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Record W2888711914 · doi:10.3390/ijerph15091821

Feasibility and Effectiveness of a Wearable Technology-Based Physical Activity Intervention in Preschoolers: A Pilot Study

2018· article· en· W2888711914 on OpenAlexaff
Wonwoo Byun, Erica Y. Lau, Timothy A. Brusseau

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2018
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntervention (counseling)Physical therapyPhysical activityMedicinePsychologyNursing

Abstract

fetched live from OpenAlex

The purpose of this pilot study was to evaluate the feasibility and the effectiveness of an intervention that employed a technology-based physical activity (PA) monitoring system and teacher-regulated strategies to promote PA in preschoolers. A total of 93 preschoolers (53% girls, 4.7 years) from 5 child care centers were recruited for a one-week intervention and randomly assigned into control (2 centers, n = 45) or intervention (3 centers, n = 48) group. Key intervention components included: (1) wearable device-based, real-time monitoring of children's PA by classroom teachers and (2) teacher-regulated strategies for providing more opportunities for PA. Sedentary behavior (SED) and PA were measured using accelerometers. Overall, children in the intervention group showed significantly lower level of SED (31.6 vs. 33.6 min/h) and higher level of total PA (28.4 vs. 26.4 min/h) than children in the control group, after adjusting for age, sex, race, parent education level, parent perception of their child's PA, BMI, and childcare centers. Teachers in the intervention group reported that the intervention was highly feasible to be implemented in their current classroom settings. In conclusion, we observed high acceptability and initial effectiveness of the current intervention. Subsequent research at larger-scale is warranted to fully evaluate the effectiveness of the intervention strategies tested in this study.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.232
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.088
GPT teacher head0.429
Teacher spread0.341 · 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

Citations32
Published2018
Admission routes1
Has abstractyes

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