Developing Medical Apps and mHealth Interventions PDF

Health Informatics Alan Davies Julia Mueller Developing Medical Apps and mHealth Interventions A Guide for Researchers, Physicians and Informaticians Health Informatics This series is directed to healthcare professionals leading the transformation of healthcare by using information and knowledge. For over 20 years, Health Informatics has offered a broad range of titles: some address specific professions such as nursing, medicine, and health administration; others cover special areas of practice such as trauma and radiology; still other books in the series focus on interdisciplinary issues, such as the computer based patient record, electronic health records, and networked healthcare systems. Editors and authors, eminent experts in their fields, offer their accounts of innovations in health informatics. Increasingly, these accounts go beyond hardware and software to address the role of information in influencing the transformation of healthcare delivery systems around the world. The series also increasingly focuses on the users of the information and systems: the organizational, behavioral, and societal changes that accompany the diffusion of information technology in health services environments. Developments in healthcare delivery are constant; in recent years, bioinformatics has emerged as a new field in health informatics to support emerging and ongoing developments in molecular biology. At the same time, further evolution of the field of health informatics is reflected in the introduction of concepts at the macro or health systems delivery level with major national initiatives related to electronic health records (EHR), data standards, and public health informatics. These changes will continue to shape health services in the twenty-first century. By making full and creative use of the technology to tame data and to transform information, Health Informatics will foster the development and use of new knowledge in healthcare. More information about this series at http://www.springer.com/series/1114 Alan Davies Julia Mueller Developing Medical Apps and mHealth Interventions A Guide for Researchers, Physicians and Informaticians Alan Davies Julia Mueller University of Manchester University of Cambridge Manchester, UK Cambridge, UK ISSN 1431-1917 ISSN 2197-3741 (electronic) Health Informatics ISBN 978-3-030-47498-0 ISBN 978-3-030-47499-7 (eBook) https://doi.org/10.1007/978-3-030-47499-7 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. 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The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland This book is dedicated to the memories of: Valerie Jane Davies 1955–2000 Bruce Nigel Davies 1953–2013 Preface Using smartphone applications as a means of collecting research data and trialing and implementing medical/health interventions is becoming increasingly popular, due in part to the ubiquitous nature of mobile technology. A large swathe of the world’s population has access to a smartphone, which is essentially a powerful hand- held computational device bristling with sensors. mHealth apps open up avenues for personalised interventions with the possibility of rich, high quality data acquisition. Data can be combined with modern scalable big data approaches, such as pattern mining and machine learning, which can be used to discover new insights into aspects of people’s mental and physical health. With all this potential, many domains are now moving into mobile technology and app development. Designing a medical or health intervention and deploying this on a mobile device in the form of an application is a task that requires expertise in many different disciplines. Between them, the authors of this book have worked in the fields of academic research, data science, software engineering, clinical practice and health psychology. We have worked on large and small interventions and have learnt many lessons along the way. It is our hope that we can translate some of this experience into useful insights to help readers who are considering implementing a medical/health intervention to navigate the complexities of this area. This book is aimed at bridging the gap between researchers, developers and clinicians to highlight best practice and the steps involved in designing, building and evaluating mHealth interventions. We offer insights into the different aspects of such projects with additional case studies to help foster better understanding, communication and working practices between experts to work collaboratively and effectively on delivering mobile health interventions. Manchester, UK Alan Davies March 2020 vii Acknowledgements We would like to thank Alex Turner, Michael Lee and Jonathan Carlton for the helpful and constructive feedback provided. We also extend our gratitude to all the people and organisations that have allowed us to kindly reproduce images and other materials used throughout this book. Alan would like to thank Monika and Victoria Golaś for their continued patience and use of the flat’s front room for the duration of the project. Julia would like to thank Nick for his invaluable patience and support. Julia would also like to thank her family Margrit, Ingo, Halina and Flora, without whom she wouldn’t be where she is today. Finally, we would like to thank Dr Caroline Jay and Professor Simon Harper for their continued support of our careers. ix Contents 1 Introduction to mHealth.................................................... 1 1.1 What Are Health Apps?............................................... 1 1.2 mHealth, eHealth and Related Terms................................. 2 1.3 Why Do We Need Health Apps?...................................... 4 1.4 Types of Health Apps.................................................. 6 1.4.1 Health Apps for Health Promotion and Prevention........ 6 1.4.2 Health Apps for Disease Management...................... 7 1.4.3 Health Apps for Remote Access to Treatment.............. 8 1.5 Common Features of Health Apps.................................... 9 1.6 Advantages and Benefits of Delivering Interventions via Health Apps............................................................ 14 1.7 Disadvantages and Challenges of Delivering Interventions via Health Apps........................................................ 17 1.8 Summary of Key Points............................................... 20 1.9 Quiz.................................................................... 21 1.10 Exercises............................................................... 22 Recommended Reading....................................................... 22 References..................................................................... 22 2 Project Development Methodologies, Management and Data Modelling..................................................................... 25 2.1 Introduction............................................................ 25 2.2 The Software Crisis.................................................... 26 2.3 Agile.................................................................... 27 2.3.1 Agile Methodologies and Frameworks..................... 31 2.3.2 Scrum......................................................... 31 2.3.3 Kanban....................................................... 36 2.3.4 Extreme Programming (XP)................................. 37 2.3.5 Feature Driven Development (FDD)........................ 39 2.3.6 Lean Software Development................................ 40 2.3.7 Crystal........................................................ 42 xi xii Contents 2.4 Agile Practices......................................................... 44 2.4.1 Minimum Viable Product (MVP)........................... 44 2.4.2 Testing in Agile.............................................. 45 2.4.3 Test Driven Development (TDD)........................... 45 2.4.4 Pair Programming............................................ 45 2.4.5 Continuous Integration (CI)................................. 46 2.4.6 Code Review................................................. 46 2.5 Requirements Gathering and Representation......................... 47 2.5.1 User Stories.................................................. 47 2.5.2 Personas...................................................... 48 2.5.3 Estimating.................................................... 49 2.5.4 Burn-Up Charts.............................................. 52 2.5.5 Burn-Down Charts........................................... 52 2.5.6 PERT......................................................... 53 2.6 Data Modelling........................................................ 54 2.6.1 Data Flow Diagrams (DFDs)................................ 54 2.6.2 Unified Modeling Language (UML) Diagrams............. 56 2.6.3 Data Journey Modelling..................................... 61 2.6.4 Project Management Tools.................................. 65 2.7 Summary of Key Points............................................... 68 2.8 Quiz.................................................................... 69 2.9 Exercises............................................................... 70 Recommended Reading....................................................... 72 References..................................................................... 72 3 Designing an mHealth Intervention........................................ 75 3.1 Introduction............................................................ 75 3.2 Challenges to DHI Development..................................... 76 3.3 Developing Ideas and Specifying Aims.............................. 76 3.4 Ethical Considerations................................................. 78 3.5 Reviewing Existing Evidence and Approaches...................... 80 3.5.1 Reviewing the Literature.................................... 80 3.5.2 Systematic Reviewing....................................... 81 3.5.3 Reviewing Apps.............................................. 82 3.5.4 Rapid Reviewing............................................. 83 3.6 Frameworks and Approaches......................................... 84 3.6.1 The MRC Guidance for Developing and Evaluating Complex Interventions....................................... 84 3.6.2 The Behaviour Change Wheel.............................. 85 3.6.3 The Person-Based Approach................................ 86 3.7 Behaviour Change Theories........................................... 88 3.7.1 Selecting a Theory........................................... 90 3.7.2 Translating Theory into Practice............................ 90 3.8 User and Stakeholder Engagement................................... 91 3.8.1 Research and Patient and Public Involvement (PPI)....... 94 Contents xiii 3.8.2 Interviews and Focus Groups................................ 96 3.8.3 Paper Prototyping, Storyboarding and Digital Prototyping................................................... 98 3.8.4 Using Think Aloud to Refine Interventions................ 98 3.8.5 Identifying and Engaging Users............................. 101 3.9 Sufficient Engagement................................................. 104 3.10 Evaluation.............................................................. 104 3.11 Summary of Key Points............................................... 106 3.12 Quiz.................................................................... 107 3.13 Exercises............................................................... 107 Recommended Reading....................................................... 108 References..................................................................... 108 4 Application Development and Testing..................................... 111 4.1 Introduction............................................................ 111 4.2 Choosing a Platform................................................... 112 4.2.1 iOS............................................................ 114 4.2.2 Programming for iOS........................................ 115 4.2.3 Tools for Health and Medical App Development........... 117 4.2.4 Android....................................................... 117 4.2.5 Programming for Android................................... 117 4.2.6 Progressive Web Apps (PWA)............................... 118 4.2.7 PWA Frameworks............................................ 120 4.2.8 Coding Setup................................................. 125 4.3 Design Patterns........................................................ 125 4.3.1 Model-View-Controller...................................... 127 4.4 Sensors and the Internet of Things (IoT)............................. 129 4.4.1 Sensors....................................................... 129 4.4.2 Internet of Things (IoT)...................................... 131 4.5 Testing.................................................................. 134 4.5.1 Test Driven Development.................................... 135 4.6 Types of Software Test................................................ 141 4.7 App Maintenance...................................................... 144 4.8 Summary of Key Points............................................... 144 4.9 Quiz.................................................................... 145 4.10 Exercises............................................................... 146 Recommended Reading....................................................... 146 References..................................................................... 146 5 Data Collection, Storage and Security..................................... 149 5.1 Introduction............................................................ 149 5.2 Data Storage........................................................... 150 5.2.1 Local Storage................................................. 150 5.2.2 The Back End................................................ 150 5.2.3 Cloud Storage................................................ 151 5.2.4 Structured Databases......................................... 152 xiv Contents 5.2.5 Migrations.................................................... 158 5.2.6 NoSQL Databases............................................ 158 5.2.7 Graph Databases............................................. 162 5.3 Legal Aspects.......................................................... 165 5.3.1 European Union/European Economic Area (EU/EEA).... 166 5.3.2 USA.......................................................... 167 5.3.3 Regulatory Compliance...................................... 168 5.4 Geo-Location.......................................................... 169 5.5 Information Governance (IG) and Data Management Plans (DMP).................................................................. 170 5.6 Data Security........................................................... 171 5.6.1 HTTP and HTTPS........................................... 172 5.6.2 GET and POST Requests.................................... 173 5.6.3 TCP/IP........................................................ 174 5.6.4 Encryption.................................................... 174 5.6.5 Password Management...................................... 175 5.6.6 Cyberattacks.................................................. 179 5.6.7 The State of Data Security in mHealth Apps............... 180 5.7 Users’ Access to their Own Data..................................... 181 5.8 Blockchain............................................................. 183 5.8.1 Applying Blockchain to Health Data....................... 185 5.9 Summary of Key Points............................................... 186 5.10 Quiz.................................................................... 187 5.11 Exercises............................................................... 188 Recommended Reading....................................................... 189 References..................................................................... 189 6 Feeding Back Information to Patients and Users with Visualisations........................................................... 191 6.1 Introduction............................................................ 191 6.2 Visualisations.......................................................... 192 6.3 Types of Visualisations................................................ 193 6.4 The Importance of Visualisations..................................... 194 6.5 Dynamic Visualisations............................................... 195 6.6 Design Principles...................................................... 196 6.6.1 Gestalt Principles............................................ 197 6.6.2 Design Layout................................................ 198 6.6.3 Preattentive Attributes....................................... 198 6.7 Visualisation for Decision Making.................................... 199 6.7.1 Probabilistic Reasoning...................................... 203 6.8 Feeding Back Complex Data to Patients............................. 205 6.8.1 Graph Literacy............................................... 207 6.9 Creating Visualisations................................................ 208 Contents xv 6.10 Using Visualisation in Apps........................................... 216 6.10.1 Collecting Data from Users.................................. 218 6.10.2 Involving Users in Visualisation Design.................... 219 6.11 Summary of Key Points............................................... 224 6.12 Quiz.................................................................... 225 6.13 Exercises............................................................... 226 Recommended Reading....................................................... 226 References..................................................................... 227 7 Usability Testing and Deployment......................................... 229 7.1 Introduction............................................................ 229 7.2 Human Computer Interaction......................................... 231 7.3 User Centered Design................................................. 231 7.4 Design and User Testing.............................................. 233 7.5 Usability Studies....................................................... 234 7.5.1 Sample Size.................................................. 235 7.6 Accessibility........................................................... 236 7.7 Affective Computing.................................................. 238 7.8 Physiological Methods................................................ 239 7.8.1 Eye-Tracking................................................. 239 7.8.2 Electroencephalography (EEG)............................. 244 7.8.3 Electromyogram (EMG)..................................... 245 7.8.4 Electrocardiogram (ECG)................................... 245 7.8.5 Galvanic Skin Response (GSR)............................. 246 7.8.6 Facial Recognition........................................... 247 7.9 Guidelines for the Inclusion of Human Factors in Approval Checking............................................................... 249 7.10 In the Wild Evaluation................................................. 249 7.11 Applying HCI to Healthcare.......................................... 253 7.12 Summary of Key Points............................................... 254 7.13 Quiz.................................................................... 254 7.14 Exercises............................................................... 255 Recommended Reading....................................................... 256 References..................................................................... 256 8 Designing an mHealth Evaluation......................................... 259 8.1 Introduction............................................................ 259 8.2 Designing an Evaluation: Initial Considerations..................... 259 8.2.1 Asking the Right Questions................................. 260 8.2.2 Specific Aspects of Apps That Affect Evaluation Design Choices............................................... 262 8.3 Choosing a Study Design............................................. 264 8.3.1 Quantitative or Qualitative................................... 264 8.3.2 Descriptive or Analytical.................................... 266 8.3.3 Observational or Experimental.............................. 267 8.3.4 Pragmatic or Explanatory.................................... 268 xvi Contents 8.4 Experimental Studies.................................................. 269 8.4.1 Randomised Controlled Trials (RCTs)...................... 270 8.4.2 Crossover Trials.............................................. 272 8.4.3 Stepped Wedge Design...................................... 273 8.5 Observational Studies................................................. 274 8.5.1 Cross-Sectional Studies...................................... 274 8.5.2 Cohort Studies............................................... 275 8.5.3 Case-Control Studies........................................ 277 8.5.4 Quasi-experimental Studies................................. 278 8.6 Feasibility and Pilot Studies........................................... 279 8.7 Sample Size............................................................ 280 8.8 Control Groups........................................................ 280 8.9 Choosing a Study Design: Minimum and Best Practice Standards............................................................... 282 8.10 Costs and Economic Evaluations..................................... 284 8.10.1 Economic Evaluation According to the NICE Evidence Standards Framework for Digital Health Technologies................................................. 286 8.11 Non-response and Dropout in Online Evaluations................... 286 8.12 Considering Harm and Risk........................................... 287 8.13 Qualitative Studies..................................................... 288 8.13.1 Interviews.................................................... 289 8.13.2 Focus Groups................................................. 292 8.13.3 Observation................................................... 294 8.14 Review by External Organisations.................................... 295 8.15 Summary of Key Points............................................... 295 8.16 Quiz.................................................................... 296 8.17 Exercises............................................................... 297 Recommended Reading....................................................... 298 References..................................................................... 298 9 Data Analysis Methods...................................................... 301 9.1 Introduction............................................................ 301 9.2 Data Sources and Outcomes in App-Based Research................ 302 9.3 Statistical Analysis Methods.......................................... 304 9.3.1 Hypotheses................................................... 304 9.3.2 Data Preparation and Cleaning.............................. 306 9.3.3 Data Types.................................................... 306 9.3.4 Descriptive Statistics......................................... 307 9.3.5 Assumptions.................................................. 308 9.3.6 Parametric Tests.............................................. 310 9.3.7 Non-parametric Tests........................................ 315 9.3.8 Analysing Categorical Data: Chi-Square Test, Fisher’s Exact Test and McNemar’s Test................... 317 9.3.9 Multiple Testing and Inflation of Error..................... 318 Contents xvii 9.3.10 Effect Sizes................................................... 319 9.3.11 Confidence Intervals......................................... 320 9.4 Data Science........................................................... 321 9.4.1 Linear Algebra............................................... 322 9.4.2 Linear Equations............................................. 322 9.4.3 Vectors and Matrices......................................... 323 9.4.4 Special Matrices.............................................. 328 9.4.5 Machine Learning............................................ 330 9.4.6 Decision Trees............................................... 334 9.4.7 Support Vector Machines (SVM)........................... 336 9.4.8 k-Nearest Neighbors (k-NN)................................ 339 9.4.9 Evaluating a Model.......................................... 341 9.4.10 Model Selection.............................................. 342 9.4.11 Natural Language Processing (NLP)........................ 343 9.5 Which Tools to Use?.................................................. 347 9.5.1 Python........................................................ 348 9.5.2 The R Project for Statistical Computing.................... 348 9.5.3 Julia........................................................... 350 9.5.4 SPSS.......................................................... 350 9.5.5 JASP.......................................................... 351 9.6 Qualitative Data Analysis............................................. 351 9.6.1 Epistemology and Ontology................................. 352 9.6.2 Thematic Analysis........................................... 354 9.6.3 Framework Analysis......................................... 354 9.6.4 Grounded Theory............................................ 357 9.6.5 Content Analysis............................................. 359 9.7 Summary of Key Points............................................... 360 9.8 Quiz.................................................................... 361 9.9 Exercises............................................................... 362 Recommended Reading....................................................... 363 References..................................................................... 364 Solutions to Quizzes.............................................................. 367 Index............................................................................... 373 About the Authors Dr. Alan Davies is a lecturer in Health Data Sciences at the University of Manchester in the Division of Informatics, Imaging and Data Sciences. Alan has a background in Computer Science (PhD) and Nursing Science (BSc) and has previously worked as a software engineer and app developer in industry. Alan also worked in interventional cardiology, assisting with routine and emergency cardiac proce- dures where he authored several medical textbooks on electrocardiogram (ECG) interpretation. More recently he has worked for the University of Manchester as a Research Software Engineer (RSE) and Data Scientist prior to becoming a lecturer. He delivers modules on the MSc in Health Infor- matics joint award delivered by UCL and the University of Manchester, where he is a module lead on the ‘Usable Health Systems Design’ module and the ‘Mod- ern Information Engineering’ module. He also teaches on the MSc in Health Data Science on various modules, including ‘Understanding Data and Decision Making’ and ‘Health Information Systems and Technologies’. Alan’s research is focused on mHealth interventions and how patients interpret medical data. Alan was a Data Science fellow at AstraZeneca, as well as a member of the Universities Research Ethics commit- tee (UREC). Alan completed his PhD in the Interac- tion Analysis and Modelling lab at the University of Manchester using eye-tracking methods to examine how clinicians interpret ECGs. xix xx About the Authors Dr. Julia Mueller is a Research Associate in the Med- ical Research Council Epidemiology Unit at the Uni- versity of Cambridge. She works within the programme on Prevention of Diabetes and Related Metabolic Dis- orders in High Risk Groups. Prior to this role, she worked as a Lecturer in Health- care Sciences in the Division of Population Health, Health Services Research and Primary Care at the Uni- versity of Manchester, leading a course unit on Digital Public Health on the Master of Public Health pro- gramme. Much of her research relates to digital health, with a particular focus on health-related behaviour change interventions such as mobile apps for chronic disease management. She is also a strong proponent of patient and public engagement and has organised and participated in various events and initiatives to help patients become involved in research and to disseminate research findings to members of the public. Julia completed a BSc in Psychology at the Georg- August-University in Goettingen, Germany, and an MSc in Health Psychology at the University of St Andrews. Her studies piqued her interest in the links between psychological and physical well-being, and in the poten- tial of Web-based interventions for improving health. In 2018, Julia completed a PhD research project at the University of Manchester about the role of Web-based information in help-seeking of those worried about lung cancer. Acronyms BM body mass index CBT cognitive behavioural therapy COPD chronic obstructive pulmonary disease DHI digital health intervention DHT digital health technology ECG Electrocardiogram EEG electroencephalogram FDD Feature-driven development GUI graphical user interface HCI Human computer interaction HCP healthcare professional IDE Integrated development environment MRC Medical Research Council NHS National Health Service OS Operating System PWA progressive web apps RCT Randomised controlled trial TDD Test-driven development UCD User Centered Design UI User interface UX User experience WHO world health organisation XP eXtreme programming xxi Chapter 1 Introduction to mHealth 1.1 What Are Health Apps? Health apps, or mHealth interventions are mobile applications that aim to promote and maintain health. Mobile applications, also referred to as mobile apps or simply apps are software applications that are designed to run on mobile (i.e. handheld) devices such as smartphones, tablets and wearable devices like smartwatches. Mobile apps run directly on the device, whereas web and desktop applications run within a web browser or a desktop machine, respectively. Health apps aim to promote and maintain health by supporting behaviour change and/or decision making. This spans a wide variety of different interventions with different aims. In this book, we define health apps as interventions aiming to change behaviour, support patients, and improve patient outcomes, delivered via smartphones or other mobile devices. They are typically used by patients, carers, or healthcare professionals. Examples of typical health apps or mHealth interventions Apps to promote healthy lifestyles, like physical exercise or healthy diets Apps to assist decision-making, e.g. an app to help healthcare professionals decide whether to prescribe antibiotics Apps to help people with long-term health problems to self-manage their condition Apps to facilitate interaction and communication between patients (peer support), between patients and healthcare professionals (telehealth), or between healthcare professionals Apps to track patient outcomes over time to allow continuous assessment © Springer Nature Switzerland AG 2020 1 A. Davies, J. Mueller, Developing Medical Apps and mHealth Interventions, Health Informatics, https://doi.org/10.1007/978-3-030-47499-7_1 2 1 Introduction to mHealth Possible aims of health apps enabling users to be better informed about their health allowing users to share experiences with others in similar positions (e.g. other patients with the same condition) changing perceptions and cognitions around health assessing and monitoring specified health states or health behaviours improving communication between patients and healthcare professionals (HCPs) supporting users to change their behaviour to promote healthier lifestyles 1.2 mHealth, eHealth and Related Terms Before delving further into mHealth and health apps, clarification of commonly used terminology in this field is helpful. There are several terms that are frequently used in the general field of digital health, often with considerable overlap and lack of clear definitions. Firstly, eHealth is often used synonymously with digital health. The World Health Organisation (WHO) defines eHealth as “the cost-effective and secure use of information and communications technologies in support of health and health-related fields, including health care services, health surveillance, health literature, and health education, knowledge and research”. Mobile Health or mHealth is a sub-field within eHealth. It is concerned with “the use of mobile phones and other wireless technologies to support the achievement of health objectives”. Thus, mHealth encompasses the use of mobile apps for medical care as well as prevention, health promotion and disease management. Related to the field of mHealth is uHealth, which is concerned with the use of ubiquitous technology in health care and health promotion. Ubiquitous technology refers to mobile devices or objects embedded with processors that enable them to connect via the Internet (e.g. smartphones, tablets, smartwatches, activity trackers, other wearable devices, or biometric devices). Increasingly, microprocessors and sensors are embedded in everyday objects (e.g. in hospital beds to allow real-time tracking of the availability of beds). Such technology is also referred to as Internet of Things or pervasive technology. As such, mHealth is a sub-field within uHealth. Telemedicine or telehealth (literally “healing at a distance”) refers to the use of information and communication technologies to facilitate access to medical care and information. It includes, for example, the use of text messaging, emails, telephone or video calls for consultations and other communications with healthcare professionals. Thus, there can be some overlap between mHealth/uHealth and telemedicine, for example in form of mobile apps that facilitate communication between healthcare professionals. 1.2 mHealth, eHealth and Related Terms 3 eHealth is also often used interchangeably with health informatics, though the two terms refer to different concepts. Health informatics specifically refers to the management and use of patient information, and as such is concerned with IT-based innovations in healthcare services. It encompasses the generation, management, analysis, interpretation and communication of health data. Data science can be understood as a sub-domain of health informatics which focuses specifically on data analysis. It makes use of powerful programming systems, hardware and algorithms to obtain insights from structured and unstructured data. Such advances are important given the large quantities of complex data generated through increasing digitisation. Data analysts are now often faced with health datasets of high volume and diversity which cannot be analysed using traditional database software tools, meaning that new algorithms, techniques and approaches are required. Figure 1.1 provides an overview of definitions of important topics and concepts in the field of digital health. Fig. 1.1 Overview of key concepts in the field of digital health 4 1 Introduction to mHealth 1.3 Why Do We Need Health Apps? With improvements in treatments and overall quality of life, people are living longer. The World Health Organization (WHO) estimates that by 2050, 2 billion people (more than a fifth of the world’s population) will be over 60 years of age. This means that noncommunicable diseases related to ageing such as cancer, respiratory conditions, diabetes and cardiovascular diseases are increasing. Moreover, other determinants of chronic health problems such as obesity are increasing. In the UK, for example, the number of diabetes diagnoses increased from 1.4 million in 1996 to 4.5 million in 2015, and a further increase to 5 million is expected by 2025. Worldwide, diabetes is expected to affect 1 in 10 people by 2040. Globally, noncommunicable diseases account for over 70% of all deaths, and 80% of all premature deaths. In addition to being the most prevalent global health problem, noncommunicable diseases are also the most costly. Costs extend beyond immediate treatment costs, for example in terms of reduced employment and productivity resulting in loss of income to individual households, and loss of national economic output. Effective and low-cost interventions are needed to address this growing burden. Given these changes in the health profiles of the general population, paired with wider economic crises resulting in decreasing public spending on health and falling government commitment to health, financial pressures on health services are increasing. According to the WHO, the majority (80%) of cardiovascular diseases, stroke and diabetes as well as 40% of cancers could be prevented through lifestyle changes in the population. Developments in technology have often been cited as a potential solution to the healthcare crisis. Technology can help streamline processes, increase efficiency, and empower patients to manage their own health better. For example, infusion pumps have automated processes of giving injections which previously had to be undertaken manually by nurses. This frees up nurses’ time and ensures more patients can be treated. In the UK, the Topol Review, which was published in 2019, specifically explored how digital health technologies can be harnessed to tackle problems faced by the National Health Service (NHS) related to increasing demand coupled with financial constraints. The review concludes that digital technologies will help improve services offered by healthcare professionals (rather than replace them) and will free up time which healthcare professionals can spend with patients. Technology, and in particular mobile technology, is becoming increasingly prevalent across the world, further highlighting its potential to play a key role in tackling the global healthcare crisis. Globally, there are more than 3 billion smartphone users. Among advanced economies, 76% of adults report owning a smartphone, with some countries such as South Korea reporting smartphone penetration as high as 95%. Emerging economies are rapidly catching up, with a median 45% reporting smartphone ownership. Moreover, around 90% of the 1.3 Why Do We Need Health Apps? 5 population in advanced economies and 60% in emerging economies are connected to the Internet. Due to the widespread penetration of smartphone ownership and Internet connec- tivity, health apps present a cost-effective, scalable means of disseminating health interventions to large numbers of people. The Topol Review specifically highlights health apps as a key development that will help tackle pressures faced by the NHS. It estimates that smartphone apps will affect more than 80% of the NHS healthcare workforce. Health apps could potentially empower people to self-manage their health rather than relying on over-stretched healthcare services. A systematic review on health apps designed to improve self-management of key symptoms of chronic conditions found apps for diabetes, chronic lung diseases, and cardiovascular diseases. Several were associated with significant improvements in clinical outcomes, such as blood glucose levels in people with diabetes, improved physical functioning in people with cardiovascular diseases, or lung function parameters in people with chronic lung diseases. Aside from allowing patients to self-manage their health conditions, health apps can also assist healthcare professionals with important tasks such as decision- making, health record management, and communication. Again, this can free up resources and enhance efficiency to help under-resourced healthcare systems deal with increasing demands and pressures. For example, a study at Toronto General Hospital explored the implementation of smartphones for communication among medical teams, with different communication channels depending on the level of urgency [26, 46]. In subsequent surveys, the teams reported improvements in communications and reductions in disruptions to workflows. Although response time to emergencies did not change significantly following the implementation of the smartphone-based communication system, team members did report spending less time attempting to contact physicians, suggesting that time was potentially freed up for other processes [26, 46]. In another example, researchers in Boston developed an app which allows clinicians to capture and securely store clinical images in electronic health records. Clinicians who used the app reportedly found it easy to use and helpful for clinical practice. Visual data can be extremely helpful for clinical diagnoses and monitoring of disease progression. For example, clinicians often ask patients to take regular photos of suspicious moles to document any changes indicating melanoma. The app was developed to streamline previous processes for recording visual data which involved clinicians taking photos on personal phones, emailing the photos to their email accounts, and then opening this email on a hospital workstation in order to copy and paste images from the email into the electronic health record. App functions such as decision support and enhanced communication can also be harnessed to tackle other challenges facing healthcare systems worldwide including the global influenza pandemic, antimicrobial resistance, and vaccine hesitancy. For example, the “Antimicrobial Companion app” provides practitioners with access to clinical guidelines as well as facilitating decision-making. 6 1 Introduction to mHealth It should be noted that, despite these promising findings, tangible impacts of health apps on wider healthcare utilisation and the efficiency of healthcare systems have not been systematically assessed. Therefore it is unclear whether the potential of apps to reduce pressures on healthcare services are realised in practice. In the UK, for example, a King’s Fund review cautioned that, despite the potential of technology to deliver considerable savings for the National Health Service, they are often not implemented at the scale needed to realise their full potential. Thus, further research is necessary to assess the extent and scale of the impact of mHealth on health services and health outcomes. 1.4 Types of Health Apps The following sections introduce different types of health apps including apps for health promotion and prevention, disease management and remote access to treatment. 1.4.1 Health Apps for Health Promotion and Prevention Many health apps target determinants of health to promote health and prevent health problems. There are currently thousands of apps available on app stores which claim to help users lead healthier lifestyles, for example by supporting users to stop smoking, adopt healthier diets, exercise, or drink less alcohol. This is of particular relevance given the increasing prevalence of non-communicable diseases which are largely attributable to lifestyle factors. Case study: “Craving to Quit” – an mHealth intervention to support smoking cessation “Craving to Quit” was developed by researchers from Yale University. It drew on mindfulness training – based on ancient Buddhist practices – to reduce smoking rates among smokers motivated to quit, and to reduce the association between craving and smoking. The app used minfulness- based meditation practices to help users manage cravings and body sensations related to them, and to foster acceptance and retrain the mind. Additionally, the app made use of experience sampling to measure smoking, craving, and other factors up to six times per day. Evaluation: The app was evaluated by comparing it to a control app which had the same look and feel as Craving to Quit, but included only experience sampling. The primary outcome was one-week point-prevalence absti- (continued) 1.4 Types of Health Apps 7 nence from smoking at 6 months. The authors found no significant differences between participants who received Craving to Quit, and those who used the control app, regardless of whether abstinence was self-reported or verified by CO-monitoring. The groups also did not differ in smoking rates. There was some preliminary evidence to suggest that the mindfulness app led to a reduced association between cravings and smoking. 1.4.2 Health Apps for Disease Management Health apps are used frequently to help patients (and healthcare professionals) manage diseases, particularly long-term conditions such as diabetes, chronic respi- ratory conditions, cancer, and mental health problems. Health apps can help patients keep track of their symptoms, medication and other disease-related variables. They can also be used to provide patients with educational information about their condition. Features can be added to support patients with self-management. For example, automatic reminders can help patients adhere to treatments. Health apps can also support patients with the psychological difficulties associated with chronic diseases by drawing on psychological therapeutic methods like cognitive behavioural therapy and mindfulness meditation. For example, patients with chronic obstructive pulmonary disease often experience anxiety due to breathing difficulties, which can significantly impair quality of life. Health apps can help alleviate symptoms and reduce anxiety by helping users practice breathing techniques as well as mindfulness-based meditations. Such self-management interventions are particularly important in light of the increasing burden of non-communicable diseases paired with rising financial pressures on healthcare systems. Importantly, not all health apps are patient-facing; apps may also support decision-making by healthcare professionals. For example, Tuon et al. exam- ined antimicrobial prescriptions at a university hospital before and after implemen- tation of an app providing antimicrobial use guidance. The authors found significant reductions in prescriptions for several antibiotics, as well as a reduction in costs related to antibiotics. Case study: An mHealth intervention for atrial fibrillation Guo et al. developed and tested an app for management of atrial fibrillation, with different versions for patients and healthcare professionals respectively. The app involved the following features: (continued) 8 1 Introduction to mHealth Personal health record: The app was able to record clinical features in a personal health record, including atrial fibrillation features, details about the patients’ medical history, results from laboratory tests, and current treatments (e.g. drugs and other pharmacologic treatments). Clinical decision support: The app automatically recommended different treatment methods based on entries to the personal health record. For example, the app calculated patients’ stroke risk and recommended anticoagulants if indicated. Patient’s educational programme: The educational programme included self-support items which aimed to improve patients’ knowledge of atrial fibrillation and to help patients manage their condition at home. Patient involvement with self-care: Patients were encouraged to monitor and record their heart rate, blood pressure, and feedback on their treatment. Structured follow-up: At regular intervals, the app assessed drug therapy, thrombotic events, bleeding events, quality of life, and treatment satisfaction with automatic reminder notifications. Data security: Access to the app was protected by a user-sensitive pass- word. The public health records were stored on a cloud platform. Evaluation: A cluster-randomised controlled trial was conducted to test effects of the app. Participants who used the app showed significant improve- ments in knowledge of atrial fibrillation, quality of life, and drug adherence, compared to those in the control arm of the study. 1.4.3 Health Apps for Remote Access to Treatment Health apps can facilitate remote access to treatment, by (a) providing users with effective treatments within the app itself (e.g. audio meditations) or (b) by facilitating communication with healthcare professionals (or other individuals who are able to provide support). This is important for those living in remote or underserved areas (e.g. in rural areas), those with mobility issues, as well as those with competing responsibilities (e.g. work, care-giving). For those who are well versed in the use of technology, health apps can offer easy access and round-the- clock availability at low cost. For example, the National Institute for Health and Care Excellence in the UK now recommends digital cognitive behavioural therapy for children and young people suffering from mild depression. 1.5 Common Features of Health Apps 9 Case study: An mHealth telehealth intervention for posttraumatic stress disorder Kuhn et al. developed an app to support military veterans with post- traumatic stress disorder (PTSD) by facilitating psychoeducation and self- management. The app was developed using participatory design principles by conducting focus groups with people with PTSD and using their suggestions to develop content and features. Features requested by users included the ability to be used discreetly when PTSD symptoms arise (e.g. in a supermarket queue), and the ability to track PTSD symptoms. The app included the following sections: Psychoeducation: This section provided educational information about PTSD, its symptoms, progression, and available treatments. Self assessment: In this section, users were able to record their symptoms using a validated self-report measure. Users were able to schedule assess- ments at regular intervals and set reminder notifications. After inputting information, the app provided users with an overview of their symptoms in form of a graph, as well as feedback on the severity and progression of their symptoms. Coping mechanisms: This section provided users with various tools for self-management of symptoms, such as breathing exercises and progressive muscle relaxation. Finding support: This section provided users with links and telephone numbers to help them find professional help. Users were also able to add contact details from their own support network (e.g. family, friends). At the time of publication, the app had been widely used (over 130,000 downloads in 78 different countries). It was tested with 45 veterans with PTSD. Participants used the app for several days and then rated user satisfaction and perceived helpfulness using standardised questionnaires. They also discussed their perceptions of the app in focus groups. 1.5 Common Features of Health Apps Health apps are typically complex interventions that include various different features and components that allow them to promote health, illness management, decision making and behaviour change. Many health apps include information and education as a key component (Fig. 1.2). Patient education is becoming increasingly important as healthcare systems place stronger emphasis on self-care in order to manage rising demands. Decision support aids are also commonly used in many apps. This often entails data input by the user, which the app then uses to return a recommendation for 10 1 Introduction to mHealth Fig. 1.2 Example of an app providing information about lung functions to educate patients with respiratory conditions. App created by the authors further action. For example, Figure 1.3 shows an app which healthcare professionals can use to make decisions about antimicrobial prescribing for urinary tract infec- tions. Health apps often include behaviour change support. This can take many different forms. For example, in the app shown in Fig. 1.4, users receive a reminder when they are due to take or repurchase their medication. This “nudges” users to engage in the desired behaviour, i.e. adhering to a treatment regimen. Health apps are also increasingly used as a platform for self-assessment and monitoring. Both researchers and clinicians frequently rely on patients recalling past events accurately, often over a long time frame. However recall is typically imperfect or biased. For example, research suggests people are often unable to accurately report the amount of sleep they obtain or the symptoms they have experienced over longer time frames. Moreover, healthcare professionals often make decisions about treatment regimens based on patients’ symptom profile when they present during consultations, but this “snapshot” may not provide an accurate picture of the general symptom profile. Health apps are a useful tool to support self-assessment and monitoring because they allow ecological momentary assessment (EMA) (or experience sampling) as well as monitoring via sensors. EMA involves repeated sampling of participants’ behaviours and experiences in real time, in their natural environments. This 1.5 Common Features of Health Apps 11 Fig. 1.3 Example of an app providing decision support to healthcare professionals on antimi- crobial prescribing for urinary tract infections. (© Tactuum, https://play.google.com/store/ apps/details?id=com.tactuum.quris.nes.antimicrobial&hl=en or https://qrs.ly/ykb0cmf. Screenshot reproduced with permission) helps to build a more detailed understanding of patients’ health. For example, the app shown in Fig. 1.5 sent users daily reminders to log their level of breathless- ness over a timeframe of several weeks. This enabled users to view how their symptoms varied over time. This information could also be useful for healthcare professionals to support decision making on optimal treatment. Another useful feature often incorporated into health apps is communication and interaction. Through text messaging and (video) call functions, health apps can enable rapid communication. The app shown in Fig. 1.6, for example, uses a secure messaging system to allow healthcare professionals to communicate medical information quickly and efficiently to other healthcare professionals. Messages and images are transferred using end-to-end encryption to ensure confidential informa- tion can be communicated securely. The app also uses two-factor authentication to enhance security. Figure 1.7 shows another example of an app which facilitates in-app consultations. 12 1 Introduction to mHealth Fig. 1.4 Example of an app which aims to support users in adhering to their treatment regime by alerting them via automatic reminders when medication is due to be taken or repurchased. (© Smartpatient, https://play.google.com/store/apps/details?id=eu.smartpatient.mytherapy Screen- shots reproduced with permission) The two examples mentioned above relate to communication among healthcare professionals, and between healthcare professionals and patients. Health apps can also enable communication between patients as a form of peer support. The app in Fig. 1.8 facilitates communication between people with chronic pain by allowing users to interact and exchange advice. Peer support apps are particularly useful for patients who would otherwise struggle to identify peers (e.g. for rare conditions), or for those who are unable to access face-to-face support (e.g. if they have mobility issues or live in remote areas), or for sensitive conditions that people may feel uncomfortable discussing in person (e.g. if there is a perceived social stigma). Finally, another note-worthy feature of many health apps is the incorporation of theory-based psychological interventions. Many apps translate psychological interventions that were originally developed for face-to-face interactions into digital formats. For example, various apps incorporate established psychotherapy methods like cognitive behavioural therapy (CBT) or mindfulness. Figure 1.9 shows an app designed to reduce anxiety and stress which includes mindfulness-based components (e.g. recorded meditation exercises) and CBT-based components to challenge and reframe negative or anxious thoughts. 1.5 Common Features of Health Apps 13 Fig. 1.5 Example of an app which used experience sampling/ecological momentary assessment to help patients monitor their breathlessness over time. App developed by the authors Fig. 1.6 Example of an app which uses secure messaging to facilitate communication between healthcare professionals. (© MedXAU, https://play.google.com/store/apps/details?id=com.medx. android&hl=en Screenshots reproduced with permission) 14 1 Introduction to mHealth Fig. 1.7 Example of an app which can be used to access in-app consultations with licensed clin- icians. (© MyPocketDoctor https://play.google.com/store/apps/details?id=com.mypocketdoctor. android Screenshots reproduced with permission) Fig. 1.8 Example of an app which facilitates peer support among people with chronic pain. (© Chronic Pain Support, https://play.google.com/store/apps/details?id=com.myhealthteams. mychronicpainteam. Screenshots reproduced with permission) 1.6 Advantages and Benefits of Delivering Interventions via Health Apps Delivering interventions via mobile phones – as opposed to face-to-face settings or use of other media – can be advantageous for several reasons. 1.6 Advantages and Benefits of Delivering Interventions via Health Apps 15 Fig. 1.9 Example of an app which uses psychological therapy methods like cognitive behavioural therapy and mindfulness. (© Stress & Anxiety Companion, https://play.google.com/store/apps/ details?id=com.companionapps.anxietycompanion. Screenshots reproduced with permission) Affordability: Interventions delivered via digital platforms are often cheaper than face-to-face interventions, and, apart from initial development costs, their main- tenance and running costs tend to be low. A systematic review of economic evaluations of mHealth interventions found that 74% of the included studies found evidence for the cost-effectiveness of mHealth interventions, although recom- mended economic outcome items were frequently not reported. Scalability: There is enormous potential for mHealth interventions to be scaled up to large audiences , whereas face-to-face interventions typically require more substantial logistical efforts and resources to be delivered to larger audiences, such as staff training and venue costs. Flexibility: Health apps are accessible round-the-clock and from various locations (though an Internet connection may be required). This makes it easier for users to fit their use of the intervention to their unique schedule, as opposed to face-to-face interventions, which are administered at a specific time and place.This is particularly important for those with competing priorities such as work or caring responsibilities. Tailoring and personalisation: Health apps can be adapted to specific characteris- tics and requirements of users, while traditional interventions are often static. For example, information presented via a leaflet is usually generic, while information presented via an app or a website can easily be tailored to user characteristics and preferences. Health apps can be tailored in terms of content (Fig. 1.10) and in terms of functionality (Fig. 1.11). 16 1 Introduction to mHealth Fig. 1.10 Example of web-based intervention that tailored content to users based on their reported symptoms and risk factors based on clinical guidelines. Website developed by the authors Fig. 1.11 Example of an app which allows users to tailor features like reminders to their own preferences. App developed by the authors Interactivity: Apps offer advantages over static materials like leaflets and booklets by enabling interactivity. This can be in form of interactions with other users (e.g. other patients or healthcare professionals), or simply in form of interactions between the user and the interface. Simple interactions – like a change in the graphical user 1.7 Disadvantages and Challenges of Delivering Interventions via Health Apps 17 interface when a user taps a certain area of the screen – can help keep the user engaged and attentive. Anonymity: Health apps can offer increased anonymity, which may be important for those with sensitive health concerns. For example, someone with moderate anxiety or depression may feel comfortable downloading an app, whereas they might not feel comfortable seeking face-to-face professional support due to the attached stigma. A systematic review on the factors affecting engagement with digital health interventions found that perceived anonymity plays an important role, e.g. for interventions relating to sexually transmitted diseases and mental health. Widespread availability: With over 2.5 billion smartphone owners across the world and increasingly prevalent Internet penetration, health apps are nearly universally accessible, particularly in advanced economies, where approximately 76% of the population own smartphones. Smartphones are almost ubiquitous among younger populations aged 18–34 years in advanced economies, with an average of (95%) owning a smartphone. Although smartphone ownership is lower among older populations (55% among those aged 50 years and above in advanced economies), the age gap between the generations has been decreasing noticeably since 2015. It is important to note, however, that smartphone ownership varies considerably by country, with only around 45% smartphone ownership in emerging economies. Unequal distributions of smartphone ownership and Internet access are further discussed in Sect. 1.7. 1.7 Disadvantages and Challenges of Delivering Interventions via Health Apps Despite numerous advantages and benefits, there are also a number of important challenges and limitations that need to be taken into account when considering the delivery of a health intervention via mobile technology. Lack of regulation: A main issue with health apps is that content is not officially regulated, which means that the accuracy and safety is not guaranteed. Virtually anyone who is technically able to develop an app can upload this to app stores, with no requirements for medical quality control. For example, Taki et al. systematically assessed infant feeding websites and apps. Out of 46 apps, 78% were rated poor quality, and none were rated excellent. The reviewed apps showed issues with navigability, design, readability, accessibility (e.g. for those with visual impairments), and content. A lack of regulation and evidence base can lead to ineffective interventions, and, at worst, inaccurate medical information which can lead to serious negative consequences. For example, consider the pregnancy and diet app in Fig. 1.12, which encourages women to eat salmon and mackerel during pregnancy. The app fails to mention that these should be consumed in moderation 18 1 Introduction to mHealth Fig. 1.12 Example of an app providing medically inaccurate and potentially harmful information. The app encourages pregnant women to eat oily fish like mackerel and salmon, without mentioning that these can contain harmful pollutants like dioxins and polychlorinated biphenyls. Image created for the purpose of this book, based on existing apps. because they may contain harmful dioxins and polychlorinated biphenyls. Another example of apps providing inaccurate advice are apps that claim to promote safer consumption of alcohol by providing estimates of blood alcohol concentration to enable users to determine whether they are safe to drive. Such apps do not in fact have any capacity to estimate blood alcohol concentration. There are initiatives which aim to mitigate such issues by providing users with access to quality- controlled health apps, such as the NHS Apps Library of the UK National Health Service. All apps in the library are required to meet standards sourced by NHS Digital, including evidence of clinical safety, security and technical stability. Lack of face-to-face contact: Although remote consultations and support can be convenient and may sometimes be the only option open to patients, the importance of personal contact should not be underestimated. Face-to-face contact may be important when physical examinations are required, or to build trustful relationships between clinicians and patients with clear respective responsibilities. Without this contact, patient engagement and compliance with treatments may decrease. However, the extant literature shows ample evidence that healthcare delivered via telemedicine (i.e. video or messaging formats) has no detrimental effects on health outcomes, professional practice or satisfaction [6, 35]. For example, Tates compared web-based versus face-to-face consultations for gynecological 1.7 Disadvantages and Challenges of Delivering Interventions via Health Apps 19 health problems and found no differences in information exchange, interpersonal relationship building, or shared decision making. Digital divide: Not all population groups have equal access to digital technology, and almost half of the world’s population are currently still cut off from the Internet. Even where access is available, users may encounter further barriers, for example due to the high levels of (health) literacy often required for health app usage. Similar divides are apparent at a global level; in developed countries, 81% of the population have Internet access, whereas only 41.3% of people in developing countries use the Internet. Apart from lack of access to Internet connectivity or mobile devices, there are further accessibility issues for specific user groups. For example, most apps do not include text-to-speech/speech-to-text features and are therefore not accessible to users who are unable to read or type, e.g. users who are blind, have manual dexterity issues, or are illiterate. Moreover, both chronic and acute health conditions are often accompanied by disability-like impairments that might affect mobility, cognition, or perception and thereby impact on people’s ability to interact with mobile devices and interfaces. Generally, those who are older, less educated, and of lower socio-economic status are less likely to have access to or use smartphones. At the same time, these groups are often particularly vulnerable to health problems. As such, there are concerns over whether digital health interventions risk further isolating and disadvantaging those with the worst health outcomes, thereby widening health inequalities. Rapid change of technology vs. slow pace of evidence-based development and evaluation: Aside from the lack of regulation, the availability of evidence-based apps is further hampered by lengthy processes involved in the evaluation of apps and publication of the findings. For independent evaluations by academic institutions, this will often involve several months or even years, from first applying for funding to undertaking the evaluation and finally publishing results in academic journals. Given the rapid pace at which technology changes, the evaluated technology will often be outdated by the time evaluations are completed and published. Data security concerns: Health apps often involve input of personal, sensitive data by users. This can include for example names and contact details, demographic characteristics (age, gender...), health data (e.g. weight and blood pressure mea- surements, symptom tracking data...), and information about the users’ location (e.g. GPS coordinates). In some cases these data are stored simply on the device itself, but in many cases, data are transferred from the device to remote databases (e.g. for research or data processing). As such, there is a considerable risk of data breaches, either through errors/oversights, or malicious attacks. For example, on reviewing security hazards related to digital communication between patients and clinical teams, Griffiths et al. found that there is a risk of inadvertently disclos- ing sensitive information, for example when users accidentally send messages to the wrong recipient (oversight/error) or hacking and interception of communication (malicious attacks). Despite existing laws on data protection such as the European Union’s General Data Protection Regulation (GDPR) and established guidelines for 20 1 Introduction to mHealth good practice, the majority of the most popular mobile health apps do not follow regulations and guidelines, entailing serious data privacy risks. Data security is discussed further in Chap. 5. Resistance to implementation: Many users as well as healthcare professionals may exhibit some resistance to implementing and using health apps. For example, there may be concerns among healthcare professionals that health apps do not meet guidelines for good medical practice, or that incorporation of mHealth may be disruptive to workflows and therefore time-consuming [7, 28, 32]. Users may resist uptake of health apps, for example if they are sceptical of their effectiveness in improving health outcomes, if they do not trust their accuracy and credibility, if they perceive health risks to using apps, or if they feel worried or fearful about taking an active role in their own health management. Technical failures: As with all technology there is a risk of technical failures that could lead to errors or disrupt and thereby compromise patient safety or app functionality. For example, Griffiths et al. evaluated digital communication strategies between young people with long-term health conditions and their clinical care teams. They identified important technical problems that could lead to barriers to communication, including lack of Internet access in certain situations, battery failures, freezing of apps, and limited storage on devices resulting in failure to record content. In Reade et al. pilot study testing an app for tracking chronic pain, physical activity and weather data, participants’ batteries depleted quickly because accelerometer data capture used a large amount of power. This led to potential data losses as well as user disengagement. High dropout: Although the ease with which apps can be accessed – often free of charge and requiring no formal commitment – means that they are likely to be accessed by large numbers of users, this also means that user retention is often low. Users often decide within the first 3–7 days whether they will continue using an app, and most apps lose over three quarters of their users only 3 days after download. After 3 months, only 5% of users remain. Although there are millions of apps on app stores, only a few thousand are able to sustain continuous engagement. 1.8 Summary of Key Points This chapter introduced the field of mHealth and provided an overview of different types of mHealth interventions, including apps for health promotion and prevention of disease, apps for disease management, and apps which facilitate remote access to treatment. The importance of mHealth has surged over recent years and is likely to rise further, given increasing pressures on healthcare systems worldwide due to limited financial resources and demographic shifts in the population. mHealth involves many benefits that are likely to help support healthcare professionals and free up time to spend with patients, but they also involve limitations and challenges that require careful consideration to mitigate risk and harm. 1.9 Quiz 21 mHealth is concerned with the use of mobile phones and other wireless technolo- gies to achieve health objectives Health apps (or “mHealth interventions”) are mobile applications that aim to promote and maintain health by supporting behaviour change and/or decision making. Health apps are of particular importance in light of rising pressures on health- care systems worldwide, with most healthcare systems struggling to meet the demands of an increasingly ageing population coupled with a surge in long-term health conditions such as diabetes. Health apps can span a wide array of aims, such as health promotion and prevention, supporting (self-)management of chronic conditions, tracking of health outcomes over time, supporting decision making, enabling remote access to treatment, and facilitating peer support. Apps can provide a means of disseminating interventions that is relatively affordable and scalable. They offer considerable advantages over more static materials through their flexibility and interactivity. Valid concerns remain regarding the lack of regulation, data security, technical failures, and barriers to access experienced by those who are unable or struggle to purchase or operate mobile devices and Internet connectivity (who also tend to be those most vulnerable to poor health outcomes). Further challenges include high dropout rates among users and potential resistance to innovation among stakeholders and/or users. 1.9 Quiz 1. An app which allows users to send text messages to their healthcare professionals would be considered... (a) an mHealth intervention (b) a telehealth intervention (c) a uHealth intervention (d) all of the above 2. Which of the following items is not an example of ecological momentary assessment (EMA)? (a) An app uses smartphones’ in-built accelerometer to track physical activity levels of users as they go about their everyday lives. (b) An app asks users to rate their breathlessness levels after they have under- taken a physical exercise at a clinic as part of a study. (c) An app requires people with asthma to input their symptoms daily over a period of several months. Answers to the quiz can be found in “Solutions to Quizzes”. 22 1 Introduction to mHealth 1.10 Exercises In this chapter, we discussed the concept of the “digital divide”. Those who experience barriers to accessing technology and the Internet are often also those who are most vulnerable to poor health outcomes. What conclusion would you draw from this; should healthcare services refrain from implementing mHealth interventions? What measures could they take to mitigate the effects of the digital divide? Reflect. Recommended Reading 1. Castle-Clarke S. What will new technology mean for the NHS and its patients? Four big technological trends. Technical report, The King’s Fund and the Nuffield Trust; 2018. 2. National Health Service. 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