The Diabetes Management App for Diabetic Mellitus' Wellbeing: A Perspective on Optimal Matching of Social Support


Student thesis: Doctoral Thesis

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Award date26 Apr 2022


Chronic illnesses such as diabetes are leading causes of death, resulting in substantial economic costs worldwide. To prevent life-threatening risks, chronic patients should perform self-monitoring (SM) of their chronic illnesses. Nevertheless, the complexity of chronic illness management presents a challenge for chronic patients to perform healthy behaviors and maintain psychological health. Smart health devices and applications, along with technological advances in artificial intelligence (AI), makes information technology (IT)-enabled self-monitoring (ITSM) a promising approach to reducing healthcare costs and addressing the challenge.

The growing popular smart health devices and applications are changing the conventional services for chronic patients who need the involvement of professional healthcare providers and their families. Traditional professional support and family support have time and distance barriers in practice. Patients’ visits to medical professionals are not often enough to receive immediate advice and feedback on their healthy behaviors, which need to be adapted daily. Patients are typically middle-aged and elderly, and they may not be living with their key family caregivers. The latter are usually busy at work or studying during the daytime and cannot provide responsive offline support to the patients. This dissertation suggests that ITSM offers a unique opportunity to overcome the two mentioned professional and family support barriers. On the one hand, AI-empowered devices and applications serve as a surrogate for formal support that medical professionals typically provide patients. Such technologies can generate professional and personalized support messages to patients through the analysis of extensive patient data. On the other hand, AI-empowered devices and applications enable informal sources like patients’ family members to deliver support.

Despite these promises, research on smart health devices and applications for chronic patients’ health management is still in the infancy stage. The existing social support studies on medical professionals mainly considered information systems as a channel to convey professional support messages, while the literature on family support focused on offline support. Beyond a traditional view that social support is within human-to-human interaction, this dissertation considers these smart health devices and applications as a formal source that can provide professional social support, a human-to-machine interaction. This dissertation also explores patients’ family support enabled by these devices and applications. However, few studies have offered theoretical understandings and empirical evidence on whether or how these technologies affect chronic patients’ healthy behaviors and psychological health, two critical effects of chronic illness management.

Social support theories have been one of the most well-documented theories in understanding mental and physical health outcomes. Nevertheless, the existing models failed to match support with patients’ needs for optimal health outcomes. Task specificity models proposed that patients’ needs should be fulfilled by different forms/functioning types of support. In contrast, the pyramid of social-environmental support models and hierarchy compensatory models proposed that people’s needs preferences have two distinct sources of support. Evidence from these models showed that the effect of social support is not always beneficial for chronic patients. Grounded in social support theories, this dissertation aims to address the research gaps in the existing literature: (1) the lack of simultaneous understanding of behavioral and psychological effects of social support; (2) the lack of understanding of the effect of integrating different aspects of support (i.e., forms of support and sources of support) for optimum outcomes; and (3) the lack of understanding of the contingent effects determining the conditions under which different social supports work.

Extending the optimal match theory of social support to chronic illness management, a theoretical model is proposed and empirically tested. The research model theorizes the direct effects of focal supports identified by integrating two aspects of support (i.e., support forms: informational vs. emotional supports; and support sources: the app vs. the primary family member). Furthermore, two contingent factors, stage of chronic illness management and illness severity, that reflect situational differences of optimal support are proposed in this study.

A two-wave online survey with 254 subjects is conducted to collect data for testing the research model. The results show that: (1) regarding direct effects, both IT informational support (ITInS) and IT emotional support (ITEmS) independently improve healthy behavioral intention (HBI). However, they have insignificant effects on psychological distress (PD). Family members’ emotional support (FEmS) significantly reduces PD and motivates HBI. (2) Regarding the contingent effect of stage of chronic illness management, ITInS becomes less beneficial (decrease in PD and increase in HBI) from the early to the late stage. By contrast, IT emotional support (ITEmS) becomes more beneficial from the early to the late stage (decrease in PD and increase in HBI). The effects of FEmS do not differ between the early stage and the late stage. (3) Regarding the contingent effect of illness severity, illness severity only moderates the relationship between the two IT supports and HBI (strengthening the positive effect of ITInS but weakening the positive effect of ITEmS). Conversely, illness severity only moderates the relationship between family members’ support and PD (strengthening the negative effect of FEmS).

Overall, this dissertation demonstrates that supports enabled by smart health devices and applications can simultaneously improve chronic patients’ healthy behaviors and relieve psychological distress. This can be achieved through matching different aspects of support: support forms and support sources and through considering contingency factors: stage of illness management and illness severity. The interesting patterns of findings and implications to both research and practice are discussed in this dissertation.

    Research areas

  • chronic illness, information technology (IT)-enabled self-monitoring (ITSM), smart health devices and apps, emotional support, informational support, family support, stage of chronic illness management, psychological distress, healthy behaviors, optimal match theory