Trial reportPloS one2014
Reporting the reliability of accelerometer data with and without missing values.
Trial report in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review.JMIR mHealth and uHealth · 2021Pooled it
- Data-driven approach to quantify trust in medical devices using Bayesian networks.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- How Many Days are Necessary to Represent Typical Daily Leg Movement Behavior for Infants at Risk of Developmental Disabilities?Sensors (Basel, Switzerland) · 2020Article
- Validity and Wearability of Consumer-based Fitness Trackers in Free-living Children.International journal of exercise science · 2019Article
- Reliability of accelerometer-determined physical activity and sedentary behavior in school-aged children: a 12-country study.International journal of obesity supplements · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesParticipants with complete accelerometer data often represent a low proportion of the total sample and, in some cases, may be distinguishable from participants with incomplete data. Because traditional reliability methods characterize the consistency of complete data, little is known about reliability properties for an entire sample. This study employed Generalizability theory to report an index of reliability characterizing complete (7 days) and observable (1 to 7 days) accelerometer data.
designCross-sectional.
methodsAccelerometer data from the Study of Early Child Care and Youth Development were analyzed in this study. Missing value analyses were conducted to describe the pattern and mechanism of missing data. Generalizability coefficients were derived from variance components to report reliability parameters for complete data and also for the entire observable sample. Analyses were conducted separately by age (9, 11, 12, and 15 yrs) and daily wear time criteria (6, 8, 10, and 12 hrs).
resultsParticipants with complete data were limited (<34%) and, most often, data were not considered to be missing completely at random. Across conditions, reliability coefficients for complete data were between 0.74 and 0.87. Relatively lower reliability properties were found across all observable data, ranging from 0.52 to 0.67. Sample variability increased with longer wear time criteria, but decreased with advanced age.
conclusionsA reliability coefficient that includes all participants, not just those with complete data, provides a global perspective of reliability that could be used to further understand group level associations between activity and health outcomes.
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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.