Associations Between Adverse Childhood Experiences and Early Adolescent Physical Activity in the United States

OBJECTIVE

To determine the associations between the number of adverse childhood experiences (ACEs) and objectively-measured physical activity (PA) in a population-based, demographically diverse cohort of 9-14-year-olds and to determine which subtypes of ACEs were associated with physical activity levels.

METHODS

We analyzed data (n = 7046) from the Adolescent Brain Cognitive Development (ABCD) Study 4.0 release at baseline and year 2 follow-up. ACE (cumulative score and subtypes) and physical activity (average Fitbit daily steps assessed at Year 2) were analyzed using linear regression analyses. Covariates included race and ethnicity, sex, household income, parent education, body mass index, study site, twins/siblings, and data collection period.

RESULTS

Adjusted models suggest an inverse association between number of ACEs and Fitbit daily steps, with ≥4 (compared to 0) ACEs associated with 567 fewer daily steps (95% CI -902.2, -232.2). Of the ACEs subtypes, emotional abuse (B = -719.3, 95% CI -1430.8, -7.9), physical neglect (B = -423.7, 95% CI -752.8, -94.6), household mental illness (B = -317.1, 95% CI -488.3, -145.9), and household divorce or separation (B = -275.4, 95% CI -521.5, -29.2) were inversely and statistically significant associated with Fitbit daily steps after adjusting for confounders.

CONCLUSIONS

Our results suggest that there is an inverse, dose-dependent relationship between cumulative number of ACEs and physical activity as measured by daily steps. This work highlights the importance of screening for ACEs among young people at an early age to help identify those who could benefit from interventions or community programs that support increased physical activity.

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.

Exploring the effects of fitbit incentive on treatment outcomes in veterans undergoing intensive pain rehabilitation program

OBJECTIVE

This study compares clinical pain outcomes between patients in a pain treatment program that received a Fitbit, to patients that did not. We also explored: (1) cognitive, emotional, and psychological factors that may have impacted the decision to opt in to receiving a Fitbit; and (2) whether the choice to receive a Fitbit impacted changes in cognitive, emotional, and psychological factors following treatment.

METHODS

Among 58 patients in a multidisciplinary pain treatment program at a Veterans Affairs Healthcare System hospital, 31 patients opted to receive a Fitbit as adjunct treatment, while 27 did not. This study utilized patient-reported and practitioner-collected data from the pain treatment program.

RESULTS

Compared to the non-Fitbit group, the Fitbit group displayed a significant decrease in average pain intensity, however showed no correlation between Fitbit activity and average pain intensity. Additionally, treatment satisfaction was the only predictor of treatment group, when modeling pre- and post-treatment outcomes changes.

CONCLUSION

The implementation of a Fitbit may lead to improved pain intensity. Initial evidence suggests that opting to receive a Fitbit during a pain treatment program indicates treatment engagement leading to greater treatment satisfaction. Future work is needed to verify and expand upon this potential mechanism.

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.

Exploring the Major Barriers to Physical Activity in Persons With Multiple Sclerosis: Observational Longitudinal Study

BACKGROUND

Physical activity (PA) represents a low-cost and readily available means of mitigating multiple sclerosis (MS) symptoms and alleviating the disease course. Nevertheless, persons with MS engage in lower levels of PA than the general population.

OBJECTIVE

This study aims to enhance the understanding of the barriers to PA engagement in persons with MS and to evaluate the applicability of the Barriers to Health Promoting Activities for Disabled Persons (BHADP) scale for assessing barriers to PA in persons with MS, by comparing the BHADP score with self-reported outcomes of fatigue, depression, self-efficacy, and health-related quality of life, as well as sensor-measured PA.

METHODS

Study participants (n=45; median age 46, IQR 40-51 years; median Expanded Disability Status Scale score 4.5, IQR 3.5-6) were recruited among persons with MS attending inpatient neurorehabilitation. They wore a Fitbit Inspire HR (Fitbit Inc) throughout their stay at the rehabilitation clinic (phase 1; 2-4 wk) and for the 4 following weeks at home (phase 2; 4 wk). Sensor-based step counts and cumulative minutes in moderate to vigorous PA were computed for the last 7 days at the clinic and at home. On the basis of PA during the last 7 end-of-study days, we grouped the study participants as active (≥10,000 steps/d) and less active (<10,000 steps/d) to explore PA barriers compared with PA level. PA barriers were repeatedly assessed through the BHADP scale. We described the relevance of the 18 barriers of the BHADP scale assessed at the end of the study and quantified their correlations with the Spearman correlation test. We evaluated the associations of the BHADP score with end-of-study reported outcomes of fatigue, depression, self-efficacy, and health-related quality of life with multivariable regression models. We performed separate regression analyses to examine the association of the BHADP score with different sensor-measured outcomes of PA.

RESULTS

The less active group reported higher scores for the BHADP items Feeling what I do doesn't help, No one to help me, and Lack of support from family/friends. The BHADP items Not interested in PA and Impairment were positively correlated. The BHADP score was positively associated with measures of fatigue and depression and negatively associated with self-efficacy and health-related quality of life. The BHADP score showed an inverse relationship with the level of PA measured but not when dichotomized according to the recommended PA level thresholds.

CONCLUSIONS

The BHADP scale is a valid and well-adapted tool for persons with MS because it reflects common MS symptoms such as fatigue and depression, as well as self-efficacy and health-related quality of life. Moreover, decreases in PA levels are often related to increases in specific barriers in the lives of persons with MS and should hence be addressed jointly in health care management.

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.

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.

Six-Month Pilot Testing of a Digital Health Tool to Support Effective Self-Care in People With Heart Failure: Mixed Methods Study

BACKGROUND

Digital tools may support people to self-manage their heart failure (HF). Having previously outlined the human-centered design development of a digital tool to support self-care of HF, the next step was to pilot the tool over a period of time to establish people's acceptance of it in practice.

OBJECTIVE

This study aims to conduct an observational pilot study to examine the usability, adherence, and feasibility of a digital health tool for HF within the Irish health care system.

METHODS

A total of 19 participants with HF were provided with a digital tool comprising a mobile app and the Fitbit Charge 4 and Aria Air smart scales for a period of 6 months. Changes to their self-care were assessed before and after the study with the 9-item European HF Self-care Behavior Scale (EHFScBS) and the Minnesota Living with HF Questionnaire (MLwHFQ) using a Wilcoxon signed rank test. After the study, 3 usability questionnaires were implemented and descriptively analyzed: the System Usability Scale (SUS), Wearable Technology Motivation Scale (WTMS), and Comfort Rating Scale (CRS). Participants also undertook a semistructured interview regarding their experiences with the digital tool. Interviews were analyzed deductively using the Theoretical Domains Framework.

RESULTS

Participants wore their devices for an average of 86.2% of the days in the 6-month testing period ranging from 40.6% to 98%. Although improvements in the EHFScBS and MLwHFQ were seen, these changes were not significant (P=.10 and P=.70, respectively, where P>.03, after a Bonferroni correction). SUS results suggest that the usability of this system was not acceptable with a median score of 58.8 (IQR 55.0-60.0; range 45.0-67.5). Participants demonstrated a strong motivation to use the system according to the WTMS (median 6.0, IQR 5.0-7.0; range 1.0-7.0), whereas the Fitbit was considered very comfortable as demonstrated by the low CRS results (median 0.0, IQR 0.0-0.0; range 0.0-2.0). According to participant interviews, the digital tool supported self-management through increased knowledge, improved awareness, decision-making, and confidence in their own data, and improving their social support through a feeling of comfort in being watched.

CONCLUSIONS

The digital health tool demonstrated high levels of adherence and acceptance among participants. Although the SUS results suggest low usability, this may be explained by participants uncertainty that they were using it fully, rather than it being unusable, especially given the experiences documented in their interviews. The digital tool targeted key self-management behaviors and feelings of social support. However, a number of changes to the tool, and the health service, are required before it can be implemented at scale. A full-scale feasibility trial conducted at a wider level is required to fully determine its potential effectiveness and wider implementation needs.

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.

Validation of the relationship between rapid eye movement sleep and sleep-related erections in healthy adults by a feasible instrument Fitbit Charge2

BACKGROUND

Sleep, particularly rapid eye movement sleep, has been found to be associated with sleep-related erections. While RigiScan is currently a more accurate method for monitoring nocturnal erectile events, the Fitbit, a smart wearable device, shows great potential for sleep monitoring.

OBJECTIVES

To analyze the relationship between sleep-related erections and sleep by recruiting sexually active, healthy men for simultaneous monitoring of sleep and nocturnal penile tumescence and rigidity.

PATIENTS AND METHODS

Using Fitbit Charge2 and RigiScan, we simultaneously monitored nocturnal sleep and erections in 43 healthy male volunteers, and analyzed the relationship between sleep periods and erectile events with the Statistical Package for Social Sciences.

RESULTS

Among all erectile events, 89.8% were related to rapid eye movement, and 79.2% of all rapid eye movement periods were associated with erectile events. Moreover, a statistical correlation was shown between the duration of rapid eye movement and the time of total erectile events (first night: 𝜌 = 0.316, p = 0.039; second night: 𝜌 = 0.370, p = 0.015).

DISCUSSION AND CONCLUSION

Our study shows a potential link between sleep-related erections and rapid eye movement sleep, which has implications for the current examination of sleep-related erections and further research into the mechanisms of erectile function. Meanwhile, the wearable device Fitbit has shown a potential promise for sleep monitoring in patients with erectile dysfunction. The results provide an alternative approach for further research on the relationship between erectile function and sleep with large sample sizes in the future.

Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker

OBJECTIVE

The aim of this study was to compare self-reported total energy intake (TEI) collected using an online multiple-pass 24-h dietary recall tool (Intake24) with total energy expenditure (TEE) estimated from Fitbit Charge 2-improved algorithms in adults from the NoHoW trial (12-mo weight maintenance after free-living weight loss).

METHODS

Bland-Altman plots were used to assess the level of agreement between TEI and TEE at baseline and after 12 mo. The ratio of TEI to TEE was also calculated.

RESULTS

Data from 1323 participants (71% female) was included in the analysis (mean ± SD: age 45 ± 12 y, body mass index 29.7 ± 5.4 kg/m2, initial weight loss 11.5 ± 6.5 kg). The TEI was lower than TEE on average by 33%, with limits of agreement ranging from -91% to +25%. Men, younger individuals, those with higher body mass index, those with the greater weight loss before enrollment, and those who gained weight during the study underestimated to a greater extent.

CONCLUSIONS

These findings contribute to the ongoing research examining the validity of technology-based dietary assessment tools.

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.

A pilot randomised controlled trial exploring the feasibility and efficacy of a human-AI sleep coaching model for improving sleep among university students

Objective

Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality.

Methods

In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention.

Results

Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95% CI: [-1.55-0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9-59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7-61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep.

Conclusions

The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

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 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.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.

Effects of personalized exercise prescriptions and social media delivered through mobile health on cancer survivors' physical activity and quality of life

PURPOSE

This study aimed to examine the effects of a multi-component mobile health intervention (wearable, apps, and social media) on cancer survivors' (CS') physical activity (PA), quality of life, and PA determinants compared to exercise prescription only, social media only, and attention control conditions.

METHODS

A total of 126 CS (age = 60.37 ± 7.41 years, mean ± SD) were recruited from the United States. The study duration was 6 months and participants were randomly placed into 4 groups. All participants received a Fitbit tracker and were instructed to install its companion app to monitor their daily PA. They (1) received previously established weekly personalized exercise prescriptions via email, (2) received weekly Facebook health education and interacted with one another, (3) received both Conditions 1 and 2, or (4) were part of the control condition, meaning they adopted usual care. CS PA daily steps, quality of life (i.e., physical health and mental health), and PA determinants (e.g., self-efficacy, social support) were measured at baseline, 3 months, and 6 months.

RESULTS

The final sample size included 123 CS. The results revealed only the multi-component condition had greater improvements in PA daily steps than the control condition post-intervention (95% confidence interval (95%CI): 368-2951; p < 0.05). Similarly, those in the multi-component condition had significantly greater increased physical health than the control condition (95%CI: -0.41 to -0.01; p < 0.05) over time. In addition, the social media condition had significantly greater increased perceived social support than the control condition (95%CI: 0.01-0.93; p < 0.05). No other significant differences on outcomes were identified.

CONCLUSION

The study findings suggest that the implementation of a multi-component mobile health intervention had positive effects on CS PA steps and physical health. Also, offering social media intervention has the potential to improve CS perceived social support.

Chronotype-Dependent Sleep Loss Is Associated with a Lower Amplitude in Circadian Rhythm and a Higher Fragmentation of REM Sleep in Young Healthy Adults

In modern society, the time and duration of sleep on workdays are primarily determined by external factors, e.g., the alarm clock. This can lead to a misalignment of the intrinsically determined sleep timing, which is dependent on the individual chronotype, resulting in reduced sleep quality. Although this is highly relevant given the high incidence of sleep disorders, little is known about the effect of this misalignment on sleep architecture. Using Fitbit trackers and questionnaire surveys, our study aims to elucidate sleep timing, sleep architecture, and subjective sleep quality in young healthy adults (n = 59) under real-life conditions (average of 82.4 ± 9.7 days). Correlations between variables were calculated to identify the direction of relationships. On workdays, the midpoint of sleep was earlier, the sleep duration was shorter, and tiredness upon waking was higher than on free days. A higher discrepancy between sleep duration on workdays and free days was associated with a lower stability of the circadian rhythm of REM sleep and also with a higher fragmentation of REM sleep. Similarly, a higher tiredness upon waking on free days, thus under intrinsically determined sleep timing conditions, was associated with a lower proportion and a higher fragmentation of REM sleep. This suggests that the misalignment between extrinsically and intrinsically determined sleep timing affects the architecture of sleep stages, particularly REM sleep, which is closely connected to sleep quality.

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's 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 Singed Ranks 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 Fitbit outcomes didn't.

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.

Does providing feedback and guidance on sleep perceptions using sleep wearables improve insomnia? Findings from "Novel Insomnia Treatment Experiment": a randomized controlled trial

STUDY OBJECTIVES

Insomnia is a disorder diagnosed based on self-reported sleep complaints. Differences between self-reported and sensor-based sleep parameters (sleep-wake state discrepancy) are common but not well-understood in individuals with insomnia. This two-arm, parallel-group, single-blind, superiority randomized-controlled trial examined whether monitoring sleep using wearable devices and providing support for interpretation of sensor-based sleep data improved insomnia symptoms or impacted sleep-wake state discrepancy.

METHODS

A total of 113 (age M = 47.53; SD = 14.37, 64.9% female) individuals with significant insomnia symptoms (Insomnia Severity Index(ISI) ≥10) from the community were randomized 1:1 (permuted block randomization) to receive 5 weeks (1) Intervention (n = 57): feedback about sensor-based sleep (Fitbit and EEG headband) with guidance for data interpretation and ongoing monitoring, and (2) Control (n = 56): sleep education and hygiene. Both groups received one individual session and two check-in calls. The ISI (primary outcome), sleep disturbance (SDis), sleep-related impairment (SRI), depression, and anxiety were assessed at baseline and post-intervention.

RESULTS

In total, 103 (91.2%) participants completed the study. Intention-to-treat multiple regression with multiple imputations showed that after controlling for baseline values, compared to the Control group (n = 51), the Intervention group (n = 52) had lower ISI (p = .011, d = 0.51) and SDis (p = .036, d = 0.42) post-intervention, but differences in SRI, depression, anxiety, and sleep-wake state discrepancy parameters (total sleep time, sleep onset latency, and wake after sleep onset) were not meaningful (P-values >.40).

CONCLUSIONS

Providing feedback and guidance about sensor-based sleep parameters reduced insomnia severity and sleep disturbance but did not alter sleep-wake state discrepancy in individuals with insomnia more than sleep hygiene and education. The role of sleep wearable devices among individuals with insomnia requires further research.

CLINICAL TRIAL REGISTRATION

The Novel Insomnia Treatment Experiment (NITE): the effectiveness of incorporating appropriate guidance for sleep wearables in users with insomnia. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378452, Australia New Zealand Clinical Trials Registry: ACTRN12619001636145.

Feasibility of Fitbit Use in Adolescent Survivors of Pediatric Cancer: Who Consistently Uses It and for How Long?

Purpose: Wearable activity trackers with real-time feedback and goal-setting features are being incorporated into programs to increase physical activity among childhood cancer survivors. This analysis describes the adoption and use of a Fitbit® Flex™ among adolescent-aged survivors of childhood cancer without incentives, reminders, or interventions to encourage use. Procedures: Cancer survivors aged 13-18 and ≥2 years from therapy received a Fitbit Flex with instructions to wear it daily. Researchers downloaded participants' daily total steps and active minutes for 1 year. Participants were classified as consistent (≥5 days/week for >4 weeks during the first 12 weeks) or inconsistent users, and comparisons between user types were made. Longitudinal use of the Fitbit and participants' 1-year acceptability evaluations are described. Results: Overall, 67.1% (47/70) of survivors enrolled, and Fitbit data were available for 36 participants. Initially, 30.6% (11/36) were consistent users. Consistent users had lower body mass index z-scores at enrollment (0.4 ± 0.7 vs. 1.2 ± 0.9; p = 0.01), but were otherwise comparable with inconsistent users. Over time survivors' use declined; at 12 months, only one participant was using his or her Fitbit. Survivors who completed a survey (n = 22) reported their Fitbit helped them self-monitor their exercise (72.7%, 16/22) and lead a more active lifestyle (63.6%, 14/22), but found it challenging to charge and not lose, forget about, or break the device. Conclusions: In the real-world setting, only a small subset of adolescent-aged survivors will initially consistently use a Fitbit and their interest diminishes over time. To maximize engagement, interventions incorporating wearable activity trackers likely need to include additional behavior change strategies.

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.

Data-driven physical actigraphy patterns relate to cognitive and vascular health in older adults

Health benefits of physical activity (PA) are well known; however, specific PA patterns that relate most strongly to cognitive aging outcomes are poorly understood. We characterized latent profiles of PA among older adults and examined associations with cognition and vascular burden. 124 functionally normal older adults wore a Fitbit™ for 30 days. Daily average step count, sedentary time (0 steps/min), and high-intensity time (≥120 steps/min) were calculated. Participants completed neurocognitive testing assessing cognitive domains of executive functioning and memory; medical history, from which vascular burden (i.e., a count of cardiovascular conditions) was calculated; and brain MRI (n = 44). Subgroups with similar PA patterns were identified via latent profile analysis. Three latent PA classes emerged: Class 1Low PA (n = 49), Class 2Average PA (n = 59), and Class 3High-intensity PA (n = 16). PA class related to executive functioning and vascular burden, driven by better outcomes in Class 3 than Class 1. Sex-stratified analyses revealed these associations were strongest in males. Post hoc analyses showed a positive association between high-intensity PA and white matter integrity among males. High-intensity PA related to better cognitive and vascular health, particularly among males. Findings inform physical activity-specific and person-specific recommendations for optimal cognitive aging.

Use of a Fitbit-like device in rats: Sex differences, relation to EEG sleep, and use to measure the long-term effects of adolescent ethanol exposure

BACKGROUND

Sleep difficulties and rhythm disturbances are some of the problems associated with adolescent binge drinking. Recently, animal models of alcohol-induced insomnia have been developed. However, studies in human subjects have recently focused not only on nighttime EEG findings but also on daytime sleepiness and disrupted activity levels as typically measured by activity tracking devices such as the "Fitbit." We sought to develop and test a Fitbit-like device (the "FitBite") in rats and use it to track rest-activity cycles following adolescent alcohol exposure.

METHODS

The effects of 5 weeks of adolescent ethanol vapor or control conditions were evaluated in 48 male and female Wistar rats using FitBite activity while intoxicated, and during acute (24 h post-vapor exposure) and chronic withdrawal (4 weeks post-vapor exposure). Data were analyzed using activity count and cosinor analyses. Fourteen rats were subsequently implanted with cortical electrodes, and data from the FitBite were compared with EEG data to determine how well the FitBite could identify sleep and activity cycles.

RESULTS

Female rats were generally more active than males, with higher circadian rhythm amplitudes and mesors (rhythm-adjusted means) across a 24-h period. There were significant correlations between EEG-estimated sleep and activity counts using the FitBite. When the rats were tested during intoxication after 4 weeks of ethanol vapor exposure, they had significantly less overall activity. Disruptions in circadian rhythm were also found with significant decreases in the circadian amplitude, mesor, and a later shift in the acrophase. At 24 h of ethanol withdrawal, rats had more episodes of activity with shorter durations during the daytime, when rats are expected to spend more of their time sleeping. This effect remained at 4 weeks following withdrawal, but circadian rhythm disruptions were no longer present.

CONCLUSIONS

A Fitbit-like device can be successfully used in rats to assess rest-activity cycles. Adolescent alcohol exposure produced circadian rhythm disturbances that were not observed after withdrawal. Fragmentation of ultradian rest-activity cycles during the light period was found at 24 h and 4 weeks after withdrawal and support data demonstrating the presence of sleep disturbance long after alcohol withdrawal.

Assessment of well-being using Fitbit technology in college students, faculty and staff completing breathing meditation during COVID-19: A pilot study

This pilot study aimed to explore the intersection of mindfulness, physical activity, and mental well-being within higher education populations during the COVID-19 pandemic. College students, faculty, and staff (n = 34) from a public university participated in the study during spring, summer, and fall 2021. All participants wore a Fitbit for two weeks and were assigned to a treatment group (n = 17), who completed a daily five-minute breathing meditation during the second week, and a control group (n = 17), who did not complete breathing meditation. Amount of sleep and physical activity were measured with the Fitbit. Surveys assessed feasibility and acceptability of the intervention, along with perceived anxiety, depression, well-being, worry, and mindfulness at baseline and after the two-week study. Results demonstrated that the intervention was feasible, and that daily breathing meditation may help reduce anxiety and may lead to greater physical activity and rapid eye movement (REM) sleep. This pilot study lays the foundation for further research into mindfulness, physical activity, and mental health, which may have important implications for promoting mental well-being in college populations following the COVID-19 pandemic.

Sleep Duration on Workdays Is Correlated with Subjective Workload and Subjective Impact of High Workload on Sleep in Young Healthy Adults

Psychosocial stress is widespread worldwide and particularly affects young adults. There is a close and bidirectional relationship between sleep quality and mental health. Sleep duration, which is an important feature of sleep quality, shows both intra-individual variations and inter-individual differences. Internal clocks control individual sleep timing, which, in turn, defines the chronotype. On workdays, however, the end and duration of sleep are largely limited by external factors, such as alarm clocks, especially in later chronotypes. The aim of this study is to investigate whether there is a relationship between sleep timing and duration on workdays and measures for psychosocial stress, such as anxiety and depression; subjective workload; and the subjective impact of a high workload on sleep. We used a combination of Fitbit wearable actigraphy data and a questionnaire survey of young, healthy medical students and calculated correlations between the respective variables. We found that a shorter sleep duration on workdays is associated with a higher subjective workload and a higher subjective impact of a high workload on sleep, which, in turn, are associated with higher measures of anxiety and depression. Our study contributes to understanding the importance of sleep timing/duration and their regularity on weekdays for subjectively perceived psychosocial stress.

Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis

BACKGROUND

Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions.

OBJECTIVE

The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques.

METHODS

We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively.

RESULTS

Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages.

CONCLUSIONS

No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

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).

Using Wearable Technology to Quantify Physical Activity Recovery: Secondary Report From the AFTER (App-Facilitated Tele-Rehabilitation) Program for COVID-19 Survivors Randomized Study

BACKGROUND

Knowledge on physical activity recovery after COVID-19 survival is limited. The AFTER (App-Facilitated Tele-Rehabilitation) program for COVID-19 survivors randomized participants, following hospital discharge, to either education and unstructured physical activity or a telerehabilitation program. Step count data were collected as a secondary outcome, and we found no significant differences in total step count trajectories between groups at 6 weeks. Further step count data were not analyzed.

OBJECTIVE

The purpose of this analysis was to examine step count trajectories and correlates among all participants (combined into a single group) across the 12-week study period.

METHODS

Linear mixed models with random effects were used to model daily steps over the number of study days. Models with 0, 1, and 2 inflection points were considered, and the final model was selected based on the highest log-likelihood value.

RESULTS

Participants included 44 adults (41 with available Fitbit [Fitbit LLC] data). Initially, step counts increased by an average of 930 (95% CI 547-1312; P<.001) steps per week, culminating in an average daily step count of 7658 (95% CI 6257-9059; P<.001) at the end of week 3. During the remaining 9 weeks of the study, weekly step counts increased by an average of 67 (95% CI -30 to 163; P<.001) steps per week, resulting in a final estimate of 8258 (95% CI 6933-9584; P<.001) steps.

CONCLUSIONS

Participants showed a marked improvement in daily step counts during the first 3 weeks of the study, followed by more gradual improvement in the remaining 9 weeks. Physical activity data and step count recovery trajectories may be considered surrogates for physiological recovery, although further research is needed to examine this relationship.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04663945; https://tinyurl.com/2p969ced.

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.

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.

Does changing perceptions of sleep by incorporating sleep wearables improve insomnia? Protocol for a randomized study (the Novel Insomnia Treatment Experiment)

Study Objectives

Insomnia is common in the general population and is diagnosed based on self-reported sleep complaints. There is a frequent discrepancy between objectively recorded and self-reported sleep (sleep-wake state discrepancy), especially in individuals with insomnia. Although sleep-wake state discrepancy is well-documented in the literature, it is not well understood. This protocol describes the methodology of a randomized control study, which will examine whether providing monitoring and feedback about objectively recorded sleep with support for interpretation of sleep-wake state discrepancy improves insomnia symptoms and will explore the potential mechanisms of change.

Methods

Participants are 90 individuals with insomnia symptoms (Insomnia Severity Index [ISI] ≥10). Participants will be randomized to one of two conditions: (1) Intervention: feedback about objectively recorded sleep (actigraph and optional electroencephalogram headband) with guidance for data interpretation, (2) Control: sleep hygiene session. Both conditions will involve individual sessions and two check-in calls. The primary outcome is ISI score. Secondary outcomes include sleep-related impairment, symptoms of anxiety and depression, and other sleep and quality of life measures. Outcomes will be assessed using validated instruments at baseline and post-intervention.

Discussion

With increasing number of wearable devices that measure sleep, there is a need to understand how sleep data provided by these devices could be utilized in the treatment of insomnia. Findings from this study have the potential to better understand sleep-wake state discrepancy in insomnia and uncover new approaches to supplement current insomnia treatment.

The piety of optimization: The rhetoric of health awareness in ParticipACTION and Fitbit

This article uses the tools of rhetorical study to investigate how health awareness, as both a concept and a set of beliefs that reinforce ideals of health, permeates everyday life and affects ways of being. I explore how health awareness is communicated through both public health and commercial marketing campaigns, and argue that as the sources of information change, so too do the ideas of health that we are asked to be aware of. Through an analysis of the websites of ParticipACTION, a publicly funded health and fitness campaign, and Fitbit, a corporation that produces wearable technologies, I show that these organizations provide their audiences with instructions for self-conduct in the pursuit of health through the piety that time is a resource to be managed. Through this piety, ParticipACTION and Fitbit's websites each reify an altar of health where health is represented as a socially and physically fitter (optimized) self, always just out of reach and attainable in the future. I conclude with a call for critical descriptions of health awareness to move beyond the explanatory power of neoliberalization of health, and turn to the work of Rachel Sanders, Annmarie Mol, and Donna Haraway as possible avenues for resisting optimization.

Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study

Objective

Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs. work from office; WFO). Here, we examined the effects of WFH on sleep and activity patterns in the transition to normality during the later stages of the Covid-19 pandemic (Aug 2021-Jan 2022).

Methods

Two-hundred and twenty-five working adults enrolled in a public health study were followed for 22  weeks. Sleep and activity data were collected with a consumer fitness tracker (Fitbit Versa 2). Over three 2-week periods (Phase 1/week 1-2: August 16-29, 2021; Phase 2/week 11-12: October 25-November 7, 2021; Phase 3/week 21-22: January 3-16, 2022), participants provided daily Fitbit sleep and activity records. Additionally, they completed daily phone-based ecological momentary assessment (EMA), providing ratings of sleep quality, wellbeing (mood, stress, motivation), and information on daily work arrangements (WFH, WFO, no work). Work arrangement data were used to examine the effects of WFH vs. WFO on sleep, activity, and wellbeing.

Results

The proportion of WFH vs. WFO days fluctuated over the three measurement periods, mirroring evolving Covid restrictions. Across all three measurement periods WFH days were robustly associated with later bedtimes (+14.7 min), later wake times (+42.3 min), and longer Total Sleep Time (+20.2 min), compared to WFO days. Sleep efficiency was not affected. WFH was further associated with lower daily step count than WFO (-2,471 steps/day). WFH was associated with higher wellbeing ratings compared to WFO for those participants who had no children. However, for participants with children, these differences were not present.

Conclusion

Pandemic-initiated changes in sleep and physical activity were sustained during the later stage of the pandemic. These changes could have longer term effects, and conscious effort is encouraged to harness the benefits (i.e., longer sleep), and mitigate the pitfalls (i.e., less physical activity). These findings are relevant for public health as hybrid WHF is likely to persist in a post-pandemic world.

Objective and subjective measures of daily physical activity in persons with Multiple Sclerosis beginning a rehabilitation regime: A cross-sectional study

INTRODUCTION

Fatigue, and balance and gait disorders can impact on physical activity (PA) levels in persons with Multiple Sclerosis (pwMS). To date, several studies have examined PA in pwMS during daily life, but little is known about PA levels in pwMS during a rehabilitation period. The present study investigated PA levels (daily steps, light physical activity (LPA), and moderate-vigorous physical activity (MVPA)) in inpatient and outpatients with MS during their rehabilitation period and described the relationship between objectively measured PA and levels of disability, quality of life, fatigue, and self-efficacy.

METHODS

In this exploratory cross-sectional study, we examined 40 pwMS during their inpatient or outpatient rehabilitation regime. Participants included in the study (N = 34) wore a Fitbit Versa tracker for one week recording daily steps, minutes of LPA, and minutes of MVPA (primary outcomes). They underwent a clinical assessment of physical activity levels (Godin Leisure-Time Exercise Questionnaire(GLTEQ)), fatigue (Fatigue Severity Scale(FSS)), walking ability (10 Meter Walk Test(10MWT), 2-Min Walk Test(2MWT), 12-item Multiple Sclerosis Walking scale(MSWS-12)), quality of life (12-Item Short Form Survey (SF-12)), and self-efficacy (Self-Efficacy in Multiple Sclerosis scale (SEMS)) (secondary outcomes). Multiple linear regressions (MLR) models were used to test whether the demographic difference between the two groups influenced the estimation of objective variables measured by Fitbit. Finally, correlations between objectively measured physical activity and subjective clinical scales were estimated with Spearman correlations.

RESULTS

Our sample consisted of 21 females and 13 males with a mean (interquartile range) age of 52 (20) years and an Expanded Disability Status scale(EDSS) score of 6.0 (1.50) points; baseline characteristics of inpatients (N = 18) and outpatients (N = 16) differed statistically only in EDSS levels (p-value = 0.008) and use of assistive devices (p=0.007). The whole sample performed (mean±standard deviation) 3969±2190 steps per day, with no significant difference between inpatients (3318±1515) and outpatients (4660±2606). No statistical difference was found between the groups in LPA (p-value=0.064). A significant difference in MVPA (p-value < 0.001) was found between inpatients and outpatients, 1.52±3.98 and 14.69±11.56 min per day, respectively. Significant correlations were found between FSS and MVPA both in the whole sample (r(32)= -0.62, p < 0.001) and in the outpatients group (r(14) = -0.66, p = 0.005), and between 10MWT and daily steps (whole sample:(r(32) = 0.48, p = 0.005), outpatients:(r(14)= -0.51, p = 0.05)).

CONCLUSIONS

PwMS in our study overall engaged in reduced and less intense levels of daily PA with respect to the guidelines with inpatients performing almost no vigorous activities. Considering the importance of PA in improving physical and mental well-being, clinicians and researchers should develope strategies to increase daily PA of PwMS during their rehabilitation programs and daily life.

Validity of Fitbit activity monitoring for adults with progressive muscle diseases

PURPOSE

Measuring physical activity informs activity recommendations in clinical practice and provides outcomes in clinical trials that are meaningful to patients. Activity assessment in muscle disease is challenging and there is insufficient evidence to support any single activity measure; however, multi-modal activity measurement might have potential.

MATERIALS AND METHODS

This two-part study included 20 and 95 adults with progressive muscle diseases with mobility ranging from independent to assisted, including wheelchair users. Their activity was measured using a multi-sensor Fitbit activity monitor, for which criterion validity and acceptability were tested in study 1 and validity, reliability, and responsiveness were tested in the longitudinal, home-based study 2.

RESULTS

Study 1: Fitbit was acceptable and had strong criterion validity (rho/kappa ≥0.90), although up to 15% measurement error. Study 2: Fitbit had satisfactory concurrent and construct validity, reliability, and responsiveness. However, Fitbit active minutes registered 75 min more activity per week than gold standard moderate and vigorous physical activity (MVPA) time.

CONCLUSIONS

Fitbit had satisfactory measurement properties for monitoring physical activity in adults with progressive muscle diseases. However, Fitbit should not be considered an exact step counter, heart rate monitor or calorimeter and Fitbit active minutes are not synonymous with MVPA time.Implications for rehabilitationPeople with progressive muscle diseases mobilise independently, with walking aids and with wheelchairs; physical activity measurement can be challenging in this population.Multisensor smart activity monitoring by Fitbit had satisfactory validity, reliability, responsiveness, and acceptability for the estimation of physical activity in adults with progressive muscle diseases.Fitbit active minutes are not synonymous with moderate and vigorous physical activity (MVPA) time measured using a research grade accelerometer.

The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions

BACKGROUND AND OBJECTIVES

Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions.

METHODS

In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS).

RESULTS

The results showed promising estimation performance with R² of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT.

CONCLUSIONS

This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

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.

Fitbit Activity, Quota-Based Pacing, and Physical and Emotional Functioning Among Adults With Chronic Pain

Physical activity can improve function in patients with chronic pain, however, adherence is low, in part due to inconsistent activity patterns. Smart wearable activity trackers, such as Fitbits, may help promote activity. In our program for chronic pain, we examined: 1) Fitbit activity patterns (ie, step count, moderate-to-vigorous physical activity (MVPA), sedentary behavior), and 2) whether achievement of weekly, individualized Fitbit step goals was associated with functional outcomes. We conducted a secondary analysis of Fitbit data from our 10-week mind-body activity program for chronic pain (GetActive-Fitbit arm, N = 41). Participants self-reported emotional and physical function and completed performance-based and accelerometer-based assessments. From week 1 to week 10, 30% of participants increased >800 steps; 32.5% increased MVPA; and 30% decreased sedentary behavior. Only step count significantly changed across time with mean steps peaking at week 8 (M = +1897.60, SD = 467.67). Fitbit step goal achievement was associated with improvements in anxiety (ß = -.35, CI [-2.80, -.43]), self-reported physical function (ß = -.34, CI [-5.17, 8.05]), and performance-based physical function (ß = .29, CI [-71.93, 28.38]), but not accelerometer-based physical function or depression. Adhering to individualized Fitbit step goals in the context of a mind-body activity program may improve anxiety and self-reported and performance-based physical function. PERSPECTIVE: We examine Fitbit activity patterns and the association between quota-based pacing and functional outcomes within a mind-body activity program for adults with chronic pain. Complementing quota-based pacing and coping skills with Fitbits may be a useful approach to promote activity engagement and behavior change in chronic pain populations.

A Smartphone Physical Activity App for Patients in Alcohol Treatment: Single-Arm Feasibility Trial

BACKGROUND

Alcohol use disorder (AUD) is a significant public health concern worldwide. Alcohol consumption is a leading cause of death in the United States and has a significant negative impact on individuals and society. Relapse following treatment is common, and adjunct intervention approaches to improve alcohol outcomes during early recovery continue to be critical. Interventions focused on increasing physical activity (PA) may improve AUD treatment outcomes. Given the ubiquity of smartphones and activity trackers, integrating this technology into a mobile app may be a feasible, acceptable, and scalable approach for increasing PA in individuals with AUD.

OBJECTIVE

This study aims to test the Fit&Sober app developed for patients with AUD. The goals of the app were to facilitate self-monitoring of PA engagement and daily mood and alcohol cravings, increase awareness of immediate benefits of PA on mood and cravings, encourage setting and adjusting PA goals, provide resources and increase knowledge for increasing PA, and serve as a resource for alcohol relapse prevention strategies.

METHODS

To preliminarily test the Fit&Sober app, we conducted an open pilot trial of patients with AUD in early recovery (N=22; 13/22, 59% women; mean age 43.6, SD 11.6 years). At the time of hospital admission, participants drank 72% of the days in the last 3 months, averaging 9 drinks per drinking day. The extent to which the Fit&Sober app was feasible and acceptable among patients with AUD during early recovery was examined. Changes in alcohol consumption, PA, anxiety, depression, alcohol craving, and quality of life were also examined after 12 weeks of app use.

RESULTS

Participants reported high levels of satisfaction with the Fit&Sober app. App metadata suggested that participants were still using the app approximately 2.5 days per week by the end of the intervention. Pre-post analyses revealed small-to-moderate effects on increase in PA, from a mean of 5784 (SD 2511) steps per day at baseline to 7236 (SD 3130) steps per day at 12 weeks (Cohen d=0.35). Moderate-to-large effects were observed for increases in percentage of abstinent days (Cohen d=2.17) and quality of life (Cohen d=0.58) as well as decreases in anxiety (Cohen d=-0.71) and depression symptoms (Cohen d=-0.58).

CONCLUSIONS

The Fit&Sober app is an acceptable and feasible approach for increasing PA in patients with AUD during early recovery. A future randomized controlled trial is necessary to determine the efficacy of the Fit&Sober app for long-term maintenance of PA, ancillary mental health, and alcohol outcomes. If the efficacy of the Fit&Sober app could be established, patients with AUD would have a valuable adjunct to traditional alcohol treatment that can be delivered in any setting and at any time, thereby improving the overall health and well-being of this population.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02958280; https://www.clinicaltrials.gov/ct2/show/NCT02958280.

The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.

Monitoring postoperative ambulation and sleep after head and neck surgery: A feasibility and utility study using wearable devices

BACKGROUND

To evaluate use of wearable activity devices to monitor trends in ambulation and sleep after head and neck surgery.

METHODS

Patients utilized Fitbit devices after surgery. Daily activity and sleep scores, step counts, and total sleep time (TST) were obtained.

RESULTS

There were 30 patients within the final cohort. Trends in step counts after specific procedures were identified, and higher subjective activity level correlated with step counts (r = 0.25, 95% CI = 0.07-0.41). Among patients with complete step data (n = 24), POD1 steps ≥200 were associated with reduced length of stay (4.5 ± 1.7 days vs. 7.2 ± 4.0 days, 95% CI = 0.33-5.0). Mean TST was 5.4 ± 2.5 h, TST correlated with subjective sleep scores (r = 0.25, 95% CI = 0.03-0.044), and clinical events associated with sleep-wake transitions on Fitbit devices were identified.

CONCLUSIONS

Wearable activity devices appear feasible for monitoring trends in postoperative ambulation and sleep. Use of these devices may facilitate postoperative recovery.

Wearable Use in an Observational Study Among Older Adults: Adherence, Feasibility, and Effects of Clinicodemographic Factors

Introduction

Wearables have great potential to improve monitoring and delivery of physical activity interventions to older adults with downstream benefits to multisystem health and longevity; however, benefits obtained from wearables depend on their uptake and usage. Few studies have examined person-specific factors that relate to wearable adherence. We characterized adherence to using a wearable activity tracker for 30 days and examined associations between adherence and demographics, cognitive functioning, brain volumes, and technology familiarity among community-dwelling older adults.

Methods

Participants were 175 older adults enrolled in the UCSF Longitudinal Brain Aging Study who were asked to wear a Fitbit^TM Flex 2 during waking hours for 30 days. Sixty two of these participants were also asked to sync their devices to the Fitbit smartphone app daily to collect minute-level data. We calculated adherence to wearing the Fitbit daily (i.e., proportion of days with valid activity data) and adherence to daily device syncing (i.e., proportion of days with minute-level activity data). Participants also completed a brain MRI and in-person cognitive testing measuring memory, executive functioning, and processing speed. Spearman correlations, Wilcoxon rank sum tests, and logistic regression tested relationships between wearable adherence and clinicodemographic factors.

Results

Participants wore the Fitbits for an average of 95% of study days and were 85% adherent to the daily syncing protocol. Greater adherence to wearing the device was related to female sex. Greater adherence to daily device syncing was related to better memory, independent of demographic factors. Wearable adherence was not significantly related to age, education, executive functioning, processing speed, brain gray matter volumes, or self-reported familiarity with technology. Participants reported little-to-no difficulty using the wearable and all reported willingness to participate in another wearable study in the future.

Conclusions

Older adults have overall high adherence to wearable use in the current study protocol. Person-specific factors, however, may represent potential barriers to equitable uptake of wearables for physical activity among older adults, including demographics and cognitive functioning. Future studies and clinical providers utilizing wearable activity trackers with older adults may benefit from implementation of reminders (e.g., texts, calls) for device use, particularly among men and individuals with memory impairment.

A Clinician's Guide to Smartwatch "Interrogation"

Purpose of Review

Wearable technology is rapidly evolving and the data that it can provide regarding an individual’s health is becoming increasingly important for clinicians to consider. The purpose of this review is to help inform health care providers of the benefits of smartwatch interrogation, with a focus on reviewing the various parameters and how to apply the data in a meaningful way.

Recent Findings

This review details interpretation of various parameters found commonly in newer smartwatches such as heart rate, step count, ECG, heart rate recovery (HRR), and heart rate variability (HRV), while also discussing potential pitfalls that a clinician should be aware of.

Summary

Smartwatch interrogation is becoming increasingly relevant as the continuous data it provides helps health care providers make more informed decisions regarding diagnosis and treatment. For this reason, we recommend health care providers familiarize themselves with the technology and integrate it into clinical practice.

Making Every Step Count: Minute-by-Minute Characterization of Step Counts Augments Remote Activity Monitoring in People With Multiple Sclerosis

Background

Ambulatory disability is common in people with multiple sclerosis (MS). Remote monitoring using average daily step count (STEPS) can assess physical activity (activity) and disability in MS. STEPS correlates with conventional metrics such as the expanded disability status scale (Expanded Disability Status Scale; EDSS), Timed-25 Foot walk (T25FW) and timed up and go (TUG). However, while STEPS as a summative measure characterizes the number of steps taken over a day, it does not reflect variability and intensity of activity.

Objectives

Novel analytical methods were developed to describe how individuals spends time in various activity levels (e.g., continuous low versus short bouts of high) and the proportion of time spent at each activity level.

Methods

94 people with MS spanning the range of ambulatory impairment (unaffected to requiring bilateral assistance) were recruited into FITriMS study and asked to wear a Fitbit continuously for 1-year. Parametric distributions were fit to minute-by-minute step data. Adjusted R² values for regressions between distributional fit parameters and STEPS with EDSS, TUG, T25FW and the patient-reported 12-item MS Walking scale (MSWS-12) were calculated over the first 4-weeks, adjusting for sex, age and disease duration.

Results

Distributional fits determined that the best statistically-valid model across all subjects was a 3-compartment Gaussian Mixture Model (GMM) that characterizes the step behavior within 3 levels of activity: high, moderate and low. The correlation of GMM parameters for baseline step count measures with clinical assessments was improved when compared with STEPS (adjusted R² values GMM vs. STEPS: TUG: 0.536 vs. 0.419, T25FW: 0.489 vs. 0.402, MSWS-12: 0.383 vs. 0.378, EDSS: 0.557 vs. 0.465). The GMM correlated more strongly (Kruskal-Wallis: p = 0.0001) than STEPS and gave further information not included in STEPS.

Conclusions

Individuals' step distributions follow a 3-compartment GMM that better correlates with clinic-based performance measures compared with STEPS. These data support the existence of high-moderate-low levels of activity. GMM provides an interpretable framework to better understand the association between different levels of activity and clinical metrics and allows further analysis of walking behavior that takes step distribution and proportion of time at three levels of intensity into account.

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

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