Observational studyJMIR mHealth and uHealth2022
Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study.
Observational study in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis 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
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Characterization of physical health variables across fall risk appraisal groups: a cross-sectional study.BMC geriatrics · 2026Article
- Sedentary behavior type matters: compositional analysis of 24-hour movement behaviors and cognitive function in older adults.Journal of activity, sedentary and sleep behaviors · 2026Article
- Associations of accelerometry-derived time in major activity intensities with cognitive outcomes: a compositional data analysis approach.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Effect of a digital-based integrated exercise and sleep intervention for older adults with depression: study protocol for a stepped-wedge cluster randomized controlled trial.BMC geriatrics · 2026Article
- The Concurrent Validity of the ActiGraph LEAP Compared to the ActiGraph GT9X Link for Measuring Physical Activity.Journal for the measurement of physical behaviour · 2026Article
- Impact of accelerometer epoch summary measure on associations between physical activity and all-cause mortality in Whitehall II and UK Biobank.Scientific reports · 2025Article
- Visualising accelerometer-based 24/7 human movement behaviour data: an umbrella review and framework development from the LABDA project.Journal of activity, sedentary and sleep behaviors · 2025Review
- Device-independent Physical Activity metrics and adiposity-related indicators among children and adolescents in the United States.Journal of sports sciences · 2025Article
- Use of Digital Biomarkers from Sensing Technologies to Explore the Relationship Between Daytime Activity Levels and Sleep Quality in Nursing Home Residents with Dementia: A Proof-of-Concept Study.Sensors (Basel, Switzerland) · 2025Article
- Associations of accelerometer-measured light-intensity physical activity with mortality and incidence of cardiovascular diseases and cancers: A prospective cohort study.Journal of sport and health science · 2025Article
- Food-Specific Morning and Night Intake Scores Are Associated with Impaired Glucose Tolerance during Pregnancy.The Journal of nutrition · 2025Article
- Learning causal effect of physical activity distribution: an application of functional treatment effect estimation with unmeasured confounding.Journal of applied statistics · 2025Article
- Association between accelerometer-measured physical activity volume and sleep duration in older adults: a cross-sectional interpretable machine learning analysis.Frontiers in public health · 2025Article
- Effects of caffeine on accelerometer measured sleep and physical activity among older adults under free-living conditions.BMC public health · 2024Article
- Predicting poor performance on cognitive tests among older adults using wearable device data and machine learning: a feasibility study.npj aging · 2024Article
- Association between physical activity based on wearables and self-reported health status among adolescents: NHANES 2011-2014.BMC public health · 2024Article
- Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention.BMC medicine · 2024Review
- Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study.Sensors (Basel, Switzerland) · 2024Article
- Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data.Annals of clinical and translational neurology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundGiven the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures.
objectiveWe aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data.
methodsHigh-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data.
resultsData were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published.
conclusionsThe findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.
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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.