Impact of mobile health interventions during the perinatal period on maternal psychosocial outcomes: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review was to evaluate the effectiveness of mother-targeted mobile health (mHealth) education interventions during the perinatal period on maternal psychosocial outcomes in high-income countries. INTRODUCTION: The perinatal period is an exciting yet challenging period for mothers that requires physical, emotional and social adjustment to new norms and expectations. In recent years, there has been an increase in the use of mHealth by new mothers who are seeking health information through online or mobile applications. While there have been systematic reviews on the impact of mHealth interventions on maternal and newborn health in low- and middle-income countries, the impact of these interventions on maternal psychosocial health outcomes in high-income countries remains uncertain. INCLUSION CRITERIA: This review considered studies of mHealth education interventions targeting mothers in high-income countries (as defined by the World Bank) during the perinatal period. Interventions must have started between the antenatal period (conception through birth) through six weeks postpartum. All experimental study designs were included. Outcomes included self-efficacy, social support, postpartum anxiety and postpartum depression. METHODS: PubMed, CINAHL, PsycINFO and Embase were searched for published studies in English on December 16, 2018. Gray literature was also searched for non-peer reviewed articles, including Google Scholar, mHealth intelligence and clinical trials databases. Critical appraisal was undertaken by two independent reviewers using standardized critical appraisal instruments from JBI. Quantitative data were extracted from included studies independently by two reviewers using the standardized data extraction tool from JBI. All conflicts were solved through consensus with a third reviewer. Quantitative data were, where possible, pooled in statistical meta-analysis using RevMan. Where statistical pooling was not possible, findings were reported narratively. RESULTS: Of the 1,607 unique articles identified, 106 full-text papers were screened and 24 articles were critically appraised, with 21 included in the final review. Eleven were quasi-experimental and 10 were randomized controlled trials. The mHealth intervention approach varied, with text message and mobile applications being the most common. Length of intervention ranged from four weeks to six months. The topics of the mHealth intervention varied widely, with the most common topic being postpartum depression. Mothers who received an mHealth intervention targeting postpartum depression showed a decreased score on the Edinburgh Postnatal Depression Scale when measured post-intervention (odds ratio = -6.01, 95% confidence interval = -8.34 to -3.67, p < 0.00001). The outcomes related to self-efficacy, social support and anxiety showed mixed findings of effectiveness (beneficial and no change) across the studies identified. CONCLUSIONS: This review provides insight into the effectiveness of mHealth interventions targeting mothers in high-income countries in the perinatal period to enhance four psychosocial outcomes: self-efficacy, social support, anxiety and depression. Despite a wide variety of outcome measurements used, the predominant findings suggest that there are insufficient data to conclude that mHealth interventions can improve self-efficacy and anxiety outcomes. Potential benefits on social support were related to interventions targeting postnatal behaviors. Postpartum depression was the mostly commonly reported outcome. Findings related to the comparison of pre-post outcomes and intervention versus control demonstrated that mHealth interventions targeting postpartum depression were associated with a reduction in postpartum 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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".