Health behavior and disease self-management indicators in patients with cardiovascular diseases using a health app: Findings from an RCT

Sonia Lippke; Luisa Korte; Vinayak Anand Kumar; Andreas Fach; Tiara Ratz; Sonia Lippke; Luisa Korte; Vinayak Anand Kumar; Andreas Fach; Tiara Ratz

doi:10.3934/publichealth.2025015

AIMS Public Health

2025, Volume 12, Issue 1: 233-258. doi: 10.3934/publichealth.2025015

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Research article Topical Sections

Health behavior and disease self-management indicators in patients with cardiovascular diseases using a health app: Findings from an RCT

1.
Health Promotion and Prevention Unit, Department of Health Sciences, Hamburg University of Applied Sciences/Hochschule für Angewandte Wissenschaften Hamburg (HAW Hamburg), Hamburg, Germany
2.
Health Psychology & Behavioral Medicine Lab, School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany
3.
Apprevent GmbH, Bremen, Germany
4.
Klinikum Links der Weser, Bremen, Germany
5.
AO Innovation Translation Center, Clinical Operations, AO Foundation, Switzerland

Received: 30 August 2024 Revised: 22 October 2024 Accepted: 08 January 2025 Published: 26 February 2025

Background
Prevention of acute cardiovascular events in patients with cardiovascular disease (CVD) requires promoting health-protective behaviors (e.g., physical activity) and reducing health-compromising behaviors (e.g., sitting). Digital interventions addressing health behavior offer great potential. Based on a multiple behavior change theory, an intervention in the form of a digital health application (app) was evaluated in a pilot trial, testing the following hypotheses (H): H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely related; H2: changes in health behaviors and disease self-management indicators over time (T0 to T1) are more pronounced in the intervention group (IG, app users) than in the control group (CG); H3: within the IG, changes in systolic and diastolic blood pressure indicate a positive trajectory.
Methods
A 12-week randomized controlled trial (RCT) was conducted with two measurement points. The IG received an app addressing self-management and health behavior change. A total of N = 40 CVD patients were randomized equally to the CG (45% women; mean age = 60.6 years) and the IG (35% women; mean age = 61.5 years).
Results
Findings support H1 with correlations between behaviors (r = −0.66–0.79) and disease self-management (r = −0.06–0.70). H2 was also partially supported, with significant improvements over time in self-management indicators, especially self-care maintenance, in the IG (Eta² = 0.35; p < 0.001). H3 could not be confirmed as no significant changes were found.
Conclusions
This study provides evidence that an app addressing different behavior change techniques (BCTs) can help to manage CVD by promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account may increase the effectiveness of behavioral intervention, thereby improving individual and public health. Replications with larger samples and more objective measures are needed.
- cardiovascular diseases (CVDs),
- non-communicable diseases (NCDs),
- disease self-management,
- digital health application,
- randomized controlled trial,
- multiple behavior change
Citation: Sonia Lippke, Luisa Korte, Vinayak Anand Kumar, Andreas Fach, Tiara Ratz. Health behavior and disease self-management indicators in patients with cardiovascular diseases using a health app: Findings from an RCT[J]. AIMS Public Health, 2025, 12(1): 233-258. doi: 10.3934/publichealth.2025015

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Abstract

Background

Prevention of acute cardiovascular events in patients with cardiovascular disease (CVD) requires promoting health-protective behaviors (e.g., physical activity) and reducing health-compromising behaviors (e.g., sitting). Digital interventions addressing health behavior offer great potential. Based on a multiple behavior change theory, an intervention in the form of a digital health application (app) was evaluated in a pilot trial, testing the following hypotheses (H): H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely related; H2: changes in health behaviors and disease self-management indicators over time (T0 to T1) are more pronounced in the intervention group (IG, app users) than in the control group (CG); H3: within the IG, changes in systolic and diastolic blood pressure indicate a positive trajectory.

Methods

A 12-week randomized controlled trial (RCT) was conducted with two measurement points. The IG received an app addressing self-management and health behavior change. A total of N = 40 CVD patients were randomized equally to the CG (45% women; mean age = 60.6 years) and the IG (35% women; mean age = 61.5 years).

Results

Findings support H1 with correlations between behaviors (r = −0.66–0.79) and disease self-management (r = −0.06–0.70). H2 was also partially supported, with significant improvements over time in self-management indicators, especially self-care maintenance, in the IG (Eta² = 0.35; p < 0.001). H3 could not be confirmed as no significant changes were found.

Conclusions

This study provides evidence that an app addressing different behavior change techniques (BCTs) can help to manage CVD by promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account may increase the effectiveness of behavioral intervention, thereby improving individual and public health. Replications with larger samples and more objective measures are needed.

Acknowledgments

We acknowledge the support with proofreading and formatting this manuscript by Gloria Peiker and Tran Ngoc Huong Quan. The authors are grateful to Dr. med. Jan Homoth and Claas Oltmann giving input and Prof. Dr. Viviane Scherenberg and Prof. Dr. med. Harm Wienbergen for providing comments and suggestions on earlier drafts of the study protocol. Further, we thank the whole apprevent GmbH team for the collaboration.
This research was funded by the application manufacturer apprevent GmbH and partially funded by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 956501. Parts of this manuscript were conducted as part of the Rehabilitation Research Priority Professorship in cooperation with the German Pension Insurance Association, funded by the BMBF and the BWFGB as part of the federal-state program “FH-Personal” (go-2-prof:in project at HAW Hamburg).

Authors' contribution

Conceptualization, S.L., T.R., and A.F.; Methodology, T.R., L.K. and A.F.; Validation, S.L. and V.A.K.; Formal Analysis, L.K. and S.L.; Investigation, T.R. and L.K.; Resources, S.L. and A.F.; Data Curation, V.A.K.; Writing—Original Draft Preparation, S.L.; Writing—Review and Editing, T.R., L.K. and V.A.K.; Visualization, L.K. and S.L.; Supervision, A.F. and T.R.; Project Administration, L.K.; Funding Acquisition, S.L. and A.F. All authors have read and agreed to the published version of the manuscript.

Conflicts of interest

The reCardial study is initiated and funded by the app manufacturer apprevent GmbH, which is directed by Dr. Jan Homoth, Michael Wegner and Rüdiger Weiß. The funder was involved in participant management and interpretation of data; in the writing of the manuscript and in the decision to publish the results. The apprevent GmbH has no involvement in study design. Tiara Ratz was a former employee of the “reCardial” project at apprevent GmbH. Andreas Fach is associated with the apprevent GmbH. Luisa Korte works as a research associate within apprevent GmbH. Sonia Lippke and Vinayak Anand Kumar declare that they do not have any conflict of interest.

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