Factors Impacting Mobile Health Adoption for Depression Care and Support by Adolescent Mothers in Nigeria: Preliminary Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth), the use of mobile technology in health care, is increasingly being used for mental health service delivery even in low- and middle-income countries to scale up treatment, and a variety of evidence supports their potential in different populations. OBJECTIVE: This study aims to use the Social Cognitive Theory (SCT) as a lens to explain knowledge of mHealth use for mental health care, personal behavioral capabilities, and the external social contexts that can impact the adoption of an mHealth app for depression care among perinatal adolescents in Nigeria. METHODS: At the preliminary stage of a user-centered design (UCD), 4 focus group discussions were conducted among 39 participants: 19 perinatal adolescents with a history of depression and 20 primary care providers. Guided by the SCT, a popular model used for predicting and explaining health behaviors, we documented participants' knowledge of mHealth use for health purposes, advantages, and challenges to the adoption of an mHealth app by young mothers, and approaches to mitigate challenges. Data collection and analysis was an iterative process until saturation of all topic areas was reached. RESULTS: The mean age for young mothers was 17.3 (SD 0.9) years and 48 (SD 5.8) years for care providers. Mistrust from relatives on mobile phone use for therapeutic purposes, avoidance of clinic appointments, and sharing of application contents with friends were some challenges to adoption identified in the study population. Supportive personal factors and expressions of self-efficacy on mobile app use were found to be insufficient for adoption. This is because there are social complications and disapprovals that come along with getting pregnant at a young age. Adequate engagement of parents, guardians, and partners on mHealth solutions by care providers was identified as necessary to the uptake of digital tools for mental health care in this population. CONCLUSIONS: The SCT guided the interpretations of the study findings. Young mothers expressed excitement at the use of mHealth technology to manage perinatal depression. Real-life challenges, however, need to be attended to for successful implementation of such interventions. Communications between care providers and patients' relatives on the therapeutic use of mHealth are vital to the success of a mHealth mental health management plan for depression in young mothers in Nigeria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".