Multidimensional Cognitive Behavioral Therapy for Obesity Applied by Psychologists Using a Digital Platform: Open-Label Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Developing effective, widely useful, weight management programs is a priority in health care because obesity is a major health problem. OBJECTIVE: This study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile services Noom Coach and InBody. METHODS: and no clinical problems besides obesity were randomized into experimental and control groups. The experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8 weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated using independent t tests and a per-protocol framework. RESULTS: Mean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (-3.1%, SD 4.5, vs -0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group (-6.3%, SD 8.8, vs -0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly reduced compared to the control group (-15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and -7.1%, SD 35.1, vs 14.4%, SD 41.2, P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at 8 weeks was significantly improved compared to the control group (-2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9 kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term clinical outcomes. CONCLUSIONS: These findings confirm that technology-based interventions should be multidimensional and are most effective with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".