Characterising physical activity patterns in community-dwelling older adults using digital phenotyping: a 2-week observational study protocol

• الحاوية / القاعدة: BMJ Open
• المؤلفون الرئيسيون: Bruno Bonnechère, Dominique Hansen, Kim Daniels, Sharona Vonck, Jolien Robijns, Annemie Spooren
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: BMJ Publishing Group 2025-05-01
• الوصول للمادة أونلاين: https://bmjopen.bmj.com/content/15/5/e095769.full

Introduction Physical activity (PA) is crucial for older adults’ well-being and mitigating health risks. Encouraging active lifestyles requires a deeper understanding of the factors influencing PA, which conventional approaches often overlook by assuming stability in these determinants over time. However, individual-level determinants fluctuate over time in real-world settings. Digital phenotyping (DP), employing data from personal digital devices, enables continuous, real-time quantification of behaviour in natural settings. This approach offers ecological and dynamic assessments into factors shaping individual PA patterns within their real-world context. This paper presents a study protocol for the DP of PA behaviour among community-dwelling older adults aged 65 years and above.Methods and analysis This 2-week multidimensional assessment combines supervised (self-reported questionnaires, clinical assessments) and unsupervised methods (continuous wearable monitoring and ecological momentary assessment (EMA)). Participants will wear a Garmin Vivosmart V.5 watch, capturing 24/7 data on PA intensity, step count and heart rate. EMA will deliver randomised prompts four times a day via the Smartphone Ecological Momentary Assessment3 application, collecting real-time self-reports on physical and mental health, motivation, efficacy and contextual factors. All measurements align with the Behaviour Change Wheel framework, assessing capability, opportunity and motivation. Machine learning will analyse data, employing unsupervised learning (eg, hierarchical clustering) to identify PA behaviour patterns and supervised learning (eg, recurrent neural networks) to predict behavioural influences. Temporal patterns in PA and EMA responses will be explored for intraday and interday variability, with follow-up durations optimised through random sliding window analysis, with statistical significance evaluated in RStudio at a threshold of 0.05.Ethics and dissemination The study has been approved by the ethical committee of Hasselt University (B1152023000011). The findings will be presented at scientific conferences and published in a peer-reviewed journal.Trial registration number NCT06094374.