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Objective: The use of smartwatches has attracted considerable interest in developing smart digital health interventions and improving health and well-being during the past few years. This work presents a systematic review of the literature on smartwatch interventions in healthcare. The main characteristics and individual health-related outcomes of smartwatch interventions within research studies are illustrated, in order to acquire evidence of their benefit and value in patient care., Methods: A literature search in the bibliographic databases of PubMed and Scopus was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to identify research studies incorporating smartwatch interventions. The studies were grouped according to the intervention's target disease, main smartwatch features, study design, target age and number of participants, follow-up duration, and outcome measures., Results: The literature search identified 13 interventions incorporating smartwatches within research studies with people of middle and older age. The interventions targeted different conditions: cardiovascular diseases, diabetes, depression, stress and anxiety, metastatic gastrointestinal cancer and breast cancer, knee arthroplasty, chronic stroke, and allergic rhinitis. The majority of the studies (76%) were randomized controlled trials. The most used smartwatch was the Apple Watch utilized in 4 interventions (31%). Positive outcomes for smartwatch interventions concerned foot ulcer recurrence, severity of symptoms of depression, utilization of healthcare resources, lifestyle changes, functional assessment and shoulder range of motion, medication adherence, unplanned hospital readmissions, atrial fibrillation diagnosis, adherence to self-monitoring, and goal attainment for emotion regulation. Challenges in using smartwatches included frequency of charging, availability of Internet and synchronization with a mobile app, the burden of using a smartphone in addition to a patient's regular phone, and data quality., Conclusion: The results of this review indicate the potential of smartwatches to bring positive health-related outcomes for patients. Considering the low number of studies identified in this review along with their moderate quality, we implore the research community to carry out additional studies in intervention settings to show the utility of smartwatches in clinical contexts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: Mobile Health (mHealth) refers to using mobile devices to support health. This study aimed to identify specific methodological challenges in systematic reviews (SRs) of mHealth interventions and to develop guidance for addressing selected challenges., Study Design and Setting: Two-phase participatory research project. First, we sent an online survey to corresponding authors of SRs of mHealth interventions. On a five-category scale, survey respondents rated how challenging they found 24 methodological aspects in SRs of mHealth interventions compared to non-mHealth intervention SRs. Second, a subset of survey respondents participated in an online workshop to discuss recommendations to address the most challenging methodological aspects identified in the survey. Finally, consensus-based recommendations were developed based on the workshop discussion and subsequent interaction via email with the workshop participants and two external mHealth SR authors., Results: We contacted 953 corresponding authors of mHealth intervention SRs, of whom 50 (5 %) completed the survey. All the respondents identified at least one methodological aspect as more or much more challenging in mHealth intervention SRs than in non-mHealth SRs. A median of 11 (IQR 7.25-15) out of 24 aspects (46 %) were rated as more or much more challenging. Those most frequently reported were: defining intervention intensity and components (85 %), extracting mHealth intervention details (71 %), dealing with dynamic research with evolving interventions (70 %), assessing intervention integrity (69 %), defining the intervention (66 %) and maintaining an updated review (65 %). Eleven survey respondents participated in the workshop (five had authored more than three mHealth SRs). Eighteen consensus-based recommendations were developed to address issues related to mHealth intervention integrity and to keep mHealth SRs up to date., Conclusion: mHealth SRs present specific methodological challenges compared to non-mHealth interventions, particularly related to intervention integrity and keeping SRs current. Our recommendations for addressing these challenges can improve mHealth SRs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children., Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices., Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective., Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals. Clinical and Translational Impact Statement-This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Pervasive technologies such as Artificial Intelligence, Virtual Reality and the Internet of Things, despite their great potential for improved workability and well-being of older workers, entail wide ethical concerns. Aligned with these considerations we emphasize the need to present from the viewpoint of ethics the risks of personalized ICT solutions that aim to remedy health and support the well-being of the ageing population at workplaces. The ethical boundaries of digital technologies are opaque. The main motivation is to cope with the uncertainties of workplaces' digitization and develop an ethics framework, termed SmartFrameWorK, for personalized health support through ICT tools at workplace environments. SmartFrameWorK is built upon a five-dimensional approach of ethics norms: autonomy, privacy, transparency, trustworthiness and accountability to incite trust in digital workplace technologies. A typology underpins these principles and guides the ethical decision-making process with regard to older worker particular needs, context, data type-related risks and digital tools' use throughout their lifecycle. Risk analysis of pervasive technology use and multimodal data collection, highlighted the imperative for ethically aware practices for older workers' activity and behaviour monitoring. The SmartFrameWorK methodology has been applied in a case study to provide evidence that personalized digital services could elicit trust in users through a well-defined framework. Ethics compliance is a dynamic process from participants' engagement to data management. Defining ethical determinants is pivotal towards building trust and reinforcing better workability and well-being in older workers., Competing Interests: Conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

Childhood obesity is a major public health challenge which is linked with the occurrence of diseases such as diabetes and cancer. The COVID-19 pandemic has forced changes to the lifestyle behaviors of children, thereby making the risk of developing obesity even greater. Novel preventive tools and approaches are required to fight childhood obesity. We present a social robot-based platform which utilizes an interactive motivational strategy in communication with children, collects self-reports through the touch of tangible objects, and processes behavioral data, aiming to: (a) screen and assess the behaviors of children in the dimensions of physical activity, diet, and education, and (b) recommend individualized goals for health behavior change. The platform was integrated through a microservice architecture within a multi-component system targeting childhood obesity prevention. The platform was evaluated in an experimental study with 30 children aged 9-12 years in a real-life school setting, showing children's acceptance to use it, and an 80% success rate in achieving weekly personal health goals recommended by the social robot-based platform. The results provide preliminary evidence on the implementation feasibility and potential of the social robot-based platform toward the betterment of children's health behaviors in the context of childhood obesity prevention. Further rigorous longer-term studies are required., Competing Interests: Conflict of interestThe authors of this manuscript declare no conflicts of interest., (© The Author(s) 2022.)

IoT technologies generate intelligence and connectivity and develop knowledge to be used in the decision-making process. However, research that uses big data through global interconnected infrastructures, such as the 'Internet of Things' (IoT) for Active and Healthy Ageing (AHA), is fraught with several ethical concerns. A large-scale application of IoT operating in diverse piloting contexts and case studies needs to be orchestrated by a robust framework to guide ethical and sustainable decision making in respect to data management of AHA and IoT based solutions. The main objective of the current article is to present the successful completion of a collaborative multiscale research work, which addressed the complicated exercise of ethical decision making in IoT smart ecosystems for older adults. Our results reveal that among the strong enablers of the proposed ethical decision support model were the participatory and deliberative procedures complemented by a set of regulatory and non-regulatory tools to operationalize core ethical values such as transparency, trust, and fairness in real care settings for older adults and their caregivers.

This study conducted a preliminary usability assessment of the Virtual Supermarket Test (VST), a serious game-based self-administered cognitive screening test for mild cognitive impairment (MCI). Twenty-four healthy older adults with subjective cognitive decline and 33 patients with MCI self-administered the VST and then completed the System Usability Scale (SUS). The average SUS score was 83.11 (SD = 14.6). The SUS score was unaffected by age, education, touch device familiarity, and diagnosis of MCI. SUS score correlated with VST performance ( r  = -0.496, p  = 0.000). Results of this study indicate good usability of the VST., Competing Interests: The authors have no conflict of interest to report., (© 2022 – The authors. Published by IOS Press.)

Background: Major chronic diseases such as cardiovascular disease (CVD), diabetes, and cancer impose a significant burden on people and health care systems around the globe. Recently, deep learning (DL) has shown great potential for the development of intelligent mobile health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere., Objective: The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this rapidly developing field., Methods: A search was conducted on the bibliographic databases Scopus and PubMed to identify papers with a focus on the deployment of DL algorithms that used data captured from mobile devices (eg, smartphones, smartwatches, and other wearable devices) targeting CVD, diabetes, or cancer. The identified studies were synthesized according to the target disease, the number of enrolled participants and their age, and the study period as well as the DL algorithm used, the main DL outcome, the data set used, the features selected, and the achieved performance., Results: In total, 20 studies were included in the review. A total of 35% (7/20) of DL studies targeted CVD, 45% (9/20) of studies targeted diabetes, and 20% (4/20) of studies targeted cancer. The most common DL outcome was the diagnosis of the patient's condition for the CVD studies, prediction of blood glucose levels for the studies in diabetes, and early detection of cancer. Most of the DL algorithms used were convolutional neural networks in studies on CVD and cancer and recurrent neural networks in studies on diabetes. The performance of DL was found overall to be satisfactory, reaching >84% accuracy in most studies. In comparison with classic machine learning approaches, DL was found to achieve better performance in almost all studies that reported such comparison outcomes. Most of the studies did not provide details on the explainability of DL outcomes., Conclusions: The use of DL can facilitate the diagnosis, management, and treatment of major chronic diseases by harnessing mHealth data. Prospective studies are now required to demonstrate the value of applied DL in real-life mHealth tools and interventions., (©Andreas Triantafyllidis, Haridimos Kondylakis, Dimitrios Katehakis, Angelina Kouroubali, Lefteris Koumakis, Kostas Marias, Anastasios Alexiadis, Konstantinos Votis, Dimitrios Tzovaras. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.04.2022.)

Sedentary behavior is considered as a major public health challenge, linked with many chronic diseases and premature mortality. In this paper, we propose a steps counting -based machine learning approach for the prediction of sedentary behavior. Our work focuses on analyzing historical data from multiple users of wearable physical activity trackers and exploring the performance of four machine learning algorithms, i.e., Logistic Regression, Random Forest, XGBoost, Convolutional Neural Networks, as well as a Majority Vote Ensemble of the algorithms. To train and test our models we employed a crowd sourced dataset containing a month's data of 33 users. For further evaluation, we employed a dataset containing 6 months of data of an additional user. The results revealed that while all models succeed in predicting next-day sedentary behavior, the ensemble model outperforms all baselines, as it manages to predict sedentary behavior and reduce false positives more effectively. On the multi-subjects test dataset, our ensemble model achieved an accuracy of 82.12% with a sensitivity of 74.53% and a specificity of 85.71%. On the additional unseen dataset, we achieved 76.88% in accuracy, 63.27% in sensitivity and 81.75% in specificity. These outcomes provide the ground towards the development of real-life artificially intelligent systems for sedentary behavior prediction.

Background: A vast amount of mobile apps have been developed during the past few months in an attempt to "flatten the curve" of the increasing number of COVID-19 cases., Objective: This systematic review aims to shed light into studies found in the scientific literature that have used and evaluated mobile apps for the prevention, management, treatment, or follow-up of COVID-19., Methods: We searched the bibliographic databases Global Literature on Coronavirus Disease, PubMed, and Scopus to identify papers focusing on mobile apps for COVID-19 that show evidence of their real-life use and have been developed involving clinical professionals in their design or validation., Results: Mobile apps have been implemented for training, information sharing, risk assessment, self-management of symptoms, contact tracing, home monitoring, and decision making, rapidly offering effective and usable tools for managing the COVID-19 pandemic., Conclusions: Mobile apps are considered to be a valuable tool for citizens, health professionals, and decision makers in facing critical challenges imposed by the pandemic, such as reducing the burden on hospitals, providing access to credible information, tracking the symptoms and mental health of individuals, and discovering new predictors., (©Haridimos Kondylakis, Dimitrios G Katehakis, Angelina Kouroubali, Fokion Logothetidis, Andreas Triantafyllidis, Ilias Kalamaras, Konstantinos Votis, Dimitrios Tzovaras. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.12.2020.)

Background: Digital health interventions based on tools for Computerized Decision Support (CDS) and Machine Learning (ML), which take advantage of new information, sensing and communication technologies, can play a key role in childhood obesity prevention and treatment., Objectives: We present a systematic literature review of CDS and ML applications for the prevention and treatment of childhood obesity. The main characteristics and outcomes of studies using CDS and ML are demonstrated, to advance our understanding towards the development of smart and effective interventions for childhood obesity care., Methods: A search in the bibliographic databases of PubMed and Scopus was performed to identify childhood obesity studies incorporating either CDS interventions, or advanced data analytics through ML algorithms. Ongoing, case, and qualitative studies, along with those not providing specific quantitative outcomes were excluded. The studies incorporating CDS were synthesized according to the intervention's main technology (e.g., mobile app), design type (e.g., randomized controlled trial), number of enrolled participants, target age of children, participants' follow-up duration, primary outcome (e.g., Body Mass Index (BMI)), and main CDS feature(s) and their outcomes (e.g., alerts for caregivers when BMI is high). The studies incorporating ML were synthesized according to the number of subjects included and their age, the ML algorithm(s) used (e.g., logistic regression), as well as their main outcome (e.g., prediction of obesity)., Results: The literature search identified 8 studies incorporating CDS interventions and 9 studies utilizing ML algorithms, which met our eligibility criteria. All studies reported statistically significant interventional or ML model outcomes (e.g., in terms of accuracy). More than half of the interventional studies (n = 5, 63 %) were designed as randomized controlled trials. Half of the interventional studies (n = 4, 50 %) utilized Electronic Health Records (EHRs) and alerts for BMI as means of CDS. From the 9 studies using ML, the highest percentage targeted at the prognosis of obesity (n = 4, 44 %). In the studies incorporating more than one ML algorithms and reporting accuracy, it was shown that decision trees and artificial neural networks can accurately predict childhood obesity., Conclusions: This review has found that CDS tools can be useful for the self-management or remote medical management of childhood obesity, whereas ML algorithms such as decision trees and artificial neural networks can be helpful for prediction purposes. Further rigorous studies in the area of CDS and ML for childhood obesity care are needed, considering the low number of studies identified in this review, their methodological limitations, and the scarcity of interventional studies incorporating ML algorithms in CDS tools., Competing Interests: Declaration of Competing Interest The authors of this manuscript declare no conflicts of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Literature supports the use of serious games and virtual environments to assess cognitive functions and detect cognitive decline. This promising assessment method, however, has not yet been translated into self-administered screening instruments for pre-clinical dementia., Objective: The aim of this study is to assess the performance of a novel self-administered serious game-based test, namely the Virtual Supermarket Test (VST), in detecting mild cognitive impairment (MCI) in a sample of older adults with subjective memory complaints (SMC), in comparison with two well-established screening instruments, the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE)., Methods: Two groups, one of healthy older adults with SMC (N = 48) and one of MCI patients (N = 47) were recruited from day centers for cognitive disorders and administered the VST, the MoCA, the MMSE, and an extended pencil and paper neuropsychological test battery., Results: The VST displayed a correct classification rate (CCR) of 81.91% when differentiating between MCI patients and older adults with SMC, while the MoCA displayed of CCR of 72.04% and the MMSE displayed a CCR of 64.89%., Conclusion: The three instruments assessed in this study displayed significantly different performances in differentiating between healthy older adults with SMC and MCI patients. The VST displayed a good CCR, while the MoCA displayed an average CCR and the MMSE displayed a poor CCR. The VST appears to be a robust tool for detecting MCI in a population of older adults with SMC.

Background: Mobile health (mHealth) technology has the potential to play a key role in improving the health of patients with chronic non-communicable diseases., Objectives: We present a review of systematic reviews of mHealth in chronic disease management, by showing the features and outcomes of mHealth interventions, along with associated challenges in this rapidly growing field., Methods: We searched the bibliographic databases of PubMed, Scopus, and Cochrane to identify systematic reviews of mHealth interventions with advanced technical capabilities (e.g., Internet-linked apps, interoperation with sensors, communication with clinical platforms, etc.) utilized in randomized clinical trials. The original studies included the reviews were synthesized according to their intervention features, the targeted diseases, the primary outcome, the number of participants and their average age, as well as the total follow-up duration., Results: We identified 5 reviews respecting our inclusion and exclusion criteria, which examined 30 mHealth interventions. The highest percentage of the interventions targeted patients with diabetes (n = 19, 63%), followed by patients with psychotic disorders (n = 7, 23%), lung diseases (n = 3, 10%), and cardiovascular disease (n = 1, 3%). 14 studies showed effective results: 9 in diabetes management, 2 in lung function, and 3 in mental health. Significantly positive outcomes were reported in 8 interventions (n = 8, 47%) from 17 studies assessing glucose concentration, one intervention assessing physical activity, 2 interventions (n = 2, 67%) from 3 studies assessing lung function parameters, and 3 mental health interventions assessing N-back performance, medication adherence, and number of hospitalizations. Divergent features were adopted in 14 interventions with significantly positive outcomes, such as personalized goal setting (n = 10, 71%), motivational feedback (n = 5, 36%), and alerts for health professionals (n = 3, 21%). The most significant found challenges in the development and evaluation of mHealth interventions include the design of studies with high quality, the construction of robust interventions in combination with health professional inputs, and the identification of tools and methods to improve patient adherence., Conclusions: This review found mixed evidence regarding the health benefits of mHealth interventions for patients living with chronic diseases. Further rigorous studies are needed to assess the outcomes of personalized mHealth interventions toward the optimal management of chronic diseases., (Copyright © 2019 Elsevier B.V. All rights reserved.)

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., (©Andreas K Triantafyllidis, Athanasios Tsanas. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.04.2019.)

Cardiovascular diseases (CVDs) are a leading cause of premature death worldwide. International guidelines recommend routine delivery of all phases of cardiac rehabilitation (CR). Uptake of traditional CR remains suboptimal, as attendance at formal hospital-based CR programs is low, with community-based CR rates and individual long-term exercise maintenance even lower. Home-based CR programs have been shown to be equally effective in clinical and health-related quality of life outcomes and yet are not readily available. The aim of the current study was to develop the PATHway intervention (physical activity toward health) for the self-management of CVD. Increasing physical activity in individuals with CVD was the primary behavior. The PATHway intervention was theoretically informed by the behavior change wheel and social cognitive theory. All relevant intervention functions, behavior change techniques, and policy categories were identified and translated into intervention content. Furthermore, a person-centered approach was adopted involving an iterative codesign process and extensive user testing. Education, enablement, modeling, persuasion, training, and social restructuring were selected as appropriate intervention functions. Twenty-two behavior change techniques, linked to the six intervention functions and three policy categories, were identified for inclusion and translated into PATHway intervention content. This paper details the use of the behavior change wheel and social cognitive theory to develop an eHealth intervention for the self-management of CVD. The systematic and transparent development of the PATHway intervention will facilitate the evaluation of intervention effectiveness and future replication.

Background: Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes., Objectives: We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs., Methods: The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems., Results: The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance., Conclusions: Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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.

Background: The benefits of regular physical activity for health and quality of life are unarguable. New information, sensing and communication technologies have the potential to play a critical role in computerised decision support and coaching for physical activity., Objectives: We provide a literature review of recent research in the development of physical activity interventions employing computerised decision support, their feasibility and effectiveness in healthy and diseased individuals, and map out challenges and future research directions., Methods: We searched the bibliographic databases of PubMed and Scopus to identify physical activity interventions with computerised decision support utilised in a real-life context. Studies were synthesized according to the target user group, the technological format (e.g., web-based or mobile-based) and decision-support features of the intervention, the theoretical model for decision support in health behaviour change, the study design, the primary outcome, the number of participants and their engagement with the intervention, as well as the total follow-up duration., Results: From the 24 studies included in the review, the highest percentage (n = 7, 29%) targeted sedentary healthy individuals followed by patients with prediabetes/diabetes (n = 4, 17%) or overweight individuals (n = 4, 17%). Most randomized controlled trials reported significantly positive effects of the interventions, i.e., increase in physical activity (n = 7, 100%) for 7 studies assessing physical activity measures, weight loss (n = 3, 75%) for 4 studies assessing diet, and reductions in glycosylated hemoglobin (n = 2, 66%) for 3 studies assessing glycose concentration. Accelerometers/pedometers were used in almost half of the studies (n = 11, 46%). Most adopted decision support features included personalised goal-setting (n = 16, 67%) and motivational feedback sent to the users (n = 15, 63%). Fewer adopted features were integration with electronic health records (n = 3, 13%) and alerts sent to caregivers (n = 4, 17%). Theoretical models of decision support in health behaviour to drive the development of the intervention were not reported in most studies (n = 14, 58%)., Conclusions: Interventions employing computerised decision support have the potential to promote physical activity and result in health benefits for both diseased and healthy individuals, and help healthcare providers to monitor patients more closely. Objectively measured activity through sensing devices, integration with clinical systems used by healthcare providers and theoretical frameworks for health behaviour change need to be employed in a larger scale in future studies in order to realise the development of evidence-based computerised systems for physical activity monitoring and coaching., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Introduction: Successive confidential enquiries into maternal deaths in the UK have identified an urgent need to develop a national early warning score (EWS) specifically for pregnant or recently pregnant women to aid more timely recognition, referral and treatment of women who are developing life-threatening complications in pregnancy or the puerperium. Although many local EWS are in use in obstetrics, most have been developed heuristically. No current obstetric EWS has defined the thresholds at which an alert should be triggered using evidence-based normal ranges, nor do they reflect the changing physiology that occurs with gestation during pregnancy., Methods and Analysis: An observational cohort study involving 1000 participants across three UK sites in Oxford, London and Newcastle. Pregnant women will be recruited at approximately 14 weeks' gestation and have their vital signs (heart rate, blood pressure, respiratory rate, oxygen saturation and temperature) measured at 4 to 6-week intervals during pregnancy. Vital signs recorded during labour and delivery will be extracted from hospital records. After delivery, participants will measure and record their own vital signs daily for 2 weeks. During the antenatal and postnatal periods, vital signs will be recorded on an Android tablet computer through a custom software application and transferred via mobile internet connection to a secure database. The data collected will be used to define reference ranges of vital signs across normal pregnancy, labour and the immediate postnatal period. This will inform the design of an evidence-based obstetric EWS., Ethics and Dissemination: The study has been approved by the NRES committee South East Coast-Brighton and Sussex (14/LO/1312) and is registered with the ISRCTN (10838017). All participants will provide written informed consent and can withdraw from the study at any point. All data collected will be managed anonymously. The findings will be disseminated in international peer-reviewed journals and through research conferences., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Introduction: Exercise-based cardiac rehabilitation (CR) independently alters the clinical course of cardiovascular diseases resulting in a significant reduction in all-cause and cardiac mortality. However, only 15%-30% of all eligible patients participate in a phase 2 ambulatory programme. The uptake rate of community-based programmes following phase 2 CR and adherence to long-term exercise is extremely poor. Newer care models, involving telerehabilitation programmes that are delivered remotely, show considerable promise for increasing adherence. In this view, the PATHway (Physical Activity Towards Health) platform was developed and now needs to be evaluated in terms of its feasibility and clinical efficacy., Methods and Analysis: In a multicentre randomised controlled pilot trial, 120 participants (m/f, age 40-80 years) completing a phase 2 ambulatory CR programme will be randomised on a 1:1 basis to PATHway or usual care. PATHway involves a comprehensive, internet-enabled, sensor-based home CR platform and provides individualised heart rate monitored exercise programmes (exerclasses and exergames) as the basis on which to provide a personalised lifestyle intervention programme. The control group will receive usual care. Study outcomes will be assessed at baseline, 3 months and 6 months after completion of phase 2 of the CR programme. The primary outcome is the change in active energy expenditure. Secondary outcomes include cardiopulmonary endurance capacity, muscle strength, body composition, cardiovascular risk factors, peripheral endothelial vascular function, patient satisfaction, health-related quality of life (HRQoL), well-being, mediators of behaviour change and safety. HRQoL and healthcare costs will be taken into account in cost-effectiveness evaluation., Ethics and Dissemination: The study will be conducted in accordance with the Declaration of Helsinki. This protocol has been approved by the director and clinical director of the PATHway study and by the ethical committee of each participating site. Results will be disseminated via peer-reviewed scientific journals and presentations at congresses and events., Trial Registration Number: NCT02717806. This trial is currently in the pre-results stage., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

The current institution-based model for healthcare service delivery faces enormous challenges posed by an aging population and the prevalence of chronic diseases. For this reason, pervasive healthcare, i.e., the provision of healthcare services to individuals anytime anywhere, has become a major focus for the research community. In this paper, we map out the current state of pervasive healthcare research by presenting an overview of three emerging areas in personalized health monitoring, namely: 1) mobile phone sensing via in-built or external sensors, 2) self-reporting for manually captured health information, such as symptoms and behaviors, and 3) social sharing of health information within the individual's community. Systems deployed in a real-life setting as well as proofs-of-concept for achieving pervasive health are presented, in order to identify shortcomings and increase our understanding of the requirements for the next generation of pervasive healthcare systems addressing these three areas.

Objective: There is significant interest in the role of digital health technology in enabling optimal monitoring of heart failure patients. To harness this potential, it is vital to account for users' capacity and preferences in the development of technological solutions. We adopted an iterative approach focussed on learning from users' interactions with a mobile-health monitoring system., Methods: We used a participatory mixed methods research approach to develop and evaluate a mobile-health monitoring system. Fifty-eight heart failure patients were recruited from three health care settings in the UK and provided with Internet-enabled tablet computers that were wirelessly linked to sensor devices for blood pressure, heart rate and weight monitoring. One to two home visits were conducted with a subgroup of 29 participants to evaluate the usability of the system over a median follow-up period of six months. The thematic analysis of observational data and 45 interviews was informed by the domestication of technology theory., Results: Our findings indicate that digital health technologies need to create and extend connections with health professionals, be incorporated into users' daily routines, and be personalised according to users' technological competencies and interest in assuming a proactive or more passive role in monitoring their condition., Conclusions: Users' patterns of engagement with health technology changes over time and varies according to their need and capacity to use the technology. Incorporating diverse user experiences in the development and maintenance of mobile-health systems is likely to increase the extent of successful uptake and impacts on outcomes for patients and providers., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Sensor-based health systems can often become difficult to use, extend and sustain. The authors propose a framework for designing sensor-based health monitoring systems aiming to provide extensible and usable monitoring services in the scope of pervasive patient care. The authors' approach relies on a distributed system for monitoring the patient health status anytime-anywhere and detecting potential health complications, for which healthcare professionals and patients are notified accordingly. Portable or wearable sensing devices measure the patient's physiological parameters, a smart mobile device collects and analyses the sensor data, a Medical Center system receives notifications on the detected health condition, and a Health Professional Platform is used by formal caregivers in order to review the patient condition and configure monitoring schemas. A Service-oriented architecture is utilised to provide extensible functional components and interoperable interactions among the diversified system components. The framework was applied within the REMOTE ambient-assisted living project in which a prototype system was developed, utilising Bluetooth to communicate with the sensors and Web services for data exchange. A scenario of using the REMOTE system and preliminary usability results show the applicability, usefulness and virtue of our approach.

Rehabilitation is important for patients with cardiovascular diseases (CVD) to improve health outcomes and quality of life. However, adherence to current exercise programmes in cardiac rehabilitation is limited. We present the design and development of a Decision Support System (DSS) for telerehabilitation, aiming to enhance exercise programmes for CVD patients through ensuring their safety, personalising the programme according to their needs and performance, and motivating them toward meeting their physical activity goals. The DSS processes data originated from a Microsoft Kinect camera, a blood pressure monitor, a heart rate sensor and questionnaires, in order to generate a highly individualised exercise programme and improve patient adherence. Initial results within the EU-funded PATHway project show the potential of our approach.

Aims: Previous generations of home monitoring systems have had limited usability. We aimed to develop and evaluate a user-centred and adaptive system for health monitoring and self-management support in patients with heart failure., Methods and Results: Patients with heart failure were recruited from three UK centres and provided with Internet-enabled tablet computers that were wirelessly linked with sensor devices for blood pressure, heart rate, and weight monitoring. Patient observations, interviews, and concurrent analyses of the automatically collected data from their monitoring devices were used to increase the usability of the system. Of the 52 participants (median age 77 years, median follow-up 6 months [interquartile range, IQR, 3.6-9.2]), 24 (46%) had no, or very limited prior, experience with digital technologies. It took participants about 1.5 min to complete the daily monitoring tasks, and the rate of failed attempts in completing tasks was <5%. After 45 weeks of observation, participants still used the system on 4.5 days per week (confidence interval 3.2-5.7 days). Of the 46 patients who could complete the final survey, 93% considered the monitoring system as easy to use and 38% asked to keep the system for self-management support after the study was completed., Conclusion: We developed a user-centred home monitoring system that enabled a wide range of heart failure patients, with differing degrees of IT literacy, to monitor their health status regularly. Despite no active medical intervention, patients felt that they benefited from the reassurance and sense of connectivity that the monitoring system provided.

Background: Despite their potential for improving health outcomes, mobile-based home monitoring systems for heart failure have not yet been taken up widely by the patients and providers., Objectives: To design and iteratively move towards a personalised mobile health monitoring system for patients living with heart failure, according to their health care and usability needs., Methods: We present an iterative approach to refining a remote health monitoring system that is based on interactions between different actors (patients, clinicians, social scientists and engineers) and supports the collection of quantitative and qualitative information about user experience and engagement. Patients were provided with tablet computers and commercially available sensing devices (a blood pressure monitor, a set of weighing scales, and a pulse oximeter) in order to complete physiological measurements at home, answer symptom-specific questionnaires, review their personal readings, view educational material on heart failure self-management, and communicate with their health professionals. The system supported unobtrusive remote software upgrades via an application distribution channel and the activation or deactivation of functional components by health professionals during run-time operation. We report early findings from the application of this approach in a cohort of 26 heart failure patients (mean age 72±15 years), their caregivers and healthcare professionals who participated in the SUPPORT-HF (Seamless User-centred Proactive Provision Of Risk-stratified Treatment for Heart Failure) study over a one-year study period (mean patient follow-up duration=270±62 days)., Results: The approach employed in this study led to several system upgrades dealing in particular with patient requirements for better communication with the development team and personalised self-monitoring interfaces. Engagement with the system was constantly high throughout the study and during the last week of the evaluation, 23 patients (88%) used the system at least once and 16 patients (62%) at least three times., Conclusions: Designers of future mobile-based home monitoring systems for heart failure and other chronic conditions could leverage the described approach as a means of meeting patients' needs during system use within the home environment and facilitating successful uptake., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Although a plethora of remote health monitoring systems have been proposed for chronic conditions, the challenge posed by the changing patient needs and the requirement for personalization in health monitoring to move beyond proprietary, difficult to extend, and unsustainable solutions still pertains. In this direction, we describe a mobile health system based on a smartphone, portable/wearable sensors for measuring the patient's physiological parameters, and back-end platforms for the health professionals to monitor the patient condition and configure monitoring plans in an individualized manner. A prototype system was developed based on a Service-oriented Architecture and integrating commercially available sensing devices. An experimental study has been conducted with 53 patients in order to investigate the usability of the proposed system. The patients were able to perform the majority of the target tasks successfully (Success Rate = 77%), while the perceived usability using the System Usability Scale (SUS) was found to be above average (SUS score = 73%), indicating that the patients overall perceived the system as both easy to use and useful.

The ongoing efforts toward continuity of care and the recent advances in information and communication technologies have led to a number of successful personal health systems for the management of chronic care. These systems are mostly focused on monitoring efficiently the patient's medical status at home. This paper aims at extending home care services delivery by introducing a novel framework for monitoring the patient's condition and safety with respect to the medication treatment administered. For this purpose, considering a body area network (BAN) with advanced sensors and a mobile base unit as the central communication hub from the one side, and the clinical environment from the other side, an architecture was developed, offering monitoring patterns definition for the detection of possible adverse drug events and the assessment of medication response, supported by mechanisms enabling bidirectional communication between the BAN and the clinical site. Particular emphasis was given on communication and information flow aspects that have been addressed by defining/adopting appropriate formal information structures as well as the service-oriented architecture paradigm. The proposed framework is illustrated via an application scenario concerning hypertension management.

A flexible, scaleable and cost-effective medical telemetry system is described for monitoring sleep-related disorders in the home environment. The system was designed and built for real-time data acquisition and processing, allowing for additional use in intensive care unit scenarios where rapid medical response is required in case of emergency. It comprises a wearable body area network of Zigbee-compatible wireless sensors worn by the subject, a central database repository residing in the medical centre and thin client workstations located at the subject's home and in the clinician's office. The system supports heterogeneous setup configurations, involving a variety of data acquisition sensors to suit several medical applications. All telemetry data is securely transferred and stored in the central database under the clinicians' ownership and control.