Internet-Delivered Self-help for Adults With ADHD (MyADHD): Usability Study
Bibliographic record
Abstract
BACKGROUND: Although effective pharmacological treatment exists, many adults with attention-deficit/hyperactivity disorder (ADHD) prefer a nonpharmacological option for managing their symptoms. Internet-delivered self-help interventions have the potential to address this unmet supportive care need reported by adults with ADHD, at relatively low costs. However, if the intervention does not offer optimal functions, content, and layout, it could decrease adherence and engagement and potentially compromise the effectiveness of such interventions. Thus, there is a need for examining the usability and factors that enhance and impair the usability of internet-delivered self-help interventions. OBJECTIVE: This study evaluates the usability of an internet-delivered self-help intervention for adults with ADHD (MyADHD). The main goals were to (1) collect qualitative and quantitative data on usability and (2) identify usability problems. METHODS: Individual think-aloud interviews and staged usability testing (N=5) were conducted to evaluate the usability of the MyADHD intervention in terms of function, content, and design. MyADHD end users provided iterative feedback to maximize engagement and usability. They performed tasks involved in operating the intervention and provided "think-aloud" commentary and postsession usability ratings. The interviews were recorded, transcribed verbatim, and analyzed. RESULTS: Participants were satisfied with the overall usability of the program. The average perceived usability score out of 100 was 70 for the first round of testing and improved to 77.5 after applying modifications, with a mean score of 75.5 (SD 5.9) for all rounds of usability testing. The analysis of the interviews revealed 3 central themes: functionality, content, and layout. CONCLUSIONS: Optimizing the usability of internet-delivered self-guided interventions is a critical step in the design and development process. The usability testing in this study provided valuable information from users' perspectives on the content and platform of the intervention. Analysis revealed the need for intervention enhancement with regard to design, functionality, and content from the perspective of potential end users. Overall, participants saw value in the MyADHD intervention and were confident that they could use it for the self-management of symptoms and expressed the desire to use the entire intervention when it becomes available. Through this development process, we produced an intervention that is likely to be used successfully and is ready for deployment in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT04511169; https://clinicaltrials.gov/ct2/show/NCT04511169.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".