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Journal of Physical Activity & Health May 2019: Improving sidewalks may encourage physical activity by providing safe, defined, and connected walking spaces. However, it is unknown if reduced health care...
: Improving sidewalks may encourage physical activity by providing safe, defined, and connected walking spaces. However, it is unknown if reduced health care expenditures assumed by increased physical activity offset the investment for sidewalk improvements. : This cost-effectiveness analysis of sidewalk improvements in Houston, TX, was among adults enrolled in the Houston Travel-Related Activity in Neighborhoods Study, 2013-2017 . The 1-year change in physical activity was measured using self-report (n = 430) and accelerometry (n = 228) and expressed in metabolic equivalent (MET) hours per year (MET·h·y). Cost-effectiveness ratios were calculated by comparing annualized sidewalk improvement costs (per person) with 1-year changes in physical activity. : The estimated cost-effectiveness ratio were $0.01 and -$0.46 per MET·h·y for self-reported and accelerometer-derived physical activity, respectively. The cost-effectiveness benchmark was $0.18 (95% confidence interval, $0.06-$0.43) per MET·h·y gained based on the volume of physical activity necessary to avoid health care costs. : Improving sidewalks was cost-effective based on self-reported physical activity, but not cost-effective based on accelerometry. Study findings suggest that improving sidewalks may not be a sufficient catalyst for changing total physical activity; however, other benefits of making sidewalks more walkable should be considered when deciding to invest in sidewalk improvements.
Topics: Accelerometry; Cost-Benefit Analysis; Environment Design; Exercise; Female; Humans; Male
PubMed: 30982380
DOI: 10.1123/jpah.2018-0329 -
Sensors (Basel, Switzerland) Oct 2022Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity...
Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.
Topics: Accelerometry; Algorithms; Human Activities; Humans
PubMed: 36236586
DOI: 10.3390/s22197482 -
Animal : An International Journal of... Jul 2017Temporo-spatial observation of the leg could provide important information about the general condition of an animal, especially for those such as sheep and other...
Temporo-spatial observation of the leg could provide important information about the general condition of an animal, especially for those such as sheep and other free-ranging farm animals that can be difficult to access. Tri-axial accelerometers are capable of collecting vast amounts of data for locomotion and posture observations; however, interpretation and optimization of these data records remain a challenge. The aim of the present study was to introduce an optimized method for gait (walking, trotting and galloping) and posture (standing and lying) discrimination, using the acceleration values recorded by a tri-axial accelerometer mounted on the hind leg of sheep. The acceleration values recorded on the vertical and horizontal axes, as well as the total acceleration values were categorized. The relative frequencies of the acceleration categories (RFACs) were calculated in 3-s epochs. Reliable RFACs for gait and posture discrimination were identified with discriminant function and canonical analyses. Post hoc predictions for the two axes and total acceleration were conducted, using classification functions and classification scores for each epoch. Mahalanobis distances were used to determine the level of accuracy of the method. The highest discriminatory power for gait discrimination yielded four RFACs on the vertical axis, and five RFACs each on the horizontal axis and total acceleration vector. Classification functions showed the highest accuracy for walking and galloping. The highest total accuracy on the vertical and horizontal axes were 90% and 91%, respectively. Regarding posture discrimination, the vertical axis exhibited the highest discriminatory power, with values of RFAC (0, 1]=99.95% for standing; and RFAC (-1, 0]=99.50% for lying. The horizontal axis showed strong discrimination for the lying side of the animal, as values were in the acceleration category of (0, 1] for lying on the left side and (-1, 0] on the right side. The algorithm developed by the method employed in the present study facilitates differentiation of the various types of gait and posture in animals from fewer data records, and produces the most reliable acceleration values from only one axis within a short time frame. The present study introduces an optimized method by which the tri-axial accelerometer can be used in gait and posture discrimination in sheep as an animal model.
Topics: Acceleration; Accelerometry; Algorithms; Animals; Behavior, Animal; Dairying; Female; Gait; Humans; Locomotion; Male; Posture; Sheep; Video Recording
PubMed: 27903315
DOI: 10.1017/S175173111600255X -
International Journal of Environmental... Jul 2019Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2...
Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler's unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants' performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was "carried" by an adult (median = 144 counts/5 s; < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for "carried" were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the "carried" behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and "carried" as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being "carried" from ambulatory movements.
Topics: Accelerometry; Child Behavior; Child, Preschool; Data Analysis; Exercise; Female; Goals; Hip; Humans; Infant; Machine Learning; Male; Sedentary Behavior; Video Recording; Wrist
PubMed: 31330889
DOI: 10.3390/ijerph16142598 -
Clinical Neurophysiology : Official... Oct 2021To develop and test wearable monitoring of surface electromyography and motion for detection and quantification of positive and negative myoclonus in patients with...
OBJECTIVE
To develop and test wearable monitoring of surface electromyography and motion for detection and quantification of positive and negative myoclonus in patients with progressive myoclonic epilepsy type 1 (EPM1).
METHODS
Surface electromyography and three-dimensional acceleration were measured from 23 EPM1 patients from the biceps brachii (BB) of the dominant and the extensor digitorum communis (EDC) of the non-dominant arm for 48 hours. The patients self-reported the degree of myoclonus in a diary once an hour. Severity of myoclonus with action was evaluated by using video-recorded Unified Myoclonus Rating Scale (UMRS). Correlations of monitored parameters were quantified with the UMRS scores and the self-reported degrees of myoclonus.
RESULTS
The monitoring-based myoclonus index correlated significantly (p < 0.001) with the UMRS scores (ρ = 0.883 for BB and ρ = 0.823 for EDC) and with the self-reported myoclonus degrees (ρ = 0.483 for BB and ρ = 0.443 for EDC). Ten patients were assessed as probably having negative myoclonus in UMRS, while our algorithm detected that in twelve patients.
CONCLUSIONS
Wearable monitoring was able to detect both positive and negative myoclonus in EPM1 patients.
SIGNIFICANCE
Our method is suitable for quantifying objective, real-life treatment effects at home and progression of myoclonus.
Topics: Accelerometry; Adolescent; Adult; Electromyography; Female; Humans; Male; Middle Aged; Myoclonus; Unverricht-Lundborg Syndrome; Wearable Electronic Devices; Young Adult
PubMed: 34454274
DOI: 10.1016/j.clinph.2021.06.026 -
The International Journal of Behavioral... Jun 2012Despite their increased use, no studies have examined the validity of Actical accelerometry cut points for moderate physical activity (PA) in underserved (low-income,...
BACKGROUND
Despite their increased use, no studies have examined the validity of Actical accelerometry cut points for moderate physical activity (PA) in underserved (low-income, high-crime), minority populations. The high rates of chronic disease and physical inactivity in these populations likely impact the measurement of PA. There is growing concern that traditionally defined cut points may be too high for older or inactive adults. The present study aimed to determine the self-selected pace associated with instructions to "walk for exercise" and the corresponding accelerometry estimates (e.g., Actical counts/minute) for underserved, African American adults.
METHOD
Fifty one participants (61% women) had a mean age of 60.1 (SD = 9.9) and a mean body mass index of 30.5 kg/m2 (SD = 6.0). They performed one seated task, one standing task, and three walking tasks: "strolling"; "walking for exercise"; and "walking in an emergency."
RESULTS
The average pace for strolling, walking for exercise, and walking in an emergency were 1.62 miles per hour (mph; SD = .51), 2.51 mph (SD = .53), and 2.86 mph (SD = .58), respectively. The average Actical counts/minute for the five activities were: 4 (SD = 15), 16 (SD = 29), 751 (SD = 591), 2006 (SD = 1095), and 2617 (SD = 1169), respectively. Regression analyses showed that the predicted counts/minute for a pace of 2.0 mph (which is used as the criterion for moderate exercise in this study) was 1075 counts/minute (SEM = 73).
CONCLUSIONS
The cut point associated with subjectively determined moderate PA is similar to those previously published for older adults and extends the use of adjusted cut points to African American populations. These results indicate that accurate cut points can be obtained using this innovative methodology.
Topics: Accelerometry; Black or African American; Aged; Body Mass Index; Female; Health Behavior; Humans; Male; Middle Aged; Monitoring, Physiologic; Motor Activity; Poverty; Social Environment; Surveys and Questionnaires; Walking
PubMed: 22697280
DOI: 10.1186/1479-5868-9-73 -
Sensors (Basel, Switzerland) May 2022Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and...
Physical activity (PA) is globally recognized as a pillar of general health. Step count, as one measure of PA, is a well known predictor of long-term morbidity and mortality. Despite its popularity in consumer devices, a lack of methodological standards and clinical validation remains a major impediment to step count being accepted as a valid clinical endpoint. Previous works have mainly focused on device-specific step-count algorithms and often employ sensor modalities that may not be widely available. This may limit step-count suitability in clinical scenarios. In this paper, we trained neural network models on publicly available data and tested on an independent cohort using two approaches: generalization and personalization. Specifically, we trained neural networks on accelerometer signals from one device and either directly applied them or adapted them individually to accelerometer data obtained from a separate subject cohort wearing multiple distinct devices. The best models exhibited highly accurate step-count estimates for both the generalization (96-99%) and personalization (98-99%) approaches. The results demonstrate that it is possible to develop device-agnostic, accelerometer-only algorithms that provide highly accurate step counts, positioning step count as a reliable mobility endpoint and a strong candidate for clinical validation.
Topics: Accelerometry; Algorithms; Deep Learning; Exercise; Humans; Neural Networks, Computer
PubMed: 35684609
DOI: 10.3390/s22113989 -
Telemedicine Journal and E-health : the... Oct 2015Studies have shown self-monitoring can modify health behaviors, including physical activity (PA). This study tested the utility of a wearable sensor/device (Fitbit(®)...
BACKGROUND
Studies have shown self-monitoring can modify health behaviors, including physical activity (PA). This study tested the utility of a wearable sensor/device (Fitbit(®) One™; Fitbit Inc., San Francisco, CA) and short message service (SMS) text-messaging prompts to increase PA in overweight and obese adults.
MATERIALS AND METHODS
Sixty-seven adults wore a Fitbit One tracker for 6 weeks; half were randomized to also receive three daily SMS-based PA prompts. The Fitbit One consisted of a wearable tracker for instant feedback on performance and a Web site/mobile application (app) for detailed summaries. Outcome measures were objectively measured steps and minutes of PA by intensity using two accelerometers: Actigraph™ (Pensacola, FL) GT3X+ (primary measure) at baseline and Week 6 and Fitbit One (secondary measure) at baseline and Weeks 1, 2, 3, 4, 5, and 6.
RESULTS
Mixed-model repeated-measures analysis of primary measures indicated a significant within-group increase of +4.3 (standard error [SE]=2.0) min/week of moderate- to vigorous-intensity PA (MVPA) at 6-week follow-up (p=0.04) in the comparison group (Fitbit only), but no study group differences across PA levels. Secondary measures indicated the SMS text-messaging effect lasted for only 1 week: the intervention group increased by +1,266 steps (SE=491; p=0.01), +17.8 min/week MVPA (SE=8.5; p=0.04), and +38.3 min/week total PA (SE=15.9; p=0.02) compared with no changes in the comparison group, and these between-group differences were significant for steps (p=0.01), fairly/very active minutes (p<0.01), and total active minutes (p=0.02).
CONCLUSIONS
These data suggest that the Fitbit One achieved a small increase in MVPA at follow-up and that the SMS-based PA prompts were insufficient in increasing PA beyond 1 week. Future studies can test this intervention in those requiring less help and/or test strategies to increase participants' engagement levels.
Topics: Accelerometry; Adolescent; Adult; Aged; Exercise; Exercise Therapy; Female; Humans; Male; Middle Aged; Monitoring, Physiologic; Obesity; Text Messaging
PubMed: 26431257
DOI: 10.1089/tmj.2014.0176 -
Anesthesiology Nov 2018WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Quantitative neuromuscular monitoring is required to ensure neuromuscular function has recovered completely at the... (Comparative Study)
Comparative Study Observational Study
WHAT WE ALREADY KNOW ABOUT THIS TOPIC
WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Quantitative neuromuscular monitoring is required to ensure neuromuscular function has recovered completely at the time of tracheal extubation. The TOFscan (Drager Technologies, Canada) is a new three-dimensional acceleromyography device that measures movement of the thumb in multiple planes. The aim of this observational investigation was to assess the agreement between nonnormalized and normalized train-of-four values obtained with the TOF-Watch SX (Organon, Ireland) and those obtained with the TOFscan during recovery from neuromuscular blockade.
METHODS
Twenty-five patients were administered rocuronium, and spontaneous recovery of neuromuscular blockade was allowed to occur. The TOFscan and TOF-Watch SX devices were applied to opposite arms. A preload was applied to the TOF-Watch SX, and calibration was performed before rocuronium administration. Both devices were activated, and train-of-four values were obtained every 15 s. Modified Bland-Altman analyses were conducted to compare train-of-four ratios measured with the TOFscan to those measured with the TOF-Watch SX (when train-of-four thresholds of 0.2 to 1.0 were achieved).
RESULTS
Bias and 95% limits of agreement between the TOF-Watch SX and the TOFscan at nonnormalized train-of-four ratios between 0.2 and 1.0 were 0.021 and -0.100 to 0.141, respectively. When train-of-four measures with the TOF-Watch SX were normalized, bias and 95% limits of agreement between the TOF-Watch SX and the TOFscan at ratios between 0.2 and 1.0 were 0.015 and -0.097 to 0.126, respectively.
CONCLUSIONS
Good agreement between the TOF-Watch SX with calibration and preload application and the uncalibrated TOFscan was observed throughout all stages of neuromuscular recovery.
Topics: Accelerometry; Anesthesia Recovery Period; Arm; Equipment Design; Female; Humans; Male; Middle Aged; Neuromuscular Blockade; Neuromuscular Monitoring; Prospective Studies; Thumb
PubMed: 30130260
DOI: 10.1097/ALN.0000000000002400 -
Journal of Neuroengineering and... Jul 2020LSVT-BIG® is an intensively delivered, amplitude-oriented exercise therapy reported to improve mobility in individuals with Parkinson's disease (PD). However, questions...
BACKGROUND
LSVT-BIG® is an intensively delivered, amplitude-oriented exercise therapy reported to improve mobility in individuals with Parkinson's disease (PD). However, questions remain surrounding the efficacy of LSVT-BIG® when compared with similar exercise therapies. Instrumented clinical tests using body-worn sensors can provide a means to objectively monitor patient progression with therapy by quantifying features of motor function, yet research exploring the feasibility of this approach has been limited to date. The aim of this study was to use accelerometer-instrumented clinical tests to quantify features of gait, balance and fine motor control in individuals with PD, in order to examine motor function during and following LSVT-BIG® therapy.
METHODS
Twelve individuals with PD undergoing LSVT-BIG® therapy, eight non-exercising PD controls and 14 healthy controls were recruited to participate in the study. Functional mobility was examined using features derived from accelerometry recorded during five instrumented clinical tests: 10 m walk, Timed-Up-and-Go, Sit-to-Stand, quiet stance, and finger tapping. PD subjects undergoing therapy were assessed before, each week during, and up to 13 weeks following LSVT-BIG®.
RESULTS
Accelerometry data captured significant improvements in 10 m walk and Timed-Up-and-Go times with LSVT-BIG® (p < 0.001), accompanied by increased stride length. Temporal features of the gait cycle were significantly lower following therapy, though no change was observed with measures of asymmetry or stride variance. The total number of Sit-to-Stand transitions significantly increased with LSVT-BIG® (p < 0.001), corresponding to a significant reduction of time spent in each phase of the Sit-to-Stand cycle. No change in measures related to postural or fine motor control was observed with LSVT-BIG®. PD subjects undergoing LSVT-BIG® showed significant improvements in 10 m walk (p < 0.001) and Timed-Up-and-Go times (p = 0.004) over a four-week period when compared to non-exercising PD controls, who showed no week-to-week improvement in any task examined.
CONCLUSIONS
This study demonstrates the potential for wearable sensors to objectively quantify changes in motor function in response to therapeutic exercise interventions in PD. The observed improvements in accelerometer-derived features provide support for instrumenting gait and sit-to-stand tasks, and demonstrate a rescaling of the speed-amplitude relationship during gait in PD following LSVT-BIG®.
Topics: Accelerometry; Aged; Exercise Therapy; Feasibility Studies; Female; Humans; Male; Parkinson Disease; Wearable Electronic Devices
PubMed: 32660495
DOI: 10.1186/s12984-020-00729-8