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Record W4410723843 · doi:10.2196/59074

Evaluating Fitbits for Assessment of Physical Activity and Sleep in Pediatric Pain: Feasibility and Acceptability Pilot Study

2025· article· en· W4410723843 on OpenAlexvenueno aff
Bridget A. Nestor, Andreas M. Baumer, Justin Chimoff, Benoit Delecourt, Camila Koike, Nicole Tacugue, Roland Brusseau, Nathalie Roy, Israel A. Gaytan-Fuentes, Navil F. Sethna, Joe Kossowsky

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsPreprintSleep (system call)MedicinePhysical therapyPhysical activityComputer science

Abstract

fetched live from OpenAlex

Background: Consumer-grade wearables, such as Fitbits, are a promising, cost-effective methodology for objectively assessing sleep and physical activity in youth with pain. Objective: This study investigated the acceptability and feasibility of implementing Fitbits for youth with acute and chronic pain in and out of hospital settings while maintaining data security and patient confidentiality. Methods: We investigated participant experience of Fitbit use over 3 to 4 weeks for a sample of youth with acute pain undergoing either orthopedic or cardiac surgical procedures (N=34, mean age 14.46, SD 3.70 years, 47.06% [n=36] female) and a sample of youth with chronic pain enrolled in an intensive interdisciplinary pain treatment program (N=28, mean age 15.00, SD 2.33 years, 82.14% [n=23] female). We assessed the acceptability of Fitbit use through survey items probing comfort (0=extremely uncomfortable to 10=extremely comfortable), perceived burdensomeness (0=not burdensome at all to 10=extremely burdensome), and open-ended issues or concerns. Feasibility was assessed by tracking the daily compliant wear of the Fitbit device, which was operationalized as more than 600 minutes of daily wear time. We tested for group differences in acceptability and feasibility between orthopedic and cardiac patients within the acute pain sample and between the acute pain and chronic pain samples. We created an automated data pipeline to ensure data security, patient confidentiality, and quality. Results: Acceptability findings revealed high levels of reported comfort (acute pain: mean 8.56, SD 1.43; chronic pain: mean 8.27, SD 1.69) and low levels of perceived burdensomeness (acute: mean 0.68, SD 1.17; chronic: mean 1.15, SD 1.38) related to Fitbit wearing in both samples. No significant differences in these acceptability outcomes emerged between orthopedic and cardiac patients or between the acute pain and chronic pain groups (P values>.10). Transient concerns of mild wrist irritation and sleep discomfort were occasionally reported across both samples (15.79% [n=9] of participants). Feasibility findings indicated high feasibility (acute: median compliance rate of 86.67%; chronic: median compliance rate of 96.65%) for the study duration in both samples. Mann-Whitney U tests indicated significantly higher median compliance rates per participant among orthopedic as compared with cardiac patients (U=146.5, P=.04) and significantly higher median compliance rates per participant among the chronic pain group as compared with the acute pain group (U=186, P<.001), including significantly higher median compliant days (U=162, P<.001) and study days (U=234.5, P<.001) per participant. Conclusions: These findings support the use of Fitbits as an acceptable and feasible method for collecting objective data on sleep and physical activity for youth experiencing pain. Findings also highlight the logistics of implementing consumer-grade wearable devices throughout all stages of the clinical research process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.427
GPT teacher head0.667
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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