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Record W3186731821 · doi:10.2196/26690

Evaluating Digital Program Support for the Physical Activity 4 Everyone (PA4E1) School Program: Mixed Methods Study

2021· article· en· W3186731821 on OpenAlexvenueno aff
Matthew Mclaughlin, Jed Duff, Tom McKenzie, Elizabeth Campbell, Rachel Sutherland, John Wiggers, Luke Wolfenden

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilNSW Ministry of HealthHunter Medical Research InstituteCentral Coast Local Health District
KeywordsComputer scienceMedical educationMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Effectively scaled-up physical activity interventions are urgently needed to address the high prevalence of physical inactivity. To facilitate scale-up of an efficacious school-based physical activity program (Physical Activity 4 Everyone [PA4E1]), provision of implementation support to physical education (PE) teachers was adapted from face-to-face and paper-based delivery modes to partial delivery via a website. A lack of engagement (usage and subjective experience) with digital delivery modes, including websites, may in part explain the typical reduction in effectiveness of scaled-up interventions that use digital delivery modes. A process evaluation focused on the PA4E1 website was undertaken. OBJECTIVE: The 2 objectives were to (1) describe the usage of the PA4E1 program website by in-school champions (PE teachers leading the program within their schools) and PE teachers using quantitative methods; (2) examine the usage, subjective experience, and usability of the PA4E1 program website from the perspective of in-school champions using mixed methods. METHODS: The first objective used website usage data collected across all users (n=273) throughout the 9 school terms of the PA4E1 implementation support. The 4 usage measures were sessions, page views, average session duration, and downloads. Descriptive statistics were calculated and explored across the duration of the 26-month program. The second objective used mixed methods, triangulating data from the first objective with data from a think-aloud survey and usability test completed by in-school champions (n=13) at 12 months. Qualitative data were analyzed thematically alongside descriptive statistics from the quantitative data in a triangulation matrix, generating cross-cutting themes using the "following a thread" approach. RESULTS: For the first objective, in-school champions averaged 48.0 sessions per user, PE teachers 5.8 sessions. PE teacher sessions were of longer duration (10.5 vs 7.6 minutes) and included more page views (5.4 vs 3.4). The results from the mixed methods analysis for the second objective found 9 themes and 2 meta-themes. The first meta-theme indicated that the website was an acceptable and appropriate delivery mode, and usability of the website was high. The second meta-theme found that the website content was acceptable and appropriate, and identified specific suggestions for improvement. CONCLUSIONS: Digital health interventions targeting physical activity often experience issues of lack of user engagement. By contrast, the findings from both the quantitative and mixed methods analyses indicate high usage and overall acceptability and appropriateness of the PA4E1 website to school teachers. The findings support the value of the website within a multidelivery mode implementation intervention to support schools to implement physical activity promoting practices. The analysis identified suggested intervention refinements, which may be adopted for future iterations and further scale-up of the PA4E1 program. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12617000681358; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=372870.

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)0.033
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.454
Teacher spread0.376 · 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 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

Citations9
Published2021
Admission routes1
Has abstractyes

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