Seniors’ Body Weight Dissatisfaction and Longitudinal Associations With Weight Changes, Anorexia of Aging, and Obesity
Bibliographic record
Abstract
OBJECTIVE: We examined longitudinal associations between weight dissatisfaction, weight changes, anorexia of aging, and obesity among 1,793 seniors followed over 4 years between 2003 and 2009. METHOD: Obesity prevalence (body mass index [BMI] ≥ 30) and prevalence/incidence of weight dissatisfaction, anorexia of aging (self-reported appetite loss), and weight changes ≥5% were assessed. Predictors of weight loss ≥5%, anorexia of aging, and weight dissatisfaction were examined using logistic regressions. RESULTS: Half of seniors experienced weight dissatisfaction (50.6%, 95% confidence interval [CI] = [48.1, 53.1]). Anorexia of aging and obesity prevalence was 7.0% (95% CI = [5.7, 8.3]) and 25.1% (95% CI = [22.9, 27.3]), whereas incidence of weight gain/loss ≥5% was 6.6% (95% CI = [1.3, 11.9]) and 8.8% (95% CI = [3.3, 14.3]). Weight gain ≥5% predicts men's subsequent weight dissatisfaction (odds ratio [OR] = 6.66, 95% CI = [2.06, 21.60]). No other association was observed. DISCUSSION: Weight dissatisfaction is frequent but not associated with subsequent eating disorders. In men, weight gain predicted weight dissatisfaction. Seniors' weight dissatisfaction does not necessarily equate weight changes. Due to its high prevalence, it is of public health interest to understand how seniors' weight dissatisfaction may impact health.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".