Medical journal of the Islamic Republic of Iran · 2026
Attitudes toward Mobile App Use in Individuals with Type 1 Diabetes Mellitus: A Quasi-Experimental Mixed-Methods Investigation of a Digital Support Tool for Diabetes Self-Management Behavior.
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Alaei-Shahmiri Fariba, Ghafoury Roya, Rimaz Shahnaz, Rahimi Shakiba, Faghihi Mitra, Maghsoomi Zohreh, E Khamseh Mohammad
Аннотация
BACKGROUND: Mobile applications can enhance diabetes self-management by visualizing glucose trends and promoting engagement. However, evidence on their behavioral and clinical effectiveness in environments with constrained resources remains limited. This study evaluated the impact of the Iranian Asan-Chek mobile app on glycemic outcomes and user experiences among adults with type 1 diabetes mellitus (T1DM).
METHODS: In a 12-week quasi-experimental mixed-methods study, 148 adults with T1DM were recruited. Seventy-four participants used the Bluetooth-enabled Asan-Chek app (intervention group) and 74 performed structured self-monitoring of blood glucose (control group). Primary outcomes included changes in HbA1c and qualitative insights into user engagement and experience. Semi-structured interviews with 15 purposively selected participants (13 app users, 2 controls, varied in age and diabetes duration) explored attitudes, perceived benefits, and barriers. Transcripts were analyzed thematically.
RESULTS: HbA1c decreased by 0.62% in the app group and by 0.41% in the control group (both P < 0.001), without any notable divergence between the intervention and control participants (P = 0.202). Changes in BMI, insulin requirements, and glycemic variability during the course of the study exhibited no statistical disparity between the two groups. Qualitative analysis uncovered three motifs: (i) heightened self efficacy and perceived control arising from instant feedback; (ii) stronger motivation prompted by color coded trend charts and reminders and (iii) usability and economic barriers: interface complexity, data driven anxiety, test strip cost, and finger prick fatigue.
CONCLUSION: Over twelve weeks, qualitative results highlighted that the Asan-Chek app provided motivational and behavioral advantages for individuals with T1DM but did not outperform structured SMBG in attaining HbA1c targets. Addressing challenges through optimized app interface, comprehensive user training, affordable supplies via reimbursement, and AI-driven feedback may help convert these behavioral gains into lasting glycemic improvement.
METHODS: In a 12-week quasi-experimental mixed-methods study, 148 adults with T1DM were recruited. Seventy-four participants used the Bluetooth-enabled Asan-Chek app (intervention group) and 74 performed structured self-monitoring of blood glucose (control group). Primary outcomes included changes in HbA1c and qualitative insights into user engagement and experience. Semi-structured interviews with 15 purposively selected participants (13 app users, 2 controls, varied in age and diabetes duration) explored attitudes, perceived benefits, and barriers. Transcripts were analyzed thematically.
RESULTS: HbA1c decreased by 0.62% in the app group and by 0.41% in the control group (both P < 0.001), without any notable divergence between the intervention and control participants (P = 0.202). Changes in BMI, insulin requirements, and glycemic variability during the course of the study exhibited no statistical disparity between the two groups. Qualitative analysis uncovered three motifs: (i) heightened self efficacy and perceived control arising from instant feedback; (ii) stronger motivation prompted by color coded trend charts and reminders and (iii) usability and economic barriers: interface complexity, data driven anxiety, test strip cost, and finger prick fatigue.
CONCLUSION: Over twelve weeks, qualitative results highlighted that the Asan-Chek app provided motivational and behavioral advantages for individuals with T1DM but did not outperform structured SMBG in attaining HbA1c targets. Addressing challenges through optimized app interface, comprehensive user training, affordable supplies via reimbursement, and AI-driven feedback may help convert these behavioral gains into lasting glycemic improvement.