Needs and Expectations for the myNewWay Blended Digital and Face-to-Face Psychotherapy Model of Care for Depression and Anxiety (Part 1): Participatory Design Study including People with Lived and Living Experience
Bibliographic record
Abstract
BACKGROUND: Digital mental health interventions (DMHIs) are effective in reducing symptoms of depression and anxiety. Low user engagement and uptake of DMHIs observed in previous research may be addressed by involving the intended target audience in the design of the DMHI from the outset. OBJECTIVE: This study is phase 1 of a multiphase project aimed at designing, developing, and evaluating a blended DMHI for depression and anxiety in Australia. Our objective was to partner with adults with lived and living experiences of depression and anxiety on their needs and expectations of a new transdiagnostic DMHI for depression and anxiety. This included identifying strategies that would help increase their engagement with the DMHI and their preferences for integrating the DMHI with psychotherapy. METHODS: A mixed methods participatory design approach was used to collect quantitative and qualitative data via a web-based survey (n=324) and semistructured interviews (n=21). Feedback was collected on participants' needs and expectations for the DMHI, including accessibility, content, features, functionality, format, data sharing, preferred clinical support pathways, and barriers to and facilitators of user engagement. Qualitative interview data were analyzed using reflexive thematic analysis. RESULTS: Most participants (190/257, 73.9%) preferred a DMHI delivered as a smartphone app that could be used at any time of the day. Ease of use and a well-designed interface were important, as was a positive, encouraging, and uplifting DMHI look and feel. Other preferences included symptom tracking, diverse therapeutic content, and features that facilitated social connection and peer support (eg, online community and stories of lived and living experience). Participants also suggested several strategies to enhance engagement with the DMHI, including personalization, reminders, short and achievable activities, and goal setting. Participants reported a strong interest in sharing information from their DMHI with mental health professionals (to facilitate therapy), especially regarding changes to their emotions. CONCLUSIONS: Transdiagnostic DMHIs for depression and anxiety have great potential to improve access to affordable, evidence-based mental health support. Involving people with lived and living experiences of depression and anxiety in the design, development, and conceptualization of DMHIs may improve uptake, acceptance, engagement, usability, and ultimately, treatment outcomes.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".