Understanding the effectiveness and design of parent-oriented mobile health interventions: a systematic review and narrative synthesis
Bibliographic record
Abstract
BACKGROUND: Parents of children with a health condition experience high levels of distress which can have long-term impact on the child and parent. Dyadic interventions have the potential to decrease this distress, however several barriers to access including time constraints have been reported. Mobile health (mHealth) interventions can address several of these barriers. GOAL: The goal of this systematic review was to review and synthesize the literature examining the effects of parent-oriented mHealth interventions and their content and design. METHODS: We searched PubMed/MEDLINE, Embase, PsycINFO, CINAHL and Cochrane Central databases from January 2013 to 2023 using a search strategy based on telemedicine and parents/caregivers. Included studies were randomized controlled trials assessing the effect of parent-oriented mHealth interventions on child and parent health. The Cochrane risk-of-bias tool was used to assess for bias in studies. Trial details and design and content features of interventions were extracted. Outcomes were organized using the Van Houtven's Framework for Informal Caregiver Interventions. Results are presented narratively. RESULTS: Fifty papers pertaining to 49 unique studies met our inclusion criteria. More than half of the studies scored high-risk for bias. Interventions targeted a wide range of pediatric conditions. Intervention type included texting (n = 17) and investigator-developed mobile applications (n = 16). Interventions significantly improved parent psychological health and child health outcomes. Key intervention features and design included the use/application of codesign and a theory-driven intervention. CONCLUSION: Parent-oriented mHealth interventions identified in this review significantly improved both parent and child health outcomes. Therefore, these interventions have the potential to support parents outside of a clinical setting.
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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 | 0.066 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".