Evidence map›Paper›PMID 33565991›Full record

ArticleJMIR research protocols2021

App-Based Salt Reduction Intervention in School Children and Their Families (AppSalt) in China: Protocol for a Mixed Methods Process Evaluation.

Yuewen Sun, Rong Luo, Yuan Li, Feng J He, Monique Tan, Graham A MacGregor, Hueiming Liu, Puhong Zhang

Abstract read
In one paragraph

Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuewen SunThe George Institute for Global Health, Peking University Health Science Center, Beijing, China.ORCID https://orcid.org/0000-0002-1681-4848
Rong LuoThe George Institute for Global Health, Peking University Health Science Center, Beijing, China.ORCID https://orcid.org/0000-0003-2725-9939
Yuan LiThe George Institute for Global Health, Peking University Health Science Center, Beijing, China.ORCID https://orcid.org/0000-0003-1887-7065
Feng J HeBarts and The London School of Medicine & Dentistry, Queen Mary University of London, London, United Kingdom.ORCID https://orcid.org/0000-0003-2807-4119
Monique TanBarts and The London School of Medicine & Dentistry, Queen Mary University of London, London, United Kingdom.ORCID https://orcid.org/0000-0003-4287-5553
Graham A MacGregorBarts and The London School of Medicine & Dentistry, Queen Mary University of London, London, United Kingdom.ORCID https://orcid.org/0000-0001-9206-4500
Hueiming LiuThe George Institute for Global Health, Sydney, Australia.ORCID https://orcid.org/0000-0001-9077-8673
Puhong ZhangThe George Institute for Global Health, Peking University Health Science Center, Beijing, China.ORCID https://orcid.org/0000-0003-4610-9848

Funding

Department of Health 16/136/77Medical Research Council MR/J015903/1
6 · The paper itself

Abstract

backgroundThe app-based salt reduction intervention program in school children and their families (AppSalt) is a multicomponent mobile health (mHealth) intervention program, which involves multiple stakeholders, including students, parents, teachers, school heads, and local health and education authorities. The complexity of the AppSalt program highlights the need for process evaluation to investigate how the implementation will be achieved at different sites.

objectiveThis paper presents a process evaluation protocol of the AppSalt program, which aims to monitor the implementation of the program, explain its causal mechanisms, and provide evidence for scaling up the program nationwide.

methodsA mixed methods approach will be used to collect data relating to five process evaluation dimensions: fidelity, dose delivered, dose received, reach, and context. Quantitative data, including app use logs, activity logs, and routine monitoring data, will be collected alongside the intervention process to evaluate the quantity and quality of intervention activities. The quantitative data will be summarized as medians, means, and proportions as appropriate. Qualitative data will be collected through semistructured interviews of purposely selected intervention participants and key stakeholders from local health and education authorities. The thematic analysis technique will be used for analyzing the qualitative data with the support of NVivo 12. The qualitative data will be triangulated with the quantitative data during the interpretation phase to explain the 5 process evaluation dimensions.

resultsThe intervention activities of the AppSalt program were initiated at 27 primary schools in three cities since October 2018. We have completed the 1-year intervention of this program. The quantitative data for this study, including app use log, activity logs, and the routine monitoring data, were collected and organized during the intervention process. After completing the intervention, we conducted semistructured interviews with 32 students, 32 parents, 9 teachers, 9 school heads, and 8 stakeholders from local health and education departments. Data analysis is currently underway.

conclusionsUsing mHealth technology for salt reduction among primary school students is an innovation in China. The findings of this study will help researchers understand the implementation of the AppSalt program and similar mHealth interventions in real-world settings. Furthermore, this process evaluation will be informative for other researchers and policy makers interested in replicating the AppSalt program and designing their salt reduction intervention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19430.

Indexed as

health educationmobile healthmobile phoneprocess evaluationsalt reduction

Identifiers

PMID33565991
PMCPMC7904395

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.