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Needs and preferences of women with prior severe preeclampsia regarding app-based cardiovascular health promotion

A Correction to this article was published on 09 January 2023

This article has been updated

Abstract

Background

Women with prior severe preeclampsia are at an increased risk for cardiovascular diseases later in life compared to women who had a normotensive pregnancy. The objective of this study was to assess their needs and preferences regarding app-based cardiovascular health promotion.

Methods

Patients (n = 35) of the Follow-Up PreEClampsia Outpatient Clinic (FUPEC), Erasmus MC, the Netherlands, participated in an anonymous online survey. The main outcomes under study were women’s needs for health behavior promotion, and their preferences with respect to intervention delivery. Descriptive statistics were used to evaluate needs, and thematic analysis was used to analyze preferences.

Results

Women’s primary need for health behavior promotion pertained to their fat and sugar intake and physical activity; for some, to their mental health (practices), fruit and vegetable intake, salt intake, and water intake; and for a few, to their alcohol and tobacco use. Most women preferred an app-based intervention to include, in descending order: the tracking of health-related metrics, an interactive platform, the use of behavior change strategies, the provision of information, and personalization.

Conclusion

Cardiovascular health promotion targeting women with prior severe preeclampsia should feel relevant to its audience. App-based interventions are likely to be well received if they target fat and sugar intake and physical activity. These interventions should preferably track health-related metrics, be interactive, contain behavior change strategies, provide information, and be personalized. Adopting these findings during intervention design could potentially increase uptake, behavior change, and behavior change maintenance in this population.

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Introduction

The leading cause of death in women worldwide are cardiovascular diseases (CVDs), contributing to 35% of female deaths [1]. Some risk factors for CVDs are unique to women, such as hypertensive disorders of pregnancy. These disorders affect 5 to 10% of pregnancies globally, and their prevalence is increasing [2, 3]. Of all hypertensive disorders of pregnancy, preeclampsia, which complicates 2 to 5% of all pregnancies [4], increases CVD risk the most: it has been linked to a two- to eightfold increased risk throughout the lifespan [5,6,7]. Maternal factors associated with an increased risk of preeclampsia are antiphospholipid antibody syndrome, prior preeclampsia, chronic hypertension, pre-gestational diabetes, and obesity [8]. Given that the causes and the early diagnosis of preeclampsia are not yet fully understood [4, 9], attention to the cardiovascular follow-up and risk management of women with prior preeclampsia is warranted [6, 7]. CVD risk can be decreased substantially by participating in healthy lifestyle behaviors [10, 11]. Therefore, efficacious health promotion interventions are warranted in this high-risk group [12].

Health behavior change interventions have been widely applied in the general population with demonstrable efficacy [13,14,15,16,17]. When it comes to the medium of interventions, web-based interventions have several advantages over face-to-face interventions: they are comparatively low cost, have a wide reach, and provide flexibility in intervention location and time [18, 19]. Web-based interventions were previously found to appeal to women with prior preeclampsia due to the flexibility they provide, i.e., they fit more easily into the demanding and unpredictable schedules of (often young and working) mothers [20,21,22]. Access to web-based interventions may be further enhanced by delivering them via mobile phone optimized web browsers or dedicated mobile apps, instead of desktop optimized web browsers, called mHealth [23]. Health apps are proliferating rapidly —there are now more than 350,000 available for download [24]. A prerequisite of health app use is owning a smartphone: about half of the world’s population [25] and 84% of the Dutch population meets this criterion [26]. Over half of Dutch women already use health apps, primarily to monitor their health behaviors, with another quarter being open to using one in the future [26].

It is increasingly recognized that to design an intervention that resonates with its intended audience, one should assess the needs and preferences of the study population prior to the development of the intervention protocol. This may increase intervention uptake, and enhance behavior change and maintenance [27]. Both quantitative and qualitative methods have been previously used to yield insight on needs and preferences, and to provide recommendations for the design of mHealth interventions in the general population [28], and in specific patient groups, such as hypertensive and CVD patients [30,29,31], and obstetric and gynecological patients [33,32,33,35]. In women with prior preeclampsia, previous research has offered insights into preferences for postpartum lifestyle counseling [21], the factors that influence the use of mHealth to monitor preeclampsia-related symptoms [36], and the acceptability of a specific web-based health promotion intervention [37]. To our knowledge, the needs of women with prior severe preeclampsia regarding the behavioral target of app-based cardiovascular health promotion, and their preferences for the delivery of such an intervention, have not been previously assessed.

With this study we aim to further understanding of the needs and preferences of women with prior severe preeclampsia for app-based cardiovascular health promotion. We define needs as the extent to which women struggle to participate in certain health-promoting behaviors, plan to make positive changes to these behaviors, and are interested in participating in an app-delivered program targeting these behaviors. More specifically, our objective is to gain insight into women’s needs regarding behaviors related to cardiovascular health, namely: physical activity, fat and sugar intake, fruit and vegetable intake, salt intake, water intake, mental well-being (practices), alcohol use, and tobacco use [11]. Our second aim is to understand women’s preferences regarding the delivery of app-based cardiovascular health promotion. Our related objective is to explore their wishes regarding app content, functionalities, and interface.

Method

Study setting

Study participants were recruited from an outpatient clinic for women with prior severe preeclampsia. In the Erasmus Medical Center (Erasmus MC), cardiovascular follow-up and risk management is provided for women with prior severe preeclampsia at the multidisciplinary Follow-Up PreEClampsia Outpatient Clinic (FUPEC), unique in the Netherlands [38]. Presently there are around 1500 patients enrolled at the clinic, with an additional 100 to 150 women registering each year.

Study population

Participant recruitment at the FUPEC and online data collection took place between September and November 2020 (n = 35). Inclusion criterion for participation was having experienced severe preeclampsia at least once, as per the definition of the American Congress of Obstetricians and Gynecologists [39]. Exclusion criteria for participation were: <18 years of age, pregnant at time of inclusion, < 3 months after delivery, any circumstance preventing moderate-to-vigorous intensity physical activity (e.g., illness, injury, surgery, rehabilitation), no working knowledge of Dutch, and no possession of a smartphone. No upper age limit was applied. These exclusion criteria were employed to obtain a sample of women similar to those who will enroll in an app-based cardiovascular health promotion program [40]. A total of six women were excluded: three women were < 3 months after delivery, and three women had insufficient knowledge of Dutch. Invited women were informed that participation in the study was voluntary, and that they could withdraw at any point, without having to provide a reason. Women who chose to participate signed an informed consent form prior to participation. As is common practice for studies using qualitative methods, a specific target sample size was not pre-defined. Instead, recruitment was planned to be considered complete after data saturation was reached on the qualitative outcomes under study [41]. Responses received from participants were read while the online survey was still open for further recruitment. When, during this iterative process of data familiarization, study authors LLK and MFVDB agreed that new themes were not expected to arise from the inclusion of additional participants, the online survey was closed.

Patient and public involvement

Patients and members of the public were not involved in the design, conduct, or reporting of this study.

Design

An anonymous online survey was administered.

Sampling strategy

The study used criterion sampling, i.e., participants had to have prior experience with severe preeclampsia [42].

Procedure

Women were asked at their FUPEC consultation whether they were interested in joining the study. Women who did not show up at their scheduled consultation were asked via email. Those that expressed interest either at the consultation or via email received the survey. Of the 122 women asked, 119 agreed to receive the survey. Of these women, 55 started the survey, and 35 provided complete responses. Only complete responses were used in the current analyses. Women who did not provide complete responses (n = 20) were comparable to the study sample (n = 35) in for example age, educational level, and when they had experienced severe preeclampsia (data not shown). The survey assessed four topics: demographics, needs for app-based cardiovascular health promotion, perceived determinants of physical activity, and preferences for app-based cardiovascular health promotion. The current study used data on the first, second, and fourth topics. Data on the third topic was collected for the purpose of a qualitative assessment of physical activity determinants, the results of which will be published separately. The survey was hosted online on the data capture tool Limesurvey [43]. Data were imported into IBM SPSS Statistics and NVivo for analyses [44, 45].

Main outcome measures

The main outcomes of this study were participants’ needs and preferences with respect to app-based cardiovascular health promotion. The recruitment materials and survey were developed by members of the research team, including JERVL and JJD as clinicians and MFVDB as medical student of the Follow-Up PreEClampsia Outpatient Clinic (FUPEC). Questions assessing needs were based on previous studies gauging the needs of a population prior to developing an mHealth intervention [29, 33], and were surveying health behaviors that are relevant for cardiovascular health promotion [11]. Questions assessing preferences were based on prior studies that examined the preferences of a population regarding content, functionality and interface before developing an mHealth intervention [29, 33, 34], and by the persuasive design framework of web-based interventions [46]. The questions have not been previously validated.

Participants answered one question each about the three components of needs: struggling to participate in certain health-promoting behaviors, planning to make positive changes to these behaviors, and being interested in participating in an app-delivered program targeting these behaviors. Spearman’s rho correlation analyses were performed between the three items for each health behavior to support their validity as positively related, but distinct components (for coefficients and significance levels see supporting information, supporting Table 1 ). We assessed needs regarding physical activity, fat and sugar intake, fruit and vegetable intake, salt intake, water intake, mental well-being (practices), alcohol use, and tobacco use [11]. First, participants reported on their struggle to follow a healthy lifestyle concerning these behaviors (e.g., “How often do you struggle to make healthy choices when it comes to fat and sugar intake?”) on a seven-point scale (1 = very rarely to 7 = very often). Second, participants reported their behavior change intentions regarding these behaviors (e.g., “How often do you think of making positive changes to your physical activity?”) on a seven-point scale (1 = very rarely to 7 = very often). Last, participants reported their interest in partaking in an app-based intervention targeting these behaviors (e.g., “How interested would you be in partaking in an app-based intervention targeting fruit and vegetable intake?”) on a seven-point scale (1 = not interested to 7 = very interested). For questions about alcohol and tobacco use, the response option not applicable (N/A) was included to accommodate for women who do not engage in these behaviors. Participants were assumed to engage in all the other studied behaviors to some extent, therefore, the response option N/A was not added.

Participants also reported their preferences for the delivery of an app-based intervention. To this aim, participants responded to a series of open-ended questions assessing three aspects of intervention delivery: content (e.g., “What should this app contain?”), functionality (e.g., “What should this app do?”), and interface (e.g., “How and with whom would you like to communicate via the app?”). Participants also reported on the acceptable number of weeks and hours per week of the intervention (“Time demand: What do you think is reasonable?”).

Participants reported their demographic characteristics: age (years), number of children (number), living situation (with or without partner, with or without children), educational level (lower, middle, higher; classified using the International Standard Classification of Education [47]), paid employment status (yes, no; if yes, number of hours per week), when they had experienced severe preeclampsia (between three months and one year ago; between one and three years ago; over three years ago), and whether preeclampsia-related health complaints were still present (yes, no; if yes, what complaints).

Data analysis

Participants’ demographic characteristics, and responses on scaled items used to identify the needs of the population in terms of health promotion target behavior were reported using descriptive statistics. Scale ratings between 1 and 4 were collapsed into No, and ratings between 5 and 7 were collapsed into Yes. For alcohol and tobacco use, N/A was collapsed into No as well.

Thematic analysis was used to identify themes in participants’ preferences regarding intervention delivery [4850]. Inductive content analysis for emergent themes was applied, consistent with guidelines for the analysis of qualitative data using the grounded theory approach [51]. After reading and re-reading participants’ responses, LLK and MvdB defined coding instances, and identified five recurring themes in these instances. They then returned to the data independently and categorized each coding instance into one of the five themes. A small number of coding instances were categorized as belonging to two themes (for examples of the thematic analysis procedure see supporting information, supporting Table 2). Initial interrater percent agreement was 91%. Subsequently, categorizations were revisited until 100% agreement was reached.

Results

Characteristics of the study population

Table 1 shows the characteristics of the study population (n = 35). Participants had a median age of 35 years. Most women had one child (54%) and were living with a partner (80%). The majority were highly educated (80%) and in paid employment (80%), working a median of 28 h per week. Most women experienced severe preeclampsia more than three years ago (54%). Half of women were still experiencing health complaints related to preeclampsia (49%), such as fatigue and anxiety, and problems with concentration and memory (examples of participants’ complaints are published under supporting information, supporting Table 3).

Table 1 Characteristics of the study population

Needs regarding health promotion target behavior

Table 2 shows the needs of participants in terms of the target behavior of the intervention. In descending order, participants struggled to follow a healthy lifestyle with respect to their fat and sugar intake (43%), physical activity (31%), water intake (20%), mental well-being (practices) (17%), salt intake (15%), alcohol use (11%), fruit and vegetable intake (6%), and tobacco use (6%). Results regarding planning to make positive changes to these behaviors, and being interested in participating in an app-based intervention targeting these behaviors showed a similar pattern; although generally, more women reported planning to make positive changes and being interested in an app-based intervention, than they reported struggling with health behaviors.

Table 2 Needs regarding health promotion target behavior

Preferences regarding intervention delivery

Table 3 shows the five themes of preferred intervention delivery, in descending order: tracking of health-related metrics (i.e., monitoring outcomes over time), interactivity (i.e., two-way communication with other people or app), behavior change strategy (i.e., methods to alter determinants of behavior), information (i.e., health-related information), and personalization (i.e., tailored delivery). Example quotes of each theme are presented below in English (example quotes are published in their original language under supporting information, supporting Table 4). Table 3 also summarizes participants’ preferred intervention duration: 12 weeks (interquartile range 5 to 52 weeks), 2 h and 45 min per intervention week (interquartile range 1 to 5 h).

Table 3 Preferences regarding intervention delivery

Tracking

The majority of participants (89%) mentioned the tracking of various health-related metrics as a preferred component of the intervention, for example “Measurement of steps, heart rate, exercise intensity”, “Tracking nutrition”, and “Monitoring well-being”.

Interactivity

Three out of four women (74%) preferred the program to contain interactive elements, such as “Exercising together remotely”, “Points for exercise and drinking [water], competition with participants”, and “Asking questions to specialists and be able to approach fellow [preeclampsia] sufferers”.

Behavior change strategy

Nearly three-quarter of participants (69%) mentioned behavior change strategies that they would like the app to include, for example “Tips on how to build up a daily routine”, “Amount of exercise per day/week and intervene accordingly: stimulate if it is not enough, reward if it is sufficient” (Note: this coding instance was also coded as another theme, Tracking), and “Tips (exercises, e.g., meditation) for reducing stress, busy mind, relaxation”.

Information

Over half of women (57%) reported provision of information to be a desired element of the intervention, such as “Lots of information, but not just to ‘scare’ you, as in, if you don’t move, you get this disease! Instead, for example, it has been proven that if you exercise X times a week, your blood pressure drops by X. Digestible, smaller bits of information”, “Relationship between preeclampsia and exercise, and what effects this can have”, and “High blood pressure in combination with exercise, how much do you have to sweat or be out of breath, what is enough in terms of amount of exercise. Which exercises can help with certain complaints, which exercises help to create a basic level of fitness and how do you train from there. What food can you eat before, during and after exercise”.

Personalization

Over half of participants (54%) preferred the program to be personalized, for example “Reminder of exercises, goals; compliments on results/knowledge/overview” (Note: this coding instance was also coded as another theme, Interactivity), “Enough choices to turn things on and off”, and “During the recovery process, I would have liked to have received feedback about which aspects were ‘normal’, and which need more attention or patience, and how to deal with them”.

Discussion

The objective of this study was to identify the needs and preferences of women with prior severe preeclampsia for app-based cardiovascular health promotion. Women’s primary need for health behavior promotion pertained to their fat and sugar intake and physical activity; for some, their mental health (practices), fruit and vegetable intake, salt intake, and water intake; and for a few, their alcohol and tobacco use. Most women preferred the intervention to include, in descending order: the tracking of health-related metrics, an interactive platform, the use of behavior change strategies, the provision of information, and personalization.

Interpretation of key findings

Our results indicate that women’s primary need lied in addressing their fat and sugar intake and physical activity. Both of these behaviors are closely linked to CVD risk, emphasizing the need for interventions that target these behaviors in this priority population [6, 11]. Participants’ interest in improving these behaviors could be due to their awareness of their heightened CVD risk [52, 53], further strengthened by their wish to provide a healthy environment to their children [54]. Previous research showed that women with prior preeclampsia wish to receive support in adopting a healthy lifestyle [21], and that their post-partum period is a window of opportunity for behavior change [55].

After their need to address fat and sugar intake and physical activity, women’s following priority was to gain better means to manage their mental health: half of participants reported to still experience health complaints related to preeclampsia, such as fatigue and anxiety, and problems with concentration and memory. Previous research has identified a negative impact of preeclampsia on mental health [56, 57]. A healthy lifestyle, such as engaging in physical activity, has been linked to improved mental health, therefore, future interventions should target multiple needs simultaneously [5860].

Participants’ need to address their fruit and vegetable intake, salt intake and water intake was modest, and their need to address alcohol and tobacco use was low. Implementing interventions targeting these behaviors in this group may yield low uptake and little behavior change.

We found that women with prior severe preeclampsia have a desire to gain information as part of a cardiovascular health promotion program, further emphasizing that providing informational lifestyle counselling is consistent with patients’ preferences [22, 52]. Some women wanted to receive information on the relationship between preeclampsia, lifestyle behaviors, and CVD risk. Therefore, clinicians might want to consider devoting more time to elaborating on evidence-based recommendations to manage CVD risk after severe preeclampsia through the adoption of preventive health behaviors [6]. Moreover, participants were interested in receiving more detailed information on the interrelation between different health behaviors such as diet and physical activity, and how certain physical or mental health complaints may be alleviated. Therefore, informational intervention content could be enriched by consulting various specialists, such as dieticians, physiotherapists, or psychologists.

Additionally, participants preferred to receive more than ‘just information’: they were open to receiving instruction on behavior change strategies, such as planning, incentives, and stress reduction [61]. The primary preference of participants was the tracking of their health-related metrics, such as dietary intake, physical activity intensity, and mental well-being. This finding is in line with studies demonstrating that patient autonomy is an integral part of the successful self-management of chronic diseases [62]. Interactivity was also a prominent preference of our participants: for example, they wanted to use the app to communicate with specialists, and to chat with and exercise together with other women who had severe preeclampsia. Interactive game-like elements were also described, such as the collection of points and competition with other participants. Finally, our participants described a wish for the intervention to contain personalized elements, such as the option to customize content and the provision of feedback. These results, and previous findings that these intervention elements can enhance intervention effects and user usage and adherence, suggest that future app-based programs aimed to improve cardiovascular health in women with prior severe preeclampsia would benefit from including such elements in their delivery [63, 64, 65].

Strengths and limitations

The current study has several strengths. It is the first study to conduct an in-depth assessment of the needs and preferences of women with prior severe preeclampsia for app-based cardiovascular health promotion. Secondly, it yields several applicable suggestions for intervention researchers to inform the design of apps for women with prior severe preeclampsia, potentially increasing intervention uptake, behavior change, and behavior change maintenance. Finally, our findings could be applicable to other populations, such as women with a history of other types of hypertensive pregnancy disorders, or other pregnancy complications, such as intrauterine growth restriction or gestational diabetes. However, our study also had some limitations that should be taken into account when interpreting the results. It could be that as our study population was drawn from an outpatient clinic specialized in the cardiovascular follow-up and risk management of women with prior severe preeclampsia, participants had a higher awareness of their increased risk for CVDs than most women with prior preeclampsia. Second, some participant quotes offered little context, limiting the interpretation of preferences (e.g., participants did not specify whether a behavior change strategy would be useful in all health behavior contexts, or only the context they used to exemplify the strategy). Third, our study population was highly educated, limiting the generalizability of our findings to all socioeconomic groups, e.g. in terms of preferences regarding health apps. Fourth, the size of our study sample might have been too small to allow for generalizations to be made based on our quantitative findings, i.e. regarding needs. Fifth, while the experience of medical staff has provided some input on the comprehensibility and acceptability of our study materials, future studies should also including members of the target population in the pilot testing phase to garner external and lay perspectives. Finally, our study did not assess the extent to which self-perceived need for behavior change reflects actual unhealthy behavioral habits, i.e., quantitative data on participants’ health behavior was not collected, nor were participants informed of ideal values of all health behaviors under study.

Conclusion

Cardiovascular health promotion targeting women with prior severe preeclampsia should feel relevant to its audience. App-based interventions are likely to be well received if they target fat and sugar intake and physical activity. These interventions should preferably track health-related metrics, be interactive, contain behavior change strategies, provide information, and be personalized. Adopting these findings during intervention design could potentially increase uptake, behavior change, and behavior change maintenance in this population.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available because participants were not asked to provide informed consent for the sharing of their data with third parties, but are available from the corresponding author on reasonable request.

Change history

References

  1. Abubakar II, Tillmann T, Banerjee A. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2015;385(9963):117–71.

    Article  Google Scholar 

  2. Regitz-Zagrosek V, Roos-Hesselink JW, Bauersachs J, Blomström-Lundqvist C, Cifkova R, De Bonis M, et al. 2018 ESC guidelines for the management of cardiovascular diseases during pregnancy: the task force for the management of cardiovascular diseases during pregnancy of the European Society of Cardiology (ESC). Eur Heart J. 2018;39(34):3165–241.

    Article  Google Scholar 

  3. Benschop L, Duvekot JJ, van Lennep JER. Future risk of cardiovascular disease risk factors and events in women after a hypertensive disorder of pregnancy. Heart. 2019;105(16):1273–8.

    Article  Google Scholar 

  4. Hutcheon JA, Lisonkova S, Joseph KS. Epidemiology of pre-eclampsia and the other hypertensive disorders of pregnancy. Best Pract Res Clin Obstet Gynecol. 2011;25(4):391–403.

    Article  Google Scholar 

  5. Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ. 2007;335(7627):974.

    Article  Google Scholar 

  6. Heida KY, Bots ML, de Groot CJ, van Dunné FM, Hammoud NM, Hoek A, et al. Cardiovascular risk management after reproductive and pregnancy-related disorders: A Dutch multidisciplinary evidence-based guideline. Eur J Prev Cardiol. 2016;23(17):1863–79.

    Article  Google Scholar 

  7. Rich-Edwards JW, Fraser A, Lawlor DA, Catov JM. Pregnancy characteristics and women’s future cardiovascular health: an underused opportunity to improve women’s health? Epidemiol Rev. 2014;36(1):57–70.

    Article  Google Scholar 

  8. Bartsch E, Medcalf KE, Park AL, Ray JG. Clinical risk factors for pre-eclampsia determined in early pregnancy: systematic review and meta-analysis of large cohort studies. BMJ. 2016;353:i1753.

    Article  Google Scholar 

  9. Lagana AS, Vitale SG, Sapia F, Valenti G, Corrado F, Padula F, et al. miRNA expression for early diagnosis of preeclampsia onset: hope or hype? J Maternal-Fetal Neonatal Med. 2018;31(6):817–21.

    Article  CAS  Google Scholar 

  10. WHO. Prevention of cardiovascular disease: guidelines for assessment and management of cardiovascular risk. WHO; 2007.

  11. Eduardo S. Life’s Simple 7: Vital But Not Easy. J Am Heart Assoc. 2018;7(11):e009324.

    Article  Google Scholar 

  12. Thomas H, Diamond J, Vieco A, Chaudhuri S, Shinnar E, Cromer S, et al. Global Atlas of Cardiovascular Disease. Glob Heart. 2018;13(3):143–63.

    Article  Google Scholar 

  13. Brewer NT, Hall MG, Noar SM, Parada H, Stein-Seroussi A, Bach LE, et al. Effect of pictorial cigarette pack warnings on changes in smoking behavior: a randomized clinical trial. JAMA Intern Med. 2016;176(7):905–12.

    Article  Google Scholar 

  14. Allais O, Bazoche P, Teyssier S. Getting more people on the stairs: the impact of point-of-decision prompts. Soc Sci Med. 2017;192:18–27.

    Article  Google Scholar 

  15. Milkman KL, Beshears J, Choi JJ, Laibson D, Madrian BC. Using implementation intentions prompts to enhance influenza vaccination rates. Proc Natl Acad Sci. 2011;108(26):10415–20.

    Article  CAS  Google Scholar 

  16. Benartzi S, Beshears J, Milkman KL, Sunstein CR, Thaler RH, Shankar M, et al. Should governments invest more in nudging? Psychol Sci. 2017;28(8):1041–55.

    Article  Google Scholar 

  17. Ali MK, Echouffo-Tcheugui JB, Williamson DF. How effective were lifestyle interventions in real-world settings that were modeled on the Diabetes Prevention Program? Health Aff (Millwood). 2012;31(1):67–75.

    Article  Google Scholar 

  18. Lustria MLA, Cortese J, Noar SM, Glueckauf RL. Computer-tailored health interventions delivered over the Web: review and analysis of key components. Patient Educ Couns. 2009;74(2):156–73.

    Article  Google Scholar 

  19. Neville LM, O’Hara B, Milat A. Computer-tailored physical activity behavior change interventions targeting adults: a systematic review. Int J Behav Nutr Phys Activity. 2009;6(1):30.

    Article  Google Scholar 

  20. Skurnik G, Roche AT, Stuart JJ, Rich-Edwards J, Tsigas E, Levkoff SE, et al. Improving the postpartum care of women with a recent history of preeclampsia: a focus group study. Hypertens Pregnancy. 2016;35(3):371–81.

    Article  Google Scholar 

  21. Hoedjes M, Berks D, Vogel I, Duvekot JJ, Oenema A, Franx A, et al. Preferences for Postpartum Lifestyle Counseling Among Women Sharing an Increased Cardiovascular and Metabolic Risk: A Focus Group Study. Hypertens Pregnancy. 2011;30(1):83–92.

    Article  Google Scholar 

  22. Seely EW, Rich-Edwards J, Lui J, Nicklas JM, Saxena A, Tsigas E, et al. Risk of future cardiovascular disease in women with prior preeclampsia: a focus group study. BMC Pregnancy Childbirth. 2013;13(1):240.

    Article  Google Scholar 

  23. Free C, Phillips G, Felix L, Galli L, Patel V, Edwards P. The effectiveness of M-health technologies for improving health and health services: a systematic review protocol. BMC Res Notes. 2010;3(1):1–7.

    Article  Google Scholar 

  24. Byambasuren O, Beller E, Glasziou P. Current Knowledge and Adoption of Mobile Health Apps Among Australian General Practitioners: Survey Study. JMIR Mhealth Uhealth. 2019;7(6):e13199.

    Article  Google Scholar 

  25. Analytics S. Half the World Owns a Smartphone: Strategy Analytics; 2021 [updated 24 07 2021. Available from: https://www.businesswire.com/news/home/20210624005926/en/Strategy-Analytics-Half-the-World-Owns-a-Smartphone/.

  26. ICT&Health. Helft Nederlanders gebruikt gezondheids-apps: ICT&Health; 2019 [updated 20 02 2019. Available from: https://www.icthealth.nl/nieuws/helft-nederlanders-gebruikt-gezondheids-apps/#:~:text=De%20helft%20van%20de%20Nederlanders,gebruikt%20deze%20gezondheidsapp%20het%20meest.

  27. Bartholomew LK, Parcel GS, Kok G. Intervention Mapping: A Process for Developing Theory and Evidence-Based Health Education Programs. Health Educ Behav. 1998;25(5):545–63.

    Article  CAS  Google Scholar 

  28. McClure JB, Heffner J, Hohl S, Klasnja P, Catz SL. Design Considerations for mHealth Programs Targeting Smokers Not Yet Ready to Quit: Results of a Sequential Mixed-Methods Study. JMIR mHealth. 2017;5(3):e31.

    Article  Google Scholar 

  29. Sebern MD, Sulemanjee N, Sebern MJ, Garnier-Villarreal M, Whitlatch CJ. Does an intervention designed to improve self-management, social support and awareness of palliative-care address needs of persons with heart failure, family caregivers and clinicians? J Clin Nurs. 2018;27(3–4):e643-e57.

    Google Scholar 

  30. Hirschey J, Bane S, Mansour M, Sperber J, Agboola S, Kvedar J, et al. Evaluating the Usability and Usefulness of a Mobile App for Atrial Fibrillation Using Qualitative Methods: Exploratory Pilot Study. JMIR Hum Factors. 2018;5(1):e13.

    Article  Google Scholar 

  31. Morrissey EC, Casey M, Glynn LG, Walsh JC, Molloy GJ. Smartphone apps for improving medication adherence in hypertension: patients’ perspectives. Patient Prefer Adherence. 2018;12:813–22.

    Article  Google Scholar 

  32. Lupton D. The use and value of digital media for information about pregnancy and early motherhood: a focus group study. BMC Pregnancy Childbirth. 2016;16(1):171.

    Article  Google Scholar 

  33. Goetz M, Müller M, Matthies LM, Hansen J, Doster A, Szabo A, et al. Perceptions of Patient Engagement Applications During Pregnancy: A Qualitative Assessment of the Patient’s Perspective. JMIR Mhealth Uhealth. 2017;5(5):e73.

    Article  Google Scholar 

  34. Velu AV, van Beukering MDM, Schaafsma FG, Frings-Dresen MHW, Mol BWJ, van der Post JAM, et al. Barriers and Facilitators for the Use of a Medical Mobile App to Prevent Work-Related Risks in Pregnancy: A Qualitative Analysis. JMIR Res Protoc. 2017;6(8):e163.

    Article  Google Scholar 

  35. Shorey S, Yang YY, Dennis CL. A Mobile Health App-Based Postnatal Educational Program (Home-but not Alone): Descriptive Qualitative Study. J Med Internet Res. 2018;20(4):e119.

    Article  Google Scholar 

  36. Rhoads SJ, Serrano CI, Lynch CE, Ounpraseuth ST, Gauss CH, Payakachat N, et al. Exploring Implementation of m-Health Monitoring in Postpartum Women with Hypertension. Telemed J E Health. 2017;23(10):833–41.

    Article  Google Scholar 

  37. Hutchesson MJ, Taylor R, Shrewsbury VA, Vincze L, Campbell LE, Callister R, et al. Be Healthe for Your Heart: A Pilot Randomized Controlled Trial Evaluating a Web-Based Behavioral Intervention to Improve the Cardiovascular Health of Women with a History of Preeclampsia. Int J Environ Res Public Health. 2020;17(16):5779.

    Article  Google Scholar 

  38. Benschop L, Duvekot JJ, Versmissen J, van Broekhoven V, Steegers EAP, Roeters van Lennep JE. Blood pressure profile 1 year after severe preeclampsia. Hypertension. 2018;71(3):491–8.

    Article  CAS  Google Scholar 

  39. Practice ACoO. ACOG practice bulletin. Diagnosis and management of preeclampsia and eclampsia. Number 33, January 2002. American College of Obstetricians and Gynecologists. International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics. 2002;77(1):67–75.

  40. Kókai LL, Ceallaigh Ó, Wijtzes DT, Roeters AI, van Lennep JE, Hagger MS, Cawley J, et al. Moving from intention to behaviour: a randomised controlled trial protocol for an app-based physical activity intervention (i2be). BMJ Open. 2022;12(1):e053711.

    Article  Google Scholar 

  41. Guest G, Bunce A, Johnson L. How Many Interviews Are Enough?:An Experiment with Data Saturation and Variability. Field Methods. 2006;18(1):59–82.

    Article  Google Scholar 

  42. Moser A, Korstjens I. Series. Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis. Eur J Gen Pract. 2018;24(1):9–18.

    Article  Google Scholar 

  43. Limesurvey GmbH. LimeSurvey: An Open Source survey tool. Hamburg.

  44. NVivo. NVivo qualitative data analysis software. Version 12 ed. QSR International Pty Ltd; 2018.

  45. Corp. I. IBM SPSS Statistics for Windows. Version 250. Armonk: IBM Corp; 2017.

    Google Scholar 

  46. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JEWC. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. 2012;14(6):e152.

    Article  Google Scholar 

  47. Statistics UIf. International standard classification of education: ISCED 2011. UNESCO Institute for Statistics Montreal; 2012.

  48. Belotto MJ. Data analysis methods for qualitative research: Managing the challenges of coding, interrater reliability, and thematic analysis. Qualitative Rep. 2018;23(11):2622–33.

    Google Scholar 

  49. Smith JA. Qualitative psychology: A practical guide to research methods. Sage Publications, Inc; 2003.

  50. Vaismoradi M, Jones J, Turunen H, Snelgrove S. Theme development in qualitative content analysis and thematic analysis. 2016.

  51. Thomas DR. A general inductive approach for qualitative data analysis. 2003.

  52. Brown MC, Bell R, Collins C, Waring G, Robson SC, Waugh J, et al. Women’s perception of future risk following pregnancies complicated by preeclampsia. Hypertens Pregnancy. 2013;32(1):60–73.

    Article  CAS  Google Scholar 

  53. You WB, Wolf M, Bailey SC, Pandit AU, Waite KR, Sobel RM, et al. Factors associated with patient understanding of preeclampsia. Hypertens Pregnancy. 2012;31(3):341–9.

    Article  Google Scholar 

  54. Hoedjes M, Berks D, Vogel I, Franx A, Duvekot JJ, Oenema A, et al. Motivators and barriers to a healthy postpartum lifestyle in women at increased cardiovascular and metabolic risk: a focus-group study. Hypertens Pregnancy. 2012;31(1):147–55.

    Article  Google Scholar 

  55. Sattar N, Greer IA. Pregnancy complications and maternal cardiovascular risk: opportunities for intervention and screening? BMJ. 2002;325(7356):157–60.

    Article  Google Scholar 

  56. Roes EM, Raijmakers MT, Schoonenberg M, Wanner N, Peters WH, Steegers EA. Physical well-being in women with a history of severe preeclampsia. J Matern Fetal Neonatal Med. 2005;18(1):39–45.

    Article  Google Scholar 

  57. Stern C, Trapp E-M, Mautner E, Deutsch M, Lang U, Cervar-Zivkovic M. The impact of severe preeclampsia on maternal quality of life. Qual Life Res. 2014;23(3):1019–26.

    Article  Google Scholar 

  58. Mottola MF. Exercise in the postpartum period: practical applications. Curr Sports Med Rep. 2002;1(6):362–8.

    Article  Google Scholar 

  59. Puetz TW. Physical activity and feelings of energy and fatigue. Sports Med. 2006;36(9):767–80.

    Article  Google Scholar 

  60. Schultchen D, Reichenberger J, Mittl T, Weh TRM, Smyth JM, Blechert J, et al. Bidirectional relationship of stress and affect with physical activity and healthy eating. Br J Health Psychol. 2019;24(2):315–33.

    Article  Google Scholar 

  61. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81–95.

    Article  Google Scholar 

  62. Francisco N, Nervo V, Geraldine F, Morten K, Erik G, Cristiano S. Self-Care Technologies in HCI: Trends, Tensions, and Opportunities. ACM Trans Comput-Hum Interact. 2015;22(6):Article 33.

    Google Scholar 

  63. Kelders SM, Kok RN, Ossebaard HC, Van Gemert-Pijnen JEWC. Persuasive system design does matter: a systematic review of adherence to web-based interventions. J Med Internet Res. 2012;14(6):e152.

    Article  Google Scholar 

  64. Mohr D, Cuijpers P, Lehman K. Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions. J Med Internet Res. 2011;13(1):e30.

    Article  Google Scholar 

  65. Kroeze W, Werkman A, Brug J. A systematic review of randomized trials on the effectiveness of computer-tailored education on physical activity and dietary behaviors. Ann Behav Med. 2006;31(3):205–23.

    Article  Google Scholar 

  66. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. 2013. https://doi.org/10.1001/jama.2013.281053

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Acknowledgements

Particular thanks are due to the women who participated in this study.

Funding

This study was funded by the Erasmus University Rotterdam, Smarter Choices for Better Health Initiative (Action Line Prevention), grant number 108348.

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Authors and Affiliations

Authors

Contributions

LLK: conceptualization, methodology, revising the manuscript;

MvdB: conceptualization, methodology, reviewing drafts of the manuscript;

MH: conceptualization, methodology, reviewing drafts of the manuscript;

DC: conceptualization, methodology, reviewing drafts of the manuscript;

KR: conceptualization, methodology, reviewing drafts of the manuscript;

HvK: conceptualization, methodology, reviewing drafts of the manuscript;

AB: conceptualization, methodology, reviewing drafts of the manuscript;

JJD: methodology, clinical guidance, reviewing drafts of the manuscript;

JRvL: methodology, clinical guidance, reviewing drafts of the manuscript;

AW: conceptualization, methodology, supervision, reviewing drafts of the manuscript;

All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Lili L. Kókai.

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Informed consent was obtained from all subjects. This study adheres most strictly to all applicable legal, ethical, and safety provisions of the Netherlands and the EU. The study was conducted in accordance with the principles of the Declaration of Helsinki [66]. The Medical Ethics Committee of the Erasmus MC has approved this study (MEC-2020-0390).

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Material 1: Correlations between components of needs per health behavior

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Kókai, L.L., van der Bijl, M.F., Hagger, M.S. et al. Needs and preferences of women with prior severe preeclampsia regarding app-based cardiovascular health promotion. BMC Women's Health 22, 427 (2022). https://doi.org/10.1186/s12905-022-02004-5

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