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A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior

Michael Kebede, Annie Green Howard, Yumeng Ren, Blake Anuskiewicz, Chongzhi Di, Melissa A. Troester +1 more
PLOS ONE2024CC BY 4.0
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Published articlePublication date: Jan 22, 2024Added to atlas: Oct 15, 2026Source
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Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

This systematic scoping review examined how latent class analysis (LCA) has been applied to accelerometry-assessed physical activity and sedentary behaviour in studies published through 2021. Of 2,555 papers found, 12 papers from 8 unique studies (sample sizes 217-7,931) met the inclusion criteria. All 8 studies used physical activity measures and 75% used sedentary behaviour measures, while the accelerometer variables fed into the LCA models varied widely between studies.

  • Signals: Accelerometer
  • Result: Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%).
  • Result: About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes.
At a glance
Venue
PLOS ONE
Published
2024
DOI
10.1371/journal.pone.0283884 (opens in a new tab)
arXiv
Not reported
PMID
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Licence
CC BY 4.0 (opens in a new tab)
Signals
Not reported
Form factor
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Subjects
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Code
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Added
Oct 15, 2026

Abstract

Background

Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry.

Methods

Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods.

Results

Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217–7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes.

Conclusions

This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.

Verbatim fromthe publisher's DOI record· Publisher licence: http://creativecommons.org/licenses/by/4.0/

A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior: ChatPPG research summary card

How it works

How Method works: Setup → Method → ResultSetup: Signals (Accelerometer). Method: Method. Result: Result (Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%).); Result (About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes.).SetupSignals: AccelerometerxyzSignalsAccelerometerMethodMethodResultResult: Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%).ResultAcross 8 unique studies, latent class…Result: About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes.ResultAbout two-thirds (63%) of the studies…

Cite

@article{kebedelcaaccelerometrysc2024,
  title = {{A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior}},
  author = {Michael Kebede and Annie Green Howard and Yumeng Ren and Blake Anuskiewicz and Chongzhi Di and Melissa A. Troester and Kelly R. Evenson},
  year = {2024},
  journal = {PLOS ONE},
  doi = {10.1371/journal.pone.0283884}
}

History

  1. Added to the ChatPPG research hub

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