Weight Loss Intervention in Young Adults with Severe Learning Disabilities: The Additive Effect of Cognitive Behavioral Treatment (a Pilot Study)
Bibliographic record
Abstract
The prevalence of obesity is reported to be higher among people with intellectual disabilities compared to the general population. Factors which were suggested to contribute to this increased prevalence include: low adherence to healthy diets, poor level of physical activity, intellectual disabilities and the lack of residential settings supporting independence. This study was designed to evaluate the additive effect of an intervention in the form of cognitive behavioral treatment (CBT) in a multi-strategy weight loss program for young adults with severe learning disabilities living in group residences in the community. The study population included 28 subjects (12 males) who were randomly assigned to one of two groups. Both groups had a weekly meeting with a dietician and were invited to take part in walking groups. One group (intervention group) had on top a weekly session of CBT. The results of our study indicate that adding a CBT component to a conventional program aiming at improved nutritional understanding (prudent diet, physical activity) of subjects with LD may improve the success for change in life habits, yet, in our study this effect was noticeable only on follow up few months after study termination. Neither group differences at baseline nor differences in the scoring for the locus of control questionnaire at baseline could predict this outcome. This delayed impact warrants further investigation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".