Telemedicine in Eating Disorder Treatment: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Telemedicine has emerged as a promising tool to enhance adherence and monitoring in patients with eating disorders (EDs). Traditional face-to-face cognitive therapies remain the gold standard; however, integrating telemedicine may provide additional support and improve patient engagement and retention. Given the increasing use of digital health interventions, it is crucial to assess their safety and effectiveness in complementing conventional treatments. OBJECTIVE: We aimed to evaluate the safety and effectiveness of telemedicine as a complementary tool for cognitive face-to-face therapies to promote adherence and monitoring of patients with EDs. METHODS: We consulted the National Institute for Health and Care Excellence, the Canadian Agency for Drugs and Technologies in Health (now known as Canada's Drug Agency), MEDLINE (Ovid), Embase, Web of Science, Cochrane Library, international HTA database (International Network of Agencies for Health Technology Assessment), CINAHL (EBSCO), and PsycINFO (EBSCO) websites and databases in December 2024 to identify eligible systematic reviews, synthesis reports, or meta-analyses that address telemedicine as a complementary therapy to face-to-face care in patients with EDs. Two researchers performed an independent critical reading of the systematic reviews and assessed the risk of bias using AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews, version 2). RESULTS: We initially identified 1004 studies, but only 5 (0.5%) systematic reviews met the inclusion criteria. Email, vodcasts, smartphone apps, and SMS text messaging were the principal telemedicine channels. Telemedicine interventions were safe, helpful, and motivating; improved retention rates and patient-physician communication; and reduced ED symptoms. CONCLUSIONS: Telemedicine interventions showed promising, positive findings as a complementary tool for face-to-face ED treatment that must be interpreted cautiously. The limited number of systematic reviews selected and their moderate to critically low quality underscore the need for further research in this area.
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".