Using real-time data capture strategies and within-subject studies to better understand the physical activity health paradox

• Published in: British journal of sports medicine p. bjsports-2024-108363
• Main Authors: Dunton, Genevieve F, Belcher, Britni R
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 13.06.2025
• Subjects: Low income groups; Sensors; Smartphones; Socioeconomic factors; United States > US; Women; Cardiometabolic risk; Cardiovascular Diseases; Physical activity
• DOI: 10.1136/bjsports-2024-108363
• ISSN: 0306-3674

[...]research does not sufficiently adjust for effects of socioeconomic indicators such as income or education level to examine whether these factors are associated with both OPA and cardiometabolic outcomes, thus confounding possible associations between them.4 Using real-time data capture strategies to study OPA and cardiometabolic risk To address the empirical gaps described above and better establish a causal linkage, evidence is needed to understand whether OPA leads to acute within-subject (eg, same-day or next-day) changes in psychosocial and cardiometabolic responses using real-time data capture strategies such as mobile devices and wearable sensors in real-world settings. A growing collection of commercially available wearable sensors and monitoring devices has been shown to collect reliable and valid measures of heart rate, heart rate variability, blood pressure and interstitial glucose metrics.5 Furthermore, accelerometers embedded within smartphones and smartwatches can capture exposure variables across the 24-hour activity cycle for any given day and employ composition data analytic strategies to understand how different combinations of OPA, LTPA, sedentary behaviour and sleep contribute to same-day or next-day acute cardiometabolic indicators.6 Such a step is critical in disentangling the interactive roles these behaviours and better understanding the extent to which this attenuates or exacerbates each other. Furthermore, real-time self-report methods using smartphone-based or smartwatch-based EMA can assess subjective characteristics of OPA and LTPA that may otherwise be susceptible to recall errors when assessed retrospectively and cannot be easily captured using accelerometers or other monitor-based wearable devices.7 Event-contingent EMA surveys triggered when elevated heart rate or body movement is detected8 can measure the psychological load of the activity (eg, stress), timing and duration of strenuous postures, and repetitive or painful body movements. When these approaches are implemented through multimeasurement burst designs in longitudinal panel studies, researchers will be able to study the cumulative impact of these short-term effects on long-term cardiometabolic outcomes such as heart disease and diabetes.