Evidence map›Paper›PMID 40885746›Full record

ArticleNPJ digital medicine2025

Adherence to digital pregnancy care - lessons learned from the SMART start feasibility study.

Katharina M Jaeger, Michael Nissen, Heike Leutheuser, Nina Danzberger, Adriana Titzmann, Constanza A Pontones, Chloë Goossens, Philipp Ziegler, Sabrina Uhrig, Lothar Haeberle and 9 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Evaluation of the uptake and usability of the pregnant women surveillance system (SIMG): A cross-sectional pilot study.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
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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

19 authors.

Katharina M JaegerMachine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. katharina.jaeger@fau.de.
Michael NissenMachine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Heike LeutheuserAmbient Assisted Living & Medical Assistance Systems, Department of Computer Science, University of Bayreuth, Bayreuth, Germany.
Nina DanzbergerDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Adriana TitzmannDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Constanza A PontonesDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Chloë GoossensDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Philipp ZieglerDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Sabrina UhrigDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Lothar HaeberleDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Hannah BleherDepartment of Social Ethics, University of Bonn, Bonn, Germany.
Kristina KastDepartment of Healthcare Management, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Johannes KornhuberDepartment of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Oliver SchoeffskiDepartment of Healthcare Management, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Matthias BraunDepartment of Social Ethics, University of Bonn, Bonn, Germany.
Peter A FaschingDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Matthias W BeckmannDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Bjoern M EskofierMachine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Hanna HuebnerDepartment of Obstetrics and Gynecology, Uniklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Funding

Bundesministerium für Gesundheit 2519DAT400
6 · The paper itself

Abstract

The World Health Organization increasingly highlights the role of digital health technologies in supporting prenatal care. Despite this potential, the real-world implementation of such technologies remains limited, even in high-income countries with established analog systems. We developed a comprehensive digital pregnancy care framework, SMART Start and evaluated it in a prospective study involving 528 pregnant individuals in Germany. This study is registered at the German Clinical Trials Register (DRKS00036867). Participants were equipped with a mobile app and self-examination technologies. The mobile app featured study functionality, pregnancy-related questionnaires, digital maternity records, and pregnancy-supportive content. Self-examination technologies included a standard care kit for home measurements of routine prenatal care parameters (weight, blood pressure, urinalysis), and an innovative kit with novel sensors (smartwatch, sleep analyzer). Here, we analyzed the adherence to digital pregnancy care and present the lessons learned from a clinical and technical perspective. Among all participants, 49% engaged with at least one digital package. Weekly weight tracking reached adherence rates up to 67% in the first 14 weeks. Adherence to blood pressure and urinalysis measurements was lower, peaking at 20 and 28%, respectively, but remained stable over time. Questionnaire completion rates varied in dependence on their length and complexity. 31% of users disengaged at the time of registration. While overall retention time did not significantly differ across participant subgroups (all p > 0.05), adherence analyses revealed meaningful group-level differences in engagement with specific self-examination protocols. This discrepancy underscores that continued participation does not necessarily imply consistent engagement with all components of the digital care model. The adherence to the study schedule demonstrated that pregnant individuals are generally willing and capable of engaging in home-based, multimodal self-monitoring; however, the importance of adaptive scheduling, patient-centered feedback, agile development, and interdisciplinary collaboration should be addressed by future studies. The presented SMART Start framework offers a pathway towards data-driven, personalized pregnancy care while potentially reducing the demand for conventional healthcare infrastructure.

Identifiers

PMID40885746
PMCPMC12398578

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Registered trials

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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.