Mixed-Methods Analysis of Factors Impacting Use of a Postoperative mHealth App
Bibliographic record
Abstract
BACKGROUND: Limited communication and care coordination following discharge from hospitals may contribute to surgical complications. Smartphone apps offer a novel mechanism for communication and care coordination. However, factors which may affect patient app use in a postoperative, at-home setting are poorly understood. OBJECTIVE: The objectives of this study were to (1) gauge interest in smartphone app use among patients after colorectal surgery and (2) better understand factors affecting patient app use in a postoperative, at-home setting. METHODS: A prospective feasibility study was performed at a hospital that principally serves low socioeconomic status patients. After colorectal surgery, patients were enrolled and given a smartphone app, which uses previously validated content to provide symptom-based recommendations. Patients were instructed to use the app daily for 14 days after discharge. Demographics and usability data were collected at enrollment. Usability was measured with the System Usability Scale (SUS). At follow-up, the SUS was repeated and patients underwent a structured interview covering ease of use, willingness to use, and utility of use. Two members of the research team independently reviewed the field notes from follow-up interviews and extracted the most consistent themes. Chart and app log reviews identified clinical endpoints. RESULTS: We screened 115 patients, enrolled 20 patients (17.4%), and completed follow-up interviews with 17 patients (85%). Reasons for nonenrollment included: failure to meet inclusion criteria (47/115, 40.9%), declined to participate (26/115, 22.6%), and other reasons (22/115, 19.1%). There was no difference in patient ratings between usability at first-use and after extended use, with SUS scores greater than the 95th percentile at both time points. Despite high usability ratings, 6/20 (30%) of patients never used the app at home after hospital discharge and 2/20 (10%) only used the app once. Interviews revealed three themes related to app use: (1) patient-related barriers could prevent use even though the app had high usability scores; (2) patients viewed the app as a second opinion, rather than a primary source of information; and (3) many patients viewed the app as an external burden. CONCLUSIONS: Use patterns in this study, and response rates after prompts to contact the operative team, suggest that apps need to be highly engaging to be adopted by patients. The growing penetration of smartphones and the proliferation of app-based interventions are unlikely to improve care coordination and communication, unless apps address the barriers and patient perceptions identified in this study. This study shows that high usability alone is not sufficient to motivate patients to use smartphone apps in the postoperative period.
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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 | 0.047 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".