The Impact of Telemonitoring and Telehealth Coaching on Depression, Anxiety, and Stress Scales in Overweight and Obese Individuals: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
(1) Background: The literature has demonstrated several pathways that link obesity with stress. Thus, new approaches to weight management programs must also integrate health coaching and telemonitoring for overall health and wellbeing. This study aimed to measure stress, anxiety, and depression scales (DASS-21) in overweight and obese participants who joined a pilot randomized controlled trial (RCT) and the association between changes in DASS-21 scores and changes in anthropometric measures. (2) Methods: Fifty participants were enrolled in a randomized controlled trial and divided into two groups: the intervention group, which received a hypocaloric diet remotely, weekly telemonitoring, and monthly telehealth coaching, and the control group, which only followed a hypocaloric diet without any support. The Arabic version of the Depression Anxiety Stress Scales (DASS-21) was used. (3) Results: The data reveal that participants from the intervention group exhibited a significant decrease in the anxiety scale after 3 months compared with the control group. In addition, the correlations between depression, anxiety, stress, and all anthropometric measures in the intervention group showed a moderately significant positive correlation between changes in waist circumference and depression. (4) Conclusions: The findings confirm that integrating health coaching and telemonitoring can improve wellbeing and weight loss.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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".