CONTACT: a non-randomised feasibility study of bluetooth-enabled wearables for contact tracing in UK care homes during the COVID-19 pandemic.

BACKGROUND: The need for effective non-pharmaceutical infection prevention measures such as contact tracing in pandemics remains in care homes, but traditional approaches to contact tracing are not feasible in care homes. The CONTACT intervention introduces Bluetooth-enabled wearable devices (BLE wearables) as a potential solution for automated contact tracing. Using structured reports and reports triggered by positive COVID-19 cases in homes, we fed contact patterns and trends back to homes to support better-informed infection prevention decisions and reduce blanket application of restrictive measures. This paper reports on the evaluation of feasibility and acceptability of the intervention prior to a planned definitive cluster randomised trial of the CONTACT BLE wearable intervention. METHODS: CONTACT was a non-randomised mixed-method feasibility study over 2 months in four English care homes. Recruitment was via care home research networks, with individual consent. Data collection methods included routine data from the devices, case report forms, qualitative interviews (with staff and residents), field observation of care, and an adapted version of the NoMaD survey instrument to explore implementation using Normalisation Process Theory. Quantitative data were analysed using descriptive statistical methods. Qualitative data were thematically analysed using a framework approach and Normalisation Process Theory. Intervention and study delivery were evaluated against predefined progression criteria. RESULTS: Of 156 eligible residents, 105 agreed to wear a device, with 102 (97%) starting the intervention. Of 225 eligible staff, 82% (n = 178) participated. Device loss and damage were significant: 11% of resident devices were lost or damaged, ~ 50% were replaced. Staff lost fewer devices, just 6%, but less than 10% were replaced. Fob wearables needed more battery changes than card-type devices (15% vs. 0%). Structured and reactive feedback was variably understood by homes but unlikely to be acted on. Researcher support for interpreting reports was valued. Homes found information useful when it confirmed rather than challenged preconceived contact patterns. Staff privacy concerns were a barrier to adoption. Study procedures added to existing work, making participation burdensome. Study participation benefits did not outweigh perceived burden and were amplified by the pandemic context. CONTACT did not meet its quantitative or qualitative progression criteria. CONCLUSION: CONTACT found a large-scale definitive trial of BLE wearables for contact tracing and feedback-informed IPC in care homes unfeasible and unacceptable - at least in the context of shifting COVID-19 pandemic demands. Future research should co-design interventions and studies with care homes, focusing on successful intervention implementation as well as technical effectiveness. TRIAL REGISTRATION: ISRCTN registration: 11204126 registered 17/02/2021.

Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study.

BACKGROUND: Aging is a risk factor for falls, frailty, and disability. The utility of wearables to screen for physical performance and frailty at the population level is an emerging research area. To date, there is a limited number of devices that can measure frailty and physical performance simultaneously. OBJECTIVE: The aim of this study is to evaluate the accuracy and validity of a continuous digital monitoring wearable device incorporating gait mechanics and heart rate recovery measurements for detecting frailty, poor physical performance, and falls risk in older adults at risk of falls. METHODS: This is a substudy of 156 community-dwelling older adults ≥60 years old with falls or near falls in the past 12 months who were recruited for a fall prevention intervention study. Of the original participants, 22 participants agreed to wear wearables on their ankles. An interview questionnaire involving demographics, cognition, frailty (FRAIL), and physical function questions as well as the Falls Risk for Older People in the Community (FROP-Com) was administered. Physical performance comprised gait speed, timed up and go (TUG), and the Short Physical Performance Battery (SPPB) test. A gait analyzer was used to measure gait mechanics and steps (FRAIL-functional: fatigue, resistance, and aerobic), and a heart rate analyzer was used to measure heart rate recovery (FRAIL-nonfunctional: weight loss and chronic illness). RESULTS: The participants' mean age was 74.6 years. Of the 22 participants, 9 (41%) were robust, 10 (46%) were prefrail, and 3 (14%) were frail. In addition, 8 of 22 (36%) had at least one fall in the past year. Participants had a mean gait speed of 0.8 m/s, a mean SPPB score of 8.9, and mean TUG time of 13.8 seconds. The sensitivity, specificity, and area under the curve (AUC) for the gait analyzer against the functional domains were 1.00, 0.84, and 0.92, respectively, for SPPB (balance and gait); 0.38, 0.89, and 0.64, respectively, for FRAIL-functional; 0.45, 0.91, and 0.68, respectively, for FROP-Com; 0.60, 1.00, and 0.80, respectively, for gait speed; and 1.00, 0.94, and 0.97, respectively, for TUG. The heart rate analyzer demonstrated superior validity for the nonfunctional components of frailty, with a sensitivity of 1.00, specificity of 0.73, and AUC of 0.83. CONCLUSIONS: Agreement between the gait and heart rate analyzers and the functional components of the FRAIL scale, gait speed, and FROP-Com was significant. In addition, there was significant agreement between the heart rate analyzer and the nonfunctional components of the FRAIL scale. The gait and heart rate analyzers could be used in a screening test for frailty and falls in community-dwelling older adults but require further improvement and validation at the population level.

Pilot study for the development of an automatically generated and wearable-based early warning system for the detection of deterioration of hospitalized patients of an acute care hospital.

BACKGROUND: Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. METHODS: We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland-Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. RESULTS: Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high. CONCLUSIONS: In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods. TRIAL REGISTRATION: January 26, 2023, NCT05699967.

Enhancing cardiac postoperative care: a smartwatch-integrated remote telemonitoring platform for health screening with ECG analysis.

Aims: The integration of smartwatches into postoperative cardiac care transforms patient monitoring, systematically tracking vital signs and delivering real-time data to a centralized platform. This study focuses on developing a platform for seamless integration, assessing reliability, and evaluating the impact on post-cardiac surgery. The goal is to establish a robust foundation for understanding the efficacy and dependability of smartwatch-based telemonitoring, enhancing care for this population. Methods and results: A total of 108 cardiac surgery patients were divided into telemonitoring (TLM) and control (CTL) groups. The TLM group utilized smartwatches for continuous monitoring of vital parameters (SpO2, HR, BP, ECG) over 30 ± 3 days. Statistical analyses (Pearson, Intraclass Correlation, Bland-Altman, Tost Test) were employed to compare smartwatch measurements with traditional methods. Significant correlations and concordance were observed, particularly in HR and BP measurements. Challenges were noted in SpO2 measurement. The ECG algorithm exhibited substantial agreement with cardiologists (Kappa: 0.794; p > 0.001), highlighting its reliability. The telemonitoring platform played a crucial role in early detection of clinical changes, including prompt Emergency Department (ED) visits, contributing significantly to preventing outcomes that could lead to mortality, such as asymptomatic Atrioventricular block. Positive patient responses affirmed technological efficacy, especially in identifying cardiac arrhythmias like atrial fibrillation. Conclusion: The integration of smartwatches into remote telemonitoring for postoperative cardiac care demonstrates substantial potential, improving monitoring and early complication detection, thereby enhancing patient outcomes. The FAPO-X Study (Assisted Digital Telemonitoring with Wearables in Patients After Cardiovascular Surgery; NCT05966857) underscores the promising role of telemonitoring in postoperative cardiac care.

The acceptability of using wearable electronic devices to monitor physical activity of patients with Multiple Myeloma undergoing treatment: a systematic review.

Introduction: Multiple myeloma (MM) is diagnosed in 6,000 people in the UK yearly. A performance status measure, based on the patients' reported level of physical activity, is used to assess patients' fitness for treatment. This systematic review aims to explore the current evidence for the acceptability of using wearable devices in patients treated for MM to measure physical activity directly. Methods: Three databases were searched (MEDLINE, EMBASE and CINAHL) up until 7th September 2023. Prospective studies using wearable devices to monitor physical activity in patients on treatment for MM were included. Bias across the studies was assessed using the CASP tool. Results: Nine studies, with 220 patients on treatment for MM, were included. Only two studies had a low risk of bias. Different wearable device brands were used for varying lengths of time and were worn on either the wrist, upper arm, or chest. Adherence, reported in seven studies, ranged from 50% to 90%. Six studies reported an adherence greater than 75%. Although physical activity was also measured in a heterogenous manner, most studies reported reduced physical activity during treatment, associated with a higher symptom burden. Conclusion: Monitoring patients receiving treatment for MM with a wearable device appears acceptable as an objective measure to evaluate physical activity. Due to the heterogeneity of the methods used, the generalisability of the results is limited. Future studies should explore the data collected prospectively and their ability to predict relevant clinical outcomes.

Assessing sleep in primary brain tumor patients using smart wearables and patient-reported data: Feasibility and interim analysis of an observational study.

Background: Sleep-wake disturbances are common and disabling in primary brain tumor (PBT) patients but studies exploring longitudinal data are limited. This study investigates the feasibility and relationship between longitudinal patient-reported outcomes (PROs) and physiologic data collected via smart wearables. Methods: Fifty-four PBT patients ≥ 18 years wore Fitbit smart-wearable devices for 4 weeks, which captured physiologic sleep measures (eg, total sleep time, wake after sleep onset [WASO]). They completed PROs (sleep hygiene index, PROMIS sleep-related impairment [SRI] and Sleep Disturbance [SD], Morningness-Eveningness Questionnaire [MEQ]) at baseline and 4 weeks. Smart wearable use feasibility (enrollment/attrition, data missingness), clinical characteristics, test consistency, PROs severity, and relationships between PROs and physiologic sleep measures were assessed. Results: The majority (72%) wore their Fitbit for the entire study duration with 89% missing < 3 days, no participant withdrawals, and 100% PRO completion. PROMIS SRI/SD and MEQ were all consistent/reliable (Cronbach's alpha 0.74-0.92). Chronotype breakdown showed 39% morning, 56% intermediate, and only 6% evening types. Moderate-severe SD and SRI were reported in 13% and 17% at baseline, and with significant improvement in SD at 4 weeks (P = .014). Fitbit-recorded measures showed a correlation at week 4 between WASO and SD (r = 0.35, P = .009) but not with SRI (r = 0.24, P = .08). Conclusions: Collecting sleep data with Fitbits is feasible, PROs are consistent/reliable, > 10% of participants had SD and SRI that improved with smart wearable use, and SD was associated with WASO. The skewed chronotype distribution, risk and impact of sleep fragmentation mechanisms warrant further investigation. Trial Registration: NCT04 669 574.

Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration.

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487-0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001).

Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects.

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses.

Blended learning in physical education: application and motivation.

Background: The aim of the research was to evaluate outdoor aerobic sport activities (OASA) in the physical education (PE) of university students using wearables and their potential to personalize the learning process and enhance motivation. Methods: In total, 368 university students participated. The OASA structure and the key points of application in PE were described. Descriptive statistics of the training units (n = 3,680) were processed. The students recorded their training data in the Strava app (10 sessions per semester), and the data were shared in the online sport community created on the Strava platform. Motivation was evaluated using a questionnaire. The focus was both on intrinsic motivation and extrinsic "ICT" motivation, based on Strava app features and tools. Results: The most preferred outdoor aerobic sport activities were running (58%), cycling (13%), and walking (16%). The results provided insight into motivation and performance analysis. Students' motivation to participate in OASA was mainly in health concerns, such as staying in shape (94%), staying healthy (90%), and psychological concerns, such as having fun (88%), improving state of mind (88%), or relieving stress (83%). In achievement concerns, the motivation was a personal challenge (72%), while competing with others was ranked lowest (32%). The Strava app was a motivating tool for students to record, monitor, and analyze their individual activities and feel "connectedness" to the online sport community. 70% of students were motivated by the non-competitive character of PE, which gave them a personalized opportunity to train without being compared to others. Discussion: The OASA management, with the use of blended learning methods and the Strava app, uses a motivational approach to create, support, and maintain students' healthy habits of physical activity through PE lessons. The need for students to be motivated to exercise can be confirmed in the analysis of the statistical descriptive parameters of running, cycling, and walking. There was a tendency for students to complete only the minimum required distance/time (not more). On the other hand, students enjoyed the training, and 99% of students confirmed that they would enroll again. That fact underlined the importance of motivating students with an effective learning strategy and giving support and guidance.

Validating the Use of Continuous Glucose Monitors With Nondiabetic Recreational Runners.

PURPOSE: Continuous glucose monitors (CGMs) are becoming increasingly popular among endurance athletes despite unconfirmed accuracy. We assessed the concurrent validity of the FreeStyle Libre 2 worn on 2 different sites at rest, during steady-state running, and postprandial. METHODS: Thirteen nondiabetic, well-trained recreational runners (age = 40 [8] y, maximal aerobic oxygen consumption = 46.1 [6.4] mL·kg-1·min-1) wore a CGM on the upper arm and chest while treadmill running for 30, 60, and 90 minutes at intensities corresponding to 50%, 60%, and 70% of maximal aerobic oxygen consumption, respectively. Glucose was measured by manually scanning CGMs and obtaining a finger-prick capillary blood glucose sample. Mean absolute relative difference, time in range, and continuous glucose Clarke error grid analysis were used to compare paired CGM and blood glucose readings. RESULTS: Across all intensities of steady-state running, we found a mean absolute relative difference of 13.8 (10.9) for the arm and 11.4 (9.0) for the chest. The coefficient of variation exceeded 70%. Approximately 47% of arm and 50% of chest paired glucose measurements had an absolute difference ≤10%. Continuous glucose Clarke error grid analysis indicated 99.8% (arm) and 99.6% (chest) CGM data fell in clinically acceptable zones A and B. Time-in-range analysis showed reduced accuracy at lower glucose levels. However, CGMs accurately detected trends in mean glucose readings over time. CONCLUSIONS: CGMs are not valid for point glucose monitoring but appear to be valid for monitoring glucose trends during steady-state exercise. Accuracy is similar for arm and chest. Further research is needed to determine whether CGMs can detect important events such as hypoglycemia during exercise.

Wearables are a viable digital health tool for older Indigenous adults living remotely in Australia (research).

Objective: Health programs for Indigenous people are most effective, acceptable, and sustainable when Indigenous perspectives are prioritized. Codesign builds on Indigenous people's creativity and propensity to experiment with new technologies and ensures research is designed and implemented in a culturally safe and respectful manner. Limited research has focused on older Indigenous people as partners in digital health. No research has focused on the acceptability and feasibility of older Indigenous people using wearables for heart health monitoring. This study provides insights into the acceptability and feasibility for ≥55-year-old Indigenous people living in remote locations to use wearables (watches and patches) to detect atrial fibrillation (AF) and high blood pressure. Methods: This mixed methods study was codesigned and coimplemented with the local Aboriginal Controlled Health Service in a remote area of New South Wales, Australia. It included active involvement and codesign with the participants. The devices used in this study included a Withings Scan watch and a Biobeat patch. Results: Despite challenging conditions (>36°C) and variable internet connectivity, 11 Indigenous older adults participated in a five-day wearables program in a remote location. Participants indicated that using digital health devices was acceptable and feasible for older Indigenous users. They described high levels of comfort, safety and convenience when using wearables (patches and watches) to detect AF. They were active participants in codesigning the program. Conclusion: Older Indigenous Australians are motivated to use wearable health devices. They are keen to participate in codesign innovative health tech programs to ensure new health technologies are acceptable to Indigenous people and feasible for remote locations.

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies.

BACKGROUND: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor. OBJECTIVE: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs. METHODS: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported. RESULTS: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies. CONCLUSIONS: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

Detection of sedentary time and bouts using consumer-grade wrist-worn devices: a hidden semi-Markov model.

BACKGROUND: Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts. METHODS: We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL. RESULTS: In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes). CONCLUSIONS: STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.

Central Hemodynamic and Thermoregulatory Responses to Food Intake as Potential Biomarkers for Eating Detection: Systematic Review.

BACKGROUND: Diet-related diseases, such as type 2 diabetes, require strict dietary management to slow down disease progression and call for innovative management strategies. Conventional diet monitoring places a significant memory burden on patients, who may not accurately remember details of their meals and thus frequently falls short in preventing disease progression. Recent advances in sensor and computational technologies have sparked interest in developing eating detection platforms. OBJECTIVE: This review investigates central hemodynamic and thermoregulatory responses as potential biomarkers for eating detection. METHODS: We searched peer-reviewed literature indexed in PubMed, Web of Science, and Scopus on June 20, 2022, with no date limits. We also conducted manual searches in the same databases until April 21, 2024. We included English-language papers demonstrating the impact of eating on central hemodynamics and thermoregulation in healthy individuals. To evaluate the overall study quality and assess the risk of bias, we designed a customized tool inspired by the Cochrane assessment framework. This tool has 4 categories: high, medium, low, and very low. A total of 2 independent reviewers conducted title and abstract screening, full-text review, and study quality and risk of bias analysis. In instances of disagreement between the 2 reviewers, a third reviewer served as an adjudicator. RESULTS: Our search retrieved 11,450 studies, and 25 met our inclusion criteria. Among the 25 included studies, 32% (8/25) were classified as high quality, 52% (13/25) as medium quality, and 16% (4/25) as low quality. Furthermore, we found no evidence of publication bias in any of the included studies. A consistent postprandial increase in heart rate, cardiac output, and stroke volume was observed in at least 95% (heart rate: 19/19, cardiac output: 18/19, stroke volume: 11/11) of the studies that investigated these variables' responses to eating. Specifically, cardiac output increased by 9%-100%, stroke volume by 18%-41%, and heart rate by 6%-21% across these studies. These changes were statistically significant (P<.05). In contrast, the 8 studies that investigated postprandial thermoregulatory effects displayed grossly inconsistent results, showing wide variations in response with no clear patterns of change, indicating a high degree of variability among these studies. CONCLUSIONS: Our findings demonstrate that central hemodynamic responses, particularly heart rate, hold promise for wearable-based eating detection, as cardiac output and stroke volume cannot be measured by any currently available noninvasive medical or consumer-grade wearables. TRIAL REGISTRATION: PROSPERO CRD42022360600; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360600.

Better understanding fall risk: AI-based computer vision for contextual gait assessment.

Contemporary research to better understand free-living fall risk assessment in Parkinson's disease (PD) often relies on the use of wearable inertial-based measurement units (IMUs) to quantify useful temporal and spatial gait characteristics (e.g., step time, step length). Although use of IMUs is useful to understand some intrinsic PD fall-risk factors, their use alone is limited as they do not provide information on extrinsic factors (e.g., obstacles). Here, we update on the use of ergonomic wearable video-based eye-tracking glasses coupled with AI-based computer vision methodologies to provide information efficiently and ethically in free-living home-based environments to better understand IMU-based data in a small group of people with PD. The use of video and AI within PD research can be seen as an evolutionary step to improve methods to understand fall risk more comprehensively.

Monetisation and Servitisation of Wearable Device-Derived Lived Experience Data to Establish a Community-Centric Wellbeing Ecosystem.

Existing healthcare systems are struggling to cope with the rapid increase in mental health diseases. Wearable devices such as smartwatches introduce a new opportunity in this regard. The realisation of this opportunity depends on the engagement of the community in sharing their lived experience data. A health data marketplace is introduced in this regard, where individuals can monetise their wearable device-derived lived experience data by selling it to consumers such as researchers, medical practitioners, and artificial intelligence service providers.

Leveraging mHealth Technologies for Public Health.

Traditional public health surveillance efforts are generally based on self-reported data. Although well validated, these methods may nevertheless be subjected to limitations such as biases, delays, and costs or logistical challenges. An alternative is the use of smart technologies (eg, smartphones and smartwatches) to complement self-report indicators. Having embedded sensors that provide zero-effort, passive, and continuous monitoring of health variables, these devices generate data that could be leveraged for cases in which the data are related to the same self-report metric of interest. However, some challenges must be considered when discussing the use of mobile health technologies for public health to ensure digital health equity, privacy, and best practices. This paper provides, through a review of major Canadian surveys and mobile health studies, an overview of research involving mobile data for public health, including a mapping of variables currently collected by public health surveys that could be complemented with self-report, challenges to technology adoption, and considerations on digital health equity, with a specific focus on the Canadian context. Population characteristics from major smart technology brands-Apple, Fitbit, and Samsung-and demographic barriers to the use of technology are provided. We conclude with public health implications and present our view that public health agencies and researchers should leverage mobile health data while being mindful of the current barriers and limitations to device use and access. In this manner, data ecosystems that leverage personal smart devices for public health can be put in place as appropriate, as we move toward a future in which barriers to technology adoption are decreasing.

Remotely Powered Two-Wire Cooperative Sensors for Bioimpedance Imaging Wearables.

Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in functional 2D images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) electrodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the conventional star arrangement. Prior research demonstrated cooperative sensors for measuring bioimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to remove batteries from the sensors and have the central unit supply power over the two-wire bus. Building from our previous research, this paper presents the application of this method to the measurement of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application-specific integrated circuit (ASIC). The initial experimental results reveal that both approaches are feasible, but the ASIC approach offers advantages for medical safety, as well as lower power consumption and a smaller size.

Monitoring postures and motions of hospitalized patients using sensor technology: a scoping review.

BACKGROUND: Sensor technology could provide solutions to monitor postures and motions and to help hospital patients reach their rehabilitation goals with minimal supervision. Synthesized information on device applications and methodology is lacking. OBJECTIVES: The purpose of this scoping review was to provide an overview of device applications and methodological approaches to monitor postures and motions in hospitalized patients using sensor technology. METHODS: A systematic search of Embase, Medline, Web of Science and Google Scholar was completed in February 2023 and updated in March 2024. Included studies described populations of hospitalized adults with short admission periods and interventions that use sensor technology to objectively monitor postures and motions. Study selection was performed by two authors independently of each other. Data extraction and narrative analysis focused on the applications and methodological approaches of included articles using a personalized standard form to extract information on device, measurement and analysis characteristics of included studies and analyse frequencies and usage. RESULTS: A total of 15.032 articles were found and 49 articles met the inclusion criteria. Devices were most often applied in older adults (n = 14), patients awaiting or after surgery (n = 14), and stroke (n = 6). The main goals were gaining insight into patient physical behavioural patterns (n = 19) and investigating physical behaviour in relation to other parameters such as muscle strength or hospital length of stay (n = 18). The studies had heterogeneous study designs and lacked completeness in reporting on device settings, data analysis, and algorithms. Information on device settings, data analysis, and algorithms was poorly reported. CONCLUSIONS: Studies on monitoring postures and motions are heterogeneous in their population, applications and methodological approaches. More uniformity and transparency in methodology and study reporting would improve reproducibility, interpretation and generalization of results. Clear guidelines for reporting and the collection and sharing of raw data would benefit the field by enabling study comparison and reproduction.

In a clinical setting, wearables are currently used to monitor postures and motions in a wide variety of study applications and hospital populations.Measurement of postures and motions in the hospital setting is characterized by methodological heterogeneity. This poses a significant challenge, impacting the interpretation of results and hindering meaningful comparisons between studiesFollowing guidelines for reporting and the collection and sharing of raw data would benefit the field.