Automatic messaging for improving patients engagement in diabetes management: an exploratory study

Effects of an Electronic Medical Records-Linked Diabetes Self-Management System on Treatment Targets in Real Clinical Practice: Retrospective, Observational Cohort Study

BACKGRUOUND

This study evaluated the effects of a mobile diabetes management program called "iCareD" (College of Medicine, The Catholic University of Korea) which was integrated into the hospital's electronic medical records system to minimize the workload of the healthcare team in the real clinical practice setting.

METHODS

In this retrospective observational study, we recruited 308 patients. We categorized these patients based on their compliance regarding their use of the iCareD program at home; compliance was determined through self-monitored blood glucose inputs and message subscription rates. We analyzed changes in the ABC (hemoglobin A1c, blood pressure, and low-density lipoprotein cholesterol) levels from the baseline to 12 months thereafter, based on the patients' iCareD usage patterns.

RESULTS

The patients comprised 92 (30%) non-users, 170 (55%) poor-compliance users, and 46 (15%) good-compliance users; the ABC target achievement rate showed prominent changes in good-compliance groups from baseline to 12 months (10.9% vs. 23.9%, P<0.05), whereas no significant changes were observed for poor-compliance users and non-users (13.5% vs. 18.8%, P=0.106; 20.7% vs. 14.1%, P=0.201; respectively).

CONCLUSION

Implementing the iCareD can improve the ABC levels of patients with diabetes with minimal efforts of the healthcare team in real clinical settings. However, the improvement of patients' compliance concerning the use of the system without the vigorous intervention of the healthcare team needs to be solved in the future.

Pervasive Healthcare Internet of Things: A Survey

Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. We classify the vast amount of publications into different categories such as sensors, communication, artificial intelligence, infrastructure, and security. Intensively covering 118 research works, we survey (1) applications, (2) key components, their roles and connections, and (3) the challenges. Our survey also discusses the potential solutions to overcome the challenges in this research field.

Multimedia Automation Access Control of Big Data Open Resources Based on Blockchain

In order to better mine the value of data, the author proposes a research on the automatic access control of big data open resources multimedia based on blockchain and introduces big data access control BBAC-BD (blockchain-based access control mechanism for big data environment). The author designed a strategy management contract based on the Bloom filter, as a probabilistic data structure with extremely high space utilization efficiency and proposed the strategic management contract (PAP CONTRACT) and the strategic decision contract (PDP CONTRACT). In this way, the nontampering, auditability, and verifiability of the access control information are guaranteed; then, the access control method based on smart contracts is adopted to realize the user-driven, whole-process transparent, and dynamic and automatic access control of big data resources. The simulation results show that the greater the ratio of n/k, the better the optimization effect, and the greater the ratio, the lower the corresponding misjudgment rate, but it will also take up more space costs. At the same time, the true value of the false positive rate is generally less than the theoretical value of the false positive rate. When the performance of Hash (strategy to retrieve) is better, the result of Hash distribution is more uniform. Under the condition of m = 3, the misjudgment rate acceptable for the expected use can be achieved, and the increase in the number of Hashes will not bring a significant increase in revenue. Freed from the traditional model of providing access control services based on third parties, solve the problem of transparency of authority judgments; at the same time, through smart contracts, based on the strategy published by the resource owner on the blockchain, realize automatic access control to big data resources; and make the judicial process more flexible and the judgment result more credible. The BBAC-BD mechanism realizes a safe, reliable, and transparent new access control architecture, and it can effectively promote the safe circulation and sharing of big data.

Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review

Background

Complications due to type 2 diabetes (T2D) can be mitigated through proper self-management that can positively change health behaviors. Technological tools are available to help people living with, or at risk of developing, T2D to manage their condition, and such tools provide a large repository of patient-generated health data (PGHD). Analytics can provide insights into the health behaviors of people living with T2D.

Objective

The aim of this review is to investigate what can be learned about the health behaviors of those living with, or at risk of developing, T2D through analytics from PGHD.

Methods

A scoping review using the Arksey and O’Malley framework was conducted in which a comprehensive search of the literature was conducted by 2 reviewers. In all, 3 electronic databases (PubMed, IEEE Xplore, and ACM Digital Library) were searched using keywords associated with diabetes, behaviors, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted, after which studies were selected. Critical examination took place through a descriptive-analytical narrative method, and data extracted from the studies were classified into thematic categories. These categories reflect the findings of this study as per our objective.

Results

We identified 43 studies that met the inclusion criteria for this review. Although 70% (30/43) of the studies examined PGHD independently, 30% (13/43) combined PGHD with other data sources. Most of these studies used machine learning algorithms to perform their analysis. The themes identified through this review include predicting diabetes or obesity, deriving factors that contribute to diabetes or obesity, obtaining insights from social media or web-based forums, predicting glycemia, improving adherence and outcomes, analyzing sedentary behaviors, deriving behavior patterns, discovering clinical correlations from behaviors, and developing design principles.

Conclusions

The increased volume and availability of PGHD have the potential to derive analytical insights into the health behaviors of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavior patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, which constitutes a unique source of data for these applications that would not be possible through the use of other data sources.

Chronic patient remote monitoring through the application of big data and internet of things

Chronic patients could benefit from the technological advances, but the clinical approaches for this kind of patients are still limited. This paper describes a system for chronic patients monitoring both, in home and external environments. For this purpose, we used novel technologies as big data, cloud computing and internet of things (IoT). Additionally, the system has been validated for three use cases: cardiovascular disease (CVD), hypertension (HPN) and chronic obstructive pulmonary disease (COPD), which were selected for their incidence in the population. This system is innovative within e-health, mainly due to the use of a big data architecture based on open-source components, also it provides a scalable and distributed environment for storage and processing of biomedical sensor data. The proposed system enables the incorporation of non-medical data sources in order to improve the self-management of chronic diseases and to develop better strategies for health interventions for chronic and dependents patients.

Mobile Health in Chronic Disease Management and Patient Empowerment: Exploratory Qualitative Investigation Into Patient-Physician Consultations

Background

Chronic diseases often present severe consequences for those affected. The management and treatment of chronic diseases largely depend on patients’ lifestyle choices and how they cope with the disease in their everyday lives. Accordingly, the ability of patients to self-manage diseases is a highly relevant topic. In relation to self-management, studies refer to patient empowerment as strengthening patients’ voices and enabling them to assert control over their health and treatment. Mobile health (mHealth) provides cost-efficient means to support self-management and foster empowerment.

Objective

There is a scarcity of research investigating how mHealth affects patient empowerment during patient-physician consultations. The objective of this study is to address this knowledge gap by investigating how mHealth affects consultations and patient empowerment.

Methods

We relied on data from an ethnographic field study of 6 children and adolescents diagnosed with juvenile idiopathic arthritis. We analyzed 6 patient-physician consultations and drew on Michel Foucault’s concepts of power and power technology.

Results

Our results suggest that the use of mHealth constitutes practices that structure the consultations around deviations and noncompliant patient behavior. Our analysis shows how mHealth is used to discipline patients and correct their behavior. We argue that the use of mHealth during consultations may unintentionally lead to relevant aspects of patients’ lives related to the disease being ignored; thus, inadvertently, patients’ voices may be silenced.

Conclusions

Our results show that concrete uses of mHealth may conflict with extant literature on empowerment, which emphasizes the importance of strengthening the patients’ voices and enabling patients to take more control of their health and treatment. We contribute to the state-of-the-art knowledge by showing that the use of mHealth may have unintended consequences that do not lead to empowerment. Our analysis underscores the need for further research to investigate how mHealth impacts patient empowerment during consultations.

A reinforcement learning based algorithm for personalization of digital, just-in-time, adaptive interventions

Suboptimal health related behaviors and habits; and resulting chronic diseases are responsible for majority of deaths globally. Studies show that providing personalized support to patients yield improved results by preventing and/or timely treatment of these problems. Digital, just-in-time and adaptive interventions are mobile phone-based notifications that are being utilized to support people wherever and whenever necessary in coping with their health problems. In this research, we propose a reinforcement learning-based mechanism to personalize interventions in terms of timing, frequency and preferred type(s). We simultaneously employ two reinforcement learning models, namely intervention-selection and opportune-moment-identification; capturing and exploiting changes in people's long-term and momentary contexts respectively. While the intervention-selection model adapts the intervention delivery with respect to type and frequency, the opportune-moment-identification model tries to find the most opportune moments to deliver interventions throughout a day. We propose two accelerator techniques over the standard reinforcement learning algorithms to boost learning performance. First, we propose a customized version of eligibility traces for rewarding past actions throughout an agent's trajectory. Second, we utilize the transfer learning method to reuse knowledge across multiple learning environments. We validate the proposed approach in a simulated experiment where we simulate four personas differing in their daily activities, preferences on specific intervention types and attitudes towards the targeted behavior. Our experiments show that the proposed approach yields better results compared to the standard reinforcement learning algorithms and successfully capture the simulated variations associated with the personas.

mHealth and Engagement Concerning Persons With Chronic Somatic Health Conditions: Integrative Literature Review

Background

Chronic somatic health conditions are a global public health challenge. Being engaged in one’s own health management for such conditions is important, and mobile health (mHealth) solutions are often suggested as key to promoting engagement.

Objective

The aim of this study was to review, critically appraise, and synthesize the available research regarding engagement through mHealth for persons with chronic somatic health conditions.

Methods

An integrative literature review was conducted. The PubMed, CINAHL, and Inspec databases were used for literature searches. Quality assessment was done with the guidance of Critical Appraisal Skills Programme (CASP) checklists. We used a self-designed study protocol comprising 4 engagement aspects—cognitive, behavioral and emotional, interactional, and the usage of mHealth—as part of the synthesis and analysis.

Results

A total of 44 articles met the inclusion criteria and were included in the analysis. mHealth usage was the most commonly occurring engagement aspect, behavioral and emotional aspects the second, cognitive aspects the third, and interactional aspects of engagement the least common aspect in the included articles. The results showed that there is a mix of enablers and barriers to engagement in relation to the 4 engagement aspects. The perceived meaningfulness and need for the solution and its content were important to create and maintain engagement. When perceived as meaningful, suitable, and usable, mHealth can support knowledge gain and learning, facilitate emotional and behavioral aspects such as a sense of confidence, and improve interactions and communications with health care professionals.

Conclusions

mHealth solutions have the potential to support health care engagement for persons with chronic somatic conditions. More research is needed to further understand how, by which means, when, and among whom mHealth could further improve engagement for this population.

A Survey of Healthcare Internet-of-Things (HIoT): A Clinical Perspective

In combination with current sociological trends, the maturing development of IoT devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data that is orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, HIoT data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this paper, we survey existing and emerging technologies that can enable this vision for the future of healthcare, particularly in the clinical practice of healthcare. Three main technology areas underlie the development of this field: (a) sensing, where there is an increased drive for miniaturization and power efficiency; (b) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure, and (c) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout the paper, we use a case study to concretely illustrate the impact of these trends. We conclude our paper with a discussion of the emerging directions, open issues, and challenges.

An expandable approach for design and personalization of digital, just-in-time adaptive interventions

Objective

We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables.

Materials and Methods

We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.

Results

We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.

Conclusion

While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

User Centered Design to Improve Information Exchange in Diabetes Care Through eHealth : Results from a Small Scale Exploratory Study

Heterogeneity of people with diabetes makes maintaining blood glucose control and achieving therapy adherence a challenge. It is fundamental that patients get actively involved in the management of the disease in their living environments. The objective of this paper is to evaluate the use and acceptance of a self-management system for diabetes developed with User Centered Design Principles in community settings. Persons with diabetes and health professionals were involved the design, development and evaluation of the self-management system; which comprised three iterative cycles: scenario definition, user archetype definition and system development. A comprehensive system was developed integrating modules for the management of blood glucose levels, medication, food intake habits, physical activity, diabetes education and messaging. The system was adapted for two types of principal users (personas): Type 1 Diabetes user and Type 2 Diabetes user. The system was evaluated by assessing the use, the compliance, the attractiveness and perceived usefulness in a multicenter randomized pilot study involving 20 patients and 24 treating professionals for a period of four weeks. Usage and compliance of the co-designed system was compared during the first and the last two weeks of the study, showing a significantly improved behaviour of patients towards the system for each of the modules. This resulted in a successful adoption by both type of personas. Only the medication module showed a significantly different use and compliance (p= 0.01) which can be explained by the different therapeutic course of the two types of diabetes. The involvement of patients to make their own decisions and choices form design stages was key for the adoption of a self-management system for diabetes.

What are the important design features of personal health records to improve medication adherence for patients with long-term conditions? A systematic literature review

OBJECTIVES

This systematic literature review aims to identify important design features of the electronic personal health record (PHR) that may improve medication adherence in the adult population with long-term conditions.

DATA SOURCES

PubMed (including MEDLINE), CINAHL, Science Direct (including EMBASE), BioMed Central, ACM digital, Emerald Insight, Google Scholar and Research Gate.

METHODS

Studies that were published between 1 January 2002 and 31 May 2018 in English were included if the participants were adults, with at least one long-term condition, were able to self-administer their medication and were treated in primary care settings. The quality of evidence was assessed with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system and the risk of bias was appraised using the Cochrane risk of bias tool.

RESULTS

From a total of 27 studies that matched the inclusion criteria, 12 were excluded due to low quality of evidence, 10 were rated moderate and 5 were rated high quality. All the included studies had low sample size and limited follow-up duration. Thirteen of the included studies found that the use of a PHR has increased medication adherence. The identified design features are reminders, education, personalisation and tailoring, feedback and alerts, gamification, medication management, medical appointment management, diary and self-monitoring, health condition management, set goals, patient's blog and tethered. It was impossible to draw conclusions as to which feature is important to what group of patients and why. The most frequently identified conditions were HIV and diabetes. This review did not identify any papers with negative results. It was not possible to numerically aggregate the PHR effect due to high heterogeneity of the medication adherence measurement, study type, participants and PHRs used.

CONCLUSION

Although we found recurrent evidence that PHRs can improve medication adherence, there is little evidence to date to indicate which design features facilitate this process.

PROSPERO REGISTRATION NUMBER

CRD42017060542.

Conformity of Diabetes Mobile apps with the Chronic Care Model

Background

Despite the growing use of mobile applications (apps) for chronic disease management, the evidence on the effectiveness of this technology on clinical and behavioural outcomes of the patients is scant. Many studies highlight the importance of the theoretical foundations of mobile-based interventions. One of the most widely accepted models for the management of chronic diseases, such as diabetes, is the Chronic Care Model (CCM). In this study, we investigated the conformity of the selected diabetes mobile apps with CCM.

Method

We searched online journal databases related to diabetes mobile apps to find common features. Then considering the components of the CCM as a reference model, features of some popular and top-ranking apps were compared with CCM.

Results

Among 23 studied apps, 34 per cent of them had medium conformity and 66 per cent of these apps were in weak conformity. The self-management support component is covered by 100 per cent of them. Ninety-five per cent of apps have covered the proactive follow-up component.

Conclusions

App conformance with CCM is generally weak. App developers are recommended to give greater consideration to established theoretical models in their design and implementation.

Assessment of Psychological Dimensions in Telemedicine Care for Gestational Diabetes Mellitus: A Systematic Review of Qualitative and Quantitative Studies

Background and Objective: Gestational Diabetes Mellitus (GDM) is a complex and wide spread problem and is considered one of the most frequent chronic metabolic conditions during pregnancy. According to a recent consensus conference held in Italy, new technologies can play a role in the so-called process of fertilization of the individual's ecosystem engagement, representing support for systemic collaboration among the main actors. The current systematic review aimed at providing an update of the literature about telemedicine for GDM, considering the role of psychological dimensions such as empowerment/self-efficacy, engagement and satisfaction.

Methods: The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The data sources were PubMed, ScienceDirect, Cochrane, and Scopus databases.

Results: Thirteen articles were identified as eligible and relevant for the final qualitative synthesis, but none was specific for the topic of engagement. The quality or research bias of the studies presents methodological limits. Most studies had clinical outcomes as a primary object. Concerning empowerment and self-efficacy, there were only preliminary findings reporting any improvements derived from using telemedicine approaches. Conversely, there were more consistent and positive results concerning the satisfaction of patients and clinicians.

Conclusions: These results are not sufficient to state a conclusive evaluation of positive effects of telemedicine use for GDM care. A more in-depth investigation of engagement and empowerment dimensions is necessary, as some benefits for the management of chronic conditions were already detected. Further investigations will also be necessary concerning the acceptability and feasibility of telemedicine systems by clinicians.

Impacting diabetes self-management in women with gestational diabetes mellitus using short messaging reminders

BACKGROUND AND PURPOSE

Gestational diabetes mellitus (GDM) has been associated with multiple complications, including increase risk of gestational hypertension, cesarean delivery, macrosomia, stillbirth, and preeclampsia. The purpose of this study was to determine the acceptability of text messaging in women with GDM and further refine intervention materials and study procedures (recruitment, enrollment, intervention, retention, and data collection).

METHODS

Nineteen women diagnosed with GDM completed a baseline demographic questionnaire followed by 4 weeks of daily text messages that included either a direct reminder to test their blood glucose levels and keep up with their treatment plan or an educational message. A postintervention survey was administered to assess the satisfaction with the messaging program.

CONCLUSION

The use of daily text messages in the treatment plan of patients diagnosed with GDM seems to be acceptable shown by an overall satisfaction with the messages and a willingness to use the messages in future pregnancies. Half of the participants also felt that the messages helped them to eat healthier.

IMPLICATIONS FOR PRACTICE

This study demonstrated a real opportunity for a low-cost intervention in the management plan of GDM.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Background

Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.

Objective

The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.

Methods

A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.

Results

We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.

Conclusions

We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

Tailored Communication Within Mobile Apps for Diabetes Self-Management: A Systematic Review

BACKGROUND

The prevalence of diabetes is increasing and with the requirements for self-management and risk of late complications, it remains a challenge for the individual and society. Patients can benefit from support from health care personnel in their self-management, and the traditional communication between patients and health care personnel is changing. Smartphones and apps offer a unique platform for communication, but apps with integrated health care personnel communication based on patient data are yet to be investigated to provide evidence of possible effects.

OBJECTIVE

Our goal was to systematically review studies that aimed to evaluate integrated communication within mobile apps for tailored feedback between patients with diabetes and health care personnel in terms of (1) study characteristics, (2) functions, (3) study outcomes, (4) effects, and (5) methodological quality.

METHODS

A systematic literature search was conducted following our International Prospective Register of Systematic Reviews (PROSPERO) protocol, searching for apps with integrated communication for persons with diabetes tested in a controlled trial in the period 2008 to 2016. We searched the databases PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central, Excerpta Medica database (EMBASE), ClinicalTrials.gov, and the World Health Organization (WHO) International Clinical Trials Registry Platform. The search was closed in September 2016. Reference lists of primary articles and review papers were assessed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and we applied the Cochrane risk of bias tool to assess methodological quality.

RESULTS

We identified 2822 citations and after duplicate removal, we assessed 1128 citations. A total of 6 papers were included in this systematic review, reporting on data from 431 persons participating in small trials of short duration. The integrated communication features were mostly individualized as written non-real-time feedback. The number of functions varied from 2 to 9, and blood glucose tracking was the most common. HbA_1c was the most common primary outcome, but the remaining reported outcomes were not standardized and comparable. Because of both the heterogeneity of the included trials and the poor methodological quality of the studies, a meta-analysis was not possible. A statistically significant improvement in the primary measure of outcome was found in 3 of the 6 included studies, of which 2 were HbA_1c and 1 was mean daytime ambulatory blood pressure. Participants in the included trials reported positive usability or feasibility postintervention in 5 out of 6 trials. The overall methodological quality of the trials was, however, scored as an uncertain risk of bias.

CONCLUSIONS

This systematic review highlights the need for more trials of higher methodological quality. Few studies offer an integrated function for communication and feedback from health care personnel, and the research field represents an area of heterogeneity with few studies of highly rigorous methodological quality. This, in combination with a low number of participants and a short follow-up, is making it difficult to provide reliable evidence of effects for stakeholders.

An m-Health system for education and motivation in cardiac rehabilitation: the experience of HeartCycle guided exercise

Introduction

Home-based programmes for cardiac rehabilitation play a key role in the recovery of patients with coronary artery disease. However, their necessary educational and motivational components have been rarely implemented with the help of modern mobile technologies. We developed a mobile health system designed for motivating patients to adhere to their rehabilitation programme by providing exercise monitoring, guidance, motivational feedback, and educational content.

Methods

Our multi-disciplinary approach is based on mapping “desired behaviours” into specific system’s specifications, borrowing concepts from Fogg’s Persuasive Systems Design principles. A randomised controlled trial was conducted to compare mobile-based rehabilitation (55 patients) versus standard care (63 patients).

Results

Some technical issues related to connectivity, usability and exercise sessions interrupted by safety algorithms affected the trial. For those who completed the rehabilitation (19 of 55), results show high levels of both user acceptance and perceived usefulness. Adherence in terms of started exercise sessions was high, but not in terms of total time of performed exercise or drop-outs. Educational level about heart-related health improved more in the intervention group than the control. Exercise habits at 6 months follow-up also improved, although without statistical significance.

Discussion

Results indicate that the adopted design methodology is promising for creating applications that help improve education and foster better exercise habits, but further studies would be needed to confirm these indications.

Use of an holistic approach for effective adoption of User-Centred-Design techniques in diabetes disease management: Experiences in user need elicitation

One of the most important challenges of designing eHealth tools for Chronic Disease Management is to understand how transforming cutting-edge innovations in something that can impact the current clinical practice and improve the performance of the health care systems. The adoption of User Centered Design techniques is fundamental in order to integrate these systems in an effective and successful way. The work presented in this paper describe the methodologies used in the context of two multidisciplinary research projects, METABO and MOSAIC. The adoption of the methodologies have been driven by the CeHRes Roamap, a holistic framework that support participatory development of eHealth. The work reported in this paper describes the results of the first two (out of the five) phases in eliciting user needs.

An evaluation of patients' experienced usability of a diabetes mHealth system using a multi-method approach

OBJECTIVE

mHealth systems are becoming more common to aid patients in their diabetes self-management, but recent studies indicate a need for thorough evaluation of patients' experienced usability. Current evaluations lack a multi-method design for data collection and structured methods for data analyses. The purpose of this study was to provide a feasibility test of a multi-method approach for both data collection and data analyses for patients' experienced usability of a mHealth system for diabetes type 2 self-management.

MATERIALS AND METHODS

A random sample of 10 users was selected from a larger clinical trial. Data collection methods included user testing with eight representative tasks and Think Aloud protocol, a semi-structured interview and a questionnaire on patients' experiences using the system. The Framework Analysis (FA) method and Usability Problem Taxonomy (UPT) were used to structure, code and analyze the results. A usability severity rating was assigned after classification.

RESULTS

The combined methods resulted in a total of 117 problems condensed into 19 usability issues with an average severity rating of 2.47 or serious. The usability test detected 50% of the initial usability problems, followed by the post-interview at 29%. The usability test found 18 of 19 consolidated usability problems while the questionnaire uncovered one unique issue. Patients experienced most usability problems (8) in the Glucose Readings View when performing complex tasks such as adding, deleting, and exporting glucose measurements. The severity ratings were the highest for the Glucose Diary View, Glucose Readings View, and Blood Pressure View with an average severity rating of 3 (serious). Most of the issues were classified under the artifact component of the UPT and primary categories of Visualness (7) and Manipulation (6). In the UPT task component, most issues were in the primary category Task-mapping (12).

CONCLUSIONS

Multiple data collection methods yielded a more comprehensive set of usability issues. Usability testing uncovered the largest volume of usability issues, followed by interviewing and then the questionnaire. The interview did not surface any unique consolidated usability issues while the questionnaire surfaced one. The FA and UPT were valuable in structuring and classifying problems. The resulting descriptions serve as a communication tool in problem solving and programming. We recommend the usage of multiple methods in data collection and employing the FA and UPT in data analyses for future usability testing.