Fitbit Device Research Articles

Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis.

BackgroundAlthough it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.ObjectiveThe purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.MethodsWe conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.ResultsA total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of −2.99 beats per minute (k comparison=74), −2.77 kcal per minute (k comparison=29), and −3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: −23.99 to 18.01, −12.75 to 7.41, and −13.07 to 6.86, respectively).ConclusionsFitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

A Remote Health Coaching, Text-Based Walking Program in Ethnic Minority Primary Care Patients With Overweight and Obesity: Feasibility and Acceptability Pilot Study.

BackgroundOver half of US adults have at least one chronic disease, including obesity. Although physical activity is an important component of chronic disease self-management, few reach the recommended physical activity goals. Individuals who identify as racial and ethnic minorities are disproportionally affected by chronic diseases and physical inactivity. Interventions using consumer-based wearable devices have shown promise for increasing physical activity among patients with chronic diseases; however, populations with the most to gain, such as minorities, have been poorly represented to date.ObjectiveThis study aims to assess the feasibility, acceptability, and preliminary outcomes of an 8-week text-based coaching and Fitbit program aimed at increasing the number of steps in a predominantly overweight ethnic minority population.MethodsOverweight patients (BMI >25 kg/m2) were recruited from an internal medicine clinic located in an inner-city academic medical center. Fitbit devices were provided. Using 2-way SMS text messaging, health coaches (HCs) guided patients to establish weekly step goals that were specific, measurable, attainable, realistic, and time-bound. SMS text messaging and Fitbit activities were managed using a custom-designed app. Program feasibility was assessed via the recruitment rate, retention rate (the proportion of eligible participants completing the 8-week program), and patient engagement (based on the number of weekly text message goals set with the HC across the 8-week period). Acceptability was assessed using a qualitative, summative evaluation. Exploratory statistical analysis included evaluating the average weekly steps in week 1 compared with week 8 using a paired t test (2-tailed) and modeling daily steps over time using a linear mixed model.ResultsOf the 33 patients initially screened; 30 (91%) patients were enrolled in the study. At baseline, the average BMI was 39.3 (SD 9.3) kg/m2, with 70% (23/33) of participants presenting as obese. A total of 30% (9/30) of participants self-rated their health as either fair or poor, and 73% (22/30) of participants set up ≥6 weekly goals across the 8-week program. In total, 93% (28/30) of participants completed a qualitative summative evaluation, and 10 themes emerged from the evaluation: patient motivation, convenient SMS text messaging experience, social support, supportive accountability, technology support, self-determined goals, achievable goals, feedback from Fitbit, challenges, and habit formation. There was no significant group change in the average weekly steps for week 1 compared with week 8 (mean difference 7.26, SD 6209.3; P=.99). However, 17% (5/30) of participants showed a significant increase in their daily steps.ConclusionsOverall, the results demonstrate the feasibility and acceptability of a remotely delivered walking study that included an HC; SMS text messaging; a wearable device (Fitbit); and specific, measurable, attainable, realistic, and time-bound goals within an ethnic minority patient population. Results support further development and testing in larger samples to explore efficacy.

Feasibility and Acceptability of a Physical Activity Tracker and Text Messages to Promote Physical Activity During Chemotherapy for Colorectal Cancer: Pilot Randomized Controlled Trial (Smart Pace II).

BackgroundWe conducted a pilot 2-arm randomized controlled trial to assess the feasibility of a digital health intervention to increase moderate-to-vigorous physical activity in patients with colorectal cancer (CRC) during chemotherapy.ObjectiveThis study aimed to determine whether a digital health physical activity intervention is feasible and acceptable during chemotherapy for CRC.MethodsPotentially eligible patients with CRC expected to receive at least 12 weeks of chemotherapy were identified in person at the University of California, San Francisco, and on the web through advertising. Eligible patients were randomized 1:1 to a 12-week intervention (Fitbit Flex, automated SMS text messages) versus usual care. At 0 and 12 weeks, patients wore an Actigraph GT3X+ accelerometer for 7 days and completed surveys, body size measurements, and an optional 6-minute walk test. Participants could not be masked to their intervention arm, but people assessing the body size and 6-minute walk test outcomes were masked. The primary outcomes were adherence (eg, Fitbit wear and text response rate) and self-assessed acceptability of the intervention. The intervention would be considered feasible if we observed at least 80% complete follow-up and 70% adherence and satisfaction, a priori.ResultsFrom 2018 to 2020, we screened 240 patients; 53.3% (128/240) of patients were ineligible and 26.7% (64/240) declined to participate. A total of 44 patients (44/240, 18%) were randomized to the intervention (n=22) or control (n=22) groups. Of these, 57% (25/44) were women; 68% (30/44) identified as White and 25% (11/44) identified as Asian American or Pacific Islander; and 77% (34/44) had a 4-year college degree. The median age at enrollment was 54 years (IQR 45-62 years). Follow-up at 12 weeks was 91% (40/44) complete. In the intervention arm, patients wore Fitbit devices on a median of 67 out of 84 (80%) study days and responded to a median of 17 out of 27 (63%) questions sent via SMS text message. Among 19 out of 22 (86%) intervention patients who completed the feedback survey, 89% (17/19) were satisfied with the Fitbit device; 63% (12/19) were satisfied with the SMS text messages; 68% (13/19) said the SMS text messages motivated them to exercise; 74% (14/19) said the frequency of SMS text messages (1-3 days) was ideal; and 79% (15/19) said that receiving SMS text messages in the morning and evening was ideal.ConclusionsThis pilot study demonstrated that many people receiving chemotherapy for CRC are interested in participating in digital health physical activity interventions. Fitbit adherence was high; however, participants indicated a desire for more tailored SMS text message content. Studies with more socioeconomically diverse patients with CRC are required.Trial RegistrationClinicalTrials.gov NCT03524716; https://clinicaltrials.gov/ct2/show/NCT03524716

Exploring the Effects of In-App Components on Engagement With a Symptom-Tracking Platform Among Participants With Major Depressive Disorder (RADAR-Engage): Protocol for a 2-Armed Randomized Controlled Trial.

BackgroundMulti-parametric remote measurement technologies (RMTs) comprise smartphone apps and wearable devices for both active and passive symptom tracking. They hold potential for understanding current depression status and predicting future depression status. However, the promise of using RMTs for relapse prediction is heavily dependent on user engagement, which is defined as both a behavioral and experiential construct. A better understanding of how to promote engagement in RMT research through various in-app components will aid in providing scalable solutions for future remote research, higher quality results, and applications for implementation in clinical practice.ObjectiveThe aim of this study is to provide the rationale and protocol for a 2-armed randomized controlled trial to investigate the effect of insightful notifications, progress visualization, and researcher contact details on behavioral and experiential engagement with a multi-parametric mobile health data collection platform, Remote Assessment of Disease and Relapse (RADAR)–base.MethodsWe aim to recruit 140 participants upon completion of their participation in the RADAR Major Depressive Disorder study in the London site. Data will be collected using 3 weekly tasks through an active smartphone app, a passive (background) data collection app, and a Fitbit device. Participants will be randomly allocated at a 1:1 ratio to receive either an adapted version of the active app that incorporates insightful notifications, progress visualization, and access to researcher contact details or the active app as usual. Statistical tests will be used to assess the hypotheses that participants using the adapted app will complete a higher percentage of weekly tasks (behavioral engagement: primary outcome) and score higher on self-awareness measures (experiential engagement).ResultsRecruitment commenced in April 2021. Data collection was completed in September 2021. The results of this study will be communicated via publication in 2022.ConclusionsThis study aims to understand how best to promote engagement with RMTs in depression research. The findings will help determine the most effective techniques for implementation in both future rounds of the RADAR Major Depressive Disorder study and, in the long term, clinical practice.Trial RegistrationClinicalTrials.gov NCT04972474; http://clinicaltrials.gov/ct2/show/NCT04972474International Registered Report Identifier (IRRID)DERR1-10.2196/32653

Texting Older Sisters to Step (TOSS) Using Fitbits to Promote Physical Activity: A Feasibility Study

Black women are disproportionately diagnosed with obesity (BMI ≥ 30 kg/m2). Obesity is a preventable but complex, public health problem that is multifaceted, chronic, and approximately 58% of Black women 60 years and older are classified as obese, compared to 38% of their White counterparts. This 12 week, pre/post, 2-group study aimed to determine if a peer-informed physical activity (PA) intervention with peer support would be feasible among community- dwelling, obese, older Black women to promote regular PA. Forty-eight potential participants were screened, 24 categorized as obese were enrolled and completed the study. The mean age was 64 (SD 3.0) years. Steps were measured by a Fitbit-Inspire with data successfully collected on 98% of days with the treatment group averaging a daily increase of 700-steps more than the control. Evaluation of intervention’s acceptability revealed that 100% enjoyed the study and using the Fitbit device. Text message readability was 100% and 95% said the study was motivational. Additionally, 8.3% said daily prompts were too frequent, 12% indicated that future studies should include additional social support, and 88% did not comment on the Fitbit community option for support, suggesting that this feature was not practical. Findings demonstrated that this intervention meets the criteria of being scalable, low cost, feasible, and acceptable for the older Black women. Using self-monitoring techniques in combination with at least one other behavioral strategy, such as our TOSS messages (cues for motivation) as the delivery channel for health promotion messages are a promising approach to increase PA behaviors.

A meta-analysis of Fitbit devices: same company, different models, different validity evidence

Fitbit devices are among the most commonly used physical activity devices used by the general public. Multiple studies have examined the validity evidence of Fitbit devices of estimating energy expenditure during physical activity compared to criterion references. However, the literature lacks objective, summary validity evidence that supports the use of various models of Fitbit devices. Therefore, this study aims (a) to examine the validity evidence among the various models of Fitbit devices and (b) to investigate the influence of several device factors on the validity evidence of Fitbit models using meta-analysis. A total of 402 articles were identified through five databases. Upon review of the articles, 29 studies were included in the meta-analysis. Seven different moderator variables, including Fitbit model, device placement, type of device, heart rate capability, release year of devices, activity types and sedentary activity, were identified and included in the meta-analysis to examine their impact on the validity evidence of Fitbit devices. The summarised validity coefficient of energy expenditure during physical activity estimated by Fitbit devices and measured by criterion references was r=.64 (k = 29, 95% CI [.59, .69], p<.001). Fitbit model was not found to be a significant factor impacting validity evidence of Fitbit devices, but heart rate capability, activity types and sedentary activity were found to be significant factors impacting validity evidence. This study found that not all Fitbit models have a similar ability in estimating energy expenditure during physical activity. Continued research is needed in examining the validity evidence of Fitbit devices, especially considering some factors may affect the validity evidence in measuring energy expenditure during physical activity.

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review.

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients. PubMed. A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review. In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care. Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices.

Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.

Early results on the efficacy and acceptability of a cardiac rehabilitation programme that transitioned to a fully virtual platform with adoption of wearable technology for covid era

Abstract Background/Introduction Cardiaovascular prevention/rehabilitation programmes continue to reduce cardiovascular mortality even with contemporary treatment. During covid the majority of face-to-face programmes were suspended but these services have never been more crucial as control of cardiovascular risk factors can mitigate the morbidity/mortality risk from covid. Programmes must now however be delivered in a way that reduces patient exposure. Here we describe how we rapidly transitioned our previously fully face to face cardiovascular prevention/programme to a completely virtual platform adopting Fitbit as wearable technology. Methods The previously face-to-face initial assessment (IA) conducted by the multidisciplinary team (MDT) – nurse, dietician and physiotherapist is now delivered via video/phone as per patient preference. Patients are provided with equipment kits (tape measures, blood pressure monitors (BP), Fitbit smartwatches and Fibricheck app as required. The virtual IA includes assessment of: Smoking habit, blood pressure (BP), heart rate, lipid profile and HbA1c (taken in community phlebotomy hub), cardioprotective medications, weight, BMI, waist circumference, Mediterranean Diet Score, functional capacity via the Duke Activity Status Index, habitual activity levels, risk stratification for exercise, hospital anxiety and depression scores (HADS) and quality of life (QOL). Patients receive education and tailored advice with SMART goals as well as a written care plan. The subsequent 12 programme is comprised of Results Between April and November 2020 n=262 had a virtual IA (94% of those offered and n=114 (95% of those offered) attended an end of programme assessment. 64% were male and the mean age was 64.1 years. Acceptance of the Fitbit device was 72% of those offered. Table 1 below shows the main clinical and patient-reported outcomes in those attending both an IA and EOP with the data for the same 6 months the year prior (face to face programme) also for comparison. Programme satisfaction ratings were high with 85% rating the programme as excellent or very good. Conclusions Transitioning a previously fully face to face cardiac rehabilitation programme to a wholly virtual platform was feasible and acceptable to patients. Early data analysis would suggest that the virtual programme achieves similar clinical and patient reported outcomes. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Funded under Transformation Funding Programme, Department of Health, Northern Ireland Table 1

378 Sleep Time Measured Using Fitbit Devices in Emergency Medicine Residents Based on Rotation

Emergency medicine (EM) residents rotate through various other environments during their training which often have variable hours and schedule patterns. These variations likely affect the amount of sleep residents are able to get. This study aims to determine if EM residents at a level one trauma center sleep less during more time- intensive rotations. This was an IRB approved, prospective, observational study that obtained EM residents’ sleep times using a Fitbit Charge 3 device. Each willing participant received a Fitbit device and was asked to wear it at all times for a three month period. Each resident’s corresponding rotation for the month was also recorded and associated with their data. The device automatically records sleep time for each 24-hour period and syncs to a database that records sleep times for each subject number. Data was interpreted using descriptive statistics, calculating median times for each subject throughout each rotation. Medians were used in order to avoid unnecessary skew for single sleep time outliers. The average median sleep time was then calculated for each rotation. Sleep data was collected for 33 EM residents, 10 female and 23 male, with rotators through nine different environments: EM, medical intensive care unit (MICU), surgical intensive care unit (SICU), pediatric intensive care unit (PICU), trauma, elective, anesthesia, orthopedics, and obstetrics. EM rotators had an average median sleep time of 424.7 minutes (N=32), approximately 7.1 hours daily. Residents on SICU slept 391.8 minutes on average (N=7), orthopedics 398.2 minutes (N=3), trauma 399.8 minutes (N=5), MICU 418.4 minutes (N=8), anesthesia 438.3 minutes (N=6), PICU 439.5 minutes (N=3), elective 455.4 minutes (N=7), and obstetrics 482.8 minutes (N=2), with N representing the number of EM residents with sleep data for at least one month of that rotation. EM rotators had the most robust data set, with 32 out of 33 residents having at least one month of sleep data for emergency medicine. The average sleep time for rotators in EM fell between the other eight rotations. SICU rotators slept the least on average, 391.8 minutes or 6.5 hours. Both trauma and MICU rotators slept less on average than emergency medicine rotators. Orthopedics also had a lower average sleep time, however it is important to note that only three subjects had at least one month of sleep data for their orthopedics rotation, as this is classically a less time-consuming off-service rotation. SICU, orthopedics, trauma, and MICU all had average median sleep times less than seven hours daily. Overall, it does seem that EM residents at our institution sleep a varying amount based on their current rotation which likely affects other elements of lifestyle and wellness. Understanding why certain rotations lead to poorer sleep quantity could aid program leadership in making positive scheduling changes for future generations of trainees.

151 Analyzing Sleep by Shift Recorded on Fitbit Devices Worn by Emergency Medicine Residents

Study Objectives: Sleep is crucial for the optimal function of all human beings, physicians included. As administrations have begun to focus on the well-being of healthcare workers, more studies are needed to determine how current working patterns affect emotional and physical well-being of these workers. It is therefore important to understand the effects of the demands of residency training on sleep quality. The goal of this study was to better evaluate the characteristics of junior physicians’ sleep and to determine how shift work impacts sleep quality in these doctors.

The role of mindfulness and relaxation in improved sleep quality following a mind-body and activity program for chronic pain.

Poor sleep quality is prevalent among individuals with chronic pain and contributes to increased physical and emotional dysfunction. However, treatments that improve sleep quality among individuals with chronic pain are scant. A previously developed mind-body activity program for chronic pain has been shown to be feasible and associated with improvements in pain and physical and emotional function. Using secondary data-analysis, the purpose of this study was to understand whether participants also experienced significant and sustained improvements in sleep quality over time and whether these improvements were explained by change in two core treatment targets, relaxation and mindfulness. Participants with heterogenous chronic pain (N = 82) were randomized to a mind-body activity intervention with (GetActive-Fitbit; n=41) or without (GetActive; n=41) a Fitbit device. Sleep quality was measured with the PSQI, mindfulness with the CAMS-R, and relaxation with the relaxation subscale of the MOCS-A. Mediation was tested via mixed-models analysis. Both intervention groups experienced significant and comparable improvements in sleep quality from baseline to post-treatment, which were sustained through a 3-month follow-up. Mindfulness and relaxation also improved significantly over time and these improvements were associated with improved sleep quality. Mindfulness and relaxation fully mediated improvement in sleep quality (medium to large effect sizes). Results suggest that, despite not targeting sleep explicitly, the two mind-body activity programs hold promise for sustainably improving sleep quality among patients with chronic pain. Targeting mindfulness and relaxation may facilitate these improvements.

Physical activity, patient-reported symptoms, and clinical events: Insights into postprocedural recovery from personal digital devices

BackgroundPersonal digital devices may offer insights into patient recovery and an approach for remote monitoring after procedures.ObjectiveTo examine associations between activity measured using personal digital devices, patient-reported outcome measures (PROMs), and clinical events among patients after catheter ablation for atrial fibrillation (AF) or bariatric surgery.MethodsWe aggregated personal digital device, PROM, and electronic health record data in a study conducted at 2 health systems. We used Fitbit devices for step count assessments, KardiaMobile for cardiac rhythm assessments, and PROMs for pain and palpitations over 5 weeks.ResultsAmong 59 patients, 30 underwent AF ablation and 29 bariatric surgery. Thirty-six patients (63%) reported pain. There was no difference in median [interquartile range] daily steps between patients with and those without pain (4419 [3286–7041] vs 3498 [2609–5888]; P = .23). Among AF ablation patients, 21 (70%) reported palpitations. Median daily steps were lower among those with palpitations than among those without (4668 [3021–6116] vs 8040 [6853–10,394]; P = .03). When accounting for within-subject correlation, recordings of AF were associated with a significant mean decrease in median daily steps (–351; 95% confidence interval –524 to –177; P <.01). Patients who received a new antiarrhythmic drug prescription had AF recorded in a median of 5 [5–5] of 5 total weeks, whereas patients who did not receive a new antiarrhythmic recorded AF in a median of 1 [0–3] week (P = .02).ConclusionPersonal digital device and PROM data can provide insight into postprocedural recovery outside of usual clinical settings and may inform follow-up and clinical decision-making. (ClinicalTrials.gov Identifier: NCT03436082)

Prediction of Stress and Mood using Neural Network, LSTM and Transfer learning

Stress can be a feeling of emotional or physical tension. It can come from any thought or event that makes you feel frustrated, disturbed, angry or nervous. It also affect the mood of the person. This study is conducting to predict the stress and mood based on heart rate variability which can be collected using Fitbit devices or Apple watches nowadays. In this work SWELL dataset available from the Kaggle repository is used. Neural Network and LSTM is used to predict the stress and mood. Predicting the stress is considered as first task and as mood prediction as second task. For second task prediction, the model created for first task is reused as pretrained model where we make use of transfer learning.

API-led Connectivity Using Mulesoft For Healthcare

The API-led business model is a new way of thinking about how to engage with partners and customers through APIs. The traditional approaches were designed for business use cases having limited endpoints and speed of delivery is not critical. Traditional approaches include P2P and E2E. To overcome these drawbacks, a new approach of API-led connectivity is used. The primary aim of API-led connectivity is to allow the integration flows to be reused by means of many events and to be reused within the integration platform. This paper focuses on creating an API- led network which is agile, reusable, and fits business requirements. This network will be built for the health care system using Allscripts and Fitbit APIs. To implement API -led networks in this paper MuleSoft platform is used and API is built using the API development cycle. Depending on the business requirements on the search for API and connector already present on Anypoint App exchange. Using available component flow is created to connect EHR system systems and different devices like Fitbit. Both API are protected with OAuth 2.0 which is handled with a separate flow for initiating the communication between the system and source API. After doing the required transformation to data fetch from source API data is pushed to the Salesforce object. Using different services provided by Mulesoft, an API-led network was built in which EHR and the Fitbit device were integrated. As this development process followed agile behavior it helped in meeting business requirements for building networks. The data was successfully fetched, processed, and transferred from the EHR system and the Fitbit device to the Salesforce object.

Use of Fitbit Devices in Physical Activity Intervention Studies Across the Life Course: Narrative Review.

BackgroundCommercial off-the-shelf activity trackers (eg, Fitbit) allow users to self-monitor their daily physical activity (PA), including the number of steps, type of PA, amount of sleep, and other features. Fitbits have been used as both measurement and intervention tools. However, it is not clear how they are being incorporated into PA intervention studies, and their use in specific age groups across the life course is not well understood. ObjectiveThis narrative review aims to characterize how PA intervention studies across the life course use Fitbit devices by synthesizing and summarizing information on device selection, intended use (intervention vs measurement tool), participant wear instructions, rates of adherence to device wear, strategies used to boost adherence, and the complementary use of other PA measures. This review provides intervention scientists with a synthesis of information that may inform future trials involving Fitbit devices. MethodsWe conducted a search of the Fitabase Fitbit Research Library, a database of studies published between 2012 and 2018. Of the 682 studies available on the Fitabase research library, 60 interventions met the eligibility criteria and were included in this review. A supplemental search in PubMed resulted in the inclusion of 15 additional articles published between 2019 and 2020. A total of 75 articles were reviewed, which represented interventions conducted in childhood; adolescence; and early, middle, and older adulthood. ResultsThere was considerable heterogeneity in the use of Fitbit within and between developmental stages. Interventions for adults typically required longer wear periods, whereas studies on children and adolescents tended to have more limited device wear periods. Most studies used developmentally appropriate behavior change techniques and device wear instructions. Regardless of the developmental stage and intended Fitbit use (ie, measurement vs intervention tool), the most common strategies used to enhance wear time included sending participants reminders through texts or emails and asking participants to log their steps or synchronize their Fitbit data daily. The rates of adherence to the wear time criteria were reported using varying metrics. Most studies supplemented the use of Fitbit with additional objective or self-reported measures for PA. ConclusionsOverall, the heterogeneity in Fitbit use across PA intervention studies reflects its relative novelty in the field of research. As the use of monitoring devices continues to expand in PA research, the lack of uniformity in study protocols and metrics of reported measures represents a major issue for comparability purposes. There is a need for increased transparency in the prospective registration of PA intervention studies. Researchers need to provide a clear rationale for the use of several PA measures and specify the source of their main PA outcome and how additional measures will be used in the context of Fitbit-based interventions.

670 Changes in Childhood Sleep Patterns in an Intervention Study Prior to and During COVID19 Restrictions

IntroductionWe conducted a childhood sleep promotion study between March 2019 and December 2020 in Philadelphia. COVID19 was first detected in Pennsylvania in March 2020 and non-essential services were strictly curtailed (including school closures), with easing of curtailments by the fall 2020 (including hybrid schooling in some districts). We determined if changes in sleep duration were consistent during pre-, earlier, and later COVID19 periods.MethodsTypically developing children (9-12y) with sleep duration <8.5 hours per weeknight were enrolled. Sleep was measured using Fitbit devices during a baseline week and a 7-week intervention period. A factorial design was used to test five candidate intervention components: 1) sleep goal; 2) electronic device reduction messaging; 3) daily routine messaging; 4) child-directed financial incentive; and 5) parent-directed financial incentive. Sleep data were transmitted to a mobile health platform that automated delivery of the intervention components. We categorized participants when they completed the study: 1) Spring-Fall 2019 semesters (pre-COVID19); 2) Spring 2020 semester (started pre-COVID19, with strict restrictions impacting intervention periods); or 3) Fall 2020 semester (easing of COVID19 restrictions). Mixed effect modelling determined sleep changes.ResultsMean age of participants was 11.6y (51% female and 29% Black participants). Pre-COVID19 (N=59), average sleep duration increased from baseline by 21 (95% CI: 10, 30) minutes per weeknight during the intervention. In spring 2020 (N=18), the average sleep duration increase was two times larger in magnitude at 41 (95% CI: 25, 59) minutes per weeknight. For fall 2020 (N=20), the average sleep duration increase was 24 (95% CI: 7, 40) minutes per weeknight. Changes in sleep timing from baseline during the intervention were consistent pre-COVID19 and in the fall 2020 (e.g., ≈15 minutes earlier sleep onset throughout the intervention period), whereas sleep timing changes were dynamic in the spring 2020 (e.g., 41 minutes earlier for week 1, and 44 minutes later for week 7).ConclusionThis sleep intervention demonstrated increases in sleep duration pre-COVID19, with marked duration increases and dynamic timing changes coinciding with COVID19 restrictions during earlier (Spring 2020), but not later (Fall 2020), weeks of the COVID19 pandemic in Pennsylvania.Support (if any)K0 1 HL1 2 3 6 1 2 and CHOP

Sustainability of Improvements in Adaptive Coping Following Mind-Body and Activity Training for Chronic Pain.

The strategies patients use to cope with chronic pain are key determinants of pain-related treatment outcomes and are often targeted in psychosocial interventions for chronic pain. However, improvements in coping often fade after intervention completion. Here, we test whether previously reported improvements in coping following two novel mind-body and activity interventions are maintained 3months after completion. Eighty-two patients with heterogeneous chronic pain were randomized to two identical mind-body and activity interventions, one with the addition of a Fitbit device (GetActive-Fitbit) and one without it (GetActive; n = 41 each). Participants completed measures of pain-catastrophizing, kinesiophobia, mindfulness, adaptive coping, and pain-resilience at baseline, post-intervention, and at 3-month follow-up. At follow-up, participants in both groups exhibited sustained improvements in all five coping measures compared to baseline (significant in both groups for all measures except for p = .05 in kinesiophobia in GetActive and p = .07 in pain resilience in GetActive-Fitbit). Overall, GetActive and GetcActive-Fitbit are promising interventions to sustainably improve coping with chronic pain. This trial is registered under ClinicalTrials.gov identifier NCT03412916.

Using Fitbit as an mHealth Intervention Tool to Promote Physical Activity: Potential Challenges and Solutions.

Consumer-based physical activity (PA) trackers, also known as wearables, are increasingly being used in research studies as intervention or measurement tools. One of the most popular and widely used brands of PA trackers is Fitbit. Since the release of the first Fitbit in 2009, hundreds of experimental studies have used Fitbit devices to facilitate PA self-monitoring and behavior change via goal setting and feedback tools. Fitbit’s ability to capture large volumes of PA and physiological data in real time creates enormous opportunities for researchers. At the same time, however, it introduces a number of challenges (eg, technological, operational, logistical), most of which are not sufficiently described in study publications. Currently, there are no technical reports, guidelines, nor other types of publications discussing some of these challenges and offering guidance to researchers on how to best incorporate Fitbit devices in their study design and intervention to achieve their research goals. As a result, researchers are often left alone to discover and address some of these issues during the study through “trial and error.” This paper aims to address this gap. Drawing on our cumulative experience of conducting multiple studies with various Fitbit PA trackers over the years, we present and discuss various key challenges associated with the use of Fitbit PA trackers in research studies. Difficulties with the use of Fitbit PA trackers are encountered throughout the entire research process. Challenges and solutions are categorized in 4 main categories: study preparation, intervention delivery, data collection and analysis, and study closeout. Subsequently, we describe a number of empirically tested strategies used in 4 of our interventional studies involving participants from a broad range of demographic characteristics, racial/ethnic backgrounds, and literacy levels. Researchers should be prepared to address challenges and issues in a timely fashion to ensure that the Fitbit effectively assists participants and researchers in achieving research and outcome goals.

Usability of affordable feedback-based technologies to improve mobility and physical activity in rehabilitation: a mixed methods study

Purpose To explore physiotherapists’ views on the usability of feedback-based technologies used in physical rehabilitation. Materials and methods A mixed methods study which was nested within a randomised controlled trial to investigate the effectiveness of affordable feedback-based technologies to improve mobility and physical activity within aged care and neurological rehabilitation. Technologies included virtual reality systems, handheld device apps and wearable devices. Physiotherapists (n = 11) who were involved in prescribing technologies during the trial rated the usability of 11 different devices using the System Usability Scale (SUS), then attended a focus group. Descriptive statistics and framework analysis were used for analysis. Results Fitbit devices (mean 89.8, SD 9.3), Fysiogaming (mean 75.6, SD 15.3) and Xbox Kinect (mean 75.5, SD 11.2) rated in the acceptable range (>70) on the SUS. Three key factors on usability emerged from the focus groups: (1) Key device features relating to practicalities (ease of set up and use, reliability, safety) and therapeutic benefit (customisation, high active practice time, useful feedback) are important for usability; (2) Usability depends on the context of use; and (3) Usability can be enhanced with technical, clinical, environmental and financial support. Conclusions Health service managers and clinicians should consider key device features identified, contextual factors of their service, and supports available when selecting technologies for use in clinical practice. Further collaboration between clinicians, researchers and technology developers would benefit future technology development, particularly taking into consideration the identified key device features from this study. IMPLICATIONS FOR REHABILITATION Technology selection should be based on key device features relating to both practicalities and therapeutic benefit. Contextual factors and available supports should also be considered when selecting technologies. Key usability features identified in this study such as ease of set up, reliability and customisability should be considered in the design of future feedback-based technologies to optimise usability in physical rehabilitation.