Al Zahidy Misk A, Simha Sue, Branda Megan, Borras-Osorio Mariana, Haemmerle Maeva, Tran Viet-Thi, Ridgeway Jennifer L, Montori Victor M
Knowledge and Evaluation Research Unit, Mayo Clinic, Rochester, MN.
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN.
Mayo Clin Proc Digit Health. 2024 Nov 16;3(1):100180. doi: 10.1016/j.mcpdig.2024.11.001. eCollection 2025 Mar.
OBJECTIVE: To understand the contribution of digital medicine tools (eg, continuous glucose monitoring systems, scheduling, and messaging applications) to treatment burden in patients with diabetes. PATIENTS AND METHODS: Between October and November 2023, we invited patients with type 1 or type 2 diabetes to participate in semistructured interviews. The interviewees completed the Treatment Burden Questionnaire as they reflected on how digital medicine tools affect their daily routines. A published taxonomy of treatment burden guided the qualitative content analysis of interview transcripts. RESULTS: In total, 20 patients agreed to participate and completed interviews (aged 21-77 years, 55% female, 60% living with type 2 diabetes). We found 5 categories of tasks related to the use of digital medicine tools that patients had to complete (eg, calibrating continuous glucose monitors), 3 factors that made these tasks burdensome (eg, cost of device replacements), and 2 categories of consequences of burdensome tasks on patient wellbeing (eg, fatigue from device alarms). CONCLUSION: Patients identified how digital medicine tools contribute to their treatment burden. The resulting digital burden taxonomy can be used to inform the design, implementation, and prescription of digital medicine tools including support for patients as they normalize them in their lives.
目的:了解数字医学工具(如持续葡萄糖监测系统、日程安排和信息应用程序)对糖尿病患者治疗负担的影响。 患者与方法:2023年10月至11月期间,我们邀请1型或2型糖尿病患者参与半结构化访谈。受访者在思考数字医学工具如何影响其日常生活时,完成了治疗负担问卷。已发表的治疗负担分类法指导了访谈记录的定性内容分析。 结果:共有20名患者同意参与并完成访谈(年龄21 - 77岁,55%为女性,60%患有2型糖尿病)。我们发现了与患者必须完成的数字医学工具使用相关的5类任务(如校准持续葡萄糖监测仪)、使这些任务变得繁重的3个因素(如设备更换成本)以及繁重任务对患者健康产生的2类后果(如设备警报导致的疲劳)。 结论:患者明确了数字医学工具如何加重他们的治疗负担。由此产生的数字负担分类法可用于为数字医学工具的设计、实施和处方提供信息,包括在患者将其融入生活时为他们提供支持。
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