A Digital Mental Health Support Program for Depression and Anxiety in Populations With Attention-Deficit/Hyperactivity Disorder: Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: A total of 1 in 2 adults with attention-deficit/hyperactivity disorder (ADHD) struggles with major depressive or anxiety disorders. The co-occurrence of these disorders adds to the complexity of finding utility in as well as adherence to a treatment option. Digital therapeutic solutions may present a promising alternative treatment option that could mitigate these challenges and alleviate symptoms. OBJECTIVE: This study aims to investigate (1) the feasibility and acceptance of a digital mental health intervention, (2) participants' engagement and retention levels, and (3) the potential efficacy with respect to anxiety and depression symptoms in a population with ADHD. Our main hypothesis was that a digital, data-driven, and personalized intervention for adults with coexisting ADHD and depressive or anxiety symptoms would show high engagement and adherence, which would be accompanied by a decrease in depressive and anxiety symptoms along with an increase in quality of life and life satisfaction levels. METHODS: This real-world data, single-arm study included 30 adult participants with ADHD symptomatology and coexisting depressive or anxiety symptoms who joined a 16-week digital, data-driven mental health support program. This intervention is based on a combination of evidence-based approaches such as cognitive behavioral therapy, mindfulness, and positive psychology techniques. The targeted symptomatology was evaluated using the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Barkley Adult ADHD Rating Scale-IV. Quality of life aspects were evaluated using the Satisfaction With Life Scale and the Life Satisfaction Questionnaire, and user feedback surveys were used to assess user experience and acceptability. RESULTS: The study retention rate was 97% (29/30), and high engagement levels were observed, as depicted by the 69 minutes spent on the app per week, 5 emotion logs per week, and 11.5 mental health actions per week. An average decrease of 46.2% (P<.001; r=0.89) in depressive symptoms and 46.4% (P<.001; r=0.86) in anxiety symptoms was observed, with clinically significant improvement for more than half (17/30, 57% and 18/30, 60%, respectively) of the participants. This was followed by an average increase of 23% (P<.001; r=0.78) and 20% (P=.003; r=0.8) in Satisfaction With Life Scale and Life Satisfaction Questionnaire scores, respectively. The overall participant satisfaction level was 4.3 out of 5. CONCLUSIONS: The findings support the feasibility, acceptability, and value of the examined digital program for adults with ADHD symptomatology to address the coexisting depressive or anxiety symptoms. However, controlled trials with larger sample sizes and more diverse participant profiles are required to provide further evidence of clinical efficacy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".