1,721,193 research outputs found
Replication Data for: Systematic Review of the Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate
No full textIntroduction: Consumer-wearable activity trackers are small electronic devices engineered to monitor and record fitness and health-related measures. The purpose of this systematic review is to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure.
Method: We extracted information about commercial wearable devices (e.g., price, size, battery life, sensors, measurements, algorithms) using an Internet search conducted from November 2016- January 2017. From this search we identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only included articles published in the English language up to May 2019. Studies were excluded if they did not identify the device used and if they did not examine the validity and/or reliability of a device. Studies including the general population and all special populations were included. We operationalized validity as criterion (as compared to other measures) and construct (degree to which device is measuring what it purports) validity. Reliability measures focused on intradevice and interdevice reliability.
Results: We included 158 publications examining 9 different commercial wearable device brands. Fitbit was by far the most studied brand. In lab-based settings Fitbit, Apple, and Samsung appeared to measure steps accurately. Heart rate was more variable with Apple Watch, Garmin was the most accurate and Fitbit tended towards underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.
Conclusion: Activity trackers are still an emerging market and the devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research
Replication Data for: Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.
No full textObjectives
There is considerable promise for using commercial wearable devices for measuring physical activity at the population level. The objective of this study was to examine whether commercial wearable devices could accurately predict lying, sitting, and different physical activity intensity in a lab based protocol.
Methods
We recruited a convenience sample of 46 participants (26 women) to wear three devices, a GENEActiv, and Apple Watch Series 2, a Fitbit Charge HR2. Participants completed a 65-minute protocol with 40-minutes of total treadmill time and 25-minutes of sitting or lying time. Indirect calorimetry was used to measure energy expenditure. The outcome variable for the study was the activity class; lying, sitting, walking self-paced, 3 METS, 5 METS, and 7 METS. Minute-by-minute heart rate, steps, distance, and calories from Apple Watch and Fitbit were included in four different machine learning models.
Results
Our analysis dataset included 3656 and 2608 minutes of Apple Watch and Fitbit data, respectively. We test decision trees, support vector machines, random forest, and rotation forest models. Rotation forest models had the highest classification accuracies at 82.6% for Apple Watch and 89.3% for Fitbit. Classification accuracies for Apple Watch data ranged from 72.5% for sitting to 89.0% for 7 METS. For Fitbit, accuracies varied between 86.2 for sitting to 92.6% for 7 METS.
Conclusion
This study demonstrated that commercial wearable devices, Apple Watch and Fitbit, were able to predict physical activity type with a reasonable accuracy. The results support the use of minute by minute data from Apple Watch and Fitbit combined machine learning approaches for scalable physical activity type classification at the population level
Bike Share Strike Data
No full textDaily bicycle share data for Philadelphia, Chicago, Boston, and Washington from 201
Bike Share Strike Data
No full textDaily bicycle share data for Philadelphia, Chicago, Boston, and Washington from 201
Replication Data for: Systematic Review of the Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate
No full textIntroduction: Consumer-wearable activity trackers are small electronic devices engineered to monitor and record fitness and health-related measures. The purpose of this systematic review is to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure.
Method: We extracted information about commercial wearable devices (e.g., price, size, battery life, sensors, measurements, algorithms) using an Internet search conducted from November 2016- January 2017. From this search we identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only included articles published in the English language up to May 2019. Studies were excluded if they did not identify the device used and if they did not examine the validity and/or reliability of a device. Studies including the general population and all special populations were included. We operationalized validity as criterion (as compared to other measures) and construct (degree to which device is measuring what it purports) validity. Reliability measures focused on intradevice and interdevice reliability.
Results: We included 158 publications examining 9 different commercial wearable device brands. Fitbit was by far the most studied brand. In lab-based settings Fitbit, Apple, and Samsung appeared to measure steps accurately. Heart rate was more variable with Apple Watch, Garmin was the most accurate and Fitbit tended towards underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.
Conclusion: Activity trackers are still an emerging market and the devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research
Active Living Feature Score
No full textIn this research, Ali Alfosool proposes Active Living Feature Score or ALF-Score, a novel approach to measure walkability more accurately and efficiently while addressing existing limitations. ALF-Score incorporates road network structure to derive various features such as network science centralities and network embedding which are crucial in better understanding the road structure. ALF-Score utilizes user opinion to build high-confidence ground-truth that is used to generate models capable of estimating walkability scores based on user opinion. By incorporating machine learning approaches in my pipelines, he was able to achieve a much higher granularity and higher spatial resolution of walkability scores at point level
Urban Sprawl Index for Canada
No full textThe objective this research is to develop a multi-dimensional, nationwide urban sprawl index for Canada at a small-area level (i.e., Census Tract, CT) using spatial statistical modelling, filling the gap that a comprehensive urban sprawl index at a fine spatial scale is missing in Canada.
We provide the data for 2011 and 2016 census years in 3 different formats (CSV, GeoJSON, and Shapefiles).
This was as part of the Canadian Urban Environmental Health Research Consortium (CANUE). The code for the analysis, meta data, and data are available here. https://github.com/walkabillylab/sprawl_scor
Replication Data for: The impact of physical activity cut-point choice on childhood activity estimates
No full textThis dataset is the replication data for the paper "The impact of physical activity cut-point choice on childhood activity estimates". It includes accelerometer data from 617 children who contributed a total of 1314 participant-weeks of valid data after processing. The detailed description of each column is as follows:
1. participant_week_index. Because we are considering participant week as our basic analysis object, so each participant week is assigned with a unique index.
2. datetime. The time is at the minute level.
3. time_class. There are 3 types of time in our study which are:
3.1. time class [1] represents school time. 09:15 am to 15:00 pm from Monday to Friday.
3.2. time class [2] represents leisure time. 06:00am to 09:15am and 15:00pm to 22:00pm from Monday to Friday, and 06:00am to 22:00pm on Saturday and Sunday.
3.3. time class [3] represents other time.
4. counts_per_minute. The accelerometer data were collected at 100 Hz epochs and reduced to vector magnitude (VM) with a 1-second epoch using ActiLife 6 data analysis software. Further, the VM within a minute is accumulated to get counts_per_minute.
5. wear_and_awake. There are 2 values:
5.1. value [0] represents that the participant is sleeping or doesn't wear the device.
5.2. value [1] represents the participant is awake and wear the device.
6. activity_level. There are 4 types under standard threshold:
6.1. N/A represents "not applicable". Because we only consider the accelerometer data during leisure hours, the activity level will be labeled as N/A if the time is not in leisure hours.
6.2. SED if CPM <= 150.
6.3. LPA if CPM is in (150, 1951]
6.4. MVPA if CPM > 195
Replication Data for: The impact of physical activity cut-point choice on childhood activity estimates
No full textThis dataset is the replication data for the paper "The impact of physical activity cut-point choice on childhood activity estimates". It includes accelerometer data from 617 children who contributed a total of 1314 participant-weeks of valid data after processing. The detailed description of each column is as follows:
1. participant_week_index. Because we are considering participant week as our basic analysis object, so each participant week is assigned with a unique index.
2. datetime. The time is at the minute level.
3. time_class. There are 3 types of time in our study which are:
3.1. time class [1] represents school time. 09:15 am to 15:00 pm from Monday to Friday.
3.2. time class [2] represents leisure time. 06:00am to 09:15am and 15:00pm to 22:00pm from Monday to Friday, and 06:00am to 22:00pm on Saturday and Sunday.
3.3. time class [3] represents other time.
4. counts_per_minute. The accelerometer data were collected at 100 Hz epochs and reduced to vector magnitude (VM) with a 1-second epoch using ActiLife 6 data analysis software. Further, the VM within a minute is accumulated to get counts_per_minute.
5. wear_and_awake. There are 2 values:
5.1. value [0] represents that the participant is sleeping or doesn't wear the device.
5.2. value [1] represents the participant is awake and wear the device.
6. activity_level. There are 4 types under standard threshold:
6.1. N/A represents "not applicable". Because we only consider the accelerometer data during leisure hours, the activity level will be labeled as N/A if the time is not in leisure hours.
6.2. SED if CPM <= 150.
6.3. LPA if CPM is in (150, 1951]
6.4. MVPA if CPM > 195
Replication Data for: Classification of sleep, sedentary behaviour, and physical activity using commercial wearable devices
No full textThis dataset is the replication data for the paper "Classification of sleep, sedentary behaviour, and physical activity using commercial wearable devices". It includes the data collected at 1Hz heart rate, steps, distance, and calories from Apple Watch and Fitbit. We also collected participants age, self-report height and weight. We created physical activity and sleep labels using the data collected by GENEActiv device. Each dataset includes 57097 and 21489 minutes of data for Apple Watch and Fitbit, respectively. The heart rate attribute for both datasets is interpolated using linear interpolation.
Attribute Information: 1- Heartrate (bpm), 2- Calories (kcal), 3- Steps (count), 4- Distance (km), 5- Age (numerical), 6- Gender (M/F), 7- Weight (lb), 8- Height (ft), 9- Activity (Sleep/Sedentary/Light/Moderate/Vigorous)
Implementation: Is available at BEAP Lab GitHub page here</p
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