A practical validation study of a commercial accelerometer using good and poor sleepers.
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ABSTRACT:
SUBMITTER: Dickinson DL
PROVIDER: S-EPMC5221738 | biostudies-other | 2016 Jul
REPOSITORIES: biostudies-other
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Project description:PurposeConfounding due to poor health is a concern in accelerometer-based studies of physical activity and health, but detailed investigations of this source of bias are lacking.MethodsUS adults (n = 4840) from the National Health and Nutrition Examination Survey (2003 to 2006) wore an accelerometer for 1 to 7 d (mean = 5.7 d) and were followed for mortality through 2015. Logistic regression was used to examine odds ratios between poor health (chronic conditions, self-reported health, mobility limitations, frailty) and low physical activity levels; Cox models were used to estimate adjusted hazard ratios (HR) and 95% CI for mortality associations for a 1 h·d increase in moderate-to-vigorous-intensity physical activity (MVPA) using two commonly used cut-points (MVPA760, MVPA2020). Modeling scenarios with shorter and longer follow-up time, increasing adjustment for poor health, by age group, and after excluding early years of follow-up were used to assess bias.ResultsOver a mean of 10.1 yr of follow-up, 1165 deaths occurred. Poor health was associated with low MVPA760 levels and increased mortality risk. In fully adjusted MVPA760 models, HR was 26% stronger comparing 0 to 4 yr (HR = 0.46) with 0 to 12 yr of follow-up (HR = 0.62), particularly in older adults (65 yr and older). Increasing statistical adjustment for poor health attenuated MVPA760 associations by 13% to 15%, and exclusion of the first 2 yr of follow-up had limited effects. Comparable results were obtained with the MVPA2020 cut-point.ConclusionsWe did not find evidence that confounding by health status resulted in entirely spurious MVPA-mortality associations; however, potential bias was appreciable in modeling scenarios involving shorter follow-up (<6 yr), older adults, and more limited statistical adjustment for poor health. The strength of MVPA-mortality associations in studies reflecting these scenarios should be interpreted cautiously.
| S-EPMC7669589 | biostudies-literature
Project description:Study objectivesSleep-wake dysfunction is bidirectionally associated with the pathogenesis and evolution of stroke. Longitudinal and prospective measurement of sleep after chronic stroke remains poorly characterized because of a lack of validated objective and ambulatory sleep measurement tools in neurological populations. This study aimed to validate a multisensor sleep monitor, the SenseWear Armband (SWA), in patients with ischemic stroke and control patients using at-home polysomnography.MethodsTwenty-eight radiologically confirmed patients with ischemic stroke (aged 69.61 ± 7.35 years; mean = 4.1 years poststroke) and 16 control patients (aged 73.75 ± 7.10 years) underwent overnight at-home polysomnography in tandem with the SWA. Lin's concordance correlation coefficient and reduced major axis regressions were employed to assess concordance of SWA vs polysomnography-measured total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. Subsequently, data were converted to 30-second epochs to match at-home polysomnography. Epoch-by-epoch agreement between SWA and at-home polysomnography was estimated using crude agreement, Cohen's kappa, sensitivity, and specificity.ResultsTotal sleep time was the most robustly quantified sleep-wake variable (concordance correlation coefficient = 0.49). The SWA performed poorest for sleep measures requiring discrimination of wakefulness (sleep onset latency; concordance correlation coefficient = 0.16). The sensitivity of the SWA was high (95.90%) for patients with stroke and for control patients (95.70%). The specificity of the SWA was fair-moderate for patients with stroke (40.45%) and moderate for control patients (45.60%). Epoch-by-epoch agreement rate was fair (78%) in patients with stroke and fair (74%) in controls.ConclusionsThe SWA shows promise as an ambulatory tool to estimate macro parameters of sleep-wake; however, agreement at an epoch level is only moderate-fair. Use of the SWA warrants caution when it is used as a diagnostic tool or in populations with significant sleep-wake fragmentation.
| S-EPMC7853221 | biostudies-literature
Project description:Women's Health Study Accelerometer Data
| PRJNA601248 | ENA
Project description:Women's Health Study Accelerometer Data
| PRJNA601249 | ENA
Project description:Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.
| S-EPMC8457921 | biostudies-literature
Project description:Collar-mounted canine activity monitors can use accelerometer data to estimate dog activity levels, step counts, and distance traveled. With recent advances in machine learning and embedded computing, much more nuanced and accurate behavior classification has become possible, giving these affordable consumer devices the potential to improve the efficiency and effectiveness of pet healthcare. Here, we describe a novel deep learning algorithm that classifies dog behavior at sub-second resolution using commercial pet activity monitors. We built machine learning training databases from more than 5000 videos of more than 2500 dogs and ran the algorithms in production on more than 11 million days of device data. We then surveyed project participants representing 10,550 dogs, which provided 163,110 event responses to validate real-world detection of eating and drinking behavior. The resultant algorithm displayed a sensitivity and specificity for detecting drinking behavior (0.949 and 0.999, respectively) and eating behavior (0.988, 0.983). We also demonstrated detection of licking (0.772, 0.990), petting (0.305, 0.991), rubbing (0.729, 0.996), scratching (0.870, 0.997), and sniffing (0.610, 0.968). We show that the devices' position on the collar had no measurable impact on performance. In production, users reported a true positive rate of 95.3% for eating (among 1514 users), and of 94.9% for drinking (among 1491 users). The study demonstrates the accurate detection of important health-related canine behaviors using a collar-mounted accelerometer. We trained and validated our algorithms on a large and realistic training dataset, and we assessed and confirmed accuracy in production via user validation.
| S-EPMC8228965 | biostudies-literature
Project description:Magnetic sensing is present in our everyday interactions with consumer electronics and demonstrates the potential for the measurement of extremely weak biomagnetic fields, such as those of the heart and brain. In this work, we leverage the many benefits of microelectromechanical system (MEMS) devices to fabricate a small, low-power, and inexpensive sensor whose resolution is in the range of biomagnetic fields. At present, biomagnetic fields are measured only by expensive mechanisms such as optical pumping and superconducting quantum interference devices (SQUIDs), suggesting a large opportunity for MEMS technology in this work. The prototype fabrication is achieved by assembling micro-objects, including a permanent micromagnet, onto a postrelease commercial MEMS accelerometer using a pick-and-place technique. With this system, we demonstrate a room-temperature MEMS magnetic gradiometer. In air, the sensor's response is linear, with a resolution of 1.1 nT cm-1, spans over 3 decades of dynamic range to 4.6 µT cm-1, and is capable of off-resonance measurements at low frequencies. In a 1 mTorr vacuum with 20 dB magnetic shielding, the sensor achieves a 100 pT cm-1 resolution at resonance. This resolution represents a 30-fold improvement compared with that of MEMS magnetometer technology and a 1000-fold improvement compared with that of MEMS gradiometer technology. The sensor is capable of a small spatial resolution with a magnetic sensing element of 0.25 mm along its sensitive axis, a >4-fold improvement compared with that of MEMS gradiometer technology. The calculated noise floor of this platform is 110 fT cm-1 Hz-1/2, and thus, these devices hold promise for both magnetocardiography (MCG) and magnetoencephalography (MEG) applications.
| S-EPMC8433323 | biostudies-literature
Project description:BackgroundWhile there are many psychophysical reports of impaired magnocellular pathway function in developmental dyslexia (DD), few have investigated parietal function, the major projection of this pathway, in good and poor readers closely matched for nonverbal intelligence. In view of new feedforward-feedback theories of visual processing, impaired magnocellular function raises the question of whether all visually-driven functions or only those associated with parietal cortex functions are equally impaired and if so, whether parietal performance is more closely related to general ability levels than reading ability.MethodsReading accuracy and performance on psychophysical tasks purported to selectively activate parietal cortex such as motion sensitivity, attentional tracking, and spatial localization was compared in 17 children with DD, 16 younger reading-age matched (RA) control children, and 46 good readers of similar chronological-age (CA) divided into CA-HighIQ and a CA-LowIQ matched to DD group nonverbal IQ.ResultsIn the age-matched groups no significant differences were found between DD and CA controls on any of the tasks relating to parietal function, although performance of the DD group and their nonverbal IQ scores was always lower. As expected, CA and RA group comparisons indicated purported parietal functioning improves with age. No difference in performance was seen on any of the parietally driven tasks between the DD and age-nonverbal IQ matched groups, whereas performance differentiated the DD group from the age-matched, higher nonverbal IQ group on several such tasks. An unexpected statistical difference in performance between lower reading age (DD and RA children) and all higher reading age (CA) children was seen on a test of chromatic sensitivity, whereas when high and low nonverbal IQ normal readers were compared performance was not differentConclusionThe results indicate that performance on purported parietal functions improves with age and may be more associated with nonverbal mentation than reading accuracy. Performance on a cognitively demanding task, traditionally considered to rely on ventral stream functions, was more related to reading accuracy.
| S-EPMC1553455 | biostudies-literature
Project description:the use of movement monitors (accelerometers) for measuring physical activity (PA) in intervention and population-based studies is becoming a standard methodology for the objective measurement of sedentary and active behaviors and for the validation of subjective PA self-reports. A vital step in PA measurement is the classification of daily time into accelerometer wear and nonwear intervals using its recordings (counts) and an accelerometer-specific algorithm.the purpose of this study was to validate and improve a commonly used algorithm for classifying accelerometer wear and nonwear time intervals using objective movement data obtained in the whole-room indirect calorimeter.we conducted a validation study of a wear or nonwear automatic algorithm using data obtained from 49 adults and 76 youth wearing accelerometers during a strictly monitored 24-h stay in a room calorimeter. The accelerometer wear and nonwear time classified by the algorithm was compared with actual wearing time. Potential improvements to the algorithm were examined using the minimum classification error as an optimization target.the recommended elements in the new algorithm are as follows: 1) zero-count threshold during a nonwear time interval, 2) 90-min time window for consecutive zero or nonzero counts, and 3) allowance of 2-min interval of nonzero counts with the upstream or downstream 30-min consecutive zero-count window for detection of artifactual movements. Compared with the true wearing status, improvements to the algorithm decreased nonwear time misclassification during the waking and the 24-h periods (all P values < 0.001).the accelerometer wear or nonwear time algorithm improvements may lead to more accurate estimation of time spent in sedentary and active behaviors.
| S-EPMC3184184 | biostudies-literature
Project description:Background and aimsWearable inertial sensors may offer additional kinematic parameters of the shoulder compared to traditional instruments such as goniometers when elaborate and time-consuming data processing procedures are undertaken. However, in clinical practice simple-real time motion analysis is required to improve clinical reasoning. Therefore, the aim was to assess the criterion validity between a portable "off-the-shelf" sensor-software system (IMU) and optical motion (Mocap) for measuring kinematic parameters during active shoulder movements.Methods24 healthy participants (9 female, 15 male, age 29 ± 4 years, height 177 ± 11 cm, weight 73 ± 14 kg) were included. Range of motion (ROM), total range of motion (TROM), peak and mean angular velocity of both systems were assessed during simple (abduction/adduction, horizontal flexion/horizontal extension, vertical flexion/extension, and external/internal rotation) and complex shoulder movements. Criterion validity was determined using intraclass-correlation coefficients (ICC), root mean square error (RMSE) and Bland and Altmann analysis (bias; upper and lower limits of agreement).ResultsROM and TROM analysis revealed inconsistent validity during simple (ICC: 0.040-0.733, RMSE: 9.7°-20.3°, bias: 1.2°-50.7°) and insufficient agreement during complex shoulder movements (ICC: 0.104-0.453, RMSE: 10.1°-23.3°, bias: 1.0°-55.9°). Peak angular velocity (ICC: 0.202-0.865, RMSE: 14.6°/s-26.7°/s, bias: 10.2°/s-29.9°/s) and mean angular velocity (ICC: 0.019-0.786, RMSE:6.1°/s-34.2°/s, bias: 1.6°/s-27.8°/s) were inconsistent.ConclusionsThe "off-the-shelf" sensor-software system showed overall insufficient agreement with the gold standard. Further development of commercial IMU-software-solutions may increase measurement accuracy and permit their integration into everyday clinical practice.
| S-EPMC9364332 | biostudies-literature