JMIR Diabetes最新文献

{"title":"Toward Diabetes Device Development That Is Mindful to the Needs of Young People Living With Type 1 Diabetes: A Data- and Theory-Driven Qualitative Study.","authors":"Nicola Brew-Sam,&nbsp;Anne Parkinson,&nbsp;Madhur Chhabra,&nbsp;Adam Henschke,&nbsp;Ellen Brown,&nbsp;Lachlan Pedley,&nbsp;Elizabeth Pedley,&nbsp;Kristal Hannan,&nbsp;Karen Brown,&nbsp;Kristine Wright,&nbsp;Christine Phillips,&nbsp;Antonio Tricoli,&nbsp;Christopher J Nolan,&nbsp;Hanna Suominen,&nbsp;Jane Desborough","doi":"10.2196/43377","DOIUrl":"https://doi.org/10.2196/43377","url":null,"abstract":"<p><strong>Background: </strong>An important strategy to understand young people's needs regarding technologies for type 1 diabetes mellitus (T1DM) management is to examine their day-to-day experiences with these technologies.</p><p><strong>Objective: </strong>This study aimed to examine young people's and their caregivers' experiences with diabetes technologies in an exploratory way and relate the findings to the existing technology acceptance and technology design theories. On the basis of this procedure, we aimed to develop device characteristics that meet young people's needs.</p><p><strong>Methods: </strong>Overall, 16 in-person and web-based face-to-face interviews were conducted with 7 female and 9 male young people with T1DM (aged between 12 and 17 years) and their parents between December 2019 and July 2020. The participants were recruited through a pediatric diabetes clinic based at Canberra Hospital. Data-driven thematic analysis was performed before theory-driven analysis to incorporate empirical data results into the unified theory of acceptance and use of technology (UTAUT) and value-sensitive design (VSD). We used the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist for reporting our research procedure and findings. In this paper, we summarize the key device characteristics that meet young people's needs.</p><p><strong>Results: </strong>Summarized interview themes from the data-driven analysis included aspects of self-management, device use, technological characteristics, and feelings associated with device types. In the subsequent theory-driven analysis, the interview themes aligned with all UTAUT and VSD factors except for one (privacy). Privacy concerns or related aspects were not reported throughout the interviews, and none of the participants made any mention of data privacy. Discussions around ideal device characteristics focused on reliability, flexibility, and automated closed loop systems that enable young people with T1DM to lead an independent life and alleviate parental anxiety. However, in line with a previous systematic review by Brew-Sam et al, the analysis showed that reality deviated from these expectations, with inaccuracy problems reported in continuous glucose monitoring devices and technical failures occurring in both continuous glucose monitoring devices and insulin pumps.</p><p><strong>Conclusions: </strong>Our research highlights the benefits of the transdisciplinary use of exploratory and theory-informed methods for designing improved technologies. Technologies for diabetes self-management require continual advancement to meet the needs and expectations of young people with T1DM and their caregivers. The UTAUT and VSD approaches were found useful as a combined foundation for structuring the findings of our study.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"8 ","pages":"e43377"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9320035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile Health Apps for the Control and Self-management of Type 2 Diabetes Mellitus: Qualitative Study on Users' Acceptability and Acceptance.","authors":"Marloes Bults,&nbsp;Catharina Margaretha van Leersum,&nbsp;Theodorus Johannes Josef Olthuis,&nbsp;Robin Enya Marije Bekhuis,&nbsp;Marjolein Elisabeth Maria den Ouden","doi":"10.2196/41076","DOIUrl":"https://doi.org/10.2196/41076","url":null,"abstract":"<p><strong>Background: </strong>Mobile health apps are promising tools to help patients with type 2 diabetes mellitus (T2DM) improve their health status and thereby achieve diabetes control and self-management. Although there is a wide array of mobile health apps for T2DM available at present, apps are not yet integrated into routine diabetes care. Acceptability and acceptance among patients with T2DM is a major challenge and prerequisite for the successful implementation of apps in diabetes care.</p><p><strong>Objective: </strong>This study provides an in-depth understanding of the perceptions of patients with T2DM before use (acceptability) and after use (acceptance) regarding 4 different mobile health apps for diabetes control and self-management.</p><p><strong>Methods: </strong>A descriptive qualitative research design was used in this study. Participants could choose 1 of the 4 selected apps for diabetes control and self-management (ie, Clear.bio in combination with FreeStyle Libre, mySugr, MiGuide, and Selfcare). The selection was based on a systematic analysis of the criteria for (functional) requirements regarding monitoring, data collection, provision of information, coaching, privacy, and security. To explore acceptability, 25 semistructured in-depth interviews were conducted with patients with T2DM before use. This was followed by 4 focus groups to discuss the acceptance after use. The study had a citizen science approach, that is, patients with T2DM collaborated with researchers as coresearchers. All coresearchers actively participated in the preparation of the study, data collection, and data analysis. Data were collected between April and September 2021. Thematic analysis was conducted using a deductive approach using AtlasTi9.</p><p><strong>Results: </strong>In total, 25 coresearchers with T2DM participated in this study. Of them, 12 coresearchers tested Clear, 5 MiGuide, 4 mySugr, and 4 Selfcare. All coresearchers participated in semistructured interviews, and 18 of them attended focus groups. Personal health was the main driver of app use. Most coresearchers were convinced that a healthy lifestyle would improve blood glucose levels. Although most coresearchers did not expect that they need to put much effort into using the apps, the additional effort to familiarize themselves with the app use was experienced as quite high. None of the coresearchers had a health care professional who provided suggestions on using the apps. Reimbursement from insurance companies and the acceptance of apps for diabetes control and self-management by the health care system were mentioned as important facilitating conditions.</p><p><strong>Conclusions: </strong>The research showed that mobile health apps provide support for diabetes control and self-management in patients with T2DM. Integrating app use in care as usual and guidelines for health care professionals are recommended. Future research is needed on how to increase the implementation of mobil","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"8 ","pages":"e41076"},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10772486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives on Promoting Physical Activity Using eHealth in Primary Care by Health Care Professionals and Individuals With Prediabetes and Type 2 Diabetes: Qualitative Study.","authors":"Yohannes Woldamanuel,&nbsp;Jenny Rossen,&nbsp;Susanne Andermo,&nbsp;Patrik Bergman,&nbsp;Linda Åberg,&nbsp;Maria Hagströmer,&nbsp;Unn-Britt Johansson","doi":"10.2196/39474","DOIUrl":"https://doi.org/10.2196/39474","url":null,"abstract":"<p><strong>Background: </strong>The trend of an exponential increase in prediabetes and type 2 diabetes (T2D) is projected to continue rising worldwide. Physical activity could help prevent T2D and the progression and complications of the disease. Therefore, we need to create opportunities for individuals to acquire the necessary knowledge and skills to self-manage their chronic condition through physical activity. eHealth is a potential resource that could facilitate self-management and thus improve population health. However, there is limited research on users' perception of eHealth in promoting physical activity in primary care settings.</p><p><strong>Objective: </strong>This study aims to explore the perspectives of health care professionals and individuals with prediabetes and T2D on eHealth to promote physical activity in primary care.</p><p><strong>Methods: </strong>A qualitative approach was applied using focus group discussions among individuals with prediabetes or T2D (14 participants in four groups) and health care professionals (10 participants in two groups). The discussions were audio-recorded and transcribed verbatim. Qualitative content analysis was used inductively to code the data.</p><p><strong>Results: </strong>Three main categories emerged: utility, adoption process, and accountability. The utility of eHealth was described as a motivational, entertaining, and stimulating tool. Registration of daily medical measurements and lifestyle parameters in a cohesive digital platform was recognized as a potential resource for strengthening self-management skills. The adoption process includes eHealth to increase the accessibility of care and personalize the support of physical activity. However, participants stated that digital technology might only suit some and could increase health care providers' administrative burden. Accountability refers to the knowledge and skills to optimize eHealth and ensure data integrity and security.</p><p><strong>Conclusions: </strong>People with prediabetes and T2D and health care professionals positively viewed an integration of eHealth technology in primary care to promote physical activity. A cohesive platform using personal metrics, goal-setting, and social support to promote physical activity was suggested. This study identified eHealth illiteracy, inequality, privacy, confidentiality, and an increased workload on health care professionals as factors of concern when integrating eHealth into primary care. Continuous development of eHealth competence was reported as necessary to optimize the implementation of eHealth technology in primary care.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"8 ","pages":"e39474"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10776846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycemic Outcomes and Feature Set Engagement Among Real-Time Continuous Glucose Monitoring Users With Type 1 or Non-Insulin-Treated Type 2 Diabetes: Retrospective Analysis of Real-World Data.","authors":"Robert Dowd,&nbsp;Lauren H Jepson,&nbsp;Courtney R Green,&nbsp;Gregory J Norman,&nbsp;Roy Thomas,&nbsp;Keri Leone","doi":"10.2196/43991","DOIUrl":"https://doi.org/10.2196/43991","url":null,"abstract":"<p><strong>Background: </strong>The benefits of real-time continuous glucose monitoring (RT-CGM) are well established for patients with type 1 diabetes (T1D) and patients with insulin-treated type 2 diabetes (T2D). However, the usage and effectiveness of RT-CGM in the context of non-insulin-treated T2D has not been well studied.</p><p><strong>Objective: </strong>We aimed to assess glycemic metrics and rates of RT-CGM feature utilization in users with T1D and non-insulin-treated T2D.</p><p><strong>Methods: </strong>We retrospectively analyzed data from 33,685 US-based users of an RT-CGM system (Dexcom G6; Dexcom, Inc) who self-identified as having either T1D (n=26,706) or T2D and not using insulin (n=6979). Data included glucose concentrations, alarm settings, feature usage, and event logs.</p><p><strong>Results: </strong>The T1D cohort had lower proportions of glucose values in the 70 mg/dl to 180 mg/dl range than the T2D cohort (52.1% vs 70.8%, respectively), with more values indicating hypoglycemia or hyperglycemia and higher glycemic variability. Discretionary alarms were enabled by a large majority in both cohorts. The data sharing feature was used by 38.7% (10,327/26,706) of those with T1D and 10.4% (727/6979) of those with T2D, and the mean number of followers was higher in the T1D cohort. Large proportions of patients with T1D or T2D enabled and customized their glucose alerts. Retrospective analysis features were used by the majority in both cohorts (T1D: 15,783/26,706, 59.1%; T2D: 3751/6979, 53.8%).</p><p><strong>Conclusions: </strong>Similar to patients with T1D, patients with non-insulin-treated T2D used RT-CGM system features, suggesting beneficial, routine engagement with data by patients and others involved in their care. Motivated patients with diabetes could benefit from RT-CGM coverage.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"8 ","pages":"e43991"},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10767851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Social Support in a Diabetes Online Community: Mixed Methods Content Analysis.","authors":"Cidila Da Moura Semedo,&nbsp;Peter A Bath,&nbsp;Ziqi Zhang","doi":"10.2196/41320","DOIUrl":"https://doi.org/10.2196/41320","url":null,"abstract":"<p><strong>Background: </strong>Patients with diabetes may experience different needs according to their diabetes stage. These needs may be met via online health communities in which individuals seek health-related information and exchange different types of social support. Understanding the social support categories that may be more important for different diabetes stages may help diabetes online communities (DOCs) provide more tailored support to web-based users.</p><p><strong>Objective: </strong>This study aimed to explore and quantify the categorical patterns of social support observed in a DOC, taking into consideration users' different diabetes stages, including prediabetes, type 2 diabetes (T2D), T2D with insulin treatment, and T2D remission.</p><p><strong>Methods: </strong>Data were collected from one of the largest DOCs in Europe: Diabetes.co.uk. Drawing on a mixed methods content analysis, a qualitative content analysis was conducted to explore what social support categories could be identified in users' posts. A total of 1841 posts were coded by 5 human annotators according to a modified version of the Social Support Behavior Code, including 7 different social support categories: achievement, congratulations, network support, seeking emotional support, seeking informational support, providing emotional support, and providing informational support. Subsequently, quantitative content analysis was conducted using chi-square post hoc analysis to compare the most prominent social support categories across different stages of diabetes.</p><p><strong>Results: </strong>Seeking informational support (605/1841, 32.86%) and providing informational support (597/1841, 32.42%) were the most frequent categories exchanged among users. The overall distribution of social support categories was significantly different across the diabetes stages (χ²₁₈=287.2; P<.001). Users with prediabetes sought more informational support than those in other stages (P<.001), whereas there were no significant differences in categories posted by users with T2D (P>.001). Users with T2D under insulin treatment provided more informational and emotional support (P<.001), and users with T2D in remission exchanged more achievement (P<.001) and network support (P<.001) than those in other stages.</p><p><strong>Conclusions: </strong>This is the first study to highlight what, how, and when different types of social support may be beneficial at different stages of diabetes. Multiple stakeholders may benefit from these findings that may provide novel insights into how these categories can be strategically used and leveraged to support diabetes management.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"8 ","pages":"e41320"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10751477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog): Observational Study Using Ecological Momentary Assessment of Cognition.","authors":"Luciana Mascarenhas Fonseca, Roger W Strong, Shifali Singh, Jane D Bulger, Michael Cleveland, Elizabeth Grinspoon, Kamille Janess, Lanee Jung, Kellee Miller, Eliza Passell, Kerry Ressler, Martin John Sliwinski, Alandra Verdejo, Ruth S Weinstock, Laura Germine, Naomi S Chaytor","doi":"10.2196/39750","DOIUrl":"10.2196/39750","url":null,"abstract":"<p><strong>Background: </strong>Individuals with type 1 diabetes represent a population with important vulnerabilities to dynamic physiological, behavioral, and psychological interactions, as well as cognitive processes. Ecological momentary assessment (EMA), a methodological approach used to study intraindividual variation over time, has only recently been used to deliver cognitive assessments in daily life, and many methodological questions remain. The Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) study uses EMA to deliver cognitive and self-report measures while simultaneously collecting passive interstitial glucose in adults with type 1 diabetes.</p><p><strong>Objective: </strong>We aimed to report the results of an EMA optimization pilot and how these data were used to refine the study design of the GluCog study. An optimization pilot was designed to determine whether low-frequency EMA (3 EMAs per day) over more days or high-frequency EMA (6 EMAs per day) for fewer days would result in a better EMA completion rate and capture more hypoglycemia episodes. The secondary aim was to reduce the number of cognitive EMA tasks from 6 to 3.</p><p><strong>Methods: </strong>Baseline cognitive tasks and psychological questionnaires were completed by all the participants (N=20), followed by EMA delivery of brief cognitive and self-report measures for 15 days while wearing a blinded continuous glucose monitor. These data were coded for the presence of hypoglycemia (<70 mg/dL) within 60 minutes of each EMA. The participants were randomized into group A (n=10 for group A and B; starting with 3 EMAs per day for 10 days and then switching to 6 EMAs per day for an additional 5 days) or group B (N=10; starting with 6 EMAs per day for 5 days and then switching to 3 EMAs per day for an additional 10 days).</p><p><strong>Results: </strong>A paired samples 2-tailed t test found no significant difference in the completion rate between the 2 schedules (t₁₇=1.16; P=.26; Cohen d_z=0.27), with both schedules producing >80% EMA completion. However, more hypoglycemia episodes were captured during the schedule with the 3 EMAs per day than during the schedule with 6 EMAs per day.</p><p><strong>Conclusions: </strong>The results from this EMA optimization pilot guided key design decisions regarding the EMA frequency and study duration for the main GluCog study. The present report responds to the urgent need for systematic and detailed information on EMA study designs, particularly those using cognitive assessments coupled with physiological measures. Given the complexity of EMA studies, choosing the right instruments and assessment schedules is an important aspect of study design and subsequent data interpretation.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"8 ","pages":"e39750"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9502390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation Plan for a High-Frequency, Low-Touch Care Model at Specialized Type 1 Diabetes Clinics: Model Development.","authors":"Stephanie de Sequeira,&nbsp;Justin Presseau,&nbsp;Gillian L Booth,&nbsp;Lorraine L Lipscombe,&nbsp;Isabelle Perkins,&nbsp;Bruce A Perkins,&nbsp;Rayzel Shulman,&nbsp;Gurpreet Lakhanpal,&nbsp;Noah Ivers","doi":"10.2196/37715","DOIUrl":"https://doi.org/10.2196/37715","url":null,"abstract":"<p><strong>Background: </strong>Individuals with type 1 diabetes (T1D) are more likely to achieve optimal glycemic management when they have frequent visits with their health care team. There is a potential benefit of frequent, telemedicine interventions as an effective strategy to lower hemoglobin A1c (HbA1c).</p><p><strong>Objective: </strong>The objective is this study was to understand the provider- and system-level factors affecting the successful implementation of a virtual care intervention in type 1 diabetes (T1D) clinics.</p><p><strong>Methods: </strong>Semistructured interviews were conducted with managers and certified diabetes educators (CDEs) at diabetes clinics across Southern Ontario before the COVID-19 pandemic. Deductive analysis was carried out using the Theoretical Domains Framework, followed by mapping to behavior change techniques to inform potential implementation strategies for high-frequency virtual care for T1D.</p><p><strong>Results: </strong>There was considerable intention to deliver high-frequency virtual care to patients with T1D. Participants believed that this model of care could lead to improved patient outcomes and engagement but would likely increase the workload of CDEs. Some felt there were insufficient resources at their site to enable them to participate in the program. Member checking conducted during the pandemic revealed that clinics and staff had already developed strategies to overcome resource barriers to the adoption of virtual care during the pandemic.</p><p><strong>Conclusions: </strong>Existing enablers for high-frequency virtual care for T1D can be leveraged, and barriers can be overcome with targeted clinical incentives and support.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"7 4","pages":"e37715"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9782362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10424619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified e-Delphi Process for the Selection of Patient-Reported Outcome Measures for Children and Families With Type 1 Diabetes Using Continuous Glucose Monitors: Delphi Study.","authors":"Payal Shah,&nbsp;Jennifer K Raymond,&nbsp;Juan Espinoza","doi":"10.2196/38660","DOIUrl":"https://doi.org/10.2196/38660","url":null,"abstract":"<p><strong>Background: </strong>Type 1 diabetes (T1D) management is complex and associated with significant psychosocial burden. Continuous glucose monitors (CGM) can improve disease management and outcomes and introduce new or exacerbate existing psychosocial concerns. Patient-reported outcome measures (PROMs) can be used to capture this information, but there is no consensus on which PROMs should be used in pediatric CGM research.</p><p><strong>Objective: </strong>Here we describe the process to (1) identify PROMs that could be used to assess the impact of CGMs on pediatric patients with T1D, (2) implement a modified electronic Delphi (e-Delphi) methodology to arrive at an expert consensus on which PROMs are most suitable for clinical and research applications, and (3) establish a periodicity table for the administration of PROMs over time in a real-world evidence study.</p><p><strong>Methods: </strong>To identify appropriate PROMs for pediatric patients and families with T1D and CGMs, we conducted an asynchronous, e-Delphi process with a multidisciplinary group of experts from around the country. We identified candidate instruments through a literature review. The 3-round e-Delphi process was conducted via a study website, email, and web-based forms. Participants provided opinions on the usefulness of instruments, age validation, feasibility, time, and frequency of administration.</p><p><strong>Results: </strong>In total, 16 experts participated in the e-Delphi process; 4 of whom consistently participated in all 3 rounds. We identified 62 candidate instruments, which were narrowed down to 12 final PROMs across 5 domains: diabetes distress and burden (n=4), autonomy (n=2), quality of life (n=1), psychosocial (n=3), and technology acceptance (n=2). A quarterly administration schedule was developed to reduce burden on participants.</p><p><strong>Conclusions: </strong>PROMs can provide critical insights into the psychosocial well-being of patients. The specific measures identified in the paper are particularly well suited for pediatric patients with T1D using CGMs. Clinical implementation could help health care providers, patients, and families to engage in more comprehensive disease management.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"7 4","pages":"e38660"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10569715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Information and Communication Technology-Based Self-management System DialBeticsLite in Treating Abdominal Obesity in Japanese Office Workers: Prospective Single-Arm Pilot Intervention Study.","authors":"Yuki Kawai,&nbsp;Kayo Waki,&nbsp;Satoko Yamaguchi,&nbsp;Tomomi Shibuta,&nbsp;Kana Miyake,&nbsp;Shigeko Kimura,&nbsp;Tsuguyoshi Toyooka,&nbsp;Ryo Nakajima,&nbsp;Kazushi Uneda,&nbsp;Hiromichi Wakui,&nbsp;Kouichi Tamura,&nbsp;Masaomi Nangaku,&nbsp;Kazuhiko Ohe","doi":"10.2196/40366","DOIUrl":"https://doi.org/10.2196/40366","url":null,"abstract":"<p><strong>Background: </strong>Making lifestyle changes is an essential element of abdominal obesity (AO) reduction. To support lifestyle modification and self-management, we developed an information and communication technology-based self-management system-DialBeticsLite-with a fully automated dietary evaluation function for the treatment of AO.</p><p><strong>Objective: </strong>The objective of this study was to evaluate the preliminary efficacy and feasibility of DialBeticsLite among Japanese office workers with AO.</p><p><strong>Methods: </strong>A 2- to 3-month prospective single-arm pilot intervention study was designed to assess the effects of the intervention using DialBeticsLite. The information and communication technology system was composed of 4 modules: data transmission (body weight, blood pressure, blood glucose, and pedometer count); data evaluation; exercise input; and food recording and dietary evaluation. Eligible participants were workers who were aged ≥20 years and with AO (waist circumference ≥85 cm for men and ≥90 cm for women). Physical parameters, blood tests, nutritional intake, and self-care behavior were compared at baseline and after the intervention.</p><p><strong>Results: </strong>A total of 48 participants provided completed data for analysis, which yielded a study retention rate of 100%. The average age was 46.8 (SD 6.8) years, and 92% (44/48) of participants were male. The overall average measurement rate of DialBeticsLite, calculated by dividing the number of days with at least one measurement by the number of days of the intervention, was 98.6% (SD 3.4%). In total, 85% (41/48) of the participants reported that their participation in the study helped them to improve their lifestyle. BMI, waist circumference, and visceral fat area decreased significantly after the intervention (P<.001). In addition, the daily calorie intake reduced significantly (P=.02). There was a significant improvement in self-care behavior in terms of exercise and diet (P=.001).</p><p><strong>Conclusions: </strong>Using DialBeticsLite was shown to be a feasible and potentially effective method for reducing AO by providing users with a motivational framework to evaluate their lifestyle behaviors.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":"7 4","pages":"e40366"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10336156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone Apps for Surveillance of Gestational Diabetes: Scoping Review.","authors":"Suzanne Smyth, Eimear Curtin, Elizabeth Tully, Zara Molphy, Fionnuala Breathnach","doi":"10.2196/38910","DOIUrl":"10.2196/38910","url":null,"abstract":"<p><strong>Background: </strong>Developments and evolutions in the information and communication technology sector have provided a solid foundation for the emergence of mobile health (mHealth) in recent years. The cornerstone to management of gestational diabetes mellitus (GDM) is the self-management of glycemic indices, dietary intake, and lifestyle adaptations. Given this, it is readily adaptable to incorporation of remote monitoring strategies involving mHealth solutions.</p><p><strong>Objective: </strong>We sought to examine and assess the available smartphone apps which enable self-monitoring and remote surveillance of GDM with a particular emphasis on the generation of individualized patient feedback.</p><p><strong>Methods: </strong>Five databases were searched systematically for any studies evaluating mHealth-supported smartphone solutions for GDM management from study inception until January 2022. The studies were screened and assessed for eligibility of inclusion by 2 independent reviewers. Ultimately, 17 studies were included involving 1871 patients across 11 different countries. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) conceptual framework was adhered to for data extraction and categorization purposes.</p><p><strong>Results: </strong>All studies analyzed as part of this review facilitated direct uploading of data from the handheld glucometer to the downloaded patient-facing smartphone app. Glycemic data were captured by all studies and were reassuringly found to be either improved or noninferior to extant models of hospital-based care. Feedback was delivered in either an automated fashion through in-app communication from the health care team or facilitated through bidirectional communication with the app and hospital portal. Although resource utilization and cost-effective analyses were reported in some studies, the results were disparate and require more robust analysis. Where patient and staff satisfaction levels were evaluated, the response was overwhelmingly positive for mHealth smartphone-delivered care strategies. Emergency cesarean section rates were reduced; however, elective cesarean sections were comparatively increased among studies where the mode of delivery was assessed. Most reviewed studies did not identify any differences in maternal, perinatal, or neonatal health when app-based care was compared with usual in-person review.</p><p><strong>Conclusions: </strong>This comprehensive scoping review highlights the feasibility, reliability, and acceptability of app-assisted health care for the management of GDM. Although further exploration of the economic benefit is required prior to implementation in a real-world clinical setting, the prospect of smartphone-assisted health care for GDM is hugely promising.</p>","PeriodicalId":52371,"journal":{"name":"JMIR Diabetes","volume":" ","pages":"e38910"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9723973/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40505206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}