Addressing Depression Comorbid With Diabetes or Hypertension in Resource-Poor Settings: A Qualitative Study About User Perception of a Nurse-Supported Smartphone App in Peru
Bibliographic record
Abstract
BACKGROUND: Smartphone apps could constitute a cost-effective strategy to overcome health care system access barriers to mental health services for people in low- and middle-income countries. OBJECTIVE: The aim of this paper was to explore the patients' perspectives of CONEMO (Emotional Control, in Spanish: Control Emocional), a technology-driven, psychoeducational, and nurse-supported intervention delivered via a smartphone app aimed at reducing depressive symptoms in people with diabetes, hypertension or both who attend public health care centers, as well as the nurses' feedback about their role and its feasibility to be scaled up. METHODS: This study combines data from 2 pilot studies performed in Lima, Peru, between 2015 and 2016, to test the feasibility of CONEMO. Interviews were conducted with 29 patients with diabetes, hypertension or both with comorbid depressive symptoms who used CONEMO and 6 staff nurses who accompanied the intervention. Using a content analysis approach, interview notes from patient interviews were transferred to a digital format, coded, and categorized into 6 main domains: the perceived health benefit, usability, adherence, user satisfaction with the app, nurse's support, and suggestions to improve the intervention. Interviews with nurses were analyzed by the same approach and categorized into 4 domains: general feedback, evaluation of training, evaluation of study activities, and feasibility of implementing this intervention within the existing structures of health system. RESULTS: Patients perceived improvement in their emotional health because of CONEMO, whereas some also reported better physical health. Many encountered some difficulties with using CONEMO, but resolved them with time and practice. However, the interactive elements of the app, such as short message service, android notifications, and pop-up messages were mostly perceived as challenging. Satisfaction with CONEMO was high, as was the self-reported adherence. Overall, patients evaluated the nurse accompaniment positively, but they suggested improvements in the technological training and an increase in the amount of contact. Nurses reported some difficulties in completing their tasks and explained that the CONEMO intervention activities competed with their everyday work routine. CONCLUSIONS: Using a nurse-supported smartphone app to reduce depressive symptoms among people with chronic diseases is possible and mostly perceived beneficial by the patients, but it requires context-specific adaptations regarding the implementation of a task shifting approach within the public health care system. These results provide valuable information about user feedback for those building mobile health interventions for depression.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".