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Record W7047908144

Investigating the predictors of exercise identity formation in new exercisers

2021· dissertation· en· W7047908144 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)RecreationPhysical activityObservational studyMental healthBivariate analysisSocial cognitive theoryDemographicsLongitudinal study
DOInot available

Abstract

fetched live from OpenAlex

Background: While the physical and mental health advantages of regular physical activity are evident, 68% of adult Canadians are not meeting PA guidelines. Over the last thirty years, exercise behaviour has been mostly studied under the guise of the social cognitive framework, but emerging findings have shown identity to demonstrate predictive validity with physical activity independent of social cognitions. Exercise identity has been associated with increased frequency, duration, and intensity of exercise behaviour. Despite the bivariate correlation between identity and PA, the literature currently lacks longitudinal research to enhance the understanding of identity formation in new exercisers. Objective: The purpose of this study was to understand changes in identity among new exercisers based on the Physical Activity Self-Definition model and investigate whether exercise identity can predict exercise behaviour variations over nine weeks. Methods: Participants for this study were healthy adults (18-65) who were recruited from local gyms and recreation centres in Victoria, BC. The inclusion criteria were that participants must be new exercisers (new exercisers are those who just decided to exercise regularly or started exercising for less than 2 weeks, before baseline measurement) who were not meeting the Canadian Physical Activity guidelines upon recruitment. The study used a prospective, observational design with four measurement periods across nine weeks. Demographics were collected and exercise identity, affective attitude, commitment, capability and exercise behaviour were measured using questionnaires. The exercise Identity questionnaire was administered at 1 week, 3 weeks, 6 weeks and 9 weeks. Data analysis and longitudinal models used HLM and descriptive were generated with SPSS. Results: Affective attitude and commitment had significant correlations with identity, and identity had a significant correlation with exercise behaviour across all measurement times. Affective attitude, however, was the only significant predictor of exercise identity change over time. Capability was not associated with exercise identity. Furthermore, identity did not predict change in exercise over time. Discussion: This study provided insight into some of the factors that influence shifting exercise identity of new exercisers by testing the physical activity self-definition model (Kendzierski & Morganstein, 2009a) with longitudinal modelling. Based on the present results, it is recommended that health promoters focus on designing enjoyable programs for their novice clients, and provide a positive affective attitude toward exercising during each session. Although, exercise behaviours of the participants improved significantly during the course of this study, exercise identity was not able to predict the variation in exercise behaviour over 9 weeks. Overall, exercise identity formation can be a time-consuming process in adults, however, engaging in identity-related behaviours that are enjoyable can accelerate this 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 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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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