Continuous Monitoring Of Physical Activity Research Articles

Standardizing Continuous Physical Activity Monitoring in Patients with Cervical Spondylosis.

Prospective cohort study. To use a commercial wearable device to measure real-life, continuous physical activity in patients with CS and to establish age- and sex-adjusted standardized scores. Patients with cervical spondylosis (CS) often present with pain or neurologic deficits that results in functional limitations and inactivity. However, little is known regarding the influence of CS on patient's real-life physical activity. This study included 100 English-speaking adult patients with cervical degenerative diseases undergoing elective spine surgery at Stanford University who owned iPhones. Patients undergoing surgery for spine infections, trauma, or tumors, or with lumbar degenerative disease were excluded. Activity two weeks before surgery was expressed as raw daily step counts. Standardized z-scores were calculated based on age- and sex-specific values of a control population. Responses to patient-reported outcome measures (PROMs) surveys assessed convergent validity. Functional impairment was categorized based on predetermined z-score cut-off values. 30 CS with mean(±SD) age of 56.0(±13.4) years wore an Apple Watch for ≥8 hours/day in 87.1% of the days. Mean watch wear time was 15.7(±4.2) hours/day, and mean daily step count was 6,400(±3,792). There was no significant difference in activity between 13 patients (43%) with myelopathy and 17 (57%) without myelopathy. Test-Retest reliability between wearable step count measurements was excellent (ICC β=0.95). Physical activity showed a moderate positive correlation with SF36-PCS, EQ5D VAS, and PROMIS-PF. Activity performance was classified into categories of "no impairment" (step count=9,640(±2,412)), "mild impairment" (6,054(±816)), "moderate impairment" (3,481(±752)), and "severe impairment" (1,619(±240)). CS patients' physical activity is significantly lower than the general population, or the frequently stated goals of 7,000-10,000 steps/day. Standardized, continuous wearable physical activity monitoring in CS is a reliable, valid, and normalized outcome tool that may help characterize functional impairment before and after spinal interventions.

Prediction of Physical Activity Patterns in Older Patients Rehabilitating After Hip Fracture Surgery: Exploratory Study.

Building up physical activity is a highly important aspect in an older patient's rehabilitation process after hip fracture surgery. The patterns of physical activity during rehabilitation are associated with the duration of rehabilitation stay. Predicting physical activity patterns early in the rehabilitation phase can provide patients and health care professionals an early indication of the duration of rehabilitation stay as well as insight into the degree of patients' recovery for timely adaptive interventions. This study aims to explore the early prediction of physical activity patterns in older patients rehabilitating after hip fracture surgery at a skilled nursing home. The physical activity of patients aged ≥70 years with surgically treated hip fracture was continuously monitored using an accelerometer during rehabilitation at a skilled nursing home. Physical activity patterns were described in our previous study, and the 2 most common patterns were used in this study for pattern prediction: the upward linear pattern (n=15) and the S-shape pattern (n=23). Features from the intensity of physical activity were calculated for time windows with different window sizes of the first 5, 6, 7, and 8 days to assess the early rehabilitation moment in which the patterns could be predicted most accurately. Those features were statistical features, amplitude features, and morphological features. Furthermore, the Barthel Index, Fracture Mobility Score, Functional Ambulation Categories, and the Montreal Cognitive Assessment score were used as clinical features. With the correlation-based feature selection method, relevant features were selected that were highly correlated with the physical activity patterns and uncorrelated with other features. Multiple classifiers were used: decision trees, discriminant analysis, logistic regression, support vector machines, nearest neighbors, and ensemble classifiers. The performance of the prediction models was assessed by calculating precision, recall, and F1-score (accuracy measure) for each individual physical activity pattern. Furthermore, the overall performance of the prediction model was calculated by calculating the F1-score for all physical activity patterns together. The amplitude feature describing the overall intensity of physical activity on the first day of rehabilitation and the morphological features describing the shape of the patterns were selected as relevant features for all time windows. Relevant features extracted from the first 7 days with a cosine k-nearest neighbor model reached the highest overall prediction performance (micro F1-score=1) and a 100% correct classification of the 2 most common physical activity patterns. Continuous monitoring of the physical activity of older patients in the first week of hip fracture rehabilitation results in an early physical activity pattern prediction. In the future, continuous physical activity monitoring can offer the possibility to predict the duration of rehabilitation stay, assess the recovery progress during hip fracture rehabilitation, and benefit health care organizations, health care professionals, and patients themselves.

Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis.

Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury. This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury. This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population. The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.

Feasibility of Remotely Monitoring Daily Physical Activity in Individuals with Acquired Brain Injury

<h3>Research Objectives</h3> Wearable physical activity monitors (PAM) are increasingly replacing traditional patient recall questionnaires to monitor physical activity in the community. Remote objective monitoring of physical activity is needed especially in the current times of the Covid-19 pandemic. While PAM are increasingly used in multiple populations, there is no information on the feasibility and challenges of using PAM to remotely monitor physical activity in individuals with acquired brain injury. The main goal of this research is to determine the usability, ease of use and identify challenges of using PAM for remotely monitoring objective physical activity in the community in individuals with acquired brain injury. <h3>Design</h3> Mixed methods research (4-weeks). <h3>Setting</h3> In the community (real-world). <h3>Participants</h3> Ten individuals with acquired-brain-injury (Stroke and TBI). <h3>Interventions</h3> All participants wore three (wrist, ankle, waist) ActiGraph GT9X Link PAM simultaneously for 4-weeks in the community and provided their feedback weekly. <h3>Main Outcome Measures</h3> Usability, ease of use, challenges. <h3>Results</h3> Participants rated all PAM to have good usability on the System Usability Scale. They gave the best possible score on the After Scenario Questionnaire after using the remote data hub which was used daily to transfer the data remotely to the research dashboard. Qualitative content analysis showed that most challenges reported were for wrist PAM and wear attachments were the most challenging aspect of using PAM in this population. <h3>Conclusions</h3> This research helps establish the framework and challenges of using PAM to remotely monitor objective physical activity in the community in individuals with acquired brain injury. Continuous monitoring of physical activity in the community is essential to evaluate the progress and the effect of rehab interventions and for tracking daily physical activity in individuals with acquired brain injury. <h3>Author(s) Disclosures</h3> No conflicts.

Abstract 15451: Using Fitbit Measured Activity Levels as an Indicator of New York Heart Association (nyha) Functional Class

Introduction: Commercially available wearable devices for physical activity (PA) monitoring may provide objective data to facilitate assessment of NYHA class in patients with heart failure (HF). However, there is limited research exploring their ability to differentiate patients between NYHA classes. Objective: This study aims to assess the relationship between NYHA class and PA using a Fitbit wrist-based activity tracker (Charge 3) in patients with HF. Methods: We conducted a secondary analysis of baseline NYHA and PA data (daily step count and minutes of moderate-to-vigorous PA [MVPA]) collected by the iCardia4HF trial, which examined the feasibility and preliminary efficacy of a mHealth intervention for HF self-care. NYHA class was determined by a trained research assistant at the baseline visit, using standardized criteria. Daily steps and MVPA minutes were measured using Fitbit data from the first 7 days after the baseline visit. To be included in the analysis, patients had to have ≥4 days with ≥600 minutes of Fitbit wear-time/day. Results: We analyzed data from 78 participants (mean age=55.2 years [SD 10.6]; female=43.6%; Black/African American= 79.5%; mean BMI=35.6 kg/m2 [SD 8.7]; HFrEF=65.4%). The Kruskal-Wallis test demonstrated significant differences in the distribution of daily steps (p=.021) and MVPA (p=.023) across NYHA classes. Pairwise comparisons showed that patients in NYHA Class III had significantly lower daily steps and MVPA minutes than patients in Class I. There were also important differences in daily steps and MVPA between patients in NYHA Class III and II, and Class II and I, but these did not reach statistical significance. Conclusions: There was a general decrease in steps and MVPA duration with increasing NYHA class. Wrist-worn activity trackers represent a promising method for estimating NYHA class and assessing HF severity and progression through continuous PA monitoring, but larger studies are needed for validation.

Continuous Long-Term Physical Activity Monitoring in Hemodialysis Patients.

Physical inactivity is common in patients receiving hemodialysis, but activity patterns throughout the day and in relation to dialysis are largely unknown. This knowledge gap can be addressed by long-term continuous activity monitoring, but this has not been attempted and may not be acceptable to patients receiving dialysis. Ambulatory patients with end-stage kidney disease receiving thrice-weekly hemodialysis wore commercially available wrist-worn activity monitors for 6 months. Step counts were collected every 15 minutes and were linked to dialysis treatments. Physical function was assessed using the Short Physical Performance Battery (SPPB). Fast time to recovery from dialysis was defined as ≤2 hours. Mixed effects models were created to estimate step counts over time. Of 52 patients enrolled, 48 were included in the final cohort. The mean age was 60 years, and 75% were Black or Hispanic. Comorbidity burden was high, 38% were transported to and from dialysis by paratransit, and 79% had SPPB <10. Median accelerometer use (199 days) and adherence (95%) were high. Forty-two patients (of 43 responders) reported wearing the accelerometer every day, and few barriers to adherence were noted. Step counts were lower on dialysis days (3991 [95% CI, 3187 to 4796] versus 4561 [95% CI, 3757 to 5365]), but step-count intensity was significantly higher during the hour immediately after dialysis than during the corresponding time on nondialysis days (188 steps per hour increase [95% CI, 171 to 205]); these levels were the highest noted at any time. Postdialysis increases were more pronounced among patients with fast recovery time (225 [95% CI, 203 to 248] versus 134 [95% CI, 107 to 161] steps per hour) or those with SPPB ≥7. Estimates were unchanged after adjustment for demographics, diabetes status, and ultrafiltration rate. Long-term continuous monitoring of physical activity is feasible in patients receiving hemodialysis. Highly granular data collection and analysis yielded new insights into patterns of activity after dialysis treatments.

Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke.

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population. Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations. Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN. Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors.

Technology outcome measures in neuromuscular disorders: A systematic review.

Portable and wearable devices can monitor a number of physical performances and lately have been applied to patients with neuromuscular disorders (NMDs). We performed a systematic search of literature databases following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles, including all studies reporting the use of technological devices for motor function assessment in NMDs from 2000 to 2021. We also summarized the evidence on measurement properties (validity, reliability, responsiveness) of the analyzed technological outcome measures. One hundred studies fulfilled the selection criteria, most of them published in the past 10years. We defined four categories that gathered similar technologies: gait analysis tools, for clinical assessment of pace and posture; continuous monitoring of physical activity with inertial sensors, which allow "unsupervised" activity assessment; upper limb evaluation tools, including Kinect-based outcome measures to assess the reachable workspace; and new muscle strength assessment tools, such as Myotools. Inertial sensors have the evident advantage of being applied in the "in-home" setting, which has become especially appealing during the COVID-19 pandemic, although poor evidence from psychometric property assessment and results of the analyzed studies may limit their research application. Both Kinect-based outcome measures and Myotools have already been validated in multicenter studies and different NMDs, showing excellent characteristics for application in clinical trials. This overview is intended to raise awareness on the potential of the different technology outcome measures in the neuromuscular field and to be an informative source for the design of future clinical trials, particularly in the era of telemedicine.

Decrease in Mobility during the COVID-19 Pandemic and Its Association with Increase in Depression among Older Adults: A Longitudinal Remote Mobility Monitoring Using a Wearable Sensor.

Background: Social isolation during COVID-19 may negatively impact older adults’ wellbeing. To assess its impact, we measured changes in physical activity and sleep among community-dwelling older adults, from pre-to post-pandemic declaration. Method: Physical activity and sleep in older adults (n = 10, age = 77.3 ± 1.9 years, female = 40%) were remotely assessed within 3-month pre-to 6-month post-pandemic declaration using a pendant-wearable system. Depression was assessed pre-and post-pandemic declaration using the Center for Epidemiologic Studies Depression scale and was compared with 48 h continuous physical activity monitoring data before and during pandemic. Results: Compared to pre-pandemic, post-pandemic time spent in standing declined by 32.7% (Cohen’s d = 0.78, p < 0.01), walking by 52.2% (d = 1.1, p < 0.01), step-counts by 55.1% (d = 1.0, p = 0.016), and postural transitions by 44.6% (d = 0.82, p = 0.017) with increase in sitting duration by 20.5% (d = 0.5, p = 0.049). Depression symptoms increased by 150% (d = 0.8, p = 0.046). Interestingly, increase in depression was significantly correlated with unbroken-prolong sitting bout (ρ = 0.677, p = 0.032), cadence (ρ = −0.70, p = 0.024), and sleep duration (ρ = −0.72, p = 0.019). Conclusion: This is one of the early longitudinal studies highlighting adverse effect of the pandemic on objectively assessed physical activity and sleep in older adults. Our observations showed need for timely intervention to mitigate hard to reverse consequences of decreased physical activity such as depression.

Automated feature extraction from population wearable device data identified novel loci associated with sleep and circadian rhythms.

Wearable devices have been increasingly used in research to provide continuous physical activity monitoring, but how to effectively extract features remains challenging for researchers. To analyze the generated actigraphy data in large-scale population studies, we developed computationally efficient methods to derive sleep and activity features through a Hidden Markov Model-based sleep/wake identification algorithm, and circadian rhythm features through a Penalized Multi-band Learning approach adapted from machine learning. Unsupervised feature extraction is useful when labeled data are unavailable, especially in large-scale population studies. We applied these two methods to the UK Biobank wearable device data and used the derived sleep and circadian features as phenotypes in genome-wide association studies. We identified 53 genetic loci with p<5×10−8 including genes known to be associated with sleep disorders and circadian rhythms as well as novel loci associated with Body Mass Index, mental diseases and neurological disorders, which suggest shared genetic factors of sleep and circadian rhythms with physical and mental health. Further cross-tissue enrichment analysis highlights the important role of the central nervous system and the shared genetic architecture with metabolism-related traits and the metabolic system. Our study demonstrates the effectiveness of our unsupervised methods for wearable device data when additional training data cannot be easily acquired, and our study further expands the application of wearable devices in population studies and genetic studies to provide novel biological insights.

Exploring barriers to physical activity of patients at the internal medicine and surgical wards: a retrospective analysis of continuously collected data

Purpose To analyse physical activity of patients during their hospital stay and to explore the relationship between physical activity and barriers to physical activity. Methods This was a secondary analysis of physical activity data for patients admitted to the internal medicine and surgical wards. Physical activity data, collected with a wireless patch sensor, was operationalized as time spent lying, sitting/standing, and walking. Barriers to physical activity included patients’ pain levels, the use of urinary catheters, intravenous tubing, oxygen lines, drains, and level of dependence. Regression analysis explored the relationship between physical activity and barriers to physical activity. Results Physical activity data were collected in 39 patients (aged 27–88, mean 54 years) during hospital stay. Patients were admitted for a median of 10 d (interquartile range [IQR]: 7–15 d). These patients were lying for a median of 12.1 h (7.6–17.7), sitting/standing 11.8 h (6.3–15.7), and walking 0.1 h (0–0.3) per day. Time lying during the day related to pain levels (β = 0.4 h per unit increase in pain, p < 0.01) and drain use (β = 3.1 h, p < 0.01). Conclusions Patients spent the most time during the hospital stay lying in bed. Improved pain management and decreased drain use may be worth exploring to increase inpatient physical activity. Implications for rehabilitation Continuous monitoring of physical activity in patients during hospital stay is an important tool for health care professionals to improve multidisciplinary care and rehabilitation. Health care professionals should be aware of the necessity of adequate pain management and critically review the use of drains in order to improve physical activity of patients during hospital stay. Patients need extra support of health care professionals to increase physical activity during consecutive days of their hospital stay.

P5306Changes in health-behaviours of 15-year old girls depend on satisfaction with the prophylactic intervention program. Healthy Me Study

Abstract Introduction Healthy Me (HM) interventional study was oriented on improvement of physical activity (PA) in 15-year old girls but also included component of eating habits, smoking prevention and personal competencies. Innovative elements of the project involved usage of fitness-band with continuous monitoring of PA and special dedicated smartphone apps. Purpose To assess the effectiveness of 1-year prophylactic intervention with the use of mobile technology in girls between 14 and 15 year of age, in relation to the level of their satisfaction with the program. Methods 1198 girls 14–15 years old from 48 randomly selected schools from all over Poland participated in the HM during 2017/2018 school year. Schools were randomized to full or partial intervention and control group, respectively 636, 277 and 285 girls. Questionnaire data from the beginning and the end of the HM regarding four factors (low PA, eating sweets, smoking and stress level) were analyzed. Mean changes (MC) calculated according to the definition given in table 1 were applied as outcome measure. Satisfaction with the program was assessed after 1 year of participation in the HM as low, average and high. General linear models with main effects were estimated and adjusted for initial level of each factor. Results At the beginning of the study 87% participants did not fulfill criteria for recommended PA, 9% smoked during last 30 days and 67% ate sweets more than ones per week. Mean stress level was 6.92±2.92 points. For the entire study group, the only significant change after 1 year intervention was lower frequency of eating sweets (p=0,007). However, the higher satisfaction with the study resulted in the significantly higher effectiveness of HM intervention (table 1). Mean changes in the analyzed factors Analyzed factor Range Mean change in entire group (SE) Mean change by level of satisfaction p1 p2 low (SE) average (SE) high (SE) Physical activity (MVPA) 0–7 days −0.076 (0.063) −0.396 (0.137) −0.147 (0.068) 0.261 (0.119) 0.001 0.068 Smoking-past 0 – never 0.039 (0.028) 0.270 (0.066) 0.034 (0.033) −0.004 (0.057) 0.002 0.067 6–30 days or more Eating sweets 0 – never −0.296 (0.045) −0.047 (0.101) −0.285 (0.050) −0.313 (0.086) 0.069 0.016 6 – every day more than once Stress Cohen scale 0–16 points 0.105 (0.090) 0.498 (0.211) 0.138 (0.105) −0.424 (0.183) 0.003 0.823 SE: standard error; MVPA: moderate to vigorous physical activity; p1: main effect of the level of satisfaction; p2: main effect of the intervention group. Conclusion Program that was dedicated for improvement of PA in teenage girls could also profitably influence other factors related to health behaviours and stress level in this group but intervention must be perceived by participants as rewarding. It seems, that attractiveness of interventional program design, increased by mobile technologies, is key for its effectiveness for teens. Acknowledgement/Funding National Health Program, Ministry of Health

The place of living, school and family influence on the pattern of physical activity and sedentary behavior of adolescents: a cross sectional study

The aim of this study was to determine the influence of place of living, type of school, and family background on the physical activity and sedentary behavior of adolescents. 1,280 adolescents took part in the research: 694 boys and 586 girls aged 16–19 from Poland. Physical activity (PA) and sedentary behavior (SB) were evaluated by IPAQ-long version. A multiple regression analysis was used to evaluate the influence of socio-economic factors on physical activity and sedentary behavior. The results suggested a difference between boys and girls in all domains of physical activity and sedentary behavior. The results also indicated that social factors influence physical activity and sedentary behavior in different ways. After adjusting the model to BMI and age tested, the key determinants for boys’ physical activity were: type of leisure time in the family and place of living, while for girls – type of school and type of leisure time in the family. Sedentary behavior of both sexes were strongly dependent on type of school, and – for boys – parents’ education and type of leisure time in the family were also important. The results suggest the need for continuous monitoring of physical activity and seated behavior as well as taking into account a wide spectrum of social factors in which adolescents grow. Interventions and strategies to promote physical activity should focus on family involvement and uniting family and educational institutions operating in local environments.

Time spent lying, sitting, and upright during hospitalization after stroke: a prospective observation study

BackgroundEarly mobilization has been an important part of acute stroke unit treatment. However, early and intense mobilization within the first 24 h post stroke may cause an unfavorable outcome. Recently, objective measurements using body-worn sensors have been applied, enabling continuous monitoring of physical activity in the hospital setting. This study aimed to use body-worn sensors to quantify the amount of physical activity and how activity levels changed over time during hospitalization in patients with acute stroke. We also wanted to investigate which factors were associated with upright and sitting activity.MethodsThis was a prospective study including patients admitted to hospital within seven days after onset of stroke. Physical activity was measured by two sensors (ActivPALs from PAL Technologies Ltd., Glasgow, UK), one attached on sternum and one on the thigh of the unaffected side, monitoring continuously from inclusion until discharge. Data were processed in Matlab R 2015B and provided information about daily time in lying, sitting, and upright positions, and daily average duration of sitting and upright bouts. A linear mixed model was used to analyze changes over time.Results58 patients were included (31 women, mean (SD) age; 75.1 (12.0)). Patients were hospitalized for 12.1 (7.6) days and had a mean score on the National Institute of Health Stroke Scale of 6.2 (5.5) points. Time spent sitting and time spent upright increased per day during hospitalization by 22.10 min (95% Confidence interval (CI): 14.96, 29.24) and 3.75 min (95% CI: 1.70, 5.80) respectively. Increased time upright was associated with improved Modified Rankin Scale scores (− 38.09 min, 95% CI: -61.88, − 14.29) and higher Short Physical Performance Battery scores (6.97 min, 95% CI: 1.99, 11.95), while prolonged bouts of sitting were associated with more severe stroke (4.50 min, 95% CI: 0.80, 8.19), and older age (1.72 min, 95% CI: 0.20, 3.26).ConclusionsPatients increased their daily time spent sitting and upright during the initial hospital stay after stroke. Prolonged bouts of sitting were associated with older age and more severe strokes. Hence future research should investigate the benefit of interventions aimed at breaking up sitting time after stroke.

A Wearable System for Real-Time Continuous Monitoring of Physical Activity.

Over the last decades, wearable systems have gained interest for monitoring of physiological variables, promoting health, and improving exercise adherence in different populations ranging from elite athletes to patients. In this paper, we present a wearable system for the continuous real-time monitoring of respiratory frequency (fR), heart rate (HR), and movement cadence during physical activity. The system has been experimentally tested in the laboratory (by simulating the breathing pattern with a mechanical ventilator) and by collecting data from one healthy volunteer. Results show the feasibility of the proposed device for real-time continuous monitoring of fR, HR, and movement cadence both in resting condition and during activity. Finally, different synchronization techniques have been investigated to enable simultaneous data collection from different wearable modules.

Anomaly event detection for sit-to-stand transition recognition to improve mariner physical activity classification during a sea voyage

During a sea voyage, seafarers live on an unstable surface that demands unusual physical activity patterns. Previous studies recognized physical inactivity patterns in their special living environment. This study aims to develop a new sit-to-stand transition detection method based on physical activity classification models using wearable sensors to objectively assess the unstable physical activity patterns of seafarers. The sit-to-stand event detection was selected to improve the classification accuracy of the static activities including sitting and standing during the physical inactivity patterns. Anomaly detection was applied to handle imbalanced training data of sit-to-stand events. A single wearable sensor was attached in a chest pocket of the uniform to extract the upper trunk motion features such as body angle and dynamics. Two classification models with and without sit-to-stand event detection were developed and compared with each other to evaluate the efficacy of the sit-to-stand event detection in classifying the mariners’ physical activity. Our experimental results showed that the sit-to-stand event detectionbased model significantly improved the classification accuracy of static activities. We expect that the proposed model can be integrated into a continuous physical activity monitoring system for the objective assessment of mariner’s physical health in an unstable living environment.

Initial Experience With Real-Time Continuous Physical Activity Monitoring in Patients Undergoing Spine Surgery.

Multicenter prospective pilot study. To evaluate if continuous physical activity monitoring by a personal electronic 3-dimensional accelerometer device is feasible and can provide objective data that correlates with patient-reported outcomes following spine surgery. Self-reported health-related quality-of-life (HRQOL) metrics are inherently limited by being very subjective, having a low frequency of data collection, and inconsistent follow-up. Inclusion criteria: adults (18+), thoracolumbar deformity or degenerative disease, and regular access to a computer with internet connection. Physical activity parameters included: number of daily steps, maximum hourly steps, and activity intensity. Patients completed the Oswestry Disability Index (ODI), the Short-Form Health Survey 36 (SF-36), and the Scoliosis Research Society-22r (SRS22) preoperatively and postoperatively at 6 weeks, 3 months, and 6 months. Thirty-two patients were enrolled, 8 (25%) withdrew, 1 (3.1%) died, and 1 (3.1%) did not end up undergoing surgery resulting in 22 (68.8%) available patients. Mean preoperative and postoperative step ranges were 1278±767 to 17,800±6464 and 891±587 to 12,655±7038, respectively. Eleven patients improved in mean total daily steps at the final postoperative month with 2 having significant improvements (P<0.05). Five patients did not significantly change (P>0.05) and 6 patients had significantly lower mean total daily steps at 6 months (P<0.05). The entire cohort significantly improved in ODI, SF-36 Physical Component Summary, SRS Activity, SRS Appearance, SRS Mental, SRS Satisfaction, and SRS Total score at 6 months postoperative (P<0.05 for all). Both ODI and Physical Component Summary were significantly correlated with preoperative average total daily steps (r=-0.61, P=0.0058 and r=0.60, P=0.0114, respectively). No other HRQOL metrics were significantly correlated at baseline or at 6 months postoperative (P>0.05). A prospective pilot study for continuous real-time physical activity monitoring was successfully completed. This is the first study of its kind and demonstrates a foundation to continuous physical activity monitoring following spine surgery. A larger and longer prospective study is needed to confirm long-term results and its relationship with HRQOL scores.

Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents.

Sleep problems and disrupted circadian rhythms are common among older adults and may be associated with several health issues and physical functioning status. Wearable continuous monitoring of physical activity enables unobtrusive monitoring of circadian activity and sleep patterns. The objective of this retrospective study was to analyze whether physical functioning status (Activities of Daily Living assessment of Resident Assessment Instrument) is associated with diurnal activity rhythm and sleep patterns measured with wearable activity sensor in nursing home residents during their normal daily life. Continuous activity data were collected by the wearable sensor from 16 nursing home residents (average age of 90.7years, seven demented subjects, one female) in their daily life over several months (12-18 months). The subjects' physical activity and sleep were quantified by several parameters from the activity data. In the cross-sectional analysis, physical functioning status was associated with the strength (RHO = 0.78, ) and the stability (RHO = 0.72, ) of the activity rhythm when the level of dementia was not controlled. In the longitudinal analysis (12-18 months), at an individual level the activity rhythm indices and activity level had the strongest correlations with changes in physical functioning but the associations were to some extent individual. In these long-term case recordings, decrease in the physical functioning was most strongly associated with decreasing levels of activity, stability, and strength of the activity rhythm, and with increasing fragmentation of rhythm and daytime passivity. Daily wearable monitoring of physical activity may hence reveal information about functioning state and health of older adults. However, since the changes in activity patterns implying changes in physical functioning status may not be consistent between the individuals, a multivariate approach is recommended for monitoring of these changes by continuous physical activity measurement.

Effects of bilateral training on motor function, amount of activity and activity intensity measured with an accelerometer of patients with stroke.

[Purpose] This study investigated the recovery of arm function and the functional use of the affected limb in real life of stroke patients after bilateral arm training. [Subjects] Twenty patients with stroke were randomly allocated to the BT (bilateral training group, n=10) and UT (unilateral training group, n=10) groups. [Methods] The BT group performed functional tasks with both hand symmetrically, the UT group performed tasks with only the affected hand for 30 minutes a session 5 times a week over 6 weeks. Before and after the intervention, accelerometers (Actisleep), functional independence measure (FIM) and manual function test (MFT) were used to assess subjects’ abilities. [Results] The BT group showed a significant improvement in FIM total score, motor subtotal score and MFT score of affected side compared to the UT group. The BT group showed a more quantitative increase in the amount of activity of the affected side than the UT group. Regarding activity intensity, the BT group showed a decrease in the sedentary level and an increase of the moderate level on the affected side compared to the UT group. [Conclusion] We found that programs tailored to the characteristics of stroke patients and continuous monitoring of physical activity using an accelerometer minimized possible future disability and improved the patients’ quality of life.

Mood and Ambulatory Monitoring of Physical Activity Patterns in Youth with Polycystic Ovary Syndrome

Study ObjectiveTo provide initial insight into physical activity patterns and predictors of moderate to vigorous physical activity (MVPA) in youth with polycystic ovary syndrome (PCOS) by using a multisensor activity monitor. DesignCross-sectional study analyzing baseline MVPA data using real-time continuous monitoring of physical activity. Body mass index (BMI) and depressive symptoms were examined as predictors of MVPA. SettingA large, urban children's hospital in the United States. ParticipantsThirty-five youth (aged 12 to 21 years) previously diagnosed with PCOS (mean BMI = 38.0 kg/m2; mean age = 15.4 years, 79% white) who were participants in a behavioral lifestyle intervention. Main Outcome MeasuresTotal steps, total MVPA, longest continuous bout of MVPA, and frequency of MVPA bouts lasting for 5 to 9 minutes and 10 or more minutes. ResultsSixty percent of youth averaged at least 1 daily MVPA bout lasting 10 or more minutes, and 14% averaged a daily MVPA bout lasting 30 or more minutes. BMI was negatively correlated with MVPA bout duration (P = .04). Parental ratings of depression, but not self-report ratings, were predictive of participants' total MVPA (β = −.46; P = .01), number of 5- to 9-minute bouts (β = −.39; P = .03), and bouts of 10 or more minutes (β = −.35; P = .05). ConclusionYouth with PCOS may benefit from being prescribed multiple bouts of MVPA lasting less than 30 continuous minutes to meet national recommendations and achieve health benefits. BMI and parental endorsement of child's depression symptoms may be important to consider when assessing and prescribing MVPA to youth with PCOS.