ArticleMedicine and science in sports and exercise2016
Objective Assessment of Physical Activity: Classifiers for Public Health.
Article in Medicine and science in sports and exercise, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01775826 (Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity), which is not on this map. Cited by 36 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.
The trial behind it
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.
Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Adherence to 24-h Movement Guidelines in children: a systematic review and meta-analysis.European journal of pediatrics · 2025Pooled it
- Bicycle Trains, Cycling, and Physical Activity: A Pilot Cluster RCT.American journal of preventive medicine · 2017Trial
- Micro-bursts matter: vigorous intermittent lifestyle physical activity and risk of common brain disorders in a prospective cohort study.The international journal of behavioral nutrition and physical activity · 2026Article
- Accelerometer-Measured Sedentary Behavior and Risk of Future Cardiovascular Disease.Journal of the American College of Cardiology · 2025Article
- Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.BMC medical research methodology · 2024Article
- Relationship between quantitative physical activity and deterioration of locomotive function: a cross-sectional study using baseline data from a cohort.BMC geriatrics · 2024Article
- Concurrent Validity of Four Activity Monitors in Older Adults.Sensors (Basel, Switzerland) · 2024Article
- Examining Participant Adherence with Wearables in an In-the-Wild Setting.Sensors (Basel, Switzerland) · 2023Article
- A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment.Sensors (Basel, Switzerland) · 2023Article
- Influence of Epoch Length and Recording Site on the Relationship Between Tri-Axial Accelerometry-Derived Physical Activity Levels and Structural, Functional, and Hemodynamic Properties of Central and Peripheral Arteries.Frontiers in sports and active living · 2022Article
- Using Wearable Cameras to Investigate Health-Related Daily Life Experiences: A Literature Review of Precautions and Risks in Empirical Studies.Research ethics · 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
- Reallocation of time between device-measured movement behaviours and risk of incident cardiovascular disease.British journal of sports medicine · 2021Article
- Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification.Journal for the measurement of physical behaviour · 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
- Ordinal Statistical Models of Physical Activity Levels from Accelerometer Data.International journal of exercise science · 2021Article
- The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review.Sensors (Basel, Switzerland) · 2020Review
- 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
- Neighborhoods to Nucleotides - Advances and gaps for an obesity disparities systems epidemiology model.Current epidemiology reports · 2019Article
- Calibration and validation of accelerometry to measure physical activity in adult clinical groups: A systematic review.Preventive medicine reports · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
purposeWalking for health is recommended by health agencies, partly based on epidemiological studies of self-reported behaviors. Accelerometers are now replacing survey data, but it is not clear that intensity-based cut points reflect the behaviors previously reported. New computational techniques can help classify raw accelerometer data into behaviors meaningful for public health.
methodsFive hundred twenty days of triaxial 30-Hz accelerometer data from three studies (n = 78) were employed as training data. Study 1 included prescribed activities completed in natural settings. The other two studies included multiple days of free-living data with SenseCam-annotated ground truth. The two populations in the free-living data sets were demographically and physical different. Random forest classifiers were trained on each data set, and the classification accuracy on the training data set and that applied to the other available data sets were assessed. Accelerometer cut points were also compared with the ground truth from the three data sets.
resultsThe random forest classified all behaviors with over 80% accuracy. Classifiers developed on the prescribed data performed with higher accuracy than the free-living data classifier, but these did not perform as well on the free-living data sets. Many of the observed behaviors occurred at different intensities compared with those identified by existing cut points.
conclusionsNew machine learning classifiers developed from prescribed activities (study 1) were considerably less accurate when applied to free-living populations or to a functionally different population (studies 2 and 3). These classifiers, developed on free-living data, may have value when applied to large cohort studies with existing hip accelerometer data.
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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.