A Mobile Phone Text Messaging Intervention to Manage Fatigue for People With Multiple Sclerosis, Spinal Cord Injury, and Stroke: Development and Usability Testing
Bibliographic record
Abstract
BACKGROUND: Fatigue significantly affects daily functioning in persons with disabilities. Fatigue management can be challenging, and the information provided during routine physician visits to manage fatigue can be overwhelming. One way to address fatigue is to increase knowledge, skills, and confidence for self-management (ie, patient activation). Self-management programs have shown promising effects in targeting fatigue in persons with disabilities. However, satisfaction with self-management programs is low for persons with disabilities, and tailoring interventions to personalized needs has been recommended. SMS text messaging is increasingly being used to implement health behavior change interventions in a person's natural environment. Little has been done to link mobile health approaches with patient activation and self-management to address fatigue in persons with disabilities. OBJECTIVE: This study aimed to develop and test a mobile phone-based fatigue self-management SMS text messaging intervention targeting patient activation in 3 groups of persons with disabilities: persons with multiple sclerosis, persons who had a stroke, and persons with a spinal cord injury. METHODS: We used evidence-based resources and input from a consumer advisory board (CAB; composed of 2 participants from each of the 3 disability groups) and a neurologist to develop the intervention. The study was conducted using a 4-step process: development of the initial SMS text messaging library and categorization of the content into 9 content areas, review and modification of the SMS text messages by the neurologist and CAB, integration of the content library into a digital platform, and utility testing by CAB members. RESULTS: A total of 6 CAB participants rated SMS text messages covering 9 domain areas of fatigue self-management with good clarity (mean ratings=3.5-5.0 out of 5) and relevance (mean ratings=3.2-5.0 out of 5). Overall, SMS text messaging content was reported by CAB participants as helpful, clear, and well suited for a mobile health intervention. The CAB reached consensus on the time of day that SMS text messages should be sent (morning) and their frequency (once per day). This feedback led the research team to narrow down the program to deliver 48 SMS text messages, 1 per day, Monday through Thursday only, a total of 4 SMS text messages per week, over a 12-week period. The final set of SMS text messages was programmed into a digital platform with a predefined delivery schedule. The usability of the intervention was high, with 55 (83%) out of 66 responses endorsing the highest rating. CONCLUSIONS: This study demonstrates a step-by-step process for developing a fatigue self-management SMS text messaging intervention for persons with disabilities. For this population, whose access to health services is often limited, this intervention provides an alternative delivery model to increase access to fatigue information and deliver content that aligns with the person's needs.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".