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Record W2774512194 · doi:10.1111/aphw.12105

Standing Up for Student Health: An Application of the Health Action Process Approach for Reducing Student Sedentary Behavior—Randomised Control Pilot Trial

2017· article· en· W2774512194 on OpenAlexaff
Wuyou Sui, Harry Prapavessis

Bibliographic record

VenueApplied Psychology Health and Well-Being · 2017
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicinePhysical therapyCoping (psychology)Duration (music)Intervention (counseling)PopulationRandomized controlled trialPsychologyGerontologyClinical psychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Sedentary behavior (SB) has been associated with chronic diseases. University students are a high-risk population for excessive SB. The purpose of this pilot study was to determine if a Health Action Process Approach (HAPA) based intervention, specifically action and coping planning, would increase student break frequency and decrease duration. METHODS: Fifty-two university students (14 men, mean age 23.5) were randomised into an 8-week HAPA-treatment (sedentary behavior) or HAPA-control (nutrition) group. Participants completed an SB questionnaire that assessed break frequency and duration of student SB (Baseline, Weeks 1-6 Treatment, and Weeks 7-8 Follow-up), and received behavioral counselling on either dietary information or SB (Baseline and Week 3). RESULTS: = 0.23). For occupational (student) break frequency and duration, the large accompanying effect sizes favored the treatment group. CONCLUSIONS: The current pilot study provides preliminary evidence for the potential of a HAPA-based intervention for increasing student break frequency in full-time university students.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.090
GPT teacher head0.475
Teacher spread0.385 · 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 designRandomized trial
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

Citations41
Published2017
Admission routes1
Has abstractyes

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