Mobile Health for First Nations Populations: Systematic Review
Bibliographic record
Abstract
BACKGROUND: The ubiquitous presence and functionality of mobile devices offers the potential for mobile health (mHealth) to create equitable health opportunities. While mHealth is used among First Nations populations to respond to health challenges, the characteristics, uptake, and effectiveness of these interventions are unclear. OBJECTIVE: This review aimed to identify the characteristics of mHealth interventions (eg, study locations, health topic, and modality) evaluated with First Nations populations and to summarize the outcomes reported for intervention use, user perspectives including cultural responsiveness, and clinical effectiveness. In addition, the review sought to identify the presence of First Nations expertise in the design and evaluation of mHealth interventions with First Nations populations. METHODS: The methods of this systematic review were detailed in a registered protocol with the International Prospective Register of Systematic Reviews (PROSPERO, CRD42019123276). Systematic searches of peer-reviewed, scientific papers were conducted across 7 databases in October 2018. Eligible studies had a primary focus on mHealth interventions with experimental or quasi-experimental design to respond to a health challenge with First Nations people from Canada, Australia, New Zealand, and the United States. Two authors independently screened records for eligibility and assessed risk of bias using the Joanna Briggs Institute checklists. Data were synthesized narratively owing to the mix of study designs, interventions, and outcomes. The review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS: Searches yielded 1053 unique records, after review and screening, 13 studies (5 randomized controlled trials and 8 quasi-experimental designs) were included in the final analysis. Studies were conducted in Australia (n=9), the United States (n=2), and New Zealand (n=2). The most common health challenge addressed was mental health and suicide (n=5). Intervention modalities included text messaging (n=5), apps (n=4), multimedia messaging (n=1), tablet software (n=1), or a combination of short messaging service (SMS) and apps (n=1). Results showed mixed engagement with the intervention (n=3); favorable user perspectives, including acceptability and cultural appropriateness (n=6); and mixed outcomes for clinical effectiveness (n=10). A diverse range of risks of bias were identified, the most common of which included a lack of clarity about allocation and blinding protocols and group treatment for randomized controlled trials and a lack of control group and single outcome measures for quasi-experimental designs. First Nations expertise informed all mHealth studies, through authorship (n=8), affiliation with First Nations bodies (n=3), participatory study design (n=5), First Nations reference groups (n=5), or a combination of these. CONCLUSIONS: mHealth modalities, including SMS and apps, appear favorable for delivery of health interventions with First Nations populations, particularly in the area of mental health and suicide prevention. Importantly, First Nations expertise was strongly embedded within the studies, augmenting favorable use and user engagement. However, evidence of efficacy is limited.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".