Perceptions of Infertility and Semen Analysis Testing Among American Men Without Children.

OBJECTIVE: To determine whether health-conscious men are more likely to be concerned about infertility and self-initiate semen analysis at a laboratory/clinic or through a direct-to-consumer at-home product without a health care provider recommendation. METHODS: Cross-sectional survey conducted online via ResearchMatch.org between November 2019 and January 2020. Men age 18 and older without children (n = 634) were included for analysis. Outcomes were likelihood of self-initiating a semen analysis, prevalence of infertility concern. RESULTS: Of the 634 participants, 186 expressed concern about infertility but only 29% were likely to discuss these concerns with a health care provider. More men would self-initiate a semen analysis using an at-home product than through a traditional laboratory/clinic (14.2% vs 10.4%, P = .04). Odds of self-initiating a traditional semen analysis were higher for men concerned about low testosterone (odds ratio [OR] 2.30, 95% confidence interval [CI] 1.12-4.74, P = .023) and infertility (OR 3.91, 95% CI 2.14-7.15, P <.001). Self-initiating an at-home semen analysis was associated with concern for low testosterone and infertility as well as middle age (age 40-59: OR 3.02, 95% CI 1.16-7.88, P = .024) and fitness tracker use (OR: 1.95, 95% CI 1.12-3.39, P = .018). CONCLUSION: Many men were unlikely to discuss infertility concerns with a health care provider. Middle aged men and those who used fitness trackers were more likely to self-initiate fertility evaluation through at-home semen analysis. Concern about low serum testosterone was pervasive and strongly associated with concern for being infertile and self-initiating a semen analysis of any kind.

Mobile health and privacy: cross sectional study.

OBJECTIVES: To investigate whether and what user data are collected by health related mobile applications (mHealth apps), to characterise the privacy conduct of all the available mHealth apps on Google Play, and to gauge the associated risks to privacy. DESIGN: Cross sectional study SETTING: Health related apps developed for the Android mobile platform, available in the Google Play store in Australia and belonging to the medical and health and fitness categories. PARTICIPANTS: Users of 20 991 mHealth apps (8074 medical and 12 917 health and fitness found in the Google Play store: in-depth analysis was done on 15 838 apps that did not require a download or subscription fee compared with 8468 baseline non-mHealth apps. MAIN OUTCOME MEASURES: Primary outcomes were characterisation of the data collection operations in the apps code and of the data transmissions in the apps traffic; analysis of the primary recipients for each type of user data; presence of adverts and trackers in the app traffic; audit of the app privacy policy and compliance of the privacy conduct with the policy; and analysis of complaints in negative app reviews. RESULTS: 88.0% (n=18 472) of mHealth apps included code that could potentially collect user data. 3.9% (n=616) of apps transmitted user information in their traffic. Most data collection operations in apps code and data transmissions in apps traffic involved external service providers (third parties). The top 50 third parties were responsible for most of the data collection operations in app code and data transmissions in app traffic (68.0% (2140), collectively). 23.0% (724) of user data transmissions occurred on insecure communication protocols. 28.1% (5903) of apps provided no privacy policies, whereas 47.0% (1479) of user data transmissions complied with the privacy policy. 1.3% (3609) of user reviews raised concerns about privacy. CONCLUSIONS: This analysis found serious problems with privacy and inconsistent privacy practices in mHealth apps. Clinicians should be aware of these and articulate them to patients when determining the benefits and risks of mHealth apps.

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.

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.

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices.

AIMS: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. METHODS: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire-Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. RESULTS: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. CONCLUSION: Wearable devices inspire users' motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

Resistance to data loss from the Freestyle Libre: impact on glucose variability indices and recommendations for data analysis.

Like many wearables, flash glucose monitoring relies on user compliance and is subject to missing data. As recent research is beginning to utilise glucose technologies as behaviour change tools, it is important to understand whether missing data are tolerable. Complete Freestyle Libre data files were amputed to remove 1-6 h of data both at random and over mealtimes (breakfast, lunch, and dinner). Absolute percent errors (MAPE) and intraclass correlation coefficients (ICC) were calculated to evaluate agreement and reliability. Thirty-two (91%) participants provided at least 1 complete day (24 h) of data (age: 44.8 ± 8.6 years, female: 18 (56%); mean fasting glucose: 5.0 ± 0.6 mmol/L). Mean and continuous overall net glycaemic action (CONGA) (60 min) were robust to data loss (MAPE ≤3%). Larger errors were calculated for standard deviation, coefficient of variation (CV) and mean amplitude of glycaemic excursions (MAGE) at increasing missingness (MAPE: 2%-10%, 2%-9%, and 4%-18%, respectively). ICC decreased as missing data increased, with most indicating excellent reliability (>0.9) apart from certain MAGE ICCs, which indicated good reliability (0.84-0.9). Researchers and clinicians should be aware of the potential for larger errors when reporting standard deviation, CV, and MAGE at higher rates of data loss in nondiabetic populations. But where mean and CONGA are of interest, data loss is less of a concern. Novelty: As research now utilises flash glucose monitoring as behavioural change tools in nondiabetic populations, it is important to consider the influence of missing data. Glycaemic variability indices of mean and CONGA are robust to data loss, but standard deviation, CV, and MAGE are influenced at higher rates of missingness.

Sedentary time and activity behaviors after stroke rehabilitation: Changes in the first 3 months home.

BACKGROUND: Sedentary time is prevalent following stroke, limiting functional improvement, and increasing cardiovascular risk. At discharge we examined: 1) change in sedentary time and activity over the following 3 months' and 2) physical, psychological or cognitive factors predicting any change. A secondary aim examined cross-sectional associations between factors and activity at 3 months. METHODS: People with stroke (n = 34) were recruited from two rehabilitation units. An activity monitor (ActivPAL3) was worn for 7 days during the first week home and 3 months later. Factors examined included physical, psychological, and cognitive function. Linear mixed models (adjusted for waking hours) were used to examine changes in sedentary time, walking, and step count over time. Interaction terms between time and each factor were added to the model to determine if they modified change over time. Linear regression was performed to determine factors cross-sectionally associated with 3-month activity. RESULTS: ActivPAL data were available at both time points for 28 (82%) participants (mean age 69 [SD 12] years). At 3 months, participants spent 39 fewer minutes sedentary (95%CI -70,-8 p = .01), 21 minutes more walking (95%CI 2,22 p = .02) and completed 1112 additional steps/day (95%CI 268,1956 p = .01), compared to the first week home. No factors predicted change in activity. At 3 months, greater depression (ß 22 mins (95%CI 8,36) p = .004) and slower gait speed (ß - 43 mins 95%CI -59,-27 p ≤ 0.001) were associated with more sedentary time and less walking activity, respectively. CONCLUSIONS: Sedentary time reduced and walking activity increased between discharge home and 3 months later. Interventions targeting mood and physical function may warrant testing to reduce sedentary behavior 3 months following discharge.

Comparison of the Physical Activity Measured by a Consumer Wearable Activity Tracker and That Measured by Self-Report: Cross-Sectional Analysis of the Health eHeart Study.

BACKGROUND: Commercially acquired wearable activity trackers such as the Fitbit provide objective, accurate measurements of physically active time and step counts, but it is unclear whether these measurements are more clinically meaningful than self-reported physical activity. OBJECTIVE: The aim of this study was to compare self-reported physical activity to Fitbit-measured step counts and then determine which is a stronger predictor of BMI by using data collected over the same period reflecting comparable physical activities. METHODS: We performed a cross-sectional analysis of data collected by the Health eHeart Study, a large mobile health study of cardiovascular health and disease. Adults who linked commercially acquired Fitbits used in free-living conditions with the Health eHeart Study and completed an International Physical Activity Questionnaire (IPAQ) between 2013 and 2019 were enrolled (N=1498). Fitbit step counts were used to quantify time by activity intensity in a manner comparable to the IPAQ classifications of total active time and time spent being sedentary, walking, or doing moderate activities or vigorous activities. Fitbit steps per day were computed as a measure of the overall activity for exploratory comparisons with IPAQ-measured overall activity (metabolic equivalent of task [MET]-h/wk). Measurements of physical activity were directly compared by Spearman rank correlation. Strengths of associations with BMI for Fitbit versus IPAQ measurements were compared using multivariable robust regression in the subset of participants with BMI and covariates measured. RESULTS: Correlations between synchronous paired measurements from Fitbits and the IPAQ ranged in strength from weak to moderate (0.09-0.48). In the subset with BMI and covariates measured (n=586), Fitbit-derived predictors were generally stronger predictors of BMI than self-reported predictors. For example, an additional hour of Fitbit-measured vigorous activity per week was associated with nearly a full point reduction in BMI (-0.84 kg/m2, 95% CI -1.35 to -0.32) in adjusted analyses, whereas the association between self-reported vigorous activity measured by IPAQ and BMI was substantially smaller in magnitude (-0.17 kg/m2, 95% CI -0.34 to -0.00; P<.001 versus Fitbit) and was dominated by the Fitbit-derived predictor when compared head-to-head in a single adjusted multivariable model. Similar patterns of associations with BMI, with Fitbit dominating self-report, were seen for moderate activity and total active time and in comparisons between overall Fitbit steps per day and IPAQ MET-h/wk on standardized scales. CONCLUSIONS: Fitbit-measured physical activity was more strongly associated with BMI than self-reported physical activity, particularly for moderate activity, vigorous activity, and summary measures of total activity. Consumer-marketed wearable activity trackers such as the Fitbit may be useful for measuring health-relevant physical activity in clinical practice and research.

The Implication of Wearables and the Factors Affecting Their Usage among Recreationally Active People.

Regular physical activity (PA) is associated with health and well-being. Recent findings show that PA tracking using technological devices can enhance PA behavior. Consumer devices can track many different parameters affecting PA (e.g., number of steps, distance, and heart rate). However, it remains unclear what factors affect the usage of such devices. In this study, we evaluated whether there was a change in usage behavior across the first weeks of usage. Further we investigated whether external factors such as weather and day of the week influence usage behavior. Thirty nine participants received a Fitbit Charge 2 fitness tracker for a nine-week period. All participants were asked to wear the device according to their wishes. The usage time and amount of PA were assessed, and the influencing factors, such as weather conditions and day of the week, were analyzed. The results showed that usage behavior differed largely between individuals and decreased after five weeks of usage. Moreover, the steps per worn hour did not change significantly, indicating a similar amount of activity across the nine-week period when wearing the device. Further influencing factors were the day of the week (the tracker was used less on Sundays) and the temperature (usage time was lower with temperatures >25°). Tracking peoples' activity might have the potential to evaluate different interventions to increase PA.

A Pilot Randomized Controlled Trial Using SystemCHANGE™ Approach to Increase Physical Activity in Older Kidney Transplant Recipients.

BACKGROUND: Cardiovascular disease is the leading cause of death in kidney transplant recipients. Physical activity after transplant is the most modifiable nonpharmacological factor for improving cardiovascular outcomes. Few studies have tested walking interventions to enhance daily steps and health outcomes in older kidney recipients. METHODS: Using a pilot feasibility randomized clinical trial design, we tested the feasibility and efficacy of a 6-month SystemCHANGE™ (Change Habits by Applying New Goals and Experience) + Activity Tracker intervention for recruitment, retention, daily steps, and health outcomes (blood pressure, heart rate, body mass index, waist circumference, and physical function). The SystemCHANGE™ + Activity Tracker intervention taught participants to use a multicomponent intervention that connects person-centered systems solutions combined with visual feedback from a mobile activity tracker to achieve daily step goals. RESULTS: Fifty-three participants (mean age 65 years, 66% male, and 57% white) participated with 27 in the intervention and 26 in the control group. The study protocol was feasible to deliver with high adherence to the protocol in both groups. The intervention group increased daily steps at 3 months (mean difference, 608; standard error = 283, P = .03) compared to the control group. The secondary outcome of heart rate decreased for the intervention group (baseline [mean] 74.4+ 10.8 [standard deviation, SD;] vs 6 months [mean] 67.6+ 11.3 [SD]; P = .002) compared to the control group (baseline [mean] 70.67+ 10.4 [SD]; vs 6 months [mean] 70.2 + 11.1 [SD]; P = .83). CONCLUSIONS: SystemCHANGE™ + Activity Tracker intervention appears to be feasible and efficacious for increasing daily steps in older kidney recipients.

Development of cut-points for determining activity intensity from a wrist-worn ActiGraph accelerometer in free-living adults.

Despite recent popularity of wrist-worn accelerometers for assessing free-living physical behaviours, there is a lack of user-friendly methods to characterize physical activity from a wrist-worn ActiGraph accelerometer. Participants in this study completed a laboratory protocol and/or 3-8 hours of directly observed free-living (criterion measure of activity intensity) while wearing ActiGraph GT9X Link accelerometers on the right hip and non-dominant wrist. All laboratory data (n = 36) and 11 participants' free-living data were used to develop vector magnitude count cut-points (counts/min) for activity intensity for the wrist-worn accelerometer, and 12 participants' free-living data were used to cross-validate cut-point accuracy. The cut-points were: <2,860 counts/min (sedentary); 2,860-3,940 counts/min (light); and ≥3,941counts/min (moderate-to-vigorous (MVPA)). These cut-points had an accuracy of 70.8% for assessing free-living activity intensity, whereas Sasaki/Freedson cut-points for the hip accelerometer had an accuracy of 77.1%, and Hildebrand Euclidean Norm Minus One (ENMO) cut-points for the wrist accelerometer had an accuracy of 75.2%. While accuracy was higher for a hip-worn accelerometer and for ENMO wrist cut-points, the high wear compliance of wrist accelerometers shown in past work and the ease of use of count-based analysis methods may justify use of these developed cut-points until more accurate, equally usable methods can be developed.

Effect of a Pedometer-based Exercise Program on Cancer Related Fatigue and Quality of Life amongst Patients with Breast Cancer Receiving Chemotherapy.

BACKGROUND: Breast cancer is the most common cancer amongst Indian women. Cancer treatments leads to various side effects out of which Cancer-Related fatigue (CRF) is one of the most under-addressed side-effects. It is experienced the most in patients receiving chemotherapy. Exercise has been proven to be a beneficial intervention to manage CRF but the benefits of pedometer-based exercise programs is under-studied in patients with breast cancer. Hence, we set out to investigate the effects of a pedometer-based exercise program for patients with breast receiving chemotherapy. METHODS: The current study was a non-randomized controlled trial with 22 patients each in exercise and control group. The exercise group received a pedometer-based walking program, whereas the control group received standard physical activity advice. Fatigue, quality of life, functional capacity and body composition were assessed at baseline, 3rd week and 7th week. RESULTS: At the end of 7 weeks intervention, functional capacity, quality of life and skeletal mass were found to have improved with statistical significance, while the fatigue and changes in total fat did improve but were not statistically significant. CONCLUSION: A 7-week pedometer-based exercise program improved functional capacity, quality of life and percentage of skeletal mass and also shows to have prevented deterioration in fatigue levels in patients with breast cancer receiving chemotherapy.

Cardiac Irradiation Predicts Activity Decline in Patients Receiving Concurrent Chemoradiation for Locally Advanced Lung Cancer.

PURPOSE: Many patients with lung cancer are inactive due to their disease and underlying comorbidities, and activity levels can decline further during cancer therapy. Here we explore dosimetric predictors of activity decline in a cohort of patients who underwent continuous activity monitoring during definitive concurrent chemoradiotherapy (CRT) for locally advanced lung cancer. METHODS AND MATERIALS: We identified patients who participated in prospective clinical trials involving the use of a commercial fitness tracker throughout the course of CRT. For each patient, we applied linear regression to log-transformed daily step counts to compute the weekly rate of activity change from 1 week before radiation therapy (RT) initiation to 2 weeks after RT completion. Clinical and dosimetric factors were tested as predictors of activity change using linear regressions. RESULTS: Forty-six patient met the eligibility criteria. Median age was 66 years (range, 38-90). Pretreatment Eastern Cooperative Oncology Group performance status was 0, 1, and 2 for 17%, 70%, and 13%, respectively. Mean lung dose ranged from 5.0 to 23.5 Gy, mean esophagus dose from 1.1 to 39.6 Gy, and mean heart dose from 0.6 to 31.5 Gy. Median daily step count average was 5861 (interquartile range, 3540-8282) before RT and 3422 (interquartile range, 2364-5395) 2 weeks after RT completion. Rate of activity change was not significantly associated with age, performance status, or mean RT dose received by lungs or esophagus. In multivariate analysis, mean heart dose was significantly associated with rate of activity decline, with a 3.1% reduction in step count per week for every 10 Gy increase in mean heart dose (95% confidence interval: 0.5-5.7, P = .023). CONCLUSIONS: Extent of cardiac irradiation is associated with the rate of physical activity decline during CRT for lung cancer. Our novel finding contributes to the growing body of evidence that adverse effects of cardiac irradiation may be manifested at early time points.

Feasibility of FitSurvivor: A technology-enhanced group-based fitness intervention for adolescent and young adult survivors of childhood cancer.

BACKGROUND: This study evaluated the feasibility of a technology-enhanced group-based fitness intervention for adolescent and young adult (AYA) survivors of childhood cancer. PROCEDURE: AYA survivors ages 13-25 years were randomized to the intervention (eight in-person group sessions with mobile app and FitBit followed by 4 weeks of app and FitBit only) or waitlist control. Assessments were at 0, 2, 3, 6, and 9 months. Feasibility was evaluated by enrollment, retention, attendance, app engagement, and satisfaction. Secondary outcomes included physical activity, muscular strength/endurance, cardiorespiratory fitness, health-related quality of life, and fatigue. RESULTS: A total of 354 survivors were mailed participation letters; 68 (19%) were screened, of which 56 were eligible and 49 enrolled (88% of those screened eligible, 14% of total potentially eligible). Forty-nine survivors (Mage  = 18.5 years, 49% female) completed baseline assessments and were randomized (25 intervention, 24 waitlist). Thirty-seven (76%) completed the postintervention assessment and 32 (65%) completed the final assessment. On average, participants attended 5.7 of eight sessions (range 1-8). Overall intervention satisfaction was high (M = 4.3, SD = 0.58 on 1-5 scale). Satisfaction with the companion app was moderately high (M = 3.4, SD = 0.97). The intervention group demonstrated significantly greater improvement in lower body muscle strength compared to the waitlist postintervention, and small but not statistically significant changes in other secondary measures. CONCLUSIONS: A group-based intervention with a mobile app and fitness tracker was acceptable but has limited reach due to geographical barriers and competing demands experienced by AYA survivors.

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

BACKGROUND: Commercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions. This study aimed to evaluate methods to address missingness in data collected from commercial activity monitors. METHODS: This study utilised 1526 days of near complete data from 109 adults participating in a European weight loss maintenance study (NoHoW). We conducted simulation experiments to test a novel scaling methodology (NoHoW method) and alternative imputation strategies (overall/individual mean imputation, overall/individual multiple imputation, Kalman imputation and random forest imputation). Methods were compared for hourly, daily and 14-day physical activity estimates for steps, total daily energy expenditure (TDEE) and time in physical activity categories. In a second simulation study, individual multiple imputation, Kalman imputation and the NoHoW method were tested at different positions and quantities of missingness. Equivalence testing and root mean squared error (RMSE) were used to evaluate the ability of each of the strategies relative to the true data. RESULTS: The NoHoW method, Kalman imputation and multiple imputation methods remained statistically equivalent (p<0.05) for all physical activity metrics at the 14-day level. In the second simulation study, RMSE tended to increase with increased missingness. Multiple imputation showed the smallest RMSE for Steps and TDEE at lower levels of missingness (<19%) and the Kalman and NoHoW methods were generally superior for imputing time in physical activity categories. CONCLUSION: Individual centred imputation approaches (NoHoW method, Kalman imputation and individual Multiple imputation) offer an effective means to reduce the biases associated with missing data from activity monitors and maximise data retention.

Physical activity and atrial fibrillation: Data from wearable fitness trackers.

BACKGROUND: Regular 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. OBJECTIVE: The 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. METHODS: Daily 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. RESULTS: Among 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). CONCLUSION: Objective, 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.

Patient-generated health data collection using a wearable activity tracker in cancer patients-a feasibility study.

PURPOSE: Incorporation of patient-generated health data (PGHD) into clinical research requires an investigation of the validity of outcomes and feasibility of implementation. This single-arm pilot trial investigated the feasibility of using a commercially available activity tracking wearable device in cancer patients to assess adherence to the device and real-time PGHD collection in a clinical research setting. METHODS: From July to November 2017, enrolled adult patients were asked to wear a wristband-style device. Brief Fatigue Inventory (BFI) and MD Anderson Symptom Inventory (MDASI) were assessed at baseline and on day 29. Furthermore, 29-day Pittsburgh Sleep Quality Index, global impression of the devices, and NCI CTCAE v4 were evaluated. RESULTS: Of 30 patients (mean age, 58.6 years; male, 21 [70%]), 15 (50%) and 11 (36.7%) had gastrointestinal and lung cancer, respectively, and 27 (90%, 95% CI: 0.74-0.98) were well adhered (> 70%) to the device for 28 days. The mean adherence was 84.9% (range: 41.7-95.2%). More frequent PGHD synchronization tended to show better device adherence, with moderate correlation (r = 0.62, 95% CI: 0.33-0.80, p < 000.1). CONCLUSIONS: The feasibility of using a wearable activity tracker was confirmed in cancer patients receiving chemotherapy for a month. For future implementation in clinical trials, there is a need for further comprehensive assessment of the validity and reliability of wearable activity trackers. TRIAL REGISTRATION: This trial was registered at the University Hospital Medical Information Network Clinical Trials Registry as UMIN: UMIN000027575.

Postoperative complications and mobilisation following major abdominal surgery with vs. without fitness tracker-based feedback (EXPELLIARMUS): study protocol for a student-led multicentre randomised controlled trial (CHIR-Net SIGMA study group).

BACKGROUND: Postoperative complications following major abdominal surgery are frequent despite progress in surgical technique and perioperative care. Early and enhanced postoperative mobilisation has been advocated to reduce postoperative complications, but it is still unknown whether it can independently improve outcomes after major surgery. Fitness trackers (FTs) are a promising tool to improve postoperative mobilisation, but their effect on postoperative complications and recovery has not been investigated in clinical trials. METHODS: This is a multicentre randomised controlled trial with two parallel study groups evaluating the efficacy of an enhanced and early mobilisation protocol in combination with FT-based feedback in patients undergoing elective major abdominal surgery. Participants are randomly assigned (1:1) to either the experimental group, which receives daily step goals and a FT giving feedback about daily steps, or the control group, which is mobilised according to hospital standards. The control group also receives a FT, however with a blackened screen; thus no FT-based feedback is possible. Randomisation will be stratified by type of surgery (laparoscopic vs. open). The primary endpoint of the study is postoperative morbidity within 30 days measured via the Comprehensive Complication Index. Secondary endpoints include number of steps as well as a set of functional, morbidity and safety parameters. A total of 348 patients will be recruited in 15 German centres. The study will be conducted and organised by the student-led German Clinical Trial Network SIGMA. DISCUSSION: Our study aims at investigating whether the implementation of a simple mobilisation protocol in combination with FT-based feedback can reduce postoperative morbidity in patients undergoing major abdominal surgery. If so, FTs would offer a cost-effective intervention to enhance postoperative mobilisation and improve patient outcomes. TRIAL REGISTRATION: Deutsches Register Klinischer Studien (DRKS, German Clinical Trials Register): DRKS00016755, UTN U1111-1228-3320. Registered on 06.03.2019.

Using the Technology Acceptance Model to Explore Adolescents' Perspectives on Combining Technologies for Physical Activity Promotion Within an Intervention: Usability Study.

BACKGROUND: Wearable activity trackers and social media have been identified as having the potential to increase physical activity among adolescents, yet little is known about the perceived ease of use and perceived usefulness of the technology by adolescents. OBJECTIVE: The aim of this study was to use the technology acceptance model to explore adolescents' acceptance of wearable activity trackers used in combination with social media within a physical activity intervention. METHODS: The Raising Awareness of Physical Activity study was a 12-week physical activity intervention that combined a wearable activity tracker (Fitbit Flex) with supporting digital materials that were delivered using social media (Facebook). A total of 124 adolescents aged 13 to 14 years randomized to the intervention group (9 schools) participated in focus groups immediately post intervention. Focus groups explored adolescents' perspectives of the intervention and were analyzed using pen profiles using a coding framework based on the technology acceptance model. RESULTS: Adolescents reported that Fitbit Flex was useful as it motivated them to be active and provided feedback about their physical activity levels. However, adolescents typically reported that Fitbit Flex required effort to use, which negatively impacted on their perceived ease of use. Similarly, Facebook was considered to be a useful platform for delivering intervention content. However, adolescents generally noted preferences for using alternative social media websites, which may have impacted on negative perceptions concerning Facebook's ease of use. Perceptions of technological risks included damage to or loss of the device, integrity of data, and challenges with both Fitbit and Facebook being compatible with daily life. CONCLUSIONS: Wearable activity trackers and social media have the potential to impact adolescents' physical activity levels. The findings from this study suggest that although the adolescents recognized the potential usefulness of the wearable activity trackers and the social media platform, the effort required to use these technologies, as well as the issues concerning risks and compatibility, may have influenced overall engagement and technology acceptance. As wearable activity trackers and social media platforms can change rapidly, future research is needed to examine the factors that may influence the acceptance of specific forms of technology by using the technology acceptance model. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12616000899448; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370716.