Smart watch applications in atrial fibrillation detection: Current state and future directions.

INTRODUCTION: Atrial fibrillation (Afib) is a prevalent chronic arrhythmia associated with severe complications, including stroke, heart failure, and increased mortality. This review explores the use of smartwatches for Afib detection, addressing the limitations of current monitoring methods and emphasizing the potential of wearable technology in revolutionizing healthcare. RESULTS/OBSERVATION: Current Afib detection methods, such as electrocardiography, have limitations in sensitivity and specificity. Smartwatches with advanced sensors offer continuous monitoring, improving the chances of detecting asymptomatic and paroxysmal Afib. The review meticulously examines major clinical trials studying Afib detection using smartwatches, including the landmark Apple Heart Study and ongoing trials such as the Heart Watch, Heartline, and Fitbit Heart Study. Detailed summaries of participant numbers, smartwatch devices used, and key findings are presented. It also comments on the cost-effectiveness and scalability of smartwatch-based screening, highlighting the potential to reduce healthcare costs and improve patient outcomes. CONCLUSION/RELEVANCE: The integration of wearable technology into healthcare can lead to earlier diagnosis, improved patient engagement, and enhanced cardiac health monitoring. Despite ethical considerations and disparities, the potential benefits outweigh the challenges. This review calls for increased awareness, collaboration with insurance companies, and ongoing research efforts to optimize smartwatch accuracy and encourage widespread adoption of Afib detection. With insights from major trials, this review serves as a comprehensive reference for healthcare professionals and policymakers, guiding future strategies in the early diagnosis and management of atrial fibrillation.

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

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

Using Consumer Wearable Devices to Profile Postoperative Complications After Pediatric Appendectomy.

INTRODUCTION: Markers of postoperative recovery in pediatric patients are difficult for parents to evaluate after hospital discharge, who use subjective proxies to assess recovery and the onset of complications. Consumer-grade wearable devices (e.g., Fitbit) generate objective recovery data in near real time and thus may provide an opportunity to remotely monitor postoperative patients and identify complications beyond the initial hospitalization. The aim of this study was to use daily step counts from a Fitbit to compare recovery in patients with complications to those without complications after undergoing appendectomy for complicated appendicitis. METHODS: Children ages 3-17 years old undergoing laparoscopic appendectomy for complicated appendicitis were recruited. Patients wore a Fitbit device for 21 d after operation. After collection, patient data were included in the analysis if minimum wear-time criteria were achieved. Postoperative complications were identified through chart review, and step count trajectories for patients recovering with and without complications were compared. Additionally, to account for the patients experiencing a complication on different postoperative days, median daily step count for pre- and post-complication were analyzed. RESULTS: Eighty-six patients with complicated appendicitis were enrolled in the study, and fourteen children developed a postoperative complication. Three patients were excluded because they did not meet the minimum wear time requirements. Complications were divided into abscesses (n = 7, 64%), surgical site infections (n = 2, 18%), and other, which included small bowel obstruction and Clostridioides difficile infection (n = 2, 18%). Patients presented with a complication on mean postoperative day 8, while deviation from the normative recovery trajectory was evident 4 d prior. When compared to children with normative recovery, the patients with surgical complications experienced a slower increase in step count postoperatively, but the recovery trajectory was specific to each complication type. When corrected for day of presentation with complication, step count remained low prior to the discovery of the complication and increased after treatment resembling the normative recovery trajectory. CONCLUSIONS: This study profiled variations from the normative recovery trajectory in patients with complication after appendectomy for complicated appendicitis, with distinct trajectory patterns by complication type. Our findings have potentially profound clinical implications for monitoring pediatric patients postoperatively, particularly in the outpatient setting, thus providing objective data for potentially earlier identification of complications after hospital discharge.

Using Wearable Devices to Profile Demographic-Specific Recovery After Pediatric Appendectomy.

INTRODUCTION: Counseling patients and parents about the postoperative recovery expectations for physical activity after pediatric appendectomy varies significantly and is not specific to patients' demographic characteristics. Consumer wearable devices (CWD) can be used to objectively assess patients' normative postoperative recovery of physical activity. This study aimed to develop demographic-specific normative physical activity recovery trajectories using CWD in pediatric patients undergoing appendectomy. METHODS: Children ages 3-18 y old undergoing laparoscopic appendectomy for acute appendicitis were recruited. Patients wore a Fitbit device for 21 d postoperatively and daily step counts were measured. Patients with postoperative complications were excluded. Segmented regression models were fitted and time-to-plateau was estimated for patients with simple and complicated appendicitis separately for each age group, sex, race/ethnicity, and body mass index category. RESULTS: Among 147 eligible patients; 76 (51.7%) were female, 86 (58.5%) were in the younger group, and 79 (53.7%) had complicated appendicitis. Patients 3-11 y old demonstrated a faster trajectory to a physical activity plateau compared to those 12-18 in both simple (postoperative day [POD] 9 versus POD 17) and complicated appendicitis (POD 17 versus POD 21). Males and females had a similar postoperative recovery trajectory in simple and complicated appendicitis. There was no clear pattern differentiating trajectories based on race/ethnicity. Overweight/obese patients demonstrated a slower recovery trajectory in simple appendicitis. CONCLUSIONS: This study demonstrates that factors other than the disease itself, such as age, may affect recovery, suggesting the need for more tailored discharge instructions. CWDs can improve our understanding of recovery and allow for better data-driven counseling perioperatively.

Can we let our patients sleep in the hospital? A randomized controlled trial of a pragmatic sleep protocol in surgical oncology patients.

BACKGROUND: Postoperative inpatients experience increased stress due to pain and poor restorative sleep than non-surgical inpatients. OBJECTIVES AND METHODS: A total of 101 patients, undergoing major oncologic surgery, were randomized to a postoperative sleep protocol (n = 50) or standard postoperative care (n = 51), between August 2020 and November 2021. The primary endpoint of the study was postoperative sleep time after major oncologic surgery. Sleep time and steps were measured using a Fitbit Charge 4®. RESULTS: There was no statistically significant difference found in postoperative sleep time between the sleep protocol and standard group (median sleep time of 427 min vs. 402 min; p = 0.852, respectively). Major complication rates were similar in both groups (7.4% vs. 8.9%). Multivariate analysis found sex and Charlson Comorbidity Index to be significant factors affecting postoperative sleep time and step count. Postoperative delirium was only observed in the standard group, although this did not reach statistical significance. There were no in hospital mortalities. CONCLUSION: The use of a sleep protocol was found to be safe in our study population. There was no statistical difference in postoperative sleep time or major complications. Institution of a more humane sleep protocol for postoperative inpatients should be considered.

Comparing sleep measures in cancer survivors: self-reported sleep diary versus objective wearable sleep tracker.

PURPOSE: Cancer survivors are increasingly using wearable fitness trackers, but it is unclear if they match traditional self-reported sleep diaries. We aimed to compare sleep data from Fitbit and the Consensus Sleep Diary (CSD) in this group. METHODS: We analyzed data from two randomized clinical trials, using both CSD and Fitbit to collect sleep outcomes: total sleep time (TST), wake time after sleep onset (WASO), number of awakenings (NWAK), time in bed (TIB), and sleep efficiency (SE). Insomnia severity was measured by Insomnia Severity Index (ISI). We used the Wilcoxon signed rank test, Spearman's rank correlation coefficients, and the Mann-Whitney test to compare sleep outcomes and assess their ability to distinguish insomnia severity levels between CSD and Fitbit data. RESULTS: Among 62 participants, compared to CSD, Fitbit recorded longer TST by an average of 14.6 (SD = 84.9) minutes, longer WASO by an average of 28.7 (SD = 40.5) minutes, more NWAK by an average of 16.7 (SD = 6.6) times per night, and higher SE by an average of 7.1% (SD = 14.4); but shorter TIB by an average of 24.4 (SD = 71.5) minutes. All the differences were statistically significant (all p < 0.05), except for TST (p = 0.38). Moderate correlations were found for TST (r = 0.41, p = 0.001) and TIB (r = 0.44, p < 0.001). Compared to no/mild insomnia group, participants with clinical insomnia reported more NWAK (p = 0.009) and lower SE (p = 0.029) as measured by CSD, but there were no differences measured by Fitbit. CONCLUSIONS: TST was the only similar outcome between Fitbit and CSD. Our study highlights the advantages, disadvantages, and clinical utilization of sleep trackers in oncology.

Assessing the current utilization status of wearable devices in clinical research.

BACKGROUND/AIMS: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. METHODS: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. RESULTS: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. CONCLUSIONS: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED).

BACKGROUND: The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour. METHODS: The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form. RESULTS: The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence. CONCLUSIONS: The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography.

The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.

Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults.

Sleep tracking by consumers is becoming increasingly prevalent; yet, few studies have evaluated the accuracy of such devices. We sought to evaluate the accuracy of three devices (Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8) compared to the gold standard sleep assessment (polysomnography (PSG)). Thirty-five participants (aged 20-50 years) without a sleep disorder were enrolled in a single-night inpatient study, during which they wore the Oura Ring, Fitbit, and Apple Watch, and were monitored with PSG. For detecting sleep vs. wake, the sensitivity was ≥95% for all devices. For discriminating between sleep stages, the sensitivity ranged from 50 to 86%, as follows: Oura ring sensitivity 76.0-79.5% and precision 77.0-79.5%; Fitbit sensitivity 61.7-78.0% and precision 72.8-73.2%; and Apple sensitivity 50.5-86.1% and precision 72.7-87.8%. The Oura ring was not different from PSG in terms of wake, light sleep, deep sleep, or REM sleep estimation. The Fitbit overestimated light (18 min; p < 0.001) sleep and underestimated deep (15 min; p < 0.001) sleep. The Apple underestimated the duration of wake (7 min; p < 0.01) and deep (43 min; p < 0.001) sleep and overestimated light (45 min; p < 0.001) sleep. In adults with healthy sleep, all the devices were similar to PSG in the estimation of sleep duration, with the devices also showing moderate to substantial agreement with PSG-derived sleep stages.

The Impact of a Wearable Activity Tracker and Structured Feedback Program on Physical Activity in Hemodialysis Patients: The Step4Life Pilot Randomized Controlled Trial.

RATIONALE & OBJECTIVE: People with end-stage kidney disease (ESKD) have very low physical activity, and the degree of inactivity is strongly associated with morbidity and mortality. We assessed the feasibility and effectiveness of a 12-week intervention coupling a wearable activity tracker (FitBit) and structured feedback coaching versus wearable activity tracker alone on changes in physical activity in hemodialysis patients. STUDY DESIGN: Randomized controlled trial. SETTING & PARTICIPANTS: 55 participants with ESKD receiving hemodialysis who were able to walk with or without assistive devices recruited from a single academic hemodialysis unit between January 2019 and April 2020. INTERVENTIONS: All participants wore a Fitbit Charge 2 tracker for a minimum of 12 weeks. Participants were randomly assigned 1:1 to a wearable activity tracker plus a structured feedback intervention versus the wearable activity tracker alone. The structured feedback group was counseled weekly on steps achieved after randomization. OUTCOME: The outcome was step count, and the main parameter of interest was the absolute change in daily step count, averaged per week, from baseline to completion of 12 weeks intervention. In the intention-to-treat analysis, mixed-effect linear regression analysis was used to evaluate change in daily step count from baseline to 12-weeks in both arms. RESULTS: Out of 55 participants, 46 participants completed the 12-week intervention (23 per arm). The mean age was 62 (± 14 SD) years; 44% were Black, and 36% were Hispanic. At baseline, step count (structured feedback intervention: 3,704 [1,594] vs wearable activity tracker alone: 3,808 [1,890]) and other participant characteristics were balanced between the arms. We observed a larger change in daily step count in the structured feedback arm at 12 weeks relative to use of the wearable activity tracker alone arm (Δ 920 [±580 SD] versus Δ 281 [±186 SD] steps; between-group difference Δ 639 [±538 SD] steps; P<0.05). LIMITATIONS: Single-center study and small sample size. CONCLUSION: This pilot randomized controlled trial demonstrated that structured feedback coupled with a wearable activity tracker led to a greater daily step count that was sustained over 12 weeks relative to a wearable activity tracker alone. Future studies are required to determine longer-term sustainability of the intervention and potential health benefits in hemodialysis patients. FUNDING: Grants from industry (Satellite Healthcare) and government (National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK). TRIAL REGISTRATION: Registered at ClinicalTrials.gov with study number NCT05241171.

Association of daily physical activity with brain volumes and cervical spinal cord areas in multiple sclerosis.

BACKGROUND: Remote activity monitoring has the potential to evaluate real-world, motor function, and disability at home. The relationships of daily physical activity with spinal cord white matter and gray matter (GM) areas, multiple sclerosis (MS) disability and leg function, are unknown. OBJECTIVE: Evaluate the association of structural central nervous system pathology with ambulatory disability. METHODS: Fifty adults with progressive or relapsing MS with motor disability who could walk >2 minutes were assessed using clinician-evaluated, patient-reported outcomes, and quantitative brain and spinal cord magnetic resonance imaging (MRI) measures. Fitbit Flex2, worn on the non-dominant wrist, remotely assessed activity over 30 days. Univariate and multivariate analyses were performed to assess correlations between physical activity and other disability metrics. RESULTS: Mean age was 53.3 years and median Expanded Disability Status Scale (EDSS) was 4.0. Average daily step counts (STEPS) were highly correlated with EDSS and walking measures. Greater STEPS were significantly correlated with greater C2-C3 spinal cord GM areas (ρ = 0.39, p = 0.04), total cord area (TCA; ρ = 0.35, p = 0.04), and cortical GM volume (ρ = 0.32, p = 0.04). CONCLUSION: These results provide preliminary evidence that spinal cord GM area is a neuroanatomical substrate associated with STEPS. STEPS could serve as a proxy to alert clinicians and researchers to possible changes in structural nervous system pathology.

Physical activity in Norwegian teenagers and young adults with haemophilia A compared to general population peers.

INTRODUCTION: Limited evidence exists on objectively measured habitual physical activity (PA) of young people with haemophilia (PWH). AIMS: To compare different outcomes of objective PA between young PWH A and controls using a commercial activity tracker. METHODS: We enrolled males aged 13-30 years with moderate and severe haemophilia A, without inhibitors on regular prophylaxis. PA was measured with the activity tracker Fitbit Charge 3 for 12 weeks. Control group data was obtained from ≈60,000 Fitbit users, matched on age, sex and measurement period. PA variables [steps, intensities, volume, activity types, exercise frequencies and proportion meeting the World Health Organization's moderate-to-vigorous PA (MVPA) recommendations] were compared between groups descriptively and using Welch's two-sample t-test and two-sample test of proportions. RESULTS: Forty PWH A were enrolled (mean age 19.5 years, 50% teenagers, 50% adults, three (7.5%) with moderate and 37 (92.5%) with severe haemophilia). Mean daily steps and minutes MVPA were similar between PWH and controls. PWH spent more time in light PA (mean 227 vs. 192 min/day, P = .033) and exercised more frequently (mean 5.6 vs. 3.9 exercise sessions/week, P < .001). Among teenagers, 40% PWH and 8% controls reached MVPA recommendations, compared to 95% and 100% among adults. The most common type of PA was walking. CONCLUSION: This cohort of young PWH A on prophylactic treatment had PA levels comparable to controls. Still, a considerable proportion of teenagers did not meet the recommended weekly volume of MVPA, and we encourage clinicians to have a particular focus on promoting PA for this group.

Consumer Wearables Define a Normative Recovery Trajectory Following the Modified Nuss Procedure.

INTRODUCTION: The modified Nuss procedure is an elective procedure associated with a lengthy recovery, uncontrolled pain, and risk of infrequent, yet life-threatening complications. The absence of objective measures of normative postoperative recovery creates uncertainty about the postdischarge period, which remains highly dependent on the patients' and their caregivers' expectations and management of recovery. We aimed to describe an objective-normative, physical activity recovery trajectory after the modified Nuss procedure, using step counts from the Fitbit. METHODS: This observational study enrolled children ≤18 y with pectus excavatum who underwent the modified Nuss procedure from 2021 to 2022. The Fitbit was worn for 21 postoperative days. Postdischarge outcomes and health-care utilization were evaluated. For patients without postoperative complications, piecewise linear regression analysis was conducted to generate a normative recovery trajectory model of daily step counts. RESULTS: Of 80 patients enrolled, 66 (86%) met eligibility criteria (mean age, 15.1 ± 1.3 y; 89.4% male, 62.1% non-Hispanic White). The mean number of telephone and electronic message encounters regarding concerns with the patient's recovery within 30 d postoperatively was 2.1 (standard deviation = 2.7). Ten patients (15.2%) returned to the emergency department (ED) within the 30-d postoperative period. Seven patients (10.6%) presented to the ED one time, and three patients (4.5%) presented to the ED twice. Thirty-day readmission rate was four patients (6.0%). Piecewise regression model showed that patients without complications steadily increased their daily step count on each postoperative day and plateaued on day 18. CONCLUSIONS: We have developed a normative recovery trajectory following the modified Nuss procedure using step count data collected by a consumer wearable device. This offers the potential to inform preoperative patient expectations and reduce avoidable health-care utilization through informed preoperative counseling, thus laying the ground work for the use of consumer wearable devices as a postdischarge remote monitoring tool.

Challenges of using a Fitbit smart wearable among people with dementia.

OBJECTIVES: Limited research on using smart wearables such as Fitbit devices among people with dementia has shown favourable outcomes. The aim of this study was to explore the acceptability and feasibility of using a Fitbit Charge 3 among people with dementia, living in the community, who took part in the physical exercise component of the Comprehensive REsilience-building psychoSocial intervenTion pilot study. METHODS: A mixed methods study was conducted; Quantitative data relating to wear rates for the Fitbit were recorded and qualitative data were collected by group and individual interviews with the people with dementia and their caregiver about their experience of wearing/using the Fitbit in the study. RESULTS: Nine people with dementia and their caregiver completed the intervention. Only one participant wore the Fitbit consistently. Supporting set-up and use of the devices was time consuming and caregiver involvement was essential for day-to-day support: none of the people with dementia owned a smartphone. Few of them engaged with the Fitbit features, primarily only using it to check the time and only a minority wanted to keep the device beyond the intervention. DISCUSSION: When designing a study using smart wearables such as a Fitbit among people with dementia, consideration should be given to the following: the possible burden on caregivers supporting the use of the device; a lack of familiarity with this technology in the target population; dealing with missing data, and the involvement of the researcher in setting up and supporting use of the device.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial.

BACKGROUND: The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking. METHODS: We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months. DISCUSSION: The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05351359, 28/04/2022).

Effect of a Wearable Device-Based Physical Activity Intervention in North Korean Refugees: Pilot Randomized Controlled Trial.

BACKGROUND: Effective health interventions for North Korean refugees vulnerable to metabolic disorders are currently unelucidated. OBJECTIVE: This study aimed to evaluate the effects of digital health interventions in North Korean refugees using a wearable activity tracker (Fitbit device). METHODS: We conducted a prospective, randomized, open-label study on North Korean refugees aged 19-59 years between June 2020 and October 2021 with a 12-week follow-up period. The participants were randomly assigned to either an intervention group or a control group in a 1:1 ratio. The intervention group received individualized health counseling based on Fitbit data every 4 weeks, whereas the control group wore the Fitbit device but did not receive individualized counseling. The primary and secondary outcomes were the change in the mean daily step count and changes in the metabolic parameters, respectively. RESULTS: The trial was completed by 52 North Korean refugees, of whom 27 and 25 were in the intervention and control groups, respectively. The mean age was 43 (SD 10) years, and 41 (78.8%) participants were women. Most participants (44/52, 95.7%) had a low socioeconomic status. After the intervention, the daily step count in the intervention group increased, whereas that in the control group decreased. However, there were no significant differences between the 2 groups (+83 and -521 steps in the intervention and control groups, respectively; P=.500). The effects of the intervention were more prominent in the participants with a lower-than-average daily step count at baseline (<11,667 steps/day). After the 12-week study period, 85.7% (12/14) and 46.7% (7/15) of the participants in the intervention and control groups, respectively, had an increased daily step count (P=.05). The intervention prevented the worsening of the metabolic parameters, including BMI, waist circumference, fasting blood glucose level, and glycated hemoglobin level, during the study period. CONCLUSIONS: The wearable device-based physical activity intervention did not significantly increase the average daily step count in the North Korean refugees in this study. However, the intervention was effective among the North Korean refugees with a lower-than-average daily step count; therefore, a large-scale, long-term study of this intervention type in an underserved population is warranted. TRIAL REGISTRATION: Clinical Research Information Service KCT0007999; https://cris.nih.go.kr/cris/search/detailSearch.do/23622.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study.

BACKGROUND: Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive. OBJECTIVE: This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders. METHODS: We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs. RESULTS: The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments. CONCLUSIONS: Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Consumer-based activity trackers in evaluation of physical activity in myositis patients.

OBJECTIVES: Inflammatory myopathies are characterized by muscle weakness that limits the activities of daily living. Daily step count is an accepted metric of physical activity. Wearable technologies such as Fitbit® enable tracking of daily step counts. We assessed the psychometric properties of Fitbit® and compared the accuracy of Fitbit® step counts to ActiGraph®. METHODS: This was a pilot, proof of concept, prospective observational study with four visits at 0, 1, 3 and 6 months in PM, DM, necrotizing myopathy (NM) or anti-synthetase syndrome (AS) subjects. Six core set measures (manual muscle testing, physician global disease activity, patient global disease activity, and extra-muscular disease activity, HAQ-Disability Index and creatine kinase), three functional tests (six-min walk, timed up-and-go, sit-to-stand tests) and SF-36 physical function-10 (PF10) were collected at each visit. Patients wore waist-worn Fitbit® One and ActiGraph® T3X-BT concurrently for 7 days/month for 6 months. RESULTS: Twenty-four (10 DM, 8 PM/NM, 6 AS) patients (17 females/7 males; 91% Caucasian) were enrolled. Test-retest reliability of daily steps was strong in 1-month follow-up (ICC 0.89). Daily steps and peak 1-min cadence showed moderate-strong correlations with physician global disease activity, patient global disease activity, HAQ-Disability Index, SF-36 PF10 and all three functional tests. Fitbit® and ActiGraph® step counts demonstrated good agreement and strong correlation (ICC 0.96). CONCLUSION: Fitbit® daily steps and peak 1-min cadence are reliable and valid measures of physical activity in a cohort of myositis patients. This pilot data suggests that Fitbit® has a potential for use in clinical practice and trials to monitor physical activity in myositis patients, but larger studies are needed for further validation.

Comparison of free-living physical activity measurements between ActiGraph GT3X-BT and Fitbit Charge 3 in young people with haemophilia.

INTRODUCTION: Measurement of physical activity (PA) using commercial activity trackers such as Fitbit devices has become increasingly popular, also for people with haemophilia (PWH). The accuracy of the Fitbit model Charge 3 has not yet been examined. AIMS: To compare the Fitbit Charge 3 against the research-grade accelerometer ActiGraph GT3X-BT in measuring average daily steps and minutes spent in different PA intensities. METHODS: Twenty-four young PWH wore a wrist-worn Fitbit Charge 3 and hip-worn ActiGraph GT3X-BT simultaneously for seven consecutive days in free-living conditions. Correlation of and differences between the devices for daily averages of PA parameters were assessed using Pearson's correlation coefficient and paired t-test, respectively. Agreement between devices was assessed using Bland-Altman plots. RESULTS: Twenty participants (mean age 21.8) were included in the analyses. We found moderate to high correlations between Fitbit and ActiGraph measured daily averages for all PA variables, but statistically significant differences between devices for all variables except daily minutes of moderate PA. Fitbit overestimated average daily steps, minutes of light, vigorous and moderate-to-vigorous PA. Bland-Altman plots showed a measurement bias between devices for all parameters with increasing overestimation by the Fitbit for higher volumes of PA. CONCLUSION: The Fitbit Charge 3 overestimated steps and minutes of light, moderate and moderate-to-vigorous PA as compared to the ActiGraph GT3X-BT, and this bias increased with PA volume. The Fitbit should therefore be used with caution in research, and we advise users of the device to be cognizant of this overestimation.