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Moorthy P, Weinert L, Schüttler C, Svensson L, Sedlmayr B, Müller J, Nagel T. Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review. JMIR Mhealth Uhealth 2024; 12:e52179. [PMID: 38578671 PMCID: PMC11031706 DOI: 10.2196/52179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/15/2023] [Accepted: 02/01/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND Wearable devices, mobile technologies, and their combination have been accepted into clinical use to better assess the physical fitness and quality of life of patients and as preventive measures. Usability is pivotal for overcoming constraints and gaining users' acceptance of technology such as wearables and their companion mobile health (mHealth) apps. However, owing to limitations in design and evaluation, interactive wearables and mHealth apps have often been restricted from their full potential. OBJECTIVE This study aims to identify studies that have incorporated wearable devices and determine their frequency of use in conjunction with mHealth apps or their combination. Specifically, this study aims to understand the attributes and evaluation techniques used to evaluate usability in the health care domain for these technologies and their combinations. METHODS We conducted an extensive search across 4 electronic databases, spanning the last 30 years up to December 2021. Studies including the keywords "wearable devices," "mobile apps," "mHealth apps," "physiological data," "usability," "user experience," and "user evaluation" were considered for inclusion. A team of 5 reviewers screened the collected publications and charted the features based on the research questions. Subsequently, we categorized these characteristics following existing usability and wearable taxonomies. We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. RESULTS A total of 382 reports were identified from the search strategy, and 68 articles were included. Most of the studies (57/68, 84%) involved the simultaneous use of wearables and connected mobile apps. Wrist-worn commercial consumer devices such as wristbands were the most prevalent, accounting for 66% (45/68) of the wearables identified in our review. Approximately half of the data from the medical domain (32/68, 47%) focused on studies involving participants with chronic illnesses or disorders. Overall, 29 usability attributes were identified, and 5 attributes were frequently used for evaluation: satisfaction (34/68, 50%), ease of use (27/68, 40%), user experience (16/68, 24%), perceived usefulness (18/68, 26%), and effectiveness (15/68, 22%). Only 10% (7/68) of the studies used a user- or human-centered design paradigm for usability evaluation. CONCLUSIONS Our scoping review identified the types and categories of wearable devices and mHealth apps, their frequency of use in studies, and their implementation in the medical context. In addition, we examined the usability evaluation of these technologies: methods, attributes, and frameworks. Within the array of available wearables and mHealth apps, health care providers encounter the challenge of selecting devices and companion apps that are effective, user-friendly, and compatible with user interactions. The current gap in usability and user experience in health care research limits our understanding of the strengths and limitations of wearable technologies and their companion apps. Additional research is necessary to overcome these limitations.
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Affiliation(s)
- Preetha Moorthy
- Department of Biomedical Informatics, Center for Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lina Weinert
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
- Section for Oral Health, Heidelberg Institute of Global Health, Heidelberg University Hospital, Heidelberg, Germany
| | - Christina Schüttler
- Medical Center for Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany
| | - Laura Svensson
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Brita Sedlmayr
- Institute for Medical Informatics and Biometry, Carl Gustav Carus Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Julia Müller
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Till Nagel
- Human Data Interaction Lab, Mannheim University of Applied Sciences, Mannheim, Germany
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van Kessel R, Roman-Urrestarazu A, Anderson M, Kyriopoulos I, Field S, Monti G, Reed SD, Pavlova M, Wharton G, Mossialos E. Mapping Factors That Affect the Uptake of Digital Therapeutics Within Health Systems: Scoping Review. J Med Internet Res 2023; 25:e48000. [PMID: 37490322 PMCID: PMC10410406 DOI: 10.2196/48000] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/31/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Digital therapeutics are patient-facing digital health interventions that can significantly alter the health care landscape. Despite digital therapeutics being used to successfully treat a range of conditions, their uptake in health systems remains limited. Understanding the full spectrum of uptake factors is essential to identify ways in which policy makers and providers can facilitate the adoption of effective digital therapeutics within a health system, as well as the steps developers can take to assist in the deployment of products. OBJECTIVE In this review, we aimed to map the most frequently discussed factors that determine the integration of digital therapeutics into health systems and practical use of digital therapeutics by patients and professionals. METHODS A scoping review was conducted in MEDLINE, Web of Science, Cochrane Database of Systematic Reviews, and Google Scholar. Relevant data were extracted and synthesized using a thematic analysis. RESULTS We identified 35,541 academic and 221 gray literature reports, with 244 (0.69%) included in the review, covering 35 countries. Overall, 85 factors that can impact the uptake of digital therapeutics were extracted and pooled into 5 categories: policy and system, patient characteristics, properties of digital therapeutics, characteristics of health professionals, and outcomes. The need for a regulatory framework for digital therapeutics was the most stated factor at the policy level. Demographic characteristics formed the most iterated patient-related factor, whereas digital literacy was considered the most important factor for health professionals. Among the properties of digital therapeutics, their interoperability across the broader health system was most emphasized. Finally, the ability to expand access to health care was the most frequently stated outcome measure. CONCLUSIONS The map of factors developed in this review offers a multistakeholder approach to recognizing the uptake factors of digital therapeutics in the health care pathway and provides an analytical tool for policy makers to assess their health system's readiness for digital therapeutics.
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Affiliation(s)
- Robin van Kessel
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, United Kingdom
- Department of International Health, Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Andres Roman-Urrestarazu
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Michael Anderson
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, United Kingdom
| | - Ilias Kyriopoulos
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, United Kingdom
| | - Samantha Field
- Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Giovanni Monti
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, United Kingdom
| | - Shelby D Reed
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, United States
| | - Milena Pavlova
- Department of Health Services Research, Care and Public Health Research Institute, Faculty of Health Medicine and Life Science, Maastricht University, Maastricht, Netherlands
| | - George Wharton
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, United Kingdom
| | - Elias Mossialos
- LSE Health, Department of Health Policy, London School of Economics and Political Science, London, United Kingdom
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
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Mavragani A, Noyudom A, Panjatharakul P, Visudtibhan PJ. Use of and Satisfaction With Mobile Health Education During the COVID-19 Pandemic in Thailand: Cross-sectional Study. JMIR Form Res 2023; 7:e43639. [PMID: 36596210 PMCID: PMC9893882 DOI: 10.2196/43639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/05/2022] [Accepted: 12/27/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND RamaCovid is a mobile health (mHealth) education system that provides the Thai population with information about COVID-19 and self-risk assessment. RamaCovid has a chatbot system that provides automatic conversations (available 24 hours per day) and a live chat function that allows users to directly communicate with health professionals (available 4 hours per day in the evening). The system consists of (1) COVID-19 vaccine information, (2) self-care after vaccination, (3) frequently asked questions, (4) self-risk assessment, (5) hospital finding, (6) contact number finding, and (7) live chat with a health professional. OBJECTIVE This study investigates the use of and satisfaction with the RamaCovid system. METHODS Overall, 400 people were recruited via RamaCovid by broadcasting an infographic about the study. Questionnaires collected demographic data, users' experiences of RamaCovid, and the use of and satisfaction with the system. The questions were answered using a 5-point Likert scale. Descriptive statistics were used to describe the participant characteristics and their use of and satisfaction with the RamaCovid system. The Mann-Whitney U test was performed to examine the difference in use and satisfaction between the adult and older adult groups. RESULTS The participants showed high use of and satisfaction with the RamaCovid system. They used the information to take care of themselves and their family, and they gained information about their COVID-19 risk. The users were satisfied with the system because the information was easy to understand, trustworthy, and up to date. However, the older adult group had lower use of and satisfaction with the system compared to the adult group. CONCLUSIONS RamaCovid is an example of the successful implementation of mHealth education. It was an alternative way to work with the call center during the COVID-19 pandemic and increased access to health information and health care services. Providing ongoing updated information, improving the attractiveness of the media information, and the age group difference are important issues for further system development.
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Affiliation(s)
| | - Angun Noyudom
- Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Pawanrat Panjatharakul
- Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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Bae YS, Sung S, Lee J, Lee H, Chie EK. Building and implementing a contactless clinical trial protocol for patients with COVID-19: A Korean perspective. Front Med (Lausanne) 2022; 9:975243. [PMID: 36186782 PMCID: PMC9520334 DOI: 10.3389/fmed.2022.975243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction To effectively manage patients with coronavirus disease 2019 (COVID-19) while minimizing contact between medical staff, clinical trial protocol that facilitates contactless patient management was designed to predict deterioration of disease condition and monitor mental health status. Methods Through consultation with infectious disease specialists and psychiatrists, this study identified main clinical indicators related to respiratory and non-respiratory outcomes, and mental health. Telehealth devices that could collect relevant data indicators were explored. The following three modes were identified: wearable devices, video calls, and online questionnaires. Clinical trial protocol was implemented to patients confirmed with COVID-19 infection and admitted to Seongnam residential treatment centers between September 1, 2021 and December 30, 2021. Data were collected from wearable devices, video calls, online questionnaires, and from electronic health records. Participant satisfaction was assessed through an online survey at the time of discharge. Results In total, 120 asymptomatic and mildly symptomatic COVID-19 patients participated in this trial. Seven types of physiological and life log data were collected from 87 patients using wearable devices, video and audio recordings, and online mental health-related questionnaire. Most participants were satisfied with the overall trial process, but perceived difficulties in using telehealth devices. Conclusion This trial collected simultaneously generated multimodal patient data using various telehealth devices in a contactless setting for COVID-19 patients. Data collected in this study will be used to build a remote patient management system based on the prediction algorithms.
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Affiliation(s)
- Ye Seul Bae
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
- Department of Family Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Sumi Sung
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
| | - Jungeun Lee
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
| | - Hyeonji Lee
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
| | - Eui Kyu Chie
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
- Department of Radiation Oncology, College of Medicine, Seoul National University, Seoul, South Korea
- Medical Research Center, Institute of Radiation Medicine, Seoul National University, Seoul, South Korea
- *Correspondence: Eui Kyu Chie
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Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The analysis of the electrocardiogram (ECG) is critical for the diagnosis of arrhythmias. Recent advances in information and communications technology (ICT) have led to the development of wearable ECG devices and arrhythmia-detection algorithms. This study aimed to develop an ICT-based clinical trial service platform using a self-reporting technique for real-time arrhythmia detection. To establish a clinical-trial service platform, a mobile application (app), a demilitarized zone (DMZ), an internal network, and Amazon web services virtual private cloud (AWS-VPC) were developed. The ECG data acquired by a wearable device were transmitted to the mobile app, which collected the participants’ self-reported information. The mobile app transmitted raw ECG and self-reported data to the AWS-VPC and DMZ, respectively. In the AWS-VPC, the live-streaming and playback-reviewer services were operational to display the currently and previously acquired ECG data to clinicians through the web client. All the measured data were transmitted to the internal network, in which the arrhythmia-detection algorithm was executed and all the data were saved. The self-reporting technique and arrhythmia-detection algorithm are the key elements of this platform. In particular, subjective information of participants can be easily collected using a self-reporting technique. These features are particularly of critical importance for treating painless, sparsely occurring arrhythmias.
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Savoldelli A, Vitali A, Remuzzi A, Giudici V. Increasing the user experience of televisits and telemonitoring for heart failure patients in less than 6 months: a methodological approach. Int J Med Inform 2022; 161:104717. [DOI: 10.1016/j.ijmedinf.2022.104717] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/09/2022] [Accepted: 02/13/2022] [Indexed: 11/26/2022]
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Asada K, Komatsu M, Shimoyama R, Takasawa K, Shinkai N, Sakai A, Bolatkan A, Yamada M, Takahashi S, Machino H, Kobayashi K, Kaneko S, Hamamoto R. Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics. J Pers Med 2021; 11:886. [PMID: 34575663 PMCID: PMC8471764 DOI: 10.3390/jpm11090886] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryo Shimoyama
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Akira Sakai
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Masayoshi Yamada
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Kazuma Kobayashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Syuzo Kaneko
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
| | - Ryuji Hamamoto
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.A.); (M.K.); (R.S.); (K.T.); (N.S.); (A.B.); (S.T.); (H.M.); (K.K.); (S.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (A.S.); (M.Y.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
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