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Background: Sleep disturbance is prevalent among cancer patients. The quantification of this sleep disturbance is missing, especially in palliative care settings. Aim: The aim of this study was to study the sleep patterns of the patients undergoing palliative chemotherapy and radiotherapy for head and neck cancer (HNC) using a Fitbit Charge 4 sleep-tracking device. Design: Prospective observational study. Setting: A total of 110 HNC patients undergoing palliative chemotherapy and radiotherapy at a tertiary care teaching hospital in Central India. Results: Forty-four percent of patients had a poor sleep score (less than 60). Average sleep duration was 218.66 ± 139.05 min; non-rapid eye movement (NREM) sleep duration 197.7 ± 115.91 (light NREM 171.36 ± 104 and deep NREM 23.36 ± 16.73); REM sleep duration was 30.44 ± 34.14 min. The Pittsburgh Sleep Quality Index was 10.23 ± 3.45, which indicated sleep deprivation over the past 1 month. Moderate levels of anxiety, depression, confusion, and distress existed in the cohort. Statistically significant but weak correlation existed between sleep score, anxiety, and depression. Strong correlation existed between distress score and sleep score. Confusion score did not have a significant correlation with sleep score. Conclusion: HNC patients in palliative care settings were chronically sleep deprived. Sleep architecture was also disturbed. Moderate levels of anxiety, depression, confusion, and distress existed in the studied cohort; these psychosocial disturbances had a weak correlation with the sleep score and are likely to be multifactorial. Trial registration: Institutional Ethics Committee number: IHEC-LOP/2020/IM0349. The study has been registered with clinical trial registry of India with registration number CTRI/2021/03/032400 (http://www.ctri.in). [ABSTRACT FROM AUTHOR]

Introduction Rotating shiftwork schedules are known to disrupt sleep in a manner that can negatively impact safety. Consumer sleep technologies (CSTs) may be a useful tool for sleep tracking, but the standard feedback provided by CSTs may not be salient to shift-working populations. SleepTank is an app that uses the total sleep time data scored by a CST to compute a percentage that equates hours of sleep to the fuel in a car and warns the user to sleep when the "tank" is low. Royal Australian Navy aircraft maintenance workers operating on a novel rotational shift schedule were given Fitbit Versa 2s to assess sleep timing, duration, and efficiency across a 10-week period. Half of the participants had access to just the Fitbit app while the other half had access to Fitbit and the SleepTank app. The goal of this study was to evaluate differences in sleep behavior between shifts using an off-the-shelf CST and to investigate the potential of the SleepTank app to increase sleep duration during the 10-week rotational shift work schedule. Materials and Methods Royal Australian Navy volunteers agreed to wear a Fitbit Versa 2 with the SleepTank app (SleepTank condition), or without the SleepTank app (Controls), for up to 10 weeks from May to July 2023 during the trial of a novel shift rotation schedule. Participants from across 6 units worked a combination of early (6:00 AM to 2:00 PM), day (7:30 AM to 4:30 PM), late (4:00 PM to 12:00 AM), and night shifts (12:00 AM to 6:00 AM) or stable day shifts (6:00 AM to 4:00 PM). Differences in sleep behavior (time in bed, total sleep time, bedtime, wake time, sleep efficiency [SE]) between conditions and shift types were tested using Analysis of Variance. This study was approved by the Australian Departments of Defence and Veterans' Affairs Human Research Ethics Committee. Results Thirty-four participants completed the full study (n  = 17 Controls; n  = 17 SleepTank). There was a significant effect of shift type on 24-hour time in bed (TIB24; F(4,9) = 8.15, P  < .001, η2 = 0.15) and total sleep time (TST24; F(4,9) = 8.54, P  < .001, η2 = 0.18); both were shorter in early shifts and night shifts compared to other shift types. TIB24 and TST24 were not significantly different between conditions, but there was a trend for greater SE in the SleepTank condition relative to Controls (F(1,9) = 2.99, P  = .08, η2 = 0.11). Conclusions Sleep data collected by Fitbit Versa 2s indicated shorter sleep duration (TIB24, TST24) for Royal Australian Navy workers during early and late shifts relative to stable day shifts. Access to the SleepTank app did not greatly influence measures of sleep duration but may be protective against fatigue by affecting SE. Further research is needed to evaluate the utility of the SleepTank app as a means of improving sleep hygiene in real-world, shift-working environments. [ABSTRACT FROM AUTHOR]

This study aims to examine the association between wearing wearable devices and physical activity levels among people living with diabetes. 1298 wearable device users and nonusers living with diabetes from eight states of the 2017 Behavioral Risk Factors Surveillance System were included in the analysis. Unadjusted and adjusted linear regression was performed to determine the association between self-reported physical activity per week (min) and wearable device usage (users and nonusers) among people living with diabetes using survey analysis. 84.97 % (95 % CI [80.39, 88.89]) of participants were nonusers of wearable devices, while 15.03 % (95 % CI [11.11, 19.61]) were users. Across the sample, the average weekly physical activity was 427.39 mins (95 % Cl [356.43, 498.35]). Nonusers had a higher physical activity per week with 433.83 mins (95 % CI [353.59, 514.07]), while users only had 392.59 mins (95 % CI [253.48, 531.69]) of physical activity per week. However, the differences between the two groups were non-statistically significant (p=. 61). In both adjusted and unadjusted linear regressions between physical activity per week and wearable device usage, statistically significant associations were not found (unadjusted: β=-41.24, p=.62; adjusted: β=-56.41, p =.59). Further research is needed to determine the effectiveness of wearable devices in promoting physical activity among people with diabetes. Additionally, there is a need to determine how people with diabetes use wearable devices that could promote physical activity levels. • People with diabetes are currently using wearable devices. • Wearable device users with diabetes do not necessarily have higher physical activity levels than non-wearable device users with diabetes. • Using a large free-living sample in the United States, the average weekly physical activity level of people with diabetes is more than 300 minutes per week. [ABSTRACT FROM AUTHOR]

Objective: To determine whether daily postoperative step goals and feedback through a fitness tracker (FT) reduce the rate of postoperative complications after surgery. Background: Early and enhanced postoperative mobilization has been advocated to reduce postoperative complications, but it is unknown whether FT alone can reduce morbidity. Methods: EXPELLIARMUS was performed at 11 University Hospitals across Germany by the student-led clinical trial network SIGMA. Patients undergoing major abdominal surgery were enrolled, equipped with an FT, and randomly assigned to the experimental (visible screen) or control intervention (blackened screen). The experimental group received daily step goals and feedback through the FT. The primary end point was postoperative morbidity within 30 days using the Comprehensive Complication Index (CCI). All trial visits were performed by medical students in the hospital with the opportunity to consult a surgeon-facilitator who also obtained informed consent. After discharge, medical students performed the 30-day postoperative visit through telephone and electronic questionnaires. Results: A total of 347 patients were enrolled. Baseline characteristics were comparable between the 2 groups. The mean age of patients was 58 years, and 71% underwent surgery for malignant disease, with the most frequent indications being pancreatic, colorectal, and hepatobiliary malignancies. Roughly one-third of patients underwent laparoscopic surgery. No imputation for the primary end point was necessary as data completeness was 100%. There was no significant difference in the CCI between the 2 groups in the intention-to-treat analysis (mean±SD CCI experimental group: 23±24 vs. control: 22±22; 95% CI: -6.1, 3.7; P=0.628). All secondary outcomes, including quality of recovery, 6-minute walking test, length of hospital stay, and step count until postoperative day 7 were comparable between the 2 groups. Conclusions: Daily step goals combined with FT-based feedback had no effect on postoperative morbidity. The EXPELLIARMUS shows that medical students can successfully conduct randomized controlled trials in surgery. [ABSTRACT FROM AUTHOR]

BackgroundSociodemographic disparities in adolescent physical activity have been documented but mostly rely on self-reported data. Our objective was to examine differences in device-based step metrics, including daily step count (steps d-1), by sociodemographic factors among a diverse sample of 10-to-14-year-old adolescents in the US.MethodsWe analyzed prospective cohort data from Year 2 (2018-2020) of the Adolescent Brain Cognitive Development (ABCD) Study (N = 6460). Mixed-effects models were conducted to estimate associations of sociodemographic factors (sex, sexual orientation, race/ethnicity, household income, parental education, and parental marital status) with repeated measures of steps d-1 over the course of 21 days.ResultsParticipants (49.6% female, 39.0% racial/ethnic minority) accumulated an average of 9095.8 steps d-1. In mixed-effects models, 1543.6 more steps d-1 were recorded for male versus female sex, Black versus White race (328.8 more steps d-1), heterosexual versus sexual minority sexual orientation (676.4 more steps d-1), >$200,000 versus $100,000 versus

The fitness app Strava incorporates the utility of a fitness tracker with the connection of social media. Since 2009, Strava has attracted over 100 million users, yet little research has assessed the implications of its use. The purpose of this study was to explore the perceived psychosocial implications of Strava use among collegiate club runners. Semi-structured interviews were conducted with 18 runners from collegiate running clubs across the United States who were Strava users. Results indicated that runners perceived the psychosocial implications of Strava use to fall into three main themes: 1) self-presentation, 2) social pressure, and 3) motivation. The findings of this study support the utility of Strava to connect collegiate club runners and support their motivation to run while also suggesting some potential concerns related to social pressure and self-presentation that could influence mental or physical health. [ABSTRACT FROM AUTHOR]

Abstract Background The Apple Watch (AW) was the first smartwatch to provide wheelchair user (WCU) specific information on energy expenditure (EE), but was found to be inaccurate (i.e., it underestimated) and imprecise (i.e., the underestimation was variable). Insight is therefore needed into where these inaccuracies/imprecisions originate. Accordingly, the aim of this study was to investigate how much of the variation in AW EE is explained by heart rate (HR), in addition to other factors such as body mass and height, sex, age, physical activity level and disability. Methods Forty participants (20 WCU, 20 non-disabled) performed three 4-min treadmill wheelchair propulsion stages at different speed-incline combinations, on three separate days, while wearing an AW series 4 (setting: “outdoor push walking pace”). Linear mixed model analyses investigated how much of the variation in AW EE (kcal·min−1) is explained by the fixed effects AW HR (beats·min−1), body mass and height, sex, age, physical activity level and disability. Participant-ID was included as random-intercept effect. The same mixed model analyses were conducted for criterion EE and HR. Marginal R2 (R2m; fixed effects only) and conditional R2 (R2c; fixed and random effects) values were computed. An R2m close to zero indicates that the fixed effects alone do not explain much variation. Results Although criterion HR explained a significant amount of variation in criterion EE (R2m: 0.44, R2c: 0.92, p

Background: The Apple Watch (AW) was the first smartwatch to provide wheelchair user (WCU) specific information on energy expenditure (EE), but was found to be inaccurate (i.e., it underestimated) and imprecise (i.e., the underestimation was variable). Insight is therefore needed into where these inaccuracies/imprecisions originate. Accordingly, the aim of this study was to investigate how much of the variation in AW EE is explained by heart rate (HR), in addition to other factors such as body mass and height, sex, age, physical activity level and disability. Methods: Forty participants (20 WCU, 20 non-disabled) performed three 4-min treadmill wheelchair propulsion stages at different speed-incline combinations, on three separate days, while wearing an AW series 4 (setting: "outdoor push walking pace"). Linear mixed model analyses investigated how much of the variation in AW EE (kcal·min−1) is explained by the fixed effects AW HR (beats·min−1), body mass and height, sex, age, physical activity level and disability. Participant-ID was included as random-intercept effect. The same mixed model analyses were conducted for criterion EE and HR. Marginal R2 (R2m; fixed effects only) and conditional R2 (R2c; fixed and random effects) values were computed. An R2m close to zero indicates that the fixed effects alone do not explain much variation. Results: Although criterion HR explained a significant amount of variation in criterion EE (R2m: 0.44, R2c: 0.92, p < 0.001), AW HR explained little variation in AW EE (R2m: 0.06, R2c: 0.86, p < 0.001). In contrast, body mass and sex explained a significant amount of variation in AW EE (R2m: 0.74, R2c: 0.79, p < 0.001). No further improvements in fit were achieved by adding body height, age, physical activity level or disability to the AW EE model (R2m: 0.75, R2c: 0.79, p = 0.659). Conclusion: Our results remain inconclusive on whether AW heart rate is used as factor to adjust for exercise intensity in the black box AW EE estimation algorithms. In contrast, body mass explained much of the variation in AW EE, indicating that the AW EE estimation algorithm is very reliant on this factor. Future investigations should explore better individualization of EE estimation algorithms. [ABSTRACT FROM AUTHOR]

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities. [ABSTRACT FROM AUTHOR]

RATIONALE & OBJECTIVE: People with end-stage kidney disease (ESKD) have very low physical activity, and the degree of inactivity is strongly associated with morbidity and mortality. We assessed the feasibility and effectiveness of a 12-week intervention coupling a wearable activity tracker (FitBit) and structured feedback coaching versus wearable activity tracker alone on changes in physical activity in hemodialysis patients. STUDY DESIGN: Randomized controlled trial. SETTING & PARTICIPANTS: 55 participants with ESKD receiving hemodialysis who were able to walk with or without assistive devices recruited from a single academic hemodialysis unit between January 2019 and April 2020. INTERVENTIONS: All participants wore a Fitbit Charge 2 tracker for a minimum of 12 weeks. Participants were randomly assigned 1:1 to a wearable activity tracker plus a structured feedback intervention versus the wearable activity tracker alone. The structured feedback group was counseled weekly on steps achieved after randomization. OUTCOME: The outcome was step count, and the main parameter of interest was the absolute change in daily step count, averaged per week, from baseline to completion of 12 weeks intervention. In the intention-to-treat analysis, mixed-effect linear regression analysis was used to evaluate change in daily step count from baseline to 12-weeks in both arms. RESULTS: Out of 55 participants, 46 participants completed the 12-week intervention (23 per arm). The mean age was 62 (± 14 SD) years; 44% were Black, and 36% were Hispanic. At baseline, step count (structured feedback intervention: 3,704 [1,594] vs wearable activity tracker alone: 3,808 [1,890]) and other participant characteristics were balanced between the arms. We observed a larger change in daily step count in the structured feedback arm at 12 weeks relative to use of the wearable activity tracker alone arm (Δ 920 [±580 SD] versus Δ 281 [±186 SD] steps; between-group difference Δ 639 [±538 SD] steps; P

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.

This paper examines how older adults interact with fitness trackers and how that interaction influences their physical activity. We carried out qualitative interviews with 22 individuals between the ages of 55 and 72 who had used fitness trackers as part of a six-week field experiment investigating the effects of feedback from fitness trackers and the social influence of their spouses. From their comments, we derived an explorative process model explaining the mechanisms and the four stages of effects arising from personalised feedback, namely, cognitive, affective, conative, and intuitive. These effects were grouped into internal and external dimensions. Three types of goal-related decisions determined whether interviewees moved from the internal responses of cognition and emotion to the external response of behaviour change. The findings from this study elucidate how real-time personalised feedback can motivate physical activity among older adults and highlight the goal-related factors that influence this effect. [ABSTRACT FROM AUTHOR]

PurposeThere are over 1.3 million colorectal cancer (CRC) survivors in the USA, many of whom report lower health-related quality of life (HRQoL) years after treatment. This study aimed to explore the effect of digital health tools on HRQoL in CRC survivors.MethodsWe conducted a two-arm, randomized controlled trial of 42 subjects who had completed treatment for CRC. Participants in the intervention arm received a Fitbit Flex™ and daily text messages for 12 weeks. HRQoL was assessed as a secondary endpoint in both arms at enrollment and 12 weeks using the Medical Outcomes Study Short Form Survey (SF-36) and the Functional Assessment of Cancer Therapy-Colorectal (FACT-C). Survey score changes from enrollment to 12 weeks were compared between the two arms using independent t tests, and scores at enrollment and 12 weeks were compared using paired t tests.ResultsAn increase in the FACT-C functional well-being subscale was observed in individuals in the intervention arm pre- to post-intervention (median difference, 2; interquartile range (IQR), 1, 4; P = .02). Although the between-group comparison was not statistically significant, no change in the functional well-being subscale was observed in the control arm (median difference, 0; IQR, 1, 1; P = .71). No other measures of HRQoL appeared to differ within arm across time points or between arms.ConclusionA 12-week digital physical activity intervention may improve functional well-being among CRC survivors. Larger randomized studies are needed to determine if digital health tools improve functional well-being among CRC survivors and if this improvement can be sustained over time.Trial registrationNCT02966054; registration date, November 17, 2016.

BACKGROUND: Physical activity is pivotal in managing heart failure with reduced ejection fraction, and walking integrated into daily life is an especially suitable form of physical activity. This study aimed to determine whether a 6-month lifestyle walking intervention combining self-monitoring and regular telephone counseling improves functional capacity assessed by the 6-minute walk test (6MWT) in patients with stable heart failure with reduced ejection fraction compared with usual care. METHODS: The WATCHFUL trial (Pedometer-Based Walking Intervention in Patients With Chronic Heart Failure With Reduced Ejection Fraction) was a 6-month multicenter, parallel-group randomized controlled trial recruiting patients with heart failure with reduced ejection fraction from 6 cardiovascular centers in the Czech Republic. Eligible participants were ≥18 years of age, had left ventricular ejection fraction <40%, and had New York Heart Association class II or III symptoms on guidelines-recommended medication. Individuals exceeding 450 meters on the baseline 6MWT were excluded. Patients in the intervention group were equipped with a Garmin vívofit activity tracker and received monthly telephone counseling from research nurses who encouraged them to use behavior change techniques such as self-monitoring, goal-setting, and action planning to increase their daily step count. The patients in the control group continued usual care. The primary outcome was the between-group difference in the distance walked during the 6MWT at 6 months. Secondary outcomes included daily step count and minutes of moderate to vigorous physical activity as measured by the hip-worn Actigraph wGT3X-BT accelerometer, NT-proBNP (N-terminal pro-B-type natriuretic peptide) and high-sensitivity C-reactive protein biomarkers, ejection fraction, anthropometric measures, depression score, self-efficacy, quality of life, and survival risk score. The primary analysis was conducted by intention to treat. RESULTS: Of 218 screened patients, 202 were randomized (mean age, 65 years; 22.8% female; 90.6% New York Heart Association class II; median left ventricular ejection fraction, 32.5%; median 6MWT, 385 meters; average 5071 steps/day; average 10.9 minutes of moderate to vigorous physical activity per day). At 6 months, no between-group differences were detected in the 6MWT (mean 7.4 meters [95% CI, -8.0 to 22.7]; P=0.345, n=186). The intervention group increased their average daily step count by 1420 (95% CI, 749 to 2091) and daily minutes of moderate to vigorous physical activity by 8.2 (95% CI, 3.0 to 13.3) over the control group. No between-group differences were detected for any other secondary outcomes.CONCLUSIONS: Whereas the lifestyle intervention in patients with heart failure with reduced ejection fraction improved daily steps by about 25%, it failed to demonstrate a corresponding improvement in functional capacity. Further research is needed to understand the lack of association between increased physical activity and functional outcomes. [ABSTRACT FROM AUTHOR]

ObjectiveTo investigate what social, psychological, personality, and behavioral factors affect overtime heart rate changes of college students. Participants: The daily heart rates of over 600 undergraduates at the University of Notre Dame were unobtrusively recorded via Fitbit devices from August 16, 2015, to May 13, 2017. Method: Latent Growth-Curve modeling strategy is utilized to examine how daily mean heart rate and its standard deviation change over time, and what foregoing factors predict observed changes. Results: The mean heart rate increased and its standard deviation stayed the same over the 637 days. Heart rate levels go up with that of social contacts, an indicator of peer influence. Both daily heart rate levels and changes are also affected by multiple external factors. Conclusion: Human heart rate is not only a physiological phenomenon but also a social-psychological one, as it is systematically affected by peer networks, social contexts, and human activities.

Fitbit wearable devices provide users with objective data on their physical activity and sleep habits. However, little is known about how users develop their usage patterns and the key mechanisms underlying the development of such patterns. In this article, we report results from a longitudinal analysis of Fitbit usage behavior among a sample of college students. Survey and Fitbit data were collected from 692 undergraduates at the University of Notre Dame across two waves. We use a structural equation modeling strategy to examine the relationships among three dimensions of Fitbit usage behavior corresponding to three elements of the habit loop model: trust in the accuracy of Fitbit physical activity and sleep data (cue), intensity of Fitbit device use (routine), and adjustment of physical activity and sleep behaviors based on Fitbit data (reward). More than 75 percent of participants trusted the accuracy of Fitbit data and nearly half of the participants reported they adjusted their physical activities based on the data reported by their devices. Participants who trusted the Fitbit physical activity data also tended to trust the sleep data, and those who intensively used Fitbit devices tended to adjust both their physical activities and then sleep habits. Psychological states and traits such as depression, extroversion, agreeableness, and neuroticism help predict multiple dimensions of Fitbit usage behaviors. However, we find little evidence that trust, Fitbit usage, or perceived adjustment of activity or sleep were associated with actual changes in levels of sleep and activity. We discuss the implications of these findings for understanding when and how this new monitoring technology results in changes in people's behavior.

BackgroundSelf-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data.MethodsData were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended.ResultsBest practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample.ConclusionsActivity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

IntroductionEfficacy tests of physical activity interventions indicate that many have limited or short-term efficacy, principally because they do not sufficiently build on theory-based processes that determine behaviour. The current study aims to address this limitation.Methods and analysisThe efficacy of the 8-week intervention will be tested using a three-condition randomised controlled trial delivered through an app, in women with a prior hypertensive pregnancy disorder. The intervention is based on the integrated behaviour change model, which outlines the motivational, volitional and automatic processes that lead to physical activity. The mechanisms by which the behaviour change techniques lead to physical activity will be tested.Following stratification on baseline factors, participants will be randomly allocated in-app to one of three conditions (1:1:1). The information condition will receive information, replicating usual care. Additionally to what the information condition receives, the motivation condition will receive content targeting motivational processes. Additionally to what the motivation condition receives, the action condition will receive content targeting volitional and automatic processes.The primary outcome is weekly minutes of moderate-to-vigorous physical activity, as measured by an activity tracker (Fitbit Inspire 2). Secondary outcomes include weekly average of Fitbit-measured daily resting heart rate, and self-reported body mass index, waist-hip ratio, cardiorespiratory fitness and subjective well-being. Tertiary outcomes include self-reported variables representing motivational, volitional, and automatic processes. Outcome measures will be assessed at baseline, immediately post-intervention, and at 3 and 12 months post-intervention. Physical activity will also be investigated at intervention midpoint. Efficacy will be determined by available case analysis. A process evaluation will be performed based on programme fidelity and acceptability measures.Ethics and disseminationThe Medical Ethics Committee of the Erasmus MC has approved this study (MEC-2020-0981). Results will be published in peer reviewed scientific journals and presented at scientific conferences.Trial registration numberNetherlands trial register, NL9329.

IntroductionSitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method.MethodsCHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification).ResultsFor minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%-83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP's positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min).ConclusionCHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

BackgroundMalaysia is projected to experience an increase in heat, rainfall, rainfall variability, dry spells, thunderstorms, and high winds due to climate change. This may lead to a rise in heat-related mortality, reduced nutritional security, and potential migration due to uninhabitable land. Currently, there is limited data regarding the health implications of climate change on the Malaysian populace, which hinders informed decision-making and interventions.ObjectiveThis study aims to assess the feasibility and reliability of using sensor-based devices to enhance climate change and health research within the SEACO health and demographic surveillance site (HDSS) in Malaysia. We will particularly focus on the effects of climate-sensitive diseases, emphasizing lung conditions like chronic obstructive pulmonary disease (COPD) and asthma.MethodsIn our mixed-methods approach, 120 participants (>18 years) from the SEACO HDSS in Segamat, Malaysia, will be engaged over three cycles, each lasting 3 weeks. Participants will use wearables to monitor heart rate, activity, and sleep. Indoor sensors will measure temperature in indoor living spaces, while 3D-printed weather stations will track indoor temperature and humidity. In each cycle, a minimum of 10 participants at high risk for COPD or asthma will be identified. Through interviews and questionnaires, we will evaluate the devices’ reliability, the prevalence of climate-sensitive lung diseases, and their correlation with environmental factors, like heat and humidity.ResultsWe anticipate that the sensor-based measurements will offer a comprehensive understanding of the interplay between climate-sensitive diseases and weather variables. The data is expected to reveal correlations between health impacts and weather exposures like heat. Participant feedback will offer perspectives on the usability and feasibility of these digital tools.ConclusionOur study within the SEACO HDSS in Malaysia will evaluate the potential of sensor-based digital technologies in monitoring the interplay between climate change and health, particularly for climate-sensitive diseases like COPD and asthma. The data generated will likely provide details on health profiles in relation to weather exposures. Feedback will indicate the acceptability of these tools for broader health surveillance. As climate change continues to impact global health, evaluating the potential of such digital technologies is crucial to understand its potential to inform policy and intervention strategies in vulnerable regions.

Introduction: Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury. Methods: This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury. Results: This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population. Discussion: The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population. [ABSTRACT FROM AUTHOR]

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data. [ABSTRACT FROM AUTHOR]

Purpose: Wearable activity trackers with real-time feedback and goal-setting features are being incorporated into programs to increase physical activity among childhood cancer survivors. This analysis describes the adoption and use of a Fitbit® Flex™ among adolescent-aged survivors of childhood cancer without incentives, reminders, or interventions to encourage use. Procedures: Cancer survivors aged 13–18 and ≥2 years from therapy received a Fitbit Flex with instructions to wear it daily. Researchers downloaded participants' daily total steps and active minutes for 1 year. Participants were classified as consistent (≥5 days/week for >4 weeks during the first 12 weeks) or inconsistent users, and comparisons between user types were made. Longitudinal use of the Fitbit and participants' 1-year acceptability evaluations are described. Results: Overall, 67.1% (47/70) of survivors enrolled, and Fitbit data were available for 36 participants. Initially, 30.6% (11/36) were consistent users. Consistent users had lower body mass index z-scores at enrollment (0.4 ± 0.7 vs. 1.2 ± 0.9; p = 0.01), but were otherwise comparable with inconsistent users. Over time survivors' use declined; at 12 months, only one participant was using his or her Fitbit. Survivors who completed a survey (n = 22) reported their Fitbit helped them self-monitor their exercise (72.7%, 16/22) and lead a more active lifestyle (63.6%, 14/22), but found it challenging to charge and not lose, forget about, or break the device. Conclusions: In the real-world setting, only a small subset of adolescent-aged survivors will initially consistently use a Fitbit and their interest diminishes over time. To maximize engagement, interventions incorporating wearable activity trackers likely need to include additional behavior change strategies. [ABSTRACT FROM AUTHOR]

Stress, a pervasive issue in today's world, significantly impacts mental and physical health. With approximately 20% of adults experiencing mental health disorders each year, and depression affecting over 260 million people globally, effective stress manage- ment is crucial. Chronic stress is also associated with physical ailments, including cardiovascular diseases, the leading global cause of death. In parallel, the wearable device market has witnessed substantial growth, with over 300 million units sold worldwide in 2021. These devices offer a unique opportunity to collect physiological and behavioral data, making them invaluable for stress detection and prediction. Combining artificial intelligence (AI) and wearables, this paper explores their accuracy in capturing vital data and the potential to revolutionize stress management, ultimately improving mental and physical well-being. In this paper, the ensemble-based classifier known as the Histogram-based Gradient Boosting Classifier detected stress from heart rates with an accuracy of 73%. This establishes the independent nature of Heart Rates for stress prediction, emphasizing that there is no need for sophisticated ECG and HRV readings. This opens up the possibility of using sensors as used in wearable devices to effectively detect stress in our day-to-day lives. [ABSTRACT FROM AUTHOR]

ObjectiveTo explore multi-clinician perspectives on intensive care early mobility, monitoring and to assess the perceived value of technology-generated mobility metrics to provide user feedback to inform research, practice improvement, and technology development.MethodsWe performed a qualitative descriptive study. Three focus groups were conducted with critical care clinicians, including nurses (n = 10), physical therapists (n = 8) and physicians (n = 8) at an academic medical centre that implemented an intensive care early mobility programme in 2012. Qualitative thematic analysis was used to code transcripts and identify overarching themes.FindingsAlong with reaffirming the value of performing early mobility interventions, four themes for improving mobility monitoring emerged, including the need for: 1) standardised indicators for documenting mobility; 2) inclusion of both quantitative and qualitative metrics to measure mobility 3) a balance between quantity and quality of data; and 4) trending mobility metrics over time.ConclusionIntensive care mobility monitoring should be standardised and data generated should be high quality, capable of supporting trend analysis, and meaningful. By improving measurement and monitoring of mobility, future researchers can examine the arc of activity that patients in the intensive care unit undergo and develop models to understand factors that influence successful implementation.

BACKGROUND: Research supports that moderate-to-vigorous intensity physical activity (MVPA) is key to prolonged health and function. Among older adults, substantial changes to MVPA may be infeasible, thus a growing literature suggests a shift in focus to whole-day activity patterns. METHODS: With data from 795 older adults aged 65-100 in the Adult Changes in Thought Activity Monitoring study, we used linear regression to estimate associations between ActiGraph and activPAL measured activity patterns - including light intensity physical activity, steps, standing, and sedentary behaviors - and physical function as measured by a short Performance-based Physical Function (sPPF) score (range 0-12), a composite score based on three standardized physical performance tasks: gait speed, timed chair stands, and grip strength. We examined whether relationships persisted when controlling for MVPA or differed across age, gender, or quartiles of MVPA. RESULTS: In models unadjusted for MVPA, a 1-standard deviation (SD) increment of daily sitting (1.9 h more), mean sitting bout duration (8 min longer average), or time spent in sedentary activity (1.6 h more) was associated with ~ 0.3-0.4 points lower mean sPPF score (all p

The field of gastroenterology has recently seen a surge in wearable technology to monitor physical activity, sleep quality, pain, and even gut activity. The past decade has seen the emergence of wearable devices including Fitbit, Apple Watch, AbStats, and ingestible sensors. In this review, we discuss current and future devices designed to measure sweat biomarkers, steps taken, sleep efficiency, gastric electrical activity, stomach pH, and intestinal contents. We also summarize several clinical studies to better understand wearable devices so that we may assess their potential benefit in improving healthcare while also weighing the challenges that must be addressed.

To date, the effect of both fixed and time-varying individual, social, psychological, environmental, and behavioral characteristics on temporal growth trends in physical activity (PA) among younger individuals remains an under-studied topic. In this paper, we address this gap in previous work by examining how temporal growth trends in PA respond to changing social, environmental, and behavioral characteristics using a large sample of college students (N = 692) who participated in the NetHealth project at the University of Notre Dame and from which fine-grained longitudinal data on physical activity and social interaction were collected unobtrusively via the use of wearables for 637 days (August 16, 2015 to May 13, 2017). These data are augmented by periodic survey data on fixed sociodemographic and psychological variables. We estimate latent growth-curve models for daily activity status, steps, active minutes, and activity calories. We find evidence of both a generalized friendship paradox and a peer effect for PA, with the average PA level of study participants' contacts being on average larger than their own, and with this average level exerting a statistically significant effect on individual PA levels. Notably, there was limited evidence of temporal growth in PA across the 637 days of observation with null temporal effects for three out of the four PA indicators, except for daily steps taken. Finally, we find that social, psychological, and behavioral factors (e.g., large network size, high extroversion levels, and more courses taken) are systematically associated with higher PA levels in this sample. Overall, our findings highlight the importance of social, environmental, and behavioral factors (such as peer networks and daily sociability) in modulating the dynamics of PA levels among college students.

BackgroundA randomized trial of a pedometer-based intervention with weekly activity goals led to a modest increase in step count among dialysis patients. In a secondary analysis, we investigated the effect of this intervention on body composition.MethodsSixty dialysis patients were randomized to standard care or a 6-month program consisting of 3 months of pedometers and weekly step count targets and 3 months of post-intervention follow-up. We obtained bioelectrical impedance spectroscopy (BIS) data on 54 of these patients (28 control, 26 intervention) and used linear mixed-modeling (adjusted for sex and dialysis modality) to estimate differences in change in total-body muscle mass (TBMM) adjusted for height2, fat mass (kg), and body mass index (BMI) (kg/m2) between control and intervention groups.ResultsThe median age of participants was 57.5 years (53-66), and 76% were men. At baseline, there was no significant difference between groups in age, BMI, race, or body composition, but there were more men in the intervention group. After 3 months, patients in the intervention group increased their average daily steps by 2414 (95% CI 1047, 3782) more than controls (p

IntroductionObjective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury.MethodsThis pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury.ResultsThis pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population.DiscussionThe unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.

BackgroundRegular physical activity is an important determinant of cardiovascular health and quality of life. Previous investigations examining the association between exercise and atrial fibrillation (AF) have been limited by self-reported, retrospectively collected activity data.ObjectiveThe purpose of this study was to objectively quantify differences in daily physical activity among individuals with and those without AF using electronic wearable activity trackers.MethodsDaily exercise data were directly obtained from wrist-worn activity trackers (Fitbit, San Francisco, CA) among participants in the Health eHeart (HeH) study. Average daily step count was compared between individuals with and those without AF both before and after adjusting for comorbidities. AF severity was quantified using the Atrial Fibrillation Effect on QualiTy of Life (AFEQT) survey.ResultsAmong 171,284 HeH study participants, 3333 individuals (234 with AF [7%]) submitted activity data. In unadjusted analysis, AF participants ambulated an average of 723 fewer steps per day (95% confidence interval [CI] 292-1154; P = .001) compared to individuals without AF. After adjustment for demographics and comorbid diseases, participants with AF demonstrated 591 fewer steps per day (95% CI 149-1033; P = .009). Among AF patients, AF severity was associated with less physical activity. For each single point decrease in AFEQT score (corresponding to more symptomatic AF), physical activity decreased by a mean 24 steps per day (95% CI 1-46; P = .04).ConclusionObjective, automatically collected step count data demonstrate that individuals with AF engage in significantly less average daily physical activity. In addition, worsening AF symptom severity is associated with reduced daily exercise.

BACKGROUND: Physical activity and sleep are behavioral risk factors for cancer that may be influenced by environmental exposures, including built and natural environments. However, many studies in this area are limited by residence-based exposure assessment and/or self-reported, time-aggregated measures of behavior. METHODS: The Nurses Health Study 3 (NHS3) Mobile Health Substudy is a pilot study of 500 participants in the prospective NHS3 cohort who use a smartphone application and a Fitbit for seven-day periods, four times over a year, to measure minute-level location, physical activity, heart rate, and sleep. RESULTS: We have collected data on 435 participants, comprising over 6 million participant-minutes of heart rate, step, sleep, and location. Over 90% of participants had five days of ≥600 minutes of Fitbit wear-time in their first sampling week, and this percentage dropped to 70% for weeks 2 to 4. Over 819 sampling weeks, we observed an average of 7,581 minutes of heart rate and step data [interquartile range (IQR): 6,651-9,645] per participant-week, and >2 million minutes of sleep in over 5,700 sleep bouts. We have recorded location data for 5,237 unique participant-days, averaging 104 location observations per participant-day (IQR: 103-107). CONCLUSIONS: This study describes a protocol to incorporate mobile health technology into a nationwide prospective cohort to measure high-resolution objective data on environment and behavior. IMPACT: This project could provide translational insights into interventions for urban planning to optimize opportunities for physical activity and healthy sleep patterns to reduce cancer risk.See all articles in this CEBP Focus section, Modernizing Population Science.

Rationale & objectivePatients receiving dialysis report very low physical activity. We implemented a pilot trial to assess the feasibility of a pedometer-based intervention to gather preliminary evidence about its impact on physical activity, symptoms, and surrogates of cardiovascular risk.Study designPilot randomized controlled trial.Setting & participants60 dialysis patients from San Francisco dialysis clinics.InterventionParticipants were randomly assigned 1:1 to receiving pedometers with weekly step goals or usual care for 3 months.OutcomesThe primary outcome was step counts, measured using pedometers. Secondary outcomes included physical performance using the Short Physical Performance Battery, the Physical Function and Vitality scales of the 36-Item Short Form Health Survey, the Dialysis Symptoms Index, and the Center for Epidemiologic Studies-Depression Scale, with endothelial function as a secondary and heart rate variability as an exploratory surrogate measure of cardiovascular risk. Targeted enrollment was 50% and targeted completion was 85%.Results49% of approached patients were enrolled, and 92% completed the study. After 3 months, patients randomly assigned to the intervention (n=30) increased their average daily steps by 2,256 (95% CI, 978-3,537) more than the 30 controls (P

To compare the accuracy of electronic health record clinician documentation and accelerometer-based sensors with a gold standard dataset derived from clinician-annotated video to quantify early mobility activities in adult ICU patients.DesignProspective, observational study.SettingMedical ICU at an academic hospital.PatientsAdult ICU patients (n = 30) were each continuously monitored over a median of 24.4 hours, yielding 711.5 hours of video, electronic health record, and sensor data.InterventionsNone.Measurements and main resultsElectronic health record documentation estimated ambulation (intraclass correlation coefficient, 0.89; 95% CI, 0.78-0.95), sitting out-of-bed (intraclass correlation coefficient, 0.85; 95% CI, 0.72-0.93), and turning events (intraclass correlation coefficient, 0.87; 95% CI, 0.75-0.94) with excellent agreement but underestimated the number of standing, transferring, and pregait activities performed per patient. The accelerometer-based sensor had excellent agreement with video annotation for estimating duration of time spent supine (intraclass correlation coefficient, 0.99; CI, 0.97-0.99) and sitting/standing upright (intraclass correlation coefficient, 0.92; CI, 0.82-0.96) but overestimated ambulation time.ConclusionsOur results show that electronic health record documentation and sensor-based technologies accurately capture distinct but complimentary metrics for ICU mobility measurement. Innovations in artifact detection, standardization of clinically relevant mobility definitions, and electronic health record documentation enhancements may enable further use of these technologies to drive critical care research and technology leveraged data-driven ICU models of care.

Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-reported outcomes (PROs) using activity tracker data in a cohort of patients with stable ischemic heart disease (SIHD). A population of 182 patients with SIHD were monitored over a period of 12 weeks. Each subject received a Fitbit Charge 2 device to record daily activity data, and each subject completed eight Patient-Reported Outcomes Measurement Information Systems short form at the end of each week as a self-assessment of their health status. Two models were built to classify PRO scores using activity tracker data. The first model treated each week independently, whereas the second used a hidden Markov model (HMM) to take advantage of correlations between successive weeks. Retrospective analysis compared the classification accuracy of the two models and the importance of each feature. In the independent model, a random forest classifier achieved a mean area under curve (AUC) of 0.76 for classifying the physical function PRO. The HMM model achieved significantly better AUCs for all PROs (p < 0.05) other than Fatigue and Sleep Disturbance, with a highest mean AUC of 0.79 for the physical function-short form 10a. Our study demonstrates the ability of activity tracker data to classify health status over time. These results suggest that patient outcomes can be monitored in real time using activity trackers.

PurposeThis study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children.Methods59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis.ResultsFitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure (~0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs.ConclusionsFitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.

BackgroundOver 1.3 million people live with colorectal cancer in the United States. Physical activity is associated with lower risk of colorectal cancer recurrence and mortality. Interventions are needed to increase physical activity in colorectal cancer survivors.MethodsWe conducted a 2-arm non-blinded pilot randomized controlled trial at the University of California, San Francisco among 42 individuals who had completed curative-intent treatment for colorectal cancer to determine the feasibility and acceptability of a 12-week (84 days) physical activity intervention using a Fitbit Flex™ and daily text messages. Participants were randomized 1:1 to receive the intervention with print educational materials or print educational materials alone. We explored the impact of the intervention versus usual care on physical activity using ActiGraph GT3X+ accelerometers pre-/post-intervention.ResultsWe screened 406 individuals and randomized 42 to intervention (n = 21) or control (n = 21) groups. During the 12-week study, the intervention arm wore their Fitbits a median of 74 days [88% of days in study period, interquartile range: 23-83 days] and responded to a median of 34 (out of 46) text messages that asked for a reply (interquartile range: 13-38 text messages). Among the 16 intervention participants who completed the feedback survey, the majority (88%) reported that the intervention motivated them to exercise and that they were satisfied with their experience. No statistically significant difference in change in moderate-to-vigorous physical activity was found from baseline to 12 weeks between arms.ConclusionA 12-week physical activity intervention with a Fitbit and text messages was feasible and acceptable among colorectal cancer patients after curative treatment. Larger studies are needed to determine whether the intervention increases physical activity.Trial registrationClinicaltrials.gov Identifier NCT02966054 . Registered 17 November 2016, retrospectively registered.

PURPOSE: Cardiorespiratory fitness (CRF), broadly defined as the bodys ability to utilize oxygen, is a well-established prognostic marker of health, but it is not routinely measured. This may be due to the difficulty of acquiring high-quality CRF measures. The purpose of this study was to independently determine the validity of the Fitbit Charge 2s measure of CRF (Fitbit CRF). METHODS: Sixty-five healthy adults between the ages of 18 and 45 yr (55% female, 45% male) were recruited to undergo gold standard V˙O2max testing and wear a Fitbit Charge 2 continuously for 1 wk during which they were instructed to complete a qualifying outdoor run to derive the Fitbit CRF (units: mL·kg·min). This measure was compared with V˙O2max measures (units: mL·kg·min) epoched at 15 and 60 s. RESULTS: Bland-Altman analyses revealed that Fitbit CRF had a positive bias of 1.59 mL·kg·min compared with laboratory data epoched at 15 s and 0.30 mL·kg·min compared with data epoched at 60 s (n = 60). F statistics (2.09; 0.08) and P values (0.133; 0.926) from Bradley-Blackwood tests for the concordance of Fitbit CRF with 15- and 60-s laboratory data, respectively, support the null hypothesis of equal means and variances, indicating there is concordance between the two measures. Mean absolute percentage error was less than 10% for each comparison. CONCLUSIONS: The Fitbit Charge 2 provides an acceptable level of validity when measuring CRF in young, healthy, and fit adults who are able to run. Further research is required to determine if it is a potentially useful tool in clinical practice and epidemiological research to quantify, categorize, and longitudinally track risk for adverse outcomes.

Physical activity (PA) estimates from the Fitbit Flex 2 were compared to those from the ActiGraph GT9X Link in 123 elementary school children. Steps and intensity-specific estimates of PA and 3-month PA change were calculated using two different ActiGraph cut-points (Evenson and Romanzini). Fitbit estimates were 35% higher for steps compared to the ActiGraph. Fitbit and ActiGraph intensity-specific estimates were closest for sedentary and light PA, while estimates of moderate and vigorous PA varied substantially depending upon the ActiGraph cut-points used. Spearman correlations between device estimates were higher for steps (rs =.70) than for moderate (rs =.54 to.55) or vigorous (rs =.29 to.48) PA. There was low concordance between devices in assessing PA changes over time. Agreement between Fitbit Flex 2 and ActiGraph estimates may depend upon the cut-points used to classify PA intensity. However, there is fair to good agreement between devices in ranking children's steps and MVPA. [ABSTRACT FROM AUTHOR]

Background: With the worldwide rising obesity epidemic and the aging population, it is essential to deliver (cost-)effective care that results in enhanced societal participation among knee arthroplasty patients. The purpose of this study is to describe the development, content, and protocol of our (cost-)effectiveness study that assesses a perioperative integrated care program, including a personalized eHealth app, for knee arthroplasty patients aimed to enhance societal participation post-surgery compared to care as usual. Methods: The intervention will be tested in a multicentre randomized controlled trial with eleven participating Dutch medical centers (i.e., hospitals and clinics). Working patients on the waiting-list for a total- or unicompartmental knee arthroplasty with the intention to return to work after surgery will be included. After pre-stratification on medical centre with or without eHealth as usual care, operation procedure (total- or unicompartmental knee arthroplasty) and recovery expectations regarding return to work, randomization will take place at the patient-level. A minimum of 138 patients will be included in both the intervention and control group, 276 in total. The control group will receive usual care. On top of care as usual, patients in the intervention group will receive an intervention consisting of three components: 1) a personalized eHealth intervention called ikHerstel ('I Recover') including an activity tracker, 2) goal setting using goal attainment scaling to improve rehabilitation and 3) a referral to a case-manager. Our main outcome is quality of life, based on patient-reported physical functioning (using PROMIS-PF). (Cost-)effectiveness will be assessed from a healthcare and societal perspective. Data collection has been started in 2020 and is expected to finish in 2024. Discussion: Improving societal participation for knee arthroplasty is relevant for patients, health care providers, employers and society. This multicentre randomized controlled trial will evaluate the (cost-)effectiveness of a personalized integrated care program for knee arthroplasty patients, consisting of effective intervention components based on previous studies, compared to care as usual. Trial registration: Trialsearch.who.int; reference no. NL8525, reference date version 1: 14–04-2020. [ABSTRACT FROM AUTHOR]

Preoperative identification of high-risk groups has been extensively studied to improve patients' outcomes. Wearable devices, which can track heart rate and physical activity data, are starting to be evaluated for patients' management. We hypothesized that commercial wearable devices (WD) may provide data associated with preoperative evaluation scales and tests, to identify patients with poor functional capacity at increased risk for complications. We conducted a prospective observational study including seventy-year-old patients undergoing two-hour surgeries under general anesthesia. Patients were asked to wear a WD for 7 days before surgery. WD data were compared to preoperatory clinical evaluation scales and with a 6-min walking test (6MWT). We enrolled 31 patients, with a mean age of 76.1 (SD ± 4.9) years. There were 11 (35%) ASA 3–4 patients. 6MWT results averaged 328.9 (SD ± 99.5) m. Daily steps and 2 as recorded using WD and were associated with 6MWT performance (R = 0.56, p = 0.001 and r = 0.58, p = 0.006, respectively) and clinical evaluation scales. This is the first study to evaluate WD as preoperative evaluation tools; we found a strong association between 6MWT, preoperative scales, and WD data. Low-cost wearable devices are a promising tool for the evaluation of cardiopulmonary fitness. Further research is needed to validate WD in this setting. [ABSTRACT FROM AUTHOR]

IntroductionThe use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions.Materials and methodsAn initial sample of 67 schoolchildren (final sample = 62; 50% females), aged 9–12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day.ResultsResults showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3%, and lower 95% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1%, and lower 95% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6–90.3%, and lower 95% IC of ICC = 0.00–0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = −0.03–0.54).ConclusionsThe activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.

BackgroundModerate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data.ObjectiveThe objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies.MethodsUsing heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks' output in supporting MVPA behavior change studies, we applied it to 2 case studies.ResultsAmong the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P

ObjectivesIndependently, physical activity (PA), sedentary behavior (SB), and sleep are related to the development and progression of chronic diseases. Less is known about how rest-activity behaviors cluster within individuals and how rest-activity behavior profiles relate to health. In this study we aimed to investigate if adult women cluster into profiles based on how they accumulate rest-activity behavior (including accelerometer-measured PA, SB, and sleep), and if participant characteristics and health outcomes differ by profile membership.MethodsA convenience sample of 372 women (mean age 55.38 + 10.16) were recruited from four US cities. Participants wore ActiGraph GT3X+ accelerometers on the hip and wrist for a week. Total daily minutes in moderate-to-vigorous PA (MVPA) and percentage of wear-time spent in SB was estimated from the hip device. Total sleep time (hours/minutes) and sleep efficiency (% of in bed time asleep) were estimated from the wrist device. Latent profile analysis (LPA) was performed to identify clusters of participants based on accumulation of the four rest-activity variables. Adjusted ANOVAs were conducted to explore differences in demographic characteristics and health outcomes across profiles.ResultsRest-activity variables clustered to form five behavior profiles: Moderately Active Poor Sleepers (7%), Highly Actives (9%), Inactives (41%), Moderately Actives (28%), and Actives (15%). The Moderately Active Poor Sleepers (profile 1) had the lowest proportion of whites (35% vs 78-91%, p < .001) and college graduates (28% vs 68-90%, p = .004). Health outcomes did not vary significantly across all rest-activity profiles.ConclusionsIn this sample, women clustered within daily rest-activity behavior profiles. Identifying 24-hour behavior profiles can inform intervention population targets and innovative behavioral goals of multiple health behavior interventions.

Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation–maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.

ImportanceDespite data aggregation and removal of protected health information, there is concern that deidentified physical activity (PA) data collected from wearable devices can be reidentified. Organizations collecting or distributing such data suggest that the aforementioned measures are sufficient to ensure privacy. However, no studies, to our knowledge, have been published that demonstrate the possibility or impossibility of reidentifying such activity data.ObjectiveTo evaluate the feasibility of reidentifying accelerometer-measured PA data, which have had geographic and protected health information removed, using support vector machines (SVMs) and random forest methods from machine learning.Design, setting, and participantsIn this cross-sectional study, the National Health and Nutrition Examination Survey (NHANES) 2003-2004 and 2005-2006 data sets were analyzed in 2018. The accelerometer-measured PA data were collected in a free-living setting for 7 continuous days. NHANES uses a multistage probability sampling design to select a sample that is representative of the civilian noninstitutionalized household (both adult and children) population of the United States.ExposuresThe NHANES data sets contain objectively measured movement intensity as recorded by accelerometers worn during all walking for 1 week.Main outcomes and measuresThe primary outcome was the ability of the random forest and linear SVM algorithms to match demographic and 20-minute aggregated PA data to individual-specific record numbers, and the percentage of correct matches by each machine learning algorithm was the measure.ResultsA total of 4720 adults (mean [SD] age, 40.0 [20.6] years) and 2427 children (mean [SD] age, 12.3 [3.4] years) in NHANES 2003-2004 and 4765 adults (mean [SD] age, 45.2 [19.9] years) and 2539 children (mean [SD] age, 12.1 [3.4] years) in NHANES 2005-2006 were included in the study. The random forest algorithm successfully reidentified the demographic and 20-minute aggregated PA data of 4478 adults (94.9%) and 2120 children (87.4%) in NHANES 2003-2004 and 4470 adults (93.8%) and 2172 children (85.5%) in NHANES 2005-2006 (P