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Record W4387585388 · doi:10.2196/47334

The Effectiveness of mHealth Interventions Targeting Parents and Youth in Human Papillomavirus Vaccination: Systematic Review

2023· review· en· W4387585388 on OpenAlexvenueno aff
Lihong Ou, Angela Chia‐Chen Chen, Ashish Amresh

Bibliographic record

VenueJMIR Pediatrics and Parenting · 2023
Typereview
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
FundersNational Institute on Drug Abuse
KeywordsmHealthPsychological interventionCINAHLVaccinationPsycINFOMedicineCochrane LibraryMEDLINESystematic reviewFamily medicineGlobal healthMeta-analysisPublic healthNursingImmunologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The prevalence of human papillomavirus (HPV) and its related cancers is a major global concern. In the United States, routine HPV vaccination is recommended for youth aged 11 or 12 years. Despite HPV being the most common sexually transmitted infection and the vaccine's proven efficacy, the vaccination rate among US youth remains below the recommended 80% completion rate. Mobile health (mHealth) interventions have demonstrated promise in improving health. Examining and synthesizing the current evidence about the impact of mHealth interventions on vaccination coverage in youth and intervention characteristics could guide future mHealth interventions aimed at mitigating the vaccination gap and disease burden. OBJECTIVE: This study aims to conduct a systematic review to assess the effectiveness of mHealth interventions on parental intent to vaccinate youth against HPV and youth's vaccine uptake. METHODS: We searched empirical papers through databases including Google Scholar, PubMed, CINAHL, PsycINFO, and Cochrane Library. The inclusion criteria were the following: (1) published between January 2011 and December 2022; (2) using mHealth aimed to improve HPV vaccination rate; (3) targeted unvaccinated youth or their parents; and (4) measured HPV-related knowledge, vaccination intention, or vaccine uptake. Overall, 3 researchers screened and appraised the quality of the eligible papers using the Melnyk Levels of Evidence and the Cochrane Grading of Recommendations Assessment, Development, and Evaluation methodology. Disagreements in search results and result interpretation were resolved through consensus. RESULTS: Overall, 17 studies that met the inclusion criteria were included in the final review. Most studies were conducted in the United States (14/17, 82%), used a randomized controlled trial design (12/17, 71%), and adopted behavior change theories or a culture-centric approach (10/17, 59%). mHealth interventions included SMS text message reminders, motivational SMS text messages, computer-tailored or tablet-tailored interventions, smartphone apps, web-based tailored interventions, social media (Facebook) campaigns, digital videos, and digital storytelling interventions. Approximately 88% (15/17) of the mHealth interventions demonstrated positive effects on knowledge, intention, or behaviors related to HPV vaccination. Overall, 12% (2/17) reported limited or no intervention impact on vaccine uptake or vaccine series completion. Effective vaccine uptake was commonly seen in interventions based on behavior change theories and those that provided culturally relevant information. CONCLUSIONS: This systematic review identified the impact of mHealth interventions among unvaccinated youth and their parents, which showed improvement in HPV-related knowledge, vaccination intention, or vaccine initiation. The interventions that incorporated theories and culture-centric approaches revealed the most promising results. Although these outcomes are encouraging, future studies are needed to investigate factors associated with the success of interventions using SMS text messaging or social media. More studies are also needed for a better understanding of the intervention elements that boost the responses of age-specific and ethnicity-specific populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.040
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.466
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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