mHealth through quantified-self : a user study

This work was partly supported by the IU-ATC project, funded by grant EP/J016756/1 from the Engineering and Physical Sciences Research Council (EPSRC). Chonlatee Khorakhun is funded by the Scottish Informatics and Computer Science Alliance (SICSA).We describe a user study of a mHealth prototype system based on a wellbeing scenario, exploiting the quantified-self approach to measurement and monitoring. We have used off-the-shelf equipment, with opensource, web-based, software, and exploiting the increasing popularity of smartphones and self-measurement devices in a user study. We emulate a mHealth scenario as a pre-clinical experiment, as a realistic alternative to a clinical scenario, with reduced risk to sensitive patient medical data. We discuss the efficacy of this approach for future mHealth systems for remote monitoring. Our system used the popular Fitbit device for monitoring personal wellbeing data, the Diaspora online social media platform (OSMP), and a simple Android/iOS remote notification application. We implemented remote monitoring, asynchronous user interaction, multiple actors, and user-controlled security and privacy mechanisms. We propose that the use of a quantified-self approach to mHealth is particularly valuable to undertake research and systems development.Postprin

Usability and feasibility of consumer-facing technology to reduce unsafe medication use by older adults

Background Mobile health technology can improve medication safety for older adults, for instance, by educating patients about the risks associated with anticholinergic medication use. Objective This study's objective was to test the usability and feasibility of Brain Buddy, a consumer-facing mobile health technology designed to inform and empower older adults to consider the risks and benefits of anticholinergics. Methods Twenty-three primary care patients aged ≥60 years and using anticholinergic medications participated in summative, task-based usability testing of Brain Buddy. Self-report usability was assessed by the System Usability Scale and performance-based usability data were collected for each task through observation. A subset of 17 participants contributed data on feasibility, assessed by self-reported attitudes (feeling informed) and behaviors (speaking to a physician), with confirmation following a physician visit. Results Overall usability was acceptable or better, with 100% of participants completing each Brain Buddy task and a mean System Usability Scale score of 78.8, corresponding to “Good” to “Excellent” usability. Observed usability issues included higher rates of errors, hesitations, and need for assistance on three tasks, particularly those requiring data entry. Among participants contributing to feasibility data, 100% felt better informed after using Brain Buddy and 94% planned to speak to their physician about their anticholinergic related risk. On follow-up, 82% reported having spoken to their physician, a rate independently confirmed by physicians. Conclusion Consumer-facing technology can be a low-cost, scalable intervention to improve older adults’ medication safety, by informing and empowering patients. User-centered design and evaluation with demographically heterogeneous clinical samples uncovers correctable usability issues and confirms the value of interventions targeting consumers as agents in shared decision making and behavior change

Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System

Systematic review of smartphone-based passive sensing for health and wellbeing

OBJECTIVE: To review published empirical literature on the use of smartphone-based passive sensing for health and wellbeing. MATERIAL AND METHODS: A systematic review of the English language literature was performed following PRISMA guidelines. Papers indexed in computing, technology, and medical databases were included if they were empirical, focused on health and/or wellbeing, involved the collection of data via smartphones, and described the utilized technology as passive or requiring minimal user interaction. RESULTS: Thirty-five papers were included in the review. Studies were performed around the world, with samples of up to 171 (median n = 15) representing individuals with bipolar disorder, schizophrenia, depression, older adults, and the general population. The majority of studies used the Android operating system and an array of smartphone sensors, most frequently capturing accelerometry, location, audio, and usage data. Captured data were usually sent to a remote server for processing but were shared with participants in only 40% of studies. Reported benefits of passive sensing included accurately detecting changes in status, behavior change through feedback, and increased accountability in participants. Studies reported facing technical, methodological, and privacy challenges. DISCUSSION: Studies in the nascent area of smartphone-based passive sensing for health and wellbeing demonstrate promise and invite continued research and investment. Existing studies suffer from weaknesses in research design, lack of feedback and clinical integration, and inadequate attention to privacy issues. Key recommendations relate to developing passive sensing strategies matching the problem at hand, using personalized interventions, and addressing methodological and privacy challenges. CONCLUSION: As evolving passive sensing technology presents new possibilities for health and wellbeing, additional research must address methodological, clinical integration, and privacy issues. Doing so depends on interdisciplinary collaboration between informatics and clinical experts

Effectiveness of psychoeducational interventions for family carers of people

Psychoeducational interventions for family carers of people with psychosis are effective for improving compliance and preventing relapse. Whether carers benefit from these interventions has been little explored. This systematic review investigated the effectiveness of psychoeducation for improving carers' outcomes, and potential treatment moderators. We searched for randomised controlled trials (RCTs) published in English or Chinese in eight databases. Carers' outcomes included wellbeing, quality of life, global morbidities, burden, and expressed emotion. Thirty-two RCTs were included, examining 2858 carers. Intervention duration ranged from 4 to 52 weeks, and contact times ranged from 6 to 42 hours. At post intervention, findings were equivocal for carers' wellbeing (SMD 0.103, 95% CI − 0.186 to 0.392). Conversely, psychoeducation was superior in reducing carers' global morbidities (SMD − 0.230, 95% CI − 0.386 to − 0.075), perceived burden (SMD − 0.434, 95% CI − 0.567 to − 0.31), negative caregiving experiences (SMD − 0.210, 95% CI − 0.396 to − 0.025) and expressed emotion (SMD − 0.161, 95% CI − 0.367 to − 0.045). The lack of available data precluded meta-analysis of outcomes beyond short-term follow-up. Meta-regression revealed no significant associations between intervention modality, duration, or contact time and outcomes. Further research should focus on improving carers' outcomes in the longer-term and identifying factors to optimise intervention design

Analysis of the innovation outputs in mHealth for patient monitoring

Abstract—In the last decade, mobile health (mHealth) has developed as a natural consequence of the advances in mobile technologies, the growing spread of mobile devices, and their application in the provision of novel health services. mHealth has demonstrated the potential to make the health care sector more efficient and sustainable and to increase the healthcare quality. Considering the boost to the healthcare area which will be provided by mHealth, many organizations and governments have engaged in innovating in this area. In this context, this work investigated the role of innovation in the area of mHealth for patient monitoring in order to determine the trends and the performance of the innovation activities in this domain. Proxy indicators, like intellectual property statistics and scientific publication statistics, were utilized to measure the outputs of innovation during the period of time from 2006 to 2015 in Europe. Two studies were performed to provide quantitative measures for the indicators measuring innovation outputs in the domain of mHealth for patient monitoring and three main conclusions were observed. First, even if there was a lot of research in Europe in mHealth for patient monitoring, the vast majority of the enterprises did not protect their inventions. Second, a strong research collaboration in the area of mHealth for patient monitoring took place between researchers affiliated to institu- tions of different European countries and even with researchers working in Asian or American institutions. Finally, an increasing trend on the number of published articles about mHealth for patient monitoring was identified. Therefore, the findings of the studies demonstrated the great interest that has arisen the field of mHealth and the huge involvement in innovation activities in the area of mHealth for patient monitoring

Effectiveness of psychoeducational interventions for family carers of people with psychosis: A systematic review and meta-analysis.

Psychoeducational interventions for family carers of people with psychosis are effective for improving compliance and preventing relapse. Whether carers benefit from these interventions has been little explored. This systematic review investigated the effectiveness of psychoeducation for improving carers' outcomes, and potential treatment moderators. We searched for randomised controlled trials (RCTs) published in English or Chinese in eight databases. Carers' outcomes included wellbeing, quality of life, global morbidities, burden, and expressed emotion. Thirty-two RCTs were included, examining 2858 carers. Intervention duration ranged from 4 to 52weeks, and contact times ranged from 6 to 42hours. At post intervention, findings were equivocal for carers' wellbeing (SMD 0.103, 95% CI -0.186 to 0.392). Conversely, psychoeducation was superior in reducing carers' global morbidities (SMD -0.230, 95% CI -0.386 to -0.075), perceived burden (SMD -0.434, 95% CI -0.567 to -0.31), negative caregiving experiences (SMD -0.210, 95% CI -0.396 to -0.025) and expressed emotion (SMD -0.161, 95% CI -0.367 to -0.045). The lack of available data precluded meta-analysis of outcomes beyond short-term follow-up. Meta-regression revealed no significant associations between intervention modality, duration, or contact time and outcomes. Further research should focus on improving carers' outcomes in the longer-term and identifying factors to optimise intervention design

Analysis of Legal and Regulatory Frameworks in Digital Health: A Comparison of Guidelines and Approaches in the European Union and United States

The advent of digital technology in healthcare presents opportunities for the improvement of healthcare systems around the world and the move towards value-based treatment. However, this move must be accompanied by strong legal and regulatory frameworks that will not only facilitate but encourage the good use of technology. The goal of the study was to assess the amenability and furtherance of regulatory frameworks in digital health by evaluating and comparing the processes, effectiveness and outcomes of these frameworks in the European Union and United States. Methods: This study incorporated two research methodologies. The first was a research of current legal and regulatory frameworks in digital health in the European Union and United States. A comprehensive online search for publications was carried out which included laws, regulations, policies, green papers, guidelines and recommendations. This research was complemented with interviews of five purposively sampled key informants in the legal and regulatory landscape. Results: Mind-maps revealed key features and challenges of the digital health field in the topics of the current state of regulation of digital health in the EU, Germany and US, regulatory pathways for digital health devices, protection and privacy of health data, mobile health validation, risk-based classification of medical devices, regulation of clinical decision support systems, telemedicine, artificial intelligence and emerging technologies, reimbursement for digital health services and liability for digital health products. The experts expressed and explained key points where current regulation is deficient. The review of the legal frameworks revealed deficiencies which provide opportunities and recommendations to further develop and strengthen the regulatory landscape. Conclusions: A key element to a robust regulatory framework is the ability to ensure trust and confidence in using digital health technology. Technology must measure the impact on quality of life and burden of disease and not merely involve the collection of data