Evidence map›Paper›PMID 37775533›Full record

ArticleNPJ digital medicine2023

Personalization and localization as key expectations of digital health intervention in women pre- to post-pregnancy.

V Vien Lee, Smrithi Vijayakumar, Wei Ying Ng, Ni Yin Lau, Qiao Ying Leong, Delicia Shu Qin Ooi, Lin Lin Su, Yung Seng Lee, Shiao-Yng Chan, Agata Blasiak and 1 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Trial
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. N-of-1 medicine.Singapore medical journal · 2024
    Article
  11. Article
  12. Article
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

11 authors.

V Vien LeeThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. vvien.lee@nus.edu.sg.ORCID http://orcid.org/0000-0002-0216-9032
Smrithi VijayakumarThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Wei Ying NgThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0003-4781-6413
Ni Yin LauThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Qiao Ying LeongThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Delicia Shu Qin OoiDepartment of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0001-8677-0126
Lin Lin SuDepartment of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Yung Seng LeeDepartment of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. paeleeys@nus.edu.sg.ORCID http://orcid.org/0000-0002-1253-0557
Shiao-Yng ChanDepartment of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. obgchan@nus.edu.sg.ORCID http://orcid.org/0000-0002-3530-3023
Agata BlasiakThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. agata.blasiak@nus.edu.sg.ORCID http://orcid.org/0000-0003-0727-7611
Dean HoThe Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. biedh@nus.edu.sg.ORCID http://orcid.org/0000-0002-7337-296X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Health behaviors before, during and after pregnancy can have lasting effects on maternal and infant health outcomes. Although digital health interventions (DHIs) have potential as a pertinent avenue to deliver mechanisms for a healthy behavior change, its success is reliant on addressing the user needs. Accordingly, the current study aimed to understand DHI needs and expectations of women before, during and after pregnancy to inform and optimize future DHI developments. Forty-four women (13 pre-, 16 during and 15 postpregnancy; age range = 21-40 years) completed a 60-minute, semistructured, qualitative interview exploring participant's experience in their current phase, experience with digital health tools, and their needs and expectations of DHIs. Interviews were audio-recorded, transcribed verbatim and thematically analyzed. From the interviews, two core concepts emerged-personalization and localization of DHI. Between both concepts, five themes and nine subthemes were identified. Themes and subthemes within personalization cover ideas of two-way interactivity, journey organization based on phases and circumstances, and privacy trade-off. Themes and subthemes within localization cover ideas of access to local health-related resources and information, and connecting to local communities through anecdotal stories. Here we report, through understanding user needs and expectations, the key elements for the development and optimization of a successful DHI for women before, during and after pregnancy. To potentially empower downstream DHI implementation and adoption, these insights can serve as a foundation in the initial innovation process for DHI developers and be further built upon through a continued co-design process.

Identifiers

PMID37775533
PMCPMC10541409

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.