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Record W4406524316 · doi:10.1123/kr.2024-0026

Social Support in Physical Activity Interventions for Adults: An Overview of Reviews

2025· article· en· W4406524316 on OpenAlexaff
Bobbie-Ann P. Craig, Lindsay Morrison, Meghan H. McDonough, Catherine M. Sabiston, Erica Bennett, Isabelle Doré, Stephanie Won, Pamela Manzara, S. Nicole Culos‐Reed, Jennifer Hewson, Sarah Kenny, Chantelle Zimmer, Amanda Wurz, Siân Jones, Ann M. Toohey, Alexandra Giancarlo, K. Geoffrey White, Raynell McDonough

Bibliographic record

VenueKinesiology Review · 2025
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British ColumbiaUniversity of the Fraser ValleyUniversity of TorontoUniversité de MontréalUniversity of Calgary
Fundersnot available
KeywordsPsychologyPsychological interventionApplied psychologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Reviews exploring social support in physical activity for specific adult populations are numerous. There is a need to synthesize knowledge and translate evidence into practical strategies to enhance social support in physical activity. The objective of this overview of reviews was to synthesize supportive strategies in physical activity contexts for adult populations. Standardized guidelines for conducting and reporting were followed. Twenty-three reviews were identified, and data were summarized narratively. Supportive strategies were categorized into nine social functions: feeling welcomed and included, making physical activity fun, modeling physical activity, providing information, encouragement, mastery feedback, autonomy support, emotional support, and fostering social connections. This review identified supportive strategies that can be transferable to and tailored for a variety of adult populations’ physical activity contexts. Fitness professionals play a key role in tailoring support to individual participants and providing social support for physical activity, coping, and developing relationships.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.291
GPT teacher head0.536
Teacher spread0.245 · 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 designOther design
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

Citations3
Published2025
Admission routes1
Has abstractyes

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