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Die digitale Pille für chronische Krankheiten
Abstract
Laut WHO sind chronische Krankheiten wie Herz-Kreislauf-Erkrankungen, Krebs, Diabetes oder Asthma weltweit für circa 70 % aller Todesfälle verantwortlich. Leistungserbringer haben allerdings nur beschränkte Ressourcen und können den Gesundheitszustand im Alltag von Patienten nicht kontinuierlich erheben und daher auch nicht immer rechtzeitig intervenieren, bevor es zu einer allfälligen Hospitalisierung kommt. Vor diesem Hintergrund diskutiert dieser interdisziplinäre Beitrag das Potenzial digitaler Pillen. Das Ziel digitaler Pillen besteht darin, Gesundheitszustände mithilfe von Informations- und Kommunikationstechnologie möglichst kontinuierlich, zweckdienlich und bequem zu erheben und nur dann zu intervenieren, wenn es unbedingt sein muss, kurzum Patienten den Umgang mit ihrer chronischen Krankheit im Alltag zu erleichtern. Nach einer Einleitung wird das Konzept digitaler Pillen näher erläutert. Danach werden fünf digitale Pillen aus den Bereichen Gesundheitskompetenz, Prävention und Therapie näher vorgestellt. Abschließend wird das Konzept der digitalen Pille kritisch reflektiert und Potenziale sowie Herausforderungen werden diskutiert.
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Working alliance describes an important relationship quality between health professionals and patients and is robustly linked to treatment success. However, due to limited resources of health professionals, working alliance cannot always be promoted just-in-time in a ubiquitous fashion. To address this scalability problem, we investigate the direct effect of interpersonal closeness cues of text-based healthcare chatbots (THCBs) on attachment bond from the working alliance con-struct and the indirect effect on the desire to continue interacting with THCBs. The underlying research model and hypotheses are informed by counselling psychology and research on conver-sational agents. In order to investigate the hypothesized effects, we first develop a THCB codebook with 12 design dimensions on interpersonal closeness cues that are categorized into visual cues (i.e. avatar), verbal cues (i.e. greetings, address, jargon, T-V-distinction), quasi-nonverbal cues (i.e. emoticons) and relational cues (i.e. small talk, self-disclosure, empathy, humor, meta-relational talk and continuity). In a second step, four distinct THCB designs are developed along the continuum of interpersonal closeness (i.e. institutional-like, expert-like, peer-like and myself-like THCBs) and a corresponding study design for an interactive THCB-based online experiment is presented to test our hypotheses. We conclude this work-in-progress by outlining our future work.
Workplace stress can negatively affect the health condition of employees and with it, the performance of organizations. Although there exist approaches to measure work-related stress, two major limitations are the low resolution of stress data and its obtrusive measurement. The current work applies design science research with the goal to design, implement and evaluate a Stress Detection Service (SDS) that senses the degree of work-related stress solely based on mouse movements of knowledge workers. Using van Gemmert and van Galen’s stress theory and Bakker and Demerouti’s Job Demands-Resource model as justificatory knowledge, we implemented a first SDS prototype that senses mouse movements and perceived stress levels. Experimental results indicate that two feature sets of mouse movements, i.e. average deviation from an optimal mouse trajectory and average mouse speed, can classify high versus low stress with an overall accuracy of 78%. Future work regarding a second build-and-evaluate loop of a SDS, then tailored to the field setting, is discussed.
In this paper, a user interface paradigm, called Talk-and-Tools, is presented for automated e-coaching. The paradigm is based on the idea that people interact in two ways with their environment: symbolically and physically. The main goal is to show how the paradigm can be applied in the design of interactive systems that offer an acceptable coaching process. As a proof of concept, an e-coaching system is implemented that supports an insomnia therapy on a smartphone. A human coach was replaced by a cooperative virtual coach that is able to interact with a human coachee. In the interface of the system, we distinguish between a set of personalized conversations (“Talk”) and specialized modules that form a coherent structure of input and output facilities (“Tools”). Conversations contained a minimum of variation to exclude unpredictable behavior but included the necessary mechanisms for variation to offer personalized consults and support. A variety of system and user tests was conducted to validate the use of the system. After a 6-week therapy, some users spontaneously reported the experience of building a relationship with the e-coach. It is concluded that the addition of a conversational component fills an important gap in the design of current mobile systems.
Asthma, diabetes, hypertension, or major depression are non-communicable diseases (NCDs) and impose a major burden on global health. Stress is linked to both the causes and consequences of NCDs and it has been shown that biofeedback-based breathing trainings (BBTs) are effective in coping with stress. Here, diaphragmatic breathing, i.e. deep abdominal breathing, belongs to the most distinguished breathing techniques. However, high costs and low scalability of state-of-the-art BBTs that require expensive medical hardware and health professionals, represent a significant barrier for their widespread adoption. Health information technology has the potential to address this important practical problem. Particularly, it has been shown that a smartphone microphone has the ability to record audio signals from exhalation in a quality that can be compared to professional respiratory devices. As this finding is highly relevant for low-cost and scalable smartphone-based BBTs (SBBT) and – to the best of our knowledge-because it has not been investigated so far, we aim to design and evaluate the efficacy of such a SBBT. As a very first step, we apply design-science research and investigate in this research-in-progress the relationship of diaphragmatic breathing and its acoustic components by just using a smartphone's microphone. For that purpose, we review related work and develop our hypotheses based on justificatory knowledge from physiology, physics and acoustics. We finally describe a laboratory study that is used to test our hypotheses. We conclude with a brief outlook on future work.
Work-related stress has the potential to increase the risk of chronic stress, major depression and other non-communicable diseases. Organizational stress monitoring usually applies long-term self-report instruments that are designed in a retrospective manner, and thus, is obtrusive, time-consuming and, most important, fails to detect and predict short-term episodes of stress. To address this shortcoming, we apply design science research with the goal to design, implement and evaluate a stress management service for knowledge workers (stressOUT) that senses the degree of work-related stress solely based on mouse movements. Using stress theory as justificatory knowledge, we implemented stressOUT that tracks mouse movements and perceived stress levels randomly twice a day with the goal to learn features of mouse movements that are related to stress perceptions. Results of a first longitudinal field study indicate that mouse cursor speed is negatively related to perceived stress. Future work is discussed.
The personal and financial burden of asthma highly depends on a patient's disease self-management skill. Scalable mHealth apps, designed to empower patients, have the potential to play a crucial role in asthma disease management. However, the actual clinical efficacy of mHealth asthma apps is poorly understood due to the lack of both methodologically sound research and accessible evidence-based apps. We therefore apply design science with the goal to design, implement and evaluate a mHealth app for people with asthma, the Mobile Asthma Companion (MAC). The current prototype of MAC delivers health literacy knowledge triggered by nocturnal cough rates. We conclude by proposing a randomized controlled trial to test the efficacy of our prototype.
Background:
Existing research postulates a variety of components that show an impact on utilization of technology-mediated mental health information systems (MHIS) and treatment outcome. Although researchers assessed the effect of isolated design elements on the results of Web-based interventions and the associations between symptom reduction and use of components across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic benefit associated with specific service components of technology-mediated MHIS for depression.
Objective:
This systematic review aims at identifying components of technology-mediated MHIS for patients with depression. Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on what components in technology-mediated MHIS for the treatment of depression should be standard of care.
Methods:
Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference (SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were analyzed. Publication bias was examined using visual inspection of funnel plots and Begg's test. Qualitative data analysis was also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between two researchers.
Results:
Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward reduced depressive symptoms (SMD -0.58, 95% CI -0.71 to -0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15 components were identified.
Conclusions:
Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions for the treatment of depression and other mental disorders.
Background
The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual?s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual?s state can change rapidly, unexpectedly, and in his/her natural environment. PurposeDespite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods
Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusion
As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.
Background:
Depression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support.
Objective:
The objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms.
Methods:
A total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject's preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery.
Results:
A total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=-.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively.
Conclusions:
Proxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital health interventions leveraging smartphone sensors to provide context sensitive information for in-situ support and unobtrusive monitoring of critical mental health states.
MobileCoach, an open source behavioral intervention platform, has been developed to provide health professionals with an authoring tool to design evidence-based, scalable and low-cost digital health interventions (DHI). Its potential meets the lack in resources and capacity of health care systems to provide DHI for the treatment of noncommunicable diseases. In the current work, we introduce the first personalization approach for MobileCoach with the purpose of identifying the needs of participants, tailoring the treatment and, as a consequence, enhancing the capability of MobileCoach-based DHIs. The personalization approach is then exemplified by a very first prototype of a DHI for people with asthma that is able to detect coughing by just using a smartphone's microphone. First empirical results with five healthy subjects and 80 coughs indicate its technical feasibility as the detection accuracy yielded 83.3%. Future work will focus on the integration of personalized sensing and supporting applications for MobileCoach.
Objective:
To test the efficacy of a combined web- and text messaging-based intervention to reduce problem drinking in young people compared to assessment only.
Method:
Two-arm, parallel-group, cluster-randomized controlled trial with assessments at baseline and 6-month follow up. The automated intervention included online feedback, based on the social norms approach, and individually tailored text messages addressing social norms, outcome expectations, motivation, self-efficacy, and planning processes, provided over 3 months. The main outcome criterion was the prevalence of risky single-occasion drinking (RSOD, defined as drinking at least 5 standard drinks on a single occasion in men and 4 in women) in the past 30 days. Irrespective of alcohol consumption, 1,355 students from 80 Swiss vocational and upper secondary school classes, all of whom owned a mobile phone, were invited to participate in the study. Of these, 1,041 (76.8%) students participated in the study.
Results:
Based on intention-to-treat analyses, RSOD prevalence decreased by 5.9% in the intervention group and increased by 2.6% in the control group, relative to that of baseline assessment (odds ratio [OR] = 0.62, 95% confidence interval [CI] = 0.44-0.87). No significant group differences were observed for the following secondary outcomes: RSOD frequency, quantity of alcohol consumed, estimated peak blood alcohol concentration, and overestimation of peer drinking norms.
Conclusions:
The intervention program reduced RSOD, which is a major indicator of problem drinking in young people, effectively. (PsycINFO Database Record
Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference.
Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires.
Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB.
Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use.
Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.
Background:
Problems with the use of inhalers by patients were noted shortly after the launch of the metered dose inhaler (MDI) and persist today. We aimed to assess the most common errors in inhaler use over the last 40 years in patients treated with MDI or dry powder inhalers (DPI).
Methods:
A systematic search for articles reporting direct observation of inhaler technique by trained personnel covered the period 1975 to 2014. Outcomes were nature and frequencies of the three most common errors; the percentage of patients demonstrating correct, acceptable, or poor technique; and variations in these outcomes over these 40 years and when partitioned into years 1-20 and 21-40. Analyses were conducted in accordance with PRISMA and STROBE recommendations.
Results:
Data were extracted from 144 articles reporting on a total number of 54 354 subjects performing 59 584 observed tests of technique. The most frequent MDI errors were in co-ordination (45% (95% CI 41-49%)), speed and/or depth of inspiration (44% (40-47%)), and no post-inhalation breath-hold (46% (42-49%)). Frequent DPI errors were incorrect preparation in 28% (26-33%), no full expiration before inhalation in 46% (42-50%), and no post-inhalation breath-hold in 37% (33-40%). The overall prevalence of correct technique was 31% (28-35%); of acceptable, 41% (36-47%); and of poor, 31% (27-36%). There were no significant differences between the first and second twenty-year periods of scrutiny.
Conclusions:
Incorrect inhaler technique is unacceptably frequent and has not improved over the past 40 years, pointing to an urgent need for new approaches to education and drug delivery.
Advances in wireless devices and mobile technology offer many opportunities for delivering just-in-time adaptive interventions (JITAIs)-suites of interventions that adapt over time to an individual's changing status and circumstances with the goal to address the individual's need for support, whenever this need arises. A major challenge confronting behavioral scientists aiming to develop a JITAI concerns the selection and integration of existing empirical, theoretical and practical evidence into a scientific model that can inform the construction of a JITAI and help identify scientific gaps. The purpose of this paper is to establish a pragmatic framework that can be used to organize existing evidence into a useful model for JITAI construction. This framework involves clarifying the conceptual purpose of a JITAI, namely, the provision of just-in-time support via adaptation, as well as describing the components of a JITAI and articulating a list of concrete questions to guide the establishment of a useful model for JITAI construction. The proposed framework includes an organizing scheme for translating the relatively static scientific models underlying many health behavior interventions into a more dynamic model that better incorporates the element of time. This framework will help to guide the next generation of empirical work to support the creation of effective JITAIs. (PsycINFO Database Record
This book provides an introduction to the mathematics needed to model, analyze, and design feedback systems. It is an ideal textbook for undergraduate and graduate students, and is indispensable for researchers seeking a self-contained reference on control theory. Unlike most books on the subject, Feedback Systems develops transfer functions through the exponential response of a system, and is accessible across a range of disciplines that utilize feedback in physical, biological, information, and economic systems. Karl Åström and Richard Murray use techniques from physics, computer science, and operations research to introduce control-oriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Åström and Murray then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. They provide exercises at the end of every chapter, and an accompanying electronic solutions manual is available. Feedback Systems is a complete one-volume resource for students and researchers in mathematics, engineering, and the sciences.
Cultural and language differences and socioeconomic status interact with and contribute to low health literacy, defined as the inability to understand or act on medical/therapeutic instructions. Health literacy is increasingly recognized as an important factor in patient compliance, cancer screening utilization, and chronic disease outcomes. Commendable efforts have been initiated by the American Medical Association and other organizations to address low health literacy among patients. Less work has been done, however, to place health literacy in the broader context of socioeconomic and cultural differences among patients and providers that hinder communication and compliance. This review examines cultural influences on health literacy, cancer screening and chronic disease outcomes. We argue that cultural beliefs around health and illness contribute to an individual's ability to understand and act on a health care provider's instructions. This paper proposes key aspects of the intersection between health literacy and culturally varying beliefs about health which merit further exploration.
In recent years, there has been an explosion of research focused on using technology in healthcare, including Web and mobile health assessment and intervention tools, as well as smartphone sensors and smart environments for monitoring and promoting health behavior. This work has shown that technology-based therapeutic tools offer considerable promise for monitoring and responding to individuals’ health behavior in real time. They may extend the reach of clinicians or serve as stand-alone tools, may be cost-effective and offer countless opportunities for tailoring behavioral interventions that are optimally responsive to individuals’ needs and preferences. Technology may enable entirely new models of healthcare both within and outside of formal systems of care. This book defines the state of scientific research related to the development, experimental evaluation, and effective dissemination of technology-based therapeutic tools targeting behavioral health. It provides an overview of evidence-based approaches to harnessing technology in areas of substance use, mental health, diet and exercise, medication adherence, and chronic disease management. The book also defines the state of implementation research examining models for integrating technology-based behavioral healthcare systems to increase the quality and reach of evidence-based and cost-effective behavioral healthcare, and discusses public health and policy implications of this work. This book consists of contributions from an interdisciplinary group of authors who are leaders in fields of paramount importance to the arena of technology and behavioral health, including the science of behavior change, emerging technologies, health economics, novel methodologies and analytics, implementation science, regulation, privacy and security, and public policy.
Notifications can be relevant but they can also decrease productivity when delivered at the wrong point in time. Smartphones are increasingly capable of detecting relevant context information with the goal to decrease the number of these badly timed interruptions. Accordingly, research on context- aware notification management systems (CNMSs) on mobile devices has received increasing attention recently, prototypes have been built and empirically evaluated. However, there exists no systematic overview of mobile CNMSs evaluating their efficacy. The objectives of the current work are therefore to identify relevant empirical studies that have assessed the efficacy of mobile CNMSs and to discuss the findings with respect to future work. A systematic literature review and meta-analysis was conducted to address these objectives. Consistent with prior work, two efficacy metrics were applied: response rate and response delay. A keyword-based search strategy was used and resulted in 1’634 studies, out of which 8 were relevant for the topic. Findings indicate that mobile CNMSs increase the response rate, while there was only little evidence that they reduce response time, too. Implications for researchers and practitioners are discussed and future research is outlined that aims at further increasing the efficacy of mobile CNMSs.
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA, that is robust to practical systems issues. Our method and theory for the first time consider issues of high communication cost, stragglers, and fault tolerance for distributed multi-task learning. The resulting method achieves significant speedups compared to alternatives in the federated setting, as we demonstrate through simulations on real-world federated datasets.
The power of lifestyle as medicine was perceived thousands of years ago. There is now consistent and compelling science to support the important influence of lifestyle on health. Approximately 80% of chronic disease and premature death could be prevented by not smoking, being physically active, and adhering to a healthful dietary pattern. Cardiovascular disease, diabetes, stroke, dementia, and cancer are all influenced by lifestyle choices. Despite the ample evidence about what behaviors promote health, confusion still prevails among the general population. This is particularly true with regard to diet. Confusing nutrition messages from scientists, the media, the food industry, and other sources have made it all but impossible for any single authority to convey persuasively the fundamentals of healthful eating. The case is made here that a global coalition of diverse experts has the power to do what no individual can: clarify and popularize an understanding of the fundamentals of a health-promoting, sustainable pattern of diet and lifestyle, and rally the general public to their consistent support.
Digital medicine offers the possibility of continuous monitoring, behavior modification and personalized interventions at low cost, potentially easing the burden of chronic disease in cost-constrained healthcare systems.
Effective and efficient behavioral interventions are important and of high interest today. Due to shortcomings of related approaches, we introduce MobileCoach (mobile-coach.eu) as novel open source behavioral intervention platform. With its modular architecture, its rule-based engine that monitors behavioral states and triggers state transitions, we assume MobileCoach to lay a fruitful ground for evidence-based, scalable and low-cost behavioral interventions in various application domains. The code basis is made open source and thus, MobileCoach can be used and revised not only by interdisciplinary research teams but also by public bodies or business organizations without any legal constraints. Technical details of the platform are presented as well as preliminary empirical findings regarding the acceptance of one particular intervention in the public health context. Future work will integrate Internet of Things services that sense and process data streams in a way that MobileCoach interventions can be further tailored to the needs and characteristics of individual participants.
Introduction:
This article is part of a Focus Theme of Methods of Information in Medicine on Health Record Banking.
Background:
Healthcare is often ineffective and costs are steadily rising. This is in a large part due to the inaccessibility of medical and health data stored in multiple silos. Furthermore, in most cases molecular differences between individuals that result in different susceptibilities to drugs and diseases as well as targeted interventions cannot be taken into account. Technological advances in genome sequencing and the interaction of 'omics' data with environmental data on one hand and mobile health on the other, promise to generate the longitudinal health data that will form the basis for a more personalized, precision medicine.
Objectives:
For this new medicine to become a reality, however, millions of personal health data sets have to be aggregated. The value of such aggregated personal data has been recognized as a new asset class and many commercial entities are competing for this new asset (e.g. Google, Facebook, 23andMe, PatientsLikeMe). The primary source and beneficiary of personal health data is the individual. As a collective, society should be the beneficiary of both the economic and health value of these aggregated data and (health) information.
Methods:
We posit that empowering citizens by providing them with a platform to safely store, manage and share their health-related data will be a necessary element in the transformation towards a more effective and efficient precision medicine. Such health data platforms should be organized as cooperatives that are solely owned and controlled by their members and not by shareholders. Members determine which data they want to share for example with doctors or to contribute to research for the benefit of their health and that of society. Members will also decide how the revenues generated by granting third parties access to the anonymized data that they agreed to share, should be invested in research, information or education.
Results:
Currently no functional Health Data Cooperatives exist yet. The relative success of health data repositories such as 23andme and PatientsLikeMe indicates that citizens are willing to participate in research even if - and in contrast to the cooperative model - the commercial value of these data does not go back to the collective of users.
Conclusions:
In the Health Data Cooperative model, the citizens with their data would take the center stage in the healthcare system and society would benefit from the health-related and financial benefits that aggregation of these data brings.
Background:
Diabetes is a common chronic disease that is increasingly managed in primary care. Different systems have been proposed to manage diabetes care.
Objectives:
To assess the effects of different interventions, targeted at health professionals or the structure in which they deliver care, on the management of patients with diabetes in primary care, outpatient and community settings.
Search strategy:
We searched the Cochrane Effective Practice and Organisation of Care Group specialised register, the Cochrane Controlled Trials Register (Issue 4 1999), MEDLINE (1966-1999), EMBASE (1980-1999), Cinahl (1982-1999), and reference lists of articles.
Selection criteria:
Randomised trials (RCTs), controlled clinical trials (CCTs), controlled before and after studies (CBAs) and interrupted time series (ITS) analyses of professional, financial and organisational strategies aimed at improving care for people with Type 1 or Type 2 diabetes. The participants were health care professionals, including physicians, nurses and pharmacists. The outcomes included objectively measured health professional performance or patient outcomes, and self-report measures with known validity and reliability.
Data collection and analysis:
Two reviewers independently extracted data and assessed study quality.
Main results:
Forty-one studies were included involving more than 200 practices and 48,000 patients. Twenty-seven studies were RCTs, 12 were CBAs, and two were ITS. The studies were heterogeneous in terms of interventions, participants, settings and outcomes. The methodological quality of the studies was often poor. In all studies the intervention strategy was multifaceted. In 12 studies the interventions were targeted at health professionals, in nine they were targeted at the organisation of care, and 20 studies targeted both. In 15 studies patient education was added to the professional and organisational interventions. A combination of professional interventions improved process outcomes. The effect on patient outcomes remained less clear as these were rarely assessed. Arrangements for follow-up (organisational intervention) also showed a favourable effect on process outcomes. Multiple interventions in which patient education was added or in which the role of the nurse was enhanced also reported favourable effects on patients' health outcomes.
Reviewer's conclusions:
Multifaceted professional interventions can enhance the performance of health professionals in managing patients with diabetes. Organisational interventions that improve regular prompted recall and review of patients (central computerised tracking systems or nurses who regularly contact the patient) can also improve diabetes management. The addition of patient-oriented interventions can lead to improved patient health outcomes. Nurses can play an important role in patient-oriented interventions, through patient education or facilitating adherence to treatment.
To examine patient- and provider-reported psychosocial problems and barriers to effective self-care and resources for dealing with those barriers.
Cross-sectional study using face-to-face or telephone interviews with diabetic patients and health-care providers in 13 countries in Asia, Australia, Europe and North America. Participants were randomly selected adults (n = 5104) with Type 1 or Type 2 diabetes, and providers (n = 3827), including primary care physicians, diabetes specialist physicians and nurses.
Regimen adherence was poor, especially for diet and exercise; provider estimates of patient self-care were lower than patient reports for all behaviours. Diabetes-related worries were common among patients, and providers generally recognized these worries. Many patients (41%) had poor psychological well-being. Providers reported that most patients had psychological problems that affected diabetes self-care, yet providers often reported they did not have the resources to manage these problems, and few patients (10%) reported receiving psychological treatment.
Psychosocial problems appear to be common among diabetic patients worldwide. Addressing these problems may improve diabetes outcomes, but providers often lack critical resources for doing so, particularly skill, time and adequate referral sources.
Health literacy video clips for children with asthma. Poster presented at the CSS Meets & Greets CDHI event
- A Möller
- H Oswald
- U Dittler
- F Meyer
- M Schaub
- F Barata
- P Tinschert
- J-M Egger
- E Fleisch
- T Kowatsch
Kowatsch T, Barata F, Tinschert P, Dittler U, Egger J-M, Meyer F, Schaub M, Fleisch E, Oswald
H, Möller A et al (2017a) Digital health literacy intervention for children with asthma. Poster
presented at the CSS Meets & Greets CDHI event, CSS, Lucerne, 4 Dezember 2017
Text-based healthcare chatbots supporting patient and health professional teams: preliminary results of a randomized controlled trial on childhood obesity
- T Kowatsch
- M K Nißen
- I Shih
- D Rüegger
- D Volland
- A Filler
- F Künzler
- F Barata
- S Haug
- D Büchter
- B Brogle
- K Heldt
- P Gindrat
- N Farpour-Lambert
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