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Record W4404095721 · doi:10.1016/j.arrct.2024.100384

Psychometrics of Wearable Devices Measuring Physical Activity in Ambulant Children With Gait Abnormalities: A Systematic Review and Meta-analysis

2024· review· en· W4404095721 on OpenAlexaff
Huib van Moorsel, Barbara Engels, Jacek Buczny, Jan Willem Gorter, Kelly P. Arbour‐Nicitopoulos, Tim Takken, Raoul Engelbert, Manon Bloemen

Bibliographic record

VenueArchives of Rehabilitation Research and Clinical Translation · 2024
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of TorontoMcMaster University
FundersNationaal Regieorgaan Praktijkgericht Onderzoek SIA
KeywordsPhysical medicine and rehabilitationMeta-analysisGaitGait analysisWearable computerPsychometricsMedicinePhysical therapyPsychologyComputer scienceClinical psychologyPathology

Abstract

fetched live from OpenAlex

Objective To evaluate psychometrics of wearable devices measuring physical activity (PA) in ambulant children with gait abnormalities due to neuromuscular conditions. Data Sources We searched PubMed, Embase, PsycINFO, CINAHL, and SPORTDiscus in March 2023. Study Selection We included studies if (1) participants were ambulatory children (2-19y) with gait abnormalities, (2) reliability and validity were analyzed, and (3) peer-reviewed studies in the English language and full-text were available. We excluded studies of children with primarily visual conditions, behavioral diagnoses, or primarily cognitive disability. We performed independent screening and inclusion, data extraction, assessment of the data, and grading of results with 2 researchers. Data Extraction Our report follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed methodological quality with Consensus-based Standards for the selection of health measurement instruments. We extracted data on reported reliability, measurement error, and validity. We performed meta-analyses for reliability and validity coefficient values. Data Synthesis Of 6911 studies, we included 26 with 1064 participants for meta-analysis. Results showed that wearables measuring PA in children with abnormal gait have high to very high reliability (intraclass correlation coefficient [ICC] +, test-retest reliability =0.81; 95% confidence interval [CI], 0.74-0.89; I 2 =88.57%; ICC +, interdevice reliability =0.99; 95% CI, 0.98-0.99; I 2 =71.01%) and moderate to high validity in a standardized setting ( r +, construct validity =0.63; 95% CI, 0.36-0.89; I 2 =99.97%; r +, criterion validity =0.68; 95% CI, 0.57-0.79; I 2 =98.70%; r +, criterion validity cutoff point based =0.69; 95% CI, 0.58-0.80; I 2 =87.02%). The methodological quality of all studies included in the meta-analysis was moderate. Conclusions There was high to very high reliability and moderate to high validity for wearables measuring PA in children with abnormal gait, primarily due to neurological conditions. Clinicians should be aware that several moderating factors can influence an assessment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.088
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.038
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.247
GPT teacher head0.482
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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