%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e58956 %T A Hierarchical Framework for Selecting Reference Measures for the Analytical Validation of Sensor-Based Digital Health Technologies %A Bakker,Jessie P %A McClenahan,Samantha J %A Fromy,Piper %A Turner,Simon %A Peterson,Barry T %A Vandendriessche,Benjamin %A Goldsack,Jennifer C %+ Digital Medicine Society, 90 Canal Street, Boston, MA, 02114, United States, 1 7652343463, benjamin.vandendriessche@dimesociety.org %K digital health technologies %K analytical validation %K digital medicine %K reference measures %K fit-for-purpose digital clinical measures %D 2025 %7 7.2.2025 %9 Viewpoint %J J Med Internet Res %G English %X Sensor-based digital health technologies (sDHTs) are increasingly used to support scientific and clinical decision-making. The digital clinical measures they generate offer enormous benefits, including providing more patient-relevant data, improving patient access, reducing costs, and driving inclusion across health care ecosystems. Scientific best practices and regulatory guidance now provide clear direction to investigators seeking to evaluate sDHTs for use in different contexts. However, the quality of the evidence reported for analytical validation of sDHTs—evaluation of algorithms converting sample-level sensor data into a measure that is clinically interpretable—is inconsistent and too often insufficient to support a particular digital measure as fit-for-purpose. We propose a hierarchical framework to address challenges related to selecting the most appropriate reference measure for conducting analytical validation and codify best practices and an approach that will help capture the greatest value of sDHTs for public health, patient care, and medical product development. %M 39918870 %R 10.2196/58956 %U https://www.jmir.org/2025/1/e58956 %U https://doi.org/10.2196/58956 %U http://www.ncbi.nlm.nih.gov/pubmed/39918870 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e57385 %T Digital Health Innovations to Catalyze the Transition to Value-Based Health Care %A Zhang,Lan %A Bullen,Christopher %A Chen,Jinsong %K digital health %K value-based health care %K VBHC %K patient-reported outcome measures %K PROM %K digital transformation %K health care innovation %K patient-centric care %K health technology %K patient-reported outcome %K PRO %K outcome measure %K telehealth %K telemedicine %K eHealth %K personalized %K customized %K engagement %K patient-centered care %K standardization %K implementation %D 2025 %7 20.1.2025 %9 %J JMIR Med Inform %G English %X The health care industry is currently going through a transformation due to the integration of technologies and the shift toward value-based health care (VBHC). This article explores how digital health solutions play a role in advancing VBHC, highlighting both the challenges and opportunities associated with adopting these technologies. Digital health, which includes mobile health, wearable devices, telehealth, and personalized medicine, shows promise in improving diagnostic accuracy, treatment options, and overall health outcomes. The article delves into the concept of transformation in health care by emphasizing its potential to reform care delivery through data communication, patient engagement, and operational efficiency. Moreover, it examines the principles of VBHC, with a focus on patient outcomes, and emphasizes how digital platforms play a role in treatment among tertiary hospitals by using patient-reported outcome measures. The article discusses challenges that come with implementing VBHC, such as stakeholder engagement and standardization of patient-reported outcome measures. It also highlights the role played by health innovators in facilitating the transition toward VBHC models. Through real-life case examples, this article illustrates how digital platforms have had an impact on efficiencies, patient outcomes, and empowerment. In conclusion, it envisions directions for solutions in VBHC by emphasizing the need for interoperability, standardization, and collaborative efforts among stakeholders to fully realize the potential of digital transformation in health care. This research highlights the impact of digital health in creating a health care system that focuses on providing high-quality, efficient, and patient-centered care. %R 10.2196/57385 %U https://medinform.jmir.org/2025/1/e57385 %U https://doi.org/10.2196/57385 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e57332 %T Patient-Centric Approach to Personalized Electronic Medical Records via QR Code in Japan %A Izumida,Yoshihiko %A Omura,Takashi %A Fujiwara,Masahiro %A Nukaya,Shoko %A Yoneyama,Akio %A Boubacar,Sow %A Yabe,Shinichiro %A Noguchi,Rika %A Nakayama,Shima %A Muraoka,Wataru %A Okuno,Yuki %A Miyashita,Sho %A Ishihara,Yurika %A Moriwaki,Yuto %A Otani,Ryoji %A Adachi,Junichiro %A Tanabe,Kenichiro %A Yamano,Yoshihisa %A Takai,Yasushi %A Honjo,Masaru %+ Department of Endocrinology and Diabetes, Saitama Medical Center, Saitama Medical University, 1981 Kamoda, Kawagoe, Saitama, 350-8550, Japan, 81 492283570, izumida@saitama-med.ac.jp %K Sync for Science-J %K S4S-J %K electronic medical record %K personal health record %K privacy preference manager %K patient-generated health data %K Health Level 7 Fast Health Care Interoperability Resources %K HL7-FHIR %K logical observation identifiers names and codes %K LOINC %K open science %K mobile health %K app %K digital health %K digital intervention %D 2024 %7 23.12.2024 %9 Viewpoint %J Interact J Med Res %G English %X Government policies in the United States and the European Union promote standardization and value creation in the use of FAIR (findability, accessibility, interoperability, and reusability) data, which can enhance trust in digital health systems and is crucial for their success. Trust is built through elements such as FAIR data access, interoperability, and improved communication, which are essential for fostering innovation in digital health technologies. This Viewpoint aims to report on exploratory research demonstrating the feasibility of testing a patient-centric data flow model facilitating semantic interoperability on precision medical information. In this global trend, the interoperable interface called Sync for Science-J (S4S-J) for linking electronic medical records (EMRs) and personal health records was launched as part of the Basic Policy for Economic and Fiscal Management and Reform in Japan. S4S-J controls data distribution consisting of EMR and patient-generated health data and converts this information into QR codes that can be scanned by mobile apps. This system facilitates data sharing based on personal information beliefs and unlocks siloed Internet of Things systems with a privacy preference manager. In line with Japanese information handling practices, the development of a mobile cloud network will lower barriers to entry and enable accelerated data sharing. To ensure cross-compatibility and compliance with future international data standardization, S4S-J conforms to the Health Level 7 Fast Health Care Interoperability Resources standard and uses the international standardized logical observation identifiers names and codes (LOINC) to redefine medical terms used in different terminology standards in different medical fields. It is developed as an applied standard in medical information intended for industry, health care services, and research through secondary use of data. A multicenter collaborative study was initiated to investigate the effectiveness of this system; this was a registered, multicenter, randomized controlled clinical trial, the EMBRACE study of the mobile health app M♡Link for hyperglycemic disorders in pregnancy, which implements an EMR–personal health record interoperable interface via S4S-J. Nevertheless, the aforementioned new challenges, the pivotal Health Level 7 Fast Health Care Interoperability Resources system, and LOINC data mapping were successfully implemented. Moreover, the preliminary input of EMR-integrated patient-generated health data was successfully shared between authorized medical facilities and health care providers in accordance with the patients’ preferences. The patient-centric data flow of the S4S-J in Japan is expected to guarantee the right to data portability, which promotes the maximum benefit of use by patients themselves, which in turn contributes to the promotion of open science. %M 39715547 %R 10.2196/57332 %U https://www.i-jmr.org/2024/1/e57332 %U https://doi.org/10.2196/57332 %U http://www.ncbi.nlm.nih.gov/pubmed/39715547 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59888 %T Don’t Forget the Humble Text Message: 25 Years of Text Messaging in Health %A Dobson,Rosie %A Whittaker,Robyn %A Abroms,Lorien C %A Bramley,Dale %A Free,Caroline %A McRobbie,Hayden %A Stowell,Melanie %A Rodgers,Anthony %+ School of Population Health, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand, 64 93737599, r.dobson@auckland.ac.nz %K text messaging %K messaging %K SMS %K texting %K mHealth %K mobile health %D 2024 %7 17.12.2024 %9 Viewpoint %J J Med Internet Res %G English %X Since the early studies exploring the use of SMS text messaging for health intervention, text messaging has played a pivotal role in the advancement of mobile health. As an intervention modality, text messaging has provided vital learnings for the design and delivery of interventions, particularly in low-resource settings. Despite the advances in technology over the last 25 years, text messaging is still being used in largely the same way to deliver health information, behavior change interventions, and support. The strong, consistent evidence for the benefits of this type of intervention has made text messaging a routine part of health interventions around the world. Key to its success is its simplicity, alongside the benefit of being arguably the most accessible form of consumer digital health intervention. Text message interventions are well suited for public health interventions due to their low cost, vast reach, frequent use, high read rates, and ability to be tailored and personalized. Furthermore, the nature of text messaging interventions makes them ideal for the delivery of multilingual, culturally tailored interventions, which is important in the context of increasing cultural diversity in many countries internationally. Indeed, studies assessing text message–based health interventions have shown them to be effective across sociodemographic and ethnic groups and have led to their adoption into national-level health promotion programs. With a growing focus on artificial intelligence, robotics, sensors, and other advances in digital health, there is an opportunity to integrate these technologies into text messaging programs. Simultaneously, it is essential that equity remains at the forefront for digital health researchers, developers, and implementers. Ensuring digital health solutions address inequities in health experienced across the world while taking action to maximize digital inclusion will ensure the true potential of digital health is realized. Text messaging has the potential to continue to play a pivotal role in the delivery of equitable digital health tools to communities around the world for many years to come. Further new technologies can build on the humble text message, leveraging its success to advance the field of digital health. This Viewpoint presents a retrospective of text messaging in health, drawing on the example of text message–based interventions for smoking cessation, and presents evidence for the continued relevance of this mobile health modality in 2025 and beyond. %M 39689299 %R 10.2196/59888 %U https://www.jmir.org/2024/1/e59888 %U https://doi.org/10.2196/59888 %U http://www.ncbi.nlm.nih.gov/pubmed/39689299 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e58035 %T Digital Health Readiness: Making Digital Health Care More Inclusive %A Bober,Timothy %A Rollman,Bruce L %A Handler,Steven %A Watson,Andrew %A Nelson,Lyndsay A %A Faieta,Julie %A Rosland,Ann-Marie %+ Division of General Internal Medicine, University of Pittsburgh School of Medicine, UPMC Montefiore Hospital, Suite W933, Pittsburgh, PA, 15213, United States, 1 412 692 4821, bobertm@upmc.edu %K digital health %K digital health literacy %K informatics %K digital disparities %K digital health readiness %K inclusivity %K digital health tool %K literacy %K patient support %K health system %D 2024 %7 9.10.2024 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X This paper proposes an approach to assess digital health readiness in clinical settings to understand how prepared, experienced, and equipped individual people are to participate in digital health activities. Existing digital health literacy and telehealth prediction tools exist but do not assess technological aptitude for particular tasks or incorporate available electronic health record data to improve efficiency and efficacy. As such, we propose a multidomain digital health readiness assessment that incorporates a person’s stated goals and motivations for use of digital health, a focused digital health literacy assessment, passively collected data from the electronic health record, and a focused aptitude assessment for critical skills needed to achieve a person’s goals. This combination of elements should allow for easy integration into clinical workflows and make the assessment as actionable as possible for health care providers and in-clinic digital health navigators. Digital health readiness profiles could be used to match individuals with support interventions to promote the use of digital tools like telehealth, mobile apps, and remote monitoring, especially for those who are motivated but do not have adequate experience. Moreover, while effective and holistic digital health readiness assessments could contribute to increased use and greater equity in digital health engagement, they must also be designed with inclusivity in mind to avoid worsening known disparities in digital health care. %M 39383524 %R 10.2196/58035 %U https://mhealth.jmir.org/2024/1/e58035 %U https://doi.org/10.2196/58035 %U http://www.ncbi.nlm.nih.gov/pubmed/39383524 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e57158 %T Raw Photoplethysmography as an Enhancement for Research-Grade Wearable Activity Monitors %A Hibbing,Paul R %A Khan,Maryam Misal %+ Department of Kinesiology and Nutrition, University of Illinois Chicago, 1919 W Taylor St, Rm 650, Mail Code 517, Chicago, IL, 60612, United States, 1 312 355 1088, phibbing@uic.edu %K measurement %K optical sensors %K sensor fusion %K wearable electronic devices %K accelerometry %K photoplethysmography %K digital health %K exercise %K sedentary behavior %D 2024 %7 27.9.2024 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Wearable monitors continue to play a critical role in scientific assessments of physical activity. Recently, research-grade monitors have begun providing raw data from photoplethysmography (PPG) alongside standard raw data from inertial sensors (accelerometers and gyroscopes). Raw PPG enables granular and transparent estimation of cardiovascular parameters such as heart rate, thus presenting a valuable alternative to standard PPG methodologies (most of which rely on consumer-grade monitors that provide only coarse output from proprietary algorithms). The implications for physical activity assessment are tremendous, since it is now feasible to monitor granular and concurrent trends in both movement and cardiovascular physiology using a single noninvasive device. However, new users must also be aware of challenges and limitations that accompany the use of raw PPG data. This viewpoint paper therefore orients new users to the opportunities and challenges of raw PPG data by presenting its mechanics, pitfalls, and availability, as well as its parallels and synergies with inertial sensors. This includes discussion of specific applications to the prediction of energy expenditure, activity type, and 24-hour movement behaviors, with an emphasis on areas in which raw PPG data may help resolve known issues with inertial sensing (eg, measurement during cycling activities). We also discuss how the impact of raw PPG data can be maximized through the use of open-source tools when developing and disseminating new methods, similar to current standards for raw accelerometer and gyroscope data. Collectively, our comments show the strong potential of raw PPG data to enhance the use of research-grade wearable activity monitors in science over the coming years. %M 39331461 %R 10.2196/57158 %U https://mhealth.jmir.org/2024/1/e57158 %U https://doi.org/10.2196/57158 %U http://www.ncbi.nlm.nih.gov/pubmed/39331461 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e49719 %T Leveraging mHealth Technologies for Public Health %A Velmovitsky,Pedro Elkind %A Kirolos,Merna %A Alencar,Paulo %A Leatherdale,Scott %A Cowan,Donald %A Morita,Plinio Pelegrini %+ School of Public Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 5198884567, plinio.morita@uwaterloo.ca %K mobile health %K mHealth %K smart technology %K wearables %K public health %K population health %K apps %K surveys %K self-report %K surveillance %K digital public health %K mobile phone %D 2024 %7 12.9.2024 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators. Having embedded sensors that provide zero-effort, passive, and continuous monitoring of health variables, these devices generate data that could be leveraged for cases in which the data are related to the same self-report metric of interest. However, some challenges must be considered when discussing the use of mobile health technologies for public health to ensure digital health equity, privacy, and best practices. This paper provides, through a review of major Canadian surveys and mobile health studies, an overview of research involving mobile data for public health, including a mapping of variables currently collected by public health surveys that could be complemented with self-report, challenges to technology adoption, and considerations on digital health equity, with a specific focus on the Canadian context. Population characteristics from major smart technology brands—Apple, Fitbit, and Samsung—and demographic barriers to the use of technology are provided. We conclude with public health implications and present our view that public health agencies and researchers should leverage mobile health data while being mindful of the current barriers and limitations to device use and access. In this manner, data ecosystems that leverage personal smart devices for public health can be put in place as appropriate, as we move toward a future in which barriers to technology adoption are decreasing. %M 39265164 %R 10.2196/49719 %U https://publichealth.jmir.org/2024/1/e49719 %U https://doi.org/10.2196/49719 %U http://www.ncbi.nlm.nih.gov/pubmed/39265164 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e50043 %T Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture %A Pulantara,I Wayan %A Wang,Yuhan %A Burke,Lora E %A Sereika,Susan M %A Bizhanova,Zhadyra %A Kariuki,Jacob K %A Cheng,Jessica %A Beatrice,Britney %A Loar,India %A Cedillo,Maribel %A Conroy,Molly B %A Parmanto,Bambang %K integrated system %K IoT integration %K wearable %K mHealth Fitbit %K Nokia %K clinical trial management %K research study management %K study tracking %K remote assessment %K tracking %K Fitbit %K wearable devices %K device %K management %K data analysis %K behavioral %K data collection %K Internet of Things %K IoT %K mHealth %K mobile health %D 2024 %7 7.8.2024 %9 %J JMIR Mhealth Uhealth %G English %X The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device’s application programmable interface and the mobile app’s adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations. %R 10.2196/50043 %U https://mhealth.jmir.org/2024/1/e50043 %U https://doi.org/10.2196/50043 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e51974 %T Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement %A Monachelli,Rebecca %A Davis,Sharon Watkins %A Barnard,Allison %A Longmire,Michelle %A Docherty,John P %A Oakley-Girvan,Ingrid %+ Medable Inc, 525 University Ave, Palo Alto, CA, 94301, United States, 1 8778206259, oakley@stanford.edu %K adherence %K app design %K attrition %K mHealth %K user engagement %K user experience %K proof-of-concept %D 2024 %7 26.3.2024 %9 Viewpoint %J Interact J Med Res %G English %X Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants’ knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app’s design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users. %M 38416858 %R 10.2196/51974 %U https://www.i-jmr.org/2024/1/e51974 %U https://doi.org/10.2196/51974 %U http://www.ncbi.nlm.nih.gov/pubmed/38416858 %0 Journal Article %@ 2291-5222 %I %V 12 %N %P e44422 %T An Introduction to Smart Home Ward–Based Hospital-at-Home Care in China %A Cheng,Weibin %A Cao,Xiaowen %A Lian,Wanmin %A Tian,Junzhang %K smart home ward %K telemonitoring %K telemedicine %K home care %K hospital at home %K healthcare delivery %K implementation %K smart ward %K medical monitoring %K medical care %K rehabilitation %K health care %D 2024 %7 30.1.2024 %9 %J JMIR Mhealth Uhealth %G English %X Hospital-at-home has been gaining increased attention as a potential remedy for the current shortcomings of our health care system, allowing for essential health services to be provided to patients in the comfort of their own homes. The advent of digital technology has revolutionized the way we provide medical and health care, leading to the emergence of a “hospital without walls.” The rapid adoption of novel digital health care technologies is revolutionizing remote health care provision, effectively dismantling the conventional boundary separating hospitals from the comfort of patients’ homes. The Guangdong Second Provincial General Hospital has developed a 5G-powered Smart Home Ward (SHW) that extends medical care services to the home setting and is tailored to meet the needs and settings of each patient’s household. The SHW was initially tested for its suitability for treating 4 specialized diseases, including cardiovascular disease, stroke, Parkinson disease, and Alzheimer disease. Understanding and addressing the potential challenges and risks associated with SHWs is essential for the successful implementation and maintenance of safe and effective home hospitalization. %R 10.2196/44422 %U https://mhealth.jmir.org/2024/1/e44422 %U https://doi.org/10.2196/44422 %0 Journal Article %@ 2291-5222 %I %V 12 %N %P e48803 %T Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones %A Hong,Wonki %K mobile health %K mHealth %K smartphonization %K wearable deviceization %K new form factor %K sensor-integrated display %D 2024 %7 22.1.2024 %9 %J JMIR Mhealth Uhealth %G English %X Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine. %R 10.2196/48803 %U https://mhealth.jmir.org/2024/1/e48803 %U https://doi.org/10.2196/48803 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e47177 %T SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment %A Gunsilius,Chloe Zimmerman %A Heffner,Joseph %A Bruinsma,Sienna %A Corinha,Madison %A Cortinez,Maria %A Dalton,Hadley %A Duong,Ellen %A Lu,Joshua %A Omar,Aisulu %A Owen,Lucy Long Whittington %A Roarr,Bradford Nazario %A Tang,Kevin %A Petzschner,Frederike H %+ Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Carney Institute for Brain Science, 4th floor, 164 Angell Street, Providence, RI, 02912, United States, 1 401 863 6272, chloe_zimmerman@brown.edu %K acute pain %K acute-chronic pain transition %K chronic pain %K clinical outcome measurement %K digital health %K ecological momentary assessment %K EMA %K ESM %K experience sampling methodology %K mHealth %K mobile health %K pain management %K pain self-management %K patient reported outcomes %K smartphone app %D 2024 %7 12.1.2024 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users’ self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain. %M 38214952 %R 10.2196/47177 %U https://mhealth.jmir.org/2024/1/e47177 %U https://doi.org/10.2196/47177 %U http://www.ncbi.nlm.nih.gov/pubmed/38214952 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e49301 %T The Necessity of Interoperability to Uncover the Full Potential of Digital Health Devices %A Schwab,Julian D %A Werle,Silke D %A Hühne,Rolf %A Spohn,Hannah %A Kaisers,Udo X %A Kestler,Hans A %+ Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany, 49 731 500 24500, hans.kestler@uni-ulm.de %K semantic terminology %K semantic %K terminology %K terminologies %K data linkage %K interoperability %K data exchange %K SNOMED CT %K LOINC %K eHealth %K patient-reported outcome questionnaires %K requirement for standards %K standard %K standards %K PRO %K PROM %K patient reported %D 2023 %7 22.12.2023 %9 Viewpoint %J JMIR Med Inform %G English %X Personalized health care can be optimized by including patient-reported outcomes. Standardized and disease-specific questionnaires have been developed and are routinely used. These patient-reported outcome questionnaires can be simple paper forms given to the patient to fill out with a pen or embedded in digital devices. Regardless of the format used, they provide a snapshot of the patient’s feelings and indicate when therapies need to be adjusted. The advantage of digitizing these questionnaires is that they can be automatically analyzed, and patients can be monitored independently of doctor visits. Although the questions of most clinical patient-reported outcome questionnaires follow defined standards and are evaluated by clinical trials, these standards do not exist for data processing. Interoperable data formats and structures would benefit multilingual and cross-study data exchange. Linking questionnaires to standardized terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Logical Observation Identifiers, Names, and Codes (LOINC) would improve this interoperability. However, linking clinically validated patient-reported outcome questionnaires to clinical terms available in SNOMED CT or LOINC is not as straightforward as it sounds. Here, we report our approach to link patient-reported outcomes from health applications to SNOMED CT or LOINC codes. We highlight current difficulties in this process and outline ways to minimize them. %M 38133917 %R 10.2196/49301 %U https://medinform.jmir.org/2023/1/e49301 %U https://doi.org/10.2196/49301 %U http://www.ncbi.nlm.nih.gov/pubmed/38133917 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e44265 %T The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions %A Nourse,Rebecca %A Dingler,Tilman %A Kelly,Jaimon %A Kwasnicka,Dominika %A Maddison,Ralph %+ School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, 3125, Australia, 61 0392443075, rnourse@deakin.edu.au %K smart home %K health %K chronic condition %K chronic illness %K digital health %K technology %K behavior change %K wearable %K smart technology %K smart health %K economic %K cost %K security %K data storage %K implementation %D 2023 %7 18.12.2023 %9 Viewpoint %J J Med Internet Res %G English %X The effective management of chronic conditions requires an approach that promotes a shift in care from the clinic to the home, improves the efficiency of health care systems, and benefits all users irrespective of their needs and preferences. Digital health can provide a solution to this challenge, and in this paper, we provide our vision for a smart health ecosystem. A smart health ecosystem leverages the interoperability of digital health technologies and advancements in big data and artificial intelligence for data collection and analysis and the provision of support. We envisage that this approach will allow a comprehensive picture of health, personalization, and tailoring of behavioral and clinical support; drive theoretical advancements; and empower people to manage their own health with support from health care professionals. We illustrate the concept with 2 use cases and discuss topics for further consideration and research, concluding with a message to encourage people with chronic conditions, their caregivers, health care professionals, policy and decision makers, and technology experts to join their efforts and work toward adopting a smart health ecosystem. %M 38109188 %R 10.2196/44265 %U https://www.jmir.org/2023/1/e44265 %U https://doi.org/10.2196/44265 %U http://www.ncbi.nlm.nih.gov/pubmed/38109188 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 9 %N %P e49560 %T Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure %A Masel,Joanna %A Petrie,James Ian Mackie %A Bay,Jason %A Ebbers,Wolfgang %A Sharan,Aalekh %A Leibrand,Scott Michael %A Gebhard,Andreas %A Zimmerman,Samuel %+ Department of Ecology & Evolutionary Biology, University of Arizona, 1041 E Lowell St, Tucson, AZ, 85721, United States, 1 5206269888, masel@arizona.edu %K COVID-19 %K SARS-CoV-2 %K pandemic preparedness %K decentralized protocols %K smartphone %K mobile phone %K contact tracing %D 2023 %7 4.12.2023 %9 Viewpoint %J JMIR Public Health Surveill %G English %X Digital contact tracing and notification were initially hailed as promising strategies to combat SARS-CoV-2; however, in most jurisdictions, they did not live up to their promise. To avert a given transmission event, both parties must have adopted the technology, it must detect the contact, the primary case must be promptly diagnosed, notifications must be triggered, and the secondary case must change their behavior to avoid the focal tertiary transmission event. If we approximate these as independent events, achieving a 26% reduction in the effective reproduction number Rt would require an 80% success rate at each of these 6 points of failure. Here, we review the 6 failure rates experienced by a variety of digital contact tracing and contact notification schemes, including Singapore’s TraceTogether, India’s Aarogya Setu, and leading implementations of the Google Apple Exposure Notification system. This leads to a number of recommendations, for example, that the narrative be framed in terms of user autonomy rather than user privacy, and that tracing/notification apps be multifunctional and integrated with testing, manual contact tracing, and the gathering of critical scientific data. %M 38048155 %R 10.2196/49560 %U https://publichealth.jmir.org/2023/1/e49560 %U https://doi.org/10.2196/49560 %U http://www.ncbi.nlm.nih.gov/pubmed/38048155 %0 Journal Article %@ 2291-5222 %I %V 11 %N %P e45103 %T The Importance of Data Quality Control in Using Fitbit Device Data From the All of Us Research Program %A Lederer,Lauren %A Breton,Amanda %A Jeong,Hayoung %A Master,Hiral %A Roghanizad,Ali R %A Dunn,Jessilyn %K wearable device %K Fitbit %K All of Us %K data quality %K noise %K missingness %K biometric monitoring %D 2023 %7 3.11.2023 %9 %J JMIR Mhealth Uhealth %G English %X Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions. %R 10.2196/45103 %U https://mhealth.jmir.org/2023/1/e45103 %U https://doi.org/10.2196/45103 %0 Journal Article %@ 2371-4379 %I JMIR Publications %V 8 %N %P e46654 %T An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes %A Fareed,Naleef %A Swoboda,Christine %A Wang,Yiting %A Strouse,Robert %A Hoseus,Jenelle %A Baker,Carrie %A Joseph,Joshua J %A Venkatesh,Kartik %+ Department of Biomedical Informatics, College of Medicine, The Ohio State University, 370 W. 9th Avenue, Columbus, OH, 43210, United States, 1 6143660283, naleef.fareed@osumc.edu %K personalization %K mobile health %K mHealth %K pregnancy %K pregnant %K maternal %K personalized %K diabetic %K algorithm %K diabetes %K rule-based algorithms %K social determinants of health %K inclusive %K inclusivity %K design %D 2023 %7 12.10.2023 %9 Viewpoint %J JMIR Diabetes %G English %X Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts. %M 37824196 %R 10.2196/46654 %U https://diabetes.jmir.org/2023/1/e46654 %U https://doi.org/10.2196/46654 %U http://www.ncbi.nlm.nih.gov/pubmed/37824196 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45409 %T Cultural Responsivity in Technology-Enabled Services: Integrating Culture Into Technology and Service Components %A Eustis,Elizabeth H %A LoPresti,Jessica %A Aguilera,Adrian %A Schueller,Stephen M %+ Center for Anxiety and Related Disorders, Boston University, 900 Commonwealth Ave 2nd Floor, Boston, MA, 02215, United States, 1 617 353 9610, liz.eustis@gmail.com %K technology %K mobile health %K mHealth %K mental health %K cultural responsivity %K human support %K mobile phone %D 2023 %7 3.10.2023 %9 Viewpoint %J J Med Internet Res %G English %X Technology-enabled services (TESs) are clinical interventions that combine technological and human components to provide health services. TESs for mental health are efficacious in the treatment of anxiety and depression and are currently being offered as frontline treatments around the world. It is hoped that these interventions will be able to reach diverse populations across a range of identities and ultimately decrease disparities in mental health treatment. However, this hope is largely unrealized. TESs include both technology and human service components, and we argue that cultural responsivity must be considered in each of these components to help address existing treatment disparities. To date, there is limited guidance on how to consider cultural responsivity within these interventions, including specific targets for the development, tailoring, or design of the technologies and services within TESs. In response, we propose a framework that provides specific recommendations for targets based on existing models, both at the technological component level (informed by the Behavioral Intervention Technology Model) and the human support level (informed by the Efficiency Model of Support). We hope that integrating culturally responsive considerations into these existing models will facilitate increased attention to cultural responsivity within TESs to ensure they are ethical and responsive for everyone. %M 37788050 %R 10.2196/45409 %U https://www.jmir.org/2023/1/e45409 %U https://doi.org/10.2196/45409 %U http://www.ncbi.nlm.nih.gov/pubmed/37788050 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e47486 %T Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies %A Khanna,Amit %A Jones,Graham %+ GDD Connected Health and Innovation Group, Novartis Pharmaceuticals, 1 Health Plaza, East Hanover, NJ, 07936, United States, 1 8572757045, graham.jones@novartis.com %K digital health %K monitoring %K personalized medicine %K Parkinson disease %K wearables %K neurodegenerative disorder %K cognitive impairment %K economic burden %K digital technology %K symptom management %K disease control %K debilitating disease %K intervention %D 2023 %7 27.9.2023 %9 Viewpoint %J JMIR Form Res %G English %X Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring. %M 37756050 %R 10.2196/47486 %U https://formative.jmir.org/2023/1/e47486 %U https://doi.org/10.2196/47486 %U http://www.ncbi.nlm.nih.gov/pubmed/37756050 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 6 %N %P e50991 %T Personal Health Tracking: A Paradigm Shift in the Self-Care Models in Nursing %A Choi,Soyoung %+ Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, 272 Freer Hall, 906 S. Goodwin Ave, Urbana, IL, 61801, United States, 1 2173332573, soyoung@illinois.edu %K personal health data %K personal informatics %K self-care %K self-tracking %K mobile health technology %K human-technology %K human-computer %K human computer interaction %K health tracking %K framework %K frameworks %K model %K models %K mHealth %K mobile health %K informatics %D 2023 %7 20.9.2023 %9 Viewpoint %J JMIR Nursing %G English %X The rapidly evolving digital health landscape necessitates updates to existing self-care models in nursing. This viewpoint paper revisits and evaluates prevalent models, recognizing their comprehensive exploration of self-care concepts while also identifying a gap in the incorporation of personal informatics. It underscores the missing link of human-technology interplay, an essential aspect in understanding self-care practices within digital generations. The author delineates the role of personal health tracking in self-care and the achievement of desired health outcomes. Based on these insights, the author proposes a refined, digitized self-care model that incorporates mobile health (mHealth) technologies and self-tracking behaviors. The paper concludes by advocating the application of this model for future mHealth nursing interventions, providing a framework for facilitating patient self-care and improving health and well-being in the era of digital health. %M 37728970 %R 10.2196/50991 %U https://nursing.jmir.org/2023/1/e50991 %U https://doi.org/10.2196/50991 %U http://www.ncbi.nlm.nih.gov/pubmed/37728970 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e45694 %T Sustainable Development for Mobile Health Apps Using the Human-Centered Design Process %A An,Qingfan %A Kelley,Marjorie M %A Hanners,Audra %A Yen,Po-Yin %+ Department of Community Medicine and Rehabilitation, Umeå University, Biology Building 3rd floor, Linnaeus väg 9, Umeå, 90736, Sweden, 46 0764550820, qingfan.an@umu.se %K mHealth %K mobile health %K apps %K human-centered design %K sociotechnical %K sustainability %K mobile technology %K speculative design %K mobile phone %D 2023 %7 25.8.2023 %9 Viewpoint %J JMIR Form Res %G English %X Well-documented scientific evidence indicates that mobile health (mHealth) apps can improve the quality of life, relieve symptoms, and restore health for patients. In addition to improving patients’ health outcomes, mHealth apps reduce health care use and the cost burdens associated with disease management. Currently, patients and health care providers have a wide variety of choices among commercially available mHealth apps. However, due to the high resource costs and low user adoption of mHealth apps, the cost-benefit relationship remains controversial. When compared to traditional expert-driven approaches, applying human-centered design (HCD) may result in more useable, acceptable, and effective mHealth apps. In this paper, we summarize current HCD practices in mHealth development studies and make recommendations to improve the sustainability of mHealth. These recommendations include consideration of factors regarding culture norms, iterative evaluations on HCD practice, use of novelty in mHealth app, and consideration of privacy and reliability across the entire HCD process. Additionally, we suggest a sociotechnical lens toward HCD practices to promote the sustainability of mHealth apps. Future research should consider standardizing the HCD practice to help mHealth researchers and developers avoid barriers associated with inadequate HCD practices. %M 37624639 %R 10.2196/45694 %U https://formative.jmir.org/2023/1/e45694 %U https://doi.org/10.2196/45694 %U http://www.ncbi.nlm.nih.gov/pubmed/37624639 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 11 %N %P e41345 %T Mental Health in Urban Environments: Uncovering the Black Box of Person-Place Interactions Requires Interdisciplinary Approaches %A Kanning,Martina %A Yi,Li %A Yang,Chih-Hsiang %A Niermann,Christina %A Fina,Stefan %+ Department of Sport Science, University of Konstanz, Univeristätsstraße 10, Konstanz, 78464, Germany, 49 7531 88 3154, martina.kanning@uni-konstanz.de %K physical activity %K urban health %K ambulatory assessment %K environment %K mental health %K real-time data %K within-subject association %D 2023 %7 11.5.2023 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Living in urban environments affects individuals’ mental health through different pathways. For instance, physical activity and social participation are seen as mediators. However, aiming to understand underlying mechanisms, it is necessary to consider that the individual is interacting with its environment. In this regard, this viewpoint discusses how urban health research benefits from integration of socioecological and interdisciplinary perspectives, combined with innovative ambulatory data assessments that enable researchers to integrate different data sources. It is stated that neither focusing on the objective and accurate assessment of the environment (from the perspective of spatial sciences) nor focusing on subjectively measured individual variables (from the public health as well as a psychosocial perspective) alone is suitable to further develop the field. Addressing person-place interactions requires an interdisciplinary view on the level of theory (eg, which variables should be focused on?), assessment methods (eg, combination of time-varying objective and subjective measures), as well as data analysis and interpretation. Firstly, this viewpoint gives an overview on previous findings addressing the relationship of environmental characteristics to physical activity and mental health outcomes. We emphasize the need for approaches that allow us to appropriately assess the real-time interaction between a person and a specific environment and examine within-subject associations. This requires the assessment of environmental features, the spatial-temporal behavior of the individual, and the subjective experiences of the situation together with other individual factors, such as momentary affective states. Therefore, we finally focused on triggered study designs as an innovative ambulatory data assessment approach that allows us to capture real-time data in predefined situations (eg, while walking through a specific urban area). %M 37166963 %R 10.2196/41345 %U https://mhealth.jmir.org/2023/1/e41345 %U https://doi.org/10.2196/41345 %U http://www.ncbi.nlm.nih.gov/pubmed/37166963 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e41153 %T Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review %A Diaz,Claudio %A Caillaud,Corinne %A Yacef,Kalina %+ School of Computer Science, The University of Sydney, Building J12/1 Cleveland Street, Camperdown NSW, Sydney, 2006, Australia, 61 (02) 9351 2222, kalina.yacef@sydney.edu.au %K activity tracker %K wearable electronic devices %K fitness trackers %K data mining %K artificial intelligence %K health %K education %K behavior change %K physical activity %K wearable devices %K trackers %K health education %K sensor data %D 2023 %7 6.3.2023 %9 Review %J JMIR Med Inform %G English %X Background: Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants’ physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants’ physical activity evolves. Objective: The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? Methods: The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. Results: All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). Conclusions: Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce. %M 36877559 %R 10.2196/41153 %U https://medinform.jmir.org/2023/1/e41153 %U https://doi.org/10.2196/41153 %U http://www.ncbi.nlm.nih.gov/pubmed/36877559 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e42449 %T Charting a Course for Smartphones and Wearables to Transform Population Health Research %A Dixon,William G %A van der Veer,Sabine N %A Ali,Syed Mustafa %A Laidlaw,Lynn %A Dobson,Richard J B %A Sudlow,Cathie %A Chico,Tim %A MacArthur,Jacqueline A L %A Doherty,Aiden %+ Big Data Institute, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, United Kingdom, 44 01865 617794, aiden.doherty@ndph.ox.ac.uk %K mHealth %K wearable %K person-generated health data %K population health research %K devices %K research %K health %K data %K mobile health %K clinical %K digital %D 2023 %7 7.2.2023 %9 Viewpoint %J J Med Internet Res %G English %X The use of data from smartphones and wearable devices has huge potential for population health research, given the high level of device ownership; the range of novel health-relevant data types available from consumer devices; and the frequency and duration with which data are, or could be, collected. Yet, the uptake and success of large-scale mobile health research in the last decade have not met this intensely promoted opportunity. We make the argument that digital person-generated health data are required and necessary to answer many top priority research questions, using illustrative examples taken from the James Lind Alliance Priority Setting Partnerships. We then summarize the findings from 2 UK initiatives that considered the challenges and possible solutions for what needs to be done and how such solutions can be implemented to realize the future opportunities of digital person-generated health data for clinically important population health research. Examples of important areas that must be addressed to advance the field include digital inequality and possible selection bias; easy access for researchers to the appropriate data collection tools, including how best to harmonize data items; analysis methodologies for time series data; patient and public involvement and engagement methods for optimizing recruitment, retention, and public trust; and methods for providing research participants with greater control over their data. There is also a major opportunity, provided through the linkage of digital person-generated health data to routinely collected data, to support novel population health research, bringing together clinician-reported and patient-reported measures. We recognize that well-conducted studies need a wide range of diverse challenges to be skillfully addressed in unison (eg, challenges regarding epidemiology, data science and biostatistics, psychometrics, behavioral and social science, software engineering, user interface design, information governance, data management, and patient and public involvement and engagement). Consequently, progress would be accelerated by the establishment of a new interdisciplinary community where all relevant and necessary skills are brought together to allow for excellence throughout the life cycle of a research study. This will require a partnership of diverse people, methods, and technologies. If done right, the synergy of such a partnership has the potential to transform many millions of people’s lives for the better. %M 36749628 %R 10.2196/42449 %U https://www.jmir.org/2023/1/e42449 %U https://doi.org/10.2196/42449 %U http://www.ncbi.nlm.nih.gov/pubmed/36749628 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e38614 %T Beyond Pathogen Filtration: Possibility of Smart Masks as Wearable Devices for Personal and Group Health and Safety Management %A Lee,Peter %A Kim,Heepyung %A Kim,Yongshin %A Choi,Woohyeok %A Zitouni,M Sami %A Khandoker,Ahsan %A Jelinek,Herbert F %A Hadjileontiadis,Leontios %A Lee,Uichin %A Jeong,Yong %+ Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro Yuseong gu, Daejeon, 34141, Republic of Korea, 82 423507165, yong@kaist.ac.kr %K smart mask %K pathogen filtration %K COVID-19 %K protective equipment %K digital health %K wearable %K smart device %K wearable device %K sensor %K health monitoring %D 2022 %7 21.6.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Face masks are an important way to combat the COVID-19 pandemic. However, the prolonged pandemic has revealed confounding problems with the current face masks, including not only the spread of the disease but also concurrent psychological, social, and economic complications. As face masks have been worn for a long time, people have been interested in expanding the purpose of masks from protection to comfort and health, leading to the release of various “smart” mask products around the world. To envision how the smart masks will be extended, this paper reviewed 25 smart masks (12 from commercial products and 13 from academic prototypes) that emerged after the pandemic. While most smart masks presented in the market focus on resolving problems with user breathing discomfort, which arise from prolonged use, academic prototypes were designed for not only sensing COVID-19 but also general health monitoring aspects. Further, we investigated several specific sensors that can be incorporated into the mask for expanding biophysical features. On a larger scale, we discussed the architecture and possible applications with the help of connected smart masks. Namely, beyond a personal sensing application, a group or community sensing application may share an aggregate version of information with the broader population. In addition, this kind of collaborative sensing will also address the challenges of individual sensing, such as reliability and coverage. Lastly, we identified possible service application fields and further considerations for actual use. Along with daily-life health monitoring, smart masks may function as a general respiratory health tool for sports training, in an emergency room or ambulatory setting, as protection for industry workers and firefighters, and for soldier safety and survivability. For further considerations, we investigated design aspects in terms of sensor reliability and reproducibility, ergonomic design for user acceptance, and privacy-aware data-handling. Overall, we aim to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart masks as one of the promising wearable devices. By integrating biomarkers of respiration symptoms, a smart mask can be a truly cutting-edge device that expands further knowledge on health monitoring to reach the next level of wearables. %M 35679029 %R 10.2196/38614 %U https://mhealth.jmir.org/2022/6/e38614 %U https://doi.org/10.2196/38614 %U http://www.ncbi.nlm.nih.gov/pubmed/35679029 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 6 %P e31069 %T Viewing Mobile Health Technology Design Through the Lens of Amplification Theory %A Merid,Beza %A Robles,Maria Cielito %A Nallamothu,Brahmajee K %A Newman,Mark W %A Skolarus,Lesli E %+ School for the Future of Innovation in Society, Arizona State University, 1120 South Cady Mall, Tempe, AZ, 85281, United States, 1 (480) 727 8787, Beza.Merid@asu.edu %K mHealth %K digital health %K cardiovascular disease %K high blood pressure %K structural barriers to health %K racial health disparities %K Amplification Theory of Technology %D 2022 %7 10.6.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Digital health interventions designed to promote health equity can be valuable tools in the delivery of health care to hardly served patient populations. But if the design of these technologies and the interventions in which they are deployed do not address the myriad structural barriers to care that minoritized patients, patients in rural areas, and patients who have trouble paying for care often face, their impact may be limited. Drawing on our mobile health (mHealth) research in the arena of cardiovascular care and blood pressure management, this viewpoint argues that health care providers and researchers should tend to structural barriers to care as a part of their digital health intervention design. Our 3-step predesign framework, informed by the Amplification Theory of Technology, offers a model that interventionists can follow to address these concerns. %M 35687411 %R 10.2196/31069 %U https://mhealth.jmir.org/2022/6/e31069 %U https://doi.org/10.2196/31069 %U http://www.ncbi.nlm.nih.gov/pubmed/35687411 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e25116 %T Developing a Smart Home Technology Innovation for People With Physical and Mental Health Problems: Considerations and Recommendations %A Forchuk,Cheryl %A Serrato,Jonathan %A Lizotte,Daniel %A Mann,Rupinder %A Taylor,Gavin %A Husni,Sara %+ Mental Health Nursing Research Alliance, Parkwood Institute, Lawson Health Research Institute, 550 Wellington Road S, London, ON, N6C 0A7, Canada, 1 519 685 8500 ext 75802, jonathan.serrato@lhsc.on.ca %K smart home %K smart technology %K mental health %K physical health, eHealth %K comorbidity %K innovation %K communication %K connection %K uHealth %K ubiquitous health %K digital health %D 2022 %7 29.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Smart home technologies present an unprecedented opportunity to improve health and health care by providing greater communication and connectivity with services and care providers and by supporting the daily activities of people managing both mental and physical health problems. Based on our experience from conducting smart technology health studies, including a smart home intervention, we provide guidance on developing and implementing such interventions. First, we describe the need for an overarching principle of security and privacy that must be attended to in all aspects of such a project. We then describe 4 key steps in developing a successful smart home innovation for people with mental and physical health conditions. These include (1) setting up the digital infrastructure, (2) ensuring the components of the system communicate, (3) ensuring that the system is designed for the intended population, and (4) engaging stakeholders. Recommendations on how to approach each of these steps are provided along with suggested literature that addresses additional considerations, guidelines, and equipment selection in more depth. %M 35486422 %R 10.2196/25116 %U https://mhealth.jmir.org/2022/4/e25116 %U https://doi.org/10.2196/25116 %U http://www.ncbi.nlm.nih.gov/pubmed/35486422 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e36762 %T SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing %A Adamowicz,Lukas %A Christakis,Yiorgos %A Czech,Matthew D %A Adamusiak,Tomasz %+ Digital Medicine and Translational Imaging, Pfizer Inc, 610 Main Street, Cambridge, MA, 02139, United States, 1 802 324 1829, lukas.adamowicz@pfizer.com %K wearable sensors %K digital medicine %K gait analysis %K human movement analysis %K digital biomarkers %K uHealth %K wearable %K sensor %K gait %K movement %K mobility %K physical activity %K sleep %K Python %K coding %K open source %K software package %K algorithm %K machine learning %K data science %K computer programming %D 2022 %7 21.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Wearable inertial sensors are providing enhanced insight into patient mobility and health. Significant research efforts have focused on wearable algorithm design and deployment in both research and clinical settings; however, open-source, general-purpose software tools for processing various activities of daily living are relatively scarce. Furthermore, few studies include code for replication or off-the-shelf software packages. In this work, we introduce SciKit Digital Health (SKDH), a Python software package (Python Software Foundation) containing various algorithms for deriving clinical features of gait, sit to stand, physical activity, and sleep, wrapped in an easily extensible framework. SKDH combines data ingestion, preprocessing, and data analysis methods geared toward modern data science workflows and streamlines the generation of digital endpoints in “good practice” environments by combining all the necessary data processing steps in a single pipeline. Our package simplifies the construction of new data processing pipelines and promotes reproducibility by following a convention over configuration approach, standardizing most settings on physiologically reasonable defaults in healthy adult populations or those with mild impairment. SKDH is open source, as well as free to use and extend under a permissive Massachusetts Institute of Technology license, and is available from GitHub (PfizerRD/scikit-digital-health), the Python Package Index, and the conda-forge channel of Anaconda. %M 35353039 %R 10.2196/36762 %U https://mhealth.jmir.org/2022/4/e36762 %U https://doi.org/10.2196/36762 %U http://www.ncbi.nlm.nih.gov/pubmed/35353039 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e34483 %T Reimagining Connected Care in the Era of Digital Medicine %A Mann,Devin M %A Lawrence,Katharine %+ Department of Population Health, NYU Grossman School of Medicine, 227 E 30th Street, New York, NY, 10016, United States, 1 2122639026, devin.mann@nyulangone.org %K health information technology %K telehealth %K remote patient monitoring %K mobile health %K mHealth %K eHealth %K digital health %K innovation %K process model %K information technology %K digital medicine %K automation %D 2022 %7 15.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The COVID-19 pandemic accelerated the adoption of remote patient monitoring technology, which offers exciting opportunities for expanded connected care at a distance. However, while the mode of clinicians’ interactions with patients and their health data has transformed, the larger framework of how we deliver care is still driven by a model of episodic care that does not facilitate this new frontier. Fully realizing a transformation to a system of continuous connected care augmented by remote monitoring technology will require a shift in clinicians’ and health systems’ approach to care delivery technology and its associated data volume and complexity. In this article, we present a solution that organizes and optimizes the interaction of automated technologies with human oversight, allowing for the maximal use of data-rich tools while preserving the pieces of medical care considered uniquely human. We review implications of this “augmented continuous connected care” model of remote patient monitoring for clinical practice and offer human-centered design-informed next steps to encourage innovation around these important issues. %M 35436238 %R 10.2196/34483 %U https://mhealth.jmir.org/2022/4/e34483 %U https://doi.org/10.2196/34483 %U http://www.ncbi.nlm.nih.gov/pubmed/35436238 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e32696 %T Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives %A Hamberger,Marietta %A Ikonomi,Nensi %A Schwab,Julian D %A Werle,Silke D %A Fürstberger,Axel %A Kestler,Angelika MR %A Holderried,Martin %A Kaisers,Udo X %A Steger,Florian %A Kestler,Hans A %+ Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, Ulm, 89081, Germany, 49 731 500 ext 24500, hans.kestler@uni-ulm.de %K mHealth %K mobile apps %K patient empowerment %K digital health %K interaction empowerment %K patient-doctor relationship %K health care network %K intersectoral communication %D 2022 %7 13.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X In its most trending interpretation, empowerment in health care is implemented as a patient-centered approach. In the same sense, many mobile health (mHealth) apps are being developed with a primary focus on the individual user. The integration of mHealth apps into the health care system has the potential to counteract existing challenges, including incomplete or nonstandardized medical data and lack of communication, especially in the intersectional context (eg, patients, medical forces). However, concerns about data security and privacy, regional differences in regulations, lack of accessibility, and nontransparent apps hinder the successful integration of mHealth into the health care system. One approach to address this is to rethink the interpretation of empowerment. On that basis, we here examine existing approaches of individual empowerment and subsequently analyze a different view of empowerment in digital health, namely interaction empowerment. Such a change of perspective could positively influence intersectoral communication and facilitate secure data and knowledge sharing. We discuss this novel viewpoint on empowerment, focusing on more efficient integration and development of mHealth approaches. A renewed interpretation of empowerment could thus buffer current limitations of individual empowerment while also advancing digitization of the health system. %M 35416786 %R 10.2196/32696 %U https://mhealth.jmir.org/2022/4/e32696 %U https://doi.org/10.2196/32696 %U http://www.ncbi.nlm.nih.gov/pubmed/35416786 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e29510 %T Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design %A Cho,Peter Jaeho %A Yi,Jaehan %A Ho,Ethan %A Shandhi,Md Mobashir Hasan %A Dinh,Yen %A Patil,Aneesh %A Martin,Leatrice %A Singh,Geetika %A Bent,Brinnae %A Ginsburg,Geoffrey %A Smuck,Matthew %A Woods,Christopher %A Shaw,Ryan %A Dunn,Jessilyn %+ Department of Biomedical Engineering, Duke University, Room 1427, Fitzpatrick Center (FCIEMAS), 101 Science Drive, Durham, NC, 27708-0281, United States, 1 919 660 5131, jessilyn.dunn@duke.edu %K bring your own device %K wearable device %K mHealth %D 2022 %7 8.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, and treatment. Recently, there has been a surge of digital health studies where data are collected through a bring-your-own-device (BYOD) approach, in which participants who already own a specific technology may voluntarily sign up for the study and provide their digital health data. BYOD study design accelerates the collection of data from a larger number of participants than cohort design; this is possible because researchers are not limited in the study population size based on the number of devices afforded by their budget or the number of people familiar with the technology. However, the BYOD study design may not support the collection of data from a representative random sample of the target population where digital health technologies are intended to be deployed. This may result in biased study results and biased downstream technology development, as has occurred in other fields. In this viewpoint paper, we describe demographic imbalances discovered in existing BYOD studies, including our own, and we propose the Demographic Improvement Guideline to address these imbalances. %M 34913871 %R 10.2196/29510 %U https://mhealth.jmir.org/2022/4/e29510 %U https://doi.org/10.2196/29510 %U http://www.ncbi.nlm.nih.gov/pubmed/34913871 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e35600 %T Wearables in Schizophrenia: Update on Current and Future Clinical Applications %A Fonseka,Lakshan N %A Woo,Benjamin K P %+ Olive View-University of California Los Angeles Medical Center, 14445 Olive View Drive, Sylmar, CA, 91342, United States, 1 (747) 210 3000, lfonseka@ucla.edu %K wearables %K smartwatch %K schizophrenia %K digital phenotype %K wearable %K mHealth %K mobile health %K review %K clinical application %K clinical utility %K clinical use %K literature search %K diagnosis %K prevention %D 2022 %7 7.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Schizophrenia affects 1% of the world population and is associated with a reduction in life expectancy of 20 years. The increasing prevalence of both consumer technology and clinical-grade wearable technology offers new metrics to guide clinical decision-making remotely and in real time. Herein, recent literature is reviewed to determine the potential utility of wearables in schizophrenia, including their utility in diagnosis, first-episode psychosis, and relapse prevention and their acceptability to patients. Several studies have further confirmed the validity of various devices in their ability to track sleep—an especially useful metric in schizophrenia, as sleep disturbances may be predictive of disease onset or the acute worsening of psychotic symptoms. Through machine learning, wearable-obtained heart rate and motor activity were used to differentiate between controls and patients with schizophrenia. Wearables can capture the autonomic dysregulation that has been detected when patients are actively experiencing paranoia, hallucinations, or delusions. Multiple platforms are currently being researched, such as Health Outcomes Through Positive Engagement and Self-Empowerment, Mobile Therapeutic Attention for Treatment-Resistant Schizophrenia, and Sleepsight, that may ultimately link patient data to clinicians. The future is bright for wearables in schizophrenia, as the recent literature exemplifies their potential to offer real-time insights to guide diagnosis and management. %M 35389361 %R 10.2196/35600 %U https://mhealth.jmir.org/2022/4/e35600 %U https://doi.org/10.2196/35600 %U http://www.ncbi.nlm.nih.gov/pubmed/35389361 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 4 %P e32244 %T Development of a Mobile App for Clinical Research: Challenges and Implications for Investigators %A Chettri,Shibani %A Wang,Vivian %A Balkin,Eli Asher %A Rayo,Michael F %A Lee,Clara N %+ College of Public Health, The Ohio State University, 1529 N High St, Apt 614, Columbus, OH, 43201, United States, 1 2404297749, Chettri.1@osu.edu %K mHealth %K mobile app %K patient-collected data %K data security %K mobile health %K patient data %K clinical research %K research facilities %D 2022 %7 1.4.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Advances in mobile app technologies offer opportunities for researchers to feasibly collect a large amount of patient data that were previously inaccessible through traditional clinical research methods. Collection of data via mobile devices allows for several advantages, such as the ability to continuously gather data outside of research facilities and produce a greater quantity of data, making these data much more valuable to researchers. Health services research is increasingly incorporating mobile health (mHealth), but collecting these data in current research institutions is not without its challenges. Our paper uses a specific example to depict specific challenges of mHealth research and provides recommendations for investigators looking to incorporate digital app technologies and patient-collected digital data into their studies. Our experience describes how clinical researchers should be prepared to work with variable software and mobile app development timelines; research institutions that are interested in participating in mHealth research need to invest in supporting information technology infrastructures in order to be a part of the growing field of mHealth and gain access to valuable patient-collected data. %M 35363154 %R 10.2196/32244 %U https://mhealth.jmir.org/2022/4/e32244 %U https://doi.org/10.2196/32244 %U http://www.ncbi.nlm.nih.gov/pubmed/35363154 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 3 %P e30872 %T Including the Public in Public eHealth: The Need for Community Participation in the Development of State-Sponsored COVID-19–Related Mobile Apps %A Idris,Muhammed Yassin %A Korin,Maya %A Araya,Faven %A Chowdhury,Sayeeda %A Medina,Patty %A Cruz,Larissa %A Hawkins,Trey-Rashad %A Brown,Humberto %A Claudio,Luz %+ Department of Medicine, Morehouse School of Medicine, 720 Westview Dr SW, Atlanta, GA, 30310, United States, 1 404 752 1500, myidris@msm.edu %K mobile apps %K COVID-19 %K CBPR %K digital health %K eHealth %K community health %K health disparities %D 2022 %7 9.3.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The COVID-19 pandemic has overwhelmed health care systems worldwide, particularly in underresourced communities of color with a high prevalence of pre-existing health conditions. Many state governments and health care entities responded by increasing their capacity for telemedicine and disease tracking and creating mobile apps for dissemination of medical information. Our experiences with state-sponsored apps suggest that because many of these eHealth tools did not include community participation, they inadvertently contributed to widening digital health disparities. We propose that, as eHealth tools continue to expand as a form of health care, more attention needs to be given to their equitable distribution, accessibility, and usage. In this viewpoint collaboratively written by a minority-serving community-based organization and an eHealth academic research team, we present our experience participating in a community advisory board working on the dissemination of the COVID Alert NY mobile app to illustrate the importance of public participation in app development. We also provide practical recommendations on how to involve community representatives in the app development process. We propose that transparency and community involvement in the process of app development ultimately increases buy-in, trust, and usage of digital technology in communities where they are needed most. %M 35113793 %R 10.2196/30872 %U https://mhealth.jmir.org/2022/3/e30872 %U https://doi.org/10.2196/30872 %U http://www.ncbi.nlm.nih.gov/pubmed/35113793 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e28686 %T Smart Speakers: The Next Frontier in mHealth %A Sunshine,Jacob %+ Department of Anesthesiology & Pain Medicine, University of Washington, 1959 NE Pacific Street, Box 356540, Seattle, WA, 98195, United States, 1 206 543 6814, jesun@uw.edu %K digital health %K mobile health %K machine learning %K smart speaker %K smartphone %D 2022 %7 21.2.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection. %M 35188467 %R 10.2196/28686 %U https://mhealth.jmir.org/2022/2/e28686 %U https://doi.org/10.2196/28686 %U http://www.ncbi.nlm.nih.gov/pubmed/35188467 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 10 %N 2 %P e31048 %T Standardized Integration of Person-Generated Data Into Routine Clinical Care %A Zeng,Billy %A Bove,Riley %A Carini,Simona %A Lee,Jonathan Shing-Jih %A Pollak,JP %A Schleimer,Erica %A Sim,Ida %+ Division of General Internal Medicine, University of California, San Francisco, Suite 308, 1545 Divisadero St, San Francisco, CA, 94143-0320, United States, 1 415 514 8686, ida.sim@ucsf.edu %K mobile health %K data sharing %K health care %K patient-generated health data %K telemedicine %D 2022 %7 10.2.2022 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Person-generated data (PGD) are a valuable source of information on a person’s health state in daily life and in between clinic visits. To fully extract value from PGD, health care organizations must be able to smoothly integrate data from PGD devices into routine clinical workflows. Ideally, to enhance efficiency and flexibility, such integrations should follow reusable processes that can easily be replicated for multiple devices and data types. Instead, current PGD integrations tend to be one-off efforts entailing high costs to build and maintain custom connections with each device and their proprietary data formats. This viewpoint paper formulates the integration of PGD into clinical systems and workflow as a PGD integration pipeline and reviews the functional components of such a pipeline. A PGD integration pipeline includes PGD acquisition, aggregation, and consumption. Acquisition is the person-facing component that includes both technical (eg, sensors, smartphone apps) and policy components (eg, informed consent). Aggregation pools, standardizes, and structures data into formats that can be used in health care settings such as within electronic health record–based workflows. PGD consumption is wide-ranging, by different solutions in different care settings (inpatient, outpatient, consumer health) for different types of users (clinicians, patients). The adoption of data and metadata standards, such as those from IEEE and Open mHealth, would facilitate aggregation and enable broader consumption. We illustrate the benefits of a standards-based integration pipeline for the illustrative use case of home blood pressure monitoring. A standards-based PGD integration pipeline can flexibly streamline the clinical use of PGD while accommodating the complexity, scale, and rapid evolution of today’s health care systems. %M 35142627 %R 10.2196/31048 %U https://mhealth.jmir.org/2022/2/e31048 %U https://doi.org/10.2196/31048 %U http://www.ncbi.nlm.nih.gov/pubmed/35142627 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e20638 %T A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study %A MacLeod,Lucy %A Suruliraj,Banuchitra %A Gall,Dominik %A Bessenyei,Kitti %A Hamm,Sara %A Romkey,Isaac %A Bagnell,Alexa %A Mattheisen,Manuel %A Muthukumaraswamy,Viswanath %A Orji,Rita %A Meier,Sandra %+ Department of Psychiatry, Dalhousie University, 5850/5980 University Avenue, PO Box 9700, Halifax, NS, B3K 6R8, Canada, 1 782414 ext 8054, sandra.m.meier@gmail.com %K mobile sensing %K youth %K psychiatry %K feasibility %K mobile phone %D 2021 %7 26.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Internalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier. Objective: This study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada. Methods: In this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants. Results: More severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=−0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=−0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit. Conclusions: Passively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on. %M 34698650 %R 10.2196/20638 %U https://mhealth.jmir.org/2021/10/e20638 %U https://doi.org/10.2196/20638 %U http://www.ncbi.nlm.nih.gov/pubmed/34698650 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e23425 %T Enhancing Healthcare Access–Smartphone Apps in Arrhythmia Screening: Viewpoint %A Książczyk,Marcin %A Dębska-Kozłowska,Agnieszka %A Warchoł,Izabela %A Lubiński,Andrzej %+ Department of Interventional Cardiology and Cardiac Arrhythmias, Medical University of Lodz, Ul. Żeromskiego 113, Łódź, 90-549, Poland, 48 42 639 35 63, marcin_ksiazczyk@interia.pl %K arrhythmia screening %K atrial fibrillation %K mobile electrocardiography %K mobile health %K phonocardiography %K photoplethysmography %K seismocardiography %K stroke prevention %D 2021 %7 27.8.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Atrial fibrillation is the most commonly reported arrhythmia and, if undiagnosed or untreated, may lead to thromboembolic events. It is therefore desirable to provide screening to patients in order to detect atrial arrhythmias. Specific mobile apps and accessory devices, such as smartphones and smartwatches, may play a significant role in monitoring heart rhythm in populations at high risk of arrhythmia. These apps are becoming increasingly common among patients and professionals as a part of mobile health. The rapid development of mobile health solutions may revolutionize approaches to arrhythmia screening. In this viewpoint paper, we assess the availability of smartphone and smartwatch apps and evaluate their efficacy for monitoring heart rhythm and arrhythmia detection. The findings obtained so far suggest they are on the right track to improving the efficacy of early detection of atrial fibrillation, thus lowering the risk of stroke and reducing the economic burden placed on public health. %M 34448723 %R 10.2196/23425 %U https://mhealth.jmir.org/2021/8/e23425 %U https://doi.org/10.2196/23425 %U http://www.ncbi.nlm.nih.gov/pubmed/34448723 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 8 %P e25425 %T Mobile Apps as Audience-Centered Health Communication Platforms %A Mackert,Michael %A Mandell,Dorothy %A Donovan,Erin %A Walker,Lorraine %A Henson-García,Mike %A Bouchacourt,Lindsay %+ Stan Richards School of Advertising and Public Relations, The University of Texas at Austin, 300 W Dean Keeton St, Austin, TX, 78712, United States, 1 512 348 8490, mackert@utexas.edu %K health communication %K mHealth %K mobile apps %K mobile health %K prenatal health %K pregnancy %K audience-centered %D 2021 %7 17.8.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Health communication campaigns often suffer from the shortcomings of a limited budget and limited reach, resulting in a limited impact. This paper suggests a shift of these campaigns to audience-centered communication platforms—particularly, apps on mobile phones. By using a common platform, multiple interventions and campaigns can combine resources and increase user engagement, resulting in a larger impact on health behavior. Given the widespread use of mobile phones, mobile apps can be an effective and efficient tool to provide health interventions. One such platform is Father’s Playbook, a mobile app designed to encourage men to be more involved during their partner’s pregnancy. Health campaigns and interventions looking to reach expectant fathers can use Father’s Playbook as a vehicle for their messages. %M 34402797 %R 10.2196/25425 %U https://mhealth.jmir.org/2021/8/e25425 %U https://doi.org/10.2196/25425 %U http://www.ncbi.nlm.nih.gov/pubmed/34402797 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 7 %P e28664 %T Consumer Wearables and the Integration of New Objective Measures in Oncology: Patient and Provider Perspectives %A Fonseka,Lakshan N %A Woo,Benjamin KP %+ College of Osteopathic Medicine of the Pacific, Western University of Health Sciences, 615 E 3rd St, Pomona, CA, 91766, United States, 1 909 623 6116, lfonseka@ucla.edu %K consumer %K wearables %K smartwatch %K cancer %K oncology %K chemotherapy %K apps %D 2021 %7 15.7.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X With one in five adults in the United States owning a smartwatch or fitness tracker, these devices are poised to impact all aspects of medicine by offering a more objective approach to replace self-reported data. Oncology has proved to be a prototypical example, and wearables offer immediate benefits to patients and oncologists with the ability to track symptoms and health metrics in real time. We aimed to review the recent literature on consumer-grade wearables and its current applications in cancer from the perspective of both the patient and the provider. The relevant studies suggested that these devices offer benefits, such as improved medication adherence and accuracy of symptom tracking over self-reported data, as well as insights that increase patient empowerment. Physical activity is consistently correlated with stronger patient outcomes, and a patient’s real-time metrics were found to be capable of tracking medication side effects and toxicity. Studies have made associations between wearable data and telomere shortening, cardiovascular disease, alcohol consumption, sleep apnea, and other conditions. The objective data obtained by the wearable presents a more complete picture of an individual’s health than the snapshot of a 15-minute office visit and a single set of vital signs. Real-time metrics can be translated into a digital phenotype that identifies risk factors specific to each patient, and shared risk factors across one’s social network may uncover common environmental exposures detrimental to one’s health. Wearable data and its upcoming integration with social media will be the foundation for the next generation of personalized medicine. %M 34264191 %R 10.2196/28664 %U https://mhealth.jmir.org/2021/7/e28664 %U https://doi.org/10.2196/28664 %U http://www.ncbi.nlm.nih.gov/pubmed/34264191 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 6 %P e25021 %T Digital Solutions to Alleviate the Burden on Health Systems During a Public Health Care Crisis: COVID-19 as an Opportunity %A Willems,Sofie H %A Rao,Jyotsna %A Bhambere,Sailee %A Patel,Dipu %A Biggins,Yvonne %A Guite,Jessica W %+ Center for Advancement in Managing Pain, School of Nursing, The University of Connecticut, 231 Glenbrook Road, Storrs, CT, 06269-4026, United States, 1 215 964 5582, jessica.guite@uconn.com %K coronavirus %K digital health %K multiplatform %K chat %K symptom tracking %K well-being %K COVID-19 %K online platform %K symptom %K monitoring %K follow-up %D 2021 %7 11.6.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The COVID-19 pandemic has generated unprecedented and sustained health management challenges worldwide. Health care systems continue to struggle to support the needs of the majority of infected individuals that are either asymptomatic or have mild symptoms. In addition, long-term effects in the form of long-lasting COVID-19 symptoms or widespread mental health issues aggravated by the pandemic pose a burden on health care systems worldwide. This viewpoint article considers aspects of digital health care solutions and how they can play an ongoing role in safely addressing gaps in the health care support available from initially and repeatedly overwhelmed providers and systems. Digital solutions can be readily designed to address this need and can be flexible enough to adapt to the evolving management requirements of various stakeholders to reduce COVID-19 infection rates, acute hospitalizations, and mortality. Multiplatform solutions provide a hybrid model of care, which can include mobile and online platforms accompanied by direct clinician input and feedback. Desirable components to be included are discussed, including symptom tracking, patient education, well-being support, and bidirectional communication between patients and clinicians. Customizable and scalable digital health platforms not only can be readily adapted to further meet the needs of employers and public health stakeholders during the ongoing pandemic, but also hold relevance for flexibly meeting broader care management needs into the future. %M 34033575 %R 10.2196/25021 %U https://mhealth.jmir.org/2021/6/e25021 %U https://doi.org/10.2196/25021 %U http://www.ncbi.nlm.nih.gov/pubmed/34033575 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 5 %P e25937 %T Noninvasive Bioimpedance Methods From the Viewpoint of Remote Monitoring in Heart Failure %A Krzesinski,Pawel %A Sobotnicki,Aleksander %A Gacek,Adam %A Siebert,Janusz %A Walczak,Andrzej %A Murawski,Piotr %A Gielerak,Grzegorz %+ Department of Cardiology and Internal Diseases, Military Institute of Medicine, Szaserow 128, Warsaw, 04-141, Poland, 48 606939390, pkrzesinski@wim.mil.pl %K heart failure %K impedance cardiography %K remote monitoring %K overhydration %K hemodynamics %K heart %K cardiac function %K cardiac %K monitor %D 2021 %7 5.5.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Heart failure (HF) is a major clinical, social, and economic problem. In view of the important role of fluid overload in the pathogenesis of HF exacerbation, early detection of fluid retention is of key importance in preventing emergency admissions for this reason. However, tools for monitoring volume status that could be widely used in the home setting are still missing. The physical properties of human tissues allow for the use of impedance-based noninvasive methods, whose different modifications are studied in patients with HF for the assessment of body hydration. The aim of this paper is to present the current state of knowledge on the possible applications of these methods for remote (home-based) monitoring of patients with HF. %M 33949964 %R 10.2196/25937 %U https://mhealth.jmir.org/2021/5/e25937 %U https://doi.org/10.2196/25937 %U http://www.ncbi.nlm.nih.gov/pubmed/33949964 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e25496 %T The Potential of mHealth as a Game Changer for the Management of Sickle Cell Disease in India %A Kumar,Ravindra %A Das,Aparup %+ ICMR-National Institute of Research in Tribal Health, ICMR-NIRTH Campus, Nagpur Road, PO Garha, Jabalpur, 482003, India, 91 7612370800, aparupdas@nirth.res.in %K sickle cell disease %K drug adherence %K mHealth %K India %D 2021 %7 13.4.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Sickle cell disease (SCD) is a chronic genetic disease that requires lifelong therapy and monitoring. Low drug adherence and poor monitoring may lead to an increase in morbidities and low quality of life. In the era of digital technology, various mobile health (mHealth) apps are being tested for their potential in increasing drug adherence in patients with SCD. We herewith discuss the applicability and feasibility of these mHealth apps for the management of SCD in India. %M 33847598 %R 10.2196/25496 %U https://mhealth.jmir.org/2021/4/e25496 %U https://doi.org/10.2196/25496 %U http://www.ncbi.nlm.nih.gov/pubmed/33847598 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e25406 %T Digital Health Integration Assessment and Maturity of the United States Biopharmaceutical Industry: Forces Driving the Next Generation of Connected Autoinjectable Devices %A Rafiei,Ramin %A Williams,Chelsea %A Jiang,Jeannette %A Aungst,Timothy Dy %A Durrer,Matthias %A Tran,Dao %A Howald,Ralph %+ SHL Medical, Gubelstrasse 22, 6300, Zug, Switzerland, 1 5617135654, chelsea.williams@shl-medical.com %K digital health %K artificial intelligence %K drug delivery %K biopharma %K autoinjector %K injectable devices %K disease management %K autoimmune %K oncology %K rare diseases %D 2021 %7 18.3.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Autoinjectable devices continue to provide real-life benefits for patients with chronic conditions since their widespread adoption 30 years ago with the rise of macromolecules. Nonetheless, issues surrounding adherence, patient administration techniques, disease self-management, and data outcomes at scale persist despite product design innovation. The interface of drug device combination products and digital health technologies formulates a value proposition for next-generation autoinjectable devices to power the delivery of precision care at home and achieve the full potential of biologics. Success will largely be dependent on biopharma’s digital health maturity to implement this framework. This viewpoint measures the digital health maturity of the top 15 biopharmaceutical companies in the US biologics autoinjector market and establishes the framework for next-generation autoinjectable devices powering home-based precision care and the need for formal digital health training. %M 33621188 %R 10.2196/25406 %U https://mhealth.jmir.org/2021/3/e25406 %U https://doi.org/10.2196/25406 %U http://www.ncbi.nlm.nih.gov/pubmed/33621188 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 3 %P e25289 %T Using Fitbit as an mHealth Intervention Tool to Promote Physical Activity: Potential Challenges and Solutions %A Balbim,Guilherme M %A Marques,Isabela G %A Marquez,David X %A Patel,Darshilmukesh %A Sharp,Lisa K %A Kitsiou,Spyros %A Nyenhuis,Sharmilee M %+ Department of Biomedical and Health Information Sciences, College of Applied Health Sciences, University of Illinois at Chicago, 1919 W Taylor St (MC 530), Chicago, IL, 60612, United States, 1 312 355 3519, skitsiou@uic.edu %K physical activity %K fitness trackers %K Fitbit %K smartphones %K interventional studies %K adults %K older adults %K wearable %K intervention %D 2021 %7 1.3.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Consumer-based physical activity (PA) trackers, also known as wearables, are increasingly being used in research studies as intervention or measurement tools. One of the most popular and widely used brands of PA trackers is Fitbit. Since the release of the first Fitbit in 2009, hundreds of experimental studies have used Fitbit devices to facilitate PA self-monitoring and behavior change via goal setting and feedback tools. Fitbit’s ability to capture large volumes of PA and physiological data in real time creates enormous opportunities for researchers. At the same time, however, it introduces a number of challenges (eg, technological, operational, logistical), most of which are not sufficiently described in study publications. Currently, there are no technical reports, guidelines, nor other types of publications discussing some of these challenges and offering guidance to researchers on how to best incorporate Fitbit devices in their study design and intervention to achieve their research goals. As a result, researchers are often left alone to discover and address some of these issues during the study through “trial and error.” This paper aims to address this gap. Drawing on our cumulative experience of conducting multiple studies with various Fitbit PA trackers over the years, we present and discuss various key challenges associated with the use of Fitbit PA trackers in research studies. Difficulties with the use of Fitbit PA trackers are encountered throughout the entire research process. Challenges and solutions are categorized in 4 main categories: study preparation, intervention delivery, data collection and analysis, and study closeout. Subsequently, we describe a number of empirically tested strategies used in 4 of our interventional studies involving participants from a broad range of demographic characteristics, racial/ethnic backgrounds, and literacy levels. Researchers should be prepared to address challenges and issues in a timely fashion to ensure that the Fitbit effectively assists participants and researchers in achieving research and outcome goals. %M 33646135 %R 10.2196/25289 %U https://mhealth.jmir.org/2021/3/e25289 %U https://doi.org/10.2196/25289 %U http://www.ncbi.nlm.nih.gov/pubmed/33646135 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e20630 %T Five Lessons Learned From Randomized Controlled Trials on Mobile Health Interventions: Consensus Procedure on Practical Recommendations for Sustainable Research %A Pach,Daniel %A Rogge,Alizé A %A Wang,Jiani %A Witt,Claudia M %+ Institute for Social Medicine, Epidemiology and Health Economics, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Luisenstraße 57, Berlin, 10117, Germany, daniel.pach@charite.de %K mHealth %K mobile apps %K pain %K behavior change techniques (BCTs) %K recommendations %D 2021 %7 8.2.2021 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: Clinical research on mobile health (mHealth) interventions is too slow in comparison to the rapid speed of technological advances, thereby impeding sustainable research and evidence-based implementation of mHealth interventions. Objective: We aimed to establish practical lessons from the experience of our working group, which might accelerate the development of future mHealth interventions and their evaluation by randomized controlled trials (RCTs). Methods: This paper is based on group and expert discussions, and focuses on the researchers’ perspectives after four RCTs on mHealth interventions for chronic pain. Results: The following five lessons are presented, which are based on practical application, increase of speed, and sustainability: (1) explore stakeholder opinions, (2) develop the mHealth app and trial simultaneously, (3) minimize complexity, (4) manage necessary resources, and (5) apply behavior change techniques. Conclusions: The five lessons developed may lead toward an agile research environment. Agility might be the key factor in the development and research process of a potentially sustainable and evidence-based mHealth intervention. %M 33555263 %R 10.2196/20630 %U https://mhealth.jmir.org/2021/2/e20630 %U https://doi.org/10.2196/20630 %U http://www.ncbi.nlm.nih.gov/pubmed/33555263 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 7 %N 12 %P e21895 %T The Digital Therapeutic Alliance and Human-Computer Interaction %A D'Alfonso,Simon %A Lederman,Reeva %A Bucci,Sandra %A Berry,Katherine %+ School of Computing and Information Systems, University of Melbourne, Doug McDonell Building, Parkville, 3010, Australia, 61 3 9035 5511, dalfonso@unimelb.edu.au %K therapeutic alliance %K digital mental health %K affective computing %K persuasive computing %K positive computing %K mobile phone %K mHealth %D 2020 %7 29.12.2020 %9 Viewpoint %J JMIR Ment Health %G English %X The therapeutic alliance (TA), the relationship that develops between a therapist and a client/patient, is a critical factor in the outcome of psychological therapy. As mental health care is increasingly adopting digital technologies and offering therapeutic interventions that may not involve human therapists, the notion of a TA in digital mental health care requires exploration. To date, there has been some incipient work on developing measures to assess the conceptualization of a digital TA for mental health apps. However, the few measures that have been proposed have more or less been derivatives of measures from psychology used to assess the TA in traditional face-to-face therapy. This conceptual paper explores one such instrument that has been proposed in the literature, the Mobile Agnew Relationship Measure, and examines it through a human-computer interaction (HCI) lens. Through this process, we show how theories from HCI can play a role in shaping or generating a more suitable, purpose-built measure of the digital therapeutic alliance (DTA), and we contribute suggestions on how HCI methods and knowledge can be used to foster the DTA in mental health apps. %M 33372897 %R 10.2196/21895 %U http://mental.jmir.org/2020/12/e21895/ %U https://doi.org/10.2196/21895 %U http://www.ncbi.nlm.nih.gov/pubmed/33372897 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 12 %P e25137 %T Wearables in the SARS-CoV-2 Pandemic: What Are They Good for? %A Bent,Brinnae %A Dunn,Jessilyn P %+ Department of Biomedical Engineering, Duke University, 2424 Erwin Road, Durham, NC, 27705, United States, 1 9196689798, jessilyn.dunn@duke.edu %K digital medicine %K digital health %K mHealth %K wearables %K sensors %K validation %K pandemic %K COVID-19 %D 2020 %7 22.12.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Recently, companies such as Apple Inc, Fitbit Inc, and Garmin Ltd have released new wearable blood oxygenation measurement technologies. Although the release of these technologies has great potential for generating health-related information, it is important to acknowledge the repercussions of consumer-targeted biometric monitoring technologies (BioMeTs), which in practice, are often used for medical decision making. BioMeTs are bodily connected digital medicine products that process data captured by mobile sensors that use algorithms to generate measures of behavioral and physiological function. These BioMeTs span both general wellness products and medical devices, and consumer-targeted BioMeTs intended for general wellness purposes are not required to undergo a standardized and transparent evaluation process for ensuring their quality and accuracy. The combination of product functionality, marketing, and the circumstances of the global SARS-CoV-2 pandemic have inevitably led to the use of consumer-targeted BioMeTs for reporting health-related measurements to drive medical decision making. In this viewpoint, we urge consumer-targeted BioMeT manufacturers to go beyond the bare minimum requirements described in US Food and Drug Administration guidance when releasing information on wellness BioMeTs. We also explore new methods and incentive systems that may result in a clearer public understanding of the performance and intended use of consumer-targeted BioMeTs. %M 33315580 %R 10.2196/25137 %U http://mhealth.jmir.org/2020/12/e25137/ %U https://doi.org/10.2196/25137 %U http://www.ncbi.nlm.nih.gov/pubmed/33315580 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e23194 %T COVID-19 Contact Tracing Apps: A Technologic Tower of Babel and the Gap for International Pandemic Control %A Du,Li %A Raposo,Vera Lúcia %A Wang,Meng %+ Faculty of Law, University of Macau, Avenida da Universidade, Taipa, Macau, SAR, 999078, China, 853 88224733, stephendu@um.edu.mo %K COVID-19 %K contact tracing apps %K privacy %K public health %K global health %D 2020 %7 27.11.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X As the world struggles with the new COVID-19 pandemic, contact tracing apps of various types have been adopted in many jurisdictions for combating the spread of the SARS-CoV-2 virus. However, even if they are successful in containing the virus within national borders, these apps are becoming ineffective as international travel is gradually resumed. The problem rests in the plurality of apps and their inability to operate in a synchronized manner, as well as the absence of an international entity with the power to coordinate and analyze the information collected by the disparate apps. The risk of creating a useless Tower of Babel of COVID-19 contact tracing apps is very real, endangering global health. This paper analyzes legal barriers for realizing the interoperability of contact tracing apps and emphasizes the need for developing coordinated solutions to promote safe international travel and global pandemic control. %M 33156804 %R 10.2196/23194 %U http://mhealth.jmir.org/2020/11/e23194/ %U https://doi.org/10.2196/23194 %U http://www.ncbi.nlm.nih.gov/pubmed/33156804 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e17577 %T Evaluating Patient-Centered Mobile Health Technologies: Definitions, Methodologies, and Outcomes %A Bruce,Courtenay %A Harrison,Patricia %A Giammattei,Charlie %A Desai,Shetal-Nicholas %A Sol,Joshua R %A Jones,Stephen %A Schwartz,Roberta %+ System Quality & Patient Safety, Houston Methodist System, 6565 Fannin Street, Houston, TX, 77030, United States, 1 2816209040, crbruce@houstonmethodist.org %K innovation %K health care %K digital technology %K digital interventions %K patient-facing technologies %K patient-centered care %K patient centeredness %K patient experience %K patient engagement %K patient activation %K quality %K effectiveness %K quality improvement %K information technologies %K outcomes %K readmissions %K length of stay %K patient adherence %D 2020 %7 11.11.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Several recently published studies and consensus statements have demonstrated that there is only modest (and in many cases, low-quality) evidence that mobile health (mHealth) can improve patient clinical outcomes such as the length of stay or reduction of readmissions. There is also uncertainty as to whether mHealth can improve patient-centered outcomes such as patient engagement or patient satisfaction. One principal challenge behind the “effectiveness” research in this field is a lack of common understanding about what it means to be effective in the digital space (ie, what should constitute a relevant outcome and how best to measure it). In this viewpoint, we call for interdisciplinary, conceptual clarity on the definitions, methodologies, and patient-centered outcomes frequently used in mHealth research. To formulate our recommendations, we used a snowballing approach to identify relevant definitions, outcomes, and methodologies related to mHealth. To begin, we drew heavily upon previously published detailed frameworks that enumerate definitions and measurements of engagement. We built upon these frameworks by extracting other relevant measures of patient-centered care, such as patient satisfaction, patient experience, and patient activation. We describe several definitional inconsistencies for key constructs in the mHealth literature. In an effort to achieve clarity, we tease apart several patient-centered care outcomes, and outline methodologies appropriate to measure each of these patient-care outcomes. By creating a common pathway linking definitions with outcomes and methodologies, we provide a possible interdisciplinary approach to evaluating mHealth technologies. With the broader goal of creating an interdisciplinary approach, we also provide several recommendations that we believe can advance mHealth research and implementation. %M 33174846 %R 10.2196/17577 %U http://mhealth.jmir.org/2020/11/e17577/ %U https://doi.org/10.2196/17577 %U http://www.ncbi.nlm.nih.gov/pubmed/33174846 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 11 %P e19154 %T Opportunities for Mobile App–Based Adherence Support for Children With Tuberculosis in South Africa %A Morse,Rachel M %A Myburgh,Hanlie %A Reubi,David %A Archey,Ava E %A Busakwe,Leletu %A Garcia-Prats,Anthony J %A Hesseling,Anneke C %A Jacobs,Stephanie %A Mbaba,Sharon %A Meyerson,Kyla %A Seddon,James A %A van der Zalm,Marieke M %A Wademan,Dillon T %A Hoddinott,Graeme %+ Desmond Tutu Tuberculosis Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Lower Level Clinical Building, Francie van Zijl Drive, Cape Town, 7505, South Africa, 27 823416810, hmyburgh@sun.ac.za %K eHealth %K mHealth %K tuberculosis %K pediatric tuberculosis %K adherence %D 2020 %7 11.11.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Tuberculosis is the number one infectious cause of death globally. Young children, generally those younger than 5 years, are at the highest risk of progressing from tuberculosis infection to tuberculosis disease and of developing the most severe forms of tuberculosis. Most current tuberculosis drug formulations have poor acceptability among children and require consistent adherence for prolonged periods of time. These challenges complicate children’s adherence to treatment and caregivers’ daily administration of the drugs. Rapid developments in mobile technologies and apps present opportunities for using widely available technology to support national tuberculosis programs and patient treatment adherence. Pilot studies have demonstrated that mobile apps are a feasible and acceptable means of enhancing children’s treatment adherence for other chronic conditions. Despite this, no mobile apps that aim to promote adherence to tuberculosis treatment have been developed for children. In this paper, we draw on our experiences carrying out research in clinical pediatric tuberculosis studies in South Africa. We present hypothetical scenarios of children’s adherence to tuberculosis medication to suggest priorities for behavioral and educational strategies that a mobile app could incorporate to address some of the adherence support gaps faced by children diagnosed with tuberculosis. We argue that a mobile app has the potential to lessen some of the negative experiences that children associate with taking tuberculosis treatment and to facilitate a more positive treatment adherence experience for children and their caregivers. %M 33174850 %R 10.2196/19154 %U https://mhealth.jmir.org/2020/11/e19154 %U https://doi.org/10.2196/19154 %U http://www.ncbi.nlm.nih.gov/pubmed/33174850 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e20156 %T Digital Media’s Role in the COVID-19 Pandemic %A Bao,Huanyu %A Cao,Bolin %A Xiong,Yuan %A Tang,Weiming %+ UNC Project-China, No. 2 Lujing Road, Guangzhou, 510095, China, 86 15920567132, weimingtangscience@gmail.com %K COVID-19 %K digital health %K media %K pandemic %K public health %K social media %K dissemination %K health information %K mobile health %D 2020 %7 18.9.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The severe acute respiratory syndrome coronavirus 2 outbreak has had a significant impact on global health, the economy, and society as a whole. Various measures are being taken to respond to the pandemic, with digital media playing a pivotal role, especially in the use of visual data to disseminate information, mobile health to coordinate medical resources, social media to promote public health campaigns, and digital tools to assist population management and disease tracing. However, digital media also faces some challenges like misinformation, lack of guidance, and information leakage. We encourage the increased use of digital media with a focus on improving trust, building social solidarity, reducing chaos, educating the public on prevention measures, and reducing the medical burden in facility-based sites. %M 32530817 %R 10.2196/20156 %U https://mhealth.jmir.org/2020/9/e20156 %U https://doi.org/10.2196/20156 %U http://www.ncbi.nlm.nih.gov/pubmed/32530817 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e22321 %T A QR Code–Based Contact Tracing Framework for Sustainable Containment of COVID-19: Evaluation of an Approach to Assist the Return to Normal Activity %A Nakamoto,Ichiro %A Wang,Sheng %A Guo,Yan %A Zhuang,Weiqing %+ School of Internet Economics and Business, Fujian University of Technology, 999 Dongsanhuang Road, JinAn District, Fuzhou , China, 86 132 550 66365, fw107@foxmail.com %K COVID-19 %K coronavirus %K symptom-based %K quick response %K eHealth %K digital health %K telesurveillance %K pandemic %K epidemic %K interoperability %D 2020 %7 7.9.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X We discuss a pandemic management framework using symptom-based quick response (QR) codes to contain the spread of COVID-19. In this approach, symptom-based QR health codes are issued by public health authorities. The codes do not retrieve the location data of the users; instead, two different colors are displayed to differentiate the health status of individuals. The QR codes are officially regarded as electronic certificates of individuals’ health status, and can be used for contact tracing, exposure risk self-triage, self-update of health status, health care appointments, and contact-free psychiatric consultations. This approach can be effectively deployed as a uniform platform interconnecting a variety of responders (eg, individuals, institutions, and public authorities) who are affected by the pandemic, which minimizes the errors of manual operation and the costs of fragmented coordination. At the same time, this approach enhances the promptness, interoperability, credibility, and traceability of containment measures. The proposed approach not only provides a supplemental mechanism for manual control measures but also addresses the partial failures of pandemic management tools in the abovementioned facets. The QR tool has been formally deployed in Fujian, a province located in southeast China that has a population of nearly 40 million people. All individuals aged ≥3 years were officially requested to present their QR code during daily public activities, such as when using public transportation systems, working at institutions, and entering or exiting schools. The deployment of this approach has achieved sizeable containment effects and played remarkable roles in shifting the negative gross domestic product (–6.8%) to a positive value by July 2020. The number of cumulative patients with COVID-19 in this setting was confined to 363, of whom 361 had recovered (recovery rate 99.4%) as of July 12, 2020. A simulation showed that if only partial measures of the framework were followed, the number of cumulative cases of COVID-19 could potentially increase ten-fold. This approach can serve as a reliable solution to counteract the emergency of a public health crisis; as a routine tool to enhance the level of public health; to accelerate the recovery of social activities; to assist decision making for policy makers; and as a sustainable measure that enables scalability. %M 32841151 %R 10.2196/22321 %U http://mhealth.jmir.org/2020/9/e22321/ %U https://doi.org/10.2196/22321 %U http://www.ncbi.nlm.nih.gov/pubmed/32841151 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 8 %P e20679 %T A Digital Health Intervention for Weight Management for Latino Families Living in Rural Communities: Perspectives and Lessons Learned During Development %A Yin,Zenong %A Errisuriz,Vanessa L %A Evans,Martin %A Inupakutika,Devasena %A Kaghyan,Sahak %A Li,Shiyu %A Esparza,Laura %A Akopian,David %A Parra-Medina,Deborah %+ Latino Research Institute, The University of Texas at Austin, 210 W 24th St, Mailcode F9200, Austin, TX, 78712, United States, 1 512 471 4557, parramedina@austin.utexas.edu %K mhealth %K digital intervention %K Latino families %K rural population %K weight %K self-management %K diet %K lifestyle %K chronic disease %D 2020 %7 20.8.2020 %9 Viewpoint %J JMIR Form Res %G English %X Rural residents face numerous challenges in accessing quality health care for management of chronic diseases (eg, obesity, diabetes), including scarcity of health care services and insufficient public transport. Digital health interventions, which include modalities such as internet, smartphones, and monitoring sensors, may help increase rural residents’ access to health care. While digital health interventions have become an increasingly popular intervention strategy to address obesity, research examining the use of technological tools for obesity management among rural Latino populations is limited. In this paper, we share our experience developing a culturally tailored, interactive health intervention using digital technologies for a family-oriented, weight management program in a rural, primarily Latino community. We describe the formative research that guided the development of the intervention, discuss the process of developing the intervention technologies including issues of privacy and data security, examine the results of a pilot study, and share lessons learned. Our experience can help others design user-centered digital health interventions to engage underserved populations in the uptake of healthy lifestyle and disease management skills. %M 32726748 %R 10.2196/20679 %U http://formative.jmir.org/2020/8/e20679/ %U https://doi.org/10.2196/20679 %U http://www.ncbi.nlm.nih.gov/pubmed/32726748 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 8 %P e17281 %T Prescribing Behavior Change: Opportunities and Challenges for Clinicians to Embrace Digital and Mobile Health %A Agarwal,Anish %A Patel,Mitesh %+ Department of Emergency Medicine, University of Pennsylvania, Blockley Hall, Room 428, 423 Guardian Drive, Philadelphia, PA, 19146, United States, 1 610 304 2318, anish.agarwal@pennmedicine.upenn.edu %K digital health %K behavior change %K mobile health %K patient-centered data collection %D 2020 %7 4.8.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Individual behaviors impact physical and mental health. Everyday behaviors such as physical activity, diet, sleep, and tobacco use have been associated with a range of acute and chronic medical conditions. Educating, motivating, and promoting sustained healthy behaviors can be challenging for clinical providers attempting to manage their patients’ health. The ubiquity and integration of mobile and digital health devices (eg, wearable step counters, smartphone-based apps) allow for individuals to generate and record enormous amounts of patient-generated health data. Research studies have begun to reveal how mobile and digital devices offer promise in motivating individual behavior change but they have not had consistent results. In this viewpoint, we discuss the potential synergy of digital health modalities and behavioral strategies as an approach for clinicians to prescribe, motivate, monitor, and sustain healthy behaviors. We discuss the strengths, challenges, and opportunities for the future of promoting health behaviors. %M 32749997 %R 10.2196/17281 %U https://mhealth.jmir.org/2020/8/e17281 %U https://doi.org/10.2196/17281 %U http://www.ncbi.nlm.nih.gov/pubmed/32749997 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e17567 %T Medical Device Apps: An Introduction to Regulatory Affairs for Developers %A Keutzer,Lina %A Simonsson,Ulrika SH %+ Department of Pharmaceutical Biosciences, Uppsala University, Box 591, Uppsala, 75124, Sweden, 46 18 471 4000, Ulrika.Simonsson@farmbio.uu.se %K MDR %K medical device regulation %K medical devices, medical device software %K mHealth %K eHealth %K mobile apps %K smartphone apps %D 2020 %7 26.6.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The Poly Implant Prothèse (PIP) scandal in France prompted a revision of the regulations regarding the marketing of medical devices. The new Medical Device Regulation (MDR [EU]) 2017/745 was developed and entered into force on May 25, 2017. After a transition period of 3 years, the regulations must be implemented in all EU and European Economic Area member states. The implementation of this regulation bears many changes for medical device development and marketing, including medical device software and mobile apps. Medical device development and marketing is a complex process by which manufacturers must keep many regulatory requirements and obligations in mind. The objective of this paper is to provide an introduction and overview of regulatory affairs for manufacturers that are new to the field of medical device software and apps with a specific focus on the new MDR, accompanying harmonized standards, and guidance documents from the European Commission. This work provides a concise overview of the qualification and classification of medical device software and apps, conformity assessment routes, technical documentation, clinical evaluation, the involvement of notified bodies, and the unique device identifier. Compared to the previous Medical Device Directive (MDD) 93/42/EEC, the MDR provides greater detail about the requirements for software qualification and classification. In particular, rule 11 sets specific rules for the classification of medical device software and will be described in this paper. In comparison to the previous MDD, the MDR is more stringent, especially regarding the classification of health apps and software. The implementation of the MDR in May 2020 and its interpretation by the authorities will demonstrate how app and software manufacturers as well as patients will be affected by the regulation. %M 32589154 %R 10.2196/17567 %U http://mhealth.jmir.org/2020/6/e17567/ %U https://doi.org/10.2196/17567 %U http://www.ncbi.nlm.nih.gov/pubmed/32589154 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e16429 %T A Pantheoretical Framework to Optimize Adherence to Healthy Lifestyle Behaviors and Medication Adherence: The Use of Personalized Approaches to Overcome Barriers and Optimize Facilitators to Achieve Adherence %A Seixas,Azizi %A Connors,Colleen %A Chung,Alicia %A Donley,Tiffany %A Jean-Louis,Girardin %+ NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY, , United States, 1 9728490862, azizi.seixas@nyumc.org %K adherence %K mHealth %K management %K chronic diseases %K prevention %K technology %D 2020 %7 24.6.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Patient nonadherence to healthy lifestyle behaviors and medical treatments (like medication adherence) accounts for a significant portion of chronic disease burden. Despite the plethora of behavioral interventions to overcome key modifiable/nonmodifiable barriers and enable facilitators to adherence, short- and long-term adherence to healthy lifestyle behaviors and medical treatments is still poor. To optimize adherence, we aimed to provide a novel mobile health solution steeped in precision and personalized population health and a pantheoretical approach that increases the likelihood of adherence. We have described the stages of a pantheoretical approach utilizing tailoring, clustering/profiling, personalizing, and optimizing interventions/strategies to obtain adherence and highlight the minimal engineering needed to build such a solution. %M 32579121 %R 10.2196/16429 %U https://mhealth.jmir.org/2020/6/e16429 %U https://doi.org/10.2196/16429 %U http://www.ncbi.nlm.nih.gov/pubmed/32579121 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 6 %P e20369 %T Case-Initiated COVID-19 Contact Tracing Using Anonymous Notifications %A Cheng,Weibin %A Hao,Chun %+ Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, 466 Xingangzhong Road, Guangzhou, 510317, China, 86 20 89168139, chwb817@gmail.com %K COVID-19 %K surveillance %K contact tracing %K digital contact tracing %K notification %K anonymous %K labor-saving %K stigma %K privacy protection %D 2020 %7 22.6.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X We discuss the concept of a participatory digital contact notification approach to assist tracing of contacts who are exposed to confirmed cases of coronavirus disease (COVID-19); the approach is simple and affordable for countries with limited access to health care resources and advanced technology. The proposed tool serves as a supplemental contract tracing approach to counteract the shortage of health care staff while providing privacy protection for both cases and contacts. This tool can be deployed on the internet or as a plugin for a smartphone app. Confirmed cases with COVID-19 can use this tool to provide contact information (either email addresses or mobile phone numbers) of close contacts. The system will then automatically send a message to the contacts informing them of their contact status, what this status means, the actions that should follow (eg, self-quarantine, respiratory hygiene/cough etiquette), and advice for receiving early care if they develop symptoms. The name of the sender of the notification message by email or mobile phone can be anonymous or not. The message received by the contact contains no disease information but contains a security code for the contact to log on the platform to retrieve the information. %M 32501802 %R 10.2196/20369 %U http://mhealth.jmir.org/2020/6/e20369/ %U https://doi.org/10.2196/20369 %U http://www.ncbi.nlm.nih.gov/pubmed/32501802 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e14375 %T Understanding and Preventing Health Concerns About Emerging Mobile Health Technologies %A Materia,Frank T %A Faasse,Kate %A Smyth,Joshua M %+ The Pennsylvania State University, 219 Biobehavioral Health Building, University Park, PA, 16802, United States, 1 814 863 8402, Smyth@psu.edu %K mHealth %K technology %K nocebo effect %K implementation science %K medically unexplained symptoms %D 2020 %7 25.5.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X New technologies and innovations have often improved population well-being and societal function; however, these are also often initially accompanied by worry and fear. In some cases, such worries can impede, or even prevent entirely, the adoption of the technology. Mobile health (mHealth), a discipline broadly focused on employing ambulatory technologies to improve the affordability, reach, and effectiveness of health promotion and clinical intervention approaches, offers new innovations and opportunities. Despite emerging evidence supporting mHealth efficacy (eg, for improving health outcomes), some individuals have concerns about mHealth technology that may impede scalability, efficacy, and, ultimately, the public health benefits of mHealth. We present a review and conceptual framework to examine these issues, focusing on three overarching themes: biophysiological, psychological, and societal concerns. There are features of mHealth that lead to worries about the potential negative effects on an individual’s health (eg, due to exposure to electromagnetic or radio waves), despite evidence supporting the safety of these technologies. When present, such beliefs can lead to worry that gives rise to the experience of unpleasant and concerning physical symptoms—the nocebo effect. This may represent an important implementational barrier because of apprehension toward beneficial mHealth products (or features thereof, such as wireless charging, wearable or implantable sensors, etc) and may also have broader ramifications (eg, leading to economic, governmental, and legislative actions). In addition to reviewing evidence on these points, we provide a broad three-step model of implementation research in mHealth that focuses on understanding and preventing health concerns to facilitate the safe and effective scalability of mHealth (and that may be generalizable and applied to similar technologies): (1) evaluating and better discerning public perceptions and misperceptions (and how these may differ between populations), (2) developing theory-based public health communication strategies regarding the safety of mHealth, and (3) disseminating this messaging using evidence-based methods. Collectively, these steps converge on reviewing evidence regarding the potential role of worry and nocebo in mHealth and providing a model for understanding and changing attitudes and preventing unfounded negative perceptions related to mHealth technology. %M 32449688 %R 10.2196/14375 %U http://mhealth.jmir.org/2020/5/e14375/ %U https://doi.org/10.2196/14375 %U http://www.ncbi.nlm.nih.gov/pubmed/32449688 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 5 %P e17507 %T Toward Earlier Diagnosis Using Combined eHealth Tools in Rheumatology: The Joint Pain Assessment Scoring Tool (JPAST) Project %A Knitza,Johannes %A Knevel,Rachel %A Raza,Karim %A Bruce,Tor %A Eimer,Ekaterina %A Gehring,Isabel %A Mathsson-Alm,Linda %A Poorafshar,Maryam %A Hueber,Axel J %A Schett,Georg %A Johannesson,Martina %A Catrina,Anca %A Klareskog,Lars %A , %+ Department of Internal Medicine 3 – Rheumatology and Immunology, Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, Erlangen, 91054, Germany, 49 91318532093, johannes.knitza@uk-erlangen.de %K rheumatology %K eHealth %K mHealth %K symptom-checkers %K apps %D 2020 %7 15.5.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Outcomes of patients with inflammatory rheumatic diseases have significantly improved over the last three decades, mainly due to therapeutic innovations, more timely treatment, and a recognition of the need to monitor response to treatment and to titrate treatments accordingly. Diagnostic delay remains a major challenge for all stakeholders. The combination of electronic health (eHealth) and serologic and genetic markers holds great promise to improve the current management of patients with inflammatory rheumatic diseases by speeding up access to appropriate care. The Joint Pain Assessment Scoring Tool (JPAST) project, funded by the European Union (EU) European Institute of Innovation and Technology (EIT) Health program, is a unique European project aiming to enable and accelerate personalized precision medicine for early treatment in rheumatology, ultimately also enabling prevention. The aim of the project is to facilitate these goals while at the same time, reducing cost for society and patients. %M 32348258 %R 10.2196/17507 %U https://mhealth.jmir.org/2020/5/e17507 %U https://doi.org/10.2196/17507 %U http://www.ncbi.nlm.nih.gov/pubmed/32348258 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e14512 %T Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health %A Brewer,LaPrincess C %A Fortuna,Karen L %A Jones,Clarence %A Walker,Robert %A Hayes,Sharonne N %A Patten,Christi A %A Cooper,Lisa A %+ Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, United States, 1 5072661376, brewer.laprincess@mayo.edu %K health informatics %K digital health %K mobile health %K eHealth %K community-based participatory research %K health equity %D 2020 %7 14.1.2020 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The rapid proliferation of health informatics and digital health innovations has revolutionized clinical and research practices. There is no doubt that these fields will continue to have accelerated growth and a substantial impact on population health. However, there are legitimate concerns about how these promising technological advances can lead to unintended consequences such as perpetuating health and health care disparities for underresourced populations. To mitigate this potential pitfall, it is imperative for the health informatics and digital health scientific communities to understand the challenges faced by disadvantaged groups, including racial and ethnic minorities, which hinder their achievement of ideal health. This paper presents illustrative exemplars as case studies of contextually tailored, sociotechnical mobile health interventions designed with community members to address health inequities using community-engaged research approaches. We strongly encourage researchers and innovators to integrate community engagement into the development of data-driven, modernized solutions for every sector of society to truly achieve health equity for all. %M 31934874 %R 10.2196/14512 %U https://mhealth.jmir.org/2020/1/e14512 %U https://doi.org/10.2196/14512 %U http://www.ncbi.nlm.nih.gov/pubmed/31934874 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 12 %P e13305 %T Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization %A L'Hommedieu,Michelle %A L'Hommedieu,Justin %A Begay,Cynthia %A Schenone,Alison %A Dimitropoulou,Lida %A Margolin,Gayla %A Falk,Tiago %A Ferrara,Emilio %A Lerman,Kristina %A Narayanan,Shrikanth %+ Information Sciences Institute, University of Southern California, 3740 McClintock Ave, EEB 413, Los Angeles, CA, 90089, United States, 1 2137402318, mhasan@isi.edu %K research %K research techniques %K Ecological Momentary Assessment %K wearable electronic devices %D 2019 %7 10.12.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Although traditional methods of data collection in naturalistic settings can shed light on constructs of interest to researchers, advances in sensor-based technology allow researchers to capture continuous physiological and behavioral data to provide a more comprehensive understanding of the constructs that are examined in a dynamic health care setting. This study gives examples for implementing technology-facilitated approaches and provides the following recommendations for conducting such longitudinal, sensor-based research, with both environmental and wearable sensors in a health care setting: pilot test sensors and software early and often; build trust with key stakeholders and with potential participants who may be wary of sensor-based data collection and concerned about privacy; generate excitement for novel, new technology during recruitment; monitor incoming sensor data to troubleshoot sensor issues; and consider the logistical constraints of sensor-based research. The study describes how these recommendations were successfully implemented by providing examples from a large-scale, longitudinal, sensor-based study of hospital employees at a large hospital in California. The knowledge gained from this study may be helpful to researchers interested in obtaining dynamic, longitudinal sensor data from both wearable and environmental sensors in a health care setting (eg, a hospital) to obtain a more comprehensive understanding of constructs of interest in an ecologically valid, secure, and efficient way. %M 31821155 %R 10.2196/13305 %U https://mhealth.jmir.org/2019/12/e13305 %U https://doi.org/10.2196/13305 %U http://www.ncbi.nlm.nih.gov/pubmed/31821155 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 11 %P e15301 %T Mind the App: Considerations for the Future of Mobile Health in Canada %A Zawati,Ma'n H %A Lang,Michael %+ Centre of Genomics and Policy, McGill University, 740, Avenue Dr Penfield, Suite 5203, Montreal, QC, H3A 0G1, Canada, 1 5146686599, man.zawati@mcgill.ca %K smartphone %K mobile phone %K regulation %K patients %K physicians %D 2019 %7 4.11.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Over the past decade, smartphone technology has become increasingly sophisticated and ubiquitous. Modern smartphones, now owned by more than three quarters of Canadians and 94% of millennials, perform an array of functions that are potentially useful in the health care context, such as tracking fitness data, enabling health record sharing, and providing user-friendly platforms for disease management. Approximately half of smartphone users have downloaded at least one health app, and clinicians are increasingly using them in their practice. However, despite widespread use, there is little evidence that supports their safety and efficacy. Few apps have been independently evaluated and many lack basic patient protections such as privacy policies. In this context, the demand for the regulation of mobile health apps has increased. Against this backdrop, regulators, including Health Canada, have begun to propose regulating the use of smartphones in health care. In this viewpoint, we respond to Health Canada’s recent proposal to regulate smartphone use in Canada according to a risk-based model. We argue that although Health Canada’s recent proposed approach is promising, it may require complementary regulation and oversight. %M 31682580 %R 10.2196/15301 %U https://mhealth.jmir.org/2019/11/e15301 %U https://doi.org/10.2196/15301 %U http://www.ncbi.nlm.nih.gov/pubmed/31682580 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e14056 %T The SMART Framework: Integration of Citizen Science, Community-Based Participatory Research, and Systems Science for Population Health Science in the Digital Age %A Katapally,Tarun Reddy %+ Johnson Shoyama Graduate School of Public Policy, University of Regina, 2155 College Ave, Regina, SK, S4P4V5, Canada, 1 3065854544, tarun.katapally@uregina.ca %K community-based participatory research %K smartphones %K mobile phones %K population health %K mHealth %K eHealth %K digital health %K big data %K evidence-based framework %K citizen science %K participatory research %K participatory surveillance %K systems science %K ubiquitous tools %D 2019 %7 30.08.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Citizen science enables citizens to actively contribute to all aspects of the research process, from conceptualization and data collection, to knowledge translation and evaluation. Citizen science is gradually emerging as a pertinent approach in population health research. Given that citizen science has intrinsic links with community-based research, where participatory action drives the research agenda, these two approaches could be integrated to address complex population health issues. Community-based participatory research has a strong record of application across multiple disciplines and sectors to address health inequities. Citizen science can use the structure of community-based participatory research to take local approaches of problem solving to a global scale, because citizen science emerged through individual environmental activism that is not limited by geography. This synergy has significant implications for population health research if combined with systems science, which can offer theoretical and methodological strength to citizen science and community-based participatory research. Systems science applies a holistic perspective to understand the complex mechanisms underlying causal relationships within and between systems, as it goes beyond linear relationships by utilizing big data–driven advanced computational models. However, to truly integrate citizen science, community-based participatory research, and systems science, it is time to realize the power of ubiquitous digital tools, such as smartphones, for connecting us all and providing big data. Smartphones have the potential to not only create equity by providing a voice to disenfranchised citizens but smartphone-based apps also have the reach and power to source big data to inform policies. An imminent challenge in legitimizing citizen science is minimizing bias, which can be achieved by standardizing methods and enhancing data quality—a rigorous process that requires researchers to collaborate with citizen scientists utilizing the principles of community-based participatory research action. This study advances SMART, an evidence-based framework that integrates citizen science, community-based participatory research, and systems science through ubiquitous tools by addressing core challenges such as citizen engagement, data management, and internet inequity to legitimize this integration. %M 31471963 %R 10.2196/14056 %U http://mhealth.jmir.org/2019/8/e14056/ %U https://doi.org/10.2196/14056 %U http://www.ncbi.nlm.nih.gov/pubmed/31471963 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 8 %P e11966 %T Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations %A Tobore,Igbe %A Li,Jingzhen %A Yuhang,Liu %A Al-Handarish,Yousef %A Kandwal,Abhishek %A Nie,Zedong %A Wang,Lei %+ Center for Medical Robotics and Minimally Invasive Surgical Devices, Shenzhen Institutes of Advance Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University, Xili Town, Nanshan District, Shenzhen,, China, 86 755 86585213, zd.nie@siat.ac.cn %K machine learning %K deep learning %K big data %K mHealth %K medical imaging %K electronic health record %K biologicals %K biomedical %K ECG %K EEG %K artificial intelligence %D 2019 %7 02.08.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology. %M 31376272 %R 10.2196/11966 %U https://mhealth.jmir.org/2019/8/e11966/ %U https://doi.org/10.2196/11966 %U http://www.ncbi.nlm.nih.gov/pubmed/31376272 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e10299 %T Using Mobile Apps for Health Management: A New Health Care Mode in China %A Lv,Qing %A Jiang,Yutong %A Qi,Jun %A Zhang,Yanli %A Zhang,Xi %A Fang,Linkai %A Tu,Liudan %A Yang,Mingcan %A Liao,Zetao %A Zhao,Minjing %A Guo,Xinghua %A Qiu,Minli %A Gu,Jieruo %A Lin,Zhiming %+ Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Guangzhou, 510930, China, 86 20 85252205, lzm-zj99@163.com %K mHealth %K internet %K health care %K medical informatics %D 2019 %7 03.06.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: China has a large population; however, medical resources are unevenly distributed and extremely limited, and more medical services are needed. With the development and ever-increasing popularity of mobile internet communication, China has created a mode of mobile health (mHealth) care to resolve this problem. Objective: The aim of this study was (1) to describe the problems associated with China’s medical care practice, (2) explore the need for and the feasibility of internet-based medical care in China, and (3) analyze the functionality of and services offered by internet-based health care platforms for the management of chronic diseases. Methods: Data search was performed by searching national websites, the popular search engine Baidu, the App Store, and websites of internet medical care institutions, using search terms like “mobile health,” “Internet health,” “mobile medical,” “Internet medical,” “digital medical,” “digital health,” and “online doctor.” A total of 6 mobile apps and websites with the biggest enrollment targeting doctors and end users with chronic diseases in China were selected. Results: We recognized the limitations of medical and health care providers and unequal distribution of medical resources in China. An mHealth care platform is a novel and efficient way for doctors and patients to follow up and manage chronic diseases. Services offered by these platforms include reservation and payment, medical consultation, medical education assessment, pharmaceutical and medical instruments sales, electronic medical records, and chronic disease management. China’s health policies are now strongly promoting the implementation of mHealth solutions, particularly in response to the increasing burden of chronic diseases and aging in the population. Conclusions: China's internet-based medical and health care mode can benefit the populace by providing people with high-quality medical resources. This can help other countries and regions with high population density and unevenly distributed medical resources manage their health care concerns. %M 31162131 %R 10.2196/10299 %U https://mhealth.jmir.org/2019/6/e10299/ %U https://doi.org/10.2196/10299 %U http://www.ncbi.nlm.nih.gov/pubmed/31162131 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 5 %P e14124 %T Technical Guidance for Clinicians Interested in Partnering With Engineers in Mobile Health Development and Evaluation %A Shah,Lochan M %A Yang,William E %A Demo,Ryan C %A Lee,Matthias A %A Weng,Daniel %A Shan,Rongzi %A Wongvibulsin,Shannon %A Spaulding,Erin M %A Marvel,Francoise A %A Martin,Seth S %+ Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD, 21231, United States, 1 410 550 3350, lochanshah2019@gmail.com %K mHealth %K cardiology %K myocardial infarction %K personalized medicine %D 2019 %7 15.05.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The explosion of mobile health (mHealth) interventions has prompted significant investment and exploration that has extended past industry into academia. Although research in this space is emerging, it focuses on the clinical and population level impact across different populations. To realize the full potential of mHealth, an intimate understanding of how mHealth is being used by patients and potential differences in usage between various demographic groups must also be prioritized. In this viewpoint, we use our experiences in building an mHealth intervention that incorporates an iOS app, Bluetooth-enabled blood pressure cuff, and Apple Watch to share knowledge on (1) how user interaction data can be tracked in the context of health care privacy laws, (2) what is required for effective, nuanced communication between clinicians and engineers to design mHealth interventions that are patient-centered and have high clinical impact, and (3) how to handle and set up a process to handle user interaction data efficiently. %M 31094337 %R 10.2196/14124 %U http://mhealth.jmir.org/2019/5/e14124/ %U https://doi.org/10.2196/14124 %U http://www.ncbi.nlm.nih.gov/pubmed/31094337 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e11245 %T Mobile Health Interventions: Exploring the Use of Common Relationship Factors %A Grekin,Emily R %A Beatty,Jessica R %A Ondersma,Steven J %+ Department of Psychology, Wayne State University, 5057 Woodward Ave, Detroit, MI, 48202, United States, 1 313 577 2366, grekine@wayne.edu %K mobile health %K mHealth %K smartphone %K empathy %K mobile applications %K therapeutic alliance %D 2019 %7 15.04.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The use of mobile health (mHealth) interventions has risen dramatically over the past two decades. It is important to consider mHealth intervention research within the broader therapy outcome literature. Among other key findings, this broader literature suggests that common relationship factors such as empathy, positive regard, and genuineness may play a critical role in therapy effectiveness. These findings raise intriguing questions for mobile interventions. For example, can mobile interventions incorporate aspects of common factors to augment their efficacy? Will the absence of relationship-based common factors make mobile interventions less effective? This viewpoint paper addresses these questions as well as related issues such as how to operationalize relationship qualities in the context of a mobile intervention and whether common relationship factors apply to computers or computerized narrators. The paper concludes by outlining a future research agenda guided by theory and empirical studies. %M 30985281 %R 10.2196/11245 %U http://mhealth.jmir.org/2019/4/e11245/ %U https://doi.org/10.2196/11245 %U http://www.ncbi.nlm.nih.gov/pubmed/30985281 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 3 %P e11969 %T Toward an Ethically Founded Framework for the Use of Mobile Phone Call Detail Records in Health Research %A Jones,Kerina Helen %A Daniels,Helen %A Heys,Sharon %A Ford,David Vincent %+ Population Data Science, Swansea University Medical School, Swansea University, Singleton Park, Swansea, SA2 8PP, United Kingdom, 44 01792 602764, k.h.jones@swansea.ac.uk %K mobile phone data %K ethical framework %D 2019 %7 22.03.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Data derived from the plethora of networked digital devices hold great potential for public benefit. Among these, mobile phone call detail records (CDRs) present novel opportunities for research and are being used in a variety of health geography studies. Research suggests that the public is amenable to the use of anonymized CDRs for research; however, further work is needed to show that such data can be used appropriately. This study works toward an ethically founded data governance framework with social acceptability. Using a multifaceted approach, this study draws upon data governance arrangements in published health research using CDRs, with a consideration of public views and the public’s information expectations from mobile network operators, and data use scenarios of CDRs in health research. The findings were considered against a backdrop of legislative and regulatory requirements. CDRs can be used at various levels of data and geographic granularity and may be integrated with additional, publicly available or restricted datasets. As such, there may be a significant risk of identity disclosure, which must be mitigated with proportionate control measures. An indicative relative risk of the disclosure model is proposed to aid this process. Subsequently, a set of recommendations is presented, including the need for greater transparency, accountability, and incorporation of public views for social acceptability. This study addresses the need for greater clarity and consistency in data governance for CDRs in health research. While recognizing the need to protect commercial interests, we propose that these recommendations be used to contribute toward an ethically founded practical framework to promote the safe, socially acceptable use of CDR data for public benefit. This pattern needs to be repeated for the appropriate use of new and emerging data types from other networking devices and the wider internet of things. %M 30900996 %R 10.2196/11969 %U http://mhealth.jmir.org/2019/3/e11969/ %U https://doi.org/10.2196/11969 %U http://www.ncbi.nlm.nih.gov/pubmed/30900996 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 7 %P e10414 %T A Path to Better-Quality mHealth Apps %A Larson,Richard S %+ School of Medicine, University of New Mexico Health Sciences Center, MSC08 4560, 1 University of New Mexico, Albuquerque, NM, 87131-0001, United States, 1 505 272 5102, RLarson@salud.unm.edu %K mobile apps %K smartphone %K software validation %K medical device legislation %K United States Food and Drug Administration %D 2018 %7 30.07.2018 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The rapid growth of mobile health (mHealth) apps has resulted in confusion among health care providers and the public about which products rely on evidence-based medicine. Only a small subset of mHealth apps are regulated by the US Food and Drug Administration. The system similar to that used to accredit and certify laboratory testing under the Clinical Laboratory Improvement Amendment offers a potential model for ensuring basic standards of quality and safety for mHealth apps. With these products expanding into the realm of diagnosis and treatment, physicians and consumers are in a strong position to demand oversight that delivers safe and high-quality mHealth apps. %M 30061091 %R 10.2196/10414 %U http://mhealth.jmir.org/2018/7/e10414/ %U https://doi.org/10.2196/10414 %U http://www.ncbi.nlm.nih.gov/pubmed/30061091 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 6 %P e158 %T Workshop on Emerging Technology and Data Analytics for Behavioral Health %A Kotz,David %A Lord,Sarah E %A O'Malley,A James %A Stark,Luke %A Marsch,Lisa A %+ Department of Computer Science, Dartmouth College, 6211 Sudikoff, Hanover, NH, 03755, United States, 1 603 646 1439, David.F.Kotz@dartmouth.edu %K behavioral health %K mobile technology %K wearable devices %K data analytics %K mHealth %D 2018 %7 20.06.2018 %9 Viewpoint %J JMIR Res Protoc %G English %X Wearable and portable digital devices can support self-monitoring for patients with chronic medical conditions, individuals seeking to reduce stress, and people seeking to modify health-related behaviors such as substance use or overeating. The resulting data may be used directly by a consumer, or shared with a clinician for treatment, a caregiver for assistance, or a health coach for support. The data can also be used by researchers to develop and evaluate just-in-time interventions that leverage mobile technology to help individuals manage their symptoms and behavior in real time and as needed. Such wearable systems have huge potential for promoting delivery of anywhere-anytime health care, improving public health, and enhancing the quality of life for many people. The Center for Technology and Behavioral Health at Dartmouth College, a P30 “Center of Excellence” supported by the National Institute on Drug Abuse at the National Institutes of Health, conducted a workshop in February 2017 on innovations in emerging technology, user-centered design, and data analytics for behavioral health, with presentations by a diverse range of experts in the field. The workshop focused on wearable and mobile technologies being used in clinical and research contexts, with an emphasis on applications in mental health, addiction, and health behavior change. In this paper, we summarize the workshop panels on mobile sensing, user experience design, statistics and machine learning, and privacy and security, and conclude with suggested research directions for this important and emerging field of applying digital approaches to behavioral health. Workshop insights yielded four key directions for future research: (1) a need for behavioral health researchers to work iteratively with experts in emerging technology and data analytics, (2) a need for research into optimal user-interface design for behavioral health technologies, (3) a need for privacy-oriented design from the beginning of a novel technology, and (4) the need to develop new analytical methods that can scale to thousands of individuals and billions of data points. %M 29925493 %R 10.2196/resprot.9589 %U http://www.researchprotocols.org/2018/6/e158/ %U https://doi.org/10.2196/resprot.9589 %U http://www.ncbi.nlm.nih.gov/pubmed/29925493 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 4 %P e102 %T Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity %A Düking,Peter %A Fuss,Franz Konstantin %A Holmberg,Hans-Christer %A Sperlich,Billy %+ Integrative & Experimental Exercise Science & Training, Institute for Sport Sciences, University of Würzburg, Judenbühlweg 11, Würzburg, 97082, Germany, 49 931 31 ext 8479, peterdueking@gmx.de %K activity tracker %K data mining %K Internet of Things %K load management %K physical activity %K smartwatch %D 2018 %7 30.04.2018 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies. %M 29712629 %R 10.2196/mhealth.9341 %U http://mhealth.jmir.org/2018/4/e102/ %U https://doi.org/10.2196/mhealth.9341 %U http://www.ncbi.nlm.nih.gov/pubmed/29712629 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 12 %P e190 %T Methods for Evaluating the Content, Usability, and Efficacy of Commercial Mobile Health Apps %A Jake-Schoffman,Danielle E %A Silfee,Valerie J %A Waring,Molly E %A Boudreaux,Edwin D %A Sadasivam,Rajani S %A Mullen,Sean P %A Carey,Jennifer L %A Hayes,Rashelle B %A Ding,Eric Y %A Bennett,Gary G %A Pagoto,Sherry L %+ Division of Preventive and Behavioral Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, United States, 1 (508) 856 6517, danielle.jakeschoffman@umassmed.edu %K mHealth %K mobile health %K mobile applications %K telemedicine/methods %K treatment efficacy %K behavioral medicine %K chronic disease %D 2017 %7 18.12.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Commercial mobile apps for health behavior change are flourishing in the marketplace, but little evidence exists to support their use. This paper summarizes methods for evaluating the content, usability, and efficacy of commercially available health apps. Content analyses can be used to compare app features with clinical guidelines, evidence-based protocols, and behavior change techniques. Usability testing can establish how well an app functions and serves its intended purpose for a target population. Observational studies can explore the association between use and clinical and behavioral outcomes. Finally, efficacy testing can establish whether a commercial app impacts an outcome of interest via a variety of study designs, including randomized trials, multiphase optimization studies, and N-of-1 studies. Evidence in all these forms would increase adoption of commercial apps in clinical practice, inform the development of the next generation of apps, and ultimately increase the impact of commercial apps. %M 29254914 %R 10.2196/mhealth.8758 %U http://mhealth.jmir.org/2017/12/e190/ %U https://doi.org/10.2196/mhealth.8758 %U http://www.ncbi.nlm.nih.gov/pubmed/29254914 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 12 %P e176 %T Texting Condolences: Adapting mHealth Programs After Unexpected Pregnancy and Infant Outcomes %A Unger,Jennifer A %A Kinuthia,John %A John-Stewart,Grace %+ Global Center for Integrated Health of Women, Adolescents and Children, Department of Global Health, University of Washington, Harborview Medical Center, Seattle, WA,, United States, 1 206 388 8141, junger@uw.edu %K mHealth %K infant loss %K miscarriage %D 2017 %7 08.12.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Mobile health (mHealth) short message system (SMS) interventions for maternal and child health (MCH) are being implemented globally. In many low- and middle-income settings in which these mHealth interventions are being rolled out, stillbirths and neonatal and infant deaths are common. It is important that mHealth solutions do not exacerbate emotional stress and pain by continuing with routine messaging for pregnancy or infant care when someone has experienced loss. In this brief viewpoint paper, we argue that SMS programs for maternal and child health need to adapt and make available messaging for miscarriage, stillbirth, and infant loss. %M 29222078 %R 10.2196/mhealth.8303 %U http://mhealth.jmir.org/2017/12/e176/ %U https://doi.org/10.2196/mhealth.8303 %U http://www.ncbi.nlm.nih.gov/pubmed/29222078 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e155 %T Tackling Regional Public Health Issues Using Mobile Health Technology: Event Report of an mHealth Hackathon in Thailand %A Pathanasethpong,Atipong %A Soomlek,Chitsutha %A Morley,Katharine %A Morley,Michael %A Polpinit,Pattarawit %A Dagan,Alon %A Weis,James W %A Celi,Leo Anthony %+ Department of Anesthesiology, Faculty of Medicine, Khon Kaen University, 123 Mittraphab Road, Khon Kaen,, Thailand, 66 891758278, atipat@kku.ac.th %K hackathon %K mHealth %K interdisciplinary collaboration %D 2017 %7 16.10.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Hackathons are intense, short, collaborative events focusing on solving real world problems through interdisciplinary teams. This is a report of the mHealth hackathon hosted by Khon Kaen University in collaboration with MIT Sana and faculty members from Harvard Medical School with the aim to improve health care delivery in the Northeast region of Thailand. Key health challenges, such as improving population health literacy, tracking disease trajectory and outcomes among rural communities, and supporting the workflow of overburdened frontline providers, were addressed using mHealth. Many modifications from the usual format of hackathon were made to tailor the event to the local context and culture, such as the process of recruiting participants and how teams were matched and formed. These modifications serve as good learning points for hosting future hackathons. There are also many lessons learned about how to achieve a fruitful collaboration despite cultural barriers, how to best provide mentorship to the participants, how to instill in the participants a sense of mission, and how to match the participants in a fair and efficient manner. This event showcases how interdisciplinary collaboration can produce results that are unattainable by any discipline alone and demonstrates that innovations are the fruits of collective wisdom of people from different fields of expertise who work together toward the same goals. %M 29038098 %R 10.2196/mhealth.8259 %U http://mhealth.jmir.org/2017/10/e155/ %U https://doi.org/10.2196/mhealth.8259 %U http://www.ncbi.nlm.nih.gov/pubmed/29038098 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e152 %T Mobile Health Apps in OB-GYN-Embedded Psychiatric Care: Commentary %A Mehralizade,Aydan %A Schor,Shayna %A Coleman,Chad M %A Oppenheim,Claire E %A Denckla,Christy A %A Borba,Christina PC %A Henderson,David C %A Wolff,James %A Crane,Sarah %A Nettles-Gomez,Pamela %A Pal,Avik %A Milanovic,Snezana %+ Boston Medical Center, 1 Boston Medical Center Pl, Boston, MA 02118, Boston, MA,, United States, 1 617 414 1917, snezana.milanovic@bmc.org %K mHealth %K eHealth %K embedded psychiatric clinic %K postpartum depression %K mental health %K OB-GYN %K global health %K reproductive health %D 2017 %7 06.10.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X This paper explores the potential benefits of the use of mobile health (mHealth) apps in obstetrician-gynecologist (OB-GYN)-embedded psychiatric clinics in the United States. First, we highlight the increasing trend of integrating mental health care within the OB-GYN context. Second, we provide examples of successful uses of mHealth in the global health context and highlight the dearth of available research in the United States. Finally, we provide a summary of the shortcomings of currently available apps and describe the upcoming trial of a novel app currently underway at the Mother-Child Wellness Clinical and Research Center at Boston Medical Center. %M 28986335 %R 10.2196/mhealth.7988 %U http://mhealth.jmir.org/2017/10/e152/ %U https://doi.org/10.2196/mhealth.7988 %U http://www.ncbi.nlm.nih.gov/pubmed/28986335 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 10 %P e136 %T A Call to Digital Health Practitioners: New Guidelines Can Help Improve the Quality of Digital Health Evidence %A Agarwal,Smisha %A Lefevre,Amnesty E %A Labrique,Alain B %+ Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD,, United States, 1 443 287 4744, alabriqu@gmail.com %K mHealth %K checklist %K reporting %K digital health %K publishing guidelines %D 2017 %7 06.10.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: Despite the rapid proliferation of health interventions that employ digital tools, the evidence on the effectiveness of such approaches remains insufficient and of variable quality. To address gaps in the comprehensiveness and quality of reporting on the effectiveness of digital programs, the mHealth Technical Evidence Review Group (mTERG), convened by the World Health Organization, proposed the mHealth Evidence Reporting and Assessment (mERA) checklist to address existing gaps in the comprehensiveness and quality of reporting on the effectiveness of digital health programs. Objective: We present an overview of the mERA checklist and encourage researchers working in the digital health space to use the mERA checklist for reporting their research. Methods: The development of the mERA checklist consisted of convening an expert group to recommend an appropriate approach, convening a global expert review panel for checklist development, and pilot-testing the checklist. Results: The mERA checklist consists of 16 core mHealth items that define what the mHealth intervention is (content), where it is being implemented (context), and how it was implemented (technical features). Additionally, a 29-item methodology checklist guides authors on reporting critical aspects of the research methodology employed in the study. We recommend that the core mERA checklist is used in conjunction with an appropriate study-design specific checklist. Conclusions: The mERA checklist aims to assist authors in reporting on digital health research, guide reviewers and policymakers in synthesizing evidence, and guide journal editors in assessing the completeness in reporting on digital health studies. An increase in transparent and rigorous reporting can help identify gaps in the conduct of research and understand the effects of digital health interventions as a field of inquiry. %M 28986340 %R 10.2196/mhealth.6640 %U https://mhealth.jmir.org/2017/10/e136/ %U https://doi.org/10.2196/mhealth.6640 %U http://www.ncbi.nlm.nih.gov/pubmed/28986340 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 6 %P e87 %T Acceptance of Mobile Health in Communities Underrepresented in Biomedical Research: Barriers and Ethical Considerations for Scientists %A Nebeker,Camille %A Murray,Kate %A Holub,Christina %A Haughton,Jessica %A Arredondo,Elva M %+ Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, 9500 Gilman Drive, MC 0725, La Jolla, CA, 92093, United States, 1 858 534 7786, nebeker@eng.ucsd.edu %K telemedicine %K cultural diversity %K ethics, research %K ethics committees %K research %K privacy %K informed consent %D 2017 %7 28.06.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: The rapid expansion of direct-to-consumer wearable fitness products (eg, Flex 2, Fitbit) and research-grade sensors (eg, SenseCam, Microsoft Research; activPAL, PAL Technologies) coincides with new opportunities for biomedical and behavioral researchers. Underserved communities report among the highest rates of chronic disease and could benefit from mobile technologies designed to facilitate awareness of health behaviors. However, new and nuanced ethical issues are introduced with new technologies, which are challenging both institutional review boards (IRBs) and researchers alike. Given the potential benefits of such technologies, ethical and regulatory concerns must be carefully considered. Objective: Our aim was to understand potential barriers to using wearable sensors among members of Latino, Somali and Native Hawaiian Pacific Islander (NHPI) communities. These ethnic groups report high rates of disparate health conditions and could benefit from wearable technologies that translate the connection between physical activity and desired health outcomes. Moreover, these groups are traditionally under-represented in biomedical research. Methods: We independently conducted formative research with individuals from southern California, who identified as Latino, Somali, or Native Hawaiian Pacific Islander (NHPI). Data collection methods included survey (NHPI), interview (Latino), and focus group (Somali) with analysis focusing on cross-cutting themes. Results: The results pointed to gaps in informed consent, challenges to data management (ie, participant privacy, data confidentiality, and data sharing conventions), social implications (ie, unwanted attention), and legal risks (ie, potential deportation). Conclusions: Results shed light on concerns that may escalate the digital divide. Recommendations include suggestions for researchers and IRBs to collaborate with a goal of developing meaningful and ethical practices that are responsive to diverse research participants who can benefit from technology-enabled research methods. Trial Registration: ClinicalTrials.gov NCT02505165; https://clinicaltrials.gov/ct2/show/NCT02505165 (Archived by WebCite at http://www.Webcitation.org/6r9ZSUgoT) %M 28659258 %R 10.2196/mhealth.6494 %U http://mhealth.jmir.org/2017/6/e87/ %U https://doi.org/10.2196/mhealth.6494 %U http://www.ncbi.nlm.nih.gov/pubmed/28659258 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e60 %T mHealth Assessment: Conceptualization of a Global Framework %A Bradway,Meghan %A Carrion,Carme %A Vallespin,Bárbara %A Saadatfard,Omid %A Puigdomènech,Elisa %A Espallargues,Mireia %A Kotzeva,Anna %+ Universitat Oberta de Catalunya (UOC), Rambla del Poblenou, 156, Catalonia, 08018, Spain, 34 253 23 00, mcarrionr@uoc.edu %K mhealth %K evaluation %K assessment %K checklist %K framework %D 2017 %7 02.05.2017 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Background: The mass availability and use of mobile health (mHealth) technologies offers the potential for these technologies to support or substitute medical advice. However, it is worrisome that most assessment initiatives are still not able to successfully evaluate all aspects of mHealth solutions. As a result, multiple strategies to assess mHealth solutions are being proposed by medical regulatory bodies and similar organizations. Objective: We aim to offer a collective description of a universally applicable description of mHealth assessment initiatives, given their current and, as we see it, potential impact. In doing so, we recommend a common foundation for the development or update of assessment initiatives by addressing the multistakeholder issues that mHealth technology adds to the traditional medical environment. Methods: Organized by the Mobile World Capital Barcelona Foundation, we represent a workgroup consisting of patient associations, developers, and health authority representatives, including medical practitioners, within Europe. Contributions from each group’s diverse competencies has allowed us to create an overview of the complex yet similar approaches to mHealth evaluation that are being developed today, including common gaps in concepts and perspectives. In response, we summarize commonalities of existing initiatives and exemplify additional characteristics that we believe will strengthen and unify these efforts. Results: As opposed to a universal standard or protocol in evaluating mHealth solutions, assessment frameworks should respect the needs and capacity of each medical system or country. Therefore, we expect that the medical system will specify the content, resources, and workflow of assessment protocols in order to ensure a sustainable plan for mHealth solutions within their respective countries. Conclusions: A common framework for all mHealth initiatives around the world will be useful in order to assess whatever mHealth solution is desirable in different areas, adapting it to the specifics of each context, to bridge the gap between health authorities, patients, and mHealth developers. We aim to foster a more trusting and collaborative environment to safeguard the well-being of patients and citizens while encouraging innovation of technology and policy. %M 28465282 %R 10.2196/mhealth.7291 %U http://mhealth.jmir.org/2017/5/e60/ %U https://doi.org/10.2196/mhealth.7291 %U http://www.ncbi.nlm.nih.gov/pubmed/28465282 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 3 %P e97 %T Taking mHealth Forward: Examining the Core Characteristics %A Davis,Teaniese Latham %A DiClemente,Ralph %A Prietula,Michael %+ Goizueta Business School, Health Initiative, Emory University, 1300 Clifton Road, Atlanta, GA, 30322, United States, 1 404 727 8761, mj.prietula@emory.edu %K mobile health %K eHealth %K mHealth %K health policy %K health technology %K text messaging %K public health informatics %K telehealth %D 2016 %7 10.08.2016 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X The emergence of mobile health (mHealth) offers unique and varied opportunities to address some of the most difficult problems of health. Some of the most promising and active efforts of mHealth involve the engagement of mobile phone technology. As this technology has spread and as this technology is still evolving, we begin a conversation about the core characteristics of mHealth relevant to any mobile phone platform. We assert that the relevance of these characteristics to mHealth will endure as the technology advances, so an understanding of these characteristics is essential to the design, implementation, and adoption of mHealth-based solutions. The core characteristics we discuss are (1) the penetration or adoption into populations, (2) the availability and form of apps, (3) the availability and form of wireless broadband access to the Internet, and (4) the tethering of the device to individuals. These collectively act to both enable and constrain the provision of population health in general, as well as personalized and precision individual health in particular. %M 27511612 %R 10.2196/mhealth.5659 %U http://mhealth.jmir.org/2016/3/e97/ %U https://doi.org/10.2196/mhealth.5659 %U http://www.ncbi.nlm.nih.gov/pubmed/27511612 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 4 %P e95 %T Mobile Phones in Research and Treatment: Ethical Guidelines and Future Directions %A Carter,Adrian %A Liddle,Jacki %A Hall,Wayne %A Chenery,Helen %+ School of Psychological Sciences, Monash University, Monash Biomedical Imaging, 770 Blackburn Road, Melbourne, 3800, Australia, 61 (0)3 9902 9431, adrian.carter@monash.edu %K ethics %K informed consent %K mHealth %K mobile phones %K Parkinson’s disease %K privacy %K regulation %D 2015 %7 16.10.2015 %9 Viewpoint %J JMIR mHealth uHealth %G English %X Mobile phones and other remote monitoring devices, collectively referred to as "mHealth," promise to transform the treatment of a range of conditions, including movement disorders, such as Parkinson’s disease. In this viewpoint paper, we use Parkinson’s disease as an example, although most considerations discussed below are valid for a wide variety of conditions. The ability to easily collect vast arrays of personal data over long periods will give clinicians and researchers unique insights into disease treatment and progression. These capabilities also pose new ethical challenges that health care professionals will need to manage if this promise is to be realized with minimal risk of harm. These challenges include privacy protection when anonymity is not always possible, minimization of third-party uses of mHealth data, informing patients of complex risks when obtaining consent, managing data in ways that maximize benefit while minimizing the potential for disclosure to third parties, careful communication of clinically relevant information gleaned via mHealth technologies, and rigorous evaluation and regulation of mHealth products before widespread use. Given the complex array of symptoms and differences in comfort and literacy with technology, it is likely that these solutions will need to be individualized. It is therefore critical that developers of mHealth apps engage with patients throughout the development process to ensure that the technology meets their needs. These challenges will be best met through early and ongoing engagement with patients and other relevant stakeholders. %M 26474545 %R 10.2196/mhealth.4538 %U http://mhealth.jmir.org/2015/4/e95/ %U https://doi.org/10.2196/mhealth.4538 %U http://www.ncbi.nlm.nih.gov/pubmed/26474545 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 2 %P e64 %T Can Standards and Regulations Keep Up With Health Technology? %A Vincent,Christopher James %A Niezen,Gerrit %A O'Kane,Aisling Ann %A Stawarz,Katarzyna %+ UCL Interaction Centre, University College London, Gower Street, London, WC1E 6BT, United Kingdom, 44 (0)203 108 7057 ext 57057, c.vincent@ucl.ac.uk %K governmental regulations %K health services %K medical devices %K mHealth %K mobile phones %K open source initiative %K software %K standards %K technology %D 2015 %7 03.06.2015 %9 Viewpoint %J JMIR mHealth uHealth %G English %X Technology is changing at a rapid rate, opening up new possibilities within the health care domain. Advances such as open source hardware, personal medical devices, and mobile phone apps are creating opportunities for custom-made medical devices and personalized care. However, they also introduce new challenges in balancing the need for regulation (ensuring safety and performance) with the need to innovate flexibly and efficiently. Compared with the emergence of new technologies, health technology design standards and regulations evolve slowly, and therefore, it can be difficult to apply these standards to the latest developments. For example, current regulations may not be suitable for approaches involving open source hardware, an increasingly popular way to create medical devices in the maker community. Medical device standards may not be flexible enough when evaluating the usability of mobile medical devices that can be used in a multitude of different ways, outside of clinical settings. Similarly, while regulatory guidance has been updated to address the proliferation of health-related mobile phone apps, it can be hard to know if and when these regulations apply. In this viewpoint, we present three examples of novel medical technologies to illustrate the types of regulatory issues that arise in the current environment. We also suggest opportunities for support, such as advances in the way we review and monitor medical technologies. %M 26041730 %R 10.2196/mhealth.3918 %U http://mhealth.jmir.org/2015/2/e64/ %U https://doi.org/10.2196/mhealth.3918 %U http://www.ncbi.nlm.nih.gov/pubmed/26041730 %0 Journal Article %@ 2291-5222 %I JMIR Publications Inc. %V 3 %N 2 %P e62 %T Integrating mHealth and Systems Science: A Combination Approach to Prevent and Treat Chronic Health Conditions %A Oreskovic,Nicolas Michel %A Huang,Terry T %A Moon,Jon %+ Division of General Academic Pediatrics, Department of Pediatrics, Massachusetts General Hospital, 100 Cambridge Street, 5th Fl., Boston, MA, 02116, United States, 1 617 726 0593, noreskovic@mgh.harvard.edu %K mHealth %K systems science %K chronic health conditions %K obesity %K physical activity %D 2015 %7 02.06.2015 %9 Viewpoint %J JMIR mHealth uHealth %G English %X Chronic health conditions are a growing global health concern and account for over half of all deaths worldwide. Finding ways to decrease the burden of and resources allotted to chronic health conditions is of primary importance. Recent advances in technology and insights into modeling techniques offer promising approaches, which if combined, represent a novel direction that would further advance the prevention and treatment of chronic health conditions. %M 26036753 %R 10.2196/mhealth.4150 %U http://mhealth.jmir.org/2015/2/e62/ %U https://doi.org/10.2196/mhealth.4150 %U http://www.ncbi.nlm.nih.gov/pubmed/26036753