ArticleMedicine and science in sports and exercise2017
Classifiers for Accelerometer-Measured Behaviors in Older Women.
Article in Medicine and science in sports and exercise, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Sitting time and risk of cancer incidence and cancer mortality in postmenopausal women: the Women's Health Accelerometry Collaboration.Cancer causes & control : CCC · 2025Article
- Changes in activity spaces, life spaces, and exposures to physical activity-promoting environments among women with overweight or obesity.Health & place · 2025Article
- Prospective Associations of Accelerometer-Measured Machine-Learned Sedentary Behavior With Death Among Older Women: The OPACH Study.Journal of the American Heart Association · 2024Article
- Identifying Multicomponent Patterns and Correlates of Accelerometry-Assessed Physical Behaviors Among Postmenopausal Women: The Women's Health Accelerometry Collaboration.Journal for the measurement of physical behaviour · 2024Article
- Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model.Sensors (Basel, Switzerland) · 2023Article
- Maximizing the Utility and Comparability of Accelerometer Data from Large-Scale Epidemiologic Studies.Journal for the measurement of physical behaviour · 2023Article
- Accelerometer-Derived Daily Life Movement Classified by Machine Learning and Incidence of Cardiovascular Disease in Older Women: The OPACH Study.Journal of the American Heart Association · 2022Article
- Observational
- 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
- Endothelial-derived cardiovascular disease-related microRNAs elevated with prolonged sitting pattern among postmenopausal women.Scientific reports · 2021Article
- Classifying sitting, standing, and walking using plantar force data.Medical & biological engineering & computing · 2021Article
- The Relationship of Accelerometer-Assessed Standing Time With and Without Ambulation and Mortality: The WHI OPACH Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2021Article
- Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models.Sensors (Basel, Switzerland) · 2020Article
- Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach.Sensors (Basel, Switzerland) · 2020Article
- Total Sitting Time and Sitting Pattern in Postmenopausal Women Differ by Hispanic Ethnicity and are Associated With Cardiometabolic Risk Biomarkers.Journal of the American Heart Association · 2020Article
- A comparison of accelerometry analysis methods for physical activity in older adult women and associations with health outcomes over time.Journal of sports sciences · 2019Article
- Physical Activity Change in an RCT: Comparison of Measurement Methods.American journal of health behavior · 2019Article
- Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review.JMIR mHealth and uHealth · 2019Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeMachine learning methods could better improve the detection of specific types of physical activities and sedentary behaviors from accelerometer data. No studies in older populations have developed and tested algorithms for walking and sedentary time in free-living daily life. Our goal was to rectify this gap by leveraging access to data from two studies in older women.
methodsIn study 1, algorithms were developed and tested in a sample of older women (N = 39, age range = 55-96 yr) in the field. Women wore accelerometers and SenseCam (ground truth annotation) devices for 7 d, yielding 3191 h and 320 d of data. Images were annotated and time matched to accelerometer data, and random forest classifiers labeled behaviors (sitting, riding in a vehicle, standing still, standing moving, and walking/running). In study 2, we examined the concurrent validity of the algorithms using accelerometer data from an observed 400-m walk test (2983 min of data available) and 6 d of wearing both accelerometers and global positioning systems devices in a sample of 222 women (age range = 67-100; 313,290 min of data available). Analyses included sensitivity, specificity balanced accuracy, and precision, as appropriate, averaged over each test participant at the minute level for each behavior.
resultsIn study 1, the algorithms had 82.2% balanced accuracy. In study 2, the classifier had 87.9% accuracy for predicting walking. Overall machine learning classifiers and global positioning systems had 88.6% agreement.
conclusionsFree-living algorithms for walking and sedentary time yielded high levels of accuracy and concurrent validity and can be applied to existing accelerometer data from older women.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.