Fitbit Charge Research Articles

Validity of a Consumer-Based Wearable to Measure Clinical Parameters in Patients With Chronic Obstructive Pulmonary Disease and Healthy Controls: Observational Study.

Consumer-based wearables are becoming more popular and provide opportunities to track individual's clinical parameters remotely. However, literature about their criterion and known-groups validity is scarce. This study aimed to assess the validity of the Fitbit Charge 4, a wrist-worn consumer-based wearable, to measure clinical parameters (ie, daily step count, resting heart rate [RHR], heart rate variability [HRV], respiratory rate [RR], and oxygen saturation) in patients with chronic obstructive pulmonary disease (COPD) and healthy controls in free-living conditions in Belgium by comparing it with medical-grade devices. Participants wore the Fitbit Charge 4 along with three medical-grade devices: (1) Dynaport MoveMonitor for 7 days, retrieving daily step count; (2) Polar H10 for 5 days, retrieving RHR, HRV, and RR; and (3) Nonin WristOX2 3150 for 4 nights, retrieving oxygen saturation. Criterion validity was assessed by investigating the agreement between day-by-day measures of the Fitbit Charge 4 and the corresponding reference devices. Known-groups validity was assessed by comparing patients with COPD and healthy controls. Data of 30 patients with COPD and 25 age- and gender-matched healthy controls resulted in good agreement between the Fitbit Charge 4 and the corresponding reference device for measuring daily step count (intraclass correlation coefficient [ICC2,1]=0.79 and ICC2,1=0.85, respectively), RHR (ICC2,1=0.80 and ICC2,1=0.79, respectively), and RR (ICC2,1=0.84 and ICC2,1=0.77, respectively). The agreement for HRV was moderate (healthy controls: ICC2,1=0.69) to strong (COPD: ICC2,1=0.87). The agreement in measuring oxygen saturation in patients with COPD was poor (ICC2,1=0.32). The Fitbit device overestimated the daily step count and underestimated HRV in both groups. While RHR and RR were overestimated in healthy controls, no difference was observed in patients with COPD. Oxygen saturation was overestimated in patients with COPD. The Fitbit Charge 4 detected significant differences in daily step count, RHR, and RR between patients with COPD and healthy controls, similar to those identified by the reference devices, supporting known-groups validity. Although the Fitbit Charge 4 shows mainly moderate to good agreement, measures of clinical parameters deviated from the reference devices, indicating that monitoring patients remotely and interpreting parameters requires caution. Differences in clinical parameters between patients with COPD and healthy controls that were measured by the reference devices were all detected by the Fitbit Charge 4.

Actigraphy Study Endpoints to Reduce Sample Size and Facilitate Drug Development for Pediatric Pulmonary Arterial Hypertension

To investigate the feasibility of using actigraphy to measure physical activity (pA) and heart rate variability (HRV) as study endpoints in pediatric pulmonary arterial hypertension (PAH) and to compare their performance to six-minute-walk distance (6MWD), a common primary endpoint used in PAH clinical trials in adults and children who can walk and understand the test process. We conducted a prospective, multicenter, non-interventional study in pediatric PAH patients and healthy children. Actiheart™ and Fitbit Charge 2™ recorded pA and heart rate (HR) data. HRV was defined as standard deviation of daily HR. Actigraphy pA and HRV and 6MWD from the same subjects were analyzed to compare children with PAH with controls, and Panama functional classification (FC) III versus II. Power/sample size simulations were conducted to detect hypothetical treatment effect equivalent to differences seen between FC III and FC II. We enrolled 116 children; 90 and 98 adhered with Actiheart and Fitbit, respectively. Actigraphy daily pA was ∼36% lower (P<0.05) and daily HRV was ∼18% lower (P<0.05) in children with PAH (n=62) than healthy controls (n=54). Daily pA and daily HRV trended ∼17% lower in FC III than FC II, whereas 6MWD showed little difference. Simulation at 80% power showed that pA required 175 subjects per group and HRV required 40 per group to detect the difference/effect, whereas 6MWD required over our maximum sample size of 200. Actigraphy is a feasible measure in pediatric PAH. Compared with 6MWD, pA and HRV may be more sensitive in differentiating Panama FC III from II. HRV may improve actigraphy's utility in pediatric PAH.

Scrolling Your Sleep Away: The Effects of Bedtime Device Use on Sleep Among Young Adults with Poor Sleep.

Many young adults report sleep problems, including insufficient sleep and poor sleep quality. Young adults are heavily reliant on electronic devices, even using them during bedtime with adverse effects on sleep. Given the importance of adequate sleep, the present study examined the daily association between using electronic devices during bedtime and sleep in a diverse sample of young adults with poor sleep. We analyzed data from a pilot randomized controlled trial in which young adults with poor sleep [n = 46; 84% female; mean age 19.3 (SD = 2.9); 30% Asian, 19% Black/African American, 9% multiracial; 34% Hispanic/Latino] wore an electronic sleep tracking device (Fitbit Charge 3) and completed daily sleep diaries including questions about sleep and bedtime routine for 4 weeks following a behavioral sleep intervention. The effect of bedtime device use on sleep latency-time needed to fall asleep-and sleep duration was estimated by generalized linear mixed models (GLMM), adjusting for weeknights/weekend nights. Bedtime device use on a given night was significantly associated with shorter self-reported sleep duration (b = - 19.80, p = .011), but not with sleep latency. Concordance between the self-reported and Fitbit-measured sleep variables was low, and bedtime device use was not associated and Fitbit-measured sleep variables. Using electronic devices before bed negatively affected self-reported sleep duration the following night. This finding highlights the importance of minimizing nightly device use among young adults with poor sleep and suggests that the inconsistency between self-reported sleep and device use warrants further investigation.

The Utility and Limitations of Remotely Delivered Feedback Messages Regarding Physical Activity in a 12-Month Weight Loss Intervention: Insights From the SMARTER Trial Focus Groups.

The increasing prevalence of obesity necessitates innovative interventions to address this grave public health concern. Standard behavioral therapy has been the mainstay for promoting weight loss, but its access and uptake are limited. The SMARTER randomized controlled trial aimed to explore the effects of self-monitoring with real-time, remotely delivered tailored feedback to recorded physical activity (PA) on weight loss. This qualitative study examined the perceived utility and limitations of key intervention components in promoting PA and weight loss in the SMARTER trial. Twenty-three participants who received feedback engaged in Zoom-based focus groups, providing insights into their interactions with the SMARTER app, Fitbit Charge 2 tracker, Aria II scale, and daily weighing. Participants' perspectives on feedback messages, personalized app experiences, and the impact of daily weighing on mental health emerged, providing nuanced insights into the multifaceted nature of success. Themes from the focus group emphasized the importance of tailoring feedback to individual preferences related to message timing, content, and engagement. The challenges that made the participants stop using the program as instructed included disappointment with not losing weight after several weeks of engagement, inability to open feedback messages within the 60-minute window, and environmental situations that prevented them from acting on the feedback received. These findings contribute to ongoing efforts geared toward refining interventions targeting PA and weight loss among individuals with obesity and provide important insights for future studies seeking to optimize the design and delivery of remotely delivered feedback messages.

Sex Differences in Indicators of Mental and Cardiovascular Health in Cannabis-Using Undergraduates

This study investigated sex differences in indicators of mental health (anxiety, depression) and cardiovascular health (e.g., physical activity levels, heart rate, VO2 max) in a sample of cannabis-using undergraduates. The sample included 209 participants (65% female) who completed two daily diaries for 14 days on their mobile devices; half of whom ( n = 100) also wore a Fitbit Charge 4. Females (vs. males) reported significantly more psychological distress at baseline; greater use of cannabis to cope with symptoms of both physical and mental ill-health; and more risks to cardiovascular health. Discriminant function analysis revealed that the variables that significantly distinguished females from males were: 1. Lower VO2 max percentile; 2. Fewer “moderate-to-very active” minutes of physical activity; 3. Higher depression scores plus poorer perceptions of mental health and physical health. Findings from this study suggest that female emerging adults who use cannabis regularly may be uniquely vulnerable to cannabis-related risks.

Variations in objectively measured sleep parameters in patients with different premature ejaculation syndromes.

Poor sleep quality is now a cause of sexual dysfunction. To investigate variations in sleep quality among patients with different types of premature ejaculation (PE) and a control group. Patients with PE were categorized into groups according to 4 types: lifelong (LPE), acquired (APE), variable (VPE), and subjective (SPE). Basic demographic information about the participants was first collected, and then clinical data were obtained. Outcomes included the 5-item International Index of Erectile Function, Premature Ejaculation Diagnostic Tool, 7-item Generalized Anxiety Disorder, 9-item Patient Health Questionnaire, Pittsburgh Sleep Quality Index, self-estimated intravaginal ejaculation latency time (minutes), and sleep monitoring parameters obtained from a wearable device (Fitbit Charge 2). A total of 215 participants were enrolled in the study, of which 136 patients with PE were distributed as follows: LPE (31.62%), APE (42.65%), VPE (10.29%), and SPE (15.44%). Subjective scales showed that patients with APE were accompanied by a higher prevalence of erectile dysfunction, anxiety, and depression, as well as poorer sleep quality (assessed by the Pittsburgh Sleep Quality Index). The results of objective sleep parameters revealed that average durations of sleep onset latency (minutes) and wake after sleep onset (minutes) in patients with APE (mean ± SD; 20.03 ± 9.14, 55 ± 23.15) were significantly higher than those with LPE (15.07 ± 5.19, 45.09 ± 20.14), VPE (13.64 ± 3.73, 38.14 ± 11.53), and SPE (14.81 ± 4.33, 42.86 ± 13.14) and the control group (12.48 ± 3.45, 37.14 ± 15.01; P < .05). The average duration of rapid eye movement (REM; minutes) in patients with APE (71.34 ± 23.18) was significantly lower than that in patients with LPE (79.67 ± 21.53), VPE (85.93 ± 6.93), and SPE (80.86 ± 13.04) and the control group (86.56 ± 11.93; P < .05). Similarly, when compared with the control group, patients with LPE had significantly longer durations of sleep onset latency and wake after sleep onset and a significantly shorter duration of REM sleep. Our study suggests that clinicians should pay attention not only to male physical assessment but also to mental health and sleep quality. This study suggests that changes in sleep structure occur in patients with PE, which may provide some direction for future research. However, the cross-sectional study design does not allow us to conclude that sleep is a risk factor for PE. After controlling for traditional parameters such as age, erectile dysfunction, anxiety, and depression, sleep parameters are independently associated with PE. Patients with APE and LPE show significant alterations in sleep parameters, with patients with APE having notably poorer sleep quality, whereas patients with VPE and SPE have sleep parameters similar to controls.

Feasibility characteristics of wrist-worn fitness trackers in health status monitoring for post-COVID patients in remote and rural areas.

Common, consumer-grade biosensors mounted on fitness trackers and smartwatches can measure an array of biometrics that have potential utility in post-discharge medical monitoring, especially in remote/rural communities. The feasibility characteristics for wrist-worn biosensors are poorly described for post-COVID conditions and rural populations. We prospectively recruited patients in rural communities who were enrolled in an at-home rehabilitation program for post-COVID conditions. They were asked to wear a FitBit Charge 2 device and biosensor parameters were analyzed [e.g. heart rate, sleep, and activity]. Electronic patient reported outcome measures [E-PROMS] for mental [bi-weekly] and physical [daily] symptoms were collected using SMS text or email [per patient preference]. Exit surveys and interviews evaluated the patient experience. Ten patients were observed for an average of 58 days and half [N = 5] were monitored for 8 weeks or more. Five patients [50%] had been hospitalized with COVID [mean stay = 41 days] and 4 [36%] had required mechanical ventilation. As baseline, patients had moderate to severe levels of anxiety, depression, and stress; fatigue and shortness of breath were the most prevalent physical symptoms. Four patients [40%] already owned a smartwatch. In total, 575 patient days of patient monitoring occurred across 10 patients. Biosensor data was usable for 91.3% of study hours and surveys were completed 82.1% and 78.7% of the time for physical and mental symptoms, respectively. Positive correlations were observed between stress and resting heart rate [r = 0.360, p<0.01], stress and daily steps [r = 0.335, p<0.01], and anxiety and daily steps [r = 0.289, p<0.01]. There was a trend toward negative correlation between sleep time and physical symptom burden [r = -0.211, p = 0.05]. Patients reported an overall positive experience and identified the potential for wearable devices to improve medical safety and access to care. Concerns around data privacy/security were infrequent. We report excellent feasibility characteristics for wrist-worn biosensors and e-PROMS as a possible substrate for multi-modal disease tracking in post-COVID conditions. Adapting consumer-grade wearables for medical use and scalable remote patient monitoring holds great potential.

Predicting Workers' Stress: Application of a High-Performance Algorithm Using Working-Style Characteristics.

Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual's lifestyle has not been evaluated. This study aims to develop a novel, high-performance algorithm to predict an employee's stress among a group of employees with similar working characteristics. This prospective observational study evaluated participants' responses to web‑based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm.

Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development.

Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI. For 10 days, adult smokers attempting to quit were asked to complete 16 hourly EMAs/day assessing cravings, mood, activity, social context, physical context, and lapse incidence, and to wear a Fitbit Charge 4 during waking hours to passively collect data on steps and heart rate. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested, without and with the sensor data. Their ability to predict lapses for out-of-sample (i) observations and (ii) individuals were evaluated. Next, a series of individual-level and hybrid (i.e., group- and individual-level) algorithms were trained and tested. Participants (N = 38) responded to 6,124 EMAs (with 6.9% of responses reporting a lapse). Without sensor data, the best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI = 0.871-0.928). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.524-0.994; median AUC = 0.639). 15/38 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.855 (range: 0.451-1.000). Hybrid algorithms could be constructed for 25/38 participants, with a median AUC of 0.692 (range: 0.523 to 0.998). With sensor data, the best-performing group-level algorithm had an AUC of 0.952 (95% CI = 0.933-0.970). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.494-0.979; median AUC = 0.745). 11/30 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.983 (range: 0.549-1.000). Hybrid algorithms could be constructed for 20/30 participants, with a median AUC of 0.772 (range: 0.444 to 0.968). In conclusion, high-performing group-level lapse prediction algorithms without and with sensor data had variable performance when applied to out-of-sample individuals. Individual-level and hybrid algorithms could be constructed for a limited number of individuals but had improved performance, particularly when incorporating sensor data for participants with sufficient wear time. Feasibility constraints and the need to balance multiple success criteria in the JITAI development and implementation process are discussed.

Effect of desflurane maintenance on postoperative sleep quality in patients undergoing elective breast surgery: A non-inferiority randomized controlled trial

BackgroundPostoperative sleep disturbance (PSD) is prevalent in perioperative patients，and has significant impact on postoperative recovery and prognosis. The aim of this study was to investigate the effect of desflurane maintenance on postoperative sleep quality, in order to optimize patients' perioperative sleep management. MethodA total of 118 patients undergoing elective breast surgery were randomized to receive either desflurane-based volatile anesthesia (desflurane group) or propofol-based total intravenous anesthesia (propofol group) for anesthesia maintenance. The primary outcome was the quality of sleep, which was assessed by the Pittsburgh Sleep Quality Index (PSQI) on 3 days after operation (POD3). Secondary outcomes were PSQI on postoperative day 7 (POD7) and 30 (POD30), and postoperative anxiety, depression, and pain score, as well as objective sleep parameters including total sleep time (TST), WASO (Wakefulness after sleep onset), REM (Rapid eye movement) and NREM (Non-rapid Eye Movement) measured by Fitbit Charge 2TM during the initial 3 postoperative days. ResultsThe global PSQI scores on POD3 in the desflurane group was non-inferior to that in the propofol group [mean (SD) 8.47 (3.46) vs. 7.65 (3.16); mean difference (95 % CI) 0.82 (−0.43, 2.07); p < 0.001 for non-inferiority]. There were no significant differences in PSQI scores on POD3 and POD7. In addition, the score of anxiety, depression, and pain on the 3rd, 7th, and 30th day after surgery have no significant differences between the propofol and the desflurane group, respectively. The postoperative NREM was higher in the desflurane group than that in the propofol group. ConclusionThe effects of desflurane-based volatile anesthesia maintenance on postoperative sleep quality is not inferior to that of propofol-based total intravenous anesthesia, and these two drugs may have different effects on the sleep structure. Trial registrationClinicalTrials.gov Identifier: NCT04805775.

Commercially available activity monitors such as the fitbit charge and apple watch show poor validity in patients with gait aids after total knee arthroplasty

PurposeThe aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery.MethodsTwenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques.ResultsMean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from − 0.87 to 1.36 with limits of agreement (LOA) ranging between − 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between − 2.8 and 5.73 with LOA between − 13.51 and 24.97.ConclusionsThese devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

Home-based aerobic exercise feasibility in oxaliplatin-receiving newly-diagnosed cancer survivors

PurposePhysical activity (PA) is beneficial but difficult to maintain during chemotherapy. This pilot RCT explored the feasibility of the MI-Walk intervention—an 8-week motivational enhancement therapy- and home-based brisk walking intervention—among gastrointestinal (GI) cancer survivors receiving chemotherapy. MethodsSixty stage II-IV GI cancer survivors were recruited from 5 sites at their second infusion visit. Participants were randomized to receive PA education alone or the MI-Walk intervention: motivational enhancement therapy consisting of 3 motivational interviewing and self-efficacy-enhancing counseling sessions, a Fitbit Charge 2, exercise diaries, telephone follow-up, scripted motivational email messages, and optional weekly walking groups. ResultsThe enrollment and completion rates were 62% and 90%, respectively. The MI-Walk participants (n = 29; mean age = 56.79, SD = 11.72; 97% white; 79% male) reported a baseline moderate-vigorous PA duration of 250.93 (SD = 636.52) min/wk. The mean MI-Walk Intervention acceptability score was 50.32 (SD = 12.02) on a scale of 14–70. Mean Fitbit and counseling helpfulness scores on a 5-point scale were 3.67 (SD = 1.43) and 3.44 (SD = 1.36), respectively. Participants’ Fitbit moderate-vigorous PA 8-week averages ranged from 0 to 716.88 min/wk; 64% of participants adhered to ≥127 min/wk. Several characteristics (e.g., age, comorbidity, PA level, employment status, BMI, education level, gender, symptoms) were associated with enrollment, attrition, and intervention acceptability and adherence (p < 0.05). ConclusionEnrollment and retention were adequate. The Fitbit and counseling were the most helpful. Acceptability and adherence varied based on participant characteristics; therefore, intervention tailoring and further research among cancer survivors less physically active at baseline and most in need of complex exercise intervention are needed.ClinicalTrials.gov NCT03515356.

Exploring Health-Related Quality of Life in Children With Hypertrophic Cardiomyopathy and Relationship to Physical Activity.

Hypertrophic cardiomyopathy is a burdensome condition that inflicts both physical and psychological impairment on those with the disease, negatively impacting health-related quality of life (HRQoL). Given the abundance of evidence suggesting a role of physical activity (PA) in modulating HRQoL in healthy populations of children, we sought to determine the relationship between HRQoL and PA in children diagnosed with hypertrophic cardiomyopathy. A multicenter prospective observational cohort study was conducted, with patients with hypertrophic cardiomyopathy aged 10 to 19 years being provided a wrist-worn activity tracker (Fitbit Charge HR) to wear for 14 days. Patients self-reported on Pediatric Quality of Life 4.0 quality of life inventory items, which were associated with PA metrics following covariate adjustment using linear regression. A total of 56 participants were recruited to the study. The median age at enrollment was 15.5 years (interquartile range, 13.8-16.8), and 16 out of 56 (29%) of the cohort were girls. The cohort reported decreased metrics of physical, psychosocial, and total summary scores compared with health reference populations, with scores comparable with that of published populations with chronic disease. Increased physical HRQoL scores were significantly associated with increased daily steps taken, distance traveled, and flights of stairs climbed. These results show that impaired PA correlates with reduced HRQoL in children with hypertrophic cardiomyopathy, suggesting PA may partially mediate HRQoL in this population.

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wearing Fitbit Charge HR 2 devices in the Adolescent Brain Cognitive Development (ABCD) Study, glass box machine learning models identified obesity predictors from Fitbit-derived measures of sleep, cardiovascular fitness, and sociodemographic status. Key predictors of obesity include identifying as Non-White race, low household income, later bedtime, short sleep duration, variable sleep timing, low daily step counts, and high heart rates (AUCMean = 0.726). Findings highlight the importance of inadequate sleep, physical inactivity, and socioeconomic disparities, for obesity risk. Results also show the clinical applicability of wearables for continuous monitoring of sleep and cardiovascular fitness in adolescents. Identifying the tipping points in the predictors of obesity risk can inform interventions and treatment strategies to reduce obesity rates in adolescents.

Accuracy of the Apple Watch Series 4 and Fitbit Versa for Assessing Energy Expenditure and Heart Rate of Wheelchair Users During Treadmill Wheelchair Propulsion: Cross-sectional Study.

The Apple Watch (AW) Series 1 provides energy expenditure (EE) for wheelchair users but was found to be inaccurate with an error of approximately 30%, and the corresponding error for heart rate (HR) provided by the Fitbit Charge 2 was approximately 10% to 20%. Improved accuracy of estimated EE and HR is expected with newer editions of these smart watches (SWs). This study aims to assess the accuracy of the AW Series 4 (wheelchair-specific setting) and the Fitbit Versa (treadmill running mode) for estimating EE and HR during wheelchair propulsion at different intensities. Data from 20 manual wheelchair users (male: n=11, female: n=9; body mass: mean 75, SD 19 kg) and 20 people without a disability (male: n=11, female: n=9; body mass: mean 75, SD 11 kg) were included. Three 4-minute wheelchair propulsion stages at increasing speed were performed on 3 separate test days (0.5%, 2.5%, or 5% incline), while EE and HR were collected by criterion devices and the AW or Fitbit. The mean absolute percentage error (MAPE) was used to indicate the absolute agreement between the criterion device and SWs for EE and HR. Additionally, linear mixed model analyses assessed the effect of exercise intensity, sex, and group on the SW error. Interclass correlation coefficients were used to assess relative agreement between criterion devices and SWs. The AW underestimated EE with MAPEs of 29.2% (SD 22%) in wheelchair users and 30% (SD 12%) in people without a disability. The Fitbit overestimated EE with MAPEs of 73.9% (SD 7%) in wheelchair users and 44.7% (SD 38%) in people without a disability. Both SWs underestimated HR. The device error for EE and HR increased with intensity for both SWs (all comparisons: P<.001), and the only significant difference between groups was found for HR in the AW (-5.27 beats/min for wheelchair users; P=.02). There was a significant effect of sex on the estimation error in EE, with worse accuracy for the AW (-0.69 kcal/min; P<.001) and better accuracy for the Fitbit (-2.08 kcal/min; P<.001) in female participants. For HR, sex differences were found only for the AW, with a smaller error in female participants (5.23 beats/min; P=.02). Interclass correlation coefficients showed poor to moderate relative agreement for both SWs apart from 2 stage-incline combinations (AW: 0.12-0.57 for EE and 0.11-0.86 for HR; Fitbit: 0.06-0.85 for EE and 0.03-0.29 for HR). Neither the AW nor Fitbit were sufficiently accurate for estimating EE or HR during wheelchair propulsion. The AW underestimated EE and the Fitbit overestimated EE, and both SWs underestimated HR. Caution is hence required when using SWs as a tool for training intensity regulation and energy balance or imbalance in wheelchair users.

Abstract 2246: The impact of a waitlist control group on physical activity level in the activity after cancer treatment exercise oncology randomized pilot study

Abstract Background: In randomized clinical trials, control conditions such as blinding are essential for mitigating threats to validity. In exercise oncology trials, researchers are unable to blind participants to randomization, which may confer unintended consequences (i.e., crossover or dropout) that may threaten validity. Despite prior suggestions that a waitlist control is optimal for lessening these consequences, to date there is little agreement concerning control conditions in exercise oncology. Further, the use of a waitlist control is complicated by concerns regarding the ethical implications of instructing participants to delay healthy behavior change. Herein, we leveraged data from the Activity after Cancer Treatment (ACT) study to evaluate the impact of randomization to a waitlist control group on objectively assessed physical activity (PA) levels in breast cancer (BC) survivors. Methods: The ACT Study (PI: Ambrosone) was a 12-week, four-arm pilot study at Roswell Park Comprehensive Cancer Center. Participants included 37 BC survivors who were randomized to either a waitlist control group (n=10), or one of three exercise interventions of varying intensities including home-based walking (n=9), home-based Zumba (n=9), or a supervised high-intensity interval training treadmill protocol (n=9) delivered by study personnel at Roswell Park Comprehensive Cancer Center. The waitlist group was asked to maintain their usual PA during the 12-week intervention. All participants received weekly phone calls to optimize engagement and ensure Fitbit data were properly synced. For the current analysis, the primary analytic exposure was randomization assignment (control or active intervention); the analytic outcomes included steps/day, moderate-to-vigorous PA (MVPA) minutes/week and total active minutes/week as measured by the Fitbit Charge 2. ANOVA was used to assess mean group differences in steps and active minutes. Results: At study conclusion, the control group accumulated significantly fewer steps/day [6,087 (±1,353)] than the active intervention groups [8,729 (±2,585), p=0.0003]. The control group also accumulated significantly fewer MVPA minutes/week [146.2 (±114.9) vs. 264.1 (±192.3), p=0.045] and total active minutes/week [1,478 (±291.0) vs. 1,976.8 (±658.6), p=0.003] in comparison to the active intervention groups, respectively. Conclusions: BC survivors randomized to active intervention accumulated significantly more steps, MVPA and total active minutes than the waitlist group. Although the waitlist was effective for minimizing PA relative to the active arms, waitlisted participants exceeded the national step average of 4800 steps/day by 1200 steps and nearly met the minimum PA recommendation of 150 minutes of MVPA/week, potentially assuaging ethical or public health concerns regarding the delay of health-seeking behavior. Citation Format: Rikki Cannioto, Evan Davis, Karen Hulme, Chi-Chen Hong, Carol DeNysschen, Tracey O'Connor, Christine Ambrosone. The impact of a waitlist control group on physical activity level in the activity after cancer treatment exercise oncology randomized pilot study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2246.

Abstract 17: Associations Between Digital Health App Engagement and Physical Activity Among African American Women With Cardiovascular-Kidney-Metabolic Syndrome: Data From the Step It Up Digital Health-Enabled Physical Activity Intervention

Background: Physical activity (PA) improves cardiovascular-kidney-metabolic health (CKMH); however, few digital health tools to increase PA are tailored for populations disproportionately impacted by adverse social determinants, such as African American (AA) women. Less is known about the effects of engagement with digital health applications (apps) on PA among AA women from under-resourced communities. The objective was to examine associations between app engagement and PA over six months in the Step It Up (SIU) digital health PA intervention. Methods: We enrolled women with ≥Stage 1 CKM syndrome (overweight/obesity) from resource-limited Washington, DC areas into SIU. Before the study, we created the SIU app with community-engaged approaches. Participants used the SIU app and a Fitbit Charge 2 PA monitor for the study. After collecting baseline PA data for two weeks, the app provides interactive PA educational modules, motivational and place-tailored messages about PA, and CKMH self-monitoring tools for six months. We examined associations between overall app and specific feature engagement (i.e. ecological momentary stress assessments (EMA) completed when opening the app, goal setting, modules) with PA (in steps/day) using linear mixed modeling. Results: Participants (N=57) had mean age=60±14 years and BMI=37±8 kg/m 2 . Overall app engagement was positively associated with PA, with each unit increase in engagement associated with 148 more steps/day (p&lt;0.001, Table). Of the app features, engagement with the app EMA was significantly associated with higher PA (p&lt;0.001). Engagement with app goal-setting tools and modules were less significantly associated with higher PA (p=.06 and p=.07 respectively). Conclusions: Engagement with community-tailored, digital health tools like the SIU app can increase PA for AA women with CKM syndrome from under-resourced communities. Future studies should identify methods for increasing engagement in digital health interventions to improve PA and CKMH among AA women.

Benefits of physical activity self-monitoring in patients with haemophilia: a prospective study with one-year follow-up.

Activity wristbands have been shown to be effective in relation to self-monitoring activity levels and increasing exercise adherence. However, previous reports have been based on short-term follow-ups in people with haemophilia (PWH). (1) To evaluate compliance with physical activity (PA) recommendations in PWH during a 1-year follow-up period using activity wristbands to record daily steps and intensity; (2) To determine the effect of PA self-monitoring on clinical outcomes. A prospective observational study was conducted in 27 adults with severe haemophilia undergoing prophylactic treatment. The Fitbit Charge HR was used to track daily PA for an entire year. The participants were encouraged to try to reach a goal of 10,000 steps/day and to track their progress. The pre- and post-evaluation included quality of life (A36 Hemophilia-QoL Questionnaire), joint health (Haemophilia Joint Health Score), functionality (Timed Up and Go test), and muscle strength. A total of 323.63 (95%CI: 194-364) valid days (i.e.,>2000 steps) were recorded. The annual average number of steps per day taken by participants was 10,379. Sixteen (59%) PWH reached 10,000 steps/day at baseline and 17 (63%) at 1 year follow-up, with no significant differences (x2=.33; p=.56). A statistically significant improvement was observed in daily moderate activity time (p=.012) and in the 'physical health' quality of life subscale (mean difference: 2.15 points; 95%CI:.64-3.65; p=.007). Our results suggest that patients with severe haemophilia who self-managed their PA can improve their long-term quality of life in the domain of physical health and also the daily time spent in moderate-intensity PA.

Imagery rescripting and cognitive restructuring for inpatients with moderate and severe depression – a controlled pilot study

BackgroundThis controlled pilot study investigates the effect of the combined use of cognitive restructuring (CR) and imagery rescripting (IR) compared to treatment as usual among inpatients with moderate and severe depression. Alongside expert ratings and self-report tools, fitness wristbands were used as an assessment tool.MethodsIn addition to the standard inpatient care (SIC) program, 33 inpatients with moderate and severe depression were randomly assigned to an intervention group (two sessions of IR and CR) or an active treatment-as-usual (TAU) control group (two sessions of problem-solving and build-up of positive activity). Depression severity was assessed by the Hamilton Depression Rating Scale-21 (HDRS-21), the Beck Depression Inventory-II (BDI-II), and as a diagnostic adjunct daily step count via the Fitbit Charge 3™. We applied for analyses of HDRS-21 and BDI-II, 2 × 2 repeated-measures analysis of variance (ANOVA), and an asymptotic Wilcoxon test for step count.ResultsThe main effect of time on both treatments was η2 = .402. Based on the data from the HDRS-21, patients in the intervention group achieved significantly greater improvements over time than the TAU group (η2 = .34). The BDI-II data did not demonstrate a significant interaction effect by group (η2 = .067). The daily hourly step count for participants of the intervention group was significantly higher (r = .67) than the step count for the control group.ConclusionsThe findings support the utilization of imagery-based interventions for treating depression. They also provide insights into using fitness trackers as psychopathological assessment tools for depressed patients.Trial registrationThe trial is registered at the German Clinical Trials Register (Deutsches Register Klinischer Studien) under the registration number: DRKS00030809.

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease.

Wearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD. Ten participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data. FB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79-0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5% sensitivity and 93.7 ± 2.5% specificity, rapid eye movement sleep with high sensitivity and specificity (78.7 ± 31.9%, 95.6 ± 2.3%), and deep sleep with lower sensitivity but high specificity (56.4 ± 28.8%, 95.0 ± 4.8%). FB heart rate was strongly correlated with PSG, and the mean absolute error between FB and PSG heart rate data was 1.16 ± 0.42 beats/min. At home, longer sleep and shorter wake times were observed compared with hospital data, whereas percentage sleep stage times were consistent with hospital data. Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD. Doheny EP, Renerts K, Braun A, etal. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease. J Clin Sleep Med. 2024;20(7):1163-1171.