Different People, Different Supports: Using Latent Class Analysis and Behaviour Change Wheel Mapping to Inform Tailored Digital Health Implementation Strategies
This study utilizes latent class analysis and Behaviour Change Wheel mapping on a survey of 617 Canadian adults to identify three distinct population segments with varying digital competencies and support preferences, demonstrating that tailored implementation strategies are essential for improving equity and engagement in digital health beyond simply addressing digital literacy.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Health systems around the world are increasingly turning to digital tools to deliver care, hoping to make medical services more accessible and convenient. This shift promises to connect patients with doctors through screens and apps, but it also carries a hidden risk: if the tools are too difficult to use, or if people cannot access the necessary devices, the very people who need care most might be left behind. For years, experts have assumed that the main barrier to using these digital health services is a simple lack of skills. The prevailing idea was that if people just learned how to use a computer or a smartphone better, they would naturally start using virtual care. However, this assumption overlooks the complex reality of how people live, what they value, and how they prefer to learn. It treats a diverse population as a single group with a single problem, ignoring the fact that a young person with a fast internet connection might face different hurdles than an older adult with limited mobility, even if both struggle with the same technology.
A team of researchers in British Columbia, Canada, set out to test whether this "one-size-fits-all" approach to digital health was actually working. They conducted a large survey of over 600 adults living in the Fraser Health region, a diverse area with a mix of ages, incomes, and cultural backgrounds. Instead of simply asking everyone the same questions and averaging the answers, the researchers used a statistical method to group people based on their real-life characteristics, such as their age, income, ethnicity, and gender. This allowed them to see distinct patterns in how different types of people interact with technology. They found that the population naturally sorted itself into three very different groups, each with its own unique story regarding digital skills, access to devices, and how they prefer to get help.
The first group, which made up more than half of the people surveyed, consisted of younger adults from diverse racial and cultural backgrounds. These individuals were generally comfortable with technology; they owned smartphones and computers, had internet access, and felt confident using digital tools. Yet, despite having the skills and the equipment, this group was the most likely to say they had never used virtual care services like video calls with a doctor. The second group was made up of older adults, mostly white, with middle incomes. This group faced the most significant challenges: they reported lower confidence in using technology, were less likely to own a smartphone, and often struggled to borrow devices from others. The third group consisted of middle-aged adults with high incomes. They had the highest levels of digital confidence, owned the most devices, and were the most frequent users of virtual care services.
The most revealing discovery was that having the skills to use technology did not automatically mean a person would use it. The younger, diverse group proved that high competence does not guarantee engagement. Even though they could use the tools, they were not using them for healthcare. The researchers found that this group simply did not know where to find information about these services or how to start, and they preferred to get help through online channels like videos or chat guides. In contrast, the older group needed a different kind of support entirely. They preferred to learn through face-to-face interactions, such as workshops or help from a family member, and they relied heavily on traditional methods like landline phones and local newspapers. The wealthy, middle-aged group, who were already using the services, mostly wanted quick technical fixes when things went wrong, rather than basic training.
By mapping these findings onto a framework used to understand human behavior, the researchers concluded that the barriers to digital health are not just about skills. For the younger group, the problem was not a lack of ability, but a lack of motivation or habit; they needed to be nudged into using the services they already had the tools for. For the older group, the barriers were physical and social; they needed better access to devices and in-person help to build their confidence. The study suggests that health systems cannot solve the problem of digital exclusion by simply teaching everyone how to use a computer. Instead, they must recognize that different groups need different solutions. A strategy that works for a tech-savvy young adult, such as sending an email link, will likely fail for an older adult who needs a printed guide and a friendly person to show them the ropes.
The researchers emphasized that their findings are based on a snapshot in time and rely on what people said they could do, rather than testing their actual skills in a lab. However, the patterns were clear and consistent. The study argues that to make digital health truly equitable, implementation strategies must be tailored to the specific needs of these population segments. It is not enough to assume that if you build the digital bridge, people will cross it. Some people need a bridge built with different materials, while others need someone to walk across it with them first. By understanding these distinct profiles, health systems can move beyond generic solutions and design support systems that actually fit the lives of the people they are trying to serve.
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