Role of telemedicine in the management of obesity: State‐of‐the‐art review
Bibliographic record
Abstract
Obesity is a worsening public health epidemic that remains challenging to manage. Obesity substantially increases the risk of cardiovascular diseases and presents a significant financial burden on the healthcare system. Digital health interventions, specifically telemedicine, may offer an attractive and viable solution for managing obesity. During the COVID-19 pandemic, the need for a safer alternative to in-person visits led to the increased popularity of telemedicine. Multiple studies have tested the efficacy of telemedicine modalities, including digital coaching via videoconferencing sessions, e-health monitoring using wearable devices, and asynchronous forms of communication such as online chatrooms with counselors. In this review, we discuss the available evidence for telemedicine interventions in managing obesity, review current challenges and barriers to using telemedicine, and outline future directions to optimize the management of patients with obesity using telemedicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".