A 2-week assessment will monitor physical activity in older adults using wearable technology.
may provide real-time insights into physical activity patterns among older adults.
Participants will wear a Garmin Vivosmart V.5 watch to continuously record data on activity intensity, step count, and heart rate.
(EMA) will collect self-reports on health and motivation four times daily.
Machine learning techniques will be used to analyze and predict factors influencing physical activity behavior.
The study aims to capture variability in physical activity both within and between days.
Simplified
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. (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 (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 Assessmentapplication, 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. 3
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.
Key numbers
200
Sample Size
Convenient sample size chosen for the trial.
2 weeks
Duration of Monitoring
Participants will wear monitoring devices for 2 weeks.
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