Investigating the Modulators of Health Behaviour Change Using the Novel AUT Stroke Riskometer Application

Date
2021
Authors
George, Shwetha Ann
Supervisor
Feigin, Valery
Krishnamurthi, Rita
Barker-Collo, Suzanne
Item type
Thesis
Degree name
Doctor of Philosophy
Journal Title
Journal ISSN
Volume Title
Publisher
Auckland University of Technology
Abstract

Stroke is a leading cause of death and disability in New Zealand (NZ) with nine out of ten events attributed to modifiable risk factors, including lifestyle behaviours. Conventional, primary stroke prevention paradigms heavily rely on awareness and education to promote changes in lifestyle risk factors (preventative health behaviour changes (PHBCs)) (Brouwer-Goossensen et al., 2016). The Stroke Riskometer (SR) mobile application is based on such a strategy, providing users with their 5- and 10-year risk for a stroke to motivate PHBCs (Parmar et al., 2015). However, the effectiveness of such methods in promoting long-term PHBCs, particularly among unmotivated individuals, is limited (Kelly & Barker, 2016). The following research examined the influences of: (1) health beliefs, (2) socioeconomic deprivation and (3) psychosocial stress in initiating and maintaining PHBCs for stroke risk reduction among NZ-based users of the SR and study advertisement responders. In Study 1 of this three-part research, qualitative interviews based on the constructs of the Health Belief Model were performed with 37 participants. Data analysis revealed that a high perceived risk of a stroke did not motivate PHBCs among study 1 participants. Instead, other health priorities and role-modelling were more powerful promoters of change while social norms and habits were barriers for PHBC. Execution of PHBCs required robust self-regulation and clarity on how to implement PHBCs. Self-regulation was also critical to prevent relapse while significance of the original motivator and acquired PHBC-related benefits strengthened resolve to maintain changes. Study 2 aimed to investigate the food choice motivations of 179, Auckland-based participants with low and high individual and community-level socioeconomic deprivation. Participants completed questionnaires on overall diet quality (including stroke-risk-increasing foods (e.g., processed foods) and risk-reducing foods (e.g., fruits and vegetables)), food choice motivations and nutritional knowledge. Overall diet quality and intake of risk-reducing foods was significantly lower in the high individual and community-level deprivation groups. Conversely, risk-increasing food intake was proportionately higher in the latter group. Food prices were considered an important food choice motivation for participants with high individual-level deprivation. However, nutrition knowledge did not vary significantly by the degree of deprivation. Study 3 investigated the impact of chronic psychosocial stress in 19 participants using a mixed methodology approach. The Trier Inventory for Chronic Stress questionnaire was employed to identify chronic stressors while qualitative interviews explored their impact on the ability to maintain PHBCs and coping techniques used. Chronic stress originated from both work/study-related demands and social issues, initiating both acute and chronic physiological/psychological responses. Participants adopted adaptive or maladaptive techniques for rest and reward or to divert their attention from the stressor. The method adopted was based on their self-regulatory capacity and whether participants valued immediate rewards or future benefits. The overall research illustrated how an individual transitions from a state of readiness to implementing and maintaining PHBCs long-term. Insights were provided on how an individual's health beliefs, psychosocial stress and deprivation status play a critical role in the success of each stage. Therefore, client-centred strategies accounting for such factors needs to be developed for optimising long-term PHBCs.

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Keywords
Stroke prevention , Lifestyle change , Behavioural medicine , Stroke riskometer , Health belief model , Stress management , Socioeconomic deprivation
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