Evidence map›Paper›PMID 41602939›Full record

ArticleDigital health

Digital health interventions for cervical cancer screening among hard-to-reach women in health-resource-limited areas: Protocol for a controlled trial with historical controls and two randomised intervention arms.

Xinhua Jia, Xi'ao Da, Jingyi Shi, Yuting Wang, Mingyang Chen, Yao Yang, Chen Gao, Jiahuan Zhai, Hanyue Ding, Youlin Qiao

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Xinhua JiaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0002-8399-6637
Xi'ao DaSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0005-2594-4977
Jingyi ShiSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0004-3187-631X
Yuting WangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0004-0582-0347
Mingyang ChenSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0001-8852-8245
Yao YangSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0007-1252-8303
Chen GaoTencent Sustainable Social Value Inclusive Health Lab, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0002-1938-4107
Jiahuan ZhaiTencent Sustainable Social Value Inclusive Health Lab, Beijing, People's Republic of China.ORCID https://orcid.org/0009-0009-0327-8239
Hanyue DingSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0001-9977-0985
Youlin QiaoSchool of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.ORCID https://orcid.org/0000-0001-6380-0871

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cervical cancer remains a leading cause of cancer morbidity and mortality among women worldwide, with an estimated 661,021 new cases and 348,189 deaths in 2022. In China's health-resource-limited areas, a substantial share of the population remains hard to reach, and the effectiveness of data-driven identification and digital outreach for this hard-to-reach population is uncertain. Objective: This study aims to (a) use ID-card-based record linkage to identify women who have never undergone cervical cancer screening and (b) evaluate whether the integrated digital intervention reduces redundant repeat screening while improving women's knowledge, attitudes and practices (KAP) in health-resource-limited areas. Methods: We will conduct a quasi-experimental controlled trial including an external historical control cohort and two individually randomised digital intervention arms in 11 sites. Women will be identified by matching unique ID-card numbers across the national screening registry and local household records. Newly screened eligible women will be randomly allocated to one of two intervention arms: (a) tailored digital interventions and (b) generic digital interventions, while an external historical cohort (January 2022-December 2023) from the same sites, before implementation of the digital platform, will serve as the control arm. Results: Recruitment began on 15 April 2025. The trial plans to recruit 142,417 participants (122,817 in the historical control cohort and 9800 in each intervention arm). Baseline surveys commenced on 15 April 2025 and will continue until December 2026. Conclusions: If effective, this study will be among the first to evaluate a full-process digital health intervention that combines algorithm-based identification with a web-plus-WeChat platform for cervical-cancer screening in resource-limited areas of China. The findings could inform programme development and benefit hard-to-reach populations.

Indexed as

attitude and practices (KAP)colposcopy completionDigital health interventionshard-to-reach womenknowledgereferral interval

Identifiers

PMID41602939
PMCPMC12833128

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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.