Bringing Psychosocial Support to Headache Sufferers Using Information and Communication Technology: Lessons Learned from Asking Potential Users What they Want
Bibliographic record
Abstract
BACKGROUND: Headaches are a major concern for which psychosocial interventions are recommended. However, headache sufferers do not always have ready access to these interventions. Technology has been used to improve access, especially in young people. OBJECTIVES: To examine user preferences to inform the development of an Internet-based psychosocial intervention including smartphone technology, referred to as the Wireless Headache Intervention. METHODS: The methodology followed a participatory design cycle, including 25 headache sufferers (14 to 28 years of age) who informed the prototype design. All participants were familiar with smartphones and the Internet. Through two iterative cycles of focus groups stratified according to age, qualitative data were collected by asking user preferences for the different planned components of the intervention (ie, smartphone pain diary, Internet-based self-management treatment, social support) and other relevant aspects (ie, smartphone versus computer delivery, and ways of reaching target audience). NVivo 8 with content analysis was used to analyze data and reflect themes as guided by the thematic survey. RESULTS: Participants reported a preference for completing the smartphone pain diary on a daily basis. Participants believed that the program should facilitate easy access to information regarding headaches and management strategies. They also wanted access to other headache sufferers and experts. Participants believed that the program should be customizable and interactive. They reinforced the need and value of an integrated smartphone and Internet-based application. CONCLUSIONS: The results provide insight into a participatory design to guide design decisions for the type of intervention for which success relies largely on self-motivation. The results also provide recommendations for design of similar interventions that may benefit from the integration of mobile applications to Internet-based interventions. The present research contributes to the theoretical frameworks that have been formulated for the development of Internet-based applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".