ArticlePloS one2017
Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Synthesis and Validity of Accelerometer Devices and Methods Used in Epidemiological Studies of Physical Activity Bout Duration and Health Outcomes: A Systematic Review.Sports medicine - open · 2026Pooled it
- Validity of objective methods for measuring sedentary behaviour in older adults: a systematic review.The international journal of behavioral nutrition and physical activity · 2018Pooled it
- Trial
- Sensor-measured sedentariness and physical activity are differentially related to fluid and crystallized abilities in aging.Psychology and aging · 2020Trial
- Trial
- Machine and Deep Learning for Detection of Moderate-to-Vigorous Physical Activity From Accelerometer Data: Systematic Scoping Review.Interactive journal of medical research · 2026Review
- Physical activity and sedentary behavior in peritoneal dialysis patients: a comparative analysis of ActiGraph GT3X data collected via wrist and waist with placement-specific cut-points.BMC nephrology · 2025Article
- Daily Variability in Sedentary Behaviour and Physical Activity Responsiveness in Older Women.Sensors (Basel, Switzerland) · 2025Article
- Survival analysis for predicting fitness app user churn.mHealth · 2025Article
- Using wearable technology to objectively investigate physical behaviour and determine health outcomes of a physical activity intervention in patients with psoriasis.Skin health and disease · 2024Article
- Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review.Journal of medical Internet research · 2024Article
- Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry.The international journal of behavioral nutrition and physical activity · 2024Article
- Concurrent Validity of Four Activity Monitors in Older Adults.Sensors (Basel, Switzerland) · 2024Article
- Article
- Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults.Sensors (Basel, Switzerland) · 2023Article
- Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model.Sensors (Basel, Switzerland) · 2023Article
- Prediction framework for upper body sedentary working behaviour by using deep learning and machine learning techniques.Soft computing · 2022Article
- The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study.Medicine and science in sports and exercise · 2021Article
- The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study.JMIR mHealth and uHealth · 2021Article
- Are all Sedentary Behaviors Equal? An Examination of Sedentary Behavior and Associations with Indicators of Disease Risk Factors in Women.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.
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