A Mobile App for Chronic Disease Self-Management: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Health literacy is a critically important skill that helps people become active participants in their health care. Multiple studies in the United States and across the world have documented the association of health literacy with multiple health outcomes. In particular, the elderly and many members of minority groups have been shown to have low levels of health literacy; the same groups are disproportionately affected by chronic illnesses. These twin burdens affect the people most in need of the skills and knowledge required for coping with chronic illnesses. Chronic disease self-management (CDSM) is a logical target for a general health literacy intervention. In an approach that spans across specific diseases, CDSM targets problems and skills needed to cope with issues such as fatigue, pain, stress, depression, sleep disturbance, and treatment adherence. In a previous study, we showed that a computer-delivered tailored information intervention targeting health literacy could improve treatment and adherence and be cost effective, but it is not clear that this same strategy will be effective in persons with low health literacy and multiple chronic conditions. OBJECTIVE: The purpose of this study is to develop a computer-delivered mobile intervention that will provide individuals with chronic conditions the necessary information to cope with their conditions. METHODS: In this project, we will complete a qualitative study on the status and needs of individuals with more than one chronic condition. Results of this study will be used to develop a mobile tailored information app that will address self-management challenges in the areas of pain, sleep, fatigue, depression, anger, stress, memory problems, and treatment adherence. The impact of the intervention on patient quality of life, patient-provider relationships, health literacy, and patient activation will be assessed. We will also explore the extent to which health literacy mediates important outcomes, such as health-related quality of life and health service utilization. RESULTS: We are currently completing the preliminary qualitative and usability studies that will inform the content and design of the intervention. We anticipate that the intervention will be complete in 2017, and the clinical trial of its efficacy will also commence in 2017. CONCLUSIONS: Results will provide evidence on the usefulness of a mobile tailored information app for improving health literacy, patient activation, health-related quality of life, and self-reported health in patients with multiple chronic conditions. TRIAL REGISTRATION: Clinicaltrials.gov NCT02922439; https://clinicaltrials.gov/ct2/show/NCT02922439 (Archived by WebCite at http://www.webcitation.org/6pTiqDAyN).
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.139 | 0.019 |
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".