Wearable Technology and Physical Activity in Chronic Disease: Opportunities and Challenges

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial

BACKGROUND

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design.

OBJECTIVE

The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems.

METHODS

The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership.

RESULTS

The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership.

CONCLUSIONS

Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines.

CLINICALTRIAL

ClinicalTrials.gov-Identifier: NCT04359238.

Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor

Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.

Preferences of people with chronic kidney disease regarding digital health interventions that promote healthy lifestyle: qualitative systematic review with meta-ethnography

OBJECTIVES

Diet and physical activity are crucial for people with chronic kidney disease (CKD) to maintain good health. Digital health interventions can increase access to lifestyle services. However, consumers' perspectives are unclear, which may reduce the capacity to develop interventions that align with specific needs and preferences. Therefore, this review aims to synthesise the preferences of people with CKD regarding digital health interventions that promote healthy lifestyle.

DESIGN

Qualitative systematic review with meta-ethnography.

DATA SOURCES

Databases Scopus, CENTRAL, MEDLINE, CINAHL and SPORTDiscus were searched between 2000 and 2023.

ELIGIBILITY CRITERIA

Primary research papers that used qualitative exploration methods to explore the preferences of adults with CKD (≥18 years) regarding digital health interventions that promoted diet, physical activity or a combination of these health behaviours.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened title, abstract and full text. Discrepancies were resolved by a third reviewer. Consumers' quotes were extracted verbatim and synthesised into higher-order themes and subthemes.

RESULTS

Database search yielded 5761 records. One record was identified following communication with a primary author. 15 papers were included. These papers comprised 197 consumers (mean age 51.0±7.2), including 83 people with CKD 1-5; 61 kidney transplant recipients; 53 people on dialysis. Sex was reported in 182 people, including 53% male. Five themes were generated regarding consumers' preferences for digital lifestyle interventions. These included simple instruction and engaging design; individualised interventions; virtual communities of care; education and action plans; and timely reminders and automated behavioural monitoring.

CONCLUSION

Digital health interventions were considered an important mechanism to access lifestyle services. Consumers' preferences are important to ensure future interventions are tailored to specific needs and goals. Future research may consider applying the conceptual framework of consumers' preferences in this review to develop and evaluate the effect of a digital lifestyle intervention on health outcomes.

PROSPERO REGISTRATION NUMBER

CRD42023411511.

Feasibility and preliminary efficacy of a physiotherapy-led remotely delivered physical activity intervention in cancer survivors using wearable technology. The IMPETUS trial

BACKGROUND

Physical activity levels are low in cancer survivors. Remotely delivered programs which harness wearable technology may potentially be beneficial.

OBJECTIVE

To evaluate the feasibility and preliminary efficacy of a remotely delivered, physical activity intervention which harnessed wearable technology.

METHODS

This single arm pre-post longitudinal study included cancer survivors who had completed treatment in the preceding 3 years. Participants were supplied with a Fitbit One® or Flex® for 12 weeks. Physical activity goals were discussed during support phone calls. Outcome measures, assessed at baseline (T1), 12 weeks (T2), and 24 weeks (T3), included feasibility (recruitment, adherence, safety, acceptability) and efficacy [physical activity (Godin leisure time Index, ActiGraph GT3X+), quality of life (functional assessment of cancer therapy - general, short form 36 physical functioning component), functional capacity (six-minute walk test)].

RESULTS

Forty-five participants completed T1 assessments (10 males, 35 females). Thirty-nine (86.6%) of those underwent assessment at T2 and 31 (68.8%) at T3. The intervention was perceived positively with no adverse effects. There were increases in functional capacity (six-minute walk test, p = .002) between T1-T3, an increase in quality of life [short form 36 physical functioning measure (p = .0035), functional assessment of cancer total score (p = .02)] and self-report physical activity levels (p = .000123) between T1-T2, although effect sizes were generally low (d = 0.180 to d = 0.418). Objectively measured physical activity did not change.

CONCLUSION

A physical activity intervention including wearable technology was safe, feasible, and well received by cancer survivors. An intervention based on this proof of concept should be followed up in further studies.

Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning

Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model's consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.

Perspectives on Ease of Use and Value of a Self-Monitoring Application to Support Physical Activity Maintenance among Individuals Living with and beyond Cancer

BACKGROUND

Physical activity (PA) can improve the physical and psychosocial health of individuals with cancer, yet PA levels remain low. Technology may address PA maintenance barriers in oncology, though the intervention effectiveness to date remains mixed. Qualitative research can reveal the nuances of using technology-based PA maintenance tools. The present study aimed to understand the perspectives of individuals with cancer on using an app to support PA maintenance.

METHODS

Individuals were interviewed after using a self-monitoring app for 24 weeks, asking about their app use, ease of use, and perceived value for supporting PA. Analyses were guided by an interpretive description.

RESULTS

Eighteen individuals were interviewed. The participants were 37-75 years old; lived in seven Canadian provinces/territories; identified as White, South Asian, or Indigenous; and had eight different cancers. Four themes were developed: some did not need the app to stay physically active, some valued the app for helping them maintain their PA, the user experience ranged from intuitive to confusing, and the time burden of app use ranged from acceptable to overwhelming.

CONCLUSIONS

The participants provided insights on using a self-monitoring app to improve PA maintenance in oncology. Work is needed to capture additional perspectives and apply findings to the development of technology-based PA maintenance tools.

Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors

BACKGROUND

Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data.

METHODS

Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis.

RESULTS

Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00-01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization.

CONCLUSIONS

Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study

BACKGROUND

This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants.

OBJECTIVE

The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.

METHODS

A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement.

RESULTS

Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).

CONCLUSIONS

Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

Universal health coverage in China part 2: addressing challenges and recommendations

This report analyses the underlying causes of China's achievements and gaps in universal health coverage over the past 2 decades and proposes policy recommendations for advancing universal health coverage by 2030. Although strong political commitment and targeted financial investment have produced positive outcomes in reproductive, maternal, newborn, and child health and infectious diseases, a fragmented and hospital-centric delivery system, rising health-care costs, shallow benefit coverage of health insurance schemes, and little integration of health in all policies have restricted China's ability to effectively prevent and control chronic disease and provide adequate financial risk protection, especially for lower-income households. Here, we used a health system conceptual framework and we propose a set of feasible policy recommendations that draw from international experiences and first-hand knowledge of China's unique institutional landscape. Our six recommendations are: instituting a primary care-focused integrated delivery system that restructures provider incentives and accountability mechanisms to prioritise prevention; leveraging digital tools to support health behaviour change; modernising information campaigns; improving financial protection through insurance reforms; promoting a health in all policy; and developing a domestic monitoring framework with refined tracer indicators that reflects China's disease burden.

Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities

This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications.

Examining the Canadian 24-Hour Movement Guidelines among Adults with Intellectual Disability: A Pilot Study

The purpose of this pilot study was to investigate the extent to which adults with intellectual disability (ID) met the 2020 Canadian 24-Hour Movement Guidelines. Fifteen adults (six females and nine males) participated in this nine-day observational study (age = 20-64 years) in 2021-2022, during the COVID-19 pandemic. Moderate-to-vigorous physical activity (MVPA), sedentary time, and total sleep time were measured with a smartwatch to compare to the guidelines. A diary subjectively tracked physical activity. Of the 15 participants, 11 met the MVPA guidelines (73%), 4 met the sedentary behaviour guidelines (27%), 7 met the sleep guidelines (47%), and only 1 participant met all 3 of the guidelines (7%). There were no differences in physical activity or sleep between weekends and weekdays, or between males and females. Walking, cleaning dishes, and swimming were the most common types of physical activity performed by the participants. The findings of this pilot study indicate the need to improve sleep and reduce sedentary time in adults with ID. As most participants met the MVPA guidelines, few met the sedentary behaviour guidelines, and nearly half met the sleep guidelines, these data also demonstrate how important it is to assess all three aspects of the movement guidelines. All these behaviours have independent health benefits and risks, which interact to influence overall health.

Waist-What? Can a single sensor positioned at the waist detect parameters of gait at a speed and distance reflective of older adults' activity?

One of the problems facing an ageing population is functional decline associated with reduced levels of physical activity (PA). Traditionally researcher or clinician input is necessary to capture parameters of gait or PA. Enabling older adults to monitor their activity independently could raise their awareness of their activitiy levels, promote self-care and potentially mitigate the risks associated with ageing. The ankle is accepted as the optimum position for sensor placement to capture parameters of gait however, the waist is proposed as a more accessible body-location for older adults. This study aimed to compare step-count measurements obtained from a single inertial sensor positioned at the ankle and at the waist to that of a criterion measure of step-count, and to compare gait parameters obtained from the sensors positioned at the two different body-locations. Step-count from the waist-mounted inertial sensor was compared with that from the ankle-mounted sensor, and with a criterion measure of direct observation in healthy young and healthy older adults during a three-minute treadmill walk test. Parameters of gait obtained from the sensors at both body-locations were also compared. Results indicated there was a strong positive correlation between step-count measured by both the ankle and waist sensors and the criterion measure, and between ankle and waist sensor step-count, mean step time and mean stride time (r = .802-1.0). There was a moderate correlation between the step time variability measures at the waist and ankle (r = .405). This study demonstrates that a single sensor positioned at the waist is an appropriate method for the capture of important measures of gait and physical activity among older adults.

Integrating the patients' voice in designing and delivering a research study: The Yorkshire Cancer Research funded PREHABS study's experience

INTRODUCTION

Engaging with patients when designing a clinical or research project is beneficial; feedback from the intended audience provides invaluable insight form the patients' perspective. Working with patients can result in developing successful research grants and interventions. The benefit of including the voice of the patient in the Yorkshire Cancer Research funded PREHABS study is described in this article.

METHODS

Patients were included in the PREHABS study from inception to completion. The Theory of Change methodology was used to provide a framework to implement patient feedback to refine the study intervention.

RESULTS

In total, 69 patients engaged with the PREHABS project. Two patients were recruited as co-applicants on the grant and were members on the Trial Management Group. Six patients attended the pre application workshop and provided feedback on their lived experiences of being a lung cancer patient. Commentary from the patients influenced the interventions selected and the design of the prehabs study. Following ethical approval (21/EE/0048) and informed written consent, 61 patients were recruited into the PREHABS study between October 2021 and November 2022. The breakdown of recruited patients was 19 males: mean age 69.1 years (SD 8.91) and 41 females; mean age 74.9 years (SD 8.9).

CONCLUSION

It is practicable and beneficial to include patients at all stages of designing and delivering a research study. Patient feedback can help refine the study interventions to allow for maximum acceptance, recruitment and retention.

IMPLICATIONS FOR PRACTICE

Including patients in the design of radiotherapy research studies can provide invaluable insight that can support the selection and delivery of interventions that are acceptable to the patient cohort.

Effectiveness of online caries management platform in children's caries prevention: A randomized controlled trial

Purpose

To construct an online caries management platform and evaluate its efficacy in children's caries prevention based on caries risk.

Methods

The study participants were second-grade pupils. The caries risk assessment tool (CAT) was used to grade caries risk for all participants, who were randomly divided into the experimental (114 pupils) and control (111 pupils) groups. The experimental group used the Internet for caries management, while the control group was managed by traditional lecturing in classroom. The caries status of each surface of the first permanent molars was recorded. The basic information and oral health knowledge, attitude, and behaviors of participants were collected by questionnaire. One year later, outcome data were collected. Pearson's chi-squared test was used to analyze the caries risk assessment items and oral health behaviors. The Mann-Whitney U-test was used to analyze the decayed-missing-filled surfaces (DMFS) index, plaque index, and scores of oral health knowledge and attitude. P < 0.05 was considered statistically significant. This study was available on the website of Chinese Clinical Trials Register (No: MR-44-22-012947).

Results

After 1 year, the oral health knowledge score was improved by 20.58% (P < 0.001) in the experimental group and 6.02% in the control group. The plaque index was improved by 49.60% (P < 0.001) in the experimental group and 21.01% in the control group. The DMFS index increased in both groups but there were no significant differences (P = 0.608). The experimental group had a better improvement effect in caries risk assessment items than the control group, including "whether the frequency of eating sugary snacks or drinks between meals is more than 3 times/day" (P = 0.033) and the use of fluoridated toothpaste (P = 0.020). The experimental group was better than the control group in reported oral health behaviors, including frequency of eating sweets before sleep (P = 0.032), brushing time (P = 0.001), and the filled rate (proportion of FS in DMFS) of first permanent molars (P = 0.003).

Conclusions

The online caries management platform showed more advantages than traditional lecturing in improving oral health knowledge and behaviors (oral hygiene practice, sugar consumption behavior, and medical treatment behavior). This platform provides a reliable implementation path for the occurrence and continuous improvement of oral health-related behaviors.

The Impact of Wearable Devices on Physical Activity for Chronic Disease Patients: Findings from the 2019 Health Information National Trends Survey

BACKGROUND

Wearable devices are shown to be an advanced tool for chronic disease management, but their impacts on physical activity remain uninvestigated. This study aims to examine the effect of wearable devices on physical activity in general people and chronic patients.

METHODS

Our sample was from the third cycle of the fifth iteration of the Health Information National Trends Survey (HINTS), which includes a total of 5438 residents. Genetic matching was used to evaluate the effect of wearable devices on physical activity in different populations.

RESULTS

(1) Both using wearable devices and using them with high frequency will improve physical activity for the whole population. (2) Wearable devices may have greater positive effects on physical activity for chronic patients. (3) Especially in patients with hypertension, high-frequency use of wearable devices can significantly improve the duration and frequency of physical activity.

CONCLUSIONS

Wearable devices lead to more physical activity, and the benefit is more noticeable for chronic patients, particularly those with hypertension.

Physical activity, physical capacity and sedentary behavior among asthma patients

BACKGROUND: Although exercise and daily physical activity (PA) have long been known to benefit patients with chronic disorders, knowledge is limited regarding asthma.

OBJECTIVE: In a Danish setting, our aim was to measure physical activity, sedentary behavior, and physical capacity among patients with asthma. We hypothesized that people with severe asthma would be less active and more sedentary than their mild-moderate counterparts.

METHODS: Adults with asthma were recruited through respiratory outpatient clinics and subsequently examined twice, 4 weeks apart. At each visit, participants underwent a series of lung function tests, questionnaires, and maximum oxygen uptake testing (VO₂max). Between the visits, participants wore an accelerometer continuously for 4 weeks, measuring sedentary time and daily steps. Sixty patients, 27 with mild-moderate asthma (GINA 1–3) and 33 with severe asthma (GINA 4–5), completed both visits and had valid accelerometer measurements.

RESULTS: No significant differences between the two groups were found in sedentary time, number of steps or VO₂max.   VO₂max was significantly correlated with FeNO (r = −0.30, p < 0.05), Short Form-12 Mental Health (r = 0.37, p < 0.05), Asthma Control Questionnaire (r = −0.35, p < 0.05), and Mini Asthma Quality of Life Questionnaire (r = 0.36, p < 0.05).

CONCLUSION: No differences were observed between patients with mild-moderate and severe asthma regarding sedentary behavior, daily steps or level of cardiopulmonary fitness. Furthermore, patients with the highest VO₂max had the higher quality of life scores.

Abbreviations: VO₂max: Maximal Oxygen Uptake; CPET: Cardiopulmonary Exercise Testing; BMI: Body Mass Index; FEV1: Forced Expired Volume in the First Second; FVC: Forced Vital Capacity; PEF: Peak Expiratory Flow; EIB: Exercise-Induced Bronchoconstriction; COPD: Chronic Obstructive Pulmonary Disease; ACQ: Asthma Control Questionnaire; Mini-AQLQ: Mini Asthma Quality of Life Questionnaire; SF-12: Short Form 12 Health Survey; SNOT-22: Sino-Nasal Outcome Test 22; GINA: The Global Initiative for Asthma; CRP: C-reactive Protein; Hgb:Hemoglobin count; EOS: Eosinophil count; EVH: Eucapnic Voluntary Hyperventilation; FeNO: Fractional Exhaled Nitric Oxide; PA: Physical Activity ERS: European Respiratory Society; ATS: American Thoracic Society; CRS: Chronic Rhinosinusitis; AHR: Airway Hyperresponsiveness

Interventions to Reduce Sedentary Behavior in Cancer Patients and Survivors: a Systematic Review

PURPOSE OF REVIEW

Sedentary behaviors (SB) after cancer diagnosis are associated with poor prognosis for certain cancers, and cancer patients and survivors report high levels of SB. Reducing SB may be a feasible and effective intervention strategy to improve outcomes. This systematic review aims to identify and evaluate the literature on interventions to reduce SB in cancer patients and survivors.

RECENT FINDINGS

Studies were identified via database searches in December 2020. Two authors evaluated study eligibility. Data were extracted and checked, and risk of bias was assessed by the study team. Of 1401 records identified, nine studies involving 394 cancer patients or survivors were included in this review. Six were randomized trials, three were non-randomized intervention studies, and almost all (n = 8) focused on feasibility with small sample sizes. All studies were conducted within the previous 5 years in Canada, Australia, USA, and South Korea. Cancer types studied were breast (n = 3), prostate (n = 2), colorectal or peritoneal (n = 1), and mixed types (n = 3). Intervention duration of 12 weeks was most common (n = 7). Five studies had multiple intervention components, and six studies included wearable devices to measure and/or prompt behavior change. There was an overall trend where intervention groups reduced SB vs. control groups, often coupled with an increase in moderate-to-vigorous physical activity. This review suggests that there is some promise for intervention strategies to reduce SB in cancer patients and survivors. There is a need for more high-quality randomized controlled trials to understand how to best decrease SB in cancer patients and survivors.

Wearables for Measuring the Physical Activity and Sedentary Behavior of Patients With Axial Spondyloarthritis: Systematic Review

BACKGROUND

Axial spondyloarthritis (axSpA) is an inflammatory rheumatic disease associated with chronic back pain and restricted mobility and physical function. Increasing physical activity is a viable strategy for improving the health and quality of life of patients with axSpA. Thus, quantifying physical activity and sedentary behavior in this population is relevant to clinical outcomes and disease management. However, to the best of our knowledge, no systematic review to date has identified and synthesized the available evidence on the use of wearable devices to objectively measure the physical activity or sedentary behavior of patients with axSpA.

OBJECTIVE

This study aimed to review the literature on the use of wearable activity trackers as outcome measures for physical activity and sedentary behavior in patients with axSpA.

METHODS

PubMed, PEDro, and Cochrane electronic databases were searched in July 2021 for relevant original articles, with no limits on publication dates. Studies were included if they were original articles, targeted adults with a diagnosis of axSpA, and reported wearable device-measured physical activity or sedentary behavior among patients with axSpA. Data regarding the study's characteristics, the sample description, the methods used for measuring physical activity and sedentary behavior (eg, wearable devices, assessment methods, and outcomes), and the main results of the physical activity and sedentary behavior assessments were extracted.

RESULTS

A total of 31 studies were initially identified; 13 (13/31, 42%) met the inclusion criteria, including 819 patients with axSpA. All the studies used accelerometer-based wearable devices to assess physical activity. Of the 13 studies, 4 (4/31, 31%) studies also reported outcomes related to sedentary behavior. Wearable devices were secured on the wrists (3/13 studies, 23%), lower back (3/13, 23%), right hip (3/13, 23%), waist (2/13, 15%), anterior thigh (1/13, 8%), or right arm (1/13, 8%). The methods for reporting physical activity and sedentary behavior were heterogeneous. Approximately 77% (10/13) of studies had a monitoring period of 1 week, including weekend days.

CONCLUSIONS

To date, few studies have used wearable devices to quantify the physical activity and sedentary behavior of patients with axSpA. The methodologies and results were heterogeneous, and none of these studies assessed the psychometric properties of these wearables in this specific population. Further investigation in this direction is needed before using wearable device-measured physical activity and sedentary behavior as outcome measures in intervention studies in patients with axSpA.

TRIAL REGISTRATION

PROSPERO CRD42020182398; https://tinyurl.com/ec22jzkt.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/23359.

Dataset of fitness trackers and smartwatches to measuring physical activity in research

OBJECTIVES

Accelerometer-based wrist-worn fitness trackers and smartwatches (wearables) appeared on the consumer market in 2011. Many wearable devices have been released since. The objective of this data paper is to describe a dataset of 423 wearables released before July 2017.

DATA DESCRIPTION

We identified wearables and extracted information from six online and offline databases. We also visited websites for all identified companies/brands to identify additional wearables, as well as obtained additional information for each identified device. Twelve attributes were collected: wearable name, company/brand name, release year, country of origin, whether the wearable was crowd funded, form factor (fitness tracker or smartwatch), and sensors supported. Support for the following sensors were mapped: accelerometer, magnetometer, gyroscope, altimeter or barometer, global-positioning-system, and optical pulse sensor (i.e., photoplethysmograph). The search was conducted between May 15th and July 1st, 2017. The included data gives an overview of most in-scope wearables released before July 2017 and allows researchers to conduct additional analysis not performed in the related article. Further insights can be achieved by complementing this list with wearable models released after July 2017.

Monitoring and Managing Lifestyle Behaviors Using Wearable Activity Trackers: Mixed Methods Study of Views From the Huntington Disease Community

Background

There are early indications that lifestyle behaviors, specifically physical activity and sleep, may be associated with the onset and progression of Huntington disease (HD). Wearable activity trackers offer an exciting opportunity to collect long-term activity data to further investigate the role of lifestyle, physical activity, and sleep in disease modification. Given how wearable devices rely on user acceptance and long-term adoption, it is important to understand users’ perspectives on how acceptable any device might be and how users might engage over the longer term.

Objective

This study aimed to explore the perceptions, motivators, and potential barriers relating to the adoption of wearable activity trackers by people with HD for monitoring and managing their lifestyle and sleep. This information intended to guide the selection of wearable activity trackers for use in a longitudinal observational clinical study.

Methods

We conducted a mixed methods study; this allowed us to draw on the potential strengths of both quantitative and qualitative methods. Opportunistic participant recruitment occurred at 4 Huntington’s Disease Association meetings, including 1 international meeting and 3 United Kingdom–based regional meetings. Individuals with HD, their family members, and carers were invited to complete a user acceptance questionnaire and participate in a focus group discussion. The questionnaire consisted of 35 items across 8 domains using a 0 to 4 Likert scale, along with some additional demographic questions. Average questionnaire responses were recorded as positive (score>2.5), negative (score<1.5), or neutral (score between 1.5 and 2.5) opinions for each domain. Differences owing to demographics were explored using the Kruskal-Wallis and Wilcoxon rank sum tests. Focus group discussions (conducted in English) were driven by a topic guide, a vignette scenario, and an item ranking exercise. The discussions were audio recorded and then analyzed using thematic analysis.

Results

A total of 105 completed questionnaires were analyzed (47 people with HD and 58 family members or carers). All sections of the questionnaire produced median scores >2.5, indicating a tendency toward positive opinions on wearable activity trackers, such as the devices being advantageous, easy and enjoyable to use, and compatible with lifestyle and users being able to understand the information from trackers and willing to wear them. People with HD reported a more positive attitude toward wearable activity trackers than their family members or caregivers (P=.02). A total of 15 participants participated in 3 focus groups. Device compatibility and accuracy, data security, impact on relationships, and the ability to monitor and self-manage lifestyle behaviors have emerged as important considerations in device use and user preferences.

Conclusions

Although wearable activity trackers were broadly recognized as acceptable for both monitoring and management, various aspects of device design and functionality must be considered to promote acceptance in this clinical cohort.

Menopause: a cardiometabolic transition

Menopause is often a turning point for women's health worldwide. Increasing knowledge from experimental data and clinical studies indicates that cardiometabolic changes can manifest at the menopausal transition, superimposing the effect of ageing onto the risk of cardiovascular disease. The menopausal transition is associated with an increase in fat mass (predominantly in the truncal region), an increase in insulin resistance, dyslipidaemia, and endothelial dysfunction. Exposure to endogenous oestrogen during the reproductive years provides women with protection against cardiovascular disease, which is lost around 10 years after the onset of menopause. In particular, women with vasomotor symptoms during menopause seem to have an unfavourable cardiometabolic profile. Early management of the traditional risk factors of cardiovascular disease (ie, hypertension, obesity, diabetes, dyslipidaemia, and smoking) is essential; however, it is important to recognise in the reproductive history the female-specific conditions (ie, gestational hypertension or diabetes, premature ovarian insufficiency, some gynaecological diseases such as functional hypothalamic amenorrhoea, and probably others) that could enhance the risk of cardiovascular disease during and after the menopausal transition. In this Review, the first of a Series of two papers, we provide an overview of the literature for understanding cardiometabolic changes and the management of women at midlife (40-65 years) who are at higher risk, focusing on the identification of factors that can predict the occurrence of cardiovascular disease. We also summarise evidence about preventive non-hormonal strategies in the context of cardiometabolic health.

Multimode optical fiber specklegram smart bed sensor array

SIGNIFICANCE

Monitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes. We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially.

AIM

Proof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed.

APPROACH

Seven MMFs are attached to the upper surface of a mattress such that they cross in a 3  ×  4 array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A 3  ×  4 image is formed by comparing the signal at each crossing point between two fibers.

RESULTS

The MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing.

CONCLUSIONS

The sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.

Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy (the NeoFit Trial): protocol for a national, multicenter single-arm trial

BACKGROUND

Breast cancer (BC) has particular characteristics in young women, with diagnosis at more advanced stages, a poorer prognosis and highly aggressive tumors. In NeoFit, we will use an activity tracker to identify and describe various digital profiles (heart rate, physical activity, and sleep patterns) in women below the age of 45 years on neoadjuvant chemotherapy for BC.

METHODS

NeoFit is a prospective, national, multicenter, single-arm open-label study. It will include 300 women below the age of 45 years treated with neoadjuvant chemotherapy for BC. Participants will be asked to wear a Withing Steel HR activity tracker round the clock for 12 months. The principal assessments will be performed at baseline, at the end of neoadjuvant chemotherapy and at 12 months. We will evaluate clinical parameters, such as toxicity and the efficacy of chemotherapy, together with quality of life, fatigue, and parameters relating to lifestyle and physical activity. The women will complete REDCap form questionnaires via a secure internet link.

DISCUSSION

In this study, the use of an activity tracker will enable us to visualize changes in the lifestyle of young women on neoadjuvant chemotherapy for BC, over the course of a one-year period. This exploratory study will provide crucial insight into the digital phenotypes of young BC patients on neoadjuvant chemotherapy and the relationship between these phenotypes and the toxicity and efficacy of treatment. This trial will pave the way for interventional studies involving sleep and physical activity interventions.

TRIAL REGISTRATION

Clinicaltrials.gov identifier: NCT05011721 . Registration date: 18/08/2021.

Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.

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.

An Energy-Autonomous Smart Shirt Employing Wearable Sensors for Users’ Safety and Protection in Hazardous Workplaces

Wearable devices represent a versatile technology in the IoT paradigm, enabling non-invasive and accurate data collection directly from the human body. This paper describes the development of a smart shirt to monitor working conditions in particularly dangerous workplaces. The wearable device integrates a wide set of sensors to locally acquire the user’s vital signs (e.g., heart rate, blood oxygenation, and temperature) and environmental parameters (e.g., the concentration of dangerous gas species and oxygen level). Electrochemical gas-monitoring modules were designed and integrated into the garment for acquiring the concentrations of CO, O2, CH2O, and H2S. The acquired data are wirelessly sent to a cloud platform (IBM Cloud), where they are displayed, processed, and stored. A mobile application was deployed to gather data from the wearable devices and forward them toward the cloud application, enabling the system to operate in areas where a WiFi hotspot is not available. Additionally, the smart shirt comprises a multisource harvesting section to scavenge energy from light, body heat, and limb movements. Indeed, the wearable device integrates several harvesters (thin-film solar panels, thermoelectric generators (TEGs), and piezoelectric transducers), a low-power conditioning section, and a 380 mAh LiPo battery to accumulate the recovered charge. Field tests indicated that the harvesting section could provide up to 216 mW mean power, fully covering the power requirements (P¯ = 1.86 mW) of the sensing, processing, and communication sections in all considered conditions (3.54 mW in the worst-case scenario). However, the 380 mAh LiPo battery guarantees about a 16-day lifetime in the complete absence of energy contributions from the harvesting section.

Exploring the factors influencing adoption of health-care wearables among generation Z consumers in India

Purpose

The purpose of this study is to identify the major factors influencing the adoption of health-care wearables in generation Z (Gen Z) customers in India. A conceptual framework using push pull and mooring (PPM) adoption theory was developed.

Design/methodology/approach

Data was collected from 208 Gen Z customers based on 5 constructs related to the adoption of health-care wearables. Confirmatory factor analysis and structural equation modelling was used to analyse the responses. The mediation paths were analysed using bootstrapping method and examination of the standardized direct and indirect effects in the model.

Findings

The study results indicated that the antecedent factors consisted of push (real-time health information availability), pull (normative environment) and mooring (decision self-efficacy) factors. The mooring factor (MOOR) was related to the push factor but not the pull factor. The MOOR, in turn, was related to the switching intention of Gen Z customers for health wearables adoption.

Research limitations/implications

The research study extended the literature related to the PPM theory in the context of the adoption of health wearables among Gen Z customers in India.

Practical implications

The study outcome would enable managers working in health wearable organizations to understand consumer behaviour towards health wearables.

Social implications

The use of health wearables among Gen Z individuals would lead to future generations adopting a healthy lifestyle resulting in an effective workforce and better economy.

Originality/value

This was one of the few studies which have explored the PPM theory to explore the factors for the adoption of health wearables among Gen Z customers in India.

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

Mobile health in preventive cardiology: current status and future perspective

PURPOSE OF REVIEW

Despite cutting edge acute interventions and growing preventive strategies supported by robust clinical trials, cardiovascular disease (CVD) has stubbornly persisted as a leading cause of death in the United States and globally. The American Heart Association recognizes mobile health technologies (mHealth) as an emerging strategy in the mitigation of CVD risk factors, with significant potential for improving population health. The purpose of this review is to highlight and summarize the latest available literature on mHealth applications and provide perspective on future directions and barriers to implementation.

RECENT FINDINGS

While available randomized controlled trials and systematic reviews tend to support efficacy of mHealth, published literature includes heterogenous approaches to similar problems with inconsistent results. Some of the strongest recent evidence has been focused on the use of wearables in arrhythmia detection. Systematic reviews of mHealth approaches demonstrate benefit when applied to risk factor modification in diabetes, cigarette smoking cessation, and physical activity/weight loss, while also showing promise in multi risk factor modification via cardiac rehabilitation.

SUMMARY

Evidence supports efficacy of mHealth in a variety of applications for CVD prevention and management, but continued work is needed for further validation and scaling. Future directions will focus on platform optimization, data and sensor consolidation, and clinical workflow integration. Barriers include application heterogeneity, lack of reimbursement structures, and inequitable access to technology. Policies to promote access to technology will be critical to evidence-based mHealth technologies reaching diverse populations and advancing health equity.

Transdisciplinary Research in Energetics and Cancer early career investigator training program: first year results

Energy imbalance increases cancer burden by increasing cancer risk and mortality. Training early career investigators on conducting impactful energy balance and cancer research is needed. We developed a Transdisciplinary Research in Energetics and Cancer (TREC) Training Program for early career investigators. This analysis examined program satisfaction, knowledge gained, publications, and awards among Year 1 participants (i.e., fellows). The program consists of an in-person course, followed by 1 year of mentorship. Faculty and fellows completed precourse and postcourse surveys. Following the mentorship period, we surveyed fellows for TREC-related research productivity, including publications and grant funding attributed to the program. Twenty fellows were accepted into the program: 3 basic, 7 clinical, and 10 population scientists. Sixteen fellows were junior faculty and four were postdoctoral fellows. The course included ~50 lectures, small group sessions, and faculty-fellow sessions. 96.7% of attendees rated the course in the highest categories of "good/very good." Knowledge significantly improved in 37 of 39 research competencies (94.8%). In the 18 months following the course, fellows published 25 manuscripts, with 3 published in journals with impact factor ≥10. Nineteen grants were funded to TREC fellows (i.e., 7 National Institutes of Health awards, 2 American Cancer Society [ACS] awards, and 10 foundation/pilot awards), and 7 fellows received career promotions. The program's impact will be defined by the degree to which TREC fellows produce discoveries that could improve the health of populations at risk for and/or surviving cancer. Upon the conclusion of our fifth year in 2021, we will publicly disseminate the program material.

Wearable Monitors Facilitate Exercise in Adult and Pediatric Stem Cell Transplant

Hematopoietic stem cell transplant (HSCT) is a potentially curative treatment for hematopoietic malignancies, complicated by decreased performance status and quality of life. Exercise therapy improves outcomes in HSCT, but several barriers have prevented exercise from becoming routine clinical practice. Based on existing data that wearable technologies facilitate exercise participation in other sedentary and chronic illness populations, we propose the novel hypothesis that wearable technologies are a valuable tool in transcending barriers and developing exercise therapy programs for HSCT patients.

Process evaluation of the Sophia Step Study- a primary care based three-armed randomized controlled trial using self-monitoring of steps with and without counseling in prediabetes and type 2 diabetes

Background

Describing implementation features of an intervention is required to compare interventions and to inform policy and best practice. The aim of this study was to conduct a process evaluation of the first 12 months of the Sophia Step Study: a primary care based RCT evaluating a multicomponent (self-monitoring of daily steps plus counseling) and a single component (self-monitoring of steps only) physical activity intervention to standard care on cardiometabolic health.

Methods

The evaluation was guided by the Medical Research Council Guidance for complex interventions. To describe the implementation communication with the health professionals implementing the interventions, attendance records and tracking of days with self-monitored pedometer-determined steps were used. Change in physical activity behaviour was measured at baseline, 6 and 12 months as daily steps by accelerometry.

Results

During April 2013 to January 2018 188 participants were randomized and intervened directly after inclusion. Response rate was 49% and drop out was 10%. A majority, 78%, had type 2 diabetes and 22% were diagnosed with prediabetes. Mean [Standard deviation (SD)] body mass index was 30.4 (4.4) kg/m² and steps per day was 6566 (3086). The interventions were delivered as intended with minor deviation from the protocol and dose received was satisfying for both the multicomponent and single component group. The mean [95% Confidence Interval (CI)] change in daily steps from baseline to 6 months was 941(227, 1655) steps/day for the multicomponent intervention group, 990 (145, 1836) step/day for the single component group and − 506 (− 1118, 107) for the control group. The mean (95% CI) change in daily steps from baseline to 12 months was 31(− 507, 570) steps/day for the multicomponent intervention group, 144 (− 566, 853) step/day for the single component group and − 890 (− 1485, − 294) for the control group. There was a large individual variation in daily steps at baseline as well as in step change in all three groups.

Conclusions

Applying self-monitoring of steps is a feasible method to implement as support for physical activity in the primary care setting both with and without counseling support.

Trial registration

ClinicalTrials.gov, NCT02374788. Registered 2 March 2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11222-9.

Comparing Methods to Identify Wear-Time Intervals for Physical Activity With the Fitbit Charge 2

There is no established method for processing data from commercially available physical activity trackers. This study aims to develop a standardized approach to defining valid wear time for use in future interventions and analyses. Sixteen African American women (mean age = 62.1 years and mean body mass index = 35.5 kg/m2) wore the Fitbit Charge 2 for 20 days. Method 1 defined a valid day as ≥10-hr wear time with heart rate data. Method 2 removed minutes without heart rate data, minutes with heart rate ≤ mean - 2 SDs below mean and ≤2 steps, and nighttime. Linear regression modeled steps per day per week change. Using Method 1 (n = 292 person-days), participants had 20.5 (SD = 4.3) hr wear time per day compared with 16.3 (SD = 2.2) hr using Method 2 (n = 282) (p < .0001). With Method 1, participants took 7,436 (SD = 3,543) steps per day compared with 7,298 (SD = 3,501) steps per day with Method 2 (p = .64). The proposed algorithm represents a novel approach to standardizing data generated by physical activity trackers. Future studies are needed to improve the accuracy of physical activity data sets.

Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology

The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can help address these challenges. New methods of collecting 24-h dietary recalls and recording diet could enable larger samples and more repeated measures to increase statistical power and measurement precision. In addition, use of machine learning to automatically classify pictures of food could become a useful complimentary method to help improve precision and validity of dietary measurements. Diet is complex due to thousands of different foods that are consumed in varying proportions, fluctuating quantities over time, and differing combinations. Current dietary pattern methods may not integrate sufficient dietary variation, and most traditional modeling approaches have limited incorporation of interactions and nonlinearity. Machine learning could help better model diet as a complex exposure with nonadditive and nonlinear associations. Last, novel big data sources could help avoid unmeasured confounding by offering more covariates, including both omics and features derived from unstructured data with machine learning methods. These opportunities notwithstanding, application of big data and machine learning must be approached cautiously to ensure quality of dietary measurements, avoid overfitting, and confirm accurate interpretations. Greater use of machine learning and big data would also require substantial investments in training, collaborations, and computing infrastructure. Overall, we propose that judicious application of big data and machine learning in nutrition science could offer new means of dietary measurement, more tools to model the complexity of diet and its relations with diseases, and additional potential ways of addressing confounding.

A Scoping Review on College Student Physical Activity: How Do Researchers Measure Activity and Examine Inequities?

BACKGROUND

The purpose of this scoping review was to critically examine the design and quality of contemporary research involving college student physical activity participation, focusing on physical activity measurement, assessment of sociodemographic characteristics, and examination of inequities based on sociodemographic characteristics.

METHODS

Systematic searches were conducted in 4 electronic databases.

RESULTS

From 28,951 sources screened, data were extracted from 488 that met the inclusion criteria. The majority of the studies were cross-sectional in design (91.4%) and employed convenience sampling methods (83.0%). Based on the subsample of studies that reported the percentage of students meeting aerobic (n = 158; equivalent of 150 min/wk of moderate physical activity) and muscle-strengthening activity recommendations (n = 8; ≥2 times/wk), 58.7% and 47.8% of students met aerobic and muscle-strengthening recommendations, respectively. With the exception of age and sex, sociodemographic characteristics were rarely assessed, and inequities based upon them were even more scarcely examined-with no apparent increase in reporting over the past decade.

CONCLUSIONS

College student physical activity levels remain concerningly low. The generalizability of findings from the contemporary literature is limited due to study design, and acknowledgement of the influence that sociodemographic characteristics have on physical activity has largely been overlooked. Recommendations for future research directions and practices are provided.

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Usability and Acceptance of Wearable Biosensors in Forensic Psychiatry: Cross-sectional Questionnaire Study

Background

The use of wearable biosensor devices for monitoring and coaching in forensic psychiatric settings yields high expectations for improved self-regulation of emotions and behavior in clients and staff members. More so, if clients have mild intellectual disabilities (IQ 50-85), they might benefit from these biosensors as they are easy to use in everyday life, which ensures that clients can practice with the devices in multiple stress and arousal-inducing situations. However, research on (continuous) use and acceptance of biosensors in forensic psychiatry for clients with mild intellectual disabilities and their caretakers is scarce. Although wearable biosensors show promise for health care, recent research showed that the acceptance and continuous use of wearable devices in consumers is not as was anticipated, probably due to low expectations.

Objective

The main goal of this study was to investigate the associations between and determinants of the expectation of usability, the actual experienced usability, and the intention for continuous use of biosensors.

Methods

A total of 77 participants (31 forensic clients with mild intellectual disabilities and 46 forensic staff members) participated in a 1-week trial. Preceding the study, we selected 4 devices thought to benefit the participants in domains of self-regulation, physical health, or sleep. Qualitative and quantitative questionnaires were used that explored the determinants of usability, acceptance, and continuous use of biosensors. Questionnaires consisted of the System Usability Scale, the Technology Acceptance Model questionnaire, and the extended expectation confirmation model questionnaire.

Results

Only the experienced usability of the devices was associated with intended continuous use. Forensic clients scored higher on acceptance and intention for continuous use than staff members. Moderate associations were found between usability with acceptance and continuous use. Staff members showed stronger associations between usability and acceptance (r=.80, P<.001) and usability and continuous use (r=.79, P<.001) than clients, who showed more moderate correlations between usability and acceptance (r=.46, P=.01) and usability and continuous use (r=.52, P=.003). The qualitative questionnaires in general indicated that the devices were easy to use and gave clear information.

Conclusions

Contrary to expectations, it was the actual perceived usability of wearing a biosensor that was associated with continuous use and to a much lesser extent the expectancy of usability. Clients scored higher on acceptance and intention for continuous use, but associations between usability and both acceptance and continuous use were markedly stronger in staff members. This study provides clear directions on how to further investigate these associations. For example, whether this is a true effect or due to a social desirability bias in the client group must be investigated. Clients with mild intellectual disabilities might benefit from the ease of use of these devices and their continuing monitoring and coaching apps. For these clients, it is especially important to develop easy-to-use biosensors with a minimum requirement on cognitive capacity to increase usability, acceptance, and continuous use.

Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study

BACKGROUND

Consumer-based physical activity trackers have increased in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. The challenges include the large heterogeneity between activity tracker models in terms of available data types, the accuracy of recorded data, and how this data can be shared between different providers and third-party systems.

OBJECTIVE

The aim of this study is to develop a system to record data on physical activity from different providers of consumer-based activity trackers and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown.

METHODS

We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from early 2019 and onward. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly changes in the number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure between 2019 and 2020 using bar plots and two-sided paired sample t tests and Wilcoxon signed-rank tests.

RESULTS

Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction in steps and activity energy expenditure was temporary, and the following monthly comparisons showed no significant change between 2019 and 2020. A small significant increase in moderate-to-vigorous physical activity was observed for several monthly comparisons after the lockdown period and when comparing March-December 2019 with March-December 2020.

CONCLUSIONS

mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine changes in physical activity levels during the COVID-19 period.

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.

Effectiveness of Minimal Contact Interventions: An RCT

INTRODUCTION

Around 23% of adults worldwide are insufficiently active. Wearable devices paired with virtual coaching software could increase physical activity. The effectiveness of 3 minimal contact interventions (paper-based physical activity diaries, activity trackers, and activity trackers coupled with virtual coaching) in increasing physical activity energy expenditure and cardiorespiratory fitness were compared over 12 weeks among inactive adults.

METHODS

This was an open label, parallel-group RCT. Inactive adults (aged ≥18 years, N=488) were randomized to no intervention (Control; n=121), paper-based diary (Diary; n=124), activity tracker (Activity Band; n=122), or activity tracker plus virtual coaching (Activity Band PLUS; n=121) groups. Coprimary outcomes included 12-week changes in physical activity energy expenditure and fitness (May 2012-January 2014). Analyses were conducted in 2019-2020.

RESULTS

There were no differences between groups overall (physical activity energy expenditure: p=0.114, fitness: p=0.417). However, there was a greater increase in physical activity energy expenditure (4.21 kJ/kg/day, 95% CI=0.42, 8.00) in the Activity Band PLUS group than in the Diary group. There were also greater decreases in BMI and body fat percentage in the Activity Band PLUS group than in the Control group (BMI= -0.24 kg/m2, 95% CI= -0.45, -0.03; body fat= -0.48%, 95% CI= -0.88, -0.08) and in theActivity Band PLUS group than in the Diary group (BMI= -0.30 kg/m2, 95% CI= -0.50, -0.09; body fat= -0.57%, 95% CI= -0.97, -0.17).

CONCLUSIONS

Coupling activity trackers with virtual coaching may facilitate increases in physical activity energy expenditure compared with a traditional paper‒based physical activity diary intervention and improve some secondary outcomes compared with a traditional paper‒based physical activity diary intervention or no intervention.

TRIAL REGISTRATION

This study is registered at www.clinicaltrials.gov ISRCTN31844443.

NFC-Based Wearable Optoelectronics Working with Smartphone Application for Untact Healthcare

With growing interest in healthcare, wearable healthcare devices have been developed and researched. In particular, near-field communication (NFC) based wearable devices have been actively studied for device miniaturization. Herein, this article proposes a low-cost and convenient healthcare system, which can monitor heart rate and temperature using a wireless/battery-free sensor and the customized smartphone application. The authors designed and fabricated a customized healthcare device based on the NFC system, and developed a smartphone application for real-time data acquisition and processing. In order to achieve compact size without performance degradation, a dual-layered layout is applied to the device. The authors demonstrate that the device can operate as attached on various body sites such as wrist, fingertip, temple, and neck due to outstanding flexibility of device and adhesive strength between the device and the skin. In addition, the data processing flow and processing result are presented for offering heart rate and skin temperature. Therefore, this work provides an affordable and practical pathway for the popularization of wireless wearable healthcare system. Moreover, the proposed platform can easily delivery the measured health information to experts for contactless/personal health consultation.

Acceptability and Effects of Commercially Available Activity Trackers for Chronic Pain Management Among Older African American Adults

BACKGROUND

Wearable activity trackers may facilitate walking for chronic pain management.

OBJECTIVE

We assessed the acceptability of a commercially available tracker and three alternative modes of reporting daily steps among older adults in a low-income, urban community. We examined whether using the tracker (Fitbit ZipTM) was associated with improvements in functioning and activity.

DESIGN

Randomized controlled pilot and feasibility trial.

SUBJECTS

Fifty-one African American adults in Detroit, Michigan, aged 60 to 85 years, with chronic musculoskeletal pain (28 in the intervention group, 23 controls).

METHODS

Participants completed telephone surveys at baseline and eight weeks. Intervention participants wore trackers for six weeks, alternately reporting daily step counts via text messages, automated telephone calls, and syncing (two weeks each). We used multimethods to assess satisfaction with trackers and reporting modalities. Adherence was indicated by the proportion of expected days on which valid step counts were reported. We assessed changes in pain interference, physical function, social participation, walking frequency, and walking duration.

RESULTS

More than 90% of participants rated trackers as easy to use, but some had technical or dexterity-related difficulties. Text reporting yielded 79% reporting adherence vs 69% each for automated calls and syncing. Intervention participants did not show greater improvement in functioning or walking than controls.

CONCLUSIONS

With appropriate support, wearable activity trackers and mHealth reporting for chronic pain self-care are feasible for use by vulnerable older adults. Future research should test whether the effects of trackers on pain-related outcomes can be enhanced by incorporating behavior change strategies and training in evidence-based cognitive-behavioral techniques.

Evaluation framework for selecting wearable activity monitors for research

BACKGROUND

Wearable devices that support activity tracking and other measurements hold great potential to increase awareness of health behaviors and support the management of chronic health conditions. There is a scarcity of guidance for researchers of all disciplines when planning new studies to evaluate and select technologies appropriate for study purpose, population, and overall context. The aim of this study was to develop and test an evaluation framework to rapidly and systematically evaluate and select consumer-grade wearable devices that serve individual study needs in preparation for evaluations with target populations.

METHODS

The wearable evaluation framework was defined based on published literature and past research experiences of the research team. We tested the framework with example case studies to select devices for two different research projects focused on aging-in-place and gestational diabetes. We show how knowledge of target population and research goals help prioritize application of the criteria to inform device selection and how project requirements inform sequence of criteria application.

RESULTS

The framework for wearable device evaluation includes 27 distinct evaluation criteria: 12 for everyday use by users, 6 on device functionality, and 9 on infrastructure for developing the research infrastructure required to obtain the data. We evaluated 10 devices from four vendors. After prioritizing the framework criteria based on the two example case studies, we selected the Withings Steele HR, Garmin Vivosmart HR+ and Garmin Forerunner 35 for further evaluation through user studies with the target populations.

CONCLUSIONS

The aim of this paper was to develop and test a framework for researchers to rapidly evaluate suitability of consumer grade wearable devices for specific research projects. The use of this evaluation framework is not intended to identify a definitive single best device, but to systematically narrow the field of potential device candidates for testing with target study populations. Future work will include application of the framework within different research projects for further refinement.

Using Wearable Devices to Monitor Physical Activity in Patients Undergoing Aortic Valve Replacement: Protocol for a Prospective Observational Study

Background

In last few decades, several tools have been developed to measure physical function objectively; however, their use has not been well established in clinical practice.

Objective

This study aims to describe the preoperative physical function and to assess and compare 6-month postoperative changes in the physical function of patients undergoing treatment for aortic stenosis with either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). The study also aims to evaluate the feasibility of wearable devices in assessing physical function in such patients.

Methods

This is a prospective observational study. The enrollment will be conducted 1 month before patients’ SAVR/TAVR. Patients will be provided with the wearable device at baseline (activity tracker device, Garmin vívoactive 3). They will be trained in the use of the device, and they will be requested to wear it on the wrist of their preferred hand until 12 months after SAVR/TAVR. After baseline assessment, they will undergo 4 follow-up assessments at 1, 3, 6, and 12 months after SAVR/TAVR. At baseline and each follow-up, they will undergo a set of standard and validated tests to assess physical function, health-related quality of life, and sleep quality.

Results

The ethics committee of Vicenza in Veneto Region in Italy approved the study (Protocol No. 943; January 4, 2019). As of October 2020, the enrollment of participants is ongoing.

Conclusions

The use of the wearable devices for real-time monitoring of physical activity of patients undergoing aortic valve replacement is a promising opportunity for improving the clinical management and consequently, the health outcomes of such patients.

Trial Registration

Clinicaltrials.gov NCT03843320; https://tinyurl.com/yyareu5y

International Registered Report Identifier (IRRID)

DERR1-10.2196/20072

The value of WeChat application in chronic diseases management in China

The prevalence of chronic diseases in China has increased rapidly in recent decades. Although the management rate of chronic diseases has improved, there is still no unified and effective management measure for chronic diseases at present. This highlights the importance of effectively managing chronic diseases. With the development of e-health, the ways of getting medical consultation have changed. WeChat is an extremely popular social application in China. It is easy to operate and can offer multiple functions. Many researches have reported the effectiveness of WeChat in chronic diseases management. Based on the status of WeChat application in chronic diseases management and the characteristics of WeChat technology, we firstly focused on the WeChat application on the management of chronic diseases such as hypertension, diabetes, coronary heart disease and cancer. Then we discussed the value of WeChat in chronic diseases management and analyzed the potential reasons. Lastly, we discussed the limitations of present researches. WeChat can be an effective tool for the management of chronic diseases, but the promotion of this mode needs support and efforts from various aspects to eventually realize improving public health.

Harnessing consumer smartphone and wearable sensors for clinical cancer research

As smartphones and consumer wearable devices become more ubiquitous, there is a growing opportunity to capture rich mobile sensor data continuously, passively, and in real-world settings with minimal burden. In the context of cancer, changes in these passively sensed digital biomarkers may reflect meaningful variation in functional status, symptom burden, quality of life, and risk for adverse clinical outcomes. These data could enable real-time remote monitoring of patients between clinical encounters and more proactive, comprehensive, and personalized care. Over the past few years, small studies across a variety of cancer populations support the feasibility and potential clinical value of mobile sensors in oncology. Barriers to implementing mobile sensing in clinical oncology care include the challenges of managing and making sense of continuous sensor data, patient engagement issues, difficulty integrating sensor data into existing electronic health systems and clinical workflows, and ethical and privacy concerns. Multidisciplinary collaboration is needed to develop mobile sensing frameworks that overcome these barriers and that can be implemented at large-scale for remote monitoring of deteriorating health during or after cancer treatment or for promotion and tailoring of lifestyle or symptom management interventions. Leveraging digital technology has the potential to enrich scientific understanding of how cancer and its treatment affect patient lives, to use this understanding to offer more timely and personalized support to patients, and to improve clinical oncology outcomes.

A Short-Term Effect of Wearable Technology-Based Lifestyle Intervention on Body Composition in Stage I-III Postoperative Breast Cancer Survivors

BACKGROUND AND AIM

A healthy body composition can improve the prognosis of breast cancer survivors. The study aimed to describe the body composition profile of breast cancer survivors and find out whether a short-term (3 months) wearable device-based lifestyle intervention had an effect on patients' body weight and body composition.

METHODS

A before-and-after study was conducted on patients with stage I-III postoperative breast cancer, aged 18-70 years. Body composition was analyzed at baseline, and then patients went for a health education program. A wearable activity tracker and a goal of calorie consumption based on each individual's weight were provided to each participant, and they were required to be equipped for 90 days. After 3 months, body composition was analyzed again.

RESULTS

Of 113 patients who completed the study, 65.49% showed a normal body mass index (BMI) at baseline assessment, 71.68% had a body fat percentage of more than 30%, and 41.59% had less skeleton muscle mass. During the intervention, the daily step count was 8,851.28 ± 2,399.31, and 59.21% reached the set goal calorie consumption. After a 3-month intervention, the patients had a significant reduction in body weight, fat mass, BMI, body fat percentage, and visceral fat area, but not in protein mass and skeleton muscle mass. Patients of different age, molecular classification, and therapy benefited from the intervention.

CONCLUSION

Wearable technology with body composition analysis and health education for breast cancer survivors may help reduce weight and improve body composition even in a short time.

CLINICAL TRIAL REGISTRATION

http://www.chictr.org.cn/showproj.aspx?proj=40672, identifier ChiCTR1900024258.

Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity. We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with `healthy' data, and once with `simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.

A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data

Background

Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently.

Objective

This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30).

Methods

Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups.

Results

No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = -4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different.

Conclusion

This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.