An empirical study conducted by the University of Michigan (2025) examined artificial intelligence (AI) use among adults aged 50 and older, focusing on levels of trust, perceived benefits, and associated risks. Findings indicated that 46% of participants reported little to no trust in AI-generated information. This lack of trust is particularly significant given the increasing integration of AI into technologies commonly used by this population. Everyday tools such as wearable devices, digital family calendars, photo-sharing platforms, smart vehicles, and home automation systems, as well as applications in healthcare, financial management, home security, and news media, are becoming deeply embedded in daily life. As a result, AI may function as a risk factor when individuals rely on technologies they do not fully trust. This tension reflects a form of cognitive dissonance, where the necessity of using AI-integrated technologies conflicts with underlying skepticism, potentially impacting mental health and overall well-being. 

Cognitive dissonance, often framed as a negative psychological state, can be reconceptualized as a critical turning point for digital wellness. I performed an OMNI search using key words “cognitive dissonance”, “technology use”, and “adulthood” to search for empirical studies related to this risk factor. I found an empirical study that examined cognitive dissonance in technology adoption specific to smart home users. In this study, Marikyan et al. (2023) suggest that when technology fails to meet expectations, individuals experience emotional discomfort such as anger, guilt, and regret, which can lead to either avoidance behaviours or adaptive coping strategies. 

I am particularly interested in this cognitive framing, in which participants can use this information to either withdraw from tech use or use adaptive coping strategies to manage the resulting cognitive dissonance. This level 2 empirical research study can be used to further examine whether cognitive dissonance acts as a risk or protective factor for digital wellness in middle adulthood, with digital literacy and cognitive coping strategies as moderating variables. 

To further ground my research analysis, I revisited key frameworks from the Digital Wellness course materials. Week 6 emphasized the development of cognitive skills through models such as critical thinking and self-regulation. The Critical Thinking model (Falcone, 1990) identifies cognitive skills, including interpretation, analysis, and evaluation, while the Self Regulation model (Laffier et al., 2023) emphasizes behavioural skills such as self-awareness, self-monitoring, and self-control. By integrating critical thinking processes within self-regulation practices, individuals examine not only their patterns of technology use, but also the underlying beliefs, assumptions and emotional responses that shape their behaviour and interactions with AI-integrated tools. In this context, cognitive dissonance becomes an opportunity for reflective meaning-making that can promote individuals to move toward more intentional, informed, and trusted technology use, and improved digital well-being. 

This week, I used the lecture video to practice research skills and evidence-based strategies to examine a digital wellness challenge relevant to my target population, middle adulthood.

A level 1 empirical study conducted by the University of Michigan (2025) examined the use of artificial intelligence (AI) among adults aged 50 and older, focusing on trust, perceived benefits, and associated risks. Findings revealed that 46% of participants reported little to no trust in AI-generated information. This lack of trust is particularly significant given the increasing integration of AI into technologies commonly used by this population. Everyday tools such as wearable devices, digital family calendars, photo-sharing platforms, smart vehicles, and home automation systems, as well as applications in healthcare, financial management, home security, and news media, are now deeply embedded in daily life. As a result, AI may function as a risk factor when individuals rely on technologies they do not fully trust. This tension reflects a form of cognitive dissonance, in which the necessity of using AI-integrated technologies conflicts with underlying skepticism, potentially impacting mental health and overall well-being. 

Cognitive dissonance, often conceptualized as a negative psychological state, can be reframed as a moderator for digital wellness. I used this moderator to conduct an OMNI search using key words “cognitive dissonance”, “technology use”, and “adulthood”. This search led to a level 2 empirical study examining cognitive dissonance in smart home technology adoption. Marikyan et al. (2023) found that when technology fails to meet user expectations, individuals experience emotional discomfort such as anger, guilt, and regret, which may lead to either avoidance behaviours or adaptive coping strategies. This study also supports findings in a different study, where technology adoption depends on fit and emotional reactions such as frustration or anxiety can have an impact on an individual's choice to use the technology (Cohen et al., 2025). This distinction is important because it suggests that cognitive dissonance does not inherently produce negative outcomes. Dissonance creates a decision point where individuals may either disengage from technology or develop adaptive strategies to manage the incongruence. This research finding positions cognitive dissonance as both a potential risk and protective factor in digital wellness, moderated by an individual’s digital literacy and cognitive coping strategies.

To further ground my research analysis, I revisited key frameworks from the Digital Wellness course materials. Week 6 emphasized the development of cognitive skills through models such as critical thinking and self-regulation. The Critical Thinking model (Falcone, 1990) identifies cognitive skills, including interpretation, analysis, and evaluation, while the Self Regulation model (Laffier et al., 2023) emphasizes behavioural skills such as self-awareness, self-monitoring, and self-control. By integrating critical thinking processes into self-regulation practices, individuals examine not only their patterns of technology use but also the underlying beliefs, assumptions, and emotional responses that shape their behaviour and interactions with AI-integrated tools. In this context, cognitive dissonance becomes an opportunity for reflective meaning-making that can promote individuals to move toward more intentional, informed, and trusted technology use, and improved digital well-being. 

To identify practical intervention strategies, I explored approaches that address maladaptive thinking patterns and problematic behaviours. Cognitive Behaviour Therapy (CBT) offers a well-established, evidence-based framework for aligning cognition, emotion, and behaviour. A subsequent OMNI search using the keywords “cognitive behaviour therapy strategies” and “cognitive dissonance and technology” led to a Level 2 empirical study by Nascimento et al. (2025). This study surveyed 453 Instagram users aged 20-65 years through an online survey on LinkedIn. Nascimento et al. (2025) found that emotions such as guilt and regret directly predicted reductions in Instagram use, whereas cognitive dissonance influenced behaviour indirectly through these emotional responses, suggesting that behaviour change is emotionally driven rather than purely cognitive. This research supports the idea that cognitive dissonance is not necessarily harmful and can likewise serve as a moderator of positive behavioural change when individuals use emotional responses to drive self-awareness and emotional regulation practices. Guilt and regret serve as psychological mechanisms that prompt individuals to curtail their Instagram use, thereby aligning their behaviour with their beliefs. This article provides strong empirical evidence for my argument that digital wellness is not achieved through restriction alone, but through reflective alignment between cognition (beliefs), affect (emotions), and behaviour (technology use). 

Within this framework, my digital wellness tool, the Couples Check-In, serves as a practical application of these evidence-based strategies. By fostering structured opportunities for self-awareness, critical reflection, and emotional regulation, the tool supports cognitive-affective self-regulation. Individuals can navigate cognitive dissonance, build trust in their use of technology, and promote more intentional and balanced digital engagement. 

Marikyan, D., Papagiannidis, S., & Alamanos, E. (2023). Cognitive Dissonance in Technology Adoption: A Study of Smart Home Users. Information Systems Frontiers, 25(3), 1101–1123. https://doi.org/10.1007/s10796-020-10042-3

Nascimento, P., Oliveira, T., & Neves, J. (2025). Understanding SNS use reduction from the perspective of the cognitive-affective model. Internet Research, 35(3), 1379–1405. https://doi.org/10.1108/INTR-04-2023-0239

University of Michigan Institute for Healthcare Policy & Innovation. (2025). How older adults use and think about artificial intelligence. https://ihpi.umich.edu/national-poll-healthy-aging/national-findings/how-older-adults-use-and-think-about-ai