Smart Healthcare solutions in China and Europe, an international business perspective

The thesis is part of the Marie Curie Fellowship project addressing health related challenges with IoT solutions. The author tries to address the challenge for the implementation of telehealth solutions by finding out the demand of the telehealth solution in selected European economies and in China (chapter 1), analyzing the emerging business models for telehealth solution ecosystems in China (chapter 2), how to integrate telehealth solutions with institutional stakeholders (chapter 3) and why are elderly users willing to use telehealth solutions in China. Chapter 1 and chapter 2 form the theoretical background for empirical work in chapter 3 and chapter 4. The thesis addressed four research questions, namely “Which societal and social-economics unmet needs that Internet of Healthcare Things can help to resolve?”, “What are the business model innovation for tech companies in China for the smart health industry?”, “What are the facilitators and hurdles for implementing telehealth solutions”, “Are elderly users willing to use telehealth solutions in China?”. Both qualitative study and quantitative analysis has been made based on data collected by in depth interviews with stakeholders, focus group study work with urban and rural residents in China. The digital platform framework was used in chapter 2 as the theoretical framework where as the stakeholder power mapping framework was used in chapter 3. The discretion choice experiment was used in chapter 4 to design questionnaire study while ordered logit regression was used to analyze the data. Telehealth solutions have great potential to fill in the gap for lack of community healthcare and ensuring health continuity between home care setting, community healthcare and hospitals. There is strong demand for such solutions if they can prove the medical value in managing chronic disease by raising health awareness and lowering health risks by changing the patients’ lifestyle. Analyzing how to realize the value for preventive healthcare by proving the health-economic value of digital health solutions (telehealth solutions) is the focus of research. There remain hurdles to build trust for telehealth solutions and the use of AI in healthcare. Next step of research can also be extended to addressing such challenges by analyzing how to improve the transparency of algorithms by disclosing the data source, and how the algorithms were built. Further research can be done on data interoperability between the EHR systems and telehealth solutions. The medical value of telehealth solutions can improve if doctors could interpret data collected from telehealth solutions; furthermore, if doctors could make diagnosis and provide treatment, adjust healthcare management plans based on such data, telehealth solutions then can be included in insurance packages, making them more accessible

COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalised medicine

A comprehensive bibliographic review with R statistical methods of the COVID pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy

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

Influences on the Uptake of and Engagement With Health and Well-Being Smartphone Apps: Systematic Review

Background: The public health impact of health and well-being digital interventions is dependent upon sufficient real-world uptake and engagement. Uptake is currently largely dependent on popularity indicators (eg, ranking and user ratings on app stores), which may not correspond with effectiveness, and rapid disengagement is common. Therefore, there is an urgent need to identify factors that influence uptake and engagement with health and well-being apps to inform new approaches that promote the effective use of such tools. Objective: This review aimed to understand what is known about influences on the uptake of and engagement with health and well-being smartphone apps among adults. Methods: We conducted a systematic review of quantitative, qualitative, and mixed methods studies. Studies conducted on adults were included if they focused on health and well-being smartphone apps reporting on uptake and engagement behavior. Studies identified through a systematic search in Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (MEDLINE), EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsychINFO, Scopus, Cochrane library databases, DataBase systems and Logic Programming (DBLP), and Association for Computing Machinery (ACM) Digital library were screened, with a proportion screened independently by 2 authors. Data synthesis and interpretation were undertaken using a deductive iterative process. External validity checking was undertaken by an independent researcher. A narrative synthesis of the findings was structured around the components of the capability, opportunity, motivation, behavior change model and the theoretical domains framework (TDF). Results: Of the 7640 identified studies, 41 were included in the review. Factors related to uptake (U), engagement (E), or both (B) were identified. Under capability, the main factors identified were app literacy skills (B), app awareness (U), available user guidance (B), health information (E), statistical information on progress (E), well-designed reminders (E), features to reduce cognitive load (E), and self-monitoring features (E). Availability at low cost (U), positive tone, and personalization (E) were identified as physical opportunity factors, whereas recommendations for health and well-being apps (U), embedded health professional support (E), and social networking (E) possibilities were social opportunity factors. Finally, the motivation factors included positive feedback (E), available rewards (E), goal setting (E), and the perceived utility of the app (E). Conclusions: Across a wide range of populations and behaviors, 26 factors relating to capability, opportunity, and motivation appear to influence the uptake of and engagement with health and well-being smartphone apps. Our recommendations may help app developers, health app portal developers, and policy makers in the optimization of health and well-being apps

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

The CALM Project: Teaching Mindfulness Meditation in Primary Care Using Computer-Based Application

Mindfulness-based interventions (MBI) have been studied extensively and the evidence is now credible that even brief techniques, delivered electronically using web-based applications (apps), easily accessed by smart phone, computer, or tablet, are consistently effective at anxiety reduction as well demonstrating efficacy in other commonly occurring comorbidities such as depression, stress, and panic symptoms. The purpose of this DNP project was to examine feasibility and the effect of the app, Calm.com, on anxious adult patients in primary care, using the Recovery Alliance Theory as its theoretical foundation. This pilot study was longitudinal, using a pre-test/post-test measurement of the Generalized Anxiety Disorder Seven-Item Scale (GAD-7), electronic collection of time the app was used, and qualitative measures in a group of 15 adult primary care patient volunteers. The MBI consisted of 28-sessions, varying in length from 9-18 minutes consisting of education on mindfulness and guided meditation practice. Changes in the pre/post GAD-7 scores were significant (p= 0.01), with a trend toward improvement in symptoms with more use of the app, but the changes were statistically insignificant (p= 0.20). The qualitative data confirmed participants’ belief that the app was beneficial in helping them learn to relax, using short educational sessions. This project adds to the evidence that a web-based app is an evidence-based option for management of anxiety in adult primary care patients

Artificial neural network (ANN) enabled internet of things (IoT) architecture for music therapy

Alternative medicine techniques such as music therapy have been a recent interest of medical practitioners and researchers. Significant clinical evidence suggests that music has a positive influence over pain, stress and anxiety for the patients of cancer, pre and post surgery, insomnia, child birth, end of life care, etc. Similarly, the technologies of Internet of Things (IoT), Body Area Networks (BAN) and Artificial Neural Networks (ANN) have been playing a vital role to improve the health and safety of the population through offering continuous remote monitoring facilities and immediate medical response. In this article, we propose a novel ANN enabled IoT architecture to integrate music therapy with BAN and ANN for providing immediate assistance to patients by automating the process of music therapy. The proposed architecture comprises of monitoring the body parameters of patients using BAN, categorizing the disease using ANN and playing music of the most appropriate type over the patient’s handheld device, when required. In addition, the ANN will also exploit Music Analytics such as the type and duration of music played and its impact over patient’s body parameters to iteratively improve the process of automated music therapy. We detail development of a prototype Android app which builds a playlist and plays music according to the emotional state of the user, in real time. Data for pulse rate, blood pressure and breath rate has been generated using Node-Red, and ANN has been created using Google Colaboratory (Colab). MQTT broker has been used to send generated data to Android device. The ANN uses binary and categorical cross-entropy loss functions, Adam optimiser and ReLU activation function to predict the mood of patient and suggest the most appropriate type of music

mHealth Innovation in Asia: Grassroots Challenges and Practical Interventions

This book offers a detailed account of a range of mHealth initiatives across South, Southeast and East Asia. It provides readers with deep insights into the challenges such initiatives face on the ground, and a view of the diverse cultural contexts shaping strategies for overcoming these challenges. The book brings together various discussions on the broader mHealth literature, and demonstrates how a research focus on diverse Asian contexts influences the success and/or failure of current mHealth initiatives. It also highlights the important roles social scientists can play in advancing theoretical approaches, as well as planning, implementing and evaluating mHealth initiatives. The book is a valuable resource for project planners, policy developers in NGOs and government institutions, as well as academics, researchers and students in the fields of public health, communications and development studies