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Abstract Background Most studies do not produce their intended outcomes on time or within budget. However, it is challenging to identify the facilitators and barriers to successful study management when the “behind the scenes action” of especially digitally enabled health research studies are akin to a black box. Therefore, it is necessary to explore first-hand experience of the facilitators and barriers to managing digitally enabled health studies. The goal of such studies is to produce new knowledge and/or develop tools that can be translated to real-world benefits for the health and care sector, individuals, and other stakeholders. These studies now exist in a time that encourages collaborative research activities with interdisciplinary research partnerships, industry collaboration, end-user involvement and insights for policy. These expectations require teams with different work cultures, methodologies, technologies, and approaches to work together, resulting in significant benefits but also challenges. Objectives To explore the relationship between the dynamics and needs of research teams and the technology used to manage digitally enabled studies through the experience of those who worked on such studies. Methods We used an interpretive phenomenological approach to explore research team members’ experiences and perceptions of study management in the field of digitally enabled health research. We interviewed 15 research team members from eight studies. A semi-structured interview guide was used to explore concepts related to study activity management, team dynamics, resources and technologies used to manage research activities, and reflections of personal experiences. An adductive thematic analysis was performed on the transcripts. Results Five main themes were identified: 1) Project Team, 2) Study management, which included management technologies, 3) Study plan, 4) Intervention, 5) Participants. This paper focuses on the first two main themes. Sub-themes included: Roles and responsibilities, Methods, Changes, Challenges and solutions and Expectations vs. reality. Sub-themes were applicable to all main themes. Therefore, results were presented as knowledge gained from the interaction between sub-themes within each theme, i.e. referred to as “comprehensive insights” in the results section of this paper. Conclusion This interview study provides new knowledge about the realities of working in collaborative, digitally enabled health research studies and demonstrates several opportunities for improved understanding of study management. More realistic and thorough understanding of the complex system in which digitally enabled health research exists can be applied to better prepare experienced researchers and newly graduated students entering the field, as well as improve existing strategies for management.

Abstract Background Regular physical activity helps to reduce weight and improve the general well-being of individuals living with obesity. Chatbots have shown the potential to increase physical activity among their users. We aimed to explore the preferences of individuals living with obesity for the features and functionalities of a modern chatbot based on social media, Artificial intelligence (AI) and other recent and relevant technologies. Methods In this study, we used qualitative methods. Focusing on individuals’ preferences for a chatbot to increase physical activity, we conducted both individual interviews and focus groups with nine adult patients staying at Evjeklinikken, a Norwegian rehabilitation clinic for individuals living with morbid obesity. The interviews were fully transcribed and then analysed inductively using thematic analysis. Results Participants preferred motivational features such as social support, goal setting, physical activity illustrations, monitoring of physical activity behaviour and outcomes, and feedback, prompts and reminders. They also preferred features for connecting and synchronising with smartwatches and training device apps. Participants wanted a chatbot that is easy to use and allows for human assistance when needed. Regarding personalising the chatbot, the participants wanted to choose the language, number of messages, and turn functionalities on and off. Conclusions Co-designing chatbots with potential users is essential to understand their specific needs and preferences. We gained valuable insight into a diverse set of features and functionalities relevant to designing physical activity chatbots for individuals living with obesity. Behaviour change techniques are equally important as personalisation features and the option for synchronising with third-party devices. In future work, we will consider the collected needs in the development of a physical activity chatbot to ensure acceptance and adherence to the digital health intervention.

Continuous glucose monitoring (CGM) represents a significant advancement in diabetes management, playing an important role in glycemic control for patients with type 1 diabetes (T1D). Despite their benefits, their performance is affected by numerous factors such as the carbohydrate intake, alcohol consumption, and physical activity (PA). Among these, PA could cause hypoglycemic episodes, which might happen after exercising. In this work, two main contributions are presented. First, we extend the performance evaluation of two glucose monitoring devices, Eversense and Free Style Libre (FSL), for measuring glucose concentrations during high-intensity PA and normal daily activity (NDA). The impact of PA is investigated considering (1) different glucose ranges (hypoglycemia, euglycemia, and hyperglycemia); and (2) four time periods throughout the day (morning, afternoon, evening, and night). Second, we evaluate the effectiveness of machine learning (ML) models, including logistic regression, K-nearest neighbors, and support vector machine, to automatically detect PA in T1D individuals using glucose measurements. The performance analysis showed significant differences between glucose levels obtained in the PA and NDA period for Eversense and FSL devices, specially in the hyperglycemic range and two time intervals (morning and afternoon). Both Eversense and FSL devices present measurements with large variability during strenuous PA, indicating that their users should be cautious. However, glucose recordings provided by monitoring devices are accurate for NDA, reaching similar values to capillary glucose device. Lastly, ML-based models yielded promising results to determine when an individual has performed PA, reaching an accuracy value of 0.93. The results can be used to develop an individualized data-driven classifier for each patient that categorizes glucose profiles based on the time interval during the day and according to if a patient performs PA. Our work contributes to the analysis of PA on the performance of CGM devices.

We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications and wearables to share their collected health related data. The questionnaire was distributed online in English, French, and Norwegian. In addition, a flyer with information of where to locate the online questionnaire was distributed during a Swiss health conference. We used snowball sampling and encouraged participants to forward the questionnaires to friends, family, and others. Data were collected between October 2018 and March 2020. 58.1 % (n = 473) responded to the English survey, 34.3 % (n = 279) responded to the French survey, and 7.6 % (n = 62) responded to the Norwegian survey.The questionnaire contained 38 questions divided into seven themes: Background and health goals, Wearables and sensors, Mobile applications, Logging of health data, Data sharing- and integration, Social media and entertainment, and Demographics (age, gender, country of origin, chronic disease status, and chronic disease caretaker status). Answer options were single answer, multiple-choice, open-ended, or on a 4-point Likert scale.Questions were defined based on 16 in-person interviews with people without any chronic disorder, people with diabetes, and people with sickle cell disease. All questions were optional. Data were collected from 814 participants. All answers to the open-ended questions have been translated into English.This dataset is especially interesting for researchers interesting in what motivates people with and without chronic disease across countries to use mHealth tools and share their collected health data. Only a subset of variables has been analyzed so far and new research questions on motivation can potentially be answered using this dataset.

BackgroundToday’s diabetes-oriented telemedicine systems can gather and analyze many parameters like blood glucose levels, carbohydrate intake, insulin doses, and physical activity levels (steps). Information collected can be presented to patients in a variety of graphical outputs. Despite the availability of several technical means, a large percentage of patients do not reach the goals established in their diabetes treatment. ObjectiveThe objective of the study was to evaluate the benefits of the Diani telemedicine system for the treatment of patients with type 1 diabetes mellitus. MethodsData were collected during a 24-week feasibility study. Patients responded to the World Health Organization Quality of Life – BREF (WHOQOL-BREF) questionnaire and a system evaluation questionnaire. The level of glycated hemoglobin (HbA1c) and the patient’s body weight were measured, and the patient’s use of the telemedicine system and their daily physical activity level were monitored. All data were sent from the patient’s device to the Diani server using a real-time diabetes diary app. Wilcoxon and Friedman tests and the linear mixed effects method were used for data analysis. ResultsThis study involved 10 patients (men: n=5; women: n=5), with a mean age of 47.7 (SD 19.3) years, a mean duration of diabetes of 10.5 (SD 8.6) years, and a mean HbA1c value of 59.5 (SD 6.7) mmol/mol. The median number of days the patients used the system was 84. After the intervention, the mean HbA1c decreased by 4.35 mmol/mol (P=.01). The patients spent 18.6 (SD 6.8) minutes on average using the app daily. After the intervention, the number of patients who measured their blood glucose level at least 3 times a day increased by 30%. The graphical visualization of the monitored parameters, automatic transmission of measured data from the glucometer, compatibility, and interconnection of individual devices when entering data were positively evaluated by patients. ConclusionsThe Diani system was found to be beneficial for patients with type 1 diabetes mellitus in terms of managing their disease. Patients perceived it positively; it strengthened their knowledge of diabetes and their understanding of the influences of the measured values on the management of their disease. Its use had a positive effect on the HbA1c level.