INCORPORATING EXERCISE TO BUFFER AVERSIVE HEALTH EFFECTS OF LONELINESS AMONG OLDER ADULTS IN THE LIFE TRIAL
Bibliographic record
Abstract
Abstract Introduction In 2023 the US Surgeon General announced an epidemic of loneliness and isolation. Prolonged loneliness among older adults has been shown to predict dementia and cardiovascular disease (CVD). However, exercise has been demonstrated to have preventive effects on CVD and cognitive health, but its effects on buffering these outcomes manifested from loneliness, or gender effects have not been investigated. This study aimed to test two moderated-mediation models to investigate if exercise moderates the relationship between loneliness and health outcomes. Methods The Lifestyle Interventions and Independence for Elders (LIFE) Study is a randomized controlled trial (n=1,600) that assigned older adults (aged 65+) to either an intervention or control group. The present observational study analyzed participants in the control group. Measures included: exercise (accelerometry), loneliness (Center for Epidemiologic Studies Depression Scale [CES-D]), CVD risk (handgrip test), and cognitive health (global cognitive function). Model #14 from Hayes PROCESS Macro 4.0 in SPSS was used to analyze the data. Results In both models, females experienced greater loneliness compared to males(β=.25, p<.001). The CVD risk model found Moderate-to-Vigorous Physical Activity (MVPA) to independently predicted handgrip strength(β=.11, p<.001), and interacted with loneliness to predict handgrip strength(β=.05, p=.03). The cognition model also found MVPA to independently predict cognition(β=.14, p<.001), and interacts with loneliness to predict cognition(β=.07, p=.03), and also demonstrate total moderated-mediation effects(β=.02, 95%CI.003 to.367). Conclusion Exercise can buffer aversive cardiovascular risk and cognition from loneliness. Lonely older adults are a high-risk demographic that should be sought for enrolling in exercise programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".