Impact of parent-targeted eHealth educational interventions on infant procedural pain management: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review was to determine whether electronic health (eHealth) educational interventions about infant procedural pain and pain management impact parental outcomes (eg, mental health, knowledge uptake), eHealth outcomes (eg, acceptance, use), and pain management outcomes (eg, parental involvement, infant pain response). INTRODUCTION: Pain in infants is a common concern for parents. Routine postpartum care for infants in early life requires them to endure painful procedures, such as immunizations, yet infants often receive little to no pain management. Parents are an essential component of effective pain management, although they may not be aware of the roles they play. Despite the increased number of eHealth resources available to educate parents about infant pain management, their impact has yet to be synthesized. INCLUSION CRITERIA: This review considered studies that evaluated eHealth educational interventions targeted at parents during pregnancy and up to 1 year postpartum. Interventions included, but were not limited to, mobile applications, web-based applications, websites, videos, interactive training, hands-on direct simulation, short message service (SMS), and desktop applications. Primary outcomes included parental outcomes (eg, stress or anxiety, self-efficacy, knowledge, attitudes), eHealth outcomes (eg, acceptance, use), and pain management outcomes (eg, parental involvement, infant pain response). Experimental, quasi-experimental, and observational study designs were included. METHODS: MEDLINE, CINAHL, PsycINFO, Embase, Scopus, Web of Science, and SciELO were searched for studies published in English up to June 14, 2021. Citation lists of relevant reviews and included studies were also searched for additional peer-reviewed articles. Two independent reviewers conducted critical appraisal using standardized tools from JBI, and data extraction, using a data extraction form designed by the authors. Statistical pooling of quantitative data was not possible due to heterogeneity; thus, the findings were reported narratively. RESULTS: A total of 4163 unique studies were screened, with 11 studies ultimately included for synthesis. Five articles were randomized controlled trials, 5 articles were analytical cross-sectional studies, and 1 article was quasi-experimental. Studies reported on 4 unique eHealth educational interventions, all of which used video format and primarily targeted the postnatal period. The findings for all primary outcomes were mixed but suggested either improvements in outcomes or no impact. The certainty of evidence was determined as low or very low across primary outcomes for reasons related to imprecision, risk of bias, and indirectness. CONCLUSIONS: Although heterogeneity of findings limited quantitative synthesis of data, this review suggests that short and engaging educational videos have the potential to positively impact parents' knowledge, confidence, and desire to be involved in procedural pain management for their children. Most of the interventions presented in this review describe evidence-based information about procedural pain management strategies that are known to be effective for infant populations. Thus, it is reasonable to assume that infant pain response should be lower when parents appropriately apply the strategies. However, the findings of this review were not able to confirm this assumption. More research is needed to evaluate the impact of parent-targeted pain management education on infant pain response. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020151569.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".