Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study

Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study

BACKGROUND AND OBJECTIVES

Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs).

METHODS

A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland-Altman plot analysis.

RESULTS

The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices.

CONCLUSIONS

Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems.

Validity and Reliability of Wearable Technology Devices during Simulated Pickleball Game Play

Pickleball is a popular sport. Also popular is wearable technology usage. Because the validity and reliability of wearable technology during pickleball is unknown, the purpose of this research was to evaluate the ability of common devices to return heart rate and estimated energy expenditure during pickleball activity. Twenty adult participants were outfitted with a portable metabolic unit and heart rate monitor (criterion measures). Experimental devices were a Garmin Instinct, Polar Vantage M2, Polar OH1, and Polar Verity Sense. Participants played simulated pickleball for 10 min. Validity measures included mean absolute percent error (MAPE) and Lin's Concordance Correlation Coefficient (CCC), whereas reliability measures included coefficient of variation (CV) and intraclass correlation coefficient (ICC). The heart rate returned lower than 10% MAPE across all devices (Instinct = 5.73-6.32%, Verity Sense = 2.92-2.97%, OH1 = 3.39-3.45%) and greater than 0.85 CCC (Instinct = 0.85-0.88, Verity Sense = 0.96-0.96, OH1 = 0.93-0.94). The CV was below 10% (Instinct = 9.30%, Verity Sense = 2.68%, OH1 = 5.01%), and ICC was above 0.7 (Instinct = 0.77, Verity Sense = 0.98, OH1 = 0.91). The energy expenditure MAPE was greater than 10% (Instinct = 27.67-28.08%, Vantage M2 = 18.87-23.38%) with CCC lower than 0.7 (Instinct = 0.47-0.49, Vantage M2 = 0.62-0.63). Reliability thresholds were met in the Vantage M2 (CV = 6%, ICC = 0.98) but not in the Instinct (CV = 15%, ICC = 0.86). The Instinct was neither valid nor reliable for estimated energy expenditure, while the Polar Vantage M2 was reliable but not valid. All devices returned valid and reliable heart rates during pickleball.

Comparison of physiological and clinical reactions to COVID-19 and influenza vaccination

BACKGROUND

Public reluctance to receive COVID-19 vaccination is associated with safety concerns. By contrast, the seasonal influenza vaccine has been administered for decades with a solid safety record and a high level of public acceptance. We compare the safety profile of the BNT162b2 COVID-19 booster vaccine to that of the seasonal influenza vaccine.

METHODS

We study a prospective cohort of 5079 participants in Israel and a retrospective cohort of 250,000 members of MHS selected randomly. We examine reactions to BNT162b2 mRNA COVID-19 booster and to influenza vaccinations. All prospective cohort participants wore a smartwatch and completed a daily digital questionnaire. We compare pre-vaccination and post-vaccination smartwatch heart-rate data, and a stress measure based on heart-rate variability. We also examine adverse events from electronic health records.

RESULTS

In the prospective cohort, 1905 participants receive the COVID-19 booster vaccine; 899 receive influenza vaccination. Focusing on those who receive both vaccines yields a total of 689 participants in the prospective cohort and 31,297 members in the retrospective cohort. Individuals reporting a more severe reaction after influenza vaccination tend to likewise report a more severe reaction after COVID-19 vaccination. In paired analysis, the increase in both heart rate and stress measure for each participant is higher for COVID-19 than for influenza in the first 2 days after vaccination. No elevated risk of hospitalization due to adverse events is found following either vaccine. Except for Bell's palsy after influenza vaccination, no elevated risk of adverse events is found.

CONCLUSIONS

The more pronounced side effects after COVID-19 vaccination may explain the greater concern associated with it. Nevertheless, our comprehensive analysis supports the safety profile of both vaccines.

Heart Rate Monitoring Among Breast Cancer Survivors: Quantitative Study of Device Agreement in a Community-Based Exercise Program

BACKGROUND

Exercise intensity (eg, target heart rate [HR]) is a fundamental component of exercise prescription to elicit health benefits in cancer survivors. Despite the validity of chest-worn monitors, their feasibility in community and unsupervised exercise settings may be challenging. As wearable technology continues to improve, consumer-based wearable sensors may represent an accessible alternative to traditional monitoring, offering additional advantages.

OBJECTIVE

The purpose of this study was to examine the agreement between the Polar H10 chest monitor and Fitbit Inspire HR for HR measurement in breast cancer survivors enrolled in the intervention arm of a randomized, pilot exercise trial.

METHODS

Participants included breast cancer survivors (N=14; aged 38-72 years) randomized to a 12-week aerobic exercise program. This program consisted of three 60-minute, moderate-intensity walking sessions per week, either in small groups or one-on-one, facilitated by a certified exercise physiologist and held at local community fitness centers. As originally designed, the exercise prescription included 36 supervised sessions at a fitness center. However, due to the COVID-19 pandemic, the number of supervised sessions varied depending on whether participants enrolled before or after March 2020. During each exercise session, HR (in beats per minute) was concurrently measured via a Polar H10 chest monitor and a wrist-worn Fitbit Inspire HR at 5 stages: pre-exercise rest; midpoint of warm-up; midpoint of exercise session; midpoint of cool-down; and postexercise recovery. The exercise physiologist recorded the participant's HR from each device at the midpoint of each stage. HR agreement between the Polar H10 and Fitbit Inspire HR was assessed using Lin concordance correlation coefficient (rc) with a 95% CI. Lin rc ranges from 0 to 1.00, with 0 indicating no concordance and 1.00 indicating perfect concordance. Relative error rates were calculated to examine differences across exercise session stages.

RESULTS

Data were available for 200 supervised sessions across the sample (session per participant: mean 13.33, SD 13.7). By exercise session stage, agreement between the Polar H10 monitor and the Fitbit was highest during pre-exercise seated rest (rc=0.76, 95% CI 0.70-0.81) and postexercise seated recovery (rc=0.89, 95% CI 0.86-0.92), followed by the midpoint of exercise (rc=0.63, 95% CI 0.55-0.70) and cool-down (rc=0.68, 95% CI 0.60-0.74). The agreement was lowest during warm-up (rc=0.39, 95% CI 0.27-0.49). Relative error rates ranged from -3.91% to 3.09% and were greatest during warm-up (relative error rate: mean -3.91, SD 11.92%).

CONCLUSIONS

The Fitbit overestimated HR during peak exercise intensity, posing risks for overexercising, which may not be safe for breast cancer survivors' fitness levels. While the Fitbit Inspire HR may be used to estimate exercise HR, precautions are needed when considering participant safety and data interpretation.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03980626; https://clinicaltrials.gov/study/NCT03980626?term=NCT03980626&rank=1.

Technological interventions in functional capacity evaluations: An insight into current applications

BACKGROUND

Functional Capacity Evaluation (FCE) is a crucial component within return-to-work decision making. However, clinician-based physical FCE interpretation may introduce variability and biases. The rise of technological applications such as machine learning and artificial intelligence, could ensure consistent and precise results.

OBJECTIVE

This review investigates the application of information and communication technologies (ICT) in physical FCEs specific for return-to-work assessments.

METHODS

Adhering to the PRISMA guidelines, a search was conducted across five databases, extracting study specifics, populations, and technological tools employed, through dual independent reviews.

RESULTS

Nine studies were identified that used ICT in FCEs. These technologies included electromyography, heart rate monitors, cameras, motion detectors, and specific software. Notably, although some devices are commercially available, these technologies were at a technology readiness level of 5-6 within the field of FCE. A prevailing trend was the combined use of diverse technologies rather than a single, unified solution. Moreover, the primary emphasis was on the application of technology within study protocols, rather than a direct evaluation of the technology usability and feasibility.

CONCLUSION

The literature underscores limited ICT integration in FCEs. The current landscape of FCEs, marked by a high dependence on clinician observations, presents challenges regarding consistency and cost-effectiveness. There is an evident need for a standardized technological approach that introduces objective metrics to streamline the FCE process and potentially enhance its outcomes.

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.

Feasibility of Leveraging Consumer Wearable Devices with Data Platform Integration for Patient Vital Monitoring in Low-Resource Settings

Manual monitoring of vital signs, which often fails to capture the onset of deterioration, is the main monitoring modality in most Ghanaian hospitals due to the high cost and inadequate supply of patient bedside monitors. Consumer wearable devices (CWDs) are emerging, relatively low-cost technologies for continuous monitoring of physiological status; however, their validity has not been established in low-resource clinical settings. We aimed to (1) investigate the validity of the heart rate (HR) and oxygen saturation (SpO2) data from two widely used CWDs, the Fitbit Versa 2 and Xiaomi Mi Smart Band 6, against gold standard bedside monitors in one Ghanaian hospital and (2) develop a web application to capture and display CWD data in a clinician-friendly way. A healthy volunteer simultaneously wore both CWDs and blood pressure cuffs to measure HR and SpO2. To test for concordance, we conducted the Bland-Altman and mean absolute percentage error analyses. We also developed a web application that retrieves and displays CWD data in near real time as text and graphical trends. Compared to gold standards (patient monitor and manual), the Fitbit Versa 2 had 96.87% and 96.67% measurement accuracies for HR, and the Xiaomi Mi Smart Band 6 had 94.24% and 93.21% measurement accuracies for HR. The Xiaomi Mi Smart Band 6 had 98.79% measurement accuracy for SpO2. The strong concordance between CWD and gold standards supports the potential implementation of these devices as a novel method of vital sign monitoring to replace manual monitoring, thus saving costs and improving patient outcomes. Further studies are needed for confirmation.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Integrating 4 methods to evaluate physical function in patients with cancer (In4M): protocol for a prospective cohort study

INTRODUCTION

Accurate, patient-centred evaluation of physical function in patients with cancer can provide important information on the functional impacts experienced by patients both from the disease and its treatment. Increasingly, digital health technology is facilitating and providing new ways to measure symptoms and function. There is a need to characterise the longitudinal measurement characteristics of physical function assessments, including clinician-reported outcome, patient-reported ported outcome (PRO), performance outcome tests and wearable data, to inform regulatory and clinical decision-making in cancer clinical trials and oncology practice.

METHODS AND ANALYSIS

In this prospective study, we are enrolling 200 English-speaking and/or Spanish-speaking patients with breast cancer or lymphoma seen at Mayo Clinic or Yale University who will receive intravenous cytotoxic chemotherapy. Physical function assessments will be obtained longitudinally using multiple assessment modalities. Participants will be followed for 9 months using a patient-centred health data aggregating platform that consolidates study questionnaires, electronic health record data, and activity and sleep data from a wearable sensor. Data analysis will focus on understanding variability, sensitivity and meaningful changes across the included physical function assessments and evaluating their relationship to key clinical outcomes. Additionally, the feasibility of multimodal physical function data collection in real-world patients with breast cancer or lymphoma will be assessed, as will patient impressions of the usability and acceptability of the wearable sensor, data aggregation platform and PROs.

ETHICS AND DISSEMINATION

This study has received approval from IRBs at Mayo Clinic, Yale University and the US Food and Drug Administration. Results will be made available to participants, funders, the research community and the public.

TRIAL REGISTRATION NUMBER

NCT05214144; Pre-results.

Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition

Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML's potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.

Beyond the EHR: How Digital Health Tools Foster Participatory Health and Self-Care for Patients with Diabetes

Just as physicians managing patients with diabetes find that it is a data-driven process, for patients living with diabetes, it is even more so, as physicians see them every few months, but patients need to live with diabetes all the time. Fortunately, the advent of the web has allowed patients to connect with information, medical care, and other patients, while mobile and connected technologies such as smartphones have provided the flexibility to do this-and to manage and share their health information-from anywhere. Healthcare professionals who care for patients with diabetes should be aware of the digital health technologies that enable patients to better care for themselves, be more active participants in their healthcare, and improve the quality of their lives.

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Mississippi church leaders' perceptions of challenges and barriers to the use of consumer wearables among community members

Background

Wearables have begun to play a transformative role in health management and disease prevention.

Objective

This study examined the use of wearable devices in African American communities in Mississippi, USA, through the lens of church leaders.

Methods

We conducted focus groups with church leaders to record their perceptions about the use of wearables of their community members. We conducted six focus groups with a total of 89 church leaders from across the state of Mississippi. The focus groups were designed to contextualize and explain the socio-cognitive processes that provided an understanding of wearable device adoption practices among community members. Participants were male and female church leaders who were recruited from the three Mississippi Districts. The church leaders' perceptions of barriers and challenges to the adoption of consumer wearables in their communities were thoroughly analyzed using thematic analysis.

Results

There was great apprehension on the part of community members about the security of the information they entered into the wearable devices and about how that information could be used by other parties. Many community members who understood the value of proactive health behaviors could not afford the high cost of purchasing wearable devices, while others displayed a low level of comfort with technology, believing that wearable use was for younger people.

Conclusion

More expansive adoption of wearable devices in Mississippi will depend on the ability of the public health professionals, policy-makers and manufacturers to address the barriers that were identified by this study, thereby enabling the community to have full access to the potential benefits of these technologies.

Integrating metabolic expenditure information from wearable fitness sensors into an AI-augmented automated insulin delivery system: a randomised clinical trial

BACKGROUND

Exercise can rapidly drop glucose in people with type 1 diabetes. Ubiquitous wearable fitness sensors are not integrated into automated insulin delivery (AID) systems. We hypothesised that an AID can automate insulin adjustments using real-time wearable fitness data to reduce hypoglycaemia during exercise and free-living conditions compared with an AID not automating use of fitness data.

METHODS

Our study population comprised of individuals (aged 21-50 years) with type 1 diabetes from from the Harold Schnitzer Diabetes Health Center clinic at Oregon Health and Science University, OR, USA, who were enrolled into a 76 h single-centre, two-arm randomised (4-block randomisation), non-blinded crossover study to use (1) an AID that detects exercise, prompts the user, and shuts off insulin during exercise using an exercise-aware adaptive proportional derivative (exAPD) algorithm or (2) an AID that automates insulin adjustments using fitness data in real-time through an exercise-aware model predictive control (exMPC) algorithm. Both algorithms ran on iPancreas comprising commercial glucose sensors, insulin pumps, and smartwatches. Participants executed 1 week run-in on usual therapy followed by exAPD or exMPC for one 12 h primary in-clinic session involving meals, exercise, and activities of daily living, and 2 free-living out-patient days. Primary outcome was time below range (<3·9 mmol/L) during the primary in-clinic session. Secondary outcome measures included mean glucose and time in range (3·9-10 mmol/L). This trial is registered with ClinicalTrials.gov, NCT04771403.

FINDINGS

Between April 13, 2021, and Oct 3, 2022, 27 participants (18 females) were enrolled into the study. There was no significant difference between exMPC (n=24) versus exAPD (n=22) in time below range (mean [SD] 1·3% [2·9] vs 2·5% [7·0]) or time in range (63·2% [23·9] vs 59·4% [23·1]) during the primary in-clinic session. In the 2 h period after start of in-clinic exercise, exMPC had significantly lower mean glucose (7·3 [1·6] vs 8·0 [1·7] mmol/L, p=0·023) and comparable time below range (1·4% [4·2] vs 4·9% [14·4]). Across the 76 h study, both algorithms achieved clinical time in range targets (71·2% [16] and 75·5% [11]) and time below range (1·0% [1·2] and 1·3% [2·2]), significantly lower than run-in period (2·4% [2·4], p=0·0004 vs exMPC; p=0·012 vs exAPD). No adverse events occurred.

INTERPRETATION

AIDs can integrate exercise data from smartwatches to inform insulin dosing and limit hypoglycaemia while improving glucose outcomes. Future AID systems that integrate exercise metrics from wearable fitness sensors may help people living with type 1 diabetes exercise safely by limiting hypoglycaemia.

FUNDING

JDRF Foundation and the Leona M and Harry B Helmsley Charitable Trust, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

Effect of home-based online training and activity feedback on oxygen uptake in patients after surgical cancer therapy: a randomized controlled trial

BACKGROUND

Exercise training is beneficial in enhancing physical function and quality of life in cancer patients. Its comprehensive implementation remains challenging, and underlying cardiopulmonary adaptations are poorly investigated. This randomized controlled trial examines the implementation and effects of home-based online training on cardiopulmonary variables and physical activity.

METHODS

Of screened post-surgical patients with breast, prostate, or colorectal cancer, 148 were randomly assigned (1:1) to an intervention (2 × 30 min/week of strength-endurance training using video presentations) and a control group. All patients received activity feedback during the 6-month intervention period. Primary endpoint was change in oxygen uptake after 6 months. Secondary endpoints included changes in cardiac output, rate pressure product, quality of life (EORTC QoL-C30), C-reactive protein, and activity behavior.

RESULTS

One hundred twenty-two patients (62 intervention and 60 control group) completed the study period. Change in oxygen uptake between intervention and control patients was 1.8 vs. 0.66 ml/kg/min (estimated difference after 6 months: 1.24; 95% CI 0.23 to 2.55; p = 0.017). Rate pressure product was reduced in IG (estimated difference after 6 months: - 1079; 95% CI - 2157 to - 1; p = 0.05). Physical activity per week was not different in IG and CG. There were no significant interaction effects in body composition, cardiac output, C-reactive protein, or quality of life.

CONCLUSIONS

Home-based online training among post-surgery cancer patients revealed an increase of oxygen uptake and a decrease of myocardial workload during exercise. The implementation of area-wide home-based training and activity feedback as an integral component in cancer care and studies investigating long-term effects are needed.

TRIAL REGISTRATION

DRKS-ID: DRKS00020499 ; Registered 17 March 2020.

Heart rate processing algorithms and exercise duration on reliability and validity decisions in biceps-worn Polar Verity Sense and OH1 wearables

Consumer wearable technology use is widespread and there is a need to validate measures obtained in uncontrolled settings. Because no standard exists for the treatment of heart rate data during exercise, the effect of different approaches on reliability (Coefficient of Variation [CV], Intraclass Correlation Coefficient [ICC]) and validity (Mean Absolute Percent Error [MAPE], Lin's Concordance Correlation Coefficient [CCC)] were determined in the Polar Verity Sense and OH1 during trail running. The Verity Sense met the reliability (CV < 5%, ICC > 0.7) and validity thresholds (MAPE < 5%, CCC > 0.9) in all cases. The OH1 met reliability thresholds in all cases except entire session average (ICC = 0.57). The OH1 met the validity MAPE threshold in all cases (3.3-4.1%), but not CCC (0.6-0.86). Despite various heart rate data processing methods, the approach may not affect reliability and validity interpretation provided adequate data points are obtained. It is also possible that a large volume of data will artificially inflate metrics.

ehealth technology in cardiac exercise therapeutics for pediatric patients with congenital and acquired heart conditions: a summary of evidence and future directions

Many children and adolescents with congenital and acquired heart disease (CHD) are physically inactive and participate in an insufficient amount of moderate-to-vigorous intensity exercise. Although physical activity (PA) and exercise interventions are effective at improving short- and long-term physiological and psychosocial outcomes in youth with CHD, several barriers including resource limitations, financial costs, and knowledge inhibit widespread implementation and dissemination of these beneficial programs. New and developing eHealth, mHealth, and remote monitoring technologies offer a potentially transformative and cost-effective solution to increase access to PA and exercise programs for youth with CHD, yet little has been written on this topic. In this review, a cardiac exercise therapeutics (CET) model is presented as a systematic approach to PA and exercise, with assessment and testing guiding three sequential PA and exercise intervention approaches of progressive intensity and resource requirements: (1) PA and exercise promotion within a clinical setting; (2) unsupervised exercise prescription; and (3) medically supervised fitness training intervention (i.e., cardiac rehabilitation). Using the CET model, the goal of this review is to summarize the current evidence describing the application of novel technologies within CET in populations of children and adolescents with CHD and introduce potential future applications of these technologies with an emphasis on improving equity and access to patients in low-resource settings and underserved communities.

Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation

BACKGROUND

Limited data accuracy is often cited as a reason for caution in the integration of physiological data obtained from consumer-oriented wearable devices in care management pathways. The effect of decreasing accuracy on predictive models generated from these data has not been previously investigated.

OBJECTIVE

The aim of this study is to simulate the effect of data degradation on the reliability of prediction models generated from those data and thus determine the extent to which lower device accuracy might or might not limit their use in clinical settings.

METHODS

Using the Multilevel Monitoring of Activity and Sleep in Healthy People data set, which includes continuous free-living step count and heart rate data from 21 healthy volunteers, we trained a random forest model to predict cardiac competence. Model performance in 75 perturbed data sets with increasing missingness, noisiness, bias, and a combination of all 3 perturbations was compared to model performance for the unperturbed data set.

RESULTS

The unperturbed data set achieved a mean root mean square error (RMSE) of 0.079 (SD 0.001) in predicting cardiac competence index. For all types of perturbations, RMSE remained stable up to 20%-30% perturbation. Above this level, RMSE started increasing and reached the point at which the model was no longer predictive at 80% for noise, 50% for missingness, and 35% for the combination of all perturbations. Introducing systematic bias in the underlying data had no effect on RMSE.

CONCLUSIONS

In this proof-of-concept study, the performance of predictive models for cardiac competence generated from continuously acquired physiological data was relatively stable with declining quality of the source data. As such, lower accuracy of consumer-oriented wearable devices might not be an absolute contraindication for their use in clinical prediction models.

Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System

Abnormalities in cardiorespiratory measurements have repeatedly been found in patients with panic disorder (PD) during laboratory-based assessments. However, recordings performed outside laboratory settings are required to test the ecological validity of these findings. Wearable devices, such as sensor-imbedded garments, biopatches, and smartwatches, are promising tools for this purpose. We systematically reviewed the evidence for wearables-based cardiorespiratory assessments in PD by searching for publications on the PubMed, PsycINFO, and Embase databases, from inception to 30 July 2022. After the screening of two-hundred and twenty records, eight studies were included. The limited number of available studies and critical aspects related to the uncertain reliability of wearables-based assessments, especially concerning respiration, prevented us from drawing conclusions about the cardiorespiratory function of patients with PD in daily life. We also present preliminary data on a pilot study conducted on volunteers at the Villa San Benedetto Menni Hospital for evaluating the accuracy of heart rate (HR) and breathing rate (BR) measurements by the wearable Zephyr BioPatch compared with the Quark-b2 stationary testing system. Our exploratory results suggested possible BR and HR misestimation by the wearable Zephyr BioPatch compared with the Quark-b2 system. Challenges of wearables-based cardiorespiratory assessment and possible solutions to improve their reliability and optimize their significant potential for the study of PD pathophysiology are presented.

Integrating 4 Measures to Evaluate Physical Function in Patients with Cancer (In4M): Protocol for a prospective study

Introduction

Accurate, patient-centered evaluation of physical function in patients with cancer can provide important information on the functional impacts experienced by patients both from the disease and its treatment. Increasingly, digital health technology is facilitating and providing new ways to measure symptoms and function. There is a need to characterize the longitudinal measurement characteristics of physical function assessments, including clinician-reported physical function (ClinRo), patient-reported physical function (PRO), performance outcome tests (PerfO) and wearable data, to inform regulatory and clinical decision-making in cancer clinical trials and oncology practice.

Methods and analysis

In this prospective study, we are enrolling 200 English- and/or Spanish-speaking patients with breast cancer or lymphoma seen at Mayo Clinic or Yale University who will receive standard of care intravenous cytotoxic chemotherapy. Physical function assessments will be obtained longitudinally using multiple assessment modalities. Participants will be followed for 9 months using a patient-centered health data aggregating platform that consolidates study questionnaires, electronic health record data, and activity and sleep data from a wearable sensor. Data analysis will focus on understanding variability, sensitivity, and meaningful changes across the included physical function assessments and evaluating their relationship to key clinical outcomes. Additionally, the feasibility of multi-modal physical function data collection in real-world patients with cancer will be assessed, as will patient impressions of the usability and acceptability of the wearable sensor, data aggregation platform, and PROs.

Ethics and dissemination

This study has received approval from IRBs at Mayo Clinic, Yale University, and the U.S. Food & Drug Administration. Results will be made available to participants, funders, the research community, and the public.

Registration Details

The trial registration number for this study is NCT05214144.

Strengths & Limitations

This study addresses an important unmet need by characterizing the performance characteristics of multiple patient-centered physical function measures in patients with cancerPhysical function is an important and undermeasured clinical outcome. Scientifically rigorous capture and measurement of physical function constitutes a key component of cancer treatment tolerability assessment both from a regulatory and clinical perspective.This study will include patients with lymphoma or breast cancer receiving a broad range of cytotoxic chemotherapy regimens. While recruitment will occur at two academic sites, patients who ultimately receive treatment at local community sites will be included.A patient-centered health data aggregating platform facilitates the delivery of patient-reported outcome measures and collection of wearable data to researchers, while reducing patient burden compared to traditional patient-generated data collection and aggregation methodsHeterogeneity in patient willingness or comfort engaging with mobile products including smartphones and wearables, enrollment primarily at large academic centers, and the modest sample size are potential limitations to the external validity of the study.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Testing motivational and self-regulatory mechanisms of action on device-measured physical activity in the context of a weight loss maintenance digital intervention: A secondary analysis of the NoHoW trial

BACKGROUND

To date, few digital behavior change interventions for weight loss maintenance focusing on long-term physical activity promotion have used a sound intervention design grounded on a logic model underpinned by behavior change theories. The current study is a secondary analysis of the weight loss maintenance NoHoW trial and investigated putative mediators of device-measured long-term physical activity levels (six to 12 months) in the context of a digital intervention.

METHODS

A subsample of 766 participants (Age = 46.2 ± 11.4 years; 69.1% female; original NoHoW sample: 1627 participants) completed all questionnaires on motivational and self-regulatory variables and had all device-measured physical activity data available for zero, six and 12 months. We examined the direct and indirect effects of Virtual Care Climate on post intervention changes in moderate-to-vigorous physical activity and number of steps (six to 12 months) through changes in the theory-driven motivational and self-regulatory mechanisms of action during the intervention period (zero to six months), as conceptualized in the logic model.

RESULTS

Model 1 tested the mediation processes on Steps and presented a poor fit to the data. Model 2 tested mediation processes on moderate-to-vigorous physical activity and presented poor fit to the data. Simplified models were also tested considering the autonomous motivation and the controlled motivation variables independently. These changes yielded good results and both models presented very good fit to the data for both outcome variables. Percentage of explained variance was negligible for all models. No direct or indirect effects were found from Virtual Care Climate to long term change in outcomes. Indirect effects occurred only between the sequential paths of the theory-driven mediators.

CONCLUSION

This was one of the first attempts to test a serial mediation model considering psychological mechanisms of change and device-measured physical activity in a 12-month longitudinal trial. The model explained a small proportion of variance in post intervention changes in physical activity. We found different pathways of influence on theory-driven motivational and self-regulatory mechanisms but limited evidence that these constructs impacted on actual behavior change. New approaches to test these relationships are needed. Challenges and several alternatives are discussed.

TRIAL REGISTRATION

ISRCTN Registry, ISRCTN88405328. Registered December 16, 2016, https://www.isrctn.com/ISRCTN88405328.

Accuracy of the Apple Watch Series 6 and the Whoop Band 3.0 for assessing heart rate during resistance exercises

This study aimed to determine heart rate accuracy measured by wearable devices during resistance exercises at various intensities. Twenty-nine participants (16 female) aged 19-37 years participated in this cross-sectional study. Participants completed five resistance exercises; Barbell Back Squat, Barbell Deadlift, Dumbbell Curl to Overhead Press, Seated Cable Row, and Burpees. During the exercises, heart rate was concurrently measured using the Polar H10, the Apple Watch Series 6 and the Whoop 3.0. The Apple Watch had high agreement with the Polar H10 during Barbell Back Squats, Barbell Deadlift, and Seated Cable Rows (rho > 0.832) and moderate to low agreement during Dumbbell Curl to Overhead Press and Burpees (rho > 0.364). The Whoop Band 3.0 had high agreement with the Polar H10 during Barbell Back Squats (r > 0.697), moderate agreement during Barbell Deadlift and Dumbbell Curl to Overhead Press (rho > 0.564) and low agreement during Seated Cable Rows and Burpees (rho > 0.383). The results varied across exercises and intensities and indicated the most favourable outcomes for the Apple Watch. In conclusion, our data suggest that the Apple Watch Series 6 is suitable for measuring heart rate during exercise prescription or monitoring resistance exercise performance.

Stressors and Destressors in Working From Home Based on Context and Physiology From Self-Reports and Smartwatch Measurements: International Observational Study Trial

Background

The COVID-19 pandemic has greatly boosted working from home as a way of working, which is likely to continue for most companies in the future, either in fully remote or in hybrid form. To manage stress levels in employees working from home, insights into the stressors and destressors in a home office first need to be studied.

Objective

We present an international remote study with employees working from home by making use of state-of-the-art technology (ie, smartwatches and questionnaires through smartphones) first to determine stressors and destressors in people working from home and second to identify smartwatch measurements that could represent these stressors and destressors.

Methods

Employees working from home from 3 regions of the world (the United States, the United Kingdom, and Hong Kong) were asked to wear a smartwatch continuously for 7 days and fill in 5 questionnaires each day and 2 additional questionnaires before and after the measurement week. The entire study was conducted remotely. Univariate statistical analyses comparing variable distributions between low and high stress levels were followed by multivariate analysis using logistic regression, considering multicollinearity by using variance inflation factor (VIF) filtering.

Results

A total of 202 people participated, with 198 (98%) participants finishing the experiment. Stressors found were other people and daily life getting in the way of work (P=.05), job intensity (P=.01), a history of burnout (P=.03), anxiety toward the pandemic (P=.04), and environmental noise (P=.01). Destressors found were access to sunlight (P=.02) and fresh air (P<.001) during the workday and going outdoors (P<.001), taking breaks (P<.001), exercising (P<.001), and having social interactions (P<.001). The smartwatch measurements positively related to stress were the number of active intensity periods (P<.001), the number of highly active intensity periods (P=.04), steps (P<.001), and the SD in the heart rate (HR; P<.001). In a multivariate setting, only a history of burnout (P<.001) and family and daily life getting in the way of work (P<.001) were positively associated with stress, while self-reports of social activities (P<.001) and going outdoors (P=.03) were negatively associated with stress. Stress prediction models based on questionnaire data had a similar performance (F1=0.51) compared to models based on automatic measurable data alone (F1=0.47).

Conclusions

The results show that there are stressors and destressors when working from home that should be considered when managing stress in employees. Some of these stressors and destressors are (in)directly measurable with unobtrusive sensors, and prediction models based on these data show promising results for the future of automatic stress detection and management.

Trial Registration

Netherlands Trial Register NL9378; https://trialsearch.who.int/Trial2.aspx?TrialID=NL9378

Increasing adherence to the Mediterranean diet and lifestyle is associated with reduced fecal calprotectin and intra-individual changes in microbial composition of healthy subjects

The Mediterranean diet (MED) is associated with the modification of gut microbial composition. In this pilot study, we investigate the feasibility of a microbiota-targeted MED-based lifestyle intervention in healthy subjects. MED intervention integrating dietary counseling, a supporting mobile application, and daily physical activity measurement using step trackers was prospectively applied for 4 weeks. Blood and fecal samples were collected at baseline, after the 4-week intervention, and at 6 and 12 months. Blood counts, inflammatory markers, microbial and eukaryotic composition were analyzed. Dietary adherence was assessed using daily questionnaires. All 20 healthy participants (females 65%, median age 37), completed the 4-week intervention. Adherence to MED increased from 15.6 ± 4.1 (baseline) to 23.2 ± 3.6 points (4 weeks), p < .01, reflected by increased dietary fiber and decreased saturated fat intake (both p < .05). MED intervention modestly reduced fecal calprotectin, white blood cell, neutrophil, and lymphocyte counts, within the normal ranges (P < .05). Levels of butyrate producers including Faecalibacterium and Lachnospira were positively correlated with adherence to MED and the number of daily steps. Bacterial composition was associated with plant-based food intake, while fungal composition with animal-based food as well as olive oil and sweets. Increasing adherence to MED correlated with increased absolute abundances of multiple beneficial gut symbionts. Therefore, increasing adherence to MED is associated with reduction of fecal calprotectin and beneficial microbial alterations in healthy subjects. Microbiota targeted lifestyle interventions may be used to modify the intestinal ecosystem with potential implications for microbiome-mediated diseases.

Systematic Review of Fitbit Charge 2 Validation Studies for Exercise Tracking

Context

There are research-grade devices that have been validated to measure either heart rate (HR) by electrocardiography (ECG) with a Polar chest strap, or step count with ACTiGraph accelerometer. However, wearable activity trackers that measure HR and steps concurrently have been tested against research-grade accelerometers and HR monitors with conflicting results. This review examines validation studies of the Fitbit Charge 2 (FBC2) for accuracy in measuring HR and step count and evaluates the device's reliability for use by researchers and clinicians.

Design

This registered review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The robvis (risk-of-bias visualization) tool was used to assess the strength of each considered article.

Eligibility Criteria

Eligible articles published between 2018 and 2019 were identified using PubMed, CINHAL, Embase, Cochran, and World of Science databases and hand-searches. All articles were HR and/or step count validation studies for the FBC2 in adult ambulatory populations.

Study Selection

Eight articles were examined in accordance with the eligibility criteria alignment and agreement among the authors and research librarian.

Main Outcome Measures

Concordance correlation coefficients (CCC) were used to measure agreement between the tracker and criterion devices. Mean absolute percent error (MAPE) was used to average the individual absolute percent errors.

Results

Studies that measured CCC found agreement between the FBC2 and criterion devices ranged between 26% and 92% for HR monitoring, decreasing in accuracy as exercise intensity increased. Inversely, CCC increased from 38% to 99% for step count when exercise intensity increased. HR error between MAPE was 9.21% to 68% and showed more error as exercise intensity increased. Step measurement error MAPE was 12% for healthy persons aged 24-72 years but was reported at 46% in an older population with heart failure.

Conclusions

Relative agreement with criterion and low-to-moderate MAPE were consistent in most studies reviewed and support validation of the FBC2 to accurately measure HR at low or moderate exercise intensities. However, more investigation controlling testing and measurement congruency is needed to validate step capabilities. The literature supports the validity of the FBC2 to accurately monitor HR, but for step count is inconclusive so the device may not be suitable for recommended use in all populations.

Higher sensitivity monitoring of reactions to COVID-19 vaccination using smartwatches

More than 12 billion COVID-19 vaccination shots have been administered as of August 2022, but information from active surveillance about vaccine safety is limited. Surveillance is generally based on self-reporting, making the monitoring process subjective. We study participants in Israel who received their second or third Pfizer BioNTech COVID-19 vaccination. All participants wore a Garmin Vivosmart 4 smartwatch and completed a daily questionnaire via smartphone. We compare post-vaccination smartwatch heart rate data and a Garmin-computed stress measure based on heart rate variability with data from the patient questionnaires. Using a mixed effects panel regression to remove participant-level fixed and random effects, we identify considerable changes in smartwatch measures in the 72 h post-vaccination even among participants who reported no side effects in the questionnaire. Wearable devices were more sensitive than questionnaires in determining when participants returned to baseline levels. We conclude that wearable devices can detect physiological responses following vaccination that may not be captured by patient self-reporting. More broadly, the ubiquity of smartwatches provides an opportunity to gather improved data on patient health, including active surveillance of vaccine safety.

A computational framework for discovering digital biomarkers of glycemic control

Digital biomarkers can radically transform the standard of care for chronic conditions that are complex to manage. In this work, we propose a scalable computational framework for discovering digital biomarkers of glycemic control. As a feasibility study, we leveraged over 79,000 days of digital data to define objective features, model the impact of each feature, classify glycemic control, and identify the most impactful digital biomarkers. Our research shows that glycemic control varies by age group, and was worse in the youngest population of subjects between the ages of 2–14. In addition, digital biomarkers like prior-day time above range and prior-day time in range, as well as total daily bolus and total daily basal were most predictive of impending glycemic control. With a combination of the top-ranked digital biomarkers, we achieved an average F1 score of 82.4% and 89.7% for classifying next-day glycemic control across two unique datasets.

Self-Reported and Physiologic Reactions to Third BNT162b2 mRNA COVID-19 (Booster) Vaccine Dose

Despite extensive technological advances in recent years, objective and continuous assessment of physiologic measures after vaccination is rarely performed. We conducted a prospective observational study to evaluate short-term self-reported and physiologic reactions to the booster BNT162b2 mRNA (Pfizer-BioNTech, https://www.pfizer.com) vaccine dose. A total of 1,609 participants were equipped with smartwatches and completed daily questionnaires through a dedicated mobile application. The extent of systemic reactions reported after the booster dose was similar to that of the second dose and considerably greater than that of the first dose. Analyses of objective heart rate and heart rate variability measures recorded by smartwatches further supported this finding. Subjective and objective reactions after the booster dose were more apparent in younger participants and in participants who did not have underlying medical conditions. Our findings further support the safety of the booster dose from subjective and objective perspectives and underscore the need for integrating wearables in clinical trials.

Accuracy and Precision of Wearable Devices for Real-Time Monitoring of Swimming Athletes

Nowadays, the use of wearable devices is spreading in different fields of application, such as healthcare, digital health, and sports monitoring. In sport applications, the present trend is to continuously monitor the athletes’ physiological parameters during training or competitions to maximize performance and support coaches. This paper aims to evaluate the performances in heart rate assessment, in terms of accuracy and precision, of both wrist-worn and chest-strap commercial devices used during swimming activity, considering a test population of 10 expert swimmers. Three devices were employed: Polar H10 cardiac belt, Polar Vantage V2, and Garmin Venu Sq smartwatches. The former was used as a reference device to validate the data measured by the two smartwatches. Tests were performed both in dry and wet conditions, considering walking/running on a treadmill and different swimming styles in water, respectively. The measurement accuracy and precision were evaluated through standard methods, i.e., Bland–Altman plot, analysis of deviations, and Pearson’s correlation coefficient. Results show that both precision and accuracy worsen during swimming activity (with an absolute increase of the measurement deviation in the range of 13–56 bpm for mean value and 49–52 bpm for standard deviation), proving how water and arms movement act as relevant interference inputs. Moreover, it was found that wearable performance decreases when activity intensity increases, highlighting the need for specific research for wearable applications in water, with a particular focus on swimming-related sports activities.

Estimation of Heart Rate and Energy Expenditure Using a Smart Bracelet during Different Exercise Intensities: A Reliability and Validity Study

Background. With wrist-worn wearables becoming increasingly available, it is important to understand their reliability and validity in different conditions. The primary objective of this study was to examine the reliability and validity of the Lexin Mio smart bracelet in measuring heart rate (HR) and energy expenditure (EE) in people with different physical activity levels exercising at different intensities. Methods. A total of 65 participants completed one maximal oxygen uptake test and two running exercise tests wearing the Mio smart bracelet, the Polar H10 HR band, and a gas-analysis system. Results. In terms of HR measurement reliability, the Mio smart bracelet showed good reliability in a left versus right test and good test−retest reliability (p > 0.05; mean absolute percentage error (MAPE) < 10%; intraclass correlation coefficient (ICC) > 0.4). For EE measurement, the Mio smart bracelet showed good reliability in a left versus right test, good test−retest reliability on the right (p > 0.05; MAPE > 10%; ICC > 0.4), and low test−retest reliability on the left (p > 0.05; MAPE > 10%; ICC < 0.4). Regarding validity, the Mio smart bracelet showed good validity for HR measurement (p > 0.05; MAPE < 10%; ICC > 0.4) and low validity for EE measurement (p < 0.05; MAPE > 10%; ICC < 0.4). Conclusion. The Lexin Mio smart bracelet showed good reliability and validity for HR measurement among people with different physical activity levels exercising at various exercise intensities in a laboratory setting. However, the smart bracelet showed good reliability and low validity for the estimation of EE.

A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM

In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.

Effects of Interactive Music Tempo with Heart Rate Feedback on Physio-Psychological Responses of Basketball Players

This paper introduces an interactive music tempo control with closed-loop heart rate feedback to yield a sportsperson with better physio-psychological states. A total of 23 participants (13 men, 10 women; 16−32 years, mean = 20.04 years) who are professionals or school team members further guide a sportsperson to amend their physical tempo to harmonize their psychological and physical states. The self-tuning mechanism between the surroundings and the human can be amplified using interactive music tempo control. The experiments showed that listening to interactive music had a significant effect on the heart rate and rating of perceived exertion (RPE) of the basketball player compared to those listening to asynchronous music or no music during exercise (p < 0.01). Synchronized interactive music allows athletes to increase their heart rate and decrease RPE during exercise and does not require a multitude of preplanned playlists. All self-selected songs can be converted into sports-oriented music using algorithms. The algorithms of synchronous and asynchronous modes in this study can be adjusted and applied to other sports fields or recovery after exercise. In the future, other musical parameters should be adjusted in real-time based on physiological signals, such as tonality, beats, chords, and orchestration.

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

BACKGROUND

Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.

OBJECTIVE

The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.

METHODS

We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.

RESULTS

A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively).

CONCLUSIONS

Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

A Personalized Smartphone-Delivered Just-in-time Adaptive Intervention (JitaBug) to Increase Physical Activity in Older Adults: Mixed Methods Feasibility Study

BACKGROUND

Just-in-time adaptive interventions (JITAIs) provide real time in-the-moment behavior change support to people when they need it most. JITAIs could be a viable way to provide personalized physical activity (PA) support to older adults in the community. However, it is unclear how feasible it is to remotely deliver a PA intervention through a smartphone to older adults or how acceptable they would find a JITAI targeting PA in everyday life.

OBJECTIVE

The aims of this study are to describe the development of JitaBug, a personalized smartphone-delivered JITAI designed to support older adults to increase or maintain their PA level, assess the feasibility of conducting an effectiveness trial of the JitaBug intervention, and explore the acceptability of JitaBug among older adults in a free-living setting.

METHODS

The intervention was developed using the Behavior Change Wheel and consisted of a wearable activity tracker (Fitbit) and a companion smartphone app (JitaBug) that delivered goal-setting, planning, reminders, and JITAI messages to encourage achievement of personalized PA goals. Message delivery was tailored based on time of day, real time PA tracker data, and weather conditions. We tested the feasibility of remotely delivering the intervention with older adults in a 6-week trial. Data collection involved assessment of PA through accelerometery and activity tracker, self-reported mood and mental well-being through ecological momentary assessment, and contextual information on PA through voice memos. Feasibility outcomes included recruitment capability and adherence to the intervention, intervention delivery in the wild, appropriateness of data collection methodology, adverse events, and participant satisfaction.

RESULTS

Of the 46 recruited older adults (aged 56-72 years), 31 (67%) completed the intervention. The intervention was successfully delivered as intended; 87% (27/31) of the participants completed the intervention independently; 94% (2247/2390) of the PA messages were successfully delivered; 99% (2239/2261) of the Fitbit and 100% (2261/2261) of the weather data calls were successful. Valid and usable wrist-worn accelerometer data were obtained from 90% (28/31) of the participants at baseline and follow-up. On average, the participants recorded 50% (7.9/16, SD 7.3) of the voice memos, 38% (3.3/8, SD 4.2) of the mood assessments, and 50% (2.1/4, SD 1.6) of the well-being assessments through the app. Overall acceptability of the intervention was very good (23/30, 77% expressed satisfaction). Participant feedback suggested that more diverse and tailored PA messages, app use reminders, technical refinements, and an improved user interface could improve the intervention and make it more appealing.

CONCLUSIONS

This study suggests that a smartphone-delivered JITAI is an acceptable way to support PA in older adults in the community. Overall, the intervention is feasible; however, based on user feedback, the JitaBug app requires further technical refinements that may enhance use, engagement, and user satisfaction before moving to effectiveness trials.

Efficacy of the Aim2Be intervention in changing lifestyle behaviours among adolescents with overweight and obesity: A Randomized Controlled Trial (Preprint)

BACKGROUND

Aim2Be is a gamified lifestyle app designed to promote lifestyle behavior changes among Canadian adolescents and their families.

OBJECTIVE

The primary aim was to test the efficacy of the Aim2Be app with support from a live coach to reduce weight outcomes (BMI Z score [zBMI]) and improve lifestyle behaviors among adolescents with overweight and obesity and their parents versus a waitlist control group over 3 months. The secondary aim was to compare health trajectories among waitlist control participants over 6 months (before and after receiving access to the app), assess whether support from a live coach enhanced intervention impact, and evaluate whether the app use influenced changes among intervention participants.

METHODS

A 2-arm parallel randomized controlled trial was conducted from November 2018 to June 2020. Adolescents aged 10 to 17 years with overweight or obesity and their parents were randomized into an intervention group (Aim2Be with a live coach for 6 months) or a waitlist control group (Aim2Be with no live coach; accessed after 3 months). Adolescents' assessments at baseline and at 3 and 6 months included measured height and weight, 24-hour dietary recalls, and daily step counts measured with a Fitbit. Data on self-reported physical activity, screen time, fruit and vegetable intake, and sugary beverage intake of adolescents and parents were also collected.

RESULTS

A total of 214 parent-child participants were randomized. In our primary analyses, there were no significant differences in zBMI or any of the health behaviors between the intervention and control groups at 3 months. In our secondary analyses, among waitlist control participants, zBMI (P=.02), discretionary calories (P=.03), and physical activity outside of school (P=.001) declined, whereas daily screen time increased (P<.001) after receiving access to the app compared with before receiving app access. Adolescents randomized to Aim2Be with live coaching reported more time being active outside of school compared with adolescents who used Aim2Be with no coaching over 3 months (P=.001). App use did not modify any changes in outcomes among adolescents in the intervention group.

CONCLUSIONS

The Aim2Be intervention did not improve zBMI and lifestyle behaviors in adolescents with overweight and obesity compared with the waitlist control group over 3 months. Future studies should explore the potential mediators of changes in zBMI and lifestyle behaviors as well as predictors of engagement.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/study/NCT03651284.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/s13063-020-4080-2.

A Brief Review on the Evolution of Technology in Exercise and Sport in Type 1 Diabetes: Past, Present, and Future

One hundred years ago, insulin was first used to successfully lower blood glucose levels in young people living with what was then called juvenile diabetes. While insulin was not a cure for diabetes, it allowed individuals to resume a near normal life and have some freedom to eat more liberally and gain the strength they needed to live a more active lifestyle. Since then, a number of therapeutic and technical advances have arisen to further improve the health and wellbeing of individuals living with type 1 diabetes, allowing many to participate in sport at the local, regional, national or international level of competition. This review and commentary highlights some of the key advances in diabetes management in sport over the last 100 years since the discovery of insulin.

Internet of things-Enabled technologies as an intervention for childhood obesity: A systematic review

Childhood obesity is one of the most serious public health challenges of the 21st century, with consequences lasting into adulthood. Internet of Things (IoT)-enabled devices have been studied and deployed for monitoring and tracking diet and physical activity of children and adolescents as well as a means of providing remote, ongoing support to children and their families. This review aimed to identify and understand current advances in the feasibility, system designs, and effectiveness of IoT-enabled devices to support weight management in children. We searched Medline, PubMed, Web of Science, Scopus, ProQuest Central and the IEEE Xplore Digital Library for studies published after 2010 using a combination of keywords and subject headings related to health activity tracking, weight management, youth and Internet of Things. The screening process and risk of bias assessment were conducted in accordance with a previously published protocol. Quantitative analysis was conducted for IoT-architecture related findings and qualitative analysis was conducted for effectiveness-related measures. Twenty-three full studies are included in this systematic review. The most used devices were smartphone/mobile apps (78.3%) and physical activity data (65.2%) from accelerometers (56.5%) were the most commonly tracked data. Only one study embarked on machine learning and deep learning methods in the service layer. Adherence to IoT-based approaches was low but game-based IoT solutions have shown better effectiveness and could play a pivotal role in childhood obesity interventions. Researcher-reported effectiveness measures vary greatly amongst studies, highlighting the importance for improved development and use of standardised digital health evaluation frameworks.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study

Background

Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research.

Objective

This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2.

Methods

Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed.

Results

A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm).

Conclusions

Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

Accuracy of Heart Rate Measurement by the Fitbit Charge 2 During Wheelchair Activities in People With Spinal Cord Injury: Instrument Validation Study

Background

Heart rate (HR) is an important and commonly measured physiological parameter in wearables. HR is often measured at the wrist with the photoplethysmography (PPG) technique, which determines HR based on blood volume changes, and is therefore influenced by blood pressure. In individuals with spinal cord injury (SCI), blood pressure control is often altered and could therefore influence HR accuracy measured by the PPG technique.

Objective

The objective of this study is to investigate the HR accuracy measured with the PPG technique with a Fitbit Charge 2 (Fitbit Inc) in wheelchair users with SCI, how the activity intensity affects the HR accuracy, and whether this HR accuracy is affected by lesion level.

Methods

The HR of participants with (38/48, 79%) and without (10/48, 21%) SCI was measured during 11 wheelchair activities and a 30-minute strength exercise block. In addition, a 5-minute seated rest period was measured in people with SCI. HR was measured with a Fitbit Charge 2, which was compared with the HR measured by a Polar H7 HR monitor used as a reference device. Participants were grouped into 4 groups—the no SCI group and based on lesion level into the <T5 (midthoracic and lower) group, T5-T1 (high-thoracic) group, and >T1 (cervical) group. Mean absolute percentage error (MAPE) and concordance correlation coefficient were determined for each group for each activity type, that is, rest, wheelchair activities, and strength exercise.

Results

With an overall MAPE_{all lesions} of 12.99%, the accuracy fell below the standard acceptable MAPE of –10% to +10% with a moderate agreement (concordance correlation coefficient=0.577). The HR accuracy of Fitbit Charge 2 seems to be reduced in those with cervical lesion level in all activities (MAPE_{no SCI}=8.09%; MAPE_<T5=11.16%; MAPE_T1−T5=10.5%; and MAPE_>T1=20.43%). The accuracy of the Fitbit Charge 2 decreased with increasing intensity in all lesions (MAPE_rest=6.5%, MAPE_activity=12.97%, and MAPE_strength=14.2%).

Conclusions

HR measured with the PPG technique showed lower accuracy in people with SCI than in those without SCI. The accuracy was just above the acceptable level in people with paraplegia, whereas in people with tetraplegia, a worse accuracy was found. The accuracy seemed to worsen with increasing intensities. Therefore, high-intensity HR data, especially in people with cervical lesions, should be used with caution.

Use of the Xiaomi Mi Band for sleep monitoring and its influence on the daily life of older people living in a nursing home

Background: Lower quantity and poorer sleep quality are common in most older adults, especially for those who live in a nursing home. The use of wearable devices, which measure some parameters such as the sleep stages, could help to determine the influence of sleep quality in daily activity among nursing home residents. Therefore, this study aims to analyse the influence of sleep and its changes concerning the health status and daily activity of older people who lived in a nursing home, by monitoring the participants for a year with Xiaomi Mi Band 2. Methods: This is a longitudinal study set in a nursing home in [Details omitted for double-anonymized peer reviewed]. The Xiaomi Mi Band 2 will be used to measure biomedical parameters and different assessment tools will be administered to participants for evaluating their quality of life, sleep quality, cognitive state, and daily functioning. Results: A total of 21 nursing home residents participated in the study, with a mean age of 86.38 ± 9.26. The main outcomes were that sleep may influence daily activity, cognitive state, quality of life, and level of dependence in activities of daily life. Moreover, environmental factors and the passage of time could also impact sleep. Conclusions: Xiaomi Mi Band 2 could be an objective tool to assess the sleep of older adults and know its impact on some factors related to health status and quality of life of older nursing homes residents. Trial Registration: NCT04592796 (Registered 16 October 2020) Available on: https://clinicaltrials.gov/ct2/show/NCT04592796.

Accuracy of wrist-worn wearable devices for determining exercise intensity

Objective

As an indicator of exercise intensity, heart rate can be measured in a timely manner using wrist-worn devices. No study has attempted to estimate a target exercise intensity using wearable devices. The objective of the study was to evaluate the validity of prescribing exercise intensity using wrist-worn devices.

Methods

Thirty healthy subjects completed a maximal cardiopulmonary exercise test. Their heart rates were recorded using an electrocardiogram and two devices—Apple Watch Series 6 and Garmin Forerunner 945. Exercise intensity with the target heart rate was defined as resting heart rate + (maximal heart rate − resting heart rate) * n% ( n%: 40–60% for moderate-intensity exercise and 60–89% for vigorous-intensity exercise). Heart rate was analyzed at the lower and upper limits of each exercise intensity (HR40, HR60, and HR89). The mean absolute percentage error and concordance correlation coefficient were calculated, and Bland–Altman plots and scatterplots were constructed.

Results

Both devices showed a low mean absolute error (1.16–1.48 bpm for Apple and 1.35–2.25 for Garmin) and mean absolute percentage error (<1% for Apple and 1.16–1.39% for Garmin) in all intensities. A substantial correlation with electrocardiogram-measured heart rate was observed for moderate to vigorous intensity with concordance correlation coefficient > 0.95 for both devices, except that Garmin showed moderate correlation at the upper limit of vigorous activity with concordance correlation coefficient = 0.936. Moreover, Bland–Altman plots and scatterplots demonstrated a strong correlation without systematic error when the values obtained via the two devices were compared with electrocardiogram measurements.

Conclusions

Our findings indicate the high validity of exercise prescriptions based on the heart rate measured by the two devices. Additional research should explore other populations to confirm these findings.

Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports.

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

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

Monte Carlo analysis of optical heart rate sensors in commercial wearables: the effect of skin tone and obesity on the photoplethysmography (PPG) signal

Commercially available wearable devices have been used for fitness and health management and their demand has increased over the last ten years. These "general wellness" and heart-rate monitoring devices have been cleared by the Food and Drug Administration for over-the-counter use, yet anecdotal and more systematic reports seem to indicate that their error is higher when used by individuals with elevated skin tone and high body mass index (BMI). In this work, we used Monte Carlo modeling of a photoplethysmography (PPG) signal to study the theoretical limits of three different wearable devices (Apple Watch series 5, Fitbit Versa 2 and Polar M600) when used by individuals with a BMI range of 20 to 45 and a Fitzpatrick skin scale 1 to 6. Our work shows that increased BMI and skin tone can induce a relative loss of signal of up to 61.2% in Fitbit versa 2, 32% in Apple S5 and 32.9% in Polar M600 when considering the closest source-detector pair configuration in these devices.

Monitoring Physical Activity Intensity During Pregnancy

For apparently healthy pregnant women, regular physical activity is recommended. The American College of Obstetricians and Gynecologists (ACOG) created recommendations for physical activity and exercise during pregnancy in 1985. At that time, pregnant women were advised to not exceed a heart rate of 140 beats per minute with physical activity. The heart rate recommendation was subsequently removed with the recommendations published in 1994, 2002, and 2015. In 2020, the ACOG updated its recommendations on physical activity for pregnant and postpartum women. The recommendation included exercising at a "fairly light to somewhat hard" perceived intensity and at less than 60-80% of age-predicted maximum heart rate, usually not exceeding a heart rate of 140 beats per minute. Women often seek advice from healthcare providers on physical activity during pregnancy, yet providers report concern about giving appropriate physical activity guidance. This paper summarizes the key scientific literature on monitoring absolute and relative exercise intensity in relation to the current ACOG recommendations, providing background on intensity-related concepts used in the recommendation. This paper also provides practical guidance to assist healthcare providers in relaying this information to pregnant women.

Using smartwatches to observe changes in activity during recovery from critical illness following COVID-19: a 1 year multi-centre observational study. (Preprint)

Background

As a sequela of the COVID-19 pandemic, a large cohort of critical illness survivors have had to recover in the context of ongoing societal restrictions.

Objective

We aimed to use smartwatches (Fitbit Charge 3; Fitbit LLC) to assess changes in the step counts and heart rates of critical care survivors following hospital admission with COVID-19, use these devices within a remote multidisciplinary team (MDT) setting to support patient recovery, and report on our experiences with this.

Methods

We conducted a prospective, multicenter observational trial in 8 UK critical care units. A total of 50 participants with moderate or severe lung injury resulting from confirmed COVID-19 were recruited at discharge from critical care and given a smartwatch (Fitbit Charge 3) between April and June 2020. The data collected included step counts and daily resting heart rates. A subgroup of the overall cohort at one site—the MDT site (n=19)—had their smartwatch data used to inform a regular MDT meeting. A patient feedback questionnaire and direct feedback from the MDT were used to report our experience. Participants who did not upload smartwatch data were excluded from analysis.

Results

Of the 50 participants recruited, 35 (70%) used and uploaded data from their smartwatch during the 1-year period. At the MDT site, 74% (14/19) of smartwatch users uploaded smartwatch data, whereas 68% (21/31) of smartwatch users at the control sites uploaded smartwatch data. For the overall cohort, we recorded an increase in mean step count from 4359 (SD 3488) steps per day in the first month following discharge to 7914 (SD 4146) steps per day at 1 year (P=.003). The mean resting heart rate decreased from 79 (SD 7) beats per minute in the first month to 69 (SD 4) beats per minute at 1 year following discharge (P<.001). The MDT subgroup’s mean step count increased more than that of the control group (176% increase vs 42% increase, respectively; +5474 steps vs +2181 steps, respectively; P=.04) over 1 year. Further, 71% (10/14) of smartwatch users at the MDT site and 48% (10/21) of those at the control sites strongly agreed that their Fitbit motivated them to recover, and 86% (12/14) and 48% (10/21), respectively, strongly agreed that they aimed to increase their activity levels over time.

Conclusions

This is the first study to use smartwatch data to report on the 1-year recovery of patients who survived a COVID-19 critical illness. This is also the first study to report on smartwatch use within a post–critical care MDT. Future work could explore the role of smartwatches as part of a randomized controlled trial to assess clinical and economic effectiveness.

International Registered Report Identifier (IRRID)

RR2-10.12968/ijtr.2020.0102

Wearable Fitness Trackers to Predict Clinical Deterioration in Maintenance Hemodialysis: A Prospective Cohort Feasibility Study

Rationale & Objective

People receiving hemodialysis often require urgent care or hospitalizations. It is possible that reductions in a patient’s level of physical activity may serve as an “early warning” of clinical deterioration, allowing timely clinical intervention. We explored whether step count could serve as a trigger for deterioration.

Study Design

Prospective observational cohort feasibility study.

Setting & Participants

We recruited consenting adult participants from outpatient dialysis clinics in Calgary, AB, between June 28, 2019, and October 10, 2019.

Exposure and Outcomes

Participants wore a wristband fitness tracker for 4 weeks. Activity data from the trackers were imported weekly into the study database. Demographic, clinical management, functional impairment, and frailty were assessed at baseline. Clinical events (urgent care and emergency department visits and hospitalizations) were monitored during the observation period.

Analytical Approach

Box and whisker plots and line plots were used to describe each participant’s daily steps. Adjusted rate ratios (and 95 % confidence intervals) were calculated to assess the associations between the number of steps taken each day and potential predictors.

Results

Data from 46 patients were included; their median age was 64 years (range, 22 to 85), and 63 % were men. The median number of steps taken per day was 3,133 (range, 248-13,753). Fourteen events among 11 patients were reported. Within patients, step count varied considerably; it was not possible to identify a patient-specific normal range for daily step count. There was no association between step count and the occurrence of clinical events, although the number of events was very small.

Limitations

The number of participants was relatively small, and there were too few events to model to examine whether step count could predict clinical deterioration.

Conclusions

Within-patient variation in daily step count was too high to generate a normal range for patients. However, patient-specific norms over a longer period (3- or 7-day periods) appear potentially worthy of future study in a larger sample and/or over a longer follow-up.

Validation of the Apple Watch for Estimating Moderate-to-Vigorous Physical Activity and Activity Energy Expenditure in School-Aged Children

The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children’s free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.