The use of non‐invasive brain stimulation techniques to reduce body weight and food cravings: A systematic review and meta‐analysis
Bibliographic record
Abstract
Summary Several studies demonstrated non‐invasive brain stimulation (NIBS) techniques such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) are safe and simple techniques that can reduce body weight, food cravings, and food consumption in patients with obesity. However, a systematic to evaluate the efficacy of active NIBS versus sham stimulation in reducing body weight and food cravings in patients with obesity is not available. We conducted a systematic review and meta‐analysis of randomized controlled trials (RCTs) using PubMed, Embase, MEDLINE, and Cochrane Central Register of Control Trial between January 1990 and February 2022. Mean differences (MDs) for continuous outcome variables with 95% confidence intervals (95% CIs) were used to examine the effects of NIBS on body weight and body mass index (BMI), whereas the hedges's g test was used to measure the effects on food craving. Nineteen RCTs involving 571 participants were included in this study. Active neurostimulation (TMS and tDCS) was significantly more likely than sham stimulation to reduce body weight (TMS: −3.29 kg, 95% CI [−5.32, −1.26]; I2 = 48%; p < .001; tDCS: −0.82 kg, 95% CI [−1.01, −0.62]; I2 = 0.0%; p = .00) and BMI (TMS: −0.74, 95% CI [−1.17, −0.31]; I2 = 0% p = .00; tDCS: MD = −0.55, 95% CI [−2.32, 1.21]; I2 = 0% p = .54) as well as food cravings (TMS: g = −0.91, 95% CI [−1.68, −0.14]; I2 = 88 p = .00; tDCS: g = −0.32, 95% CI [−0.62, −0.02]; p = .04). Compared with sham stimulation, our findings indicate that active NIBS can significantly help to reduce body weight and food cravings. Hence, these novel techniques may be used as primary or adjunct tools in treating patients with obesity.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".