Behavioral Engagement and Activation Model Study (BEAMS): A latent class analysis of adopters and non-adopters of digital health technologies among people with Type 2 diabetes.

ABSTRACT

Many people with Type 2 diabetes (T2D) who could benefit from digital health technologies (DHTs) are either not using DHTs or do use them, but not for long enough to reach their behavioral or metabolic goals. We aimed to identify subgroups within DHT adopters and non-adopters and describe their unique profiles to better understand the type of tailored support needed to promote effective and sustained DHT use across a diverse T2D population. We conducted latent class analysis of a sample of adults with T2D who responded to an internet survey between December 2021 and March 2022. We describe the clinical and psychological characteristics of DHT adopters and non-adopters, and their attitudes toward DHTs. A total of 633 individuals were characterized as either DHT "Adopters" (n = 376 reporting any use of DHT) or "Non-Adopters" (n = 257 reporting never using any DHT). Within Adopters, three subgroups were identified 21% (79/376) were "Self-managing Adopters," who reported high health activation and self-efficacy for diabetes management, 42% (158/376) were "Activated Adopters with dropout risk," and 37% (139/376) were "Non-Activated Adopters with dropout risk." The latter two subgroups reported barriers to using DHTs and lower rates of intended future use. Within Non-Adopters, two subgroups were identified 31% (79/257) were "Activated Non-Adopters," and 69% (178/257) were "Non-Adopters with barriers," and were similarly distinguished by health activation and barriers to using DHTs. Beyond demographic characteristics, psychological, and clinical factors may help identify different subgroups of Adopters and Non-Adopters.

In this study, we characterized subgroups of adopters and non-adopters of digital health technologies (DHTs) for managing Type 2 diabetes, such as apps to track nutrition, continuous glucose monitors, and activity monitors like Fitbit. Self-efficacy for diabetes management, health activation, and perceived barriers to use DHT emerged as characteristics that distinguished subgroups. Notably, subgroups of adopters differed in their interest to use these technologies in the next 3 months; groups with low levels of self-efficacy and health activation were least interested in using them and thus at risk of discontinuing use. The ability to identify these subgroups can inform strategies tailored to each subgroup that motivate adoption of DHTs and promote long-term engagement.