Efficacy of a Text-Based Mental Health Coaching App in Improving the Symptoms of Stress, Anxiety, and Depression: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Stress, anxiety, and depression are major mental health concerns worldwide. A wide variety of digital mental health interventions have demonstrated efficacy in improving one's mental health status, and digital interventions that involve some form of human involvement have been shown to demonstrate greater efficacy than self-guided digital interventions. Studies demonstrating the efficacy of digital mental health interventions within the Asian region are scarce. OBJECTIVE: This study aimed to investigate the potential efficacy of the digital mental health intervention, ThoughtFullChat, which consists of one-on-one, asynchronous, text-based coaching with certified mental health professionals and self-guided tools, in improving self-reported symptoms of depression, anxiety, and stress. The study also aims to examine the potential differences in efficacy among occupational subgroups and between sexes. METHODS: A randomized controlled study was conducted among housemen (trainee physicians), students, faculty members, and corporate staff at International Medical University, Malaysia. A total of 392 participants were enrolled and randomized to the intervention (n=197, 50.3%) and control (n=195, 49.7%) groups. Depression, anxiety, and stress symptoms were measured using the Depression, Anxiety, and Stress Scale-21 items at baseline and after the 3-month intervention period. The Satisfaction with Life Scale and Brief Resilience Scale were also included, along with a questionnaire about demographics. RESULTS: Significant decrease was observed in depression (P=.02) and anxiety (P=.002) scores in the intervention group. A subgroup (corporate staff) also demonstrated significant decrease in stress (P=.005) alongside depression (P=.006) and anxiety (P=.002). Females showed significant improvements in depression (P=.02) and anxiety (P<.001) when compared with males. CONCLUSIONS: This study provides evidence that the ThoughtFullChat app is effective in improving the symptoms of depression, anxiety, and stress in individuals, particularly among corporate staff from the educational field. It also supports the notion that mobile mental health apps that connect users to mental health professionals in a discreet and cost-efficient manner can make important contributions to the improvement of mental health outcomes. The differential improvements among occupational subgroups and between sexes in this study indicate the need for future digital mental health app designs to consider an element of personalization focused on systemic components relating to occupation. TRIAL REGISTRATION: Clinicaltrials.gov NCT04944277; https://classic.clinicaltrials.gov/ct2/show/NCT04944277.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".