Validity of a smartwatch for detecting atrial fibrillation in patients after heart valve surgery: a prospective observational study.

OBJECTIVES: Atrial fibrillation (AF) is a common early arrhythmia after heart valve surgery that limits physical activity. We aimed to evaluate the criterion validity of the Apple Watch Series 5 single-lead electrocardiogram (ECG) for detecting AF in patients after heart valve surgery. DESIGN: We enrolled 105 patients from the University Hospital of North Norway, of whom 93 completed the study. All patients underwent single-lead ECG using the smartwatch three times or more daily on the second to third or third to fourth postoperative day. These results were compared with continuous 2-4 days ECG telemetry monitoring and a 12-lead ECG on the third postoperative day. RESULTS: On comparing the Apple Watch ECGs with the ECG monitoring, the sensitivity and specificity to detect AF were 91% (75, 100) and 96% (91, 99), respectively. The accuracy was 95% (91, 99). On comparing Apple Watch ECG with a 12-lead ECG, the sensitivity was 71% (62, 100) and the specificity was 92% (92, 100). CONCLUSION: The Apple smartwatch single-lead ECG has high sensitivity and specificity, and might be a useful tool for detecting AF in patients after heart valve surgery.

Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: a mixed methods approach.

BACKGROUND: Lack of physical activity (PA) is a risk factor for death and non-communicable disease. Despite this, more than one fourth of adults worldwide do not follow PA guidelines. As part of a feasibility study to test a complex intervention for increasing PA, we included a consumer-based activity tracker (AT) as a tool to measure PA outcomes and to track heart rate during exercise sessions. The aim of the present study was to identify factors that increase wear time when using a consumer-based AT for monitoring of participants in clinical research. METHODS: Sixteen participants aged 55-74 years, with obesity, sedentary lifestyle, and elevated cardiovascular risk were recruited to a 12-month feasibility study. Participants wore a Polar M430 AT to collect continuous PA data during a six-month intervention followed by 6 months of follow-up. We performed quantitative wear time analysis, tested the validity of the AT, and completed two rounds of qualitative interviews to investigate how individual wear-time was linked to participant responses. RESULTS: From 1 year of tracking, mean number of valid wear days were 292 (SD = 86), i.e. 80%. The Polar M430 provides acceptable measurements for total energy expenditure. Motivations for increased wear time were that participants were asked to wear it and the ability to track PA progress. Perceived usefulness included time keeping, heart rate- and sleep tracking, becoming more conscious about day-to-day activity, and improved understanding of which activity types were more effective for energy expenditure. Sources of AT annoyance were measurement inaccuracies and limited instruction for use. Suggestions for improvement were that the AT was big, unattractive, and complicated to use. CONCLUSIONS: Adherence to wearing a consumer-based AT was high. Results indicate that it is feasible to use a consumer-based AT to measure PA over a longer period. Potential success factors for increased wear time includes adequate instruction for AT use, allowing participants to choose different AT designs, and using trackers with accurate measurements. To identify accurate trackers, AT validation studies in the target cohort may be needed. TRIAL REGISTRATION: U.S. National Library of Medicine, Clinical Trial registry: NCT03807323 ; Registered 16 September 2019 - Retrospectively registered.

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.

BACKGROUND: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model. OBJECTIVE: This study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio. METHODS: Three groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required. RESULTS: The one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time. CONCLUSIONS: We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale.

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System.

BACKGROUND: Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. OBJECTIVE: The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. METHODS: We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). RESULTS: Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. CONCLUSIONS: We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes.

BACKGROUND: Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. OBJECTIVE: This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. METHODS: A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. RESULTS: The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. CONCLUSIONS: Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual's GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables.

BACKGROUND: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects. OBJECTIVE: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research. METHODS: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands. RESULTS: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands. CONCLUSIONS: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

Physicians interrupted by mobile devices in hospitals: understanding the interaction between devices, roles, and duties.

BACKGROUND: A common denominator of modern hospitals is a variety of communication problems. In particular, interruptions from mobile communication devices are a cause of great concern for many physicians. OBJECTIVE: To characterize how interruptions from mobile devices disturb physicians in their daily work. The gathered knowledge will be subsequently used as input for the design and development of a context-sensitive communication system for mobile communications suitable for hospitals. METHODS: This study adheres to an ethnographic and interpretive field research approach. The data gathering consisted of participant observations, non-structured and mostly ad hoc interviews, and open-ended discussions with a selected group of physicians. Eleven physicians were observed for a total of 135 hours during May and June 2009. RESULTS: The study demonstrates to what degree physicians are interrupted by mobile devices in their daily work and in which situations they are interrupted, such as surgery, examinations, and during patients/relatives high-importance level conversations. The participants in the study expected, and also indicated, that wireless phones probably led to more interruptions immediately after their introduction in a clinic, when compared to a pager, but this changed after a short while. The unpleasant feeling experienced by the caller when interrupting someone by calling them differs compared to sending a page message, which leaves it up to the receiver when to return the call. CONCLUSIONS: Mobile devices, which frequently interrupt physicians in hospitals, are a problem for both physicians and patients. The results from this study contribute to knowledge being used as input for designing and developing a prototype for a context-sensitive communication system for mobile communication suitable for hospitals. We combined these findings with results from earlier studies and also involved actual users to develop the prototype, CallMeSmart. This system intends to reduce such interruptions and at the same time minimize the number of communication devices needed per user.

Telehealth at UC Davis--a 20-year experience.

Telehealth at the University of California Health System began as a telefetal monitoring connection with a rural hospital in 1992 and evolved to become the Center for Health and Technology (CHT) in 2000. The Center supports the vision of the University of California Davis (UC Davis) Health System-a healthier world through bold innovation. The CHT focuses on the four pillars of the academic health center: clinical services, research and scholarly work, education, and public service. Since 1996, the Center has provided more than 33,000 telemedicine consultation (excluding teleradiology, telepathology, and phone consultations) in over 30 clinical specialties and at more than 90 locations across California. Research and continuous evaluation have played an integral role in shaping the telehealth program, as well as strategic collaborations and partnerships. In an effort to expand the field of telehealth the CHT provides telehealth training for health professionals, technical specialists, and administrators. Furthermore, it also plays an integral role in workforce development through the education of the next generation of community primary care physicians through Rural Programs In Medical Education (Rural PRIME) and continuing educational programs for working health professionals through videoconferencing and Web-based modalities. The Center is supported through a variety of funding sources, and its sustainability comes from a mix of fee-for-service payment, contracts, grants, gifts, and institutional funding. Together with key partners, UC Davis has educated and informed initiatives resulting in legislation and policies that advance telehealth. Looking toward the future, UC Davis is focused on technology-enabled healthcare and supporting synergy among electronic health records, health information exchange, mobile health, informatics, and telehealth.

Size does matter: women mentally rotate large objects faster than men.

Performance in a computerized "mental rotation" task was measured in groups of males and females while they rotated Shepard-Metzler-like cube assemblies on either a standard laptop screen (size = 36 cm) or on a large display wall (584 cm) where the stimuli appeared at considerably larger sizes and within a much wider field of view than that typically used in most spatial tasks. Males and females did not differ significantly in performance in the standard size condition with regards to response time but females performed faster than males in the large display condition. Males were also found to be significantly more accurate than females, regardless of display. We found no sign of trading accuracy for speed for either of the sexes or screen size conditions. We surmise that such an effect may be due to differences in task-solving strategies between the sexes, where a holistic strategy--which may be preferred by males--is negatively affected by large object sizes, whereas a piecemeal approach, that may be preferred by females, is virtually unaffected by display size.

Virtual prenatal care: A systematic review of pregnant women's and healthcare professionals' experiences, needs, and preferences for quality care.

INTRODUCTION: Exploitation of telehealth in prenatal care has the potential to reduce the access barrier to care and empower women to participate in their own care. This review aims to assess the practical implications of virtual prenatal care and identify the needs and experiences associated with it. METHODS: A systematic literature review was conducted in four electronic databases: PubMed, Web of Science, Scopus, and Cochrane. The keywords used were "pregnancy", "virtual visit", "prenatal", and others. The search included all relevant studies published from 2011 to 2021 written in English. Articles mentioning virtual prenatal care incorporating synchronous communication between pregnant women and health care professionals were included. Those unrelated to prenatal care or employing asynchronous means of virtual care were excluded. The review was structured following the PRISMA guidelines. Different quality appraisal methods such as JBI, CASP, NOS, and Cochrane were used to assess the methodological quality of the literature. The data were then analyzed based on the categorization of the studies. RESULTS: Overall, 2863 articles were identified, of which 19 met the inclusion criteria after removing duplicates, screening of abstracts, and full text-four articles identified from hand-searching were incorporated, making a total of 23 eligible articles for the review. The studies' findings revealed the preference for implementing cost-effective virtual care based on the resource set, technological literacy, and consistent accessibility. Further, no significant differences in clinical outcomes were observed between two modes of care, virtual and in-person. The higher satisfaction by pregnant women and healthcare professionals indicated the continuity of the care. In addition, the hybrid model of virtual prenatal care integrated with traditional in-person care was acceptable to both low-risk and high-risk pregnant women. Virtual prenatal care substantially reduced travel time and absences from work, drops in clinic wait time and no-show rate, limited the risk of exposure during a pandemic, and increased self-accountability. CONCLUSION: Virtual prenatal care offers predominant advantages over in-person when it is carefully designed with the inclusion of pregnant women and healthcare professionals' needs. Evidence showed that providing adequate technology training, proper instruction, and guidelines for initial setup and assurance of a reliable and accessible system is vital in increasing access to care.

Health Research Requires Efficient Platforms for Data Collection from Personal Devices.

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment.

Collecting health-related research data using consumer-based wireless smart scales.

BACKGROUND: Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population's health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin. OBJECTIVE: The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research. METHOD: We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer's official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers. RESULT: We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021. CONCLUSION: The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario.

Using mobile health to encourage physical activity in individuals with intellectual disability: a pilot mixed methods feasibility study.

Background: Many individuals with intellectual disability (ID) have a sedentary lifestyle. Few interventions aimed at increasing their level of physical activity (PA) have shown lasting effects. Aim: To assess the feasibility and acceptability of a pilot intervention study using innovative mobile health (mHealth) support systems to encourage PA in individuals with ID. Methods: Nine individuals with ID and a low level of PA, aged 16-36 years, were included in the present convergent triangulation mixed method design. Two mHealth support systems (apps) were developed and tested. PA was measured with a Fitbit smartwatch, accelerometer, the International Physical Activity Questionnaire-Short Form (IPAQ-S), and Goal attainment scaling. Data were collected through online pre-, mid- (4 weeks), and post-intervention (12 weeks) questionnaires and activity trackers. Semi-structured qualitative interviews with participants and/or a family or staff member were held after the 12-week follow-up. Data were analyzed using conventional nonparametric statistics and thematic analyses. Results: The response rate and retention to the trial were 16% and 100%, respectively. Data quality was high, except for missing data from Fitbit activity trackers of approximately 30% from the 4- and 12-week follow-up stages. The feasibility challenges with activity trackers include rashes, size, non-acceptance, and loss of motivation. Participants and family members/staff reported interest in the study theme and were pleased with the data collection method. All but one participant achieved their PA goals. Most participants reported being satisfied with the apps as they were enjoyable or provided a reminder for performing physical and other activities. Social support for PA among family members also increased. However, app support from staff and family members was needed, and apps were not used regularly. Two of nine participants (22%) had increased their PA measured as steps per day with Fitbit at the 12-week follow-up. Conclusions: The acceptability and feasibility of using tailored mobile applications in natural settings to increase PA among adults with ID are promising. This study aligns with previous studies in showing the challenges to increasing PA, which requires the inclusion of family members, staff, and stakeholders. The intervention requires modifications before a randomized controlled trial can be conducted.

Dataset of motivational factors for using mobile health applications and systems.

We created and carried out a cross-sectional anonymous structured questionnaire on what motivates users of mobile health applications and wearables to share their collected health related data. The questionnaire was distributed online in English, French, and Norwegian. In addition, a flyer with information of where to locate the online questionnaire was distributed during a Swiss health conference. We used snowball sampling and encouraged participants to forward the questionnaires to friends, family, and others. Data were collected between October 2018 and March 2020. 58.1 % (n = 473) responded to the English survey, 34.3 % (n = 279) responded to the French survey, and 7.6 % (n = 62) responded to the Norwegian survey. The questionnaire contained 38 questions divided into seven themes: Background and health goals, Wearables and sensors, Mobile applications, Logging of health data, Data sharing- and integration, Social media and entertainment, and Demographics (age, gender, country of origin, chronic disease status, and chronic disease caretaker status). Answer options were single answer, multiple-choice, open-ended, or on a 4-point Likert scale. Questions were defined based on 16 in-person interviews with people without any chronic disorder, people with diabetes, and people with sickle cell disease. All questions were optional. Data were collected from 814 participants. All answers to the open-ended questions have been translated into English. This dataset is especially interesting for researchers interesting in what motivates people with and without chronic disease across countries to use mHealth tools and share their collected health data. Only a subset of variables has been analyzed so far and new research questions on motivation can potentially be answered using this dataset.

Information and communication technology-based interventions for chronic diseases consultation: Scoping review.

BACKGROUND: Medical consultations are often critical meetings between patients and health personnel to provide treatment, health-management advice, and exchange of information, especially for people living with chronic diseases. The adoption of patient-operated Information and Communication Technologies (ICTs) allows the patients to actively participate in their consultation and treatment. The consultation can be divided into three different phases: before, during, and after the meeting. The difference is identified by the activities in preparation (before), the meeting, conducted either physically or in other forms of non-face-to-face interaction (during), and the follow-up activities after the meeting (after). Consultations can be supported by various ICT-based interventions, often referred to as eHealth, mHealth, telehealth, or telemedicine. Nevertheless, the use of ICTs in healthcare settings is often accompanied by security and privacy challenges due to the sensitive nature of health information and the regulatory requirements associated with storing and processing sensitive information. OBJECTIVE: This scoping review aims to map the existing knowledge and identify gaps in research about ICT-based interventions for chronic diseases consultations. The review objective is guided by three research questions: (1) which ICTs are used by people with chronic diseases, health personnel, and others before, during, and after consultations; (2) which type of information is managed by these ICTs; and (3) how are security and privacy issues addressed? METHODS: We performed a literature search in ACM, IEEE, PubMed, Scopus, and Web of Science and included primary studies published between January 2015 and June 2020 that used ICT before, during, and/or after a consultation for chronic diseases. This review presents and discusses the findings from the included publications structured around the three research questions. RESULTS: Twenty-four studies met the inclusion criteria. Only five studies reported the use of ICTs in all three phases: before, during, and after consultations. The main ICTs identified were smartphone applications, web-based portals, cloud-based infrastructures, and electronic health record systems. Different devices like sensors and wearable devices were used in 23 studies to gather diverse information. Regarding the type of information managed by these ICTs, we identified nine categories: physiological data, treatment information, medical history, consultation media like images or videos, laboratory results, reminders, lifestyle parameters, symptoms, and patient identification. Security issues were addressed in 20 studies, while only eight of the included studies addressed privacy issues. CONCLUSIONS: This scoping review highlights the potential for a new model of consultation for patients with chronic diseases. Furthermore, it emphasizes the possibilities for consultations besides physical and remote meetings. The scoping review also revealed a narrow focus on security and privacy. Security issues were more likely to be mentioned in the included publications, although with limited details. Future research should focus more on security and privacy due to the increasing amount of sensitive information gathered and used for consultations.

Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study.

BACKGROUND: Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed. OBJECTIVE: The objective of this study was to compare the Polar Vantage watch (Polar Electro Oy) and Oura ring (generation 2; Oura Health Oy) activity trackers to research-based instruments for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration in free-living adults. METHODS: A total of 21 participants wore 2 consumer-based activity trackers (Polar watch and Oura ring), an ActiGraph accelerometer (ActiGraph LLC), and an Actiheart accelerometer and heart rate monitor (CamNtech Ltd) and completed a sleep diary for up to 7 days. We assessed Polar watch and Oura ring validity and comparability for measuring physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analyzed repeated measures correlations, Bland-Altman plots, and mean absolute percentage errors. RESULTS: The Polar watch and Oura ring values strongly correlated (P<.001) with the ActiGraph values for steps (Polar: r=0.75, 95% CI 0.54-0.92; Oura: r=0.77, 95% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar: r=0.76, 95% CI 0.62-0.88; Oura: r=0.70, 95% CI 0.49-0.82), and total energy expenditure (Polar: r=0.69, 95% CI 0.48-0.88; Oura: r=0.70, 95% CI 0.51-0.83) and strongly or very strongly correlated (P<.001) with the sleep diary-derived sleep durations (Polar: r=0.74, 95% CI 0.56-0.88; Oura: r=0.82, 95% CI 0.68-0.91). Oura ring-derived resting heart rates had a very strong correlation (P<.001) with the Actiheart-derived resting heart rates (r=0.9, 95% CI 0.85-0.96). However, the mean absolute percentage error was high for all variables except Oura ring-derived sleep duration (10%) and resting heart rate (3%), which the Oura ring underreported on average by 1 beat per minute. CONCLUSIONS: The Oura ring can potentially be used as an alternative to the Actiheart to measure resting heart rate. As for sleep duration, the Polar watch and Oura ring can potentially be used as replacements for a manual sleep diary, depending on the acceptable error. Neither the Polar watch nor the Oura ring can replace the ActiGraph when it comes to measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but they may be used as additional sources of physical activity measures in some settings. On average, the Polar Vantage watch reported higher outputs compared to those reported by the Oura ring for steps, moderate-to-vigorous physical activity, and total energy expenditure.

Dataset of consumer-based activity trackers as a tool for physical activity monitoring in epidemiological studies during the COVID-19 Pandemic.

Physical activity (PA) data were downloaded from 113 participants who owned a Garmin or Fitbit activity tracker in 2019 and 2020. Upon participant authorization, data were automatically downloaded from the Garmin and Fitbit cloud storages. The mSpider tool, a solution for automatic and continuous data extraction from activity tracker providers, were used to download participant data. Available data are daily averages by year, as well as monthly averages between 2019 and 2020, for steps, activity energy expenditure (AEE), total energy expenditure (TEE), moderate-to-vigorous physical activity (MVPA), light PA (LPA), moderate PA (MPA), vigorous PA (VPA), and sedentary time. In addition, March 2020 was divided in two, giving the daily average before and after the Norwegian COVID-19 lockdown date. Raw daily values for these variables are also included in a separate file. In addition, daily values for non-wear time are also include as raw data. In a previous study, differences between months, i.e., comparing 2019 with 2020 for months between March to December, were analysed for steps, MVPA, and AEE [1]. Further insights may be achieved by exploring other variables. This includes: (1) monthly averages for TEE, LPA, MPA, VPA, and sedentary time, (2) yearly averages (2019 and 2020) for steps, MVPA, TEE, AEE, LPA, MPA, VPA, and sedentary time (3) monthly average for steps, MVPA, TEE, AEE, LPA, MPA, VPA, and sedentary time for January, February, and March 2019, as well as March 2020. Additional analysis can also be conducted on the raw data.

Dataset of fitness trackers and smartwatches to measuring physical activity in research.

OBJECTIVES: Accelerometer-based wrist-worn fitness trackers and smartwatches (wearables) appeared on the consumer market in 2011. Many wearable devices have been released since. The objective of this data paper is to describe a dataset of 423 wearables released before July 2017. DATA DESCRIPTION: We identified wearables and extracted information from six online and offline databases. We also visited websites for all identified companies/brands to identify additional wearables, as well as obtained additional information for each identified device. Twelve attributes were collected: wearable name, company/brand name, release year, country of origin, whether the wearable was crowd funded, form factor (fitness tracker or smartwatch), and sensors supported. Support for the following sensors were mapped: accelerometer, magnetometer, gyroscope, altimeter or barometer, global-positioning-system, and optical pulse sensor (i.e., photoplethysmograph). The search was conducted between May 15th and July 1st, 2017. The included data gives an overview of most in-scope wearables released before July 2017 and allows researchers to conduct additional analysis not performed in the related article. Further insights can be achieved by complementing this list with wearable models released after July 2017.

mHealth Support to Stimulate Physical Activity in Individuals With Intellectual Disability: Protocol for a Mixed Methods Pilot Study.

BACKGROUND: Several studies have shown that individuals with intellectual disabilities (IDs) have low levels of physical activity (PA), and intervention studies on PA suggest inconsistent evidence. The use of technology as a means of motivation for PA has yet to be extensively explored and needs to be further investigated. OBJECTIVE: We aim to assess the feasibility and acceptability of procedures for an intervention arm in a future trial on mobile health (mHealth) to support PA for individuals with IDs. In addition, we aim to examine how the use of technology can influence motivation for PA among participants, their caregivers, and staff members. METHODS: A mixed methods pilot study of an intervention arm will be carried out in a planned randomized controlled trial (RCT). Ten participants with ID and their caregivers or a staff member will be included. Information will always be provided by a caregiver or a staff member, or participants with ID if possible. Assessments will be carried out at baseline, follow-up after 4 weeks, and 12 weeks, and include questionnaires on PA, social support, self-efficacy, and challenging behavior. PA will be measured with 2 different activity trackers (Fitbit and Axivity) for 1 week at all assessments. Feasibility will be assessed as recruitment and adherence rate, missing data, usability of the motivational mHealth tool, and estimates of effectiveness. Acceptability of study procedures, activity measures, and motivation for participation in PA will be additionally assessed with qualitative methods at the end of the intervention. RESULTS: Enrollment commenced in May 2021. Data collection was completed in March 2022. CONCLUSIONS: This pilot study will evaluate the feasibility and acceptability of study procedures of the intervention arm of a planned RCT to address feasibility issues, improve study procedures, and estimate effectiveness of the study measures. How the use of technology can influence motivation for PA will also be examined, which can help guide and improve future PA interventions involving the use of technology. TRIAL REGISTRATION: ClinicalTrials.gov NCT04929106; https://clinicaltrials.gov/ct2/show/NCT04929106. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37849.