Post-partum Maternal Vascular Function Is Reduced in Preeclampsia and Preterm Births: A Pilot Study

Introduction: Women with a history of preeclampsia (PreE) or preterm (PreT) birth are at elevated risk of future hypertension, ischemic heart disease, and stroke. Mechanisms for this increased risk are unknown. Flow-mediated dilation of the brachial artery (FMD) is an established surrogate for cardiovascular risk.Hypothesis: In this pilot study, we hypothesize that maternal vascular dysfunction associated with PreE is reversible, and the extent of recovery is predicated on specific maternal characteristics. Method(s): In this prospective study, subjects were recruited to three groups: PreE with delivery at 27-34 weeks;PreT delivery at 27-34 weeks without preeclampsia;and healthy controls at 39-40 weeks. Vascular function (FMD), nutrition (validated questionnaire), and physical activity (accelerometers) data were collected at 1-2 days post-partum and 3 months. Result(s): Fourteen subjects were enrolled (mean age 32+/-6 years). Systolic blood pressure was higher for PreE subjects (average 131+/-6) compared to controls (109 +/- 9, p=0.004) and PreT (110+/-8, p=0.008) at visit 1. This difference resolved at visit 2. Though non-significant, FMD (mean+/-SE) was higher in controls compared to PreE and PreT groups at visit 1 (7.7%+/-0.8 v. 7.4%+/-0.7 and 6.9%+/-1.0, Figure 1). FMD remains depressed at 3 months, but subject follow-up was impacted by the Covid 19 pandemic. Alternate Healthy Eating Index scores were non-significantly higher in the PreT group than PreE and controls. PreT subjects were less sedentary and more physically active (higher moderate-vigorous physical activity, higher total steps). Conclusion(s): Maternal FMD is reduced immediately post-partum in PreE and PreT births. The PreT group had lower FMD despite better nutrition and physical activity scores. This is a pilot study, and we are not powered for significance. Data from our small cohort support the ability to collect meaningful data in these understudied populations which could inform future studies of long-term cardiovascular risk.

Acceptability and usability of a soft, flexible, wearable device for cough detection in children with cystic fibrosis

Background: Cough is a common symptom in cystic fibrosis (CF), and an increase in cough is an important sign of worsening lung disease and pulmonary exacerbation, the most common cause of hospitalization in people with CF. Objective monitoring of cough could be an important outcome measure for clinical trials, especially in children too young to perform pulmonary function tests. There are no accurate, objective methods of quantifying the frequency, severity, and duration of cough. Devices that have been tested to measure cough are neither highly reliable nor user friendly. We developed a mechano-acoustic sensor (MAS): a 4.8- cm- x 2.8-cm- (1 inch) long, thin, lightweight, stretchable, wireless device that adheres easily and securely to the skin surface and is worn at the base of the neck. The devicewas validated in adults being monitored for COVID- 19. This study evaluated usability and acceptability to children and their parents. Method(s): In Cohort 1, a small, flexible, fully wireless accelerometer-based MASwas applied to the suprasternal notch of children with CF using gentle adhesives. Participants were asked to perform activities that included forced coughs while sitting, lying down, and performing activities such as jumping or jogging and other pharyngolaryngeal activities such as swallowing, speaking, and throat clearing. The sessions were an average of about 30 minutes long. In Cohort 2, participants were asked to test the device for a longer period of wearable time (4-6 hours) in various settings, including outpatient clinics, inpatient rooms, and outside clinic and athome environments. Upon completion, all participants from both cohorts were asked to fill out the Acceptability and Usability Questionnaire, which consisted of six questions ranked on a 4-point Likert scale. Result(s): Cohort 1 included 21 children aged 3 to 18 (mean age 9.25 +/- 4.85), and Cohort 2 included 12 children aged 7 to 18 (mean age 12.15 +/- 4.42). On 31 (94%) questionnaires returned, 35.5% of participants strongly agreed and 61.3% agreed with the statement "I [or my child] like(s) wearing the cough sensor." Similarly, most participants found the cough sensor easy to use (74.2% strongly agreed, 25.8% agreed) and comfortable to wear (64.5% strongly agreed, 29.0% agreed), although they found the adhesive sticker difficult to take off and the device too obvious or large. Conclusion(s): Although qualitative and quantitative acceptability and usability data were overall positive, we have redesigned the cough sensor for comfort and are continuing enrollment. The new sensor, 3.5 x 1.6 x 0.8 cm, is smaller and sits lower on the neck so participants can better conceal it underneath clothing (Figure 1). We are providing universal adhesive remover wipes to all participants. Future work includes long-term monitoring (1-2 weeks) of pulmonary exacerbations using the new devices and further assessing usability and acceptability from participants.(Figure Presented) Figure 1. New cough sensor design with a longer neck and a smaller body, allowing it to be better concealed underneath a shirtCopyright © 2022, European Cystic Fibrosis Society. All rights reserved

Accelerometer-Based Bed Occupancy Detection for Automatic, Non-Invasive Long-Term Cough Monitoring

We present a new machine learning based bed occupancy detection system that uses only the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed occupancy detection is necessary for automatic long-term cough monitoring since the time that the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost-effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture achieved an AUC of 0.94. To demonstrate the application of this bed occupancy detection system to a complete cough monitoring system, the daily cough rates along with the corresponding laboratory indicators of a patient undergoing TB treatment were estimated over a period of 14 days. This provides a preliminary indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring the long-term recovery of patients suffering from respiratory diseases such as TB and COVID-19. © 2013 IEEE.

Challenges of Data Collection for a Fully Remote Clinical Trial with a Multi-Component Outcome: Virtual Elm Pilot Study

Background: Due, partially, to the COVID-19 pandemic, interest in remote clinical trials has grown rapidly. The convenience associated with remote trials, for both researchers and participants, can lead to improved recruitment, retention, and engagement. Advancements in digital technology have led to increased accessibility to remote healthcare and have made possible remote data collection and intervention delivery in clinical trials. However, remote clinical trials are not ''one-sizefits- all'' and present key challenges, particularly, when there are multi-component outcomes, for example, the metabolic syndrome (MetS). Motivated by an ongoing, in-person, national, multi-site clinical trial aimed at the remission of the MetS (ELM trial), the Virtual ELM pilot study assessed the plausibility of remote data collection and delivery of a lifestyle intervention to participants with the MetS. It focused on weight loss after a 3-month treatment, which was used as a surrogate measure for the MetS. Objective(s): To assess the feasibility and remediate challenges of a fully remote data collection and intervention delivery for translation into a large-scale remote clinical trial. Method(s): A treatment-only pilot study was conducted with 10 participants with the MetS. Participants were recruited via self-referral or medical records interrogation. They attended virtual group meetings via Zoom led by trained interventionists every week for three consecutive months to practice mindful exercise and eating. Intervention tools, such as participant selfmonitoring, included daily food intake, mindful habit logs and daily steps. All data collection was completed remotely including weight and components of the MetS (waist circumference, blood pressure, glucose, HDL, and triglycerides). Other outcomes included physical activity, diet, and mindfulness. Remote data collection was conducted using a variety of tools including Snap Surveys (web-based questionnaires), Actigraph/ CentrePoint (accelerometer-based physical activity), Fitabase (weight, steps, and food logs). Accelerometers, blood pressure monitors, Fitbit activity trackers, wireless scales, and waist measuring tapes were mailed to participants, along with instructions on how to use them. Participants visited Quest Diagnostics to complete blood draws. Result(s): There were several challenges such as remote recruitment, outcomes data collection, and intervention delivery. The most distinctive challenges were completion of the accelerometer and blood draw protocols. Despite the challenges, this pilot achieved 100% retention for both baseline and follow-up outcomes assessments and 95% remote session attendance. Thirty percent of the sample achieved remission of MetS and 40% achieved weight loss >=5%. The screening-toenrollment ratio was 2.0. Conclusion(s): The Virtual ELM pilot study showed promising results for the possibility of efficient execution of a remote, large-scale trial. The study helped identify challenges associated with its virtual nature, such as physical measures and physical activity protocol completion, and resourceful delivery of the intervention content. Proactively addressing challenges in the enrollment phase, for example, screening for smartphone technology awareness and refinement in the planning phase, for example, selecting effective data capture tools, is essential for a successful, remote trial.

Use of awake prone positioning in acute COVID-19

Introduction: Awake prone positioning (APP) may reduce ventilation-perfusion mismatch in the context of acute respiratory distress syndrome. The Intensive Care Society recommends its use in COVID-19 to improve oxygenation and reduce risk of progression to invasive mechanical ventilation. This audit project measured the use of APP on an Acute Respiratory Care Unit (ARCU). Method(s): Observations and patient outcomes were recorded for non-intubated patients where a clinical decision had been made to prone. The activPALTM accelerometer was used as an objective measure of APP (prone or lateral-lie positioning). Analysis was performed using STATA v16. Result(s): Between September 2020 and February 2021, 19 individuals with a median age of 68 years were included. 74% were male. In the first 48 hours, 747 person-hours of data were recorded, with 358 person-hours spent in APP. Eight individuals spent at least 50% of their first 48 hours in APP. Lateral lie was better tolerated than full prone positioning, with a median (interquartile range, IQR) of 11.6 (8.0, 20.2) hours spent in lateral lie and median (IQR) of 1.6 (0.5, 8.3) hours spent fully proned in the first 48 hours. Median (interquartile range, IQR) improvement from baseline in respiratory rate/oxygenation (ROX) index at 48 hours was +1.65 (0.90, 1.89). Median (IQR) ROX index at 12 hours for individuals not in APP was 4.80 (3.04, 8.51) and 10.41 (9.09, 11.42) for individuals who were fully proned. Nine individuals were admitted to intensive care, 13 survived to discharge. Conclusion(s): Accelerometry is an objective method to measure time spent in APP and showed that lateral lie was preferred to full prone position in this cohort. Trends suggest possible improvement in ROX, although numbers were small.

COVID-19 induced changes in physical activity patterns, screen time and sleep among Swedish adolescents - a cohort study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic had a huge impact on daily life, even in countries such as Sweden where the restrictions were relatively mild. This paper assesses the effects of the COVID-19 pandemic restrictions on physical activity (PA) patterns, screen time, and sleep among Swedish adolescents. The exposures explored include gender, parental education, anthropometrics, and cardiovascular fitness (CVF). METHODS: Cohort data were collected from September 26th to December 6th, 2019, and from April 12th to June 9th, 2021. Participants were 13-14 years-old (7th graders) at baseline with 585 participating at both baseline and follow-up. At both baseline and follow-up PA and sedentary time were measured with accelerometers, and sleep and screen time with questionnaires. The exposure variables (gender, parental education, anthropometrics and CVF) were collected at baseline. Multilevel linear regression analyses were performed. RESULTS: Moderate-to-vigorous-physical activity (MVPA) remained unchanged while light physical activity (LiPA) decreased and sedentary time increased. Sleep duration decreased and screen time increased. Girls, adolescents with overweight/obesity (BMI and percent body fat), and those with lower CVF at baseline had less favourable changes in PA patterns, sleep and screen time. CONCLUSIONS: Although no significant (α = 0.05) changes were seen in MVPA, both LiPA and sedentary time as well as sleep and screen time changed in unfavourable ways. More intense activities are often organised and seem to have withstood the pandemic, while less intense activities decreased. Some groups were more vulnerable and will need directed intervention in the post-pandemic period as well as when future pandemics hit.

Validation of Oura ring energy expenditure and steps in laboratory and free-living.

BACKGROUND: Commercial activity trackers are increasingly used in research and compared with research-based accelerometers are often less intrusive, cheaper, with improved storage and battery capacity, although typically less validated. The present study aimed to determine the validity of Oura Ring step-count and energy expenditure (EE) in both laboratory and free-living. METHODS: Oura Ring EE was compared against indirect calorimetry in the laboratory, followed by a 14-day free-living study with 32 participants wearing an Oura Ring and reference monitors (three accelerometers positioned at hip, thigh, and wrist, and pedometer) to evaluate Oura EE variables and step count. RESULTS: Strong correlations were shown for Oura versus indirect calorimetry in the laboratory (r = 0.93), and versus reference monitors for all variables in free-living (r ≥ 0.76). Significant (p < 0.05) mean differences for Oura versus reference methods were found for laboratory measured sitting (- 0.12 ± 0.28 MET), standing (- 0.27 ± 0.33 MET), fast walk (- 0.82 ± 1.92 MET) and very fast run (- 3.49 ± 3.94 MET), and for free-living step-count (2124 ± 4256 steps) and EE variables (MET: - 0.34-0.26; TEE: 362-494 kcal; AEE: - 487-259 kcal). In the laboratory, Oura tended to underestimate EE with increasing discrepancy as intensity increased. The combined activities and slow running in the laboratory, and all MET placements, TEE hip and wrist, and step count in free-living had acceptable measurement errors (< 10% MAPE), whereas the remaining free-living variables showed close to (≤13.2%) acceptable limits. CONCLUSION: This is the first study investigating the validity of Oura Ring EE against gold standard methods. Oura successfully identified major changes between activities and/or intensities but was less responsive to detailed deviations within activities. In free-living, Oura step-count and EE variables tightly correlated with reference monitors, though with systemic over- or underestimations indicating somewhat low intra-individual validity of the ring versus the reference monitors. However, the correlations between the devices were high, suggesting that the Oura can detect differences at group-level for active and total energy expenditure, as well as step count.

COVID-19 and unfavorable changes in mental health unrelated to changes in physical activity, sedentary time, and health behaviors among Swedish adolescents: A longitudinal study.

Background: The COVID-19 pandemic has had major impact on the daily lives of adolescents. This study examined whether mental health outcomes had changed over the pandemic, and if such changes were related to changes in physical activity (PA), sedentary time, sleep, screen time, and participation in organized sports. Materials and methods: In this longitudinal study, data were collected in autumn 2019 with follow-up measurements in spring 2021. In total, 558 schools were invited and 34 schools around Stockholm with a variation in socioeconomic background were included. Physical activity and sedentary time were measured for seven consecutive days by accelerometry (Actigraph). Anxiety, health-related quality of life (HRQoL), psychosomatic health, stress, sleep duration, screen time, and organized sports participation were self-reported in questionnaires. Linear models were applied to estimate associations between changes in mental health outcomes and exposures. Results: From the baseline sample of 1,139 participants, 585 (55% girls), mean (SD) age 14.9 (0.3) years, participated in the follow-up. Between 2019 and 2021, there was a decrease in HRQoL [mean difference -1.7 (-2.3, -1.2), p < 0.001], increase in psychosomatic health problems [mean difference 1.8 (1.3, 2.3), p < 0.001], and an increase in the number of participants with high stress [from 94 (28%) to 139 (42%), p < 0.001]. Weekly light PA and sleep duration decreased and weekly sedentary time and screen time increased unrelated to changes in mental health outcomes. An increase in sleep duration during weekdays was significantly related to both a decrease in anxiety (B = -0.71, CI: -1.36, -0.06) and an increase in HRQoL (B = 1.00, CI: 0.51, 1.49). Conclusion: During the COVID-19 pandemic, mental health appears to have been impaired in Swedish adolescents, but unrelated to changes in PA, sedentary time, screen time, or participation in organized sports. However, increased sleep duration on weekdays was related to less anxiety and better HRQoL. The results may help policy makers and other stakeholders comprehend the differential effects of the COVID-19 pandemic on mental health outcomes and help guiding the planning of policy actions. Trial registration: ISRCTN15689873.

A Novel Snore Detection and Suppression Method for a Flexible Patch With MEMS Microphone and Accelerometer

Sleep apnea impacts more and more people all over the world, and obstructive sleep apnea of which is the most frequent. Hence, research on snoring detection and related suppression methods is extremely urgent. In this article, a novel low-cost flexible patch with MEMS microphone and accelerometer is developed to detect snore event and sleeping posture, and a small vibration motor embedded in the patch is designed to suppress snoring. Theoretical analyses of short-time energy, piecewise average filtering (PAF), and Mel-frequency cepstral coefficients (MFCCs) processing are described in detail, and the improved MFCCs are put forward and used as the input of the convolutional neural network (CNN). Furthermore, the snore recognition method based on the combination of similarity analysis and CNN analysis is presented, followed by the snoring suppression method. Experimental results demonstrate that the main features of the sound signals can be extracted effectively by PAF and MFCCs processing, and the data compression ratio is about 99.41%. Besides, the locations of the eigenvectors can be found accurately based on short-time energy analysis. The numbers of high similarity of snoring signals within 30 s are larger than 3, while those of non-snoring signals are often less than 3. If the preliminary screening with similarity analysis is passed, CNN analysis will be conducted to judge whether there are snoring events. The accuracy of snore recognition with CNN analysis is calculated to be as high as 99.25%. Finally, the average snoring time measured by the smart patch with snoring suppression is reduced to 15 from 135 min, which indicates that the proposed snore recognition and suppression methods are effective.

Intelligent Posture Monitoring System

With the shift to at-home work due to the Covid-19 pandemic, longer hours are spent sitting in front of a computer without proper ergonomic seating available in most home-office settings. Most home office arrangements often lack the necessary back support needed for prolonged periods of sedentary work. The goal of the proposed system is to automatically track a user's postural positions throughout the day through the use of a non-invasive, wearable system and automatically provide feedback from an algorithm to warn the user to correct or change their poor posture. This is done by placing magnets in the form of a rectangular grid on a shirt as well as an MMR sensor on the chest of the body. The onboard magnetic sensor records the data values from the grid of magnetics, which is then, along with data recorded from the onboard accelerometer, analyzed to determine the position of the user. A trained algorithm recognizes and automatically detects the spinal position of the user from the recorded data points and provides direction to alter their posture. These recommendations act as a warning system and allow the user to self-monitor and correct their own behavior to prevent back and neck pain and reduce the chance of long-lasting damage that can result from poor posture. © 2022 IEEE.

Accelerometer-Measured Physical Activity and Sedentary Time among Children in Japan before and during COVID-19: A Cross-Sectional and Longitudinal Analysis.

This study examined changes in physical activity (PA), sedentary behavior (SB), screen time, sleep, and executive function among Japanese preschoolers between COVID-19 pre-pandemic and pandemic periods, using cross-sectional and longitudinal data. Accelerometer data from 63 children aged 5-6 years were collected from three kindergartens in Tokyo, Japan, in late 2019 (pre-COVID-19). This was compared to the data of 49 children aged 5-6 years from the same kindergartens, collected in late 2020 (during COVID-19). Sixteen children in the pre-COVID-19 cohort also participated in the 2020 survey and provided data for the longitudinal analysis. The mean minutes of PA, SB, screen time, and sleep duration, as well as executive function, were compared between the pre- and during COVID-19 cohorts. After adjusting for school, sex, and accelerometer wear time, there were no significant differences in any of the measured outcomes between the two cohorts. However, the analysis of longitudinal data revealed significant increases in time spent in SB and on screens, and a decrease in light-intensity PA and sleep duration during the pandemic compared to the pre-pandemic period. Results suggest that, despite the COVID-19 pandemic, young children's activity levels and SB did not significantly differ from pre-pandemic levels. However, school-aged children's SB, light PA, and sleep time were affected, although this cannot be disentangled from the effects of the transition to school.

A Template Matching Based Cough Detection Algorithm Using IMU Data From Earbuds

Coughing is a common symptom across different clinical conditions and has gained further relevance in the past years due to the COVID-19 pandemic. An automated cough detection for continuous health monitoring could be developed using Earbud, a wearable sensor platform with audio and inertial measurement unit (IMU) sensors. Though several previous works have investigated audio-based automated cough detection, audio-based methods can be highly power-consuming for wearable sensor applications and raise privacy concerns. In this work, we develop IMU-based cough detection using a template matching-based algorithm. IMU provides a low-power privacy-preserving solution to complement audio-based algorithms. Similarly, template matching has low computational and memory needs, suitable for on-device implementations. The proposed method uses feature transformation of IMU signal and unsupervised representative template selection to improve upon our previous work. We obtained an AUC (AUC-ROC) of 0.85 and 0.83 for cough detection in a lab-based dataset with 45 participants and a controlled free-living dataset with 15 participants, respectively. These represent an AUC improvement of 0.08 and 0.10 compared to the previous work. Additionally, we conducted an uncontrolled free-living study with 7 participants where continuous measurements over a week were obtained from each participant. Our cough detection method achieved an AUC of 0.85 in the study, indicating that the proposed IMU-based cough detection translates well to the varied challenging scenarios present in free-living conditions. © 2022 IEEE.

Insomnia and sleep characteristics in post COVID-19 related fatigue

Aims: Following COVID-19 a substantial number of patients report persistent fatigue and insomnia. As these symptoms have overlapping features, insomnia can be easily underdiagnosed in post COVID-19 related fatigue (PCRF). The main object of this study was to determine the prevalence of insomnia in patients with PCRF and investigate their sleep characteristics. Data of PCRF patients were compared with those of patients with chronic fatigue syndrome (CFS/ME), a condition also characterized by persistent fatigue. Method(s): In this cross-sectional study insomnia severity, assessed with the Insomnia Severity Index (ISI), and prevalence of clinical insomnia (ISI score >=10), were determined in patients with PCRF (n = 114) and compared with CFS/ME patients (n = 59) using ANCOVA and logistic regression, respectively. Linear regression analyses were used to evaluate if fatigue severity, concentration problems, pain, depressive symptoms and having PCRF or CFS/ME were associated with insomnia severity. Sleep characteristics assessed with sleep diary and accelerometer were determined in patients with PCRF and compared with CFS/ME patients using ANCOVA. Result(s): In PCRF patients the mean (SD) insomnia severity was 11.46 (5.7) and prevalence of clinical insomnia was 64%. Both did not differ significantly from CFS/ME. Insomnia severity was significantly associated with depressive symptoms (beta = 0.49, p = 0.006) and higher age (beta = -0.08, p = 0.04). In PCRF the mean subjective sleep duration in h was 7.39 (1.00), sleep onset latency 0.97 (0.62) and wake after sleep onset 1.24 (0.72). The PCRF group reported a significantly shorter sleep duration than the CFS/ME group (p = 0.002), with a moderate effect size (d = 0.59). Conclusion(s): Insomnia severity and prevalence of clinical insomnia is high in PCRF. Insomnia should be assessed and if present treated with insomnia focused therapy in patients reporting post COVID-19 related chronic fatigue.

Hyperparameter Tuning in Convolutional Neural Network for Face Touching Activity Recognition using Accelerometer Data

People have been encouraged to wear masks and avoid touching their faces in public as part of the new measures to prevent the spread of coronavirus 2019 (COVID-19). During the COVID-19 epidemic, few research have examined the effect of everyday living on the frequency of facial touch activity. To develop a face touching avoidance system, deep learning algorithms have been proposed and have demonstrated their amazing performance. However, an important drawback of deep learning is its extensive dependence on hyperparameters. The results of deep learning algorithms may vary depending on hyperparameters, such as the size of the filters, the number of filters, the batch size, the number of epochs, and the training optimization technique used. In this paper, we present an effective approach for hyperparameter tuning of convolutional neural networks (CNNs) for efficiently recognized face touching activities based on accelerometer data. Two hyperparameter tuning methods (Grid search and Bayesian optimization) were evaluated in order to construct the CNN with high performance. The experiment results show that Bayesian optimization can provide suitable hyperparameters for CNNs for face touching recognition with the highest accuracy of 96.61%. © 2022 IEEE.

Physical Activity and Sedentary Behaviors in Primary School Children in Saudi Arabia during the COVID-19 Pandemic: Association with Parents' Behaviors.

During the COVID-19 pandemic, a few studies used accelerometers to assess physical activity (PA) and sedentary behavior in the family context. This study aimed to assess children and parents' moderate and vigorous physical activity (MVPA) and sedentary time, as well as their relationship in MVPA and sedentary time. Data were collected from 30 parent-child dyads during the COVID-pandemic for seven days, using a hip-worn accelerometer. Children and parents engaged in 65.6 and 34.6 min/day in MVPA and 442.2 and 427.9 min/day sedentary, respectively. There was no evidence of gender difference in MVPA and sedentary between boys and girls. Male parent spent more time in MVPA than female parents. A total of 50% of children and 53.3% of parents met the recommended PA. Children's MVPA and sedentary time were both correlated with that of their parents. Adjusted linear regression showed that only child MVPA was negatively associated with their parents' MVPA. There is evidence that multi-level interventions involving parents and children are more effective than interventions focusing on a single group. This study also provides evidence to support the link between MVPA and sedentary time between parents and children. Generalization of the findings is difficult due to the bias of self-selection sample.

The influence of removing the 10-minute bout requirement on the demographic, behaviour and health profiles of Canadian adults who meet the physical activity recommendations.

Background: Recently, the Canadian 24-Hour Movement Guidelines for Adults were released, and included a revised physical activity (PA) recommendation. The recommendation of 150 minutes per week of moderate-to-vigorous intensity PA (MVPA) was revised, from requiring that MVPA be accrued in bouts of 10 minutes or more (bouted) to having no bout requirement (non-bouted). The objective of this study was to assess whether there were differences in sociodemographic, health and fitness characteristics of Canadians who met the bouted and non-bouted PA recommendations. Data and methods: Using adult (aged 18 to 79 years) accelerometer data from three combined cycles of the nationally representative Canadian Health Measures Survey (N = 7,102), this study compared adherence to the bouted and non-bouted recommendations. Differences in sociodemographic, health and fitness measures were assessed using independent t-tests and chi-squares. Multivariate linear and logistic regressions controlling for age, sex, household education and smoking examined associations with health and fitness measures. Results: More adults met the PA recommendation using the non-bouted versus bouted (45.3% vs. 18.5%) requirement. Characteristics of those who met the bouted and only the non-bouted recommendations were similar. Exceptions among those who met only the non-bouted recommendation compared with meeting the bouted recommendation included fewer adults aged 65 years and older; lower MVPA, recreation PA and transport PA; and higher sedentary time, light PA and grip strength. Interpretation: Although the removal of the 10-minute bout requirement increased the proportion of Canadian adults who met the PA recommendation, there were no substantial differences in the sociodemographic and health characteristics of the populations captured by the bouted and non-bouted definitions. Results help to inform the transition in reporting for PA surveillance.

Predictors of objectively-measured sedentary time during the COVID-19 pandemic among breast cancer survivors participating in a diet and physical activity intervention

Purpose of the study: The purpose of this study was to investigate the predictors of objectively-measured sedentary time (ST) among breast cancer (BC) survivors who were 60 days post-treatment and were initiating participation in an intervention to improve diet and physical activity (PA) during the early phase of the COVID-19 pandemic. Methods: Cook and Move for Your Life (CMFYL) was a pilot and feasibility study of stage 0-III BC survivors testing the effects of a remotely-delivered and remotely-assessed nutrition and PA intervention. Women were ≥60 days post-treatment (current endocrine therapy allowed), consumed <5 servings of fruits/vegetables per day and/or engaged in <150 minutes/week of moderate to vigorous physical activity (MVPA). Hip-worn Actigraph GT3X accelerometers measured ST for 7 consecutive days at baseline. ST was defined as minutes/day (continuous) based on the Troiano cutpoint (<100 counts/minute), during awake (6am-11pm) wear time, and non-wear was identified using the Choi algorithm on the vector magnitude counts/minute. Multivariable linear regression models adjusting for wear time (average minutes/day) and minutes of MVPA/day were used to examine whether the following factors were predictors of ST at baseline: self-reported demographics, psychosocial factors (assessed via PROMIS Physical Function and PROMIS Anxiety forms), diet quality (Healthy Eating Index 2015 score), caloric intake (calories/day), and fruit and vegetable intake (servings/day). Results: Among the 84 women included in this analysis who had actigraphy measurements at baseline, the average ST/day was 684±79 minutes. On average, women were 58±10 years in age and most self-identified as non-Hispanic white (87%). The average time since diagnosis at time of enrollment was 4.5 years and 59% of women were receiving endocrine therapy at baseline. Adjusted models show that participants with a college degree had 24.7 (95%CI 2.0, 47.4) more minutes of ST than those with less than a college degree, and for every 1-point increase in PROMIS Physical Function scores participants had 2.5 (95%CI -4.9, -0.2) fewer minutes of ST. Conclusion: In a sample of BC survivors enrolled in a diet and PA intervention, higher level of education and poorer physical function were associated with higher ST during the early phase of the COVID-19 pandemic. These findings provide preliminary insight into factors associated with ST. Future work will investigate how these factors influence change in ST after participation in the CMFYL intervention.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity.

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69-74) years, with a body mass index of 31 (27-32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102-114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108-118) versus 106 (96-114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden's index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

A Personalized Healthcare Platform for Monitoring Mental Health of a Person During COVID

Stress is the human body’s response to various factors such as mental, physical, or emotional pressure. Coronavirus Disease 2019 pandemic has disrupted the mental health of most people worldwide. Stress plays a crucial role in corona virus disease patients during their medication period. Therefore, a remote mental health monitoring system has become a necessity. The physiological data captured using body sensors can provide rich information about the stress experienced by a person. Paper proposes a personalized stress indicator for monitoring a person’s mental health through a personalized healthcare platform. The physiological data from body sensors such as the galvanic skin response sensor, electrocardiogram module, and accelerometer module are sent in real-time to an Internet of things platform, ‘ThingSpeak.’ In the ThingSpeak platform, MATLAB analysis is performed to calculate the baseline threshold value of each user. Then, the stress percentage is evaluated based on the data rate above the threshold. The stress percentage is displayed on an output channel of the ThingSpeak platform. It enables remote monitoring of patients’ mental health by sending the health updates to the doctor or caretaker through email. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The TROLLEY Study: assessing travel, health, and equity impacts of a new light rail transit investment during the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic disrupted life in extraordinary ways impacting health and daily mobility. Public transit provides a strategy to improve individual and population health through increased active travel and reduced vehicle dependency, while ensuring equitable access to jobs, healthcare, education, and mitigating climate change. However, health safety concerns during the COVID-19 pandemic eroded ridership, which could have longstanding negative consequences. Research is needed to understand how mobility and health change as the pandemic recedes and how transit investments impact health and equity outcomes. METHODS: The TROLLEY (TRansit Opportunities for HeaLth, Livability, Exercise and EquitY) study will prospectively investigate a diverse cohort of university employees after the opening of a new light rail transit (LRT) line and the easing of campus COVID-19 restrictions. Participants are current staff who live either < 1 mile, 1-2 miles, or > 2 miles from LRT, with equal distribution across economic and racial/ethnic strata. The primary aim is to assess change in physical activity, travel mode, and vehicle miles travelled using accelerometer and GPS devices. Equity outcomes include household transportation and health-related expenditures. Change in health outcomes, including depressive symptoms, stress, quality of life, body mass index and behavior change constructs related to transit use will be assessed via self-report. Pre-pandemic variables will be retrospectively collected. Participants will be measured at 3 times over 2 years of follow up. Longitudinal changes in outcomes will be assessed using multilevel mixed effects models. Analyses will evaluate whether proximity to LRT, sociodemographic, and environmental factors modify change in outcomes over time. DISCUSSION: The TROLLEY study will utilize rigorous methods to advance our understanding of health, well-being, and equity-oriented outcomes of new LRT infrastructure through the COVID-19 recovery period, in a sample of demographically diverse adult workers whose employment location is accessed by new transit. Results will inform land use, transportation and health investments, and workplace interventions. Findings have the potential to elevate LRT as a public health priority and provide insight on how to ensure public transit meets the needs of vulnerable users and is more resilient in the face of future health pandemics. TRIAL REGISTRATION: The TROLLEY study was registered at ClinicalTrials.gov ( NCT04940481 ) June 17, 2021, and OSF Registries ( https://doi.org/10.17605/OSF.IO/PGEHU ) June 24, 2021, prior to participant enrollment.