Functional electrical stimulation of the facial muscles to improve symptoms in individuals with major depressive disorder: pilot feasibility study
Bibliographic record
Abstract
BACKGROUND: Currently, the mainstay of treatment in patients diagnosed with major depressive disorder (MDD) requiring medical attention is second generation anti-depressants. However, about 40% of patients treated with second-generation anti-depressants do not respond to initial treatment and approximately 70% do not achieve remission during the first-step treatment. There are a few non-pharmacological options available, but none have shown consistently positive results. There is a need for an intervention that is relatively easy to administer, produces consistently positive results and is associated with minimal side effects. In the current study, we assessed the feasibility of using transcutaneous Functional Electrical Stimulation Therapy (FEST) of the facial muscles, as a tool for improving depressive symptoms in individuals with MDD. RESULTS: Ten (10) individuals with moderate to severe MDD received three FEST sessions/week for a minimum of 10 to a maximum of 40 sessions. All study participants completed the required 10 therapy sessions, and 5 of the 10 participants completed additional 30 (totalling 40) FEST sessions. There were no adverse events or concerns regarding compliance to therapy. We found statistically significant improvements on Hamilton Rating Scale for Depression (HDS) and Inventory of Depressive Symptomatology (IDS) measures. However, no significant improvements were found on Positive and Negative Affect Scale and 10-point Visual Analogue Scale scales. Participants reported improvements in sleeping patterns, and this correlated with statistically significant improvements on sleep parameters of HDS and IDS measures. CONCLUSION: This study indicates that facial FEST is an acceptable, practical, and safe treatment in individuals with MDD. We provide preliminary evidence to show improvements in depressive symptoms following a minimum of 10 FEST sessions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".