What does this research mean for the field?

Integrating ecological momentary assessment with continuous wearable sensor data is a feasible approach that enhances the accuracy and ecological validity of physical activity assessment in older adults by capturing discrepancies between objective and self-reported measures. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

March 3, 2026Open Access

S12-4: Physical Activity Patterns in Older Adults: From Steps to Context – Optimising Digital Phenotyping for Physical Activity Monitoring in Older Adults by Integrating Wearable Data and Ecological Momentary Assessment

Key Points

PA assessment in older adults significantly improved with integrated EMA and wearable sensor data, indicating enhanced accuracy.
With 67.2% adherence to EMA prompts, the study found peaks in low and moderate intensity PA around midday.

Structured PICO

Does integrating ecological momentary assessment with wearable sensor data provide feasible and accurate physical activity monitoring in community-dwelling older adults?

Population

108 community-dwelling older adults

Intervention

Continuous physical activity monitoring using Garmin Vivo 5 wearable sensors integrated with real-time ecological momentary assessment (EMA) prompts

Outcome

Feasibility based on adherence to EMA, and associations between psychological factors (motivation, self-efficacy) and physical activity intensitypatient reported

Integrating wearable sensor data with ecological momentary assessment is feasible and highlights the discrepancy between objective and self-reported physical activity in older adults.

Abstract

Purpose: Physical activity (PA) is essential for healthy ageing, yet accurately assessing PA in older adults remains challenging due to the biases and limitations of traditional clinical assessments. This study aimed to optimise digital phenotyping strategies for evaluating PA patterns in older adults by integrating ecological momentary assessment (EMA) with continuous wearable sensor data. Methods: Over a two-week period, 108 community-dwelling older adults participated in the study. PA was continuously monitored using Garmin Vivo 5 wearable sensors, while participants provided real-time responses to EMA prompts. Feasibility was assessed based on adherence to EMA, and associations between psychological factors (motivation, self-efficacy) and PA intensity were analysed. Results: The combined EMA and wearable sensor approach proved feasible, with 67.2% overall adherence to EMA prompts (morning: 68.1%; evening: 65.4%). PA was primarily of low (51.4%) and moderate (46.2%) intensity, with activity peaks around midday. Motivation and self-efficacy were significantly associated with low-intensity PA (R = 0.20 and 0.14, respectively), particularly in the morning. Notably, no correlation was found between objective step counts and self-reported PA (R = −0.026, p = 0.65), emphasising the importance of combining both data sources. Conclusions: Integrating EMA with wearable sensor data within a temporal framework enhances the ecological validity and accuracy of PA assessment in older adults. This approach offers valuable, personalised insights that can inform targeted, time-sensitive interventions to promote PA in ageing populations. Support/Funding Source: PXL University of Applied Sciences and Arts 2/DWO/2021/HC/P133 and by Flanders Innovation and Entrepreneurship 2/DWO/2022/HC/VL041. Keywords: Physical activity, older adults, ecological momentary assessment, wearable sensors

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