Background
Chronic conditions, such as diabetes, affect over 100 million adults in the United States,1 placing a significant workload on patients to access care, follow treatment plans, and engage in daily self-management.2–6 For patients living with diabetes, this often includes monitoring blood glucose, making self-management decisions, implementing insulin dosing, and managing food choices and exercise.7–10 This cumulative workload, and its impact on a patient’s quality of life is known as treatment burden.7,11–14 Higher treatment burden is linked with poorer health outcomes, including reduced adherence to care plans and increased hospitalization rates.7,15
In response to these challenges, digital medicine tools and applications that aim at supporting self-management—such as continuous glucose monitors (CGMs), insulin pumps, patient portals, mobile apps, and telemedicine—have been increasingly integrated into diabetes care.16–20 These offer potential benefits by automatic or supporting self-management tasks.17,19–23 However, they can also introduce new work and source of strain, including technical malfunction, usability issues, and information overload, particularly those less familiar with or with limited access to digital technology.24,25 In our prior work patients describe how digital tools sometimes complicated rather than eased the workload of managing chronic illness.26
Despite this growing digital landscape, existing instruments that assess treatment burden such as the Treatment Burden Questionnaire (TBQ) and Patient Experience with Treatment and Self-management (PETS) do not capture the burden associated with digital tools.5,13,15,24,27 This gap hinders our ability to understand and address the challenges patients face by using digital medicine tools.26
To fill this gap and capture the burden of using digital medicine tools, the Treatment Burden Questionnaire Plus Digital (TBQ+D) was developed to capture the burden of treatment in patients living with diabetes, including the burden of digital medicine tools.
Methods
The study builds on previously reported concept elicitation interviews of patient work and experience of using digital tools.26 This report includes two phases (Figure 1): (1) mapping concept elicitation themes to the existing TBQ items and generating new or modified items and (2) cognitive testing and refinement to address gaps.
|
Figure 1 The process used to develop the Treatment Burden Questionnaire + Digital (TBQ+D). Bold indicates the three main phases: Concept Elicitation, Item Generation, and Cognitive Testing.
|
Participants
For both of the phases, adult patients aged 18 years or older with a diagnosis of type 1 or type 2 diabetes and attending the diabetes clinic in the Division of Endocrinology at Mayo Clinic (Rochester, Minnesota) were eligible to participate if they used at least one digital medicine tool for diabetes management (eg glucometer, insulin smartpens, pumps, etc.), were proficient in English, and were able to complete informed consent (ie, had no major functional impairment). Patients whose caregivers were the primary users of digital tools or who had significant cognitive or sensory impairments were excluded.
Eligible patients were identified from the daily appointment calendar and approached in-person by study staff. Recruitment aimed to maximize diversity in diabetes type (type 1 or type 2) and intensity of digital tool use which was measured using the Digital Medicine Tools Intensity Scale—a 7-point scale ranging from “none” to “maximal” (Figure 2).26
 |
Figure 2 Digital Medicine Tools Intensity Scale.
|
Item Generation
To guide item generation, we conducted a structured mapping exercise linking qualitative data from the concept elicitation study26 to items in the original TBQ (Table 1). Using content analysis, two team members reviewed patient responses to each TBQ item and coded themes that reflected how digital tools impacted the same burden domains. When novel burden domains emerged that were not addressed by existing TBQ items, new items were generated guided by the original item stems and adapted to reflect the digital context. A multidisciplinary expert panel reviewed all proposed additions and modifications to ensure conceptual clarity and relevance prior to cognitive testing.
 |
Table 1 Mapping Burden of Digital Tool Themes to TBQ+D Items
|
Cognitive Testing
Once the initial draft of the TBQ+D was complete, a new sample of patients was recruited to pre-test the TBQ+D over three rounds of cognitive interviews, each involving up to 10 participants. Participants completed the TBQ+D during in-person private sessions using a think-aloud protocol by the same trained researcher (M.A.Z). As they responded to each item, participants verbalized their thought processes, interpretations, and any confusion they experienced. This approach allowed researchers to assess the clarity, relevance, and wording of the questionnaire items. Participants were asked about difficulties in understanding any items, suggestions for improving item wording or content, and whether any aspects of their digital tool use experience were not captured by the questionnaire.
Data Handling
Survey responses, demographic, and clinical data were managed using REDCap electronic data capture tools hosted at Mayo Clinic (UL1TR002377).
Data Analysis
As previously published, themes from the concept elicitation phase were derived from patient interviews exploring the work and experience of using digital medicine tools.26 In the current study, two members of the research team reviewed these published themes and mapped them to items from the TBQ to evaluate item coverage and identify content gaps. When burden domains related to digital care—such as syncing errors, device alarms, technical malfunctions, or data management—were not adequately addressed by existing TBQ items, new items were generated using patient language and contextual detail.
For the cognitive testing phase, data from the think-aloud interviews were analyzed for patient interpretations of specific questions and overall survey clarity, applicability, and completeness. Notes and transcripts were reviewed after each round to identify issues with item interpretation, clarity, or relevance. Revisions were made iteratively following each round, and refinement continued until no new changes were suggested, indicating saturation.
Ethical Consideration
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Mayo Clinic Institutional Review Board (IRB Number 23–007631). Before any study procedures, each participant gave written informed consent.
Results
Item Generation
Themes informing item development were derived from a previously published concept elicitation study.26 In that work, patients described the effort, complexity, and consequences of using digital tools for self-management, identifying sources of burden such as syncing failures, technical malfunctions, device-related discomfort, and frequent alerts.
For example, the TBQ item on “pain or discomfort from device” was expanded to include discomfort specific to sensor insertions, allergic reactions to adhesives, and scarring from continuous glucose monitors. Other examples, such as device malfunctions, syncing errors, and disruptions from frequent alarms, led to the generation of new items.
Informed by these themes, eight new items were developed, and several original TBQ items were refined to incorporate digital-specific examples (eg, discomfort from sensors or issues with online scheduling). Table 1 presents a theme-to-item mapping that illustrates how patient feedback guided the adaptation process.
Cognitive Testing
Between April and May 2024, 24 eligible patients were approached to participate in cognitive testing, with 20 consenting (median age: 57 years, IQR: 36–67; 60% female; 40% with type 2 diabetes) (Table 2). Four patients declined due to time constraints. Participants represented a range of digital medicine tool use intensities, with 8 (40%) categorized as maximal-intensity users.
 |
Table 2 Patient Characteristics
|
The TBQ+D was refined across three rounds of cognitive interviews with up to 10 participants per round. Revisions after each round are summarized in Figure 3. Refinements focused on improving clarity, resolving redundancy, and enhancing item relevance. Revisions were completed when no additional changes were suggested, indicating saturation. Table 3 presents the final version of the TBQ+D.
 |
Table 3 Finalized Items of the TBQ+D
|
 |
Figure 3 Summary of item revisions for the Treatment Burden Questionnaire + Digital (TBQ+D) across cognitive testing rounds.
|
Discussion
We adapted the TBQ to capture the workload of using digital medicine tools and its negative impact on quality of life. The resulting instrument, TBQ+D, is a brief, self-reported measure of the overall burden of treatment inclusive of accessing and using digital medicine tools. Our findings demonstrate that digital burden is a distinct and meaningful component of treatment workload. By incorporating patient input throughout development, this work not only introduces a new tool to quantify digital burden in future research but also offers a patient-informed model for adapting existing measures to reflect modern care contexts.
Comparison with Prior Research
Existing research has highlighted the potential for digital medicine tools to add to patients’ workload, particularly when these tools are poorly integrated or difficult to use.17,28–31 Our previously published work confirmed that digital self-management technologies can introduce unique forms of digital treatment burden, including device malfunctions, alert fatigue, data synchronization issues, and emotional distress.26
Despite growing recognition of this burden, no existing patient-reported instruments systematically capture the digital-specific components of treatment burden for patients with diabetes or one or more chronic conditions. Measures like the TBQ and PETS offer valuable insights into treatment burden but do not explicitly capture digital burden. This study addresses that gap by adapting the TBQ to include the burden of using digital tools directly informed by patient experiences.
Implications for Clinical Practice
The development of the TBQ+D has two important implications for clinical practice. First, it offers a patient-informed instrument specifically designed to capture digital treatment burden among people with diabetes and, possibly, other chronic conditions—an increasingly important issue as chronic disease management becomes more reliant on digital tools. While additional validation is needed, the TBQ+D may help clinicians and researchers identify areas where digital tools contribute to care complexity, particularly for patients with high treatment demands.
Second, once validated, the TBQ+D can assist clinicians in recognizing that increased use of digital tools, even those meant to automate tasks, does not always lead to a reduction in burden. Patients with more complex treatment needs may face a higher digital workload, highlighting the importance of a patient-centered approach to their implementation in practice.
Limitations and Future Research
This study has two key limitations. First, it was conducted at a single academic medical center with a relatively homogenous patient population, which may limit the generalizability of the findings. The sample size, while appropriate for cognitive interviews, further limits the diversity of perspectives represented. Future work should actively recruit more racially and ethnically diverse participants to strengthen the tool’s relevance across populations.
Second, while the adaptation process included rigorous qualitative methods and patient input, this work focused on item development and cognitive testing. The third phase in the process of developing TBQ+D involves its field testing, to test hypotheses that test is validity, ie, its ability to truly capture the workload and negative effect on quality of life it may have induced by the work of accessing and using healthcare and enacting self-care tasks supported by digital tools. This process should continue with a range of other populations with chronic conditions and variable digital care use, thus advancing the evidence of the validity and responsiveness of the TBQ+D.
Conclusion
This study describes the development of the Treatment Burden Questionnaire Plus Digital (TBQ+D), an adaptation of the original TBQ designed to capture burden associated with digital medicine tools. Grounded in patient experiences and refined through cognitive testing, the TBQ+D represents a step toward measuring the burden of digital care in patients with living with diabetes.
By incorporating patient-centered design and directly addressing gaps in existing measures, the TBQ+D can support future research and clinical efforts to ensure digital tools reduce, rather than add to, the workload of living with diabetes. As the use of digital tools in care continues to grow, measuring burden of digital care will be essential to designing tools and workflows that promote patient centered care that is minimally disruptive. Future work should also include cross-cultural adaptation and translation of the TBQ+D to ensure its validity and utility in international settings.
Abbreviations
CGM, Continuous Glucose Monitoring; EHR, Electronic Health Record; IQR, Interquartile Range; REDCap, Research Electronic Data Capture; TBQ, Treatment Burden Questionnaire.
Acknowledgments
This work was funded by a competitive grant awarded by the Center for Digital Health at Mayo Clinic underwritten by the Noaber Foundation. The authors bear full responsibility for the content and any errors or omissions, and the views expressed do not necessarily reflect those of these entities.
For information on, or permission to use, the TBQ+D, please contact the authors (VMM, V-TT).
This paper has been uploaded to medRxiv as a preprint: https://pubmed.ncbi.nlm.nih.gov/40492074/.
Disclosure
The authors declare that they have no conflicts of interest related to this study.
References
1. Benavidez GA, Zahnd WE, Hung P, Eberth JM. Chronic Disease Prevalence in the US: sociodemographic and Geographic Variations by Zip Code Tabulation Area. Prev Chronic Dis. 2024;21:E14. doi:10.5888/pcd21.230267
2. Eton DT, Elraiyah TA, Yost KJ, et al. A systematic review of patient-reported measures of burden of treatment in three chronic diseases. Patient Relat Outcome Meas. 2013;4(1):7–20. doi:10.2147/PROM.S44694
3. Eton DT, Ramalho de Oliveira D, Egginton JS, et al. Building a measurement framework of burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Relat Outcome Meas. 2012;3(1):39–49. doi:10.2147/PROM.S34681
4. Eton DT, Ridgeway JL, Egginton JS, et al. Finalizing a measurement framework for the burden of treatment in complex patients with chronic conditions. Patient Relat Outcome Meas. 2015;6(1):117–126. doi:10.2147/PROM.S78955
5. Eton DT, Yost KJ, Lai JS, et al. Development and validation of the Patient Experience with Treatment and Self-management (PETS): a patient-reported measure of treatment burden. Qual Life Res. 2017;26(2):489–503. doi:10.1007/s11136-016-1397-0
6. Ridgeway JL, Egginton JS, Tiedje K, et al. Factors that lessen the burden of treatment in complex patients with chronic conditions: a qualitative study. Patient Prefer Adherence. 2014;8(3):339–351. doi:10.2147/ppa.S58014
7. Tran V-T, Barnes C, Montori VM, Falissard B, Ravaud P. Taxonomy of the burden of treatment: a multi-country web-based qualitative study of patients with chronic conditions. BMC Med. 2015;13(1):115. doi:10.1186/s12916-015-0349-3
8. Hanlon P, Bryson I, Morrison H, et al. Self-management interventions for Type 2 Diabetes: systematic review protocol focusing on patient workload and capacity support. Wellcome Open Res. 2021;6:257. doi:10.12688/wellcomeopenres.17238.1
9. Nifha CE. Type 2 Diabetes in Adults: management. [Report]. 2015.
10. Munshi M, Kahkoska AR, Neumiller JJ, et al. Realigning diabetes regimens in older adults: a 4S Pathway to guide simplification and deprescribing strategies. Lancet Diabetes Endocrinol. 2025;13(5):427–437. doi:10.1016/S2213-8587(24)00372-3. Epub 2025 Feb 17.
11. Sav A, King MA, Whitty JA, et al. Burden of treatment for chronic illness: a concept analysis and review of the literature. Health Expect. 2015;18(3):312–324. doi:10.1111/hex.12046
12. Spencer-Bonilla G, Quiñones AR, Montori VM, International Minimally Disruptive Medicine Working Group. Assessing the Burden of Treatment. J Gen Intern Med. 2017;32(10):1141–1145. doi:10.1007/s11606-017-4117-8
13. Tran V-T, Montori VM, Eton DT, Baruch D, Falissard B, Ravaud P. Development and description of measurement properties of an instrument to assess treatment burden among patients with multiple chronic conditions. BMC Med. 2012;10(1):68. doi:10.1186/1741-7015-10-68
14. Vijan S, Hayward RA, Ronis DL, Hofer TP. Brief report: the burden of diabetes therapy: implications for the design of effective patient-centered treatment regimens. J Gen Intern Med. 2005;20(5):479–482. doi:10.1111/j.1525-1497.2005.0117.x
15. Boehmer KR, Gallacher KI, Lippiett KA, Mair FS, May CR, Montori VM. Minimally Disruptive Medicine: progress 10 Years Later. Mayo Clin Proc. 2022;97(2):210–220. doi:10.1016/j.mayocp.2021.09.003
16. Hong JS, Wasden C, Han DH. Introduction of digital therapeutics. Comput Methods Programs Biomed. 2021;209:106319. doi:10.1016/j.cmpb.2021.106319
17. Kerr D, Ahn D, Waki K, et al. Digital interventions for self-management of type 2 diabetes mellitus: systematic literature review and meta-analysis. J Med Internet Res. 2024;26:e55757. doi:10.2196/55757
18. Huang ES, Sinclair A, Conlin PR, et al. The Growing Role of Technology in the Care of Older Adults With Diabetes. Diabetes Care. 2023;46(8):1455–1463. doi:10.2337/dci23-0021
19. Hsia J, Guthrie NL, Lupinacci P, et al. Randomized, controlled trial of a digital behavioral therapeutic application to improve glycemic control in adults with type 2 diabetes. Diabetes Care. 2022;45(12):2976–2981. doi:10.2337/dc22-1099
20. Ramakrishnan P, Yan K, Balijepalli C, Druyts E. Changing face of healthcare: digital therapeutics in the management of diabetes. Curr Med Res Opin. 2021;37(12):2089–2091. doi:10.1080/03007995.2021.1976737
21. Xiao Y, Wang Z, Zhang L, et al. Effectiveness of digital diabetes management technology on blood glucose in patients with type 2 diabetes at home: systematic review and meta-analysis. J Med Internet Res. 2025;27:e66441. doi:10.2196/66441
22. Verma R, Bhardwaj S, Lathia T, et al. Personalized glycemic response led digital therapeutics program improves time in range in a period of 14 days. Int J Diabetes Dev Ctries. 2022;42(4):1–8. doi:10.1007/s13410-022-01111-1
23. Wiecek E, Torres-Robles A, Cutler RL, Benrimoj SI, Garcia-Cardenas V. Impact of a multicomponent digital therapeutic mobile app on medication adherence in patients with chronic conditions: retrospective analysis. J Med Internet Res. 2020;22(8):e17834. doi:10.2196/17834
24. Mair FS, Montori VM, May CR. Digital transformation could increase the burden of treatment on patients. BMJ. 2021;375:n2909. doi:10.1136/bmj.n2909
25. Lenfant T, Ravaud P, Montori VM, Berntsen GR, Tran V-T. Five principles for the development of minimally disruptive digital medicine. BMJ. 2023;383:p2960. doi:10.1136/bmj.p2960
26. Al Zahidy MA, Simha S, Branda M, et al. Digital Medicine Tools and the Work of Being a Patient: a Qualitative Investigation of Digital Treatment Burden in Patients With Diabetes. Mayo Clin Proc Digit Health. 2025;3(1):100180. doi:10.1016/j.mcpdig.2024.11.001. Epub 2024 Nov 16.
27. Tran VT, Harrington M, Montori VM, Barnes C, Wicks P, Ravaud P. Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform. BMC Med. 2014;12(1):109. doi:10.1186/1741-7015-12-109
28. Parks AC, Williams AL, Kackloudis GM, Stafford JL, Boucher EM, Honomichl RD. The effects of a digital well-being intervention on patients with chronic conditions: observational study. J Med Internet Res. 2020;22(1):e16211. doi:10.2196/16211
29. Mumtaz H, Riaz MH, Wajid H, et al. Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front Digit Health. 2023;5:1203945. doi:10.3389/fdgth.2023.1203945
30. Huang ES, O’Grady M, Basu A, et al. The cost-effectiveness of continuous glucose monitoring in type 1 diabetes. Diabetes Care. 2010;33(6):1269–1274. doi:10.2337/dc09-2042
31. Tahsin F, Bahr T, Shaw J, Shachak A, Gray CS. The relationship between treatment burden and the use of telehealth technologies among patients with chronic conditions: a scoping review. Health Pol Technol. 2024;13(2):100855. doi:10.1016/j.hlpt.2024.100855