Novel Trial Design: CHIEF-HF.

BACKGROUND: The expense of clinical trials mandates new strategies to efficiently generate evidence and test novel therapies. In this context, we designed a decentralized, patient-centered randomized clinical trial leveraging mobile technologies, rather than in-person site visits, to test the efficacy of 12 weeks of canagliflozin for the treatment of heart failure, regardless of ejection fraction or diabetes status, on the reduction of heart failure symptoms. METHODS: One thousand nine hundred patients will be enrolled with a medical record-confirmed diagnosis of heart failure, stratified by reduced (≤40%) or preserved (>40%) ejection fraction and randomized 1:1 to 100 mg daily of canagliflozin or matching placebo. The primary outcome will be the 12-week change in the total symptom score of the Kansas City Cardiomyopathy Questionnaire. Secondary outcomes will be daily step count and other scales of the Kansas City Cardiomyopathy Questionnaire. RESULTS: The trial is currently enrolling, even in the era of the coronavirus disease 2019 (COVID-19) pandemic. CONCLUSIONS: CHIEF-HF (Canagliflozin: Impact on Health Status, Quality of Life and Functional Status in Heart Failure) is deploying a novel model of conducting a decentralized, patient-centered, randomized clinical trial for a new indication for canagliflozin to improve the symptoms of patients with heart failure. It can model a new method for more cost-effectively testing the efficacy of treatments using mobile technologies with patient-reported outcomes as the primary clinical end point of the trial. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04252287.

Validation of Oura ring energy expenditure and steps in laboratory and free-living.

BACKGROUND: Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. METHODS: Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. RESULTS: Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits. CONCLUSION: This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study.

BACKGROUND: Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems. OBJECTIVE: The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. METHODS: We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests. RESULTS: Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020. CONCLUSIONS: mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period.

Watcher: Cloud-Based Coding Activity Tracker for Fair Evaluation of Programming Assignments.

Online learning has made it possible to attend programming classes regardless of the constraint that all students should be gathered in a classroom. However, it has also made it easier for students to cheat on assignments. Therefore, we need a system to deal with cheating on assignments. This study presents a Watcher system, an automated cloud-based software platform for impartial and convenient online programming hands-on education. The primary features of Watcher are as follows. First, Watcher offers a web-based integrated development environment (Web-IDE) that allows students to start programming immediately without the need for additional installation and configuration. Second, Watcher collects and monitors the coding activity of students automatically in real-time. As Watcher provides the history of the coding activity to instructors in log files, the instructors can investigate suspicious coding activities such as plagiarism, even for a short source code. Third, Watcher provides facilities to remotely manage and evaluate students' hands-on programming assignments. We evaluated Watcher in a Unix system programming class for 96 students. The results showed that Watcher improves the quality of the coding experience for students through Web-IDE, and it offers instructors valuable data that can be used to analyze the various coding activities of individual students.

Accuracy and Precision of Consumer-Grade Wearable Activity Monitors for Assessing Time Spent in Sedentary Behavior in Children and Adolescents: Systematic Review.

BACKGROUND: A large number of wearable activity monitor models are released and used each year by consumers and researchers. As more studies are being carried out on children and adolescents in terms of sedentary behavior (SB) assessment, knowledge about accurate and precise monitoring devices becomes increasingly important. OBJECTIVE: The main aim of this systematic review was to investigate and communicate findings on the accuracy and precision of consumer-grade physical activity monitors in assessing the time spent in SB in children and adolescents. METHODS: Searches of PubMed (MEDLINE), Scopus, SPORTDiscus (full text), ProQuest, Open Access Theses and Dissertations, DART Europe E-theses Portal, and Networked Digital Library of Theses and Dissertations electronic databases were performed. All relevant studies that compared different types of consumer-grade monitors using a comparison method in the assessment of SB, published in European languages from 2015 onward were considered for inclusion. The risk of bias was estimated using Consensus-Based Standards for the Selection of Health Status Measurement Instruments. For enabling comparisons of accuracy measures within the studied outcome domain, measurement accuracy interpretation was based on group mean or percentage error values and 90% CI. Acceptable limits were predefined as -10% to +10% error in controlled and free-living settings. For determining the number of studies with group error percentages that fall within or outside one of the sides from previously defined acceptable limits, two 1-sided tests of equivalence were carried out, and the direction of measurement error was examined. RESULTS: A total of 8 studies complied with the predefined inclusion criteria, and 3 studies provided acceptable data for quantitative analyses. In terms of the presented accuracy comparisons, 14 were subsequently identified, with 6 of these comparisons being acceptable in terms of quantitative analysis. The results of the Cochran Q test indicated that the included studies did not share a common effect size (Q5=82.86; P<.001). I2, which represents the percentage of total variation across studies due to heterogeneity, amounted to 94%. The summary effect size based on the random effects model was not statistically significant (effect size=14.36, SE 12.04, 90% CI -5.45 to 34.17; P=.23). According to the equivalence test results, consumer-grade physical activity monitors did not generate equivalent estimates of SB in relation to the comparison methods. Majority of the studies (3/7, 43%) that reported the mean absolute percentage errors have reported values of <30%. CONCLUSIONS: This is the first study that has attempted to synthesize available evidence on the accuracy and precision of consumer-grade physical activity monitors in measuring SB in children and adolescents. We found very few studies on the accuracy and almost no evidence on the precision of wearable activity monitors. The presented results highlight the large heterogeneity in this area of research. TRIAL REGISTRATION: PROSPERO CRD42021251922; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=251922.

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration.

BACKGROUND: The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity. PURPOSE: This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions. METHODS: Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020. RESULTS: Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration. CONCLUSIONS: Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

Physical inactivity is recognized as one of the major risk factors for cardiovascular disease, diabetes, and many cancers. Most American adults fail to achieve recommended levels of physical activity. Interventions to promote physical activity in young adults are needed to reduce long-term chronic disease risk. The COVID-19 pandemic declaration abruptly changed many individuals' environments and lifestyles. These contextual changes adversely impacted physical activity levels but little is known about how these changes specifically impacted the dynamics of people's physical activity or responses to micro-interventions for promoting physical activity. Using data collected from Fitbit smartwatches before and after the pandemic declaration, we applied tools from control systems engineering to develop person-specific dynamic models of physical activity responses to messaging interventions, and investigated how physical activity dynamics changed from before to after the pandemic declaration. Step counts decreased significantly on weekdays. The average speed of participants' responses to intervention messages did not change significantly, but intervention response dynamics had limited consistency from before to after the pandemic declaration. In short, participants changed how they responded to interventions after the pandemic declaration but the magnitude and patterns of change varied across participants. Person-specific, adaptive interventions can be useful for promoting physical activity when behavioral systems are stimulated to reorganize by external factors.

An Evaluation of a Commercialized mHealth Intervention to Promote Physical Activity in the Workplace.

Background: UPnGO with ParticipACTION (UPnGO) was a commercialized 12-month workplace physical activity intervention, aimed at encouraging employees to sit less and move more at work. Its design took advantage of the ubiquitous nature of mobile fitness trackers and aimed to be implemented in any office-based workplace in Canada. The program was available at cost from June 2017 to April 2020. The objectives of this study are to evaluate the program and identify key lessons from the commercialization of UPnGO. Methods: Using a quasi-experimental design over 3 time points: baseline, 6 months, 12 months, five evaluation indicators were measured as guided by the RE-AIM framework. Reach was defined as the number and percentage of employees who registered for UPnGO and the number and percentage of sedentary participants registered. Effectiveness was assessed through average daily step count. Adoption was determined by workplace champion and senior leadership responses to the off-platform survey. Implementation was assessed as the percentage of participants who engaged with specific program elements at the 3-evaluation time points. Maintenance was assessed by the number of companies who renewed their contracts for UPnGO. Results: Reach across 17 organizations, 1980 employees participated in UPnGO, with 27% of participants identified as sedentary at baseline. Effectiveness Daily step count declined from 7,116 ± 3,558 steps at baseline to 6,969 ± 6,702 (p = <0.001) at 12 months. Adoption Workplace champion and senior leadership engagement declined from 189 to 21 and 106 to 5 from baseline to 12 months, respectively. Maintenance Two companies renewed their contracts beyond the first year. Conclusions: The commercialization of UPnGO was an ambitious initiative that met with limited success; however, some key lessons can be generated from the attempt. The workplace remains an important environment for PA interventions but effective mHealth PA programs may be difficult to implement and sustain long-term.

Exploring Families' Acceptance of Wearable Activity Trackers: A Mixed-Methods Study.

BACKGROUND: The family environment plays a crucial role in child physical activity (PA). Wearable activity trackers (wearables) show potential for increasing children's PA; however, few studies have explored families' acceptance of wearables. This study investigated the acceptability of using wearables in a family setting, aligning experiences with components of the Technology Acceptance Model and Theoretical Domains Framework. METHODS: Twenty-four families, with children aged 5-9 years, took part in a 5-week study, where all members were provided with a Fitbit Alta HR for 4 weeks. Acceptability was measured using weekly surveys and pre-post-questionnaires. Nineteen families participated in a focus group. Quantitative and qualitative data were integrated using the Pillar Integration Process technique. RESULTS: Pillars reflected (1) external variables impacting wearable use and PA and (2) wearable use, (3) ease of use, (4) usefulness for increasing PA and other health outcomes, (5) attitudes, and (6) intention to use a wearable, including future intervention suggestions. CONCLUSIONS: Families found the Fitbit easy to use and acceptable, but use varied, and perceived impact on PA were mixed, with external variables contributing towards this. This study provides insights into how wearables may be integrated into family-based PA interventions and highlights barriers and facilitators of family wearable use.

Comparison of Different Physical Activity Measures in a Cardiac Rehabilitation Program: A Prospective Study.

Concordant assessments of physical activity (PA) and related measures in cardiac rehabilitation (CR) is essential for exercise prescription. This study compared exercise measurement from an in-person walk test; wearable activity tracker; and self-report at CR entry, completion (8-weeks) and follow-up (16-weeks). Forty patients beginning CR completed the Six-Minute Walk Test (6MWT), Physical Activity Scale for the Elderly (PASE), and wore Fitbit-Flex for four consecutive days including two weekend days. The sample mean age was 66 years; 67% were male. Increased exercise capacity at CR completion and follow-up was detected by a 6MWT change in mean distance (39 m and 42 m; p = 0.01, respectively). Increased PA participation at CR completion was detected by Fitbit-Flex mean change in step counts (1794; p = 0.01). Relative changes for Fitbit-Flex step counts and a 6MWT were consistent with previous research, demonstrating Fitbit-Flex's potential as an outcome measure. With four days of data, Fitbit-Flex had acceptable ICC values in measuring step counts and MVPA minutes. Fitbit-Flex steps and 6MWT meters are more responsive to changes in PA patterns following exposure to a cardiac rehabilitation program than Fitbit-Flex or PASE-estimated moderate-vigorous PA (MVPA) minutes. Fitbit-Flex step counts provide a useful additional measure for assessing PA outside of the CR setting and accounts for day-to-day variations. Two weekend days and two weekdays are needed for Fitbit-Flex to estimate PA levels more precisely.

The Impact of Wearable Technologies in Health Research: Scoping Review.

BACKGROUND: Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research. OBJECTIVE: In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research. METHODS: We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing. RESULTS: We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations-wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%). CONCLUSIONS: Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Longitudinal changes in Super League match locomotor and event characteristics: A league-wide investigation over three seasons in rugby league.

The 2019 and 2020 Super League (SL) seasons included several competition rule changes. This study aimed to quantify the difference between the 2018, 2019 and 2020 SL seasons for duration, locomotor and event characteristics of matches. Microtechnology and match event data were analysed from 11 SL teams, comprising 124 players, from 416 competitive matches across a three-year data collection period. Due to an enforced suspension of league competition as a consequence of COVID-19 restrictions, and subsequent rule changes upon return to play, season 2020 was divided into season 2020a (i.e. Pre-COVID suspension) and season 2020b (i.e. Post-COVID suspension). Duration, locomotor variables, and match events were analysed per whole-match and ball-in-play (BIP) periods with differences between seasons determined using mixed-effects models. There were significant (ρ ≤ 0.05) reductions in whole-match and BIP durations for adjustables and backs in 2019 when compared to 2018; albeit the magnitude of reduction was less during BIP analyses. Despite reduced duration, adjustables reported an increased average speed suggesting reduced recovery time between bouts. Both forwards and adjustables also experienced an increase in missed tackles between 2018 and 2019 seasons. When comparing 2019 to 2020a, adjustables and backs increased their average speed and distance whilst all positional groups increased average acceleration both for whole-match and BIP analyses. When comparing 2020a to 2020b, all positional groups experienced reduced average speed and average acceleration for both whole-match and BIP analyses. Forwards experienced an increased number of tackles and carries, adjustables experienced an increased number of carries, and backs experienced an increased number of missed tackles when comparing these variables between season 2020a and 2020b. Rule changes have a greater effect on whole-match duration and locomotor characteristics than those reported during BIP periods which suggests the implemented rule changes have removed stagnant time from matches. Amendments to tackle related rules within matches (e.g., introduction of the 'six-again' rule) increases the number of collision related events such as carries and tackles.

Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling.

BACKGROUND: Depression is a prevalent mental disorder that is undiagnosed and untreated in half of all cases. Wearable activity trackers collect fine-grained sensor data characterizing the behavior and physiology of users (ie, digital biomarkers), which could be used for timely, unobtrusive, and scalable depression screening. OBJECTIVE: The aim of this study was to examine the predictive ability of digital biomarkers, based on sensor data from consumer-grade wearables, to detect risk of depression in a working population. METHODS: This was a cross-sectional study of 290 healthy working adults. Participants wore Fitbit Charge 2 devices for 14 consecutive days and completed a health survey, including screening for depressive symptoms using the 9-item Patient Health Questionnaire (PHQ-9), at baseline and 2 weeks later. We extracted a range of known and novel digital biomarkers characterizing physical activity, sleep patterns, and circadian rhythms from wearables using steps, heart rate, energy expenditure, and sleep data. Associations between severity of depressive symptoms and digital biomarkers were examined with Spearman correlation and multiple regression analyses adjusted for potential confounders, including sociodemographic characteristics, alcohol consumption, smoking, self-rated health, subjective sleep characteristics, and loneliness. Supervised machine learning with statistically selected digital biomarkers was used to predict risk of depression (ie, symptom severity and screening status). We used varying cutoff scores from an acceptable PHQ-9 score range to define the depression group and different subsamples for classification, while the set of statistically selected digital biomarkers remained the same. For the performance evaluation, we used k-fold cross-validation and obtained accuracy measures from the holdout folds. RESULTS: A total of 267 participants were included in the analysis. The mean age of the participants was 33 (SD 8.6, range 21-64) years. Out of 267 participants, there was a mild female bias displayed (n=170, 63.7%). The majority of the participants were Chinese (n=211, 79.0%), single (n=163, 61.0%), and had a university degree (n=238, 89.1%). We found that a greater severity of depressive symptoms was robustly associated with greater variation of nighttime heart rate between 2 AM and 4 AM and between 4 AM and 6 AM; it was also associated with lower regularity of weekday circadian rhythms based on steps and estimated with nonparametric measures of interdaily stability and autocorrelation as well as fewer steps-based daily peaks. Despite several reliable associations, our evidence showed limited ability of digital biomarkers to detect depression in the whole sample of working adults. However, in balanced and contrasted subsamples comprised of depressed and healthy participants with no risk of depression (ie, no or minimal depressive symptoms), the model achieved an accuracy of 80%, a sensitivity of 82%, and a specificity of 78% in detecting subjects at high risk of depression. CONCLUSIONS: Digital biomarkers that have been discovered and are based on behavioral and physiological data from consumer wearables could detect increased risk of depression and have the potential to assist in depression screening, yet current evidence shows limited predictive ability. Machine learning models combining these digital biomarkers could discriminate between individuals with a high risk of depression and individuals with no risk.

Effects of activity tracker-based counselling and live-web exercise on breast cancer survivors' sleep and waking time during Italy's COVID-19 lockdown.

The aim of the study was to compare the effects of weekly personal feedback, based on objectively measured physical activity, on daily sleep in breast cancer survivors (BCS) with those of an intervention that also included online supervised physical exercise sessions (OSPES). BCS benefiting from both personal feedback and OSPES (n = 24), from pre-lockdown (T0) to the first month (T1) of the national lockdown, experienced an increase in both total (p ≤ 0.001) and restorative (p ≤ 0.001) sleep time, inverting their trend from the first month of lockdown to its end (total sleeping time T1 vs. T2 0.01 ≤ p < .001, T1 vs. T3 p ≤ 0.001; restorative sleeping time T1 vs. T2 0.05 ≤ p < .01, T1 vs. T3 p ≤ 0.001). Supportive technology, together with the reception of weekly tailored advice and OSPES seems to improve both quality and quantity of sleep.

Estimating changes in physical behavior during lockdowns using accelerometry-based simulations in a large UK cohort.

To contain the recent COVID-19 outbreak, restrictions have been imposed, which has limited outdoor activity. These physical behavior changes can have serious health implications, but there is little objective information quantifying these changes. This study aimed to estimate the change in physical behavior levels during full lockdown conditions using objective data collected from a thigh-worn activity monitor. Data used were from 6492 individuals in the 1970 British Cohort Study, collected between 2016 and 2018. Using walking bout characteristics, days were classified as either "indoor only" (n = 861), "indoor and exercise" (n = 167), and "outdoor active" (n = 31 934). When compared to "outdoor active" days, "indoor only" days had 6590 fewer steps per day (2320 vs 8876, p < 0.001), a longer sedentary time (1.5 h, p < 0.001), longer lying time (1.4 h, p < 0.001) and shorter standing (1.9 h, p < 0.001) and stepping (1.3 h, p < 0.001) times. The "indoor and exercise" days had a smaller number of steps compared to "outdoor active" (7932 vs 8876, p < 0.05). There is a strong relationship between reduced daily stepping, and increased sedentary time, with a range of poor health outcomes. This has important implications for public health policy and messaging during pandemics.

Digital Health Engagement in the US Population: Insights From the 2018 Health Information National Trends Survey.

Objectives. To examine prevalence and predictors of digital health engagement among the US population. Methods. We analyzed nationally representative cross-sectional data on 7 digital health engagement behaviors, as well as demographic and socioeconomic predictors, from the Health Information National Trends Survey (HINTS 5, cycle 2, collected in 2018; n = 2698-3504). We fitted multivariable logistic regression models using weighted survey responses to generate population estimates. Results. Digitally seeking health information (70.14%) was relatively common, whereas using health apps (39.53%) and using a digital device to track health metrics (35.37%) or health goal progress (38.99%) were less common. Digitally communicating with one's health care providers (35.58%) was moderate, whereas sharing health data with providers (17.20%) and sharing health information on social media (14.02%) were uncommon. Being female, younger than 65 years, a college graduate, and a smart device owner positively predicted several digital health engagement behaviors (odds ratio range = 0.09-4.21; P value range < .001-.03). Conclusions. Many public health goals depend on a digitally engaged populace. These data highlight potential barriers to 7 key digital engagement behaviors that could be targeted for intervention.

Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic.

INTRODUCTION: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19. METHODS: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organization announced COVID-19 as a global pandemic. Telephone follow-up was commenced, in order to protect patients from unnecessary exposure to hospital staff and patients. Equipment was returned or collected by a 'no-contact' method. The TCC-COVID app and model of care had similar functionality to the original TCC-Cardiac app. Participants were enrolled exclusively by remote methods. Oxygen saturation and pulse rate were measured by a pulse oximeter, and symptomatology measured by questionnaire. Measurement results were manually entered into the app and transmitted to an online server for medical staff to review. RESULTS: A total of 164 patients were involved in the TCC-Cardiac trial, with 102 patients involved after the onset of the pandemic. There were no hospitalisations due to COVID-19 in this cohort. The study was successfully completed, with only three participants lost to follow-up. During the pandemic, 5 of 49 (10%) of patients in the intervention arm were readmitted compared to 12 of 53 (23%) in the control arm. Also, in this period, 28 of 29 (97%) of all clinically significant alerts received by the monitoring team were managed successfully in the outpatient setting, avoiding hospitalisation. Patients found the user experience largely positive, with the average rating for the app being 4.56 out of 5. 26 patients have currently been enrolled for TCC-COVID. Recruitment is ongoing. All patients have been safely and effectively monitored, with no major adverse clinical events or technical malfunctions. Patient satisfaction has been high. CONCLUSION: The TCC-Cardiac RCT was successfully completed despite the challenges posed by COVID-19. Use of the app had an added benefit during the pandemic as participants could be monitored safely from home. The model of care inspired the development of an app with similar functionality designed for use with patients diagnosed with COVID-19.

Adolescents' sedentary time, affect, and contextual factors: An ecological momentary assessment study.

BACKGROUND: Few adolescents achieve sufficient levels of physical activity, and many are spending most of their time in sedentary behavior. Affective response following sedentary time may influence motivation to remain sedentary. Ecological Momentary Assessment (EMA) is a real-time data capture methodology that can be used to identify factors influencing sedentary time, such as the context of the home setting, and resulting affective state within a free-living setting. The purpose of this study was to evaluate the relationship between context at home and adolescent sedentary time, and the relationship of sedentary time and subsequent affect. METHODS: Adolescents (n = 284; 10-16 y) participated in an EMA study that used random, interval-based sampling methods. Adolescents each received 22 unannounced surveys over 7-days through a smartphone application. One survey was randomly sent within each 2-h time-period. These time-periods occurred between 4:00 pm-8:00 pm on weekdays and 8:00 am-8:00 pm on the weekend. This 15-question survey included a series of questions on context (indoors/outdoors, alone/not alone) and positive affect. Adolescents concurrently wore an accelerometer at the hip, and the 30-min bout of accelerometry data prior to each survey was used in analyses. Mixed-effect location scale models were used to examine the association between context at home and sedentary time (stage 1) and the adjusted sedentary time and positive affect (stage 2), with each model adjusted for covariates. RESULTS: Adolescents were 12.6 ± 1.9 y of age on average, about half were White (58%), and engaged in high levels of sedentary behavior during the 30 min prior to the survey (21.4 ± 6.8 min). Most surveys occurred when adolescents were with others (59%) and indoors (88%). In Stage 1, both being alone and being indoors at home were positively associated with sedentary time (p <  0.001 for both). In Stage 2, adjusted sedentary time was not related to positive affect. Age was negatively related to positive affect (p <  0.001). CONCLUSIONS: Both contextual factors, being alone and indoors at home, were related to additional time spent sedentary compared to being with someone or outdoors. After adjustment, sedentary time was not related to subsequent positive affect, indicating other factors may be related to adolescent's positive affect in home settings.

Assessing acceptability and patient experience of a behavioral lifestyle intervention using fitbit technology in older adults to manage type 2 diabetes amid COVID-19 pandemic: A focus group study.

Type 2 diabetes (T2D) contributes to reduced quality of life in older adults, especially in those with comorbidities such as being overweight or obese. Personal fitness technology (Fitbit ®) has the potential to improve the management of T2D. Using a semi-structured interview guide, focus groups were conducted to explore participants' acceptability and experiences following a behavioral lifestyle intervention that integrated Fitbit in overweight/obese older adults with T2D amid the COVID-19 pandemic which began during the time of this study. Focus group transcripts were transcribed and analyzed using thematic analysis. Eighteen (18) of the 20 participants completed the program and focus group interviews. Overall, we observed high acceptability of the program, and participants reported favorable experiences such as increased knowledge of health behaviors, improved diabetes management, and improved quality of life following the behavioral lifestyle intervention, even under stressful life circumstances from COVID-19.

Effect of Statewide Lockdown in Response to COVID-19 Pandemic on Physical Activity Levels of Hemodialysis Patients.

BACKGROUND/OBJECTIVES: On March 22, 2020, a statewide stay-at-home order for nonessential tasks was implemented in New York State. We aimed to determine the impact of the lockdown on physical activity levels (PAL) in hemodialysis patients. METHODS: Starting in May 2018, we are conducting an observational study with a 1-year follow-up on PAL in patients from 4 hemodialysis clinics in New York City. Patients active in the study as of March 22, 2020, were included. PAL was defined by steps taken per day measured by a wrist-based monitoring device (Fitbit Charge 2). Average steps/day were calculated for January 1 to February 13, 2020, and then weekly from February 14 to June 30. RESULTS: 42 patients were included. Their mean age was 55 years, 79% were males, and 69% were African Americans. Between January 1 and February 13, 2020, patients took on average 5,963 (95% CI 4,909-7,017) steps/day. In the week prior to the mandated lockdown, when a national emergency was declared, and in the week of the shutdown, the average number of daily steps had decreased by 868 steps/day (95% CI 213-1,722) and 1,222 steps/day (95% CI 668-2300), respectively. Six patients were diagnosed with COVID-19 during the study period. Five of them exhibited significantly higher PAL in the 2 weeks prior to showing COVID-19 symptoms compared to COVID-19 negative patients. CONCLUSION: Lockdown measures were associated with a significant decrease in PAL in hemodialysis patients. Patients who contracted COVID-19 had higher PAL during the incubation period. Methods to increase PAL while allowing for social distancing should be explored and implemented.