Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Chowdhury AK
PROVIDER: S-EPMC6833090 | biostudies-literature | 2019 Oct
REPOSITORIES: biostudies-literature
Publications
Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological Data.
Chowdhury Alok Kumar AK Tjondronegoro Dian D Chandran Vinod V Zhang Jinglan J Trost Stewart G SG
Sensors (Basel, Switzerland) 20191017 20
This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participant ...[more]
Similar Datasets
Project description:PurposePhysical activity (PA) intensity is expressed as either absolute or relative intensity. Absolute intensity refers to the energy required to perform an activity. Relative intensity refers to a level of effort that takes into account how hard an individual is working relative to their maximum capacity. We sought to develop methods for obtaining individualized relative-intensity accelerometer cut points using data from a maximal graded exercise treadmill test (GXT) so that each individual has their own cut point.MethodsA total of 2363 men and women 38 to 50 yr old from the CARDIA fitness study wore ActiGraph 7164 accelerometers during a maximal GXT and for seven consecutive days in 2005-2006. Using mixed-effects regression models, we regressed accelerometer counts on heart rate as a percentage of maximum (%HRmax) and on RPE. Based on these two models, we obtained a moderate-intensity (%HRmax = 64% or RPE = 12) count cut point that is specific to each participant. We applied these subject-specific cut points to the available CARDIA accelerometer data.ResultsUsing RPE, the mean moderate-intensity accelerometer cut point was 4004 (SD = 1120) counts per minute. On average, cut points were higher for men (4189 counts per minute) versus women (3865 counts per minute) and were higher for Whites (4088 counts per minute) versus African Americans (3896 counts per minute). Cut points were correlated with body mass index (rho = -0.11) and GXT duration (rho = 0.33). Mean daily minutes of absolute- and relative-intensity moderate to vigorous PA were 34.1 (SD = 31.1) min·d and 9.1 (SD = 18.2) min·d, respectively. RPE cut points were higher than those based on %HRmax. This is likely due to some participants ending the GXT before achieving their HRmax.ConclusionsAccelerometer-based relative-intensity PA may be a useful measure of intensity relative to maximal capacity.
| S-EPMC6962549 | biostudies-literature
Project description:Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities - the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.
| S-EPMC5537365 | biostudies-literature
Project description:Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs-fMRI from a large data set with 839 subjects. Our study is a follow-up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs-fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.
| S-EPMC7267976 | biostudies-literature
Project description:BACKGROUND:24 h-accelerometry is now used to objectively assess physical activity (PA) in many observational studies like the German National Cohort; however, PA variability, observational time needed to estimate habitual PA, and reliability are unclear. METHODS:We assessed 24 h-PA of 50 participants using triaxial accelerometers (ActiGraph GT3X+) over 2 weeks. Variability of overall PA and different PA intensities (time in inactivity and in low intensity, moderate, vigorous, and very vigorous PA) between days of assessment or days of the week was quantified using linear mixed-effects and random effects models. We calculated the required number of days to estimate PA, and calculated PA reliability using intraclass correlation coefficients. RESULTS:Between- and within-person variance accounted for 34.4-45.5% and 54.5-65.6%, respectively, of total variance in overall PA and PA intensities over the 2 weeks. Overall PA and times in low intensity, moderate, and vigorous PA decreased slightly over the first 3 days of assessment. Overall PA (p?=?0.03), time in inactivity (p?=?0.003), in low intensity PA (p?=?0.001), in moderate PA (p?=?0.02), and in vigorous PA (p?=?0.04) slightly differed between days of the week, being highest on Wednesday and Friday and lowest on Sunday and Monday, with apparent differences between Saturday and Sunday. In nested random models, the day of the week accounted for <?19% of total variance in the PA parameters. On average, the required number of days to estimate habitual PA was around 1 week, being 7 for overall PA and ranging from 6 to 9 for the PA intensities. Week-to-week reliability was good (intraclass correlation coefficients, range, 0.68-0.82). CONCLUSIONS:Individual PA, as assessed using 24 h-accelerometry, is highly variable between days, but the day of assessment or the day of the week explain only small parts of this variance. Our data indicate that 1 week of assessment is necessary for reliable estimation of habitual PA.
| S-EPMC5910625 | biostudies-literature
Project description:Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual's psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users' Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely-perception of emotion, managing own emotions, managing other's emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed.
| S-EPMC8270480 | biostudies-literature
Project description:PurposeNational guidelines recommend adolescents achieve 60 minutes of moderate-to-vigorous physical activity (MVPA)/day, yet few adolescents meet these guidelines.MethodsWe piloted a novel quasi-randomized physical activity intervention to promote adolescent's use of their surrounding built environment among 30 intervention and 30 control overweight/obese adolescents aged 10-16 years living in greater Boston from 2013 to 2015. Location-specific MPVA was measured by accelerometry and global positioning system for three one-week periods (Time 1 [T1], Time 2 [T2], and Time 3 [T3]). One month after T1, intervention participants received individualized counseling on how to use their surrounding built environment to increase MVPA, and control participants received standard-of-care lifestyle modification counseling; both groups received their T1 physical activity data. T2 assessment occurred the week after the counseling visit and T3 assessment 3-4 months later. The main outcome was change in average daily minutes of MVPA; the secondary outcome was meeting national MVPA guidelines. Multivariable modeling accounted for covariates (baseline MVPA, body mass index, age, sex, race/ethnicity) and clustering by study group and town.ResultsAmong the 60 adolescents recruited, 55 (92%) completed data collection. Short-term (T2) intervention effects included increased average MVPA of +13.9 minutes intervention versus -.6 minutes control (p < .0001). Differential increase in mean daily MVPA was sustained at T3 (9.3 minutes more in intervention group; p = .0006). The proportion of adolescents in the intervention group who achieved 60 minutes/day of MVPA increased from 11% (T1) to 21% (T2), whereas declining (7%-0%) among controls.ConclusionsIndividualized counseling about the built environment can help increase MVPA among overweight and obese adolescents.
| S-EPMC5002229 | biostudies-literature
Project description:Night shift workers are often associated with circadian misalignment and physical discomfort, which may lead to burnout and decreased work performance. Moreover, the irregular work hours can lead to significant negative health outcomes such as poor eating habits, smoking, and being sedentary more often. This paper uses commercial wearable sensors to explore correlates and differences in the level of physical activity, sleep, and circadian misalignment indicators among day shift nurses and night shift nurses. We identify which self-reported assessments of affect, life satisfaction, and sleep quality, are associated with physiological and behavioral signals captured by wearable sensors. The results using data collected from 113 nurses in a large hospital setting, over a period of 10 weeks, indicate that night shift nurses are more sedentary, and report lower levels of life satisfaction than day-shift nurses. Moreover, night shift nurses report poorer sleep quality, which may be correlated with challenges in their attempts to fall asleep on off-days.
| S-EPMC8062546 | biostudies-literature
Project description:BackgroundSocial media technology such as Twitter allows users to share their thoughts, feelings, and opinions online. The growing body of social media data is becoming a central part of infodemiology research as these data can be combined with other public health datasets (eg, physical activity levels) to provide real-time monitoring of psychological and behavior outcomes that inform health behaviors. Currently, it is unclear whether Twitter data can be used to monitor physical activity levels.ObjectiveThe aim of this study was to establish the feasibility of using Twitter data to monitor physical activity levels by assessing whether the frequency and sentiment of physical activity-related tweets were associated with physical activity levels across the United States.MethodsTweets were collected from Twitter's application programming interface (API) between January 10, 2017 and January 2, 2018. We used Twitter's garden hose method of collecting tweets, which provided a random sample of approximately 1% of all tweets with location metadata falling within the United States. Geotagged tweets were filtered. A list of physical activity-related hashtags was collected and used to further classify these geolocated tweets. Twitter data were merged with physical activity data collected as part of the Behavioral Risk Factor Surveillance System. Multiple linear regression models were fit to assess the relationship between physical activity-related tweets and physical activity levels by county while controlling for population and socioeconomic status measures.ResultsDuring the study period, 442,959,789 unique tweets were collected, of which 64,005,336 (14.44%) were geotagged with latitude and longitude coordinates. Aggregated data were obtained for a total of 3138 counties in the United States. The mean county-level percentage of physically active individuals was 74.05% (SD 5.2) and 75.30% (SD 4.96) after adjusting for age. The model showed that the percentage of physical activity-related tweets was significantly associated with physical activity levels (beta=.11; SE 0.2; P<.001) and age-adjusted physical activity (beta=.10; SE 0.20; P<.001) on a county level while adjusting for both Gini index and education level. However, the overall explained variance of the model was low (R2=.11). The sentiment of the physical activity-related tweets was not a significant predictor of physical activity level and age-adjusted physical activity on a county level after including the Gini index and education level in the model (P>.05).ConclusionsSocial media data may be a valuable tool for public health organizations to monitor physical activity levels, as it can overcome the time lag in the reporting of physical activity epidemiology data faced by traditional research methods (eg, surveys and observational studies). Consequently, this tool may have the potential to help public health organizations better mobilize and target physical activity interventions.
| S-EPMC6682305 | biostudies-literature
Project description:Biomolecules assisted preparation of fluorescent gold nanoparticles (FL-Au NPs) has been reported in this work using glucose oxidase enzyme as both reducing and stabilizing agent and demonstrated their application through multimodal sensing strategy for selective detection of cysteine (Cys). Three different methods namely fluorescence turn OFF-ON strategy, naked eye detection and electrochemical methods are used for Cys detection by employing FL-Au NPs as a common probe. In case of fluorescence turn-OFF method a strong interaction between Au NPs and thiol results in quenching of fluorescence due to replacement of glucose oxidase by Cys at neutral pH. Second mode is based on fluorescence switch-ON strategy where initial fluorescence is significantly quenched by either excess acid or base and further addition of Cys results in appearance of rosy-red and green fluorescence respectively. Visual colour change and fluorescence emission arises due to etching of Au atoms on the surface by thiol leading to formation of Au nanoclusters. Finally, electrochemical sensing of Cys is also carried out using cyclic voltammetry in 0.1 M PBS solution. These findings provide a suitable platform for Cys detection over a wide range of pH and concentration levels and hence the sensitivity can also be tuned accordingly.
| S-EPMC5177922 | biostudies-literature
Project description:BACKGROUND:Communicating physical activity information with sufficient details, such as activity type, frequency, duration, and intensity, is vital to accurately delineate the attributes of physical activity that bring positive health impact. Unlike frequency and duration, intensity is a subjective concept that can be interpreted differently by people depending on demographics, health status, physical fitness, and exercise habits. However, activity intensity is often communicated using general degree modifiers, degree of physical exertion, and physical activity examples, which are the expressions that people may interpret differently. Lack of clarity in communicating the intensity level of physical activity is a potential barrier to an accurate assessment of exercise effect and effective imparting of exercise recommendations. OBJECTIVE:This study aimed to assess the variations in people's perceptions and interpretations of commonly used intensity descriptions of physical activities and to identify factors that may contribute to these variations. METHODS:A Web-based survey with a 25-item questionnaire was conducted using Amazon Mechanical Turk, targeting adults residing in the United States. The questionnaire included questions on participants' demographics, exercise habits, overall perceived health status, and perceived intensity of 10 physical activity examples. The survey responses were analyzed using the R statistical package. RESULTS:The analyses included 498 responses. The majority of respondents were females (276/498, 55.4%) and whites (399/498, 79.9%). Numeric ratings of physical exertion after exercise were relatively well associated with the 3 general degree descriptors of exercise intensity: light, moderate, and vigorous. However, there was no clear association between the intensity expressed with those degree descriptors and the degree of physical exertion the participants reported to have experienced after exercise. Intensity ratings of various examples of physical activity differed significantly according to respondents' characteristics. Regression analyses showed that those who reported good health or considered regular exercise was important for their health tended to rate the intensity levels of the activity examples significantly higher than their counterparts. The respondents' age and race (white vs nonwhite) were not significant predictors of the intensity rating. CONCLUSIONS:This survey showed significant variations in how people perceive and interpret the intensity levels of physical activities described with general severity modifiers, degrees of physical exertion, and physical activity examples. Considering that these are among the most widely used methods of communicating physical activity intensity in current practice, a possible miscommunication in assessing and promoting physical activity seems to be a real concern. We need to adopt a method that represents activity intensity in a quantifiable manner to avoid unintended miscommunication.
| S-EPMC7290482 | biostudies-literature