Developing a precision digital intervention to promote physical activity in older adults of low socioeconomic status

Context of the research
By 2050, one in six people worldwide will be aged 65 years or over (United Nations, 2019), placing unprecedented logistical and financial pressures on health and social care services. Correspondingly, meeting the needs of an ageing society features amongst the UK government's four Grand Challenges in their Industrial Strategy (Department for Business, Energy and Industrial Strategy, 2017). Despite the known benefits of physical activity on chronic disease risk and quality-of-life (Chodzko-Zajko et al., 2009; Sun et al., 2013), few older adults comply with recommended physical activity levels (McPhee et al., 2016).

Recent evidence has encouraged the use of mobile technologies to support physical activity (Bort-Roig et al., 2014; Yerrakalva et al., 2019). Currently, over 80% of adults aged 65-74 years report recent internet usage (Office for National Statistics [ONS], 2019a), consistent with sharp rises in smartphone ownership (Ofcom, 2017). Digital exclusion is, however, stratified by socioeconomic status (SES) or its derivatives (Kontos et al., 2014; ONS, 2019b), emulating physical activity divides (McPhee et al., 2016). Importantly, these cost-effective tools could transform the efficiency and productivity of the healthcare industry (Yu et al., 2018), offering personalised behavioural support to even the hardest to reach, low-SES older adults (Sullivan & Lachman, 2017; Yardley et al., 2015).

Aims and methods
1. First, a systematic review with meta-analysis will aim to identify the major biocultural determinants/correlates (e.g., self-efficacy, social support) of physical activity in older adults, by SES. Should differences emerge (as expected) across socioeconomic groups, the remainder of the research will focus on low-SES individuals. Otherwise, older adults of varying SES will be recruited.
2. To understand how these identified biocultural variables fluctuate over time, electronic ecological momentary assessment (EMA), a real-time data capturing strategy using smartphone surveys, will aim to uncover acute microprocesses influencing physical activity behaviour in older adults. Data analysis will involve multilevel modelling.
3. To optimise the acceptability of a just-in-time adaptive intervention (JITAI) delivering physical activity support to users when they need it most (as determined by real-time feedback) (Hardeman et al., 2019), an iterative, qualitative, person-based intervention design study will actively engage older adults using a think-aloud method.
4. Finally, a pilot randomised controlled trial will test the feasibility and preliminary efficacy of the resulting JITAI. By monitoring dynamic biocultural variables, there is scope to use machine learning and/or control systems engineering techniques to promote physical activity at the point where it is most likely to effect a change in behaviour (Nahum-Shani et al., 2015).

Potential applications and benefits
An overseas visit to the Institute for Health Promotion and Disease Prevention Research (University of Southern California), whose area of expertise is EMA, will expand my knowledge of aforementioned methods.

This project has potential for academic, economic, and societal impact. It will provide an insight into the benefits/challenges associated with implementing tailored technology-based initiatives to support physical activity maintenance in (low-SES) older adults. A better understanding of their needs may also be of interest to policymakers and practitioners.