mHealth: opportunities and challenges for diabetes intervention research

Background: Traditionally, health intervention evaluations provide long-term evidence of efficacy and safety via validated protocols, following a positivist paradigm, or approach, to research. However, modern mobile health (mHealth) technologies develop too quickly and outside of medical regulation, making it challenging for health research to keep pace. Objective: This thesis explored and tested how research can incorporate mHealth approaches and resources to evaluate mHealth interventions comprehensively, which follows the pragmatism paradigm. The works described herein were part of a larger project that designed, developed, and tested a data-sharing system between patients and their healthcare providers (HCPs) during diabetes consultations. Methods: The pragmatism paradigm underpins the mixed-methods, multi-phase design approach to exploring this overall objective. The following methods were performed using a sequential exploratory strategy. First, co-design workshops invited individuals with diabetes and HCPs to design an mHealth data-sharing system. Next, a scoping literature review identified research practices for evaluating mHealth interventions to-date. Then, app usage-logs, collected from a previous longitudinal study, were analyzed to explore how much additional information they could provide about patients’ self-management. Finally, a mixed-method study was designed to test the feasibility of combining both traditional and mHealth approaches and resources to evaluate an intervention. Results: Using the pragmatist paradigm as a scaffolding, these works provide evidence of how research can provide more comprehensive knowledge about mHealth interventions for diabetes care and self-management. Nine individuals with diabetes and six HCPs participated in the co-design workshops. Feedback included how a data-sharing system should work between patients and providers. The literature review identified how both traditional and mHealth-based approaches (n=15 methods, n=21 measures) were used together to evaluate mHealth interventions. Usage-log analysis revealed that changes in Glycosylated haemoglobin (HbA1c) differed between groups organized by usage patterns and duration of use of mHealth. The mixed-method study demonstrated how to collect comprehensive and complementary information when combining traditional and mHealth-centered approaches and resources. Conclusion: Traditional positivist approaches and resources are not adequate, on their own, to comprehensively understand the impact of mHealth interventions. The presented studies demonstrate that it is both feasible and prudent to combine traditional research with mHealth approaches, such as analyzing usage-logs, arranging co-design workshops, and other patient-centered methods in a pragmatist approach to produce comprehensive evidence of mHealth’s impacts on both patients and HCPs

Use of a Data-Sharing System During Diabetes Consultations

Patient-gathered self-management data and shared decision-making are touted as the answer to improving an individual’s health situation as well as collaboration between patients and their providers leading to more effective treatment plans. However, there is a gap between this ideal and reality – a lack of data-sharing technology. Here, we present the impact that the FullFlow System for sharing patient-gathered data during diabetes consultations, had on the patient-provider relationship and consultation discussion

Designing, implementing, and testing a modern electronic clinical study management system – the HUBRO system

Clinical trials need to adapt to the rapid development of today’s digital health technologies. The fast phase these technologies are changing today, make the clinical study administration demanding. To meet this challenge, new and more efficient platforms for performing clinical trials in this domain need to be designed. Since the process of following up such trials is very time-consuming, it calls for revisiting several of the methods for performing both randomized, and other clinical trials. We present system for electronic management of clinical studies that addresses many of the time-consuming challenges, which additionally address many of the quality assurance aspects. We also present results from testing the system in two studies with 50 and 8 participants

How mHealth can facilitate collaboration in diabetes care: qualitative analysis of codesign workshops

Background - Individuals with diabetes are using mobile health (mHealth) to track their self-management. However, individuals can understand even more about their diabetes by sharing these patient-gathered data (PGD) with health professionals. We conducted experience-based co-design (EBCD) workshops, with the aim of gathering end-users’ needs and expectations for a PGD-sharing system. Methods - N = 15 participants provided feedback about their experiences and needs in diabetes care and expectations for sharing PGD. The first workshop (2017) included patients with Type 2 Diabetes (T2D) (n = 4) and general practitioners (GPs) (n = 3). The second workshop (2018) included patients with Type 1 Diabetes (T1D) (n = 5), diabetes specialists (n = 2) and a nurse. The workshops involved two sessions: separate morning sessions for patients and healthcare providers (HCPs), and afternoon session for all participants. Discussion guides included questions about end-users’ perceptions of mHealth and expectations for a data-sharing system. Activities included brainstorming and designing paper-prototypes. Workshops were audio recorded, transcribed and translated from Norwegian to English. An abductive approach to thematic analysis was taken. Results Emergent themes were mHealth technologies’ impacts on end-users, and functionalities of a data-sharing system. Within these themes, similarities and differences between those with T1D and T2D, and between HCPs, were revealed. Patients and providers agreed that HCPs could use PGD to provide more concrete self-management recommendations. Participants’ paper-prototypes revealed which data types should be gathered and displayed during consultations, and how this could facilitate shared-decision making. Conclusion The diverse and differentiated results suggests the need for flexible and tailorable systems that allow patients and providers to review summaries, with the option to explore details, and identify an individual’s challenges, together. Participants’ feedback revealed that both patients and HCPs acknowledge that for mHealth integration to be successful, not only must the technology be validated but feasible changes throughout the healthcare education and practice must be addressed. Only then can both sides be adequately prepared for mHealth data-sharing in diabetes consultations. Subsequently, the design and performance of the joint workshop sessions demonstrated that involving both participant groups together led to efficient and concrete discussions about realistic solutions and limitations of sharing mHealth data in consultations

mHealth: Where Is the Potential for Aiding Informal Caregivers?

The health and well-being of informal caregivers often take a backseat to those that they care for. While systems, technologies, and services that provide care and support for those with chronic illnesses are established and continuously improved, those that support informal caregivers are less explored. An international survey about motivations to use mHealth technologies was posted to online platforms related to chronic illnesses. We focused on responses regarding the facilitators and challenges of achieving health goals, including the use of mHealth technologies, for the subgroup who identified as “Caregivers”. Findings indicate that mHealth technology is not yet the most important motivational factor for achieving health goals in this group, but greater future potential is suggested

Inequalities in the use of eHealth between socioeconomic groups among patients with type 1 and type 2 diabetes : cross-sectional study

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Methods and measures used to evaluate patient-operated mobile health interventions:Scoping literature review

Background: Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective: This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods: A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results: A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions: This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice

What motivates patients with NCDs to follow up their treatment?

Workshop at the 31st Medical Informatics Europe virtual conference, 29.05.21 - 31.05.21: https://efmi.org/2020/12/10/31st-medical-informatics-europe-conference-mie2021-athens-greece/.The increasing use of mobile health (mHealth) tools for self-management is considered to be important to improve health effects for patients with chronic NCDs (noncommunicable diseases). This development is supported by an increasing number of available mHealth apps. The apps range from disease management apps (e.g., diabetes diary) to health and fitness apps (e.g., dietary apps and workout apps). However, there seems to be a lack of motivation from most users to keep using these health apps over a long period of time [1]. This may be because of the way these apps were designed and developed, i.e. lack of co-participatory design techniques and lack of a tested developer guideline for creating mHealth solutions. The motivation behind this workshop is to identify motivational factors which will increase adoption and usage of mHealth apps. Since 2001, several of the presenters have been working on self-management tools for people with diabetes [2, 3]. The main tool is a diabetes diary – the “Few Touch Application” (Norwegian, “Diabetesdagboka”), available for free from Google Play, and used by several thousands of users [4-8]

mHealth: opportunities and challenges for diabetes intervention research

Background: Traditionally, health intervention evaluations provide long-term evidence of efficacy and safety via validated protocols, following a positivist paradigm, or approach, to research. However, modern mobile health (mHealth) technologies develop too quickly and outside of medical regulation, making it challenging for health research to keep pace. Objective: This thesis explored and tested how research can incorporate mHealth approaches and resources to evaluate mHealth interventions comprehensively, which follows the pragmatism paradigm. The works described herein were part of a larger project that designed, developed, and tested a data-sharing system between patients and their healthcare providers (HCPs) during diabetes consultations. Methods: The pragmatism paradigm underpins the mixed-methods, multi-phase design approach to exploring this overall objective. The following methods were performed using a sequential exploratory strategy. First, co-design workshops invited individuals with diabetes and HCPs to design an mHealth data-sharing system. Next, a scoping literature review identified research practices for evaluating mHealth interventions to-date. Then, app usage-logs, collected from a previous longitudinal study, were analyzed to explore how much additional information they could provide about patients’ self-management. Finally, a mixed-method study was designed to test the feasibility of combining both traditional and mHealth approaches and resources to evaluate an intervention. Results: Using the pragmatist paradigm as a scaffolding, these works provide evidence of how research can provide more comprehensive knowledge about mHealth interventions for diabetes care and self-management. Nine individuals with diabetes and six HCPs participated in the co-design workshops. Feedback included how a data-sharing system should work between patients and providers. The literature review identified how both traditional and mHealth-based approaches (n=15 methods, n=21 measures) were used together to evaluate mHealth interventions. Usage-log analysis revealed that changes in Glycosylated haemoglobin (HbA1c) differed between groups organized by usage patterns and duration of use of mHealth. The mixed-method study demonstrated how to collect comprehensive and complementary information when combining traditional and mHealth-centered approaches and resources. Conclusion: Traditional positivist approaches and resources are not adequate, on their own, to comprehensively understand the impact of mHealth interventions. The presented studies demonstrate that it is both feasible and prudent to combine traditional research with mHealth approaches, such as analyzing usage-logs, arranging co-design workshops, and other patient-centered methods in a pragmatist approach to produce comprehensive evidence of mHealth’s impacts on both patients and HCPs

Exploring Real-World mHealth Use for Diabetes Consultations: Pros and Pitfalls of a Pragmatic Mixed-Methods Approach

Intervention research is often highly controlled and does not reflect real-world situations. More pragmatic approaches, albeit less controllable and more challenging, offer the opportunity of identifying unexpected factors and connections. As the introduction of mHealth into formal diabetes care settings is relatively new and less often explored from the perspectives of patients and providers together, such an opportunity for exploration should be embraced. In this paper we demonstrate our experiences and results in designing and administering a pragmatic mixed-methods feasibility study to understand the impacts of a diabetes data-sharing system on patients and providers. In doing so, we aim to provide a realistic account of the pros and pitfalls of this approach to diabetes mHealth intervention research