Development of the TBQ+D: A Novel Patient-Reported Measure of The Burden of Digital Care

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Branda , View ORCID Profile Viet-Thi Tran , View ORCID Profile Jennifer L. Ridgeway , View ORCID Profile Victor M. Montori doi: https://doi.org/10.1101/2025.05.12.25327446 Misk Al Zahidy 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Misk Al Zahidy Kerly Guevara 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kerly Guevara Sue Simha 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 BS Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mariana Borras-Osorio 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mariana Borras-Osorio Megan E. Branda 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 4 Department of Quantitative Health Sciences, Mayo Clinic , Rochester, Minnesota, USA MS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Megan E. Branda Viet-Thi Tran 2 Université Paris Cité , CRESS, INSERM, INRAE, F-75004 Paris, France MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Viet-Thi Tran Jennifer L. Ridgeway 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 3 Robert D. and Patricia E. Kern Center for the Science of Healthcare Delivery, Mayo Clinic , Minnesota, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jennifer L. Ridgeway Victor M. Montori 1 Knowledge and Evaluation Research Unit, Division of Endocrinology and Diabetes, Mayo Clinic , Rochester, MN 55905 MD, MSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Victor M. Montori For correspondence: Montori.victor{at}mayo.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Patients with diabetes often manage complex treatment regimens that increasingly include the use of digital medicine tools. While several instruments measure treatment burden, none capture the specific burden introduced by digital medicine tools used in self-care. Development of patient reported measures to capture digital treatment burden are needed to assess the lived experiences and challenges patients face when using digital medicine tools. Objective To engage patients and clinical experts in adapting an existing measure of treatment burden—the Treatment Burden Questionnaire (TBQ). The adapted instrument underwent cognitive testing and refinements to ensure it could capture the burden of using digital medicine tools in the self-management and care of diabetes. Methods This two-phase study was conducted with adults with diabetes and other chronic conditions at the Division of Endocrinology at Mayo Clinic in Rochester, Minnesota. First, we mapped themes from prior concept elicitation interviews to existing TBQ items to identify content gaps related to digital burden. Based on these gaps, the study team and expert panel followed an item development guide to generate new items and adapt existing ones to better reflect the workload and burdens imposed by digital medicine tools. The resulting adapted instrument underwent three rounds of cognitive testing with adult patients living with diabetes, using a think-aloud protocol to assess clarity, relevance, and comprehensiveness. Results of cognitive testing informed iterative refinements across three rounds of interviews, leading to improved clarity, reduced redundancy, and improved relevance of items. Results The final TBQ+D retained the structure of the original 15-item TBQ and included 8 new items and modified 8 extant ones to capture burden of digital care (e.g., syncing issues, discomfort from sensors, and device malfunctions). Cognitive testing demonstrated strong content relevance and patient comprehension. Conclusion The TBQ+D appears able to measure digital treatment burden in patients with diabetes. Planned next steps include field testing the instrument to test validity hypotheses, and if successful, extending this evaluation to diverse populations and clinical and research settings. 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 – 29 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: (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. 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 (e.g. glucometer, insulin smartpens, pumps, etc.), were proficient in English, and were able to complete informed consent (i.e., 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 Download figure Open in new tab Figure 1: The development process Download figure Open in new tab Figure 2: Medicine Tools Intensity Scale Item Generation To guide item generation, we conducted a structured mapping exercise linking qualitative data from the concept elicitation study 26 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. View this table: View inline View popup 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 were 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 The Mayo Clinic Institutional Review Board (IRB Number 23-007631) approved all procedures. Informed consent was obtained from each participant. Written consent was captured prior to interviews. 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 (e.g., 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. View this table: View inline View popup Download powerpoint Table 2: Patient characteristics Participants represented a range of digital medicine tool use intensities, with 8 (40%) categorized as maximal-intensity users. 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 Table 3 . View this table: View inline View popup Download powerpoint Table 3: Summary of item revisions across cognitive testing rounds Refinements focused on improving clarity, resolving redundancy, and enhancing item relevance. Revisions were completed when no additional changes were suggested, indicating saturation. Table 4 presents the final version of the TBQ+D. View this table: View inline View popup Table 4: Finalized items of the TBQ+D 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 , 34 – 37 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, doesn’t 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. 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, i.e., 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 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. Data Availability All data produced in the present work are contained in the manuscript and any additional data are available upon reasonable request to the authors. CONFLICT OF INTEREST The authors declare that they have no conflicts of interest related to this study. MEANING OF ACRYONMOMS CGM: Continuous Glucose Monitoring EHR: Electronic Health Record IQR: Interquartile Range REDCap: Research Electronic Data Capture TBQ: Treatment Burden Questionnaire ACKNOWLEDGEMENTS 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. The TBQ+D© is protected by international copyright, with all rights reserved to VMM and V-TT. For information on, or permission to use, the TBQ+D, please contact the authors. REFERENCES 1. ↵ Benavidez GA , Zahnd WE , Hung P , Eberth JM . Peer Reviewed: Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area . 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OpenUrl Abstract / FREE Full Text 37. ↵ 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 Policy and Technology . 2024 : 100855 . 38. Endalamaw A , Zewdie A , Wolka E , Assefa Y . A scoping review of digital health technologies in multimorbidity management: mechanisms, outcomes, challenges, and strategies . BMC Health Services Research . 2025 ; 25 ( 1 ): 382 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted May 13, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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