Implementing an Activity Tracker to Increase Motivation for Physical Activity in Patients With Diabetes in Primary Care: Strengths, Weaknesses, Opportunities and Threats (SWOT) Analysis
Bibliographic record
Abstract
BACKGROUND: Many projects related to technology implementation in the context of chronic diseases have been developed over the years to better manage lifestyle medicine interventions and improve patient care. However, technology implementation in primary care settings remains challenging. OBJECTIVE: The aim is to carry out a strengths, weaknesses, opportunities, and threats (SWOT) analysis (1) to assess satisfaction among patients with type 2 diabetes using an activity tracker to increase motivation for physical activity (PA) and (2) to explore the research and health care team's perceptions of this technology's implementation in a primary care setting. METHODS: A 3-month hybrid type 1 study, which included 2 stages, was conducted in an academic primary health center in Quebec City, Quebec, Canada. In stage 1, a total of 30 patients with type 2 diabetes were randomized to the intervention (activity tracker) group or the control group. In stage 2, a SWOT analysis was performed on both patients and health care professionals to determine the components of successful technology implementation. Two questionnaires were used to gather feedback: a satisfaction and acceptability questionnaire concerning an activity tracker (15 patients in the intervention group) and a questionnaire based on the SWOT elements (15 patients in the intervention group and 7 health care professionals). Both questionnaires contained quantitative and qualitative questions. Qualitative variables from open questions were synthesized in a matrix and ranked according to apparition frequency and global importance. A thematic analysis was performed by the first author and validated by 2 coauthors separately. The information gathered was triangulated to propose recommendations that were then approved by the team. Both quantitative (randomized controlled trial participants) and qualitative (randomized controlled trial participants and team) results were combined for recommendations. RESULTS: In total, 86% (12/14) of the participants were satisfied with their activity tracker use and 75% (9/12) felt that it incited them to stick to their PA program. The main strengths of the team members' perspectives were the project initiation and involvement of a patient partner, the study design, the team, and the device. The weaknesses were the budgetary constraints, the turnover, and the technical issues. The opportunities were the primary care setting, the loan of equipment, and common technology. The threats were recruitment issues, administrative challenges, technological difficulties, and a single research site. CONCLUSIONS: Patients with type 2 diabetes were satisfied with their activity tracker used to improve motivation for PA. Health care team members agreed that implementation can be done in primary care, but some challenges remain in using this technological tool in clinical practice regularly. TRIAL REGISTRATION: ClinicalTrials.gov NCT03709966; https://clinicaltrials.gov/ct2/show/NCT03709966.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".