Fitbit Alta HR Research Articles

Museum-based art activities to stay young at heart? Results of a randomized controlled trial.

Health benefits have been reported with art activities. Heart rate is a biomarker of health state. The aim of this randomized controlled trial (RCT) was to compare the changes in heart rate over a 3 month-period in older adults participating in art-based activities at the Montreal Museum of Fine Arts (MMFA, Quebec, Canada) and in their control counterparts. Participants (mean age 71.0 ± 5.1; 84.9% female) were a subset of older community dwellers recruited in a RCT in two parallel groups (n = 28 in the intervention group and n = 25 in the control group) who had their heart rate recorded. They attended weekly participatory MMFA-based art activities over a 3-month period. Heart rate was collected via the smart watch Fitbit Alta HR at baseline (M0) and at 3 months (M3). The outcomes were mean heart rate per hour for the full day, including active and inactive hours. Heart rate for full day (p = 0.018) and active hours (p = 0.028) were slower in the intervention group compared to the control group. Decrease in mean heart rate for full day between M0 and M3 in the intervention group was higher than in the control group (p = 0.030). The linear regression showed that MMFA-based art activities decreased full day heart rate (Coefficient of regression Beta = -6.2 with p = 0.010). MMFA-based art activities significantly decreased full day heart rate, suggesting a health benefit in older community dwellers who participated in the RCT.Clinical trial registration: NCT03679715.

Effects of museum-based art activities on older community dwellers' physical activity: the A-health randomized controlled trial results.

Museum-based art activities have demonstrated health benefits in older adults. Few clinical trials, however, have examined physical health benefits specifically. This randomized controlled trial (RCT) aims to compare changes in daily step count over a 3-month period in older adults participating in museum-based art activities and their control counterparts. Using a subset of 53 participants recruited in the A-health RCT, the daily step count of 28 participants in the intervention group (age 70.5 ± 4.9 and 92.0% female) and 25 in the control group (age 71.5 ± 5.3 and 78.6% female) were recorded using a Fitbit Alta HR. Weekly art activities were carried out at the Montreal Museum of Fine Arts (MMFA, Quebec, Canada) over a 3-month period. The outcomes were the mean step count per active hours (i.e., between noon and 6pm), inactive hours (i.e., between midnight and 6 am) and over the full day (i.e., 24h) and the change in step count following the 3-month (M3) art-based intervention at the MMFA. The intervention group had a greater daily step count compared to the control group at M3, regardless of the step parameters examined (P ≤ 0.026). Linear regressions showed that the change in daily step count for the full day (P ≤ 0.010) and active hours (P ≤ 0.026) increased significantly with the MMFA art-based activities. MMFA-based art activities improved daily physical activity in older community-dwellers who participated in the RCT, confirming health benefits and suggesting the potential of museums in health promotion and disease prevention.

Resident Sleep During Traditional Home Call Compared to Night Float.

Purpose This article aims to compare resident sleep while on night float with a traditional home call. Methods We conducted a crossover observational study assessing sleep patterns of seven postgraduate year-2 ophthalmology residents at the University of Washington from 2019 to 2021 using the Fitbit Alta HR device. Overnight call was scheduled from 5 p.m. to 8 a.m. on weekdays, and 8 a.m. to 8 a.m. on weekends. The residency program implemented a partial night float rotation, during which two to three nights of consecutive call were assigned to a resident without other clinical duties. Sleep was recorded using the Fitbit Alta HR for residents while on a 5-week partial night float rotation, on 10-week home call rotations, with postcall relief, and for stretches of seven or more days without call responsibilities. Mixed model regression analysis was used to compare average sleep on home call, night float, and periods without call. Results Sleep data were recorded for a total of 1,015 nights, including 503 nights on home call rotation and 230 nights on night float rotation. Residents slept more during periods away from call compared to either night float or home call rotations ( p < 0.001). Residents experienced increased average overall sleep during 10-week rotations on night float compared to home call ( p = 0.008). While there was no difference in overnight sleep on call between night float and home call ( p = 0.701), residents experienced more sleep overall while on call on night float compared to home call due to more sleep being recorded during postcall naps ( p = 0.016). Conclusion Implementing a night float system can increase resident sleep by allowing for more sleep recovery during time away from clinical duties.

The development of a family-based wearable intervention using behaviour change and co-design approaches: move and connect

BackgroundPrevious research has explored the effectiveness of wearable activity trackers (wearables) for increasing child physical activity (PA) levels, but there have been mixed results. The use of theoretical frameworks and co-design techniques are recognised ways of increasing an intervention's acceptability and effectiveness. AimsThis study aims to use co-design workshops and an evidence-based theoretical framework (the Behaviour Change Wheel) to develop a family-based PA intervention using wearables. MethodsThree stages of intervention development outlined by the Behaviour Change Wheel were used. Co-design workshops with seven families (11 parents and 12 children) and seven PA experts were conducted where stakeholders discussed how to overcome previously identified barriers to families being active and using wearables. This resulted in the intervention's components being developed, with each component's mechanisms of action (e.g. intervention functions and behaviour change techniques) being retrospectively identified. ResultsThe ‘Move & Connect’ intervention was developed, which targets family PA and wearable use. The intervention takes a flexible approach and includes eight components, including wearable devices (Fitbit Alta HR), support resources, an introductory workshop, collective challenges, goal setting and reviewing, engagement prompts, social support and health-related resources (e.g. educational videos). The intervention incorporates six intervention functions targeting PA and wearable use: education, training, modelling, persuasion, incentivisation and environmental restructuring and 24 behaviour change techniques, including goal setting, social comparison, feedback on behaviour and graded task. ConclusionsThis is the first known study to use an evidence-based framework and co-design to develop a family-based wearable intervention. The identification of the intervention's mechanisms of action will prove useful when implementing and evaluating the ‘Move & Connect’ intervention and allow researchers to replicate its components.

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression

PurposeTo examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial. MethodsThis post hoc analysis used data from the ENGAGE-2 trial where adults (n = 106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M). ResultsFeatures of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, β = −0.78, p < .05; 6M, β = −0.80, p < .05) and low depressive symptoms (6M, β = −0.15, p < .05). Continuous decline in step count was associated with high weight (2M, β = 0.58, p < .05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M. ConclusionsFeatures of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Fitbits for Monitoring Depressive Symptoms in Older Aged Persons: Qualitative Feasibility Study.

In 2022, an estimated 1.105 billion people used smart wearables and 31 million used Fitbit devices worldwide. Although there is growing evidence for the use of smart wearables to benefit physical health, more research is required on the feasibility of using these devices for mental health and well-being. In studies focusing on emotion recognition, emotions are often inferred and dependent on external cues, which may not be representative of true emotional states. The aim of this study was to evaluate the feasibility and acceptability of using consumer-grade activity trackers for apps in the remote mental health monitoring of older aged people. Older adults were recruited using criterion sampling. Participants were provided an activity tracker (Fitbit Alta HR) and completed weekly online questionnaires, including the Geriatric Depression Scale, for 4 weeks. Before and after the study period, semistructured qualitative interviews were conducted to provide insight into the acceptance and feasibility of performing the protocol over a 4-week period. Interview transcripts were analyzed using a hybrid inductive-deductive thematic analysis. In total, 12 participants enrolled in the study, and 9 returned for interviews after the study period. Participants had positive attitudes toward being remotely monitored, with 78% (7/9) of participants experiencing no inconvenience throughout the study period. Moreover, 67% (6/9) were interested in trialing our prototype when it is implemented. Participants stated they would feel more comfortable if mental well-being was being monitored by carers remotely. Fitbit-like devices were an unobtrusive and convenient tool to collect physiological user data. Future research should integrate physiological user inputs to differentiate and predict depressive tendencies in users.

The Accuracy of Commercially Available Fitness Trackers in Patients after Stroke.

Background: Fitness trackers could represent an easy-to-use and cheap tool for continuous tracking of physical activity of stroke survivors during the period of their recovery at home. The aim of the study was to examine the accuracy of the Fitbit activity tracker in locomotor activity monitoring of stroke survivors with respect to gait disorders, walking speed, walking aid, and placement of the tracker on body. Methods: Twenty-four ambulatory stroke survivors (15 men and 9 women) with locomotion/gait disorder were involved in the study. Patients underwent two walking tests with the Fitbit Alta HR trackers attached on 5 different places on body. The accuracy of the trackers has been analyzed on 3 groups of patients—those walking without any walking aid, those using a single-point stick and those using a rolling walker. Results: For no-aid patients, the most accurate place was the waist. Patients with a single-point stick revealed the smallest deviations for a tracker attached to a healthy lower limb, and patients with a rolling walker revealed the smallest deviations for a tracker attached on the paretic lower limb. Conclusions: An accuracy comparable with the healthy population can be reached for all of the three groups of patients, while fulfilling the conditions for minimum speed of 2 km/h and optimal placement of the trackers with respect to a walking aid and aspect to impairment.

Effectiveness of Fitbit Activity Prompts in Reducing Sitting Time and Increasing Physical Activity in University Employees: A Randomized Controlled Trial

Consumer activity devices use prompts to alter sedentary and physical activity (PA) behaviors. However, it is unclear if PA prompts are effective. Purpose: To evaluate the effectiveness of PA prompts from a consumer wearable device in reducing sitting time and increasing PA in university employees. Methods: Thirty-three university employees without a history of consumer activity device wear were randomly assigned a Fitbit Alta HR that administered PA prompts (Prompt group) or had the prompt feature deactivated (No Prompt group). Participants wore an activPAL for 5–7 days to measure baseline sitting time and PA behaviors. Participants then wore the Fitbit for 12 days during waking hours and an activPAL during the last 5–7 days of the Fitbit wear period. Changes in activPAL sitting time and PA were compared across groups. Mean Fitbit steps taken in the first 50 min and the last 10 min of each hour were calculated and compared across groups during “Inactive” hours (&lt;250 steps in the first 50 min), where a prompt was given (Prompt group) or would have been given (No Prompt group). Results: Mean activPAL sitting time increased in the Prompt group (0.66 ± 1.70 hr/day) and remained stable in the No Prompt group (−0.04 ± 1.29 hr/day), with no statistically significant differences between groups (d = 0.33, p = .36). Moderate to vigorous PA increased modestly in both groups, but no significant differences were observed. Fitbit steps during the last 10 min of inactive hours did not differ across groups. Conclusion: Fitbit PA prompts did not alter sitting time or PA behaviors in university employees.

Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study.

Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7–14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1–8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk.

0348 Sleep Staging Using End-to-End Deep Learning Model Based on Nocturnal Sound for Smartphones

Abstract Introduction Convenient sleep tracking with mobile devices such as smartphones is desirable for people who want to easily objectify their sleep. The objective of this study was to introduce a deep learning model for sound-based sleep staging using audio data recorded with smartphones during sleep. Methods Two different audio datasets were used. One (N = 1,154) was extracted from polysomnography (PSG) data and the other (N = 327) was recorded using a smartphone during PSG from independent subjects. The performance of sound-based sleep staging would always depend on the quality of the audio. In practical conditions (non-contact and smartphone microphones), breathing and body movement sounds during night are so weak that the energy of such signals is sometimes smaller than that of ambient noise. The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement sound from ambient noise. The proposed neural network model consisted of two sub-models. The first sub-model extracted features from each 30-second epoch Mel spectrogram and the second one classified sleep stages through inter-epoch analysis of extracted features. Results Our model achieved 70 % epoch-by-epoch agreement for 4-class (wake, light, deep, rapid eye movement) stage classification and robust performance across various signal-to-noise conditions. More precisely, the model was correct in 77% of wake, 73% of light, 46% of deep, and 66% of REM. The model performance was not considerably affected by existence of sleep apnea but degradation observed with severe periodic limb movement. External validation with smartphone dataset also showed 68 % epoch-by-epoch agreement. Compared with some commercially available sleep trackers such as Fitbit Alta HR (0.6325 in mean per-class sensitivity) and SleepScore Max (0.565 in mean per-class sensitivity), our model showed superior performance in both PSG audio (0.655 in mean per-class sensitivity) and smartphone audio (0.6525 in mean per-class sensitivity). Conclusion To the best of our knowledge, this is the first end (Mel spectrogram-based feature extraction)-to-end (sleep staging) deep learning model that can work with audio data in practical conditions. Our proposed deep learning model of sound-based sleep staging has potential to be integrated in smartphone application for reliable at-home sleep tracking. Support (If Any)

Evaluation of Sleep Parameters and Sleep Staging (Slow Wave Sleep) in Athletes by Fitbit Alta HR, a Consumer Sleep Tracking Device.

PurposeSleep is an essential factor for athletes, and it is important to intervene in sleep to manage it. We need a device that can evaluate sleep easily and constantly. Consumer wearable devices can be useful tools for athletes. In order to use consumer wearable devices in clinical research, it is essential to conduct a validation study. Thus, we conducted a validation study to assess the Fitbit Alta HRTM (FBA)- a consumer wearable device with an accelerometer and a heart rate monitor to detect sleep stages and quality against electroencephalographic (EEG) studies in athletes.Patients and MethodsForty college athletes participated in the study. EEG was applied to participants simultaneously while wearing FBA.ResultsRegarding sleep parameters, there was a strong correlation between the total sleep time (TST)-EEG and the TST-Fitbit (r = 0.83; p < 0.001). Regarding the sleep stages, there was a modest correlation between the N3 sleep-EEG and the N3 sleep-Fitbit (r = 0.68; p < 0.001). In addition, there was a strong correlation between the percentage of N3 sleep in between sleep onset and initial rapid eye movement sleep-EEG and those on Fitbit (r = 0.73; p < 0.001).ConclusionThese results demonstrate that FBA facilitates sleep monitoring and exhibits acceptable agreement with EEG. Therefore, FBA is a useful tool in athletes’ sleep management.

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

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

Commercial Devices Provide Estimates of Energy Balance with Varying Degrees of Validity in Free-Living Adults

BackgroundThe challenges of accurate estimation of energy intake (EI) are well-documented, with self-reported values 12%–20% below expected values. New approaches rely on gold-standard assessments of the other components of energy balance, energy expenditure (EE) and energy storage (ES), to estimate EI. ObjectivesThe purpose of this study was to evaluate the validity, repeatability, and measurement error of consumer devices when estimating energy balance in a free-living population. MethodsTwenty-four healthy adults (14 women, 10 men; mean ± SD age: 30.7 ± 8.2 y) completed two 14-d assessment periods, including assessments of EE and ES using gold-standard [doubly labeled water (DLW) and DXA] and commercial devices [Fitbit Alta HR activity monitor (Alta) and Fitbit Aria wireless body composition scale (Aria)], and of EI by dietician-administered recalls. Accuracy and validity were assessed using Spearman correlation, interclass correlation, mean absolute percentage error, and equivalency testing. We also applied linear measurement error modeling including error in gold-standard devices and within-subject repeated-measures design to calibrate consumer devices and quantify error. ResultsThere was moderate to strong agreement for EE between the Fitbit Alta and DLW at each time point (rs = 0.82 and 0.66 for Times 1 and 2, respectively). There was weak agreement for ES between the Fitbit Aria and DXA (rs = 0.15 and 0.49 for Times 1 and 2, respectively). Correlations between methods to assess EI ranged from weak to strong, with agreement between the DXA/DLW-calculated EI and dietary recalls being the highest (rs = 0.63 for Time 1 and 0.73 for Time 2). Only EE from the Fitbit Alta at Time 1 was equivalent to the DLW value using equivalency testing. ConclusionsCommercial devices provide estimates of energy balance in free-living adults with varying degrees of validity compared to gold-standard techniques. EE estimates were the most robust overall, whereas ES estimates were generally poor.

Wearable technology to track activity and distress in breast cancer patients: case series

: Breast cancer is the most common cancer among women with a 5-year survival rate of up to 99% when detected early. This survival rate presents challenges to healthcare systems and providers in terms of ensuring patients receive adequate and timely support during the lengthy and often difficult journey of treatment and survivorship. The primary aim of this quality improvement project piloted the use of the Fitbit Alta HR fitness trackers in a large academic medical center in the south, where 19 breast cancer patients wore and synchronized a Fitbit Alta HR for 28 days. Participants were patients diagnosed with breast cancer (stages 0, I, II, III) who were actively receiving, or who had recently received, breast cancer treatment and care, and who had not previously used any type of health tracking device. The purpose was to understand how self-reported data could enhance patient-centered breast cancer care. Patients provided information on their physical activity and distress status, that enabled certified exercise physiologists to promote healthy lifestyle choices, identify potential treatment complications, and improve the patient’s overall quality of life. Although the sample size was small, a positive correlation between minutes asleep and number of awakenings (r=0.6852), and a negative correlation between distress levels and minutes fairly active (r=−0.3346), stood out as potential indications of the impact of distress on breast cancer patients. This project illuminated the potential value of understanding the ability to, and effectiveness of, capturing relevant patient reported outcomes as a valuable contribution to patient-centered healthcare delivery for patients undergoing treatment for breast cancer. Important findings suggest that building in the daily distress indicators to wearable devices may provide increased utility and expanded insight when such devices are used for patient reported monitoring and outcomes.

Abstract P173: Physical Activity In Relation To Fasting And Post-meal Blood Glucose In Pregnant Women With Diabetes

Introduction: Pregnant women with diabetes often show low levels of physical activity (PA) and high sedentary behavior (SED). Longitudinal studies with objective measures are needed to understand the relationships of daily PA with daily and next-day blood glucose (BG). Hypothesis: Increased steps or moderate to vigorous PA (MVPA) and decreased SED are linked with lower post-meal BG and next day fasting BG in pregnant women. Methods: Participants were 10 pregnant women with diabetes [mean age= 29.3 (SD= 3.6); mean gestational age= 21.9 (SD= 3.9); 90% (9 of 10) Latina] enrolled in a 12-week pilot PA intervention. Participants self-reported demographic and BG data (morning fasting BG, up to 3 daily post-meal BGs). Steps, MVPA (mins/day), and SED (mins/day) were measured using a Fitbit Alta HR. Participants had on average 49 (range: 21 to 77) days with valid PA and BG data, for a total of 469 observations. Multi-level models (MLMs) were fit to examine mean and day-level effects of steps, MVPA, and SED on post-meal and next-day fasting BG after adjusting for age, gestational age, education, and participant mean PA or SED. Due to the small sample size, effect sizes are emphasized in results instead of statistical significance. Results: The mean post-meal BG was 122.5 mg/dL and mean fasting BG was 92.81 mg/dL. After adjustment, an increase of mean steps by 1000 was linked to a lower mean post-meal BG by 11.79 mg/dL (p=0.22) and fasting BG by 7.26 mg/dL (p=0.54), though neither between effect was statistically significant. The within-individual effects of daily steps on post-meal and fasting BG were very small and non-significant (b=-1.78; p=0.59; b=0.72; p=0.30, respectively). A 1-minute increase in mean MVPA was associated with a slight increase in mean post-meal BG by 1.53 mg/dL (p=0.07). The within-individual effect of daily MVPA on daily post-meal BG was negligible and non-significant (b=-0.39, p=0.51). Between-individual effects showed SED had small, positive, non-significant associations with post-meal BG. Specifically, per 60-minute mean SED increase, mean post-meal BG increased by 1.02 mg/dL (p=0.44). Within-individual daily SED increases of 60 minutes were associated with increases of 1.87 mg/dL (p=0.63) in daily post-meal BG. MVPA and SED were not associated with fasting BG. Conclusions: Greater mean steps were linked to lower post-meal and fasting BG while greater SED and MVPA were linked to greater post-meal BG. However, within individual daily increases in MVPA and decreases in SED, were protective for post-meal BG, while controlling for individual mean MVPA and SED. Most effect sizes were small and results were not statistically significant in part due to the small sample size. Participants generally had well-controlled post-meal and fasting BGs, so results may not be generalizable to larger populations.

Feasibility and acceptability of a technology-based, rural weight management intervention in older adults with obesity

BackgroundOlder adults with obesity residing in rural areas have reduced access to weight management programs. We determined the feasibility, acceptability and preliminary outcomes of an integrated technology-based health promotion intervention in rural-living, older adults using remote monitoring and synchronous video-based technology.MethodsA 6-month, non-randomized, non-blinded, single-arm study was conducted from October 2018 to May 2020 at a community-based aging center of adults aged ≥65 years with a body mass index (BMI) ≥30 kg/m2. Weekly dietitian visits focusing on behavior therapy and caloric restriction and twice-weekly physical therapist-led group strength, flexibility and balance training classes were delivered using video-conferencing to participants in their homes. Participants used a Fitbit Alta HR for remote monitoring with data feedback provided by the interventionists. An aerobic activity prescription was provided and monitored.ResultsMean age was 72.9±3.9 years (82% female). Baseline anthropometric measures of weight, BMI, and waist circumference were 97.8±16.3 kg, 36.5±5.2 kg/m2, and 115.5±13.0 cm, respectively. A total of 142 participants were screened (n=27 ineligible), and 53 consented. There were nine dropouts (17%). Overall satisfaction with the trial (4.7+ 0.6, scale: 1 (low) to 5 (high)) and with Fitbit (4.2+ 0.9) were high. Fitbit was worn an average of 81.7±19.3% of intervention days. In completers, mean weight loss was 4.6±3.5 kg or 4.7±3.5% (p< 0.001). Physical function measures of 30-s sit-to-stand repetitions increased from 13.5±5.7 to 16.7±5.9 (p< 0.001), 6-min walk improved by 42.0±77.3 m (p=0.005) but no differences were observed in gait speed or grip strength. Subjective measures of late-life function improved (3.4±4.7 points, p< 0.001).ConclusionsA technology-based obesity intervention is feasible and acceptable to older adults with obesity and may lead to weight loss and improved physical function.Clinical trial registrationRegistered on Clinicaltrials.gov #NCT03104205. Registered on April 7, 2017. First participant enrolled on October 1st, 2018.

Performance of seven consumer sleep-tracking devices compared with polysomnography.

Study ObjectivesConsumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG).MethodsIn total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG.ResultsMost devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18–0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep.ConclusionsConsumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.

Protocol for Project Fizzyo, an analytic longitudinal observational cohort study of physiotherapy for children and young people with cystic fibrosis, with interrupted time-series design

IntroductionDaily physiotherapy is believed to mitigate the progression of cystic fibrosis (CF) lung disease. However, physiotherapy airway clearance techniques (ACTs) are burdensome and the evidence guiding practice remains weak. This...

Abstract A07: Connecting remote patient monitoring, DNA-based immunophenotyping, and clinical outcomes data

Abstract Our objective is to develop methods for utilizing highly repeated remotely monitored data, clinical assessments, and DNA methylation-based immunophenotyping. We used data from a pilot clinical trial examining the impact of physical activity tracking and counseling with dietary supplementation in older adults with obesity. Elevated body mass index (BMI) contributes to excess cancer burden and many specific cancers are directly associated with obesity. Obesity promotes inflammatory cytokines, such as TNF-alpha, hormonal dysregulation, such as estrogen and adipokines, in addition to impairments in insulin and cell growth regulators, all of which impact cell proliferation and cancer. Although physical activity is a key component in obesity interventions, specific relationships between physical activity, inflammatory, and immune markers are less clear. We examine integration methods for testing the association of physical activity data, biomarkers, and immune cell proportions inferred from DNA methylation in a trial investigating the impact of a multicomponent obesity intervention. Older obese (&amp;gt;= 65 years, BMI &amp;gt;= 30 kg/m2) who received primary care services at Dartmouth-Hitchcock were recruited (n = 15). All participants received regular in-person nutrition counseling, group exercises, and health coaching while being monitored through a wrist-worn physical activity monitor, and received additional protein supplementation. Baseline and 12-week measurements were taken for 12 clinical assessments, such as objective measures (i.e., hand grip strength and 5 times sit-to-stand), subjective (i.e., Late-Life Function and Disability Instrument), 32 blood-based biomarkers (i.e., leptin and TNF-alpha), and DNA methylation of whole blood. Physical activity and steps were measured continuously throughout the study period using a Fitbit Alta HR. To compare analytical methods, we used mixed-effects models, difference in difference, and elastic net regression models to examine the association of physical activity, immunomethylation phenotypes, and clinical outcomes. We followed 15 older adults (100% white, females), whose mean age was 73.4 years (SD = 6.0) years and mean BMI = 35.5 kg/m3 (SD = 4.68). All clinical assessments and blood-based biomarkers were collected on all 15 participants for both baseline and follow-up timepoints. An average of 92.2 days of data (steps, distance, total calories, activity calories, or minutes in sedentary, light activity, or fairly active, and very active) were recorded (SD = 9.5). We collected DNA methylation data on 10 participants at baseline and 14 participants in follow-up. Through developing newly linked data sources longitudinally, we will be able to examine new subpopulations of phenotypes that could elucidate the relationship between BMI and cancer impacting cancer screening, diagnosis, and treatment. Note: This abstract was not presented at the conference. Citation Format: Curtis L. Petersen, Brock C. Christensen, John A. Batsis. Connecting remote patient monitoring, DNA-based immunophenotyping, and clinical outcomes data [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A07.

Abstract A17: An innovative physical activity intervention in overweight and obese adults using continuous glucose monitor

Abstract Introduction: Regular physical activity (PA) lowers the risk of endometrial, breast, and colorectal cancer. However, nearly half of American adults are not sufficiently active. The physical inactivity rate is even higher among the overweight and obese population, which, in combination with the unfavorable effect of excess body weight, puts this population at a greater risk for cancer. Advancements in wearable sensor technology provide opportunities for personalized approaches to increase motivation to be active. The current pilot study used continuous glucose monitors (CGM) to demonstrate the immediate, physiologic benefits of PA. Methods: Insufficiently active (&amp;lt;150 mins of moderate intensity PA per week) overweight and obese (body mass index &amp;gt;= 25 kg/m2) adults (18-65 years old) without diabetes are being recruited to participate in this pilot intervention. Upon enrollment, participants go through a one-on-one PA education session that includes a discussion about the health benefits of recommended PA levels, demonstration of the acute effect of PA on daily glucose patterns using a web-based glucose simulator, calculation of personal heart rate zones for moderate intensity PA, and development of an exercise plan. After the education session, participants are given a Fitbit Alta HR wristband and are instructed to wear it over the next 10 days. Participants are also given a Freestyle Libre CGM, which consists of a sensor and a reader. The sensor is inserted into the back of the upper arm to collect interstitial glucose data every 15 minutes for 10 days after activation. The reader displays current glucose readings and a graph of the 8 previous hours of glucose data upon scanning the sensor. Participants are instructed to scan the sensor at least 4 times a day and are encouraged to achieve at least 150 minutes of moderate-intensity PA each week. At the end of the self-monitoring period, participants return their Fitbit and have their CGM sensor removed. Results: Thus far, 10 of 20 participants have enrolled in and completed the study. These participants have been 100% female, 70% obese, and 20% Hispanic with a mean age of 41.5 (SD = 8.6) years old. On average, participants scanned their sensor 6 times each day and wore the Fitbit for all 10 days. On average, participants highly rated each component of the education session (on a 5-point scale) for improving their PA knowledge (mean = 4.4), increasing their PA motivation (mean = 4.4), and providing personally relevant information (mean = 4.6). Participants also reported a very positive experience wearing the CGM, with mean ratings (on a 5-point scale) of 4.8 for usability, 4.6 for convenience, 4.9 for value, 4.9 for relevance, and 4.7 for motivating. Conclusion: These preliminary data suggest that using a CGM to motivate PA among nondiabetic adults is feasible and acceptable. Future research will be conducted to examine the effect a CGM-based PA intervention has on changing PA behavior. Citation Format: Yue Liao, Christopher J. Miller, Diana L. Urbauer, Therese B. Bevers, Ernest Hawk, Karen M. Basen-Engquist, Susan M. Schembre. An innovative physical activity intervention in overweight and obese adults using continuous glucose monitor [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A17.