The development of technology that allows for multiple measurements of an individual has expanded the potential for personalised prevention and treatment of diseases. This is also evident in the field of physical activity (PA), where wearable sensors have enriched our understanding of PA and its relationship with various health outcomes. From the use of diaries, questionnaires, and recalls, a significant growth in the integration of wearable sensors for tracking PA has occurred in recent years. Given that PA is complex to measure and can be characterised by domains, dimensions, and correlates or determinants [Citation1], wearable sensors enable a more precise measurement of the targeted construct. Wearable sensors, such as accelerometers, are small, non-invasive devices that provide a continuous method of measuring PA. While self-report of PA is still utilised, this method is limited by recall, comprehension, and social desirability bias [Citation2]. Although self-report has its place in specific areas of PA research, the inaccurate values derived from self-report restrict their usefulness in comparison to wearable sensors like accelerometers.
Accelerometers are commonly integrated into fitness trackers, smartwatches, and other activity monitors, and they utilise wearable sensors to gather information about PA [Citation3]. This information includes details on frequency, intensity, and duration, as well as sedentary behaviour, rest-activity patterns, and sleep patterns. Raw accelerometry and pattern recognition can also be used to capture specific activities and movements. Additionally, accelerometers can be combined with other sensors, such as heart rate monitors and GPS, or with diaries to provide a more comprehensive understanding of physical activity patterns. Ultimately, accelerometers could play a significant role in establishing a personalised medicine approach, which can be used in the primary and secondary prevention of various diseases and by rehabilitation personnel to monitor PA patterns in a real-world context over extended periods.
In addition, accelerometer data are now included in large-scale cohorts, such as the National Health and Nutrition Examination Survey (NHANES) and the UK Biobank. This enables us to explore PA in larger populations, which I consider essential for constructing a robust scientific basis for the role of PA in health using accelerometers. Furthermore, recent pharmaceutical trials have started to incorporate PA based on accelerometers, rather than relying on self-reports. This development will bolster the field of PA and increase our understanding of the association between PA and health outcomes.
There are several challenges associated with the measurement of PA that must be addressed. These challenges include the use of different wearable sensors, protocol design, body position, and duration of wear time. Moreover, there is limited accelerometer data available on PA in low- and middle-income countries, which are also at high risk for non-communicable diseases [Citation4]. Additionally, accelerometer data is typically collected over a period of one week and is then assumed to remain constant during subsequent periods, which may not always be accurate. In the future, it is likely that we will have the opportunity to collect data repeatedly over extended periods, enabling us to better understand changes in PA, patterns of PA, and their relationship to various health outcomes and other factors that influence health. By integrating large, valid data on PA with other data sources such as genetic, biomarkers, environmental, and lifestyle, I believe we can gain a more precise understanding of the role of PA in health and develop personalised prevention strategies.
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References
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