The impact of wearable motion sensing technology on physical activity in older adults
Open accessAbstract
Background and purpose Physical activity provides substantial health benefits. Older adults are less physically active than the rest of the population, and interventions that promote physical activity are needed. In this meta-analysis, we investigate how different wearable activity trackers (pedometers and accelerometers) may impact physical activity levels in older adults.Methods We searched MEDLINE, Embase and CINAHL for randomized controlled trials including participants that were ≥65 years, using wearable activity trackers with the intent of increasing physical activity. Studies whose comparator groups were engaged in active or inactive interventions, such as continued a physical therapy program or goal-setting counseling, were not excluded simply for implementing co-interventions. We used random-effects models to produce standardized mean differences (SMDs) for physical activity outcomes. Heterogeneity was measured using I 2.Results Nine studies met the eligibility criteria: Four using accelerometers, four using pedometers, and one comparing accelerometers and pedometers, for a total number of 939 participants. Using pooled data, we found a statistically significant effect of using accelerometers (SMD = 0.43 (95%CI 0.19-0.68), I 2 = 1.6%, p = 0.298), but not by using pedometers (SMD = 0.17 (95%CI -0.08-0.43), I 2 = 37.7%, p = 0.174) for increasing physical activity levels.Discussion and conclusions In this study, we found that accelerometers, alone or in combination with other co-interventions, increased physical activity in older adults however pedometers were not found to increase physical activity. The high risk of bias found in most studies limits these findings. High quality studies that isolate the effects of accelerometers on physical activity changes are needed.
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Fewer than 50% of patients had their stroke or cardiovascular disease risk factors appropriately assessed, treated, or managed.
“fewer than 50% of patients had their stroke or cardiovascular disease risk factors appropriately assessed, treated, or managed [3].”
From Patient-Perceived Factors Influencing Physical Activity Sensor Use in Stroke Prevention and Rehabilitation: Systematic Review of Qualitative Studies Using Thematic Synthesis (2026) · cites this paper as [3]
The Methods and Results passages on risk of bias assess the quality of wearable-activity trials, not patients’ stroke or cardiovascular risk-factor care. The Introduction reports physical-activity rates in older adults, but not whether risk factors were assessed, treated, or managed. Searches for the quoted claim, stroke, and cardiovascular disease risk factors found no passage supporting the fewer-than-50% figure. (LLM, conf=0.77)
Experimental — accuracy not yet measuredA comparable proportion of patients in an Australian stroke rehabilitation audit received information on self-management strategies or support programs.
“a recent audit of stroke rehabilitation services in Australia revealed that a comparable proportion of patients received information on self-management strategies or support programs [3].”
From Patient-Perceived Factors Influencing Physical Activity Sensor Use in Stroke Prevention and Rehabilitation: Systematic Review of Qualitative Studies Using Thematic Synthesis (2026) · cites this paper as [3]
The Results (Study Characteristics; page unavailable) describe exercise-counseling interventions in studies of older adults, not an Australian stroke rehabilitation audit. Searches for the audit and for stroke rehabilitation self-management information found no passage reporting the claimed proportion of patients. (LLM, conf=0.77)
Experimental — accuracy not yet measured
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- Authors
- Cooper, C., Gross, A., Brinkman, C., Pope, R., Allen, K., Hastings, S., Bogen, B. E., & Goode, A. P.
- Affiliations
- Duke University (US)
- Health Services Research & Development (US)
- Corresponding author
- Goode, Adam P.
- Venue
- Experimental Gerontology
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 112
- Pages
- 9-19
- ISSN
- 0531-5565 (ISSN-L)
- PMC
- PMC6436091
- OpenAlex
- W2885442775
- Cited by
- 82
- References
- 33
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 10 · 2023: 7 · 2022: 11 · 2021: 16 · 2020: 10 · 2019: 10 · 2018: 1
- FWCI
- 1.22
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Experimental Gerontology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Center of Innovation for Health Services Research in Primary Care (CIN 13-410)
- Duke Older Americans Independence Center (NIA P30 AG028716)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- wearable activity trackers, accelerometer, pedometers, physical activity, older adults, physical activity interventions, co-interventions, physical activity promotion
- MeSH terms
- Fitness Trackers, Wearable Electronic Devices, Aged, Humans, Exercise, Accelerometry
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