Technological Proficiencies, Engagement, and Practical Considerations for mHealth Programs at an Urban Safety-Net Hospital Emergency Departments: Data Analysis
Bibliographic record
Abstract
BACKGROUND: Safety-net emergency departments often serve as the primary entry point for medical care for low income predominantly minority patient populations. Herein, we sought to provide insight into the feasibility, technological proficiencies, engagement characteristics, and practical considerations for a mHealth intervention at a safety-net emergency department. OBJECTIVE: We aimed to analyze patient technological proficiency to understand the feasibility of and draw practical considerations for mobile phone technology (mHealth) solutions for patients with chronic disease served by safety-net emergency departments. METHODS: levels >8.5% were enrolled. A text message-based mHealth program to improve disease self-management was provided to all patients. Supporters of patients were randomized to receive a mailed copy or mHealth-based curriculum designed to improve diabetes support. Among enrolled patients, we surveyed mobile technological capacity and frequency of use. We performed latent class analysis to identify classes of patients by level of technological proficiency and compared demographic characteristics between the latent classes to identify demographic subgroups that may require more training or tailoring of the mHealth approach. Study engagement between classes was assessed by comparing the mean number of text messages exchanged, loss to follow-up, and early termination. RESULTS: Of 1876 patients who were approached, 44.2% (n=829) of patients had a stable mobile phone and were able to use text messages. Among them 166 met the trial inclusion and enrolled, 90% (149/166) of the cohort were ethnically diverse. Significant variance was found in technology capacity and frequency of use. Our latent class analysis classified 75% (124/166) of patients as highly technologically proficient and 25% (42/166) patients as minimally technologically proficient. Age (P<.001) and level of education (P<.001) were associated with class membership. Highly technologically proficient patients were younger and had higher levels of education (45.74 years old; high school or more: 90%) than minimally technologically proficient patients (53.64 years old; high school or more: 18%). Highly technologically proficient participants exchanged a mean of 40 text messages with the system coordinators compared to a mean of 10 text messages by minimally technologically proficient patients (P<.001). CONCLUSIONS: This study found that nearly half of the patients screened at the safety-net emergency department were equipped for an SMS text message-based mHealth intervention. In the small sample of patients who were enrolled, the majority were classified as highly technologically proficient. These highly proficient patients had greater study engagement. mHealth use in emergency departments may be an opportunity to improve health of ethnically diverse populations by pairing sophisticated chronic disease self-management program with SMS text message-based and traditional in-person interventions to reach patients through the method that is most familiar and comfortable. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2019.03.003.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".