses from the feasibility study. The Stage 2 application will follow discussion with other feasibility studies (i.e. September 2018 workshop) to consult on the design of the EA prospectus. If appropriate, potential collaborations with other feasibility studies will be formed to deliver the EA in Stage 2 project. We will also engage with other EPS researchers with appropriate technological expertise to help deliver and test the EA. The Empathy Agent provides a solution to the problem of facilitating empathic support digitally. Current e-therapies are commonly experienced as cold and lacking empathy. The EA can change this. Crucially, the EA will facilitate technologically-driven personalised support, which can be delivered in an empathetic manner and provide peer support. Existing digital peer support technologies do not facilitate tailored peer support involving an artificial intelligence learning the most effective and valued support for individuals. The EA will deliver tailored support, based on service user responses. The effectiveness of peer support in improving mental health is well known. There is considerable scope to offer wide scale access to the benefits of empathic peer support through the EA. The EA will represent consolidated real human empathy, growing smarter and more empathic with each use. As such, it offers significant value for improvements in mental health through providing empathetic support to all service users with digital access. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 1) - Developing an AI Empathy Agent |
| Organisation | University of East London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | Current e-therapies lack empathy; they are often experienced as cold and not personally relevant. This project will test the feasibility of developing an Empathy Agent (EA) that will address these issues, delivering empathic support via smartphones, powered by a Peer Support Community of service users (SUs). The feasibility study will test how SUs respond to and interact with a mock EA across three phases. The first phase will involve generating a bank of supportive responses from the SU community via an online survey. In the second phase, workshops and focus groups will simulate how the EA functions as an intermediary between the SU community and individual SUs using the crowd-sourced response bank. In the third phase, focus group feedback will be analysed to gain an understanding of both the feasibility of crowdsourcing the EA's responses, and the SU experience of the process. User feedback is vital to evaluating the viability of delivering empathic support through the AI. The results will also inform a report that will be shared with key networks and stakeholders in the areas of peer support and mental health technology. This will create new possibilities for collaboration for two major areas of development in mental health The full research project will deliver the Empathy Agent AI (EA), which will provide tailored peer support, based on the 'live' response bank of empathic responses from the feasibility study. The Stage 2 application will follow discussion with other feasibility studies (i.e. September 2018 workshop) to consult on the design of the EA prospectus. If appropriate, potential collaborations with other feasibility studies will be formed to deliver the EA in Stage 2 project. We will also engage with other EPS researchers with appropriate technological expertise to help deliver and test the EA. The Empathy Agent provides a solution to the problem of facilitating empathic support digitally. Current e-therapies are commonly experienced as cold and lacking empathy. The EA can change this. Crucially, the EA will facilitate technologically-driven personalised support, which can be delivered in an empathetic manner and provide peer support. Existing digital peer support technologies do not facilitate tailored peer support involving an artificial intelligence learning the most effective and valued support for individuals. The EA will deliver tailored support, based on service user responses. The effectiveness of peer support in improving mental health is well known. There is considerable scope to offer wide scale access to the benefits of empathic peer support through the EA. The EA will represent consolidated real human empathy, growing smarter and more empathic with each use. As such, it offers significant value for improvements in mental health through providing empathetic support to all service users with digital access. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 1) - Developing an AI Empathy Agent |
| Organisation | University of Liverpool |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | Current e-therapies lack empathy; they are often experienced as cold and not personally relevant. This project will test the feasibility of developing an Empathy Agent (EA) that will address these issues, delivering empathic support via smartphones, powered by a Peer Support Community of service users (SUs). The feasibility study will test how SUs respond to and interact with a mock EA across three phases. The first phase will involve generating a bank of supportive responses from the SU community via an online survey. In the second phase, workshops and focus groups will simulate how the EA functions as an intermediary between the SU community and individual SUs using the crowd-sourced response bank. In the third phase, focus group feedback will be analysed to gain an understanding of both the feasibility of crowdsourcing the EA's responses, and the SU experience of the process. User feedback is vital to evaluating the viability of delivering empathic support through the AI. The results will also inform a report that will be shared with key networks and stakeholders in the areas of peer support and mental health technology. This will create new possibilities for collaboration for two major areas of development in mental health The full research project will deliver the Empathy Agent AI (EA), which will provide tailored peer support, based on the 'live' response bank of empathic responses from the feasibility study. The Stage 2 application will follow discussion with other feasibility studies (i.e. September 2018 workshop) to consult on the design of the EA prospectus. If appropriate, potential collaborations with other feasibility studies will be formed to deliver the EA in Stage 2 project. We will also engage with other EPS researchers with appropriate technological expertise to help deliver and test the EA. The Empathy Agent provides a solution to the problem of facilitating empathic support digitally. Current e-therapies are commonly experienced as cold and lacking empathy. The EA can change this. Crucially, the EA will facilitate technologically-driven personalised support, which can be delivered in an empathetic manner and provide peer support. Existing digital peer support technologies do not facilitate tailored peer support involving an artificial intelligence learning the most effective and valued support for individuals. The EA will deliver tailored support, based on service user responses. The effectiveness of peer support in improving mental health is well known. There is considerable scope to offer wide scale access to the benefits of empathic peer support through the EA. The EA will represent consolidated real human empathy, growing smarter and more empathic with each use. As such, it offers significant value for improvements in mental health through providing empathetic support to all service users with digital access. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 1) - Developing an AI Empathy Agent |
| Organisation | University of Sheffield |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | Current e-therapies lack empathy; they are often experienced as cold and not personally relevant. This project will test the feasibility of developing an Empathy Agent (EA) that will address these issues, delivering empathic support via smartphones, powered by a Peer Support Community of service users (SUs). The feasibility study will test how SUs respond to and interact with a mock EA across three phases. The first phase will involve generating a bank of supportive responses from the SU community via an online survey. In the second phase, workshops and focus groups will simulate how the EA functions as an intermediary between the SU community and individual SUs using the crowd-sourced response bank. In the third phase, focus group feedback will be analysed to gain an understanding of both the feasibility of crowdsourcing the EA's responses, and the SU experience of the process. User feedback is vital to evaluating the viability of delivering empathic support through the AI. The results will also inform a report that will be shared with key networks and stakeholders in the areas of peer support and mental health technology. This will create new possibilities for collaboration for two major areas of development in mental health The full research project will deliver the Empathy Agent AI (EA), which will provide tailored peer support, based on the 'live' response bank of empathic responses from the feasibility study. The Stage 2 application will follow discussion with other feasibility studies (i.e. September 2018 workshop) to consult on the design of the EA prospectus. If appropriate, potential collaborations with other feasibility studies will be formed to deliver the EA in Stage 2 project. We will also engage with other EPS researchers with appropriate technological expertise to help deliver and test the EA. The Empathy Agent provides a solution to the problem of facilitating empathic support digitally. Current e-therapies are commonly experienced as cold and lacking empathy. The EA can change this. Crucially, the EA will facilitate technologically-driven personalised support, which can be delivered in an empathetic manner and provide peer support. Existing digital peer support technologies do not facilitate tailored peer support involving an artificial intelligence learning the most effective and valued support for individuals. The EA will deliver tailored support, based on service user responses. The effectiveness of peer support in improving mental health is well known. There is considerable scope to offer wide scale access to the benefits of empathic peer support through the EA. The EA will represent consolidated real human empathy, growing smarter and more empathic with each use. As such, it offers significant value for improvements in mental health through providing empathetic support to all service users with digital access. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 1) - Developing early detection methods to assess the risk of pressure ulcers in individuals with mental illness |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Pressure ulcers (PU) can develop if an individual spends prolonged periods sitting or lying in one position. Indeed, people with mental health conditions are at high risk, with dementia patients exhibiting a particularly high prevalence of PUs [Jaul et al., 2017]. Causes of this enhanced risk include inadequate repositioning, poor diet and dehydration, medication and communication challenges. Improved strategies to prevent PUs would lead to an improved quality of life in individuals with dementia and a reduced medical burden [Jaul et al., 2017]. This feasibility study aims to develop early screening methods to evaluate PU risk in this vulnerable population. In order to achieve early warning of skin damage this study will adopt an array of measures to establish posture and mobility as well as skin status. These measures will include body worn accelerometer technologies [1], to examine the temporal profile and orientation of an individual's posture [2]. Skin status will be monitored from biomarkers collected in sebum from the skin surface, sampled using a non-invasive technique to measure pro-inflammatory cytokines [3-5]. This feasibility study will be conducted in two distinct phases to establish the measurement protocol in i) healthy elderly volunteers and ii) in a small cohort of individuals with dementia. Future work will establish an algorithm based on these measures which predicts skin damage risk. This approach offers the potential to improve the clinical management, in particularly related to skin health, of vulnerable individuals with mental illness. Once a robust and feasible protocol for the monitoring of activity and skin health status has been defined, follow-on funding will be sought to determine the full efficacy of this approach to monitor pressure ulcer risk. This will inevitably involve a range of individuals with mental illness that are at high and low risk of pressure ulcers. The ultimate aim would be to establish an algorithm suitable for early indication of pressure ulcers risk in this vulnerable population. There are a number of funding agencies for which this research is appropriate e.g. Alzheimers Society, CYGNUS, Dementia Platforms |
| Impact | Disciplines - Bio-engineering and Tissue Health; Rehabilitation Bioengineering; PPIE |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Developing early detection methods to assess the risk of pressure ulcers in individuals with mental illness |
| Organisation | University of Southampton |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Pressure ulcers (PU) can develop if an individual spends prolonged periods sitting or lying in one position. Indeed, people with mental health conditions are at high risk, with dementia patients exhibiting a particularly high prevalence of PUs [Jaul et al., 2017]. Causes of this enhanced risk include inadequate repositioning, poor diet and dehydration, medication and communication challenges. Improved strategies to prevent PUs would lead to an improved quality of life in individuals with dementia and a reduced medical burden [Jaul et al., 2017]. This feasibility study aims to develop early screening methods to evaluate PU risk in this vulnerable population. In order to achieve early warning of skin damage this study will adopt an array of measures to establish posture and mobility as well as skin status. These measures will include body worn accelerometer technologies [1], to examine the temporal profile and orientation of an individual's posture [2]. Skin status will be monitored from biomarkers collected in sebum from the skin surface, sampled using a non-invasive technique to measure pro-inflammatory cytokines [3-5]. This feasibility study will be conducted in two distinct phases to establish the measurement protocol in i) healthy elderly volunteers and ii) in a small cohort of individuals with dementia. Future work will establish an algorithm based on these measures which predicts skin damage risk. This approach offers the potential to improve the clinical management, in particularly related to skin health, of vulnerable individuals with mental illness. Once a robust and feasible protocol for the monitoring of activity and skin health status has been defined, follow-on funding will be sought to determine the full efficacy of this approach to monitor pressure ulcer risk. This will inevitably involve a range of individuals with mental illness that are at high and low risk of pressure ulcers. The ultimate aim would be to establish an algorithm suitable for early indication of pressure ulcers risk in this vulnerable population. There are a number of funding agencies for which this research is appropriate e.g. Alzheimers Society, CYGNUS, Dementia Platforms |
| Impact | Disciplines - Bio-engineering and Tissue Health; Rehabilitation Bioengineering; PPIE |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Digital biomarkers for dementia research |
| Organisation | Alzheimer's Research UK |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Wearable devices and sensor technology present an unprecedented opportunity to look at real time picture of health, activity and experiences of a dementia patient population. For this population, activity levels, and sleep patterns are more difficult to measure where comorbidities, sleep disorders and mobility problems present causing "sensor noise". Previous work indicates that such devices are acceptable but many people lack the technology or the confidence or willingness to use them. There is also the issue of carer burden and reported versus experienced activity due to a lack of accuracy and tailoring of the sensors to this population. Understanding the targets for the design (cognitive, behavioural or functional changes, as key indicators of living well with dementia) is challenging, but effectively interpreting the resulting signals, should help to determine the impact of an intervention, for example, a new drug or a psychosocial therapy and eventually support personalised mental health intervention decision making. With this funding we aim to understand issues that are important to patients and carers, and health care providers such as admiral nurses, GPs and care home staff when it comes to using digital technology, by addressing some of the significant ethical, regulatory and technological challenges identified. The ability to remain independent in activities of daily living (Functional resilience) is the single most important objective in Alzheimer's disease (AD) care. By closely characterising the determinants and measures of functional decline and recovery, detecting them early, and intervening appropriately, the period of functional independence can be extended. In addition, the potential ability to predict change in behaviors will support care partners to provide timely and effective care. This will bring substantial personal, societal and economic benefit. Existing infrastructures, datasets, and technologies offer the opportunity to address these issues and to develop a new pipeline of remote sensor device-driven systems for data collection, curation, storage, access, and understanding of risk, suitable for clinical care and trials. The immediate goal following on from the feasibility work will be to: • Identify the combination of signals (physical, environmental, self-reported) that may predict change in behaviors or functional abilities • Understanding how to develop the sensitivity and accuracy of the sensors to predict the onset of decline in health of the person living with dementia • Provide the analytical solutions to interpret signals in a meaningful way. • Establish an ethical, fully regulatory compliant digital-assessment/biosignature that integrates smartphones, wearable's, and fixed/home sensors. We anticipate a full research proposal will follow from this to test the device in a clinical trial to provide the scientific evidence for use of the system / device in clinical practice and trials. |
| Impact | Disciplines - Digital Health; Neuroscience and Experimental Psychology; Sensing, Imaging and Signal Processing; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Digital biomarkers for dementia research |
| Organisation | Manchester University NHS Foundation Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Wearable devices and sensor technology present an unprecedented opportunity to look at real time picture of health, activity and experiences of a dementia patient population. For this population, activity levels, and sleep patterns are more difficult to measure where comorbidities, sleep disorders and mobility problems present causing "sensor noise". Previous work indicates that such devices are acceptable but many people lack the technology or the confidence or willingness to use them. There is also the issue of carer burden and reported versus experienced activity due to a lack of accuracy and tailoring of the sensors to this population. Understanding the targets for the design (cognitive, behavioural or functional changes, as key indicators of living well with dementia) is challenging, but effectively interpreting the resulting signals, should help to determine the impact of an intervention, for example, a new drug or a psychosocial therapy and eventually support personalised mental health intervention decision making. With this funding we aim to understand issues that are important to patients and carers, and health care providers such as admiral nurses, GPs and care home staff when it comes to using digital technology, by addressing some of the significant ethical, regulatory and technological challenges identified. The ability to remain independent in activities of daily living (Functional resilience) is the single most important objective in Alzheimer's disease (AD) care. By closely characterising the determinants and measures of functional decline and recovery, detecting them early, and intervening appropriately, the period of functional independence can be extended. In addition, the potential ability to predict change in behaviors will support care partners to provide timely and effective care. This will bring substantial personal, societal and economic benefit. Existing infrastructures, datasets, and technologies offer the opportunity to address these issues and to develop a new pipeline of remote sensor device-driven systems for data collection, curation, storage, access, and understanding of risk, suitable for clinical care and trials. The immediate goal following on from the feasibility work will be to: • Identify the combination of signals (physical, environmental, self-reported) that may predict change in behaviors or functional abilities • Understanding how to develop the sensitivity and accuracy of the sensors to predict the onset of decline in health of the person living with dementia • Provide the analytical solutions to interpret signals in a meaningful way. • Establish an ethical, fully regulatory compliant digital-assessment/biosignature that integrates smartphones, wearable's, and fixed/home sensors. We anticipate a full research proposal will follow from this to test the device in a clinical trial to provide the scientific evidence for use of the system / device in clinical practice and trials. |
| Impact | Disciplines - Digital Health; Neuroscience and Experimental Psychology; Sensing, Imaging and Signal Processing; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Digital biomarkers for dementia research |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Wearable devices and sensor technology present an unprecedented opportunity to look at real time picture of health, activity and experiences of a dementia patient population. For this population, activity levels, and sleep patterns are more difficult to measure where comorbidities, sleep disorders and mobility problems present causing "sensor noise". Previous work indicates that such devices are acceptable but many people lack the technology or the confidence or willingness to use them. There is also the issue of carer burden and reported versus experienced activity due to a lack of accuracy and tailoring of the sensors to this population. Understanding the targets for the design (cognitive, behavioural or functional changes, as key indicators of living well with dementia) is challenging, but effectively interpreting the resulting signals, should help to determine the impact of an intervention, for example, a new drug or a psychosocial therapy and eventually support personalised mental health intervention decision making. With this funding we aim to understand issues that are important to patients and carers, and health care providers such as admiral nurses, GPs and care home staff when it comes to using digital technology, by addressing some of the significant ethical, regulatory and technological challenges identified. The ability to remain independent in activities of daily living (Functional resilience) is the single most important objective in Alzheimer's disease (AD) care. By closely characterising the determinants and measures of functional decline and recovery, detecting them early, and intervening appropriately, the period of functional independence can be extended. In addition, the potential ability to predict change in behaviors will support care partners to provide timely and effective care. This will bring substantial personal, societal and economic benefit. Existing infrastructures, datasets, and technologies offer the opportunity to address these issues and to develop a new pipeline of remote sensor device-driven systems for data collection, curation, storage, access, and understanding of risk, suitable for clinical care and trials. The immediate goal following on from the feasibility work will be to: • Identify the combination of signals (physical, environmental, self-reported) that may predict change in behaviors or functional abilities • Understanding how to develop the sensitivity and accuracy of the sensors to predict the onset of decline in health of the person living with dementia • Provide the analytical solutions to interpret signals in a meaningful way. • Establish an ethical, fully regulatory compliant digital-assessment/biosignature that integrates smartphones, wearable's, and fixed/home sensors. We anticipate a full research proposal will follow from this to test the device in a clinical trial to provide the scientific evidence for use of the system / device in clinical practice and trials. |
| Impact | Disciplines - Digital Health; Neuroscience and Experimental Psychology; Sensing, Imaging and Signal Processing; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Dynamic well-being visualisation toolbox for digital products |
| Organisation | Cambridgeshire Community Services NHS Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | We will explore the best ways in which wellbeing measures could be provided to service users (SU) in a meaningful and visual presentation. In mental health settings outcome measures are routinely undertaken as part of SU assessment. These tools typically generate a numerical score, which is often not routinely relayed to the SU; and when provided, it is unknown how meaningful this is to the individual. Visualisation is rarely or inconsistently used for wellbeing and not developed with SU involvement. An appropriate, dynamic method to visualising wellbeing state would enable users to access their performance, track their performance and wellbeing over time, and update their outcome measures in real time. The proposed tool would empower users to better manage their wellbeing/condition. We will identify the factors associated with wellbeing by asking service users in focus groups about their experience, and through reviewing research already published. We will also establish the best way to visually represent user's progress on measures by looking at how successful apps have achieved this, and by asking service users what they find most accessible and useful, in focus groups. Finally, we will bring together these findings to develop a specification for discussion with app developers. We will work with an app development company to create a prototype, based on the findings that are brought together in WP3. We would initially be developing paper and pencil prototypes, co-producing with SUs. There are a number of approaches we could take from an iterative and participatory design involving end users. At the end of the full research proposal we will have a prototype that is ready to be deployed in a feasibility test with service users. EPS challenges will include linking services accessible by care teams and service users, developing a secure database and service maintenance in the long run. There will also challenges to do with apps being classed as medical devices by the NHS and for the software to be CE marked. We will explore the best ways in which wellbeing measures could be provided to service users (SU) in a meaningful visual presentation within an app. The overall vision is of patient and client empowerment and 'Helping Clients to Help Themselves via digital resources'. An appropriate, dynamic method to visualising wellbeing state would enable users to access their performance, track their performance and wellbeing over time, and update their outcome measures in real time. The proposed tool would empower users to better manage their wellbeing/condition; and help them identify triggers and patterns impacting on their individualised wellbeing. The co-production of this research is paramount to produce useful and usable outputs and resources that can be translated into clinical practice. |
| Impact | DOI - 10.1177/1757913919835231 Disciplines - Engineering; Computer Science; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Dynamic well-being visualisation toolbox for digital products |
| Organisation | University of Glasgow |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | We will explore the best ways in which wellbeing measures could be provided to service users (SU) in a meaningful and visual presentation. In mental health settings outcome measures are routinely undertaken as part of SU assessment. These tools typically generate a numerical score, which is often not routinely relayed to the SU; and when provided, it is unknown how meaningful this is to the individual. Visualisation is rarely or inconsistently used for wellbeing and not developed with SU involvement. An appropriate, dynamic method to visualising wellbeing state would enable users to access their performance, track their performance and wellbeing over time, and update their outcome measures in real time. The proposed tool would empower users to better manage their wellbeing/condition. We will identify the factors associated with wellbeing by asking service users in focus groups about their experience, and through reviewing research already published. We will also establish the best way to visually represent user's progress on measures by looking at how successful apps have achieved this, and by asking service users what they find most accessible and useful, in focus groups. Finally, we will bring together these findings to develop a specification for discussion with app developers. We will work with an app development company to create a prototype, based on the findings that are brought together in WP3. We would initially be developing paper and pencil prototypes, co-producing with SUs. There are a number of approaches we could take from an iterative and participatory design involving end users. At the end of the full research proposal we will have a prototype that is ready to be deployed in a feasibility test with service users. EPS challenges will include linking services accessible by care teams and service users, developing a secure database and service maintenance in the long run. There will also challenges to do with apps being classed as medical devices by the NHS and for the software to be CE marked. We will explore the best ways in which wellbeing measures could be provided to service users (SU) in a meaningful visual presentation within an app. The overall vision is of patient and client empowerment and 'Helping Clients to Help Themselves via digital resources'. An appropriate, dynamic method to visualising wellbeing state would enable users to access their performance, track their performance and wellbeing over time, and update their outcome measures in real time. The proposed tool would empower users to better manage their wellbeing/condition; and help them identify triggers and patterns impacting on their individualised wellbeing. The co-production of this research is paramount to produce useful and usable outputs and resources that can be translated into clinical practice. |
| Impact | DOI - 10.1177/1757913919835231 Disciplines - Engineering; Computer Science; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Dynamic well-being visualisation toolbox for digital products |
| Organisation | University of Nottingham |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | We will explore the best ways in which wellbeing measures could be provided to service users (SU) in a meaningful and visual presentation. In mental health settings outcome measures are routinely undertaken as part of SU assessment. These tools typically generate a numerical score, which is often not routinely relayed to the SU; and when provided, it is unknown how meaningful this is to the individual. Visualisation is rarely or inconsistently used for wellbeing and not developed with SU involvement. An appropriate, dynamic method to visualising wellbeing state would enable users to access their performance, track their performance and wellbeing over time, and update their outcome measures in real time. The proposed tool would empower users to better manage their wellbeing/condition. We will identify the factors associated with wellbeing by asking service users in focus groups about their experience, and through reviewing research already published. We will also establish the best way to visually represent user's progress on measures by looking at how successful apps have achieved this, and by asking service users what they find most accessible and useful, in focus groups. Finally, we will bring together these findings to develop a specification for discussion with app developers. We will work with an app development company to create a prototype, based on the findings that are brought together in WP3. We would initially be developing paper and pencil prototypes, co-producing with SUs. There are a number of approaches we could take from an iterative and participatory design involving end users. At the end of the full research proposal we will have a prototype that is ready to be deployed in a feasibility test with service users. EPS challenges will include linking services accessible by care teams and service users, developing a secure database and service maintenance in the long run. There will also challenges to do with apps being classed as medical devices by the NHS and for the software to be CE marked. We will explore the best ways in which wellbeing measures could be provided to service users (SU) in a meaningful visual presentation within an app. The overall vision is of patient and client empowerment and 'Helping Clients to Help Themselves via digital resources'. An appropriate, dynamic method to visualising wellbeing state would enable users to access their performance, track their performance and wellbeing over time, and update their outcome measures in real time. The proposed tool would empower users to better manage their wellbeing/condition; and help them identify triggers and patterns impacting on their individualised wellbeing. The co-production of this research is paramount to produce useful and usable outputs and resources that can be translated into clinical practice. |
| Impact | DOI - 10.1177/1757913919835231 Disciplines - Engineering; Computer Science; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Eating disorders in the digital age: Considering the safety measures required to prevent the abuse of healthy eating and fitness applications amongst young people |
| Organisation | Anna Freud Centre |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Mobile applications often promote healthier living (e.g. calorie and activity trackers, healthy eating information), but can also become a source of obsessive negative behaviour, particularly for young people living in an age of 'fitspo' (fitness inspiration) perpetuated by social media. Mobile engagement itself poses significant challenge, with vulnerable populations freely accessing content without the extent of their engagement becoming visible to others. Furthermore, such applications are designed to promote continuous engagement, potentially fostering compulsive behaviours. Eating disorders (ED) are an important issue UK-wide (affecting ~725,000 people), and for young people (an estimated 80% of 10-year-old girls are on a diet to change their body shape). Furthermore, the rise of social media has seen the emergence of new concerns, e.g. orthorexia (a fixation on so-called 'clean eating'). Since the majority of those experiencing ED may not be underweight, it can be difficult to differentiate a desire to become 'healthier' from the emergence of harmful behaviours. This project facilitates the development of safety measures that prevent mobile health and fitness application misuse by young people at risk of ED. It will do this by (i) developing understanding of how mobile applications are used by those with disordered eating/health behaviour; (ii) quantifying current safety measures taken by popular applications; (iii) providing responsibility guidelines for applications of this kind Further funding will be sought for a more comprehensive project to develop the countermeasure solutions which emerge during the design workshops and evaluate the effectiveness of technology intervention in this space. We will develop a set of context-aware applications and tools to support developers in identifying and addressing negative behaviors, turning our guidelines into practice within their health and fitness products. Possible examples include: a) The implementation of an alert and information provision service linked to the consistent low calorie logging b) The implantation of a body-positive pop up tool which displays images tagged by body positive campaigners on Instagram linked to excessive amounts of time on healthy eating and fitness pages. This proposal work is a vital first step towards tackling the challenges related to negative body image and its potential development into eating disorder. By implementing early countermeasure interventions to negative app use behaviours (e.g. obsessive logging of calories and fitness data; constant browsing on picture based social media platforms which can provide an unrealistic representation of the diversity of body shapes and sizes) it is hoped that we can counterbalance negative views of body image that young people hold, leading to better general mental health and wellbeing. |
| Impact | DOI - 10.2196/14239 Disciplines - Digital Health; Psychology; Computer Science; Social Sciences |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Eating disorders in the digital age: Considering the safety measures required to prevent the abuse of healthy eating and fitness applications amongst young people |
| Organisation | Lancaster University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Mobile applications often promote healthier living (e.g. calorie and activity trackers, healthy eating information), but can also become a source of obsessive negative behaviour, particularly for young people living in an age of 'fitspo' (fitness inspiration) perpetuated by social media. Mobile engagement itself poses significant challenge, with vulnerable populations freely accessing content without the extent of their engagement becoming visible to others. Furthermore, such applications are designed to promote continuous engagement, potentially fostering compulsive behaviours. Eating disorders (ED) are an important issue UK-wide (affecting ~725,000 people), and for young people (an estimated 80% of 10-year-old girls are on a diet to change their body shape). Furthermore, the rise of social media has seen the emergence of new concerns, e.g. orthorexia (a fixation on so-called 'clean eating'). Since the majority of those experiencing ED may not be underweight, it can be difficult to differentiate a desire to become 'healthier' from the emergence of harmful behaviours. This project facilitates the development of safety measures that prevent mobile health and fitness application misuse by young people at risk of ED. It will do this by (i) developing understanding of how mobile applications are used by those with disordered eating/health behaviour; (ii) quantifying current safety measures taken by popular applications; (iii) providing responsibility guidelines for applications of this kind Further funding will be sought for a more comprehensive project to develop the countermeasure solutions which emerge during the design workshops and evaluate the effectiveness of technology intervention in this space. We will develop a set of context-aware applications and tools to support developers in identifying and addressing negative behaviors, turning our guidelines into practice within their health and fitness products. Possible examples include: a) The implementation of an alert and information provision service linked to the consistent low calorie logging b) The implantation of a body-positive pop up tool which displays images tagged by body positive campaigners on Instagram linked to excessive amounts of time on healthy eating and fitness pages. This proposal work is a vital first step towards tackling the challenges related to negative body image and its potential development into eating disorder. By implementing early countermeasure interventions to negative app use behaviours (e.g. obsessive logging of calories and fitness data; constant browsing on picture based social media platforms which can provide an unrealistic representation of the diversity of body shapes and sizes) it is hoped that we can counterbalance negative views of body image that young people hold, leading to better general mental health and wellbeing. |
| Impact | DOI - 10.2196/14239 Disciplines - Digital Health; Psychology; Computer Science; Social Sciences |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Eating disorders in the digital age: Considering the safety measures required to prevent the abuse of healthy eating and fitness applications amongst young people |
| Organisation | University of Chester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Mobile applications often promote healthier living (e.g. calorie and activity trackers, healthy eating information), but can also become a source of obsessive negative behaviour, particularly for young people living in an age of 'fitspo' (fitness inspiration) perpetuated by social media. Mobile engagement itself poses significant challenge, with vulnerable populations freely accessing content without the extent of their engagement becoming visible to others. Furthermore, such applications are designed to promote continuous engagement, potentially fostering compulsive behaviours. Eating disorders (ED) are an important issue UK-wide (affecting ~725,000 people), and for young people (an estimated 80% of 10-year-old girls are on a diet to change their body shape). Furthermore, the rise of social media has seen the emergence of new concerns, e.g. orthorexia (a fixation on so-called 'clean eating'). Since the majority of those experiencing ED may not be underweight, it can be difficult to differentiate a desire to become 'healthier' from the emergence of harmful behaviours. This project facilitates the development of safety measures that prevent mobile health and fitness application misuse by young people at risk of ED. It will do this by (i) developing understanding of how mobile applications are used by those with disordered eating/health behaviour; (ii) quantifying current safety measures taken by popular applications; (iii) providing responsibility guidelines for applications of this kind Further funding will be sought for a more comprehensive project to develop the countermeasure solutions which emerge during the design workshops and evaluate the effectiveness of technology intervention in this space. We will develop a set of context-aware applications and tools to support developers in identifying and addressing negative behaviors, turning our guidelines into practice within their health and fitness products. Possible examples include: a) The implementation of an alert and information provision service linked to the consistent low calorie logging b) The implantation of a body-positive pop up tool which displays images tagged by body positive campaigners on Instagram linked to excessive amounts of time on healthy eating and fitness pages. This proposal work is a vital first step towards tackling the challenges related to negative body image and its potential development into eating disorder. By implementing early countermeasure interventions to negative app use behaviours (e.g. obsessive logging of calories and fitness data; constant browsing on picture based social media platforms which can provide an unrealistic representation of the diversity of body shapes and sizes) it is hoped that we can counterbalance negative views of body image that young people hold, leading to better general mental health and wellbeing. |
| Impact | DOI - 10.2196/14239 Disciplines - Digital Health; Psychology; Computer Science; Social Sciences |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Eating disorders in the digital age: Considering the safety measures required to prevent the abuse of healthy eating and fitness applications amongst young people |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Mobile applications often promote healthier living (e.g. calorie and activity trackers, healthy eating information), but can also become a source of obsessive negative behaviour, particularly for young people living in an age of 'fitspo' (fitness inspiration) perpetuated by social media. Mobile engagement itself poses significant challenge, with vulnerable populations freely accessing content without the extent of their engagement becoming visible to others. Furthermore, such applications are designed to promote continuous engagement, potentially fostering compulsive behaviours. Eating disorders (ED) are an important issue UK-wide (affecting ~725,000 people), and for young people (an estimated 80% of 10-year-old girls are on a diet to change their body shape). Furthermore, the rise of social media has seen the emergence of new concerns, e.g. orthorexia (a fixation on so-called 'clean eating'). Since the majority of those experiencing ED may not be underweight, it can be difficult to differentiate a desire to become 'healthier' from the emergence of harmful behaviours. This project facilitates the development of safety measures that prevent mobile health and fitness application misuse by young people at risk of ED. It will do this by (i) developing understanding of how mobile applications are used by those with disordered eating/health behaviour; (ii) quantifying current safety measures taken by popular applications; (iii) providing responsibility guidelines for applications of this kind Further funding will be sought for a more comprehensive project to develop the countermeasure solutions which emerge during the design workshops and evaluate the effectiveness of technology intervention in this space. We will develop a set of context-aware applications and tools to support developers in identifying and addressing negative behaviors, turning our guidelines into practice within their health and fitness products. Possible examples include: a) The implementation of an alert and information provision service linked to the consistent low calorie logging b) The implantation of a body-positive pop up tool which displays images tagged by body positive campaigners on Instagram linked to excessive amounts of time on healthy eating and fitness pages. This proposal work is a vital first step towards tackling the challenges related to negative body image and its potential development into eating disorder. By implementing early countermeasure interventions to negative app use behaviours (e.g. obsessive logging of calories and fitness data; constant browsing on picture based social media platforms which can provide an unrealistic representation of the diversity of body shapes and sizes) it is hoped that we can counterbalance negative views of body image that young people hold, leading to better general mental health and wellbeing. |
| Impact | DOI - 10.2196/14239 Disciplines - Digital Health; Psychology; Computer Science; Social Sciences |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Eating disorders in the digital age: Considering the safety measures required to prevent the abuse of healthy eating and fitness applications amongst young people |
| Organisation | York St John University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Mobile applications often promote healthier living (e.g. calorie and activity trackers, healthy eating information), but can also become a source of obsessive negative behaviour, particularly for young people living in an age of 'fitspo' (fitness inspiration) perpetuated by social media. Mobile engagement itself poses significant challenge, with vulnerable populations freely accessing content without the extent of their engagement becoming visible to others. Furthermore, such applications are designed to promote continuous engagement, potentially fostering compulsive behaviours. Eating disorders (ED) are an important issue UK-wide (affecting ~725,000 people), and for young people (an estimated 80% of 10-year-old girls are on a diet to change their body shape). Furthermore, the rise of social media has seen the emergence of new concerns, e.g. orthorexia (a fixation on so-called 'clean eating'). Since the majority of those experiencing ED may not be underweight, it can be difficult to differentiate a desire to become 'healthier' from the emergence of harmful behaviours. This project facilitates the development of safety measures that prevent mobile health and fitness application misuse by young people at risk of ED. It will do this by (i) developing understanding of how mobile applications are used by those with disordered eating/health behaviour; (ii) quantifying current safety measures taken by popular applications; (iii) providing responsibility guidelines for applications of this kind Further funding will be sought for a more comprehensive project to develop the countermeasure solutions which emerge during the design workshops and evaluate the effectiveness of technology intervention in this space. We will develop a set of context-aware applications and tools to support developers in identifying and addressing negative behaviors, turning our guidelines into practice within their health and fitness products. Possible examples include: a) The implementation of an alert and information provision service linked to the consistent low calorie logging b) The implantation of a body-positive pop up tool which displays images tagged by body positive campaigners on Instagram linked to excessive amounts of time on healthy eating and fitness pages. This proposal work is a vital first step towards tackling the challenges related to negative body image and its potential development into eating disorder. By implementing early countermeasure interventions to negative app use behaviours (e.g. obsessive logging of calories and fitness data; constant browsing on picture based social media platforms which can provide an unrealistic representation of the diversity of body shapes and sizes) it is hoped that we can counterbalance negative views of body image that young people hold, leading to better general mental health and wellbeing. |
| Impact | DOI - 10.2196/14239 Disciplines - Digital Health; Psychology; Computer Science; Social Sciences |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Managing Mental Health in a school environment |
| Organisation | Lancaster University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | 9.6% of all children and young people aged 16 and under will have some form of mental ill health (ONS, 2015). The most prevalent forms of childhood mental health conditions are behavioral disorders, which affect around 5.8% and anxiety, affecting 3.3%. The use of technology is commonplace in many homes and schools. Increasingly school based approaches are being explored as a way of addressing children mental health within a school context to address problems like mood, affective and developmental disorders in children and young people. Integrating technology within the whole school approach could lead to reduced levels of distress, improved mental health and early help for school age children and young people with mood, affective and/or developmental disorders. The feasibility study aims explore the use of biometric data from image/video surveillance, written, verbal and image based communication to identify common signs and symptoms associated with emotional and mental health needs to improve outcomes of children and young peoples. We will develop two design co-groups made up of Children and Young people and incorporate the ethical considerations of the application of technology and extraction of data within a school environment We aim to develop interventions that have the ability to intelligently combine data from multiple modalities, share this information among multiple related tasks and be applied to support the early identification of mental health needs within children and young people in school environments. Future research would that would support the ability to develop social biometric tools that would allow for real-time, real-world feedback within a school environment. In addition to this there needs to be a focus on creating tools that are both non-intrusive and ethical. These would need to fit within the everyday life of children and young people and be acceptable to children, young people, parents, careers and schools. |
| Impact | For Events & Audiences see Main Disciplines - Psychological Science; Global Health; Health Economics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Managing Mental Health in a school environment |
| Organisation | London School of Economics and Political Science (University of London) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | 9.6% of all children and young people aged 16 and under will have some form of mental ill health (ONS, 2015). The most prevalent forms of childhood mental health conditions are behavioral disorders, which affect around 5.8% and anxiety, affecting 3.3%. The use of technology is commonplace in many homes and schools. Increasingly school based approaches are being explored as a way of addressing children mental health within a school context to address problems like mood, affective and developmental disorders in children and young people. Integrating technology within the whole school approach could lead to reduced levels of distress, improved mental health and early help for school age children and young people with mood, affective and/or developmental disorders. The feasibility study aims explore the use of biometric data from image/video surveillance, written, verbal and image based communication to identify common signs and symptoms associated with emotional and mental health needs to improve outcomes of children and young peoples. We will develop two design co-groups made up of Children and Young people and incorporate the ethical considerations of the application of technology and extraction of data within a school environment We aim to develop interventions that have the ability to intelligently combine data from multiple modalities, share this information among multiple related tasks and be applied to support the early identification of mental health needs within children and young people in school environments. Future research would that would support the ability to develop social biometric tools that would allow for real-time, real-world feedback within a school environment. In addition to this there needs to be a focus on creating tools that are both non-intrusive and ethical. These would need to fit within the everyday life of children and young people and be acceptable to children, young people, parents, careers and schools. |
| Impact | For Events & Audiences see Main Disciplines - Psychological Science; Global Health; Health Economics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Managing Mental Health in a school environment |
| Organisation | University of Glasgow |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | 9.6% of all children and young people aged 16 and under will have some form of mental ill health (ONS, 2015). The most prevalent forms of childhood mental health conditions are behavioral disorders, which affect around 5.8% and anxiety, affecting 3.3%. The use of technology is commonplace in many homes and schools. Increasingly school based approaches are being explored as a way of addressing children mental health within a school context to address problems like mood, affective and developmental disorders in children and young people. Integrating technology within the whole school approach could lead to reduced levels of distress, improved mental health and early help for school age children and young people with mood, affective and/or developmental disorders. The feasibility study aims explore the use of biometric data from image/video surveillance, written, verbal and image based communication to identify common signs and symptoms associated with emotional and mental health needs to improve outcomes of children and young peoples. We will develop two design co-groups made up of Children and Young people and incorporate the ethical considerations of the application of technology and extraction of data within a school environment We aim to develop interventions that have the ability to intelligently combine data from multiple modalities, share this information among multiple related tasks and be applied to support the early identification of mental health needs within children and young people in school environments. Future research would that would support the ability to develop social biometric tools that would allow for real-time, real-world feedback within a school environment. In addition to this there needs to be a focus on creating tools that are both non-intrusive and ethical. These would need to fit within the everyday life of children and young people and be acceptable to children, young people, parents, careers and schools. |
| Impact | For Events & Audiences see Main Disciplines - Psychological Science; Global Health; Health Economics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Managing Mental Health in a school environment |
| Organisation | University of Liverpool |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | 9.6% of all children and young people aged 16 and under will have some form of mental ill health (ONS, 2015). The most prevalent forms of childhood mental health conditions are behavioral disorders, which affect around 5.8% and anxiety, affecting 3.3%. The use of technology is commonplace in many homes and schools. Increasingly school based approaches are being explored as a way of addressing children mental health within a school context to address problems like mood, affective and developmental disorders in children and young people. Integrating technology within the whole school approach could lead to reduced levels of distress, improved mental health and early help for school age children and young people with mood, affective and/or developmental disorders. The feasibility study aims explore the use of biometric data from image/video surveillance, written, verbal and image based communication to identify common signs and symptoms associated with emotional and mental health needs to improve outcomes of children and young peoples. We will develop two design co-groups made up of Children and Young people and incorporate the ethical considerations of the application of technology and extraction of data within a school environment We aim to develop interventions that have the ability to intelligently combine data from multiple modalities, share this information among multiple related tasks and be applied to support the early identification of mental health needs within children and young people in school environments. Future research would that would support the ability to develop social biometric tools that would allow for real-time, real-world feedback within a school environment. In addition to this there needs to be a focus on creating tools that are both non-intrusive and ethical. These would need to fit within the everyday life of children and young people and be acceptable to children, young people, parents, careers and schools. |
| Impact | For Events & Audiences see Main Disciplines - Psychological Science; Global Health; Health Economics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Tangible toys (TATO) with sensors and biofeedback mechanism: explorative work to assess its suitability and feasibility as a tool for treating anxious children |
| Organisation | Lancaster University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This project addresses the unmet need for availability of anxiety treatments for children by designing a technology to enhance therapeutic benefit in and out of intervention. Using narrative play technique, widely used in academic and clinical psychology, we will create unique and specific opportunities for encouraging anxious children to explore and process their fears and worries at times and in spaces that feel safe to them. Our technology will be co-designed with children and their families to reflect their everyday experience of living with anxiety. We will explore the efficacy of biofeedback technology, applied to the child and his/her choice of toys for inferring their emotions and anticipate anxious episodes. The responsiveness of such technology in recognising facial expressions, speech, and tactile information (pressure, proximity, speed), as well as interacting with the child, will stimulate a sense of trust, while also monitoring key biological indicators. The end goal of this project is to promote independency and to reduce emotional isolation in children with a range of mental health concerns. The research targets the Optimising Treatment Grand Challenge, and contributes to the cross-cutting capabilities in Disruptive Technologies for sensing and analysis, as well as in Medical Device Design and Innovation. We envisage that with our technology, anxious children, their families and clinicians will gain knowledge currently unavailable about the child's anxiety, which will then inform, empower and connect them better, to achieve greater patient-clinician alliance, and treatment efficacy. Our vision is illustrated in the following scenario of a 9-year-old boy with anxiety: Bruce is very anxious and finds school difficult, particularly around managing social interaction with peers and teachers. This is a cause of much tension, stress and pain for him and his loving parents. Their pain is exacerbated by regular letters from his school requesting meetings to improve his attendance. Bruce now uses a set of tangible figures to create and to record stories about his day at school/home. The figures are connected via Bluetooth to an application with a pop-up avatar who "listens and encourages" him to tell stories about his experience. While he plays with the figures, he wears a small biofeedback device which records his physiologically inferable mental states. The figures have sensors attached that record the movement, speed as well as proximity of the figures which are then synchronised with the data on the biofeedback device by the software. The combined data provide Bruce, his family and clinicians specific examples to discuss and to keep track of his progress during therapy sessions. Bruce is now more motivated to take part in his appointments and his school attendance is improving with understanding of his school who now know what tends to frighten or upset him. |
| Impact | Disciplines - healthcare technology; Mechanical Engineering; Computing & Communications; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Tangible toys (TATO) with sensors and biofeedback mechanism: explorative work to assess its suitability and feasibility as a tool for treating anxious children |
| Organisation | Loughborough University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This project addresses the unmet need for availability of anxiety treatments for children by designing a technology to enhance therapeutic benefit in and out of intervention. Using narrative play technique, widely used in academic and clinical psychology, we will create unique and specific opportunities for encouraging anxious children to explore and process their fears and worries at times and in spaces that feel safe to them. Our technology will be co-designed with children and their families to reflect their everyday experience of living with anxiety. We will explore the efficacy of biofeedback technology, applied to the child and his/her choice of toys for inferring their emotions and anticipate anxious episodes. The responsiveness of such technology in recognising facial expressions, speech, and tactile information (pressure, proximity, speed), as well as interacting with the child, will stimulate a sense of trust, while also monitoring key biological indicators. The end goal of this project is to promote independency and to reduce emotional isolation in children with a range of mental health concerns. The research targets the Optimising Treatment Grand Challenge, and contributes to the cross-cutting capabilities in Disruptive Technologies for sensing and analysis, as well as in Medical Device Design and Innovation. We envisage that with our technology, anxious children, their families and clinicians will gain knowledge currently unavailable about the child's anxiety, which will then inform, empower and connect them better, to achieve greater patient-clinician alliance, and treatment efficacy. Our vision is illustrated in the following scenario of a 9-year-old boy with anxiety: Bruce is very anxious and finds school difficult, particularly around managing social interaction with peers and teachers. This is a cause of much tension, stress and pain for him and his loving parents. Their pain is exacerbated by regular letters from his school requesting meetings to improve his attendance. Bruce now uses a set of tangible figures to create and to record stories about his day at school/home. The figures are connected via Bluetooth to an application with a pop-up avatar who "listens and encourages" him to tell stories about his experience. While he plays with the figures, he wears a small biofeedback device which records his physiologically inferable mental states. The figures have sensors attached that record the movement, speed as well as proximity of the figures which are then synchronised with the data on the biofeedback device by the software. The combined data provide Bruce, his family and clinicians specific examples to discuss and to keep track of his progress during therapy sessions. Bruce is now more motivated to take part in his appointments and his school attendance is improving with understanding of his school who now know what tends to frighten or upset him. |
| Impact | Disciplines - healthcare technology; Mechanical Engineering; Computing & Communications; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Tangible toys (TATO) with sensors and biofeedback mechanism: explorative work to assess its suitability and feasibility as a tool for treating anxious children |
| Organisation | Nottingham Trent University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This project addresses the unmet need for availability of anxiety treatments for children by designing a technology to enhance therapeutic benefit in and out of intervention. Using narrative play technique, widely used in academic and clinical psychology, we will create unique and specific opportunities for encouraging anxious children to explore and process their fears and worries at times and in spaces that feel safe to them. Our technology will be co-designed with children and their families to reflect their everyday experience of living with anxiety. We will explore the efficacy of biofeedback technology, applied to the child and his/her choice of toys for inferring their emotions and anticipate anxious episodes. The responsiveness of such technology in recognising facial expressions, speech, and tactile information (pressure, proximity, speed), as well as interacting with the child, will stimulate a sense of trust, while also monitoring key biological indicators. The end goal of this project is to promote independency and to reduce emotional isolation in children with a range of mental health concerns. The research targets the Optimising Treatment Grand Challenge, and contributes to the cross-cutting capabilities in Disruptive Technologies for sensing and analysis, as well as in Medical Device Design and Innovation. We envisage that with our technology, anxious children, their families and clinicians will gain knowledge currently unavailable about the child's anxiety, which will then inform, empower and connect them better, to achieve greater patient-clinician alliance, and treatment efficacy. Our vision is illustrated in the following scenario of a 9-year-old boy with anxiety: Bruce is very anxious and finds school difficult, particularly around managing social interaction with peers and teachers. This is a cause of much tension, stress and pain for him and his loving parents. Their pain is exacerbated by regular letters from his school requesting meetings to improve his attendance. Bruce now uses a set of tangible figures to create and to record stories about his day at school/home. The figures are connected via Bluetooth to an application with a pop-up avatar who "listens and encourages" him to tell stories about his experience. While he plays with the figures, he wears a small biofeedback device which records his physiologically inferable mental states. The figures have sensors attached that record the movement, speed as well as proximity of the figures which are then synchronised with the data on the biofeedback device by the software. The combined data provide Bruce, his family and clinicians specific examples to discuss and to keep track of his progress during therapy sessions. Bruce is now more motivated to take part in his appointments and his school attendance is improving with understanding of his school who now know what tends to frighten or upset him. |
| Impact | Disciplines - healthcare technology; Mechanical Engineering; Computing & Communications; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Tangible toys (TATO) with sensors and biofeedback mechanism: explorative work to assess its suitability and feasibility as a tool for treating anxious children |
| Organisation | University of Glasgow |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This project addresses the unmet need for availability of anxiety treatments for children by designing a technology to enhance therapeutic benefit in and out of intervention. Using narrative play technique, widely used in academic and clinical psychology, we will create unique and specific opportunities for encouraging anxious children to explore and process their fears and worries at times and in spaces that feel safe to them. Our technology will be co-designed with children and their families to reflect their everyday experience of living with anxiety. We will explore the efficacy of biofeedback technology, applied to the child and his/her choice of toys for inferring their emotions and anticipate anxious episodes. The responsiveness of such technology in recognising facial expressions, speech, and tactile information (pressure, proximity, speed), as well as interacting with the child, will stimulate a sense of trust, while also monitoring key biological indicators. The end goal of this project is to promote independency and to reduce emotional isolation in children with a range of mental health concerns. The research targets the Optimising Treatment Grand Challenge, and contributes to the cross-cutting capabilities in Disruptive Technologies for sensing and analysis, as well as in Medical Device Design and Innovation. We envisage that with our technology, anxious children, their families and clinicians will gain knowledge currently unavailable about the child's anxiety, which will then inform, empower and connect them better, to achieve greater patient-clinician alliance, and treatment efficacy. Our vision is illustrated in the following scenario of a 9-year-old boy with anxiety: Bruce is very anxious and finds school difficult, particularly around managing social interaction with peers and teachers. This is a cause of much tension, stress and pain for him and his loving parents. Their pain is exacerbated by regular letters from his school requesting meetings to improve his attendance. Bruce now uses a set of tangible figures to create and to record stories about his day at school/home. The figures are connected via Bluetooth to an application with a pop-up avatar who "listens and encourages" him to tell stories about his experience. While he plays with the figures, he wears a small biofeedback device which records his physiologically inferable mental states. The figures have sensors attached that record the movement, speed as well as proximity of the figures which are then synchronised with the data on the biofeedback device by the software. The combined data provide Bruce, his family and clinicians specific examples to discuss and to keep track of his progress during therapy sessions. Bruce is now more motivated to take part in his appointments and his school attendance is improving with understanding of his school who now know what tends to frighten or upset him. |
| Impact | Disciplines - healthcare technology; Mechanical Engineering; Computing & Communications; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Tangible toys (TATO) with sensors and biofeedback mechanism: explorative work to assess its suitability and feasibility as a tool for treating anxious children |
| Organisation | University of Sheffield |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This project addresses the unmet need for availability of anxiety treatments for children by designing a technology to enhance therapeutic benefit in and out of intervention. Using narrative play technique, widely used in academic and clinical psychology, we will create unique and specific opportunities for encouraging anxious children to explore and process their fears and worries at times and in spaces that feel safe to them. Our technology will be co-designed with children and their families to reflect their everyday experience of living with anxiety. We will explore the efficacy of biofeedback technology, applied to the child and his/her choice of toys for inferring their emotions and anticipate anxious episodes. The responsiveness of such technology in recognising facial expressions, speech, and tactile information (pressure, proximity, speed), as well as interacting with the child, will stimulate a sense of trust, while also monitoring key biological indicators. The end goal of this project is to promote independency and to reduce emotional isolation in children with a range of mental health concerns. The research targets the Optimising Treatment Grand Challenge, and contributes to the cross-cutting capabilities in Disruptive Technologies for sensing and analysis, as well as in Medical Device Design and Innovation. We envisage that with our technology, anxious children, their families and clinicians will gain knowledge currently unavailable about the child's anxiety, which will then inform, empower and connect them better, to achieve greater patient-clinician alliance, and treatment efficacy. Our vision is illustrated in the following scenario of a 9-year-old boy with anxiety: Bruce is very anxious and finds school difficult, particularly around managing social interaction with peers and teachers. This is a cause of much tension, stress and pain for him and his loving parents. Their pain is exacerbated by regular letters from his school requesting meetings to improve his attendance. Bruce now uses a set of tangible figures to create and to record stories about his day at school/home. The figures are connected via Bluetooth to an application with a pop-up avatar who "listens and encourages" him to tell stories about his experience. While he plays with the figures, he wears a small biofeedback device which records his physiologically inferable mental states. The figures have sensors attached that record the movement, speed as well as proximity of the figures which are then synchronised with the data on the biofeedback device by the software. The combined data provide Bruce, his family and clinicians specific examples to discuss and to keep track of his progress during therapy sessions. Bruce is now more motivated to take part in his appointments and his school attendance is improving with understanding of his school who now know what tends to frighten or upset him. |
| Impact | Disciplines - healthcare technology; Mechanical Engineering; Computing & Communications; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Towards a next generation platform for personalised neuro-therapeutic interventions in chronic pain |
| Organisation | Salford Fibromyalgia Support Group |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Chronic pain (lasting more than 3 months) can persist despite the best efforts of physicians, and can result in profound mental ill-health and disability. Research has found that the brain has an important role to play in how much pain people feel and whether they develop chronic pain symptoms. We are working towards new technologies that can both measure and treat changes in the brain that contribute to chronic pain. One new approach to treatment is applying visual, sound, touch or electrical stimulation to stimulate the brain. These new methods require technological development to be able to effectively target pain mechanisms in the brain. This project will investigate whether patients with chronic pain might find these non-intrusive methods of brain stimulation to be acceptable as a possible treatment, and to gather some initial evidence that the methods stimulate the brain in a useful way. We will also find out what requirements patients have for brain-monitoring (EEG) equipment that might help to improve brain stimulation treatments. Lastly, we want to find out whether smartphone-based pain diaries are something patients with chronic pain find useful to keep track of whether they are getting better or worse over time, for example in response to new treatments. Our vision is a platform for clinical detection, diagnosis and monitoring of brain mechanisms contributing to poor mental health, while at the same time providing novel non-pharmacological-based treatment. Target populations may include those with mental health conditions (anxiety/stress, depression, insomnia) and long-term conditions that significantly impact mental health (chronic pain, aging-related problems such as cognitive decline) in which brain-mechanistic pathways exist for intervention. Such mechanisms are under-detected and often treatment options are limited or associated with significant unwanted side effects (e.g. pharmaceuticals). Bioelectric medicine, applying visual, sound, tactile or electrical stimulation, provides an alternative therapeutic approach that is currently in need of engineering development prior to clinical validation. The proposed technology will measure and modify brain mechanisms that directly link to many aspects of mental health: • Enhancement of cognitive functions, such as working memory, to remedy poorer functioning in depression and due to aging. • Dampening of cortical excitability, for example the enhanced pain processing (nociception) due to central nervous system sensitisation that contributes to pain-related distress and interrupts sleep. • Modifying sensory cortical plasticity, for example neuroplastic mechanisms contributing to disorders of body and self-perception. The platform will comprise: • Real-time neuro-monitoring with novel user-friendly, discrete and personalisable EEG headsets and electrodes. • Real-time local (e.g. smartphone) computational modelling of dynamic neural data to detect markers of cognitive function, cortical excitability and neuroplasticity. • Neurostimulation technologies for frequency-selective neural entrainment, including via visual, auditory and tactile and electrical stimulation. Stimulation frequency and phase will be informed by EEG monitoring, constituting a closed-loop data-responsive system. • A centralised app for providing visual, auditory and tactile stimulation, and capturing numerical, free-text and free-speech user-inputted data (e.g. pain/emotion ratings, medication usage, event diaries) via keyboard and microphone. |
| Impact | DoI - 10.3390/s19071650 Disciplines - Psychology; Electrical Engineering; Neuro-rheumatology; Neuroscience & Experimental Psychology; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Towards a next generation platform for personalised neuro-therapeutic interventions in chronic pain |
| Organisation | University of Liverpool |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Chronic pain (lasting more than 3 months) can persist despite the best efforts of physicians, and can result in profound mental ill-health and disability. Research has found that the brain has an important role to play in how much pain people feel and whether they develop chronic pain symptoms. We are working towards new technologies that can both measure and treat changes in the brain that contribute to chronic pain. One new approach to treatment is applying visual, sound, touch or electrical stimulation to stimulate the brain. These new methods require technological development to be able to effectively target pain mechanisms in the brain. This project will investigate whether patients with chronic pain might find these non-intrusive methods of brain stimulation to be acceptable as a possible treatment, and to gather some initial evidence that the methods stimulate the brain in a useful way. We will also find out what requirements patients have for brain-monitoring (EEG) equipment that might help to improve brain stimulation treatments. Lastly, we want to find out whether smartphone-based pain diaries are something patients with chronic pain find useful to keep track of whether they are getting better or worse over time, for example in response to new treatments. Our vision is a platform for clinical detection, diagnosis and monitoring of brain mechanisms contributing to poor mental health, while at the same time providing novel non-pharmacological-based treatment. Target populations may include those with mental health conditions (anxiety/stress, depression, insomnia) and long-term conditions that significantly impact mental health (chronic pain, aging-related problems such as cognitive decline) in which brain-mechanistic pathways exist for intervention. Such mechanisms are under-detected and often treatment options are limited or associated with significant unwanted side effects (e.g. pharmaceuticals). Bioelectric medicine, applying visual, sound, tactile or electrical stimulation, provides an alternative therapeutic approach that is currently in need of engineering development prior to clinical validation. The proposed technology will measure and modify brain mechanisms that directly link to many aspects of mental health: • Enhancement of cognitive functions, such as working memory, to remedy poorer functioning in depression and due to aging. • Dampening of cortical excitability, for example the enhanced pain processing (nociception) due to central nervous system sensitisation that contributes to pain-related distress and interrupts sleep. • Modifying sensory cortical plasticity, for example neuroplastic mechanisms contributing to disorders of body and self-perception. The platform will comprise: • Real-time neuro-monitoring with novel user-friendly, discrete and personalisable EEG headsets and electrodes. • Real-time local (e.g. smartphone) computational modelling of dynamic neural data to detect markers of cognitive function, cortical excitability and neuroplasticity. • Neurostimulation technologies for frequency-selective neural entrainment, including via visual, auditory and tactile and electrical stimulation. Stimulation frequency and phase will be informed by EEG monitoring, constituting a closed-loop data-responsive system. • A centralised app for providing visual, auditory and tactile stimulation, and capturing numerical, free-text and free-speech user-inputted data (e.g. pain/emotion ratings, medication usage, event diaries) via keyboard and microphone. |
| Impact | DoI - 10.3390/s19071650 Disciplines - Psychology; Electrical Engineering; Neuro-rheumatology; Neuroscience & Experimental Psychology; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Towards a next generation platform for personalised neuro-therapeutic interventions in chronic pain |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Chronic pain (lasting more than 3 months) can persist despite the best efforts of physicians, and can result in profound mental ill-health and disability. Research has found that the brain has an important role to play in how much pain people feel and whether they develop chronic pain symptoms. We are working towards new technologies that can both measure and treat changes in the brain that contribute to chronic pain. One new approach to treatment is applying visual, sound, touch or electrical stimulation to stimulate the brain. These new methods require technological development to be able to effectively target pain mechanisms in the brain. This project will investigate whether patients with chronic pain might find these non-intrusive methods of brain stimulation to be acceptable as a possible treatment, and to gather some initial evidence that the methods stimulate the brain in a useful way. We will also find out what requirements patients have for brain-monitoring (EEG) equipment that might help to improve brain stimulation treatments. Lastly, we want to find out whether smartphone-based pain diaries are something patients with chronic pain find useful to keep track of whether they are getting better or worse over time, for example in response to new treatments. Our vision is a platform for clinical detection, diagnosis and monitoring of brain mechanisms contributing to poor mental health, while at the same time providing novel non-pharmacological-based treatment. Target populations may include those with mental health conditions (anxiety/stress, depression, insomnia) and long-term conditions that significantly impact mental health (chronic pain, aging-related problems such as cognitive decline) in which brain-mechanistic pathways exist for intervention. Such mechanisms are under-detected and often treatment options are limited or associated with significant unwanted side effects (e.g. pharmaceuticals). Bioelectric medicine, applying visual, sound, tactile or electrical stimulation, provides an alternative therapeutic approach that is currently in need of engineering development prior to clinical validation. The proposed technology will measure and modify brain mechanisms that directly link to many aspects of mental health: • Enhancement of cognitive functions, such as working memory, to remedy poorer functioning in depression and due to aging. • Dampening of cortical excitability, for example the enhanced pain processing (nociception) due to central nervous system sensitisation that contributes to pain-related distress and interrupts sleep. • Modifying sensory cortical plasticity, for example neuroplastic mechanisms contributing to disorders of body and self-perception. The platform will comprise: • Real-time neuro-monitoring with novel user-friendly, discrete and personalisable EEG headsets and electrodes. • Real-time local (e.g. smartphone) computational modelling of dynamic neural data to detect markers of cognitive function, cortical excitability and neuroplasticity. • Neurostimulation technologies for frequency-selective neural entrainment, including via visual, auditory and tactile and electrical stimulation. Stimulation frequency and phase will be informed by EEG monitoring, constituting a closed-loop data-responsive system. • A centralised app for providing visual, auditory and tactile stimulation, and capturing numerical, free-text and free-speech user-inputted data (e.g. pain/emotion ratings, medication usage, event diaries) via keyboard and microphone. |
| Impact | DoI - 10.3390/s19071650 Disciplines - Psychology; Electrical Engineering; Neuro-rheumatology; Neuroscience & Experimental Psychology; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Understanding, Predicting and Preventing Non-Suicidal Self-Injury and thoughts amongst adolescents (UPP) |
| Organisation | Lancaster University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study |
| Collaborator Contribution | Non-Suicidal Self-Injury (NSSI) is in the top five causes of medical admissions, with 19,000 adolescents attending emergency departments for NSSI in 2015/16; a 14% rise compared with preceding two years. It is thought that up to 40% of youths experiencing NSSI do not seek help due to stigma, and fear of negative reactions. Traditional face-to-face interventions lack capacity to reach these adolescents. Studies identify preference towards online information, due to the anonymity and acceptance it affords. Young people access education, social interactions and entertainment primarily via their smartphone, thus making it a useful device to support access to resources; promote help-seeking and self-management behaviours. Our aim is to design and develop a context-aware mobile application, that learns from user's daily online and physical activities, to flag-up likely early warning signs of self-harm. The app can then offer interventions to support the user in times of need. We will look into individual's relationships and social interactions to understand the context of the individual's experiences and mood dysregulation, and identify early warning signs. The tasks of the larger project include design and development of an app that will: 1) collect user's 'cyber' and 'physical' social interaction data such as location and app usage 2) collect self-report data 3) predict occurrence of NSSI thoughts using semi-supervised machine learning 4) prevent development of NSSI thoughts into action by sending an intervention The impact on wellbeing could be significant, by promoting self-monitoring, emotional regulation and coping skills We expect to publish the study findings in ACM CHI with focus on our participatory approach, and the challenges in a stigmatised, sensitive domain of self-harm. In addition we aim to publish in the journal of Child and Adolescent Psychiatry and Mental Health focusing on the role of technology for supporting adolescents who self-harm. On successful completion of feasibility study we aim to apply for the NewMind stage 2 funding to develop the mobile sensing application and evaluate its usefulness for older teenagers in a pilot study. |
| Impact | Disciplines - Digital Healthcare; Computer Science; Electronic and electrical engineering; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Understanding, Predicting and Preventing Non-Suicidal Self-Injury and thoughts amongst adolescents (UPP) |
| Organisation | Pennine Care NHS Foundation Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study |
| Collaborator Contribution | Non-Suicidal Self-Injury (NSSI) is in the top five causes of medical admissions, with 19,000 adolescents attending emergency departments for NSSI in 2015/16; a 14% rise compared with preceding two years. It is thought that up to 40% of youths experiencing NSSI do not seek help due to stigma, and fear of negative reactions. Traditional face-to-face interventions lack capacity to reach these adolescents. Studies identify preference towards online information, due to the anonymity and acceptance it affords. Young people access education, social interactions and entertainment primarily via their smartphone, thus making it a useful device to support access to resources; promote help-seeking and self-management behaviours. Our aim is to design and develop a context-aware mobile application, that learns from user's daily online and physical activities, to flag-up likely early warning signs of self-harm. The app can then offer interventions to support the user in times of need. We will look into individual's relationships and social interactions to understand the context of the individual's experiences and mood dysregulation, and identify early warning signs. The tasks of the larger project include design and development of an app that will: 1) collect user's 'cyber' and 'physical' social interaction data such as location and app usage 2) collect self-report data 3) predict occurrence of NSSI thoughts using semi-supervised machine learning 4) prevent development of NSSI thoughts into action by sending an intervention The impact on wellbeing could be significant, by promoting self-monitoring, emotional regulation and coping skills We expect to publish the study findings in ACM CHI with focus on our participatory approach, and the challenges in a stigmatised, sensitive domain of self-harm. In addition we aim to publish in the journal of Child and Adolescent Psychiatry and Mental Health focusing on the role of technology for supporting adolescents who self-harm. On successful completion of feasibility study we aim to apply for the NewMind stage 2 funding to develop the mobile sensing application and evaluate its usefulness for older teenagers in a pilot study. |
| Impact | Disciplines - Digital Healthcare; Computer Science; Electronic and electrical engineering; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Understanding, Predicting and Preventing Non-Suicidal Self-Injury and thoughts amongst adolescents (UPP) |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study |
| Collaborator Contribution | Non-Suicidal Self-Injury (NSSI) is in the top five causes of medical admissions, with 19,000 adolescents attending emergency departments for NSSI in 2015/16; a 14% rise compared with preceding two years. It is thought that up to 40% of youths experiencing NSSI do not seek help due to stigma, and fear of negative reactions. Traditional face-to-face interventions lack capacity to reach these adolescents. Studies identify preference towards online information, due to the anonymity and acceptance it affords. Young people access education, social interactions and entertainment primarily via their smartphone, thus making it a useful device to support access to resources; promote help-seeking and self-management behaviours. Our aim is to design and develop a context-aware mobile application, that learns from user's daily online and physical activities, to flag-up likely early warning signs of self-harm. The app can then offer interventions to support the user in times of need. We will look into individual's relationships and social interactions to understand the context of the individual's experiences and mood dysregulation, and identify early warning signs. The tasks of the larger project include design and development of an app that will: 1) collect user's 'cyber' and 'physical' social interaction data such as location and app usage 2) collect self-report data 3) predict occurrence of NSSI thoughts using semi-supervised machine learning 4) prevent development of NSSI thoughts into action by sending an intervention The impact on wellbeing could be significant, by promoting self-monitoring, emotional regulation and coping skills We expect to publish the study findings in ACM CHI with focus on our participatory approach, and the challenges in a stigmatised, sensitive domain of self-harm. In addition we aim to publish in the journal of Child and Adolescent Psychiatry and Mental Health focusing on the role of technology for supporting adolescents who self-harm. On successful completion of feasibility study we aim to apply for the NewMind stage 2 funding to develop the mobile sensing application and evaluate its usefulness for older teenagers in a pilot study. |
| Impact | Disciplines - Digital Healthcare; Computer Science; Electronic and electrical engineering; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Understanding, Predicting and Preventing Non-Suicidal Self-Injury and thoughts amongst adolescents (UPP) |
| Organisation | University of Sheffield |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study |
| Collaborator Contribution | Non-Suicidal Self-Injury (NSSI) is in the top five causes of medical admissions, with 19,000 adolescents attending emergency departments for NSSI in 2015/16; a 14% rise compared with preceding two years. It is thought that up to 40% of youths experiencing NSSI do not seek help due to stigma, and fear of negative reactions. Traditional face-to-face interventions lack capacity to reach these adolescents. Studies identify preference towards online information, due to the anonymity and acceptance it affords. Young people access education, social interactions and entertainment primarily via their smartphone, thus making it a useful device to support access to resources; promote help-seeking and self-management behaviours. Our aim is to design and develop a context-aware mobile application, that learns from user's daily online and physical activities, to flag-up likely early warning signs of self-harm. The app can then offer interventions to support the user in times of need. We will look into individual's relationships and social interactions to understand the context of the individual's experiences and mood dysregulation, and identify early warning signs. The tasks of the larger project include design and development of an app that will: 1) collect user's 'cyber' and 'physical' social interaction data such as location and app usage 2) collect self-report data 3) predict occurrence of NSSI thoughts using semi-supervised machine learning 4) prevent development of NSSI thoughts into action by sending an intervention The impact on wellbeing could be significant, by promoting self-monitoring, emotional regulation and coping skills We expect to publish the study findings in ACM CHI with focus on our participatory approach, and the challenges in a stigmatised, sensitive domain of self-harm. In addition we aim to publish in the journal of Child and Adolescent Psychiatry and Mental Health focusing on the role of technology for supporting adolescents who self-harm. On successful completion of feasibility study we aim to apply for the NewMind stage 2 funding to develop the mobile sensing application and evaluate its usefulness for older teenagers in a pilot study. |
| Impact | Disciplines - Digital Healthcare; Computer Science; Electronic and electrical engineering; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Understanding, Predicting and Preventing Non-Suicidal Self-Injury and thoughts amongst adolescents (UPP) |
| Organisation | Xenzone |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study |
| Collaborator Contribution | Non-Suicidal Self-Injury (NSSI) is in the top five causes of medical admissions, with 19,000 adolescents attending emergency departments for NSSI in 2015/16; a 14% rise compared with preceding two years. It is thought that up to 40% of youths experiencing NSSI do not seek help due to stigma, and fear of negative reactions. Traditional face-to-face interventions lack capacity to reach these adolescents. Studies identify preference towards online information, due to the anonymity and acceptance it affords. Young people access education, social interactions and entertainment primarily via their smartphone, thus making it a useful device to support access to resources; promote help-seeking and self-management behaviours. Our aim is to design and develop a context-aware mobile application, that learns from user's daily online and physical activities, to flag-up likely early warning signs of self-harm. The app can then offer interventions to support the user in times of need. We will look into individual's relationships and social interactions to understand the context of the individual's experiences and mood dysregulation, and identify early warning signs. The tasks of the larger project include design and development of an app that will: 1) collect user's 'cyber' and 'physical' social interaction data such as location and app usage 2) collect self-report data 3) predict occurrence of NSSI thoughts using semi-supervised machine learning 4) prevent development of NSSI thoughts into action by sending an intervention The impact on wellbeing could be significant, by promoting self-monitoring, emotional regulation and coping skills We expect to publish the study findings in ACM CHI with focus on our participatory approach, and the challenges in a stigmatised, sensitive domain of self-harm. In addition we aim to publish in the journal of Child and Adolescent Psychiatry and Mental Health focusing on the role of technology for supporting adolescents who self-harm. On successful completion of feasibility study we aim to apply for the NewMind stage 2 funding to develop the mobile sensing application and evaluate its usefulness for older teenagers in a pilot study. |
| Impact | Disciplines - Digital Healthcare; Computer Science; Electronic and electrical engineering; |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Unlocking the evidence from electronic patient records for smart intervention of mental health disorders - a case study in Alzheimer's Disease |
| Organisation | DeepCognito |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Alzheimer disease (AD) is the most common causes of dementia that affects an estimated 850,000 people in the UK. Given the lack of cure for AD and accelerated ageing of the population, AD has become one of the biggest health burdens in the world. Large scale electronic patient records (EPRs) collected from the daily clinical practice may provide novel perspectives on the care and treatment of AD. The goal of the feasibility funding is to build a learning platform that will use both structured and free text EPR data by focusing on particular drugs and their impacts on AD patients. As recent studies suggested strong genetic links between inflammation and AD, we seek seed funding to pilot extraction of anti-inflammatory drugs and AD severity indicators and to analyse these at population level. We will develop and validate our text mining (TM) algorithms using the Oxford UK-CRIS database as a test-bed: a de-identified EPR database from a mental health NHS trust in Oxfordshire with 100,000 patient records. Our vision is to build a framework that maximises the use of UK-CRIS for smarter and more effective interventions for AD. TM algorithms will be developed and applied to free text in the UK-CRIS to automatically and precisely extract key variables such as drugs, cognitive function scales and diagnoses. Then, for example, these can be used to study the impact of anti-inflammatory drugs on longitudinal AD trajectories. Further patient-level predictions will be modelled using Machine Learning on the rich fine-granular text information to improve tailored interventions at the right timing. For instance subgroup of AD patients may benefit from certain drugs in the early stage, but others may only in the late stage. Patients will be actively involved in this personalised intervention process with their doctors. The key engineering challenge is to precisely extract variables from UK-CRIS free text that are required for large-scale analyses and for patient-level analyses. Clinical narratives in mental health EPRs are known to be complex, detailed and containing a variety of information (e.g. family history, patient experience, descriptions of events, signs and outcomes). Therefore it is not straightforward to identify those variables without ambiguity and to place them in a correct temporal order. While general TM approaches so far have shown promises in capturing coarse-grained themes and patterns in collections of text documents, the extraction of detailed values for an individual patient has not yet been resolved. Given the size and complexity of the available data, our main research aim is to combine machine learning with knowledge-rich TM to map UK-CRIS free text to medical variables of interest for each patient. Another challenge is to develop novel data analytics for large and complex EPR data. The extracted EPR data will be composed of 2 million patients, each with thousands of variables and multiple time points. Simplified analyses may be possible with traditional epidemiological techniques, e.g. a couple of exposures and outcomes of interest with a linear model. However, new analytical approaches will be required to maximise the use of this rich and large data. For instance, in a highly data-driven approach, it will be possible to run a systematic search in the space of all possible highly multivariate linear models to find the one with best goodness of fit, rather than simply trying a linear model that might be obvious from prior medical knowledge. As another example, rather than building models with traditional classifiers (e.g. discriminant analysis or support vector machine), we could attempt more recent neural networks, which require in the order of millions of samples, which in the exceptional case of UK-CRIS we do have. |
| Impact | DoI / Paper - TBC Disciplines - Psychiatry; Computer Science; Data Science; Bioinformatics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Unlocking the evidence from electronic patient records for smart intervention of mental health disorders - a case study in Alzheimer's Disease |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Alzheimer disease (AD) is the most common causes of dementia that affects an estimated 850,000 people in the UK. Given the lack of cure for AD and accelerated ageing of the population, AD has become one of the biggest health burdens in the world. Large scale electronic patient records (EPRs) collected from the daily clinical practice may provide novel perspectives on the care and treatment of AD. The goal of the feasibility funding is to build a learning platform that will use both structured and free text EPR data by focusing on particular drugs and their impacts on AD patients. As recent studies suggested strong genetic links between inflammation and AD, we seek seed funding to pilot extraction of anti-inflammatory drugs and AD severity indicators and to analyse these at population level. We will develop and validate our text mining (TM) algorithms using the Oxford UK-CRIS database as a test-bed: a de-identified EPR database from a mental health NHS trust in Oxfordshire with 100,000 patient records. Our vision is to build a framework that maximises the use of UK-CRIS for smarter and more effective interventions for AD. TM algorithms will be developed and applied to free text in the UK-CRIS to automatically and precisely extract key variables such as drugs, cognitive function scales and diagnoses. Then, for example, these can be used to study the impact of anti-inflammatory drugs on longitudinal AD trajectories. Further patient-level predictions will be modelled using Machine Learning on the rich fine-granular text information to improve tailored interventions at the right timing. For instance subgroup of AD patients may benefit from certain drugs in the early stage, but others may only in the late stage. Patients will be actively involved in this personalised intervention process with their doctors. The key engineering challenge is to precisely extract variables from UK-CRIS free text that are required for large-scale analyses and for patient-level analyses. Clinical narratives in mental health EPRs are known to be complex, detailed and containing a variety of information (e.g. family history, patient experience, descriptions of events, signs and outcomes). Therefore it is not straightforward to identify those variables without ambiguity and to place them in a correct temporal order. While general TM approaches so far have shown promises in capturing coarse-grained themes and patterns in collections of text documents, the extraction of detailed values for an individual patient has not yet been resolved. Given the size and complexity of the available data, our main research aim is to combine machine learning with knowledge-rich TM to map UK-CRIS free text to medical variables of interest for each patient. Another challenge is to develop novel data analytics for large and complex EPR data. The extracted EPR data will be composed of 2 million patients, each with thousands of variables and multiple time points. Simplified analyses may be possible with traditional epidemiological techniques, e.g. a couple of exposures and outcomes of interest with a linear model. However, new analytical approaches will be required to maximise the use of this rich and large data. For instance, in a highly data-driven approach, it will be possible to run a systematic search in the space of all possible highly multivariate linear models to find the one with best goodness of fit, rather than simply trying a linear model that might be obvious from prior medical knowledge. As another example, rather than building models with traditional classifiers (e.g. discriminant analysis or support vector machine), we could attempt more recent neural networks, which require in the order of millions of samples, which in the exceptional case of UK-CRIS we do have. |
| Impact | DoI / Paper - TBC Disciplines - Psychiatry; Computer Science; Data Science; Bioinformatics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Unlocking the evidence from electronic patient records for smart intervention of mental health disorders - a case study in Alzheimer's Disease |
| Organisation | University of Oxford |
| Department | Oxford Hub |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | Alzheimer disease (AD) is the most common causes of dementia that affects an estimated 850,000 people in the UK. Given the lack of cure for AD and accelerated ageing of the population, AD has become one of the biggest health burdens in the world. Large scale electronic patient records (EPRs) collected from the daily clinical practice may provide novel perspectives on the care and treatment of AD. The goal of the feasibility funding is to build a learning platform that will use both structured and free text EPR data by focusing on particular drugs and their impacts on AD patients. As recent studies suggested strong genetic links between inflammation and AD, we seek seed funding to pilot extraction of anti-inflammatory drugs and AD severity indicators and to analyse these at population level. We will develop and validate our text mining (TM) algorithms using the Oxford UK-CRIS database as a test-bed: a de-identified EPR database from a mental health NHS trust in Oxfordshire with 100,000 patient records. Our vision is to build a framework that maximises the use of UK-CRIS for smarter and more effective interventions for AD. TM algorithms will be developed and applied to free text in the UK-CRIS to automatically and precisely extract key variables such as drugs, cognitive function scales and diagnoses. Then, for example, these can be used to study the impact of anti-inflammatory drugs on longitudinal AD trajectories. Further patient-level predictions will be modelled using Machine Learning on the rich fine-granular text information to improve tailored interventions at the right timing. For instance subgroup of AD patients may benefit from certain drugs in the early stage, but others may only in the late stage. Patients will be actively involved in this personalised intervention process with their doctors. The key engineering challenge is to precisely extract variables from UK-CRIS free text that are required for large-scale analyses and for patient-level analyses. Clinical narratives in mental health EPRs are known to be complex, detailed and containing a variety of information (e.g. family history, patient experience, descriptions of events, signs and outcomes). Therefore it is not straightforward to identify those variables without ambiguity and to place them in a correct temporal order. While general TM approaches so far have shown promises in capturing coarse-grained themes and patterns in collections of text documents, the extraction of detailed values for an individual patient has not yet been resolved. Given the size and complexity of the available data, our main research aim is to combine machine learning with knowledge-rich TM to map UK-CRIS free text to medical variables of interest for each patient. Another challenge is to develop novel data analytics for large and complex EPR data. The extracted EPR data will be composed of 2 million patients, each with thousands of variables and multiple time points. Simplified analyses may be possible with traditional epidemiological techniques, e.g. a couple of exposures and outcomes of interest with a linear model. However, new analytical approaches will be required to maximise the use of this rich and large data. For instance, in a highly data-driven approach, it will be possible to run a systematic search in the space of all possible highly multivariate linear models to find the one with best goodness of fit, rather than simply trying a linear model that might be obvious from prior medical knowledge. As another example, rather than building models with traditional classifiers (e.g. discriminant analysis or support vector machine), we could attempt more recent neural networks, which require in the order of millions of samples, which in the exceptional case of UK-CRIS we do have. |
| Impact | DoI / Paper - TBC Disciplines - Psychiatry; Computer Science; Data Science; Bioinformatics |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 1) - Unobtrusive behavioural monitoring via the interactions of daily living |
| Organisation | University of Nottingham |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | Daily transactions and interactions are increasingly digital; through analysing data flows this study aims to explore five data streams - activity at home via consumption of electricity & water through electrical devices; driving style; spend via bank & credit card information; position via GPS; and changes in voice pattern via pitch & word rate - that when extracted, analysed, processed, and presented in a suitable manner, could be used to track changes in behaviour that may be indicative of mood. |
| Impact | Funding secured from NIHR Nottingham BRC to develop an early warning signs system that could be applied to a range of conditions. This is also a collaboration with the NIHR Maudsley BRC. Following a study of the various systems available including Databox (Nottingham) and HAT (Warwick), the open-source RADAR-BASE system has been selected as the data collection and repository system to underpin a range of new interventions. Additional software has also been developed to add a service-user portal and allow additional streams of data to be collected. One study has commenced to explore service user requirements, feasibility and acceptability for using the new system to detect early warning signs for bipolar disorder, using a co-development approach. An additional PhD study is using the technology to develop a system to enable a prospective study to investigate the characterisation of changes involved in developing an identity as a 'person in recovery' and a feasibility study using the new system began in May 2019. Disciplines - Biomedical Engineering; Computer Science; Psychiatry; Electronic Engineering; Electromagnetic Applications; Medical Sociology |
| Start Year | 2017 |
| Description | Feasibility Study (Stage 2) - Building and Testing a Demonstrator System for the AI Empathy Agent |
| Organisation | Liverpool John Moores University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This study proposed an always-on, easy-to-use Empathy Agent (EA) to deliver peer-support in a digital arena. By crowdsourcing empathic responses generated by real users, the EA will grow smarter and more empathic with each use and thus improve over time. Currently, there are no online, automatic systems available like this; the increasingly ubiquitous chatbot technology are mostly based on prewritten scripts. The primary objective of this project is to extend the NewMind Funded Stage 1 Feasibility work by building and testing a demonstrator system of the Empathy Agent, using several service-user centred design cycles. First, service users will rate the existing bank of responses from Stage 1 in an online survey. The study will then explore recent advances in natural language processing and machine learning to construct a demonstrator system in order to provide the most appropriate responses. This system will then be trialled with a small group of servicer users to evaluate its ability to deliver empathetic responses in an appropriate manner. The system will provide instant responses crowdsourced from peers to deliver the voice of the peer support community in an empathetic and appropriate way in times of need. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Building and Testing a Demonstrator System for the AI Empathy Agent |
| Organisation | University of East London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This study proposed an always-on, easy-to-use Empathy Agent (EA) to deliver peer-support in a digital arena. By crowdsourcing empathic responses generated by real users, the EA will grow smarter and more empathic with each use and thus improve over time. Currently, there are no online, automatic systems available like this; the increasingly ubiquitous chatbot technology are mostly based on prewritten scripts. The primary objective of this project is to extend the NewMind Funded Stage 1 Feasibility work by building and testing a demonstrator system of the Empathy Agent, using several service-user centred design cycles. First, service users will rate the existing bank of responses from Stage 1 in an online survey. The study will then explore recent advances in natural language processing and machine learning to construct a demonstrator system in order to provide the most appropriate responses. This system will then be trialled with a small group of servicer users to evaluate its ability to deliver empathetic responses in an appropriate manner. The system will provide instant responses crowdsourced from peers to deliver the voice of the peer support community in an empathetic and appropriate way in times of need. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Building and Testing a Demonstrator System for the AI Empathy Agent |
| Organisation | University of Sheffield |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. |
| Collaborator Contribution | This study proposed an always-on, easy-to-use Empathy Agent (EA) to deliver peer-support in a digital arena. By crowdsourcing empathic responses generated by real users, the EA will grow smarter and more empathic with each use and thus improve over time. Currently, there are no online, automatic systems available like this; the increasingly ubiquitous chatbot technology are mostly based on prewritten scripts. The primary objective of this project is to extend the NewMind Funded Stage 1 Feasibility work by building and testing a demonstrator system of the Empathy Agent, using several service-user centred design cycles. First, service users will rate the existing bank of responses from Stage 1 in an online survey. The study will then explore recent advances in natural language processing and machine learning to construct a demonstrator system in order to provide the most appropriate responses. This system will then be trialled with a small group of servicer users to evaluate its ability to deliver empathetic responses in an appropriate manner. The system will provide instant responses crowdsourced from peers to deliver the voice of the peer support community in an empathetic and appropriate way in times of need. |
| Impact | Disciplines - Psychology; Social Psychology; Assistive Technology; Computer Science |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Detecting Mental health behaviours from Mobile Interactions (DEMMI): a focus on eating disorder and self-harm |
| Organisation | Lancaster University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | This work focuses on two, related mental health issues-eating disorders (ED) and self-harm (SH)-which mainly affect younger people, aged 16-24. By automating the identification of triggers for negative behaviours, we will be able to understand the best opportunities for introducing in-situ mobile interventions, at times when they are most needed. A small amount of previous work has explored the use of low-level activity recognition on mobile interactions as an approach for understanding mental health behaviours. However, as yet, we were unable to find any research which has specifically focused on detecting mobile behaviour specific to eating disorder or self-harm. We will use the AWARE framework as a starting point for understanding the contextual information that we can gather about young people experiencing these mental health issues, eventually leading to the development of a contextually aware application that will facilitate intervention delivery at times when they are most needed. This research will provide early data to support the detection of mental health behaviors through mobile interactions. Once we are able to identify patterns of mobile use which feed into the prediction of triggers points for self-harm and disordered eating behavior, we will be able to understand the points in time that preventative measures might be implemented, in the form of mobile based interventions. Our next stage of research will focus on the design and delivery of these interventions. We aim to apply for funding from either the Wellcome Trust, or NIHR Research for Patient Benefit stream to further this work. |
| Impact | Disciplines - Digital Health; Psychology; Computer Science; Social Sciences Feasibility Study Lead Dr Roisin McNaney (formerly Bristol University, now Monash University, Australia, has a PhD student who has received a scholarship at Monash to carry on the work. The PhD will be exploring the co-design of context aware interventions for disordered eating behaviours. They will be submitting paper "Detecting Mental health behaviours using Mobile Interactions (DeMMI): an exploratory study focusing on binge eating" to JMIR in March/April 2021 |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Detecting Mental health behaviours from Mobile Interactions (DEMMI): a focus on eating disorder and self-harm |
| Organisation | Pennine Care NHS Foundation Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | This work focuses on two, related mental health issues-eating disorders (ED) and self-harm (SH)-which mainly affect younger people, aged 16-24. By automating the identification of triggers for negative behaviours, we will be able to understand the best opportunities for introducing in-situ mobile interventions, at times when they are most needed. A small amount of previous work has explored the use of low-level activity recognition on mobile interactions as an approach for understanding mental health behaviours. However, as yet, we were unable to find any research which has specifically focused on detecting mobile behaviour specific to eating disorder or self-harm. We will use the AWARE framework as a starting point for understanding the contextual information that we can gather about young people experiencing these mental health issues, eventually leading to the development of a contextually aware application that will facilitate intervention delivery at times when they are most needed. This research will provide early data to support the detection of mental health behaviors through mobile interactions. Once we are able to identify patterns of mobile use which feed into the prediction of triggers points for self-harm and disordered eating behavior, we will be able to understand the points in time that preventative measures might be implemented, in the form of mobile based interventions. Our next stage of research will focus on the design and delivery of these interventions. We aim to apply for funding from either the Wellcome Trust, or NIHR Research for Patient Benefit stream to further this work. |
| Impact | Disciplines - Digital Health; Psychology; Computer Science; Social Sciences Feasibility Study Lead Dr Roisin McNaney (formerly Bristol University, now Monash University, Australia, has a PhD student who has received a scholarship at Monash to carry on the work. The PhD will be exploring the co-design of context aware interventions for disordered eating behaviours. They will be submitting paper "Detecting Mental health behaviours using Mobile Interactions (DeMMI): an exploratory study focusing on binge eating" to JMIR in March/April 2021 |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Detecting Mental health behaviours from Mobile Interactions (DEMMI): a focus on eating disorder and self-harm |
| Organisation | University of Bristol |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | This work focuses on two, related mental health issues-eating disorders (ED) and self-harm (SH)-which mainly affect younger people, aged 16-24. By automating the identification of triggers for negative behaviours, we will be able to understand the best opportunities for introducing in-situ mobile interventions, at times when they are most needed. A small amount of previous work has explored the use of low-level activity recognition on mobile interactions as an approach for understanding mental health behaviours. However, as yet, we were unable to find any research which has specifically focused on detecting mobile behaviour specific to eating disorder or self-harm. We will use the AWARE framework as a starting point for understanding the contextual information that we can gather about young people experiencing these mental health issues, eventually leading to the development of a contextually aware application that will facilitate intervention delivery at times when they are most needed. This research will provide early data to support the detection of mental health behaviors through mobile interactions. Once we are able to identify patterns of mobile use which feed into the prediction of triggers points for self-harm and disordered eating behavior, we will be able to understand the points in time that preventative measures might be implemented, in the form of mobile based interventions. Our next stage of research will focus on the design and delivery of these interventions. We aim to apply for funding from either the Wellcome Trust, or NIHR Research for Patient Benefit stream to further this work. |
| Impact | Disciplines - Digital Health; Psychology; Computer Science; Social Sciences Feasibility Study Lead Dr Roisin McNaney (formerly Bristol University, now Monash University, Australia, has a PhD student who has received a scholarship at Monash to carry on the work. The PhD will be exploring the co-design of context aware interventions for disordered eating behaviours. They will be submitting paper "Detecting Mental health behaviours using Mobile Interactions (DeMMI): an exploratory study focusing on binge eating" to JMIR in March/April 2021 |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Detecting Mental health behaviours from Mobile Interactions (DEMMI): a focus on eating disorder and self-harm |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | This work focuses on two, related mental health issues-eating disorders (ED) and self-harm (SH)-which mainly affect younger people, aged 16-24. By automating the identification of triggers for negative behaviours, we will be able to understand the best opportunities for introducing in-situ mobile interventions, at times when they are most needed. A small amount of previous work has explored the use of low-level activity recognition on mobile interactions as an approach for understanding mental health behaviours. However, as yet, we were unable to find any research which has specifically focused on detecting mobile behaviour specific to eating disorder or self-harm. We will use the AWARE framework as a starting point for understanding the contextual information that we can gather about young people experiencing these mental health issues, eventually leading to the development of a contextually aware application that will facilitate intervention delivery at times when they are most needed. This research will provide early data to support the detection of mental health behaviors through mobile interactions. Once we are able to identify patterns of mobile use which feed into the prediction of triggers points for self-harm and disordered eating behavior, we will be able to understand the points in time that preventative measures might be implemented, in the form of mobile based interventions. Our next stage of research will focus on the design and delivery of these interventions. We aim to apply for funding from either the Wellcome Trust, or NIHR Research for Patient Benefit stream to further this work. |
| Impact | Disciplines - Digital Health; Psychology; Computer Science; Social Sciences Feasibility Study Lead Dr Roisin McNaney (formerly Bristol University, now Monash University, Australia, has a PhD student who has received a scholarship at Monash to carry on the work. The PhD will be exploring the co-design of context aware interventions for disordered eating behaviours. They will be submitting paper "Detecting Mental health behaviours using Mobile Interactions (DeMMI): an exploratory study focusing on binge eating" to JMIR in March/April 2021 |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Detecting Mental health behaviours from Mobile Interactions (DEMMI): a focus on eating disorder and self-harm |
| Organisation | Xenzone |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | This work focuses on two, related mental health issues-eating disorders (ED) and self-harm (SH)-which mainly affect younger people, aged 16-24. By automating the identification of triggers for negative behaviours, we will be able to understand the best opportunities for introducing in-situ mobile interventions, at times when they are most needed. A small amount of previous work has explored the use of low-level activity recognition on mobile interactions as an approach for understanding mental health behaviours. However, as yet, we were unable to find any research which has specifically focused on detecting mobile behaviour specific to eating disorder or self-harm. We will use the AWARE framework as a starting point for understanding the contextual information that we can gather about young people experiencing these mental health issues, eventually leading to the development of a contextually aware application that will facilitate intervention delivery at times when they are most needed. This research will provide early data to support the detection of mental health behaviors through mobile interactions. Once we are able to identify patterns of mobile use which feed into the prediction of triggers points for self-harm and disordered eating behavior, we will be able to understand the points in time that preventative measures might be implemented, in the form of mobile based interventions. Our next stage of research will focus on the design and delivery of these interventions. We aim to apply for funding from either the Wellcome Trust, or NIHR Research for Patient Benefit stream to further this work. |
| Impact | Disciplines - Digital Health; Psychology; Computer Science; Social Sciences Feasibility Study Lead Dr Roisin McNaney (formerly Bristol University, now Monash University, Australia, has a PhD student who has received a scholarship at Monash to carry on the work. The PhD will be exploring the co-design of context aware interventions for disordered eating behaviours. They will be submitting paper "Detecting Mental health behaviours using Mobile Interactions (DeMMI): an exploratory study focusing on binge eating" to JMIR in March/April 2021 |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Detecting Mental health behaviours from Mobile Interactions (DEMMI): a focus on eating disorder and self-harm |
| Organisation | York St John University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | This work focuses on two, related mental health issues-eating disorders (ED) and self-harm (SH)-which mainly affect younger people, aged 16-24. By automating the identification of triggers for negative behaviours, we will be able to understand the best opportunities for introducing in-situ mobile interventions, at times when they are most needed. A small amount of previous work has explored the use of low-level activity recognition on mobile interactions as an approach for understanding mental health behaviours. However, as yet, we were unable to find any research which has specifically focused on detecting mobile behaviour specific to eating disorder or self-harm. We will use the AWARE framework as a starting point for understanding the contextual information that we can gather about young people experiencing these mental health issues, eventually leading to the development of a contextually aware application that will facilitate intervention delivery at times when they are most needed. This research will provide early data to support the detection of mental health behaviors through mobile interactions. Once we are able to identify patterns of mobile use which feed into the prediction of triggers points for self-harm and disordered eating behavior, we will be able to understand the points in time that preventative measures might be implemented, in the form of mobile based interventions. Our next stage of research will focus on the design and delivery of these interventions. We aim to apply for funding from either the Wellcome Trust, or NIHR Research for Patient Benefit stream to further this work. |
| Impact | Disciplines - Digital Health; Psychology; Computer Science; Social Sciences Feasibility Study Lead Dr Roisin McNaney (formerly Bristol University, now Monash University, Australia, has a PhD student who has received a scholarship at Monash to carry on the work. The PhD will be exploring the co-design of context aware interventions for disordered eating behaviours. They will be submitting paper "Detecting Mental health behaviours using Mobile Interactions (DeMMI): an exploratory study focusing on binge eating" to JMIR in March/April 2021 |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Unlocking evidence from electronic patient records for smart intervention of mental health disorders - towards systematic extension |
| Organisation | DeepCognito |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | The NewMind funded Stage 1 Feasibility Study led by Kim et al (Unlocking the evidence from electronic patient records for smart intervention of mental health disorders; a case study in Alzheimer's Disease) demonstrated the concept was possible in one CRIS centre (Oxford), extracting dementia diagnosis, 3 medications and 2 mental health scores, obtaining F1 accuracies ranging from 81.48% to 98.05%. These variables were then used to epidemiologically study subtypes of dementia and drug effects. To demonstrate scalability, this Stage 2 study aims at expanding capability to a second UK-CRIS centre (London Slam), a second prominent disorder (depression, besides Alzheimer disease), a larger dictionary of variables (6 drugs, 4 scores, 3 symptoms), and 2 socio-economic variables (e.g. EQ-5D) used by NHS to decide medical practise. These variables will then be used in translatable epidemiological analyses and to estimate best-personalised treatment per individual patient with neural networks. |
| Impact | Disciplines - Psychiatry; Computer Science; Data Science; Bioinformatics |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Unlocking evidence from electronic patient records for smart intervention of mental health disorders - towards systematic extension |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | The NewMind funded Stage 1 Feasibility Study led by Kim et al (Unlocking the evidence from electronic patient records for smart intervention of mental health disorders; a case study in Alzheimer's Disease) demonstrated the concept was possible in one CRIS centre (Oxford), extracting dementia diagnosis, 3 medications and 2 mental health scores, obtaining F1 accuracies ranging from 81.48% to 98.05%. These variables were then used to epidemiologically study subtypes of dementia and drug effects. To demonstrate scalability, this Stage 2 study aims at expanding capability to a second UK-CRIS centre (London Slam), a second prominent disorder (depression, besides Alzheimer disease), a larger dictionary of variables (6 drugs, 4 scores, 3 symptoms), and 2 socio-economic variables (e.g. EQ-5D) used by NHS to decide medical practise. These variables will then be used in translatable epidemiological analyses and to estimate best-personalised treatment per individual patient with neural networks. |
| Impact | Disciplines - Psychiatry; Computer Science; Data Science; Bioinformatics |
| Start Year | 2018 |
| Description | Feasibility Study (Stage 2) - Unlocking evidence from electronic patient records for smart intervention of mental health disorders - towards systematic extension |
| Organisation | University of Oxford |
| Department | Oxford Hub |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester was also a partner in delivering the study. |
| Collaborator Contribution | The NewMind funded Stage 1 Feasibility Study led by Kim et al (Unlocking the evidence from electronic patient records for smart intervention of mental health disorders; a case study in Alzheimer's Disease) demonstrated the concept was possible in one CRIS centre (Oxford), extracting dementia diagnosis, 3 medications and 2 mental health scores, obtaining F1 accuracies ranging from 81.48% to 98.05%. These variables were then used to epidemiologically study subtypes of dementia and drug effects. To demonstrate scalability, this Stage 2 study aims at expanding capability to a second UK-CRIS centre (London Slam), a second prominent disorder (depression, besides Alzheimer disease), a larger dictionary of variables (6 drugs, 4 scores, 3 symptoms), and 2 socio-economic variables (e.g. EQ-5D) used by NHS to decide medical practise. These variables will then be used in translatable epidemiological analyses and to estimate best-personalised treatment per individual patient with neural networks. |
| Impact | Disciplines - Psychiatry; Computer Science; Data Science; Bioinformatics |
| Start Year | 2018 |
| Description | Feasibility study (Stage 2) - Real-time brain modelling for personalised neuro-therapeutic interventions in chronic pain |
| Organisation | University of Liverpool |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Chronic pain is estimated to cost in excess of $600B per year in the US, together with considerable personal impacts on the individuals involved and their carers. This study aims to make new technological interventions for chronic pain, with a focus on fibromyalgia and osteoarthritis for which current treatment options (pharmaceuticals) are limited or associated with significant side effects. By employing technological factors such as electrical stimulation, tactile, sound or light stimulation we may create 'electroceutical' interventions which employ different pain relief mechanisms, with a different level of effectiveness, the project anticipates fewer side effects. In particular, this project seeks to create 'closed loop' devices where the stimulation parameters (such as frequency and phase) are automatically adjusted based upon currently sensed data in order to personalise the stimulation that is applied. To achieve this it will monitor brain activity (the EEG) to calculate the changes in the underlying brain networks which are being activated during an 'electroceutical' intervention, performing this 'brain connectivity' analysis in real-time. The proposal seeks to create new mathematical methods to allow this to be done on smartphone class hardware and to characterise the 'speed'/latency required for creating closed loop devices. |
| Impact | Disciplines - Psychology; Electrical Engineering; Neuro-rheumatology; Neuroscience & Experimental Psychology; |
| Start Year | 2018 |
| Description | Feasibility study (Stage 2) - Real-time brain modelling for personalised neuro-therapeutic interventions in chronic pain |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The NewMind team worked with the community to develop the Research Roadmap that provided a framework for the feasibility project funded by NewMind - of which this was one. The team organised and led the workshop at which the project was initially developed, provided formative feedback on an outline proposal, and worked with the proposers to develop a fundable project. Whilst the project was 'live', the team continued to provide 'hands-on' input, and organised opportunities for peer-to-peer support, including input from service users. Manchester also led delivery of the study. |
| Collaborator Contribution | Chronic pain is estimated to cost in excess of $600B per year in the US, together with considerable personal impacts on the individuals involved and their carers. This study aims to make new technological interventions for chronic pain, with a focus on fibromyalgia and osteoarthritis for which current treatment options (pharmaceuticals) are limited or associated with significant side effects. By employing technological factors such as electrical stimulation, tactile, sound or light stimulation we may create 'electroceutical' interventions which employ different pain relief mechanisms, with a different level of effectiveness, the project anticipates fewer side effects. In particular, this project seeks to create 'closed loop' devices where the stimulation parameters (such as frequency and phase) are automatically adjusted based upon currently sensed data in order to personalise the stimulation that is applied. To achieve this it will monitor brain activity (the EEG) to calculate the changes in the underlying brain networks which are being activated during an 'electroceutical' intervention, performing this 'brain connectivity' analysis in real-time. The proposal seeks to create new mathematical methods to allow this to be done on smartphone class hardware and to characterise the 'speed'/latency required for creating closed loop devices. |
| Impact | Disciplines - Psychology; Electrical Engineering; Neuro-rheumatology; Neuroscience & Experimental Psychology; |
| Start Year | 2018 |
| Description | Conference Presentation |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | K. Lopez-Dias, A. J Casson, J. Taylor, N. Trujillo-Barreto, A. Jones and M. Sivan "Brain frontal alpha entrainment using visual stimulation reduces pain intensity and unpleasantness in a sample of chronic pain patients," British Society of Rehabilitation Medicine, Society for Research in Rehabilitation, Association Of Chartered Physiotherapists For People With Learning Disabilities conference, Online, 2020. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.bsrm.org.uk/events/bsrm-events/profile/arehabilitation-research-practice-and-education-i... |
| Description | Feasibility Study Poster Presentations at MindTech 2018 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Poster Presentation 1 - Linked to Brown/Casson et al 'Towards a next generation platform for personalised neuro-therapeutic interventions in chronic pain', poster entitled 'The power of personalisation - using smartphone patient diaries to understand lived experience and self-management strategies' presented by Lucy Mitchinson (University of Manchester, School of Biological Sciences, Faculty of Biology, Medicine and Health & TIYGA Health) - https://twitter.com/lucymitchinson/status/1070407634099023872?s=20 Poster Presentation 2 - Linked to Waddingham et al 'Dynamic well-being visualisation toolbox for digital products' poster entitled 'Co-design of visual presentations of well-being' by Dr Michael Craven (University of Nottingham / NIHR MindTech) - https://twitter.com/NIHR_MindTech/status/1070260728765038593/photo/1 Exposure to international audience of academics, industry, charities and policymakers. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.mindtech.org.uk/news-events/events/mindtech-symposium-2018 |
| Description | Hosted Mental Health Track sessions MEIBioeng 2016 conference (held at Keble College, University of Oxford) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Hosted following sessions on MH from NewMind network members - Software Engineering Challenges in Digital Mental Health Research Dr Pauline Whelan (mHealth Applications Manager, Centre for Health Informatics)) Interactive involvement in, and access to, MH care planning Dr Helen Brooks (Research Fellow, School of Nursing, Midwifery and Social Work) Early signs of Dementia in Recording of User-Computer Interaction Prof Alistair Sutcliffe (Professor of Systems Engineering, Manchester Business School) Automated Monitoring of Symptom Severity Prof Maarten De Vos (Associate Professor of Engineering Science, Biomedical Engineering) Diagnosing mental health disorders using computerised decision trees Prof Eef Hogervorst (Professor in Psychology) A computer decision support system for detecting and managing risks associated with mental-health problems Dr Christopher Buckingham (Senior Lecturer, Engineering & Applied Science) Tactile Biofeedback for a Personalised Mental Health Intervention Dr Eiman Kanjo (Senior Lecturer, School of Science & Technology) The EIP Matrix: an online tool that allows Early Intervention in Psychosis teams to have real time visualised data on the level of fidelity to NICE recommendations Sarah Amani (Senior Programme Manager, Early Intervention in Psychosis Preparedness Programme) |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.ibme.ox.ac.uk/news-events/events/meibioeng-16 |
| Description | NewMind Feasibility Study 'Managing Mental Health in the School Environment' (Mwasambili et al) featured/referenced in AHSN Network brochure |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Featured in AHSN Network 'Diversity and innovation: A celebration of BAME innovators and our pledges to do more' (pg 15) |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://www.ahsnnetwork.com/wp-content/uploads/2019/09/Diversity-and-Innovation-WEB.pdf |
| Description | NewMind Plenary Events - research proposal discussion, presentation, and dissemination (Nov 2017 / Feb 2018 / Jan 2019) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Plenary events to present on funded research proposals (development, progress, findings), network growth, and community participation in developing and growing the Research Roadmap (used to inform future direction, funding focus, and informing policy makers). Attracted ~100 attendees to each of three events |
| Year(s) Of Engagement Activity | 2017,2018,2019 |
| URL | http://www.newmindnetwork.org.uk/previous-events/ |
| Description | NewMind Sandpit Proposal Development Workshops (6 in total - May 2016 / Sept 2016 / Feb 2017 / May 2017 / July 2017 / Nov 2017) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Network wide proposal development workshop. Events were a combination of presentations, workshop discussion and early stage feasibility study development amongst Engineering & Physical Science Researchers, Mental Health Clinicians / NHS staff, Industry/ Business, Charities, and individual Service Users. The 6 Sandpits run by the NewMind network had over 200 attendees, resulted in the development of 60 proposals, of which 29 were submitted for funding. Of these, 22 were filtered through to be heard at a Dragons' Den panel, resulting in the funding of 13 Stage 1 Feasibility Studies (@£15k 80% FEC) and four Stage 2 Feasibility Studies (@£30-45k 80% FEC). In addition to direct funding from NewMind (where successful), other collaborations between participants were spawned that have generated proposals and applications for funding from other sources. |
| Year(s) Of Engagement Activity | 2016,2017 |
| URL | http://www.newmindnetwork.org.uk/previous-events/ |
| Description | Poster & Paper presentations relating to Feasibility Study (Stage 1) 'UPP' & EDDA' (Mcnaney / Honary et al) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Attendance & Presentation at CHI conference in Glasgow, May 2019) - https://eprints.lancs.ac.uk/id/eprint/132468/ & https://eprints.lancs.ac.uk/id/eprint/132468/1/Method_Selection_and_Participant_Recruitment_in_Sensetive_HCI_Resaerch.pdf & http://www.research.lancs.ac.uk/portal/en/publications/method-selection-and-participant-recruitment-in-sensitive-hci-research(d7c38d4c-c1fa-402b-a45e-b2049c15388c).html Attendance & paper submission at NordiCHI 2018 Proceedings of the 10th Nordic Conference on Human-Computer Interaction: Workshop on mHealth and psycho-physical well-being, titled: "Considering the Challenges of Designing mHealth with and for Young People in Mental Health Domain" - http://www.research.lancs.ac.uk/portal/en/publications/considering-the-challenges-of-designing-mhealth-with-and-for-young-people-in-mental-health-domain(f92ea0e1-f27c-4a07-a9e2-a9b05b5d07cd)/export.html Poster presentations at the Digital Health Showcase event at Lancaster University in 2018 |
| Year(s) Of Engagement Activity | 2018,2019 |
| Description | Poster Presentation of NewMind Feasibility Study 'Developing early detection methods to assess the risk of pressure ulcers in individuals with mental illness' (Bader / Bostan et al) |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Attendance & poster presentation at European Pressure Ulcer Advisory Panel Meeting, Lyon, September 2019 (see https://epuap2019.org/fileadmin/user_upload/EPUAP/EPUAP19/Epuap2019_Katalog.qxp_web.pdf, p17 & p79) Presentation and exposure of research to international delegates |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://epuap2019.org/fileadmin/user_upload/EPUAP/EPUAP19/Epuap2019_Katalog.qxp_web.pdf |
| Description | Presentation of Feasibility Study 'Managing Mental Health in the School Environment' (Mwasambili et al) at two international conferences |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | 2019 Games for Change Festival, New York (June 2019) - https://g4c19.sched.com/speaker/naomi_mwasambili.1zdem7aw 2019 Games for Health Europe Festival, Eindhoven (October 2019) - https://www.gamesforhealtheurope.org/speaker/naomi-mwasambili/ & https://twitter.com/NeuroChampions/status/1181147160525639680?s=20 Exposure to international audiences |
| Year(s) Of Engagement Activity | 2019 |
| Description | Production of the NewMind Brochure |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Production of NewMind overview brochure documenting the success of the network, activities funded, providing a layman's view of the studies and further information re: publications and further research |
| Year(s) Of Engagement Activity | 2021 |
| URL | http://www.newmindnetwork.org.uk/newmind-brochure/ |