A family-based eHealth programme to reduce cardiovascular disease risk

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Introduction
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide (WHO, 2021a). Several CVD risk factors are controllable, including weight, diet, physical activity, smoking, and alcohol consumption (WHO, 2021a). CVD is caused by an accumulation of risk factors (Hajar, 2017). The more risk factors a person experiences, or has, the more likely their prospect of developing CVD. A collective family lifestyle of controllable risk factors such as overweight or obesity, consuming unhealthy diets or non-participation in physical activity can increase CVD risk in parents and children alike (Bahreynian et al., 2017). There is strong evidence showing parental risk of CVD translation to their children later in life (Lee et al., 2019). The World Health Organisation (WHO) firmly recommend early prevention methods for non-communicable disease such as CVD, through cost-effective behavioural interventions (WHO, 2020a). An example of such methods include electronic health (eHealth), defined as “the cost-effective and secure use of information and communications technologies in support of health and health-related fields” (WHO, 2022)(p1). eHealth interventions are described as “eHealth technology specifically focused on intervening in an existing context by changing behaviour and/or cognitions” (van Gemert-Pijnen et al., 2018)(p7). Thus, if controllable CVD risk factors and behaviours could be targeted via an early eHealth intervention for CVD prevention in both parents and children together; this could reduce the rate of CVD development for more years to come. Utilising the voices of the end-users within the development of interventions has been deemed an effective tool in ensuring interventions achieve their target outcomes (Mummah et al., 2016a).

Aim
The aim of this study was to co-design an eHealth family-based CVD risk reduction programme, ‘Health-e-Hearts’.

Methods
Health-e-Hearts was developed within an eHealth intervention framework – the IDEAS (Integrate, DEsign, Assess and Share) framework (Mummah et al., 2016b), which comprised seven steps completed within this study.
Step one: Three online focus groups with six families were conducted, to determine families’ needs and expectations for a newly developed eHealth family-based CVD risk reduction programme. Focus groups were transcribed and analysed using conventional content analysis. Step two: A systematic review was carried out to determine the effectiveness of previous family-based eHealth CVD risk reduction interventions. Step three: The behaviour change framework, the Behaviour Change Wheel (BCW), was used to determine how behaviours relating to CVD risk could be improved for families within the intervention being developed, using results from steps one and two. Step four: A project advisory group (PAG) was established comprising: families at risk of developing CVD, CVD experts, software developers and primary prevention practitioners. The PAG provided insight and feedback on several key areas of the study. Step five: A programme prototype was developed, with the help of the software developers from the PAG, that could be evaluated by families. The programme incorporated findings from steps one, two, three and four. Step six: Three additional online focus groups with six families were conducted to evaluate the prototype programme developed in step five. Families discussed each element of the programme (both developed and undeveloped), and feedback was provided to ensure the programme was fit for purpose. Step seven: Amendments were made to the prototype to meet the feedback from families in step six, to ultimately create a minimum viable product.

Results
Step one. Three categories emerged from the focus groups: experiences of health apps and devices; eHealth application needs of family members; and motivators for using an eHealth programme. Experiences included using health apps individually yet inconsistently. Needs included personalisation, free and easy-to-use, time efficient, and multiple content formats. Motivators for engaging with the programme included goal setting, rewards, and competition. Step two. Family-based eHealth interventions were found to be most effective in reducing parent and adolescent alcohol use with some effectiveness for changes to physical activity and dietary intake. Such interventions were least effective in modifying risk factors including body mass index (BMI) of parents and children. Intervention components identified as somewhat effective in reducing risk outcomes were use of theory, longer follow-up periods, incentivisation and regular intervention interaction. It was determined that more sufficiently powered, higher-quality trials with theory driven, clearly described interventions and unambiguous outcomes were needed in future research. Step three. The BCW offered a structured approach to designing the behaviour change intervention, by aiding the translation of barriers to families CVD risk reduction, identified from the literature (systematic review) and family focus groups, and mapped the corresponding intervention functions and behaviour change techniques (BCTs) provided by Michie et al. (2014) to address those barriers. Barriers included availability, knowledge, skills, resources/access, perceived time, motivation, support, and psychological health. Intervention functions included enablement, education, training, environmental restructuring, persuasion, incentivisation, modelling, restriction, and environmental restructuring. Suggested BCTs included, but were not limited to, information about health consequences, goal setting (behaviour), self-monitoring of behaviour, instruction on how to perform a behaviour, and behavioural practice/ rehearsal. Step four. The PAG aided several stages of the study, including providing feedback on recruitment posters for the online focus groups, and developing and evaluating questions used within the focus groups. They also provided feedback on the prototype before it was shown to families and aided the dissemination of results from the focus groups. Step five. There were several prototypes produced, informed by the findings from steps one through four. The components of the prototype evaluated by families included the homepage, family dashboard and family profiles, goal setting page, progress page, and the initial introductory learning module. Step six. Families provided constructive feedback for the prototype on all the components, and provided further ideas of how certain areas, such as the goal setting page could be improved. Step seven. The software developers and the PhD researcher worked through the suggested amendments for the programme and created a list of future must have and future could have components that could be added to the programme if fully developed.

Conclusion
In summary, this study co-designed an eHealth family-based CVD risk reduction programme, both for and with families at risk of developing CVD. This study shows that the intervention considered both short- and long-term behaviour change for families and has included evidence-based research from both the literature and findings from families themselves. The intervention was developed using behaviour change theory to overcome barriers preventing families’ CVD risk reduction and included intervention functions and BCTs that families themselves stated would both initiate and continue to motivate them to use the programme to reduce their CVD risk. Further development and feasibility testing of this intervention should be undertaken to inform a randomised controlled trial (RCT) to determine its effectiveness.

Thesis is embargoed until 31 July 2024.
Date of AwardJul 2023
Original languageEnglish
Awarding Institution
  • Queen's University Belfast
SponsorsNorthern Ireland Department for the Economy
SupervisorDavid Thompson (Supervisor), Chris Watson (Supervisor), Chantal Ski (Supervisor) & Karen McGuigan (Supervisor)

Keywords

  • Cardiovascular disease
  • family
  • eHealth
  • behaviour change
  • lifestyle factors

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