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Record W134044072 · doi:10.1177/070674370905400304

Changes over Time in Physical Activity and Psychological Distress among Older Adults

2009· article· en· W134044072 on OpenAlexaffvenue
John Cairney, Guy Faulkner, Scott Veldhuizen, Terrance J. Wade

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2009
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsBrock UniversityCentre for Addiction and Mental HealthUniversity of TorontoMcMaster University
Fundersnot available
KeywordsPsychosocialDistressPsychologyAssociation (psychology)Psychological interventionPsychological distressMental healthLongitudinal studyClinical psychologyPhysical activityMedicinePsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: While previous research has established that regular involvement in physical activity (PA) is associated with better mental health in old age, the socio-cognitive factors that mediate the association have only been partially tested. We examined whether changes in PA are associated with changes in distress during a 6-year period, and whether this association is mediated by changes in global self-esteem, mastery, and physical health status. METHOD: A residualized regression technique was used to examine changes over time in a national longitudinal survey of adults aged 65 years and older (n = 1327). RESULTS: There is a significant association between change in PA and change in distress. Separately, physical health status accounted for 30% of the explained variance of the longitudinal relation between PA and distress, while global self-esteem and mastery accounted for 39%. Combined, they accounted for 50% of the explained variance of PA on distress. CONCLUSION: These findings highlight the importance of psychosocial factors in the relation between PA and distress. Results suggest that PA interventions focused on improving mastery or self-worth, as well as physical fitness, may yield the greatest benefit in alleviating psychological distress.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.020
GPT teacher head0.304
Teacher spread0.284 · 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

Citations53
Published2009
Admission routes2
Has abstractyes

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