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1.
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature.
Triantafyllidis, AK, Tsanas, A
Journal of medical Internet research. 2019;(4):e12286
Abstract
BACKGROUND Machine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals. OBJECTIVE Our objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain. METHODS We searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction). RESULTS Our literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes. CONCLUSIONS This review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.
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Leveraging digital technology to intervene on personality processes to promote healthy aging.
Marsch, LA, Hegel, MT, Greene, MA
Personality disorders. 2019;(1):33-45
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Abstract
The scientific evidence is clear that personality processes (particularly conscientiousness and neuroticism) play an important role in healthy aging. Assuming it would be desirable to assist individuals to change their personality in directions that would promote healthy aging, the next step is designing interventions for the task. During the past decade, technological advances have made it possible to develop and evaluate interventions delivered via web and mobile digital technologies. The purpose of this article is to discuss the possibilities for leveraging technology to intervene on personality processes to promote healthy aging, with a specific emphasis on applications for older adults. We begin by reviewing interventions that target personality change to treat mental health problems and physical health, followed by the scant research leveraging digital technologies in targeting personality processes. We present a rationale for adopting a transdiagnostic model to guide intervention development and review the brief literature supporting transdiagnostic interventions when adapted for digital delivery (transdiagnostic Internet-based cognitive-behavioral therapy). We then summarize the literature on designing technology interventions to meet the specific needs of older adults and some of the impressive results from digital technology (Internet-based cognitive-behavioral therapy) intervention studies. We conclude with suggestions for addressing gaps in this important but understudied area of research, with a focus on research targeted to older adults. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Use of Technology in the Management of Obesity: A Literature Review.
Shannon, HH, Joseph, R, Puro, N, Darrell, E
Perspectives in health information management. 2019;(Fall):1c
Abstract
Technology is intended to assist with diagnosing, treating, and monitoring patients remotely. Little is known of its impact on health outcomes or how it is used for obesity management. This study reviewed the literature to identify the different types of technologies used for obesity management and their outcomes. A literature search strategy using PubMed, CINAHL, Scopus, Embase, and ABI/Inform was developed and then was vetted by two pairs of researchers. Twenty-three studies from 2010 to 2017 were identified as relevant. Mobile health, eHealth, and telehealth/telemedicine are among the most popular technologies used. Study outcome measurements include association between technology use and weight loss, changes in body mass index, dietary habits, physical activities, self-efficacy, and engagement. All studies reported positive findings between technology use and weight loss; 60 percent of the studies found statistically significant relationships. Knowledge gaps persist regarding opportunities for technology use in obesity management. Future research needs to include patient-level outcomes, cost-effectiveness, and user engagement to fully evaluate the feasibility of continued and expanded use of technology in obesity management.
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Methods of usability testing in the development of eHealth applications: A scoping review.
Maramba, I, Chatterjee, A, Newman, C
International journal of medical informatics. 2019;:95-104
Abstract
BACKGROUND The number of eHealth applications has exponentially increased in recent years, with over 325,000 health apps now available on all major app stores. This is in addition to other eHealth applications available on other platforms such as PC software, web sites and even gaming consoles. As with other digital applications, usability is one of the key factors in the successful implementation of eHealth apps. Reviews of the literature on empirical methods of usability testing in eHealth were last published in 2015. In the context of an exponentially increasing rate of App development year on year, an updated review is warranted. OBJECTIVE To identify, explore, and summarize the current methods used in the usability testing of eHealth applications. METHODS A scoping review was conducted on literature available from April 2014 up to October 2017. Four databases were searched. Literature was considered for inclusion if it was (1) focused on an eHealth application (which includes websites, PC software, smartphone and tablet applications), (2) provided information about usability of the application, (3) provided empirical results of the usability testing, (4) a full or short paper (not an abstract) published in English after March 2014. We then extracted data pertaining to the usability evaluation processes described in the selected studies. RESULTS 133 articles met the inclusion criteria. The methods used for usability testing, in decreasing order of frequency were: questionnaires (n = 105), task completion (n = 57), 'Think-Aloud' (n = 45), interviews (n = 37), heuristic testing (n = 18) and focus groups (n = 13). Majority of the studies used one (n = 45) or two (n = 46) methods of testing. The rest used a combination of three (n = 30) or four (n = 12) methods of testing usability. None of the studies used automated mechanisms to test usability. The System Usability Scale (SUS) was the most frequently used questionnaire (n = 44). The ten most frequent health conditions or diseases where eHealth apps were being evaluated for usability were the following: mental health (n = 12), cancer (n = 10), nutrition (n = 10), child health (n = 9), diabetes (n = 9), telemedicine (n = 8), cardiovascular disease (n = 6), HIV (n = 4), health information systems (n = 4) and smoking (n = 4). Further iterations of the app were reported in a minority of the studies (n = 41). The use of the 'Think-Aloud' (Pearson Chi-squared test: χ2 = 11.15, p < 0.05) and heuristic walkthrough (Pearson Chi-squared test: χ2 = 4.48, p < 0.05) were significantly associated with at least one further iteration of the app being developed. CONCLUSION Although there has been an exponential increase in the number of eHealth apps, the number of studies that have been published that report the results of usability testing on these apps has not increased at an equivalent rate. The number of digital health applications that publish their usability evaluation results remains only a small fraction. Questionnaires are the most prevalent method of evaluating usability in eHealth applications, which provide an overall measure of usability but do not pinpoint the problems that need to be addressed. Qualitative methods may be more useful in this regard. The use of multiple evaluation methods has increased. Automated methods such as eye tracking have not gained traction in evaluating health apps. Further research is needed into which methods are best suited for the different types of eHealth applications, according to their target users and the health conditions being addressed.
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Advances in Health Technology Use and Implementation in the Era of Healthy Living: Implications for Precision Medicine.
Phillips, SA, Ali, M, Modrich, C, Oke, S, Elokda, A, Laddu, D, Bond, S
Progress in cardiovascular diseases. 2019;(1):44-49
Abstract
Much of the focus of precision medicine has been directed toward genomics, despite the fact that "lifestyle and behavioral factors" are included in the description of precision medicine. Numerous structured diet and PA interventions have demonstrated success in preventing and/or reducing chronic-disease risk. The use of personal health technologies has expanded exponentially in the health care arena; there are a number of consumer-based technologies yielding health information to individual users. The explosion in technology use provides an opportunity for broader dissemination of health care services and products. In addition, tracking cardiovascular disease risk and lifestyle and behavioral aspects of healthy living (HL) profiles in those products may be an important leveraging interface for precision medicine. This review will discuss and present an overview of current health technologies, their use in promotion of HL metrics and how this data may be integrated into venues that support HL and precision medicine.
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Rehabilitation, the Great Absentee of Virtual Coaching in Medical Care: Scoping Review.
Tropea, P, Schlieter, H, Sterpi, I, Judica, E, Gand, K, Caprino, M, Gabilondo, I, Gomez-Esteban, JC, Busnatu, S, Sinescu, C, et al
Journal of medical Internet research. 2019;(10):e12805
Abstract
BACKGROUND In the last few years, several studies have focused on describing and understanding how virtual coaches (ie, coaching program or smart device aiming to provide coaching support through a variety of application contexts) could be key drivers for health promotion in home care settings. As there has been enormous technological progress in the field of artificial intelligence and data processing in the past decade, the use of virtual coaches gains an augmented attention in the considerations of medical innovations. OBJECTIVE This scoping review aimed at providing an overview of the applications of a virtual coach in the clinical field. In particular, the review focused on the papers that provide tangible information for coaching activities with an active implication for engaging and guiding patients who have an ongoing plan of care. METHODS We aimed to investigate the use of the term virtual coach in the clinical field performing a methodical review of the relevant literature indexed on PubMed, Scopus, and Embase databases to find virtual coach papers focused on specific activities dealing with clinical or medical contexts, excluding those aimed at surgical settings or electronic learning purposes. RESULTS After a careful revision of the inclusion and exclusion criteria, 46 records were selected for the full-text review. Most of the identified articles directly or indirectly addressed the topic of physical activity. Some papers were focused on the use of virtual coaching (VC) to manage overweight or nutritional issues. Other papers dealt with technological interfaces to facilitate interactions with patients suffering from different chronic clinical conditions such as heart failure, chronic obstructive pulmonary disease, depression, and chronic pain. CONCLUSIONS Although physical activity is a healthy practice that is most encouraged by a virtual coach system, in the current scenario, rehabilitation is the great absentee. This paper gives an overview of the tangible applications of this tool in the medical field and may inspire new ideas for future research on VC.
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Using Health Chatbots for Behavior Change: A Mapping Study.
Pereira, J, Díaz, Ó
Journal of medical systems. 2019;(5):135
Abstract
This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest in the health domain? We identify 30 articles related to health chatbots from 2014 to 2018. We analyze the selected articles qualitatively and extract a triplet for each of them. This data serves to provide a first overview of chatbot-mediated behavior change on the health domain. Main insights include: nutritional disorders and neurological disorders as the main illness areas being tackled; "affect" as the human competence most pursued by chatbots to attain change behavior; and "personalization" and "consumability" as the most appreciated technical enablers. On the other hand, main limitations include lack of adherence to good practices to case-study reporting, and a deeper look at the broader sociological implications brought by this technology.
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Empowering the Aging with Mobile Health: A mHealth Framework for Supporting Sustainable Healthy Lifestyle Behavior.
Faiola, A, Papautsky, EL, Isola, M
Current problems in cardiology. 2019;(8):232-266
Abstract
Healthcare providers are shifting to a value-based model that acknowledges the importance of a healthy lifestyle for managing chronic disease and mental health. This approach empowers patients to adopt and/or sustain healthy lifestyle choices through the use of innovative technologies-providing beneficial ways of delivering health literacy, self-monitoring, and patient-provider collaboration. Such pathways have the potential to enable healthy lifestyle management for a growing U.S. cohort-the "baby boomer" generation (BBG)-who are at risk for developing heart disease, stroke, arthritis, high cholesterol, and diabetes, etc. In this paper, we argue for a new mHealthy lifestyle management (MLM) model that uses mobile health technology as a means to engage BBG consumers in ways that establish their role in self-care and decision-making, as well as patient-provider collaboration that can significantly impact sustainable healthy lifestyle behaviors. By merging the domains of health informatics and human factors psychology, MLM addresses the complex challenges associated with patient-provider collaborative work, while offering a healthcare framework to BBGs in their quest to self-manage a physical and/or mental healthy lifestyle. A MLM use-case highlights the challenges and solutions for team-based clinical counseling. Finally, recommendations for future MLM tools are outlined that support patient access to personal health eTools, information, and services.
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Using MHealth to Improve Outcomes for Children Following Solid Organ Transplant.
Lerret, SM
Journal of pediatric nursing. 2019;:134-135
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Evaluating mobile phone applications for health behaviour change: A systematic review.
McKay, FH, Cheng, C, Wright, A, Shill, J, Stephens, H, Uccellini, M
Journal of telemedicine and telecare. 2018;(1):22-30
Abstract
Introduction Increasing smartphones access has allowed for increasing development and use of smart phone applications (apps). Mobile health interventions have previously relied on voice or text-based short message services (SMS), however, the increasing availability and ease of use of apps has allowed for significant growth of smartphone apps that can be used for health behaviour change. This review considers the current body of knowledge relating to the evaluation of apps for health behaviour change. The aim of this review is to investigate approaches to the evaluation of health apps to identify any current best practice approaches. Method A systematic review was conducted. Data were collected and analysed in September 2016. Thirty-eight articles were identified and have been included in this review. Results Articles were published between 2011- 2016, and 36 were reviews or evaluations of apps related to one or more health conditions, the remaining two reported on an investigation of the usability of health apps. Studies investigated apps relating to the following areas: alcohol, asthma, breastfeeding, cancer, depression, diabetes, general health and fitness, headaches, heart disease, HIV, hypertension, iron deficiency/anaemia, low vision, mindfulness, obesity, pain, physical activity, smoking, weight management and women's health. Conclusion In order to harness the potential of mobile health apps for behaviour change and health, we need better ways to assess the quality and effectiveness of apps. This review is unable to suggest a single best practice approach to evaluate mobile health apps. Few measures identified in this review included sufficient information or evaluation, leading to potentially incomplete and inaccurate information for consumers seeking the best app for their situation. This is further complicated by a lack of regulation in health promotion generally.