Socio-economic factors, mood, primary care utilization, and quality of life as predictors of intervention cessation and chronic stress in a type 2 diabetes prevention intervention (PREVIEW Study)
Bibliographic record
Abstract
BACKGROUND: Sedentary lifestyle and unhealthy diet combined with overweight are risk factors for type 2 diabetes (T2D). Lifestyle interventions with weight-loss are effective in T2D-prevention, but unsuccessful completion and chronic stress may hinder efficacy. Determinants of chronic stress and premature cessation at the start of the 3-year PREVIEW study were examined. METHODS: Baseline Quality of Life (QoL), social support, primary care utilization, and mood were examined as predictors of intervention cessation and chronic stress for participants aged 25 to 70 with prediabetes (n = 2,220). Moderating effects of sex and socio-economic status (SES) and independence of predictor variables of BMI were tested. RESULTS: Participants with children, women, and higher SES quitted intervention earlier than those without children, lower SES, and men. Lower QoL, lack of family support, and primary care utilization were associated with cessation. Lower QoL and higher mood disturbances were associated with chronic stress. Predictor variables were independent (p ≤ .001) from BMI, but moderated by sex and SES. CONCLUSIONS: Policy-based strategy in public health should consider how preventive interventions may better accommodate different individual states and life situations, which could influence intervention completion. Intervention designs should enable in-built flexibility in delivery enabling response to individual needs. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01777893.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".