User profiles in digitalized healthcare: active, potential, and rejecting - a cross-sectional study using latent...

Area of Science:

• Digital Health
• Healthcare Services Research
• Sociology of Health

Background:

• General health applications show varied usage across demographics, but this is understudied for digitalized healthcare.
• Understanding user patterns and characterizing user types in digitalized healthcare is currently unclear.

Purpose of the Study:

• To identify and characterize user types for digitalized healthcare services.
• To investigate the socio-demographic and attitudinal factors influencing these user types.

Main Methods:

• Latent class analysis was used to determine user types based on digitalized healthcare service usage.
• Multinomial logistic regressions analyzed socio-economic, demographic, and attitudinal factors for each user type.

Main Results:

• Three user types were identified: Rejecting (27.9%), Potential (53.8%), and Active (18.3%).
• Active users were less employed, less educated, and less skeptical; Potential users were younger, highly educated, employed, and less skeptical; Rejecters were older, more female, and of higher socio-economic status.

Conclusions:

• Significant socio-demographic and socio-economic disparities exist among digitalized healthcare user types.
• Not all population segments may equally benefit from healthcare digitalization.
• Interventions are needed to improve access to digital health innovations for all individuals.