Background: Type 2 Diabetes (T2D) is a chronic condition requiring personalized disease management
to prevent disease progression and complications. Mobile health applications can substantially
support patients in daily disease management. This study analyzes user demographics
of dibi, a newly launched patient companion app, which collects real-time longitudinal
data to improve understanding of the daily life of patients living with diabetes.
This first investigation explores baseline characteristics to enhance user engagement,
advance personalized care and facilitate predictive healthcare strategies.
Methods: Of 5744 dibi users, 2422 (42.2%) provided consent for data use and completed registration
(date: 15.12.24). Users answered onboarding information including diagnosis, age,
gender, treatment type and year of diagnosis. 2262 users with active consent and the
age≥18 were included in the analysis and their distribution over the different onboarding
parameters was evaluated. Users could input medication plans to the app, specifying
the product and whether they adhered to the user-defined schedule. Among those, 1253
(55.3%) users defined at least one medication plan with overall 1039 unique medications.
Further, 514 (41.0%) users utilized the adherence feature at least once to track medication
intake.
Results: The largest cohort of dibi users (2021, 89.3%) were patients with T2D and the majority
of those were male (57.4%), with the biggest shares being aged 56-65 years (33.5%)
and diagnosed 0-1 years ago (26.7%). Female T2D users were on average younger than
male users. Onboarding data revealed that most T2D users (36.9%) reported only OAD
treatment or only Lifestyle changes (17.1%). Of the T2D patients who used the medication
plans, most of them (57.0%) reported only OADs, while the second share (7.2%) reported
only non-diabetes medications. More escalated treatment was visible over the time
course of diagnosis for selected onboarding treatment as well as reported medications.
More female users than male users selected only lifestyle changes during onboarding.
The largest share of users who clicked the adherence feature utilized it once (29.6%),
and the majority of users (87.5%) reported ‘yes’ at least once.
Conclusion: This first analysis demonstrates that the app dibi itself and all features are accepted
and used mainly by T2D patients in Germany and gives insights into treatment according
to recommendations and patient reported lifestyle changes. These findings further
indicate that the characteristics of the dibi cohort align with those observed in
other studies and registries, offering a representative perspective on real-world
disease management. Patient reported data can support the evidence of clinical research
and provide insights into real patient experiences. Further outcomes such as medical
questionnaires and tracking clinical metrics such as HbA1c will give more insights
and allow correlations to treatment and lifestyle of users.
