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Record W4410919433 · doi:10.1177/07334648251345191

Self-Efficacy is Key: Examining the Role of Motivation to Engage in Healthy Lifestyle Behaviors for Dementia Prevention in Midlife

2025· article· en· W4410919433 on OpenAlexfundno aff
Stephanie M. Simone, Marina Kaplan, Tania Giovannetti

Bibliographic record

VenueJournal of Applied Gerontology · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingCanadian Institutes of Health Research
KeywordsDementiaPsychologyLonelinessPsychological interventionGerontologySelf-efficacyClinical psychologyMedicinePsychiatryDiseaseSocial psychology

Abstract

fetched live from OpenAlex

Modifiable risk factors account for nearly half of dementia cases, with the greatest impact on dementia prevention in midlife. Little is known about what motivates middle-aged adults to engage in healthy behaviors for dementia risk reduction. This study examined associations between motivation to make lifestyle changes for dementia risk reduction and engagement in health behaviors associated with dementia risk in 347 middle-aged adults. Multivariate linear regressions examined associations between motivation and engagement in health behaviors. Greater self-efficacy and higher education significantly predicted greater physical and cognitive activity and better sleep quality. Greater perceived barriers and general health motivation, lower self-efficacy, and younger age significantly predicted greater perceived loneliness. Self-efficacy consistently predicted engagement in health behaviors associated with dementia risk reduction in midlife. Thus, incorporating empirically supported strategies to increase self-efficacy in lifestyle interventions for dementia prevention may increase long-term adherence and overall success of dementia prevention efforts.

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)0.001
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 score0.126
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.036
GPT teacher head0.362
Teacher spread0.326 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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