ArticleStatistics in biosciences2019
A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers.
Article in Statistics in biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Six decades of animal accelerometry: trends, applications, and future directions.Movement ecology · 2026Review
- Effectiveness of PEER Intervention on Older Adults' Physical Activity Time Series Using Smoothing Spline ANOVA.Mathematics (Basel, Switzerland) · 2025Article
- Aging and activity patterns: actigraphy evidence from NHANES studies.Frontiers in systems biology · 2025Article
- Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?Sensors (Basel, Switzerland) · 2023Article
- Learning about Our Vices from Devices: A Model of Individual Learning with an Application to Consumer Food Waste.Journal of agricultural and resource economics · 2023Article
- Multivariate Functional Kernel Machine Regression and Sparse Functional Feature Selection.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Studies for the associations between physical activity and disease risk have been supported by newly developed wearable accelerometer-based devices. These devices record raw activity/movement information in real time on a second-by-second basis and the data can be converted to a variety of summary metrics, such as energy expenditure, sedentary time and moderate-vigorous intensity physical activity. Here we review some of the methods used to analyze the accelerometer data and the R packages that can generate activity related variables from raw data. We also discuss longitudinal data and functional data approaches to perform analyses for various research purposes.
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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.