ArticleMedicine and science in sports and exercise2018
Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.
Article in Medicine and science in sports and exercise, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Standardised Core Outcome Set for Measurement and Reporting Sedentary Behaviour Interventional Research: The CROSBI Consensus Study.International journal of environmental research and public health · 2022Pooled it
- Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- A systematic review of prolonged posture monitoring with accelerometer-based wearable sensors.PLOS digital health · 2026Article
- Comparative Analysis of Force-Sensitive Resistors and Triaxial Accelerometers for Sitting Posture Classification.Sensors (Basel, Switzerland) · 2024Article
- Measuring nurses' on-shift physical activity and sedentary time by accelerometry or heart rate monitoring: a descriptive case study illustrating the importance of context.Journal of activity, sedentary and sleep behaviors · 2023Article
- Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.JMIR mHealth and uHealth · 2022Article
- 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
- 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 Prediction of Running Velocity during the 30-15 Intermittent Fitness Test Using Accelerometry-Derived Metrics and Physiological Parameters: A Machine Learning Approach.International journal of environmental research and public health · 2021Article
- Endothelial-derived cardiovascular disease-related microRNAs elevated with prolonged sitting pattern among postmenopausal women.Scientific reports · 2021Article
- Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification.Journal for the measurement of physical behaviour · 2021Article
- How Accurate and Precise Can We Measure the Posture and the Energy Expenditure Component of Sedentary Behaviour with One Sensor?International journal of environmental research and public health · 2021Article
- Concurrent and discriminant validity of ActiGraph waist and wrist cut-points to measure sedentary behaviour, activity level, and posture in office work.BMC public health · 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
- Is Sitting Always Inactive and Standing Always Active? A Simultaneous Free-Living activPal and ActiGraph Analysis.International journal of environmental research and public health · 2020Article
- The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review.Sensors (Basel, Switzerland) · 2020Review
- Quantifying Physical Activity in Young Children Using a Three-Dimensional Camera.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
- Article
- Day-level sedentary pattern estimates derived from hip-worn accelerometer cut-points in 8-12-year-olds: Do they reflect postural transitions?Journal of sports sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to improve estimates of sitting time from hip-worn accelerometers used in large cohort studies by using machine learning methods developed on free-living activPAL data.
methodsThirty breast cancer survivors concurrently wore a hip-worn accelerometer and a thigh-worn activPAL for 7 d. A random forest classifier, trained on the activPAL data, was used to detect sitting, standing, and sit-stand transitions in 5-s windows in the hip-worn accelerometer. The classifier estimates were compared with the standard accelerometer cut point, and significant differences across different bout lengths were investigated using mixed-effect models.
resultsOverall, the algorithm predicted the postures with moderate accuracy (stepping, 77%; standing, 63%; sitting, 67%; sit-to-stand, 52%; and stand-to-sit, 51%). Daily level analyses indicated that errors in transition estimates were only occurring during sitting bouts of 2 min or less. The standard cut point was significantly different from the activPAL across all bout lengths, overestimating short bouts and underestimating long bouts.
conclusionsThis is among the first algorithms for sitting and standing for hip-worn accelerometer data to be trained from entirely free-living activPAL data. The new algorithm detected prolonged sitting, which has been shown to be the most detrimental to health. Further validation and training in larger cohorts is warranted.
Indexed as
Identifiers
What OpenQuestion holds
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.