Comparison of the Efficacy Between Standard Measurement-Base Care (MBC) and Enhanced MBC for Major Depressive Disorder: A Pilot Study
Bibliographic record
Abstract
Yuru He,1,2 Xing Wang,1,2 Zuowei Wang,3,4 Ping Zhang,5 Xiaojia Huang,3,4 Meihong Yu,5 Jill K Murphy,6 Erin E Michalak,6 Jing Liu,6 Tao Yang,1,2 Xiaorui Yang,7 Yiru Fang,1,8,9 Raymond W Lam,6 Jun Chen1,2 1Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Shanghai Key Laboratory of Psychotic Disorders, Shanghai, People’s Republic of China; 3Division of Mood Disorders, Shanghai Hongkou Mental Health Center, Shanghai, People’s Republic of China; 4Clinical Research Center for Mental Health, School of Medicine, Shanghai University, Shanghai, People’s Republic of China; 5Fengxian District Mental Health Center, Shanghai, People’s Republic of China; 6Department of Psychiatry, University of British Columbia, Vancouver, Canada; 7Department of Psychology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 8Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 9CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, People’s Republic of ChinaCorrespondence: Jun Chen, Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People’s Republic of China, Tel +86 21-34773367, Email doctorcj2010@gmail.com Raymond W Lam, Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada, Tel +1 604-822-7325, Email r.lam@ubc.caPurpose: To validate the efficacy of enhanced measurement-based care against standard measurement-based care in patients with major depressive disorder.Patients and Methods: In this pilot study of an ongoing multicenter cluster randomized controlled trails, 160 patients diagnosed with major depressive disorder were enrolled from 2 mental health centers, with a plan to include 12 centers in total. One hundred patients engaged in a six-month evaluation using a technology-enhanced measurement-based care tool, including assessments of clinical symptoms, side effects, and functionality at baseline, two months, four months and six months. Simultaneously, the remaining 60 patients underwent standard paper-based measurement-based care, utilizing the same set of scales over the same six-month period, with assessments at the same time points.Results: Patients utilizing the enhanced measurement-based care tool demonstrated a significantly higher reduction rate in PHQ-9 scores compared to those using standard paper-based measurement-based care during the two-month follow-up. Additionally, a notable positive correlation was observed between the frequency of enhanced measurement-based care tool usage and the quality of life during the two-month follow-up.Conclusion: Enhanced measurement-based care has the effect of reducing depressive symptoms. Our study emphasized that using enhanced measurement-based care via smartphones is a feasible tool for patients with major depressive disorder. Our future study, including results from additional research centers, may further validate the effectiveness of enhanced measurement-based care.Keywords: major depressive disorder, measurement-based care, enhanced measurement-based care, smartphone-based intervention
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".