mHealth Tools for the Self-Management of Patients With Multimorbidity in Primary Care Settings: Pilot Study to Explore User Experience
Bibliographic record
Abstract
BACKGROUND: Given the complex and evolving needs of individuals with multimorbidity, the adoption of mHealth tools to support self-management efforts is increasingly being explored, particularly in primary care settings. The electronic patient-reported outcomes (ePRO) tool was codeveloped with patients and providers in an interdisciplinary primary care team in Toronto, Canada, to help facilitate self-management in community-dwelling adults with multiple chronic conditions. OBJECTIVE: The objective of study is to explore the experience and expectations of patients with multimorbidity and their providers around the use of the ePRO tool in supporting self-management efforts. METHODS: We conducted a 4-week pilot study of the ePRO tool. Patients' and providers' experiences and expectations were explored through focus groups that were conducted at the end of the study. In addition, thematic analyses were used to assess the shared and contrasting perspectives of patients and providers on the role of the ePRO tool in facilitating self-management. Coded data were then mapped onto the Individual and Family Self-Management Theory using the framework method. RESULTS: In this pilot study, 12 patients and 6 providers participated. Both patients and providers emphasized the need for a more explicit recognition of self-management context, including greater customizability of content to better adapt to the complexity and fluidity of self-management in this particular patient population. Patients and providers highlighted gaps in the extent to which the tool enables self-management processes, including how limited progress toward self-management goals and the absence of direct provider engagement through the ePRO tool inhibited patients from meeting their self-management goals. Providers highlighted proximal outcomes based on their experience of the tool and specifically, they indicated that the tool offered valuable insights into the broader patient context, which helps to inform the self-management approach and activities they recommend to patients, whereas patients recognized the tool's potential in helping to improve access to different providers in a team-based primary care setting. CONCLUSIONS: This study identifies a more explicit recognition of the contextual factors that influence patients' ability to self-manage and greater adaptability to accommodate patient complexity and provider workflow as next steps in refining the ePRO tool to better support self-management efforts in primary care ahead of its application in a full-scale randomized pragmatic trial.
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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 | 0.000 | 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".