Fitbit Charge Research Articles

FEELING YOUNG TODAY, FEELING GOOD TOMORROW? MICROLONGITUDINAL DYNAMICS IN SUBJECTIVE AGE

Abstract Insights into the short-term dynamics and micro-longitudinal consequences of subjective age can drive our understanding of its long-term mechanisms across adulthood. Using data from 80 newly retired individuals (aged 59 to 76 years; 59% women) collected on 21 days, we made use of a recent methodological advance—multilevel dynamic structural equation modeling. As possible same-day correlates and micro-longitudinal consequences of subjective age, we investigated physical activity, step number, sleep quality, affect, and stress, which were either assessed via wearables (FitBit Charge HR) or daily diaries. Analyses suggest a weak autoregression of subjective age, indicating that how old one feels is determined via daily rather than lasting experiences. Indeed, there were significant same-day relations to all suggested correlates. The one effect lasting across several days was from an older subjective age on subsequent negative affect—a potential short-term mechanism contributing to the detrimental long-term influence of an older subjective age.

Personal Devices to Monitor Physical Activity and Nutritional Intake After Colorectal Cancer Surgery: Feasibility Study.

The use of self-monitoring devices is promising for improving perioperative physical activity and nutritional intake. This study aimed to assess the feasibility, usability, and acceptability of a physical activity tracker and digital food record in persons scheduled for colorectal cancer (CRC) surgery. This observational cohort study was conducted at a large training hospital between November 2019 and November 2020. The study population consisted of persons with CRC between 18- and 75 years of age who were able to use a smartphone or tablet and scheduled for elective surgery with curative intent. Excluded were persons not proficient in Dutch or following a protein-restricted diet. Participants used an activity tracker (Fitbit Charge 3) from 4 weeks before until 6 weeks after surgery. In the week before surgery (preoperative) and the fifth week after surgery (postoperative), participants also used a food record for 1 week. They shared their experience regarding usability (system usability scale, range 0-100) and acceptability (net promoter score, range -100 to +100). In total, 28 persons were included (n=16, 57% male, mean age 61, SD 8 years), and 27 shared their experiences. Scores regarding the activity tracker were as follows: preoperative median system usability score, 85 (IQR 73-90); net promoter score, +65; postoperative median system usability score, 78 (IQR 68-85); net promotor score, +67. The net promoter scores regarding the food record were +37 (preoperative) and-7 (postoperative). The perioperative use of a physical activity tracker is considered feasible, usable, and acceptable by persons with CRC in this study. Preoperatively, the use of a digital food record was acceptable, and postoperatively, the acceptability decreased.

Usability and acceptability of wearable technology in the early detection of dementia

AbstractBackgroundThe Early Detection of Neurodegeneration (EDoN) is a global initiative that aims to explore the potential of wearable technologies and smartphone applications to detect preclinical dementia, with aspirations to validate a digital toolkit for clinical practice. To enhance the development of an inclusive digital toolkit, we conducted a study to assess the usability and acceptability of different digital devices in people with cognitive impairments and their carers.MethodRecruitment was conducted across various UK networks such as Join Dementia Research. Participants received the EDoN toolkit, which includes a smartwatch (Fitbit Charge 4), EEG headband (Dreem 3) and two smartphone applications (Longevity and Mezurio). Guides were provided to support the setup process. Initial interviews were conducted approximately three days after the participant received the devices, to explore initial perspectives regarding the toolkit and experiences of the setup process. Follow‐up interviews were conducted two weeks later to explore the acceptability and usability of the toolkit. NVivo was used to thematically analyse the interview transcripts. Emerging themes were discussed and refined by the research group.ResultSixteen semi‐structured interviews were conducted with nine participants, at two‐time points. Four participants had mild cognitive impairment, two had frontotemporal dementia, one had Alzheimer’s and two were carers. We identified three key themes, which centred around usability, acceptability and inequity. Participants expressed the wearable devices were comfortable but individuals with physical disabilities or cognitive impairments struggled to use some devices. Participants valued the feedback the devices provided such as information on sleep and heart rate, although some information was not fully understood. Participants also shared their concerns around detecting preclinical dementia and the increased anxiety around the consequences of this such as "being put in a home”. Various inequities of the toolkit were uncovered such as digital exclusion relating to a lack of access to strong WiFi connection, compatible smartphones and poor digital literacy.ConclusionThese results are informative for the further development of user‐friendly digital tools for the early detection of dementia. Further work is required to ensure a digital toolkit is inclusive and provides information that can be understood by the user.

Usability and acceptability of wearable technology in the early detection of dementia

The Early Detection of Neurodegeneration (EDoN) is a global initiative that aims to explore the potential of wearable technologies and smartphone applications to detect preclinical dementia, with aspirations to validate a digital toolkit for clinical practice. To enhance the development of an inclusive digital toolkit, we conducted a study to assess the usability and acceptability of different digital devices in people with cognitive impairments and their carers. Recruitment was conducted across various UK networks such as Join Dementia Research. Participants received the EDoN toolkit, which includes a smartwatch (Fitbit Charge 4), EEG headband (Dreem 3) and two smartphone applications (Longevity and Mezurio). Guides were provided to support the setup process. Initial interviews were conducted approximately three days after the participant received the devices, to explore initial perspectives regarding the toolkit and experiences of the setup process. Follow-up interviews were conducted two weeks later to explore the acceptability and usability of the toolkit. NVivo was used to thematically analyse the interview transcripts. Emerging themes were discussed and refined by the research group. Sixteen semi-structured interviews were conducted with nine participants, at two-time points. Four participants had mild cognitive impairment, two had frontotemporal dementia, one had Alzheimer's and two were carers. We identified three key themes, which centred around usability, acceptability and inequity. Participants expressed the wearable devices were comfortable but individuals with physical disabilities or cognitive impairments struggled to use some devices. Participants valued the feedback the devices provided such as information on sleep and heart rate, although some information was not fully understood. Participants also shared their concerns around detecting preclinical dementia and the increased anxiety around the consequences of this such as "being put in a home". Various inequities of the toolkit were uncovered such as digital exclusion relating to a lack of access to strong WiFi connection, compatible smartphones and poor digital literacy. These results are informative for the further development of user-friendly digital tools for the early detection of dementia. Further work is required to ensure a digital toolkit is inclusive and provides information that can be understood by the user.

Impact of COVID-19 Pandemic on Physical Activity, Pain, Mood, and Sleep in Adults with Knee Osteoarthritis.

To examine changes in physical activity, sleep, pain and mood in people with knee osteoarthritis (OA) during the ongoing COVID-19 pandemic by leveraging an ongoing randomized clinical trial (RCT). Participants enrolled in a 12-month parallel two-arm RCT (NCT03064139) interrupted by the COVID-19 pandemic wore an activity monitor (Fitbit Charge 3) and filled out custom weekly surveys rating knee pain, mood, and sleep as part of the study. Data from 30 weeks of the parent study were used for this analysis. Daily step count and sleep duration were extracted from activity monitor data, and participants self-reported knee pain, positive mood, and negative mood via surveys. Metrics were averaged within each participant and then across all participants for pre-pandemic, stay-at-home, and reopening periods, reflecting the phased re-opening in the state of Massachusetts. Data from 28 participants showed small changes with inconclusive clinical significance during the stay-at-home and reopening periods compared to pre-pandemic for all outcomes. Summary statistics suggested substantial variability across participants with some participants showing persistent declines in physical activity during the observation period. Effects of the COVID-19 pandemic on physical activity, sleep, pain, and mood were variable across individuals with OA. Specific reasons for this variability could not be determined. Identifying factors that could affect individuals with knee OA who may exhibit reduced physical activity and/or worse symptoms during major lifestyle changes (such as the ongoing pandemic) is important for providing targeted healthcare services and management advice towards those that could benefit from it the most.

Evaluating Simple Objective Metrics for the Remote Measurement of Physical Activity: Preliminary Results from the RADAR‐AD Study

AbstractBackgroundHigher physical activity is associated with better global cognition and a lower risk of dementia (Rojer et al., 2021). Using questionnaires to assess physical activity is difficult in an Alzheimer’s disease (AD) population, since questionnaires rely on retrospective subjective recall. Wearables measure physical activity objectively and continuously. A variety exists, with some delivering raw high‐frequency research‐grade movement data, others providing summary variables only. The aim of this study is to compare simple activity metrics from two activity trackers monitoring physical activity in AD, and relate these to standard questionnaires.MethodParticipants in the RADAR‐AD study (Remote Assessment of Disease and Relapse – Alzheimer’s Disease) were asked to wear two activity trackers (dominant hand: Axivity AX3, non‐dominant hand: Fitbit Charge 3) for 8 weeks. Features calculated from the Axivity’s raw accelerometer data were: acceleration magnitude, time spent in sedentary, light, moderate‐to‐vigorous activity, and sleeping. Fitbit features included: step count and heart rate. Self‐reported activity levels (Godin Leisure Time Questionnaire), depression (Geriatric Depression Scale), social functioning (Social Functioning Scale), global cognition (Mini‐Mental State Examination), partner‐reported function (ADCS‐ADL) and neuropsychiatric symptoms (Neuropsychiatric Inventory) were assessed and related to activity levels using regression models.ResultWe included amyloid negative controls (n = 36) and amyloid positive preclinical (n = 13), prodromal (n = 15) and mild‐to‐moderate (n = 10) AD participants (Table 1). Expected associations were found between sedentary activity time and age, and between self‐reported activity and both moderate‐to‐vigorous activity time and step count (Table 2, Figure 1). Small group sizes currently make further comparisons of exploratory value only.ConclusionWearable devices have the potential to accurately capture clinically relevant aspects of daily activity and function of interest in AD population. On‐going research in the RADAR‐AD study will focus on developing disease‐specific methods of feature extraction based on raw movement data, and combining features from multiple devices, to explore this further.This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR-AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.

Recommendations for Identifying Valid Wear for Consumer-Level Wrist-Worn Activity Trackers and Acceptability of Extended Device Deployment in Children

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

Steps Ahead: Optimising physical activity in adults with cystic fibrosis: A pilot randomised trial using wearable technology, goal setting and text message feedback

BackgroundRegular participation in physical activity (PA) is encouraged for people with Cystic Fibrosis (CF). This study aimed to assess the effectiveness of an intervention using wearable technology, goal setting and text message feedback on PA and health outcomes in people with CF. MethodsThis was a pilot randomised trial conducted at University Hospital Limerick. Participants were randomly assigned to the intervention (INT) or active comparator (AC). The 12-week intervention consisted of wearable technology (Fitbit Charge 2) which was remotely monitored, and participants set step count goals. Participants were sent a one-way text message once a week over 12 weeks to positively reinforce and encourage PA participation. The AC group received the wearable technology alone. Follow up was assessed at 24 weeks. Outcomes assessed were PA, aerobic capacity, lung function, sleep, quality of life and wellbeing. ResultsStep count increased significantly for the INT group over 12 weeks when compared to the AC group (p=0.019). The INT group had a 28% week-to-week percentage change (Weeks 1-12), while the AC group reduced by 1%, p=0.023. Within group changes demonstrated that VO2 peak (ml/kg/min) significantly increased for the INT group at 12 weeks (24.4 ±7.65 to 26.13 ±7.79, p=0.003) but not at 24 weeks (24.45 ±7.05, p=0.776). There were no significant differences observed for VO2 peak (ml/kg/min) for the AC group. There was no significant effect on lung function, sleep, well-being, or quality of life for either group. ConclusionsA personalised PA intervention using wearable technology, goal setting and text message feedback increased PA and aerobic capacity in people with CF. Integration of this intervention into usual care may encourage regular PA participation for people with CF.

Evaluation of physical health status beyond daily step count using a wearable activity sensor

Physical health status defines an individual’s ability to perform normal activities of daily living and is usually assessed in clinical settings by questionnaires and/or by validated tests, e.g. timed walk tests. These measurements have relatively low information content and are usually limited in frequency. Wearable sensors, such as activity monitors, enable remote measurement of parameters associated with physical activity but have not been widely explored beyond measurement of daily step count. Here we report on results from a cohort of 22 individuals with Pulmonary Arterial Hypertension (PAH) who were provided with a Fitbit activity monitor (Fitbit Charge HR®) between two clinic visits (18.4 ± 12.2 weeks). At each clinical visit, a maximum of 26 measurements were recorded (19 categorical and 7 continuous). From analysis of the minute-to-minute step rate and heart rate we derive several metrics associated with physical activity and cardiovascular function. These metrics are used to identify subgroups within the cohort and to compare to clinical parameters. Several Fitbit metrics are strongly correlated to continuous clinical parameters. Using a thresholding approach, we show that many Fitbit metrics result in statistically significant differences in clinical parameters between subgroups, including those associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests. These results highlight the fact that daily step count is only one of many metrics that can be derived from activity monitors.

Abstract 13572: Feasibility and Preliminary Efficacy of a Novel YouTube Based Physical Activity Intervention in Adults With Overweight/Obesity

Background: Only 14% of adults with obesity attain the national recommended guidelines for physical activity (PA). Yet, there is a paucity of interventions designed to address individuals’ barriers to PA. We designed a web-based Physical Activity for The Heart (PATH) intervention to address this gap. Purpose: To test the feasibility and preliminary efficacy of PATH in promoting PA and reducing cardiovascular disease (CVD) risk in adults with overweight/obesity. Methods: In a randomized controlled trial (RCT) with 12-week follow-up, participants were randomized to PATH (n=41) or wait-list control (n=41) groups. The PATH group received access to the PATH website and met twice/month with a remote coach. The control group received a self-help PA guide and twice/month newsletters on general health. Moderate-to-vigorous intensity PA (MVPA) was assessed via waist-worn Actigraph GT3X, steps via Fitbit Charge 2™, weight via smart scale, blood pressure (BP) via Omron BP machine, and lipids and HbA1C via dry blood spot. Generalized linear mixed modeling was applied to examine between- and within-group differences in PA, adherence to PA, and CVD risk factors. Results: The sample (N=82) was on average 55.9 ±8.2 years old; 57.3% white; 80.5% female; 79% obese (BMI 35.5 ±6.2 kg/m 2 ;) and 62.2% had ≥4yr of college. Recruitment lasted 6 months, and retention was 96.3%. Nearly all intervention participants accessed the PATH website ≥twice/week (92.1%), spent ≥10 min/visit (89.5%), and reported that the site was very appealing (68.4%), navigable (97.4%) and culturally appropriate (79%). At 12 weeks, the PATH group had greater mean changes in weekly MVPA (+59.2 min vs. +1.1 min, p=.024) and daily steps (+1,243.9 steps vs. -61.1 steps, p=.002) compared to the control group. While no between-group differences were detected for meeting the national PA guidelines or CVD risk factors over time, the PATH group showed modest but significant improvements in weight, BMI, body fat, waist circumference, and systolic BP (all p<.05). Conclusion: This pilot RCT demonstrated that the PATH intervention is feasible and acceptable and provided preliminary efficacy in promoting PA among adults with overweight/obesity. Work is in progress to test the PATH intervention in a larger scale RCT.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults.

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75-0.88) and lift (4.1-11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

How is the usability of commercial activity monitors perceived by older adults and by researchers? A cross-sectional evaluation of community-living individuals

ObjectivesUsing commercial activity monitors may advance research with older adults. However, usability for the older population is not sufficiently established. This study aims at evaluating the usability of three wrist-worn...

Validation of Fitbit Charge 4 for assessing sleep in Chinese patients with chronic insomnia: A comparison against polysomnography and actigraphy.

Our research aims to assess the performance of a new generation of consumer activity trackers (Fitbit Charge 4TM: FBC) to measure sleep variables and sleep stage classifications in patients with chronic insomnia, compared to polysomnography (PSG) and a widely used actigraph (Actiwatch Spectrum Pro: AWS). We recruited 37 participants, all diagnosed with chronic insomnia disorder, for one night of sleep monitoring in a sleep laboratory using PSG, AWS, and FBC. Epoch-by-epoch analysis along with Bland-Altman plots was used to evaluate FBC and AWS against PSG for sleep-wake detection and sleep variables: total sleep time (TST), sleep efficiency (SE), waking after sleep onset (WASO), and sleep onset latency (SOL). FBC sleep stage classification of light sleep (LS), deep sleep (DS), and rapid eye movement (REM) was also compared to that of PSG. When compared with PSG, FBC notably underestimated DS (-41.4, p < 0.0001) and SE (-4.9%, p = 0.0016), while remarkably overestimating LS (37.7, p = 0.0012). However, the TST, WASO, and SOL assessed by FBC presented no significant difference from that assessed by PSG. Compared with PSG, AWS and FBC showed great accuracy (86.9% vs. 86.5%) and sensitivity (detecting sleep; 92.6% vs. 89.9%), but comparatively poor specificity (detecting wake; 35.7% vs. 62.2%). Both devices showed better accuracy in assessing sleep than wakefulness, with the same sensitivity but statistically different specificity. FBC supplied equivalent parameters estimation as AWS in detecting sleep variables except for SE. This research shows that FBC cannot replace PSG thoroughly in the quantification of sleep variables and classification of sleep stages in Chinese patients with chronic insomnia; however, the user-friendly and low-cost wearables do show some comparable functions. Whether FBC can serve as a substitute for actigraphy and PSG in patients with chronic insomnia needs further investigation.

Systematic Review of Fitbit Charge 2 Validation Studies for Exercise Tracking.

There are research-grade devices that have been validated to measure either heart rate (HR) by electrocardiography (ECG) with a Polar chest strap, or step count with ACTiGraph accelerometer. However, wearable activity trackers that measure HR and steps concurrently have been tested against research-grade accelerometers and HR monitors with conflicting results. This review examines validation studies of the Fitbit Charge 2 (FBC2) for accuracy in measuring HR and step count and evaluates the device's reliability for use by researchers and clinicians. This registered review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The robvis (risk-of-bias visualization) tool was used to assess the strength of each considered article. Eligible articles published between 2018 and 2019 were identified using PubMed, CINHAL, Embase, Cochran, and World of Science databases and hand-searches. All articles were HR and/or step count validation studies for the FBC2 in adult ambulatory populations. Eight articles were examined in accordance with the eligibility criteria alignment and agreement among the authors and research librarian. Concordance correlation coefficients (CCC) were used to measure agreement between the tracker and criterion devices. Mean absolute percent error (MAPE) was used to average the individual absolute percent errors. Studies that measured CCC found agreement between the FBC2 and criterion devices ranged between 26% and 92% for HR monitoring, decreasing in accuracy as exercise intensity increased. Inversely, CCC increased from 38% to 99% for step count when exercise intensity increased. HR error between MAPE was 9.21% to 68% and showed more error as exercise intensity increased. Step measurement error MAPE was 12% for healthy persons aged 24-72 years but was reported at 46% in an older population with heart failure. Relative agreement with criterion and low-to-moderate MAPE were consistent in most studies reviewed and support validation of the FBC2 to accurately measure HR at low or moderate exercise intensities. However, more investigation controlling testing and measurement congruency is needed to validate step capabilities. The literature supports the validity of the FBC2 to accurately monitor HR, but for step count is inconclusive so the device may not be suitable for recommended use in all populations.

Lifestyle and health changes in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application

Purpose The aim of this study was to determine changes in physical activity, nutrition, sleep behaviour and body composition in wheelchair users with a chronic disability after 12 weeks of using the WHEELS mHealth application (app). Methods A 12-week pre-post intervention study was performed, starting with a 1-week control period. Physical activity and sleep behaviour were continuously measured with a Fitbit charge 3. Self-reported nutritional intake, body mass and waist circumference were collected. Pre-post outcomes were compared with a paired-sample t-test or Wilcoxon signed-rank test. Fitbit data were analysed with a mixed model or a panel linear model. Effect sizes were determined and significance was accepted at p < .05. Results Thirty participants completed the study. No significant changes in physical activity (+1.5 √steps) and sleep quality (–9.7 sleep minutes; −1.2% sleep efficiency) were found. Significant reduction in energy (–1022 kJ, d = 0.71), protein (–8.3 g, d = 0.61) and fat (–13.1 g, d = 0.87) intake, body mass (–2.2 kg, d = 0.61) and waist circumference (–3.3 cm, d = 0.80) were found. Conclusion Positive changes were found in nutritional behaviour and body composition, but not in physical activity and sleep quality. The WHEELS app seems to partly support healthy lifestyle behaviour. Implications for Rehabilitation Healthy lifestyle promotion is crucial, especially for wheelchair users as they tend to show poorer lifestyle behaviour despite an increased risk of obesity and comorbidity. The WHEELS lifestyle app seems to be a valuable tool to support healthy nutrition choices and weight loss and to improve body satisfaction, mental health and vitality.

MHealth-Supported Gender- and Culturally Sensitive Weight Loss Intervention for Hispanic Men With Overweight and Obesity: Single-Arm Pilot Study.

BackgroundHispanic men have disproportionate rates of overweight and obesity compared with other racial and ethnic subpopulations. However, few weight loss interventions have been developed specifically for this high-risk group. Furthermore, the use of mobile health (mHealth) technologies to support lifestyle behavior changes in weight loss interventions for Hispanic men is largely untested.ObjectiveThis single-arm pilot study examined the feasibility and acceptability of integrating mHealth technology into a 12-week gender- and culturally sensitive weight loss intervention (GCSWLI) for Hispanic men with overweight and obesity.MethodsA total of 18 Hispanic men (mean age 38, SD 10.9 years; mean BMI 34.3, SD 5.5 kg/m²; 10/18, 56% Spanish monolingual) received a GCSWLI, including weekly in-person individual sessions, a daily calorie goal, and prescription of ≥225 minutes of moderate-intensity physical activity per week. mHealth technology support included tailored SMS text messaging, behavior self-monitoring support using Fitbit Charge 2, and weight tracking using a Fitbit Aria Wi-Fi Smart Scale. Changes in weight from baseline to 12 weeks were estimated using a paired 2-tailed t test. Descriptive analyses characterized the use of Fitbit and smart scales. Semistructured interviews were conducted immediately after intervention to assess the participants’ weight loss experiences and perspectives on mHealth technologies.ResultsOf 18 participants, 16 (89%) completed the 12-week assessments; the overall attrition rate was 11.1%. The mean weight loss at week 12 was −4.7 kg (95% CI 7.1 to −2.4 kg; P<.001). Participants wore the Fitbit 71.58% (962/1344) of the intervention days and logged their body weight using the smart scale (410/1344, 30.51% of the intervention days). Participants identified barriers to the use of the technology, such as lack of technological literacy and unreliable internet access for the smart scale.ConclusionsAlthough clinically significant weight loss was achieved by integrating mHealth technology into the GCSWLI, adherence to the prescribed use of technology was modest. Addressing barriers to the use of such technologies identified in our work may help to refine an mHealth intervention approach for Hispanic men.Trial RegistrationClinicalTrials.gov NCT02783521; https://clinicaltrials.gov/ct2/show/NCT02783521

Activity monitoring and patient-reported outcome measures in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients.

IntroductionMyalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disease with no validated specific and sensitive biomarker, and no standard approved treatment. In this observational study with no intervention, participants used a Fitbit activity tracker. The aims were to explore natural symptom variation, feasibility of continuous activity monitoring, and to compare activity data with patient reported outcome measures (PROMs).Materials and methodsIn this pilot study, 27 patients with mild to severe ME/CFS, of mean age 42.3 years, used the Fitbit Charge 3 continuously for six months. Patients wore a SenseWear activity bracelet for 7 days at baseline, at 3 and 6 months. At baseline and follow-up they completed the Short Form 36 Health Survey (SF-36) and the DePaul Symptom Questionnaire–Short Form (DSQ-SF).ResultsThe mean number of steps per day decreased with increasing ME/CFS severity; mild 5566, moderate 4991 and severe 1998. The day-by-day variation was mean 47% (range 25%–79%). Mean steps per day increased from the first to the second three-month period, 4341 vs 4781 steps, p = 0.022. The maximum differences in outcome measures between 4-week periods (highest vs lowest), were more evident in a group of eight patients with milder disease (baseline SF-36 PF > 50 or DSQ-SF < 55) as compared to 19 patients with higher symptom burden (SF-36 PF < 50 and DSQ-SF > 55), for SF-36 PF raw scores: 16.9 vs 3.4 points, and for steps per day: 958 versus 479 steps. The correlations between steps per day and self-reported SF-36 Physical function, SF-36 Social function, and DSQ-SF were significant. Fitbit recorded significantly higher number of steps than SenseWear. Resting heart rates were stable during six months.ConclusionContinuous activity registration with Fitbit Charge 3 trackers is feasible and useful in studies with ME/CFS patients to monitor steps and resting heart rate, in addition to self-reported outcome measures.Clinical trial registrationClinicaltrials.gov: NCT04195815.

Effectiveness of wearable technology to optimize youth soccer players’ off-training behaviour and training responses: a parallel group randomized trial

ABSTRACT Background Although youth soccer players meet and exceed the weekly physical activity (PA) guidelines also exhibit high off-training sedentary behaviour (SB) levels. Recent evidence indicated that low PA preceding training leads to decreased PA intensity in soccer practice and that SB impairs sports performance and recovery. Thus, a parallel group randomized trial examined the effect of activity wristbands on young players’ off-training PA and SB profiles; assessed the added value of wearable wristbands with PA warnings; and investigated whether manipulating off-training PA can affect the players’ training responses. Methods Thirty-two adolescent soccer players (16.1 ± 0.9 years old) were monitored during weekdays for two weeks (interspersed with one week). Players were randomly assigned to a reminder to move (REM) and a non-reminder to move group (nREM). The REM wore an activity wristband (Fitbit Charge 2) with PA warnings in the last week of research, while the nREM wore identical monitors without PA feedback. Throughout the study, off-training PA was assessed using tri-axial accelerometers, and training responses were analysed using wearable inertial monitoring units. Gardner-Altman estimation plots and a Fisher’s Exact Test of Independence estimated each group’s off-training PA changes between the monitored weeks. Complementary, an analysis of covariance (ANCOVA) quantified the effect of Fitbit wristband configurations (with a reminder to move or without a reminder to move) on the off-training PA and soccer training external load variables. The independent variable group had two levels (REM and nREM). The off-training PA and training load values collected in the first week were introduced in the model as a covariate, the groups were designated fixed factors and the off-training PA and training load from the third week were the dependent variables. The team was also included as a covariate and the between-group differences post-intervention were adjusted for baseline and team. Results Interestingly, results showed that different wearable wristbands did not influence the players’ off-training PA profiles (p > 0.05). Concomitantly, no differences were observed in training responses (p > 0.05). Conclusion Findings emphasize the importance of health and youth sports organizations in developing newer approaches for promoting healthier lifestyles, beyond training practices, with potentially favourable implications for sports performance.

Feasibility of Conducting Long-term Health and Behaviors Follow-up in Adolescents: Longitudinal Observational Study.

BackgroundMachine learning uses algorithms that improve automatically through experience. This statistical learning approach is a natural extension of traditional statistical methods and can offer potential advantages for certain problems. The feasibility of using machine learning techniques in health care is predicated on access to a sufficient volume of data in a problem space.ObjectiveThis study aimed to assess the feasibility of data collection from an adolescent population before and after a posterior spine fusion operation.MethodsBoth physical and psychosocial data were collected. Adolescents scheduled for a posterior spine fusion operation were approached when they were scheduled for the surgery. The study collected repeated measures of patient data, including at least 2 weeks prior to the operation and 6 months after the patients were discharged from the hospital. Patients were provided with a Fitbit Charge 4 (consumer-grade health tracker) and instructed to wear it as often as possible. A third-party web-based portal was used to collect and store the Fitbit data, and patients were trained on how to download and sync their personal device data on step counts, sleep time, and heart rate onto the web-based portal. Demographic and physiologic data recorded in the electronic medical record were retrieved from the hospital data warehouse. We evaluated changes in the patients’ psychological profile over time using several validated questionnaires (ie, Pain Catastrophizing Scale, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Pediatric Quality of Life Inventory). Questionnaires were administered to patients using Qualtrics software. Patients received the questionnaire prior to and during the hospitalization and again at 3 and 6 months postsurgery. We administered paper-based questionnaires for the self-report of daily pain scores and the use of analgesic medications.ResultsThere were several challenges to data collection from the study population. Only 38% (32/84) of the patients we approached met eligibility criteria, and 50% (16/32) of the enrolled patients dropped out during the follow-up period—on average 17.6 weeks into the study. Of those who completed the study, 69% (9/13) reliably wore the Fitbit and downloaded data into the web-based portal. These patients also had a high response rate to the psychosocial surveys. However, none of the patients who finished the study completed the paper-based pain diary. There were no difficulties accessing the demographic and clinical data stored in the hospital data warehouse.ConclusionsThis study identifies several challenges to long-term medical follow-up in adolescents, including willingness to participate in these types of studies and compliance with the various data collection approaches. Several of these challenges—insufficient incentives and personal contact between researchers and patients—should be addressed in future studies.

The Association Between Erectile Dysfunction and Sleep Parameters: Data from a Prospective, Controlled Cohort

Many studies have reported a possible strong relationship between poor sleep quality, sleep disruption, sleep disorders, and erectile dysfunction (ED). This study aimed to investigate the relationship between sleep quality and ED. Patients diagnosed with ED by the International Index of Erectile Function-5 (IIEF-5) questionnaire and 72 healthy adult men were included. Participants completed the questionnaire, underwent a detailed physical examination, and provided blood samples. All enrolled subjects then wore the Fitbit Charge 2 that monitored sleep throughout the night. Primary outcome measures included scores on the IIEF-5, General Anxiety Disorder-7 (GAD-7) scale, Patient Health Questionnaire-9 (PHQ-9), Pittsburgh Sleep Quality Index (PSQI), and sleep monitoring parameters obtained from Fitbit Charge 2. Finally, a total of 107 ED patients and 72 healthy adult men were enrolled in this study. Univariate analysis indicated that the GAD-7 (P < .001), PHQ-9 (P < .001), and PSQI scores (P < .001) significantly differed according to the presence/absence of ED. Further multiple logistic regression analysis showed that the PHQ-9 (odds ratio [OR]: 1.227, 95% confidence interval [CI]: 1.070-1.407; P=.003) and PSQI scores (OR: 1.220, 95%CI: 1.116-1.334; P < .001) were independent risk factors for ED. Analysis of objective sleep monitoring parameters showed that total sleep time (TST) (P=.001), sleep onset latency (SOL) (P=.026), deep sleep (N3) duration (P=.011) and rapid eye movement (REM) sleep duration (P < .001) were significantly differed between the 2 groups, with durations in the ED group significantly lower than those in the non-ED group. In addition, receiver operating characteristic (ROC) curve analysis indicated that the REM sleep duration had the highest area under the curve (AUC: 0.728) of all sleep parameters, with a P value < .001, a sensitivity of 72.2% and a specificity of 73.8%. Urologists and andrologists should be aware of impacted sleep quality and depression in ED patients. The strength of this study is that the relationship between sleep quality and ED was assessed with both a subjective scale and an objective sleep monitoring tool. However, our study only described an association between sleep quality and ED and did not establish a causal relationship. Sleep parameters are strongly associated with ED, indicating that poor sleep quality may increase the likelihood of ED. Wu X, Zhang Y, Zhang W, et al. The Association Between Erectile Dysfunction and Sleep Parameters: Data from a Prospective, Controlled Cohort. J Sex Med 2022;19:1387-1396.