The authors thank the participants and their families for their time and effort expended for participation in the study. As well, the authors acknowledge Dr. Yichuan Liu and Dr. Hasan Ayaz for their assistance with the timing calculation of the HR monitoring and Dr. Paul Diefenbach for development of the KOLLECT Active Video Gaming software. Funding for this work was provided by Coulter Foundation Grants #00006143 (O&#8217;Neil; Diefenbach, PIs) and #00008819 (O&#8217;Neil; Diefenbach, PIs).

&#160;&#160;Institutional Review Board approval was obtained. All youth provided written assent and parents provided consent prior to participation.
1.&#160;AVG data collection sessions
<ol>
	<li>The AVG game session
	<ol>
		<li>In this study, have youth with CP participate in an AVG session which is comprised of three 20 min games. See Table 5 for Youth Demographics. It was expected that a total of 30 games would be played; however, 29 games were completed because one subject only played 2 games in his AVG session.</li>
		<li>Have the subjects wear a HR monitor throughout the session to record HR and ECG responses.</li>
		<li>In the AVG session, have youth play each AVG while seated on a bench with feet flat on the floor and knees and hips flexed to 90 degrees (90/90 sitting) for postural support and stability.</li>
		<li>Use the following three gaming conditions for collection objects: 1) hand icons only; 2) feet icons only; and 3) both hand and feet icons. Use a counterbalanced order between subjects. Choose these three conditions to determine which is more effective in promoting physical activity and fitness and not too demanding to cause early, undue fatigue. 
		NOTE: Each game was designed using the phases of exercise prescription: warm-up, conditioning and cool-down. [Please see Table 1]. Additionally, there was a rest phase before game play began to document baseline HR and a recovery phase after game play to document time to return to baseline HR.</li>
		<li>Allow subjects a rest period between games for HR to return to baseline level.</li>
	</ol>
	</li>
	<li>Calculating HRV from ECG Data
	<ol>
		<li>Organize data into 5 min time intervals to ensure comparable data for each phase. Therefore, there were 6 phases defined for these calculations: 1) Rest; 2) Warm-up; 3) Conditioning 1 (first 5 min); 4) Conditioning 2 (second 5 min); 5) Cool-Down (5 min) and 6) Recovery. Dividing the conditioning phase into two 5 min phases allows examination of subject aerobic performance in shorter intervals to account for fatigue due to deconditioning 12 (Table 4).</li>
		<li>To properly calculate HRV measures for each segment of a subject&#39;s session, perform R-peak detection on the raw ECG signal12,13. Use the raw signal to avoid manipulations that could skew the data.</li>
		<li>To process the data, obtain the start times of each recording session and convert from &#39;datetime&#39; variables (MM/DD/YYYY HH:MM:SS.SS) to seconds. None of the sessions occurred across two days which allowed the MM/DD/YYYY portion to be ignored during these calculations. Acquire the start time of the game of interest from the timing table to locate each game session within the electrocardiogram (ECG) file; this time was converted to seconds after it had been extracted from the timing file. The timing file contained start times for each phase of the game as well as the end of the recovery period (Table 2).</li>
		<li>Calculate the rest period as the 5 min prior to the game start and the Recovery Phase as the 5 min after the end of the Cooldown Phase. Once these times were obtained, obtain the location (S) of the game phase of interest within the ECG file through the following equation: 
		<img alt="Equation 1" src="/files/ftp_upload/59230/59230eq1.jpg" />&#160; (1) 
		where Phase is set to either Rest, Warmup, Conditioning 1, Conditioning 2, Cooldown, or Recovery; the time was divided by 1/Frequency to account for the ECG sample rate. The HR monitor had a sampling rate of 250 Hz and therefore contained a measure every 4 ms.
		<ol>
			<li>Change this number by altering the sampling rate with the first prompt from the Peak_Detection.m program to account for the use of alternate recording devices. Choose which 5 min segment to work with while running the peak detection program. This was done via a prompt to the user. Set the end time to 5 min after the start time and take the frequency of the recording device into consideration.</li>
		</ol>
		</li>
		<li>Once the 5 min section had been chosen, calculate a threshold for peak-detection based upon the average and standard deviation of the waveform.
		<ol>
			<li>Set the threshold as&#160;<img alt="Equation 2" src="/files/ftp_upload/59230/59230eq2.jpg" />but this can be increased in the program if the data are uniform to reduce false-positive detection from T peaks which are higher than their corresponding R peaks. Examples of these false positives can be seen in Figure 1.</li>
			<li>Along with a minimum height for the R peak, assign a minimum distance between peaks to minimize the detection of incorrect peaks around the desired R. Set this value to 75 which corresponded to 0.3 s between peaks or 200 beats per min (bpm) (this value changes with frequency). The value of 200 bpm is higher than any HR achieved by the subjects in this study and can be changed based on the population being studied.</li>
		</ol>
		</li>
		<li>Once the threshold was calculated, let the program run through the waveform and attempt to discern all the R&#39;s for RR interval and HRV calculations. Generate a preliminary plot so that the user could review it for irregularities such as those shown in Figure 1 or Figure 2.
		<ol>
			<li>Correct these irregularities manually by editing the Detection variable which contains the microvolt (&#181;V) reading of the peak in column 1 and the location in the current game session (s/0.004) in the second column. In most cases the proper R peaks can easily be found by zooming into the problem location as seen in Figure 1. Many data sessions are fairly uniform as shown in Figure 3 and will therefore only require a few corrections. Some cases, however are fairly messy and require more time to review and obtain proper R locations.</li>
			<li>If the fluctuations in the waveform make it excessively difficult to properly locate a peak, ignore small segments ~1-2 s and attribute to ectopic beats which are not used in HRV calculations12.</li>
		</ol>
		</li>
		<li>After the R&#39;s have been located, run the HRV_Measures program. Calculate RR intervals first as they are the basis of the HRV measures used in this study12.
		<ol>
			<li>Obtain a matrix of intervals and ignore any interval greater than 1.5 s (40 bpm) as it was due to the aforementioned ectopic beats being removed from the calculations. Save these RR intervals for further calculations and verification of data. Use these intervals to calculate the Root Mean Square of the Successive Differences (RMSSD) with the following equation: 
			<img alt="Equation 3" src="/files/ftp_upload/59230/59230eq3.jpg" />&#160;RMSSD = (2) 
			Where N = Number of RR Intervals (R-R)i = Interval between neighboring QRS Peaks (R-R)i+1 = Interval between subsequent set of peaks</li>
		</ol>
		</li>
		<li>Choose this variable as it has been shown to be efficacious on intervals ranging from 1 min to 24 h in length13,14,15,16,17 and can therefore be used to assess these 5 min intervals in the game phases. Along with RMSSD, obtain the Standard Deviation of NN intervals to measure changes in HR throughout the phase14,16,18.</li>
		<li>Use the RR intervals to calculate NN50, the number of intervals that differ from the previous interval by more than 50 ms12 which has also been used on intervals ranging from one min to 24 h16,17,19,20,21.
		<ol>
			<li>Calculate the NN50 variable via a simple count function that checked whether or not the difference between consecutive RR interval lengths was greater than 50 ms. Once NN50 was obtained in this manner, divide by the total number of intervals to calculate pNN50 which is the percentage of intervals that differ by more than 50 ms. This calculation allowed the measured data to be compared across subjects, games, and even sessions of varying lengths as it is a unit-less variable13,14,16,17.</li>
		</ol>
		</li>
		<li>Calculate mean RR interval length for each phase and subject as a separate HRV measure16,17,19,22,23,24. Use this measure to calculate Average HR by dividing the mean RR interval by 60 s. Both of these measures are easily comparable across game sessions to observe the trend of the subject&#39;s activity16,17,19,22,23,24.</li>
		<li>Once these measures were calculated, calculate the Low Frequency and High Frequency Power Spectral Density (PSD) for both the raw ECG of the 5-min interval and the RR interval matrix by obtaining PSD from Fast-Fourier transforms13,14,17,19,25. All these data were then stored in a table, an example of which is shown in Table 4.</li>
	</ol>
	</li>
</ol>
2. Acquire ECG Data from the Patient
<ol>
	<li>Prepare the HR monitor chest strap and Bluetooth module for application to the subject.
	<ol>
		<li>Ensure that the Bluetooth module has been fully charged (3 h) using the charge cradle.</li>
		<li>Plug the module into the data computer via the charge cradle and open the config tool. Enter a name for logging purposes.</li>
		<li>Select the HR device, click the Time tab and select Set Date/Time to sync the module to the correct time and date. The device can now be removed from the charge cradle.</li>
		<li>Moisten the conductive areas (beige) on the HR monitor chest strap by placing a hand in water and rubbing the conductive areas.</li>
		<li>Place the HR monitor Bluetooth module into the chest strap with the conductive surfaces of the module lined up with those of the chest strap: it will click into place.</li>
		<li>Press and hold the button on the module until the lights flash. The module is now on and recording.</li>
		<li>Apply the HR monitor chest strap (with Bluetooth module) to the player with the module aligned with the left mid-axillary line and the strap just under the pectoral muscles. Once properly positioned, tighten the device so that it will not move during the session but is not uncomfortable for the player.</li>
	</ol>
	</li>
	<li>Acquire a signal and view the live feed.
	<ol>
		<li>Plug the connector into the USB port of computer that will be used to view the data.</li>
		<li>Open the Live View program and enter Setup Mode by clicking the icon with the wrench and screwdriver.</li>
		<li>Choose a player from the list if appropriate or add a new subject with the New button in the bottom left corner of the screen.</li>
		<li>Enter Subject information as desired for identification purposes (name, age, gender, height, weight).</li>
		<li>Click on the Hardware tab and select the current subject.</li>
		<li>Click Assign in the bottom of the tab and select the current device (listed as 01 if no other devices are present). Then click assign in the pop-up box.</li>
		<li>Click on the Team tab. Highlight the subject and then click the right arrow button to place the player on Team A.</li>
		<li>Click on the Deployment tab and then move the newly created team to the first tab.</li>
		<li>Open the Live Mode tab by clicking the blue Wi-Fi symbol in the top left corner.</li>
		<li>Use the Live Mode tab to monitor HR, respiratory rate, and posture of the subject in real time. 
		NOTE: Signal strength, battery power, and confidence of the measures can also be viewed.</li>
		<li>Record accurate timing (MM/DD/YYYY HH:MM:SS) of the start and end of each session and phase for processing.</li>
	</ol>
	</li>
	<li>Download the ECG Data from the HR monitor.
	<ol>
		<li>Remove the strap from the player at the end of the session and remove the Bluetooth Module from the chest strap.</li>
		<li>Place the module in the charge cradle and plug it into a computer with the software program installed.</li>
		<li>Open the log.</li>
		<li>Select the device from the dropdown menu. All sessions currently on the device are displayed with dates and times.</li>
		<li>Uncheck the box that says Use Default Save Location and chose a new save location.</li>
		<li>Click Save. A progress bar will then appear. Saving may take up to an hour depending on the length of the session.</li>
		<li>Rename the date, once it has been saved.</li>
	</ol>
	</li>
</ol>
3. Data Analysis and Calculation of Heart Rate Variability Measures
<ol>
	<li>Prepare files for processing .
	<ol>
		<li>Name ECG files as &#39;KOLLECT_Subject#_AVG4&#39; (e.g., KOLLECT_01_AVG4.csv&#39;).</li>
		<li>Generate a timing table in comma separated variable (.csv) format to draw timing data from during data processing. See Table 1 for an example of the correct format.</li>
		<li>Import the Date-time data from the .csv file and right click on the name of the newly created variable and change it to &#39;Timing.mat&#39;.</li>
	</ol>
	</li>
	<li>Preliminary R peak detection.
	<ol>
		<li>Open and run Peak _ Detection . m.</li>
		<li>Enter the frequency of ECG recording device when prompted by the program.</li>
		<li>Enter the player number for the data to be analyzed when prompted. 
		NOTE: Some players did not complete active video game 4 (AVG4) and therefore only players 1-10 are used for this study. Other numbers will provide an error message.</li>
		<li>Enter the number of the game to be analyzed (1, 2, or 3) when prompted.</li>
		<li>Enter the phase to be analyzed (Rest, Warmup (WU), Conditioning (Con), Rest, or Recovery).
		<ol>
			<li>Enter an offset in minutes if desired, or enter 0 for no offset.</li>
		</ol>
		</li>
		<li>Select the magnifying tool and select an area of the plot that is output to create a window with a width of approximately 2,000 (s/0.004) and a height that will show the full waveform as shown in Figure 3. Zoom in or out if the window is not easily inspected visually.</li>
		<li>Visually inspect the graph to evaluate if the peaks detected are correctly labeled. See Figure 1 for example of incorrectly detected and missed peaks caused by irregular ECG data (Figure 2).</li>
	</ol>
	</li>
	<li>Peak Correction
	<ol>
		<li>Correct the incorrectly detected or missing peaks by locating the Detection variable and double clicking in the workspace.</li>
		<li>Utilize the Data Cursor tool on the plot of the ECG waveform to obtain the x and y coordinates of the incorrect peak; X (time*frequency) is the first column in Detection.mat and Y (Voltage) is the second column (Figure 3).
		<ol>
			<li>Right click the text box that appears and click Select Cursor Update Function.</li>
			<li>Select TooltipUpdate.m from the folder containing the files used for this analysis. This will allow the tooltip to display more exact values.</li>
		</ol>
		</li>
		<li>If the point is a false positive, remove it from the array by clicking on its row in the Detection.mat variable and pressing Control and the Minus key. An example of false positive detection can be seen in Figure 3.</li>
		<li>Edit incorrectly marked peaks that are adjacent to unmarked peaks, as shown by the two T peaks marked as R in Figure 1, by changing their values to match that of the unmarked peak.</li>
		<li>Obtain the value of the missed peak can be obtained with the Data Cursor tool.</li>
		<li>Add additional rows to Detection. mat using control and the plus key for peaks missed due to low voltage levels.</li>
		<li>Enter the values in numerical order to avoid negative values during the calculation process (i.e., add the peak located at 11000 between the peaks at 10908 and 11167) (Figure 5).</li>
		<li>Ensure that values are entered correctly before continuing through the full session as numbers are occasionally clipped off when entered.</li>
		<li>Repeat step 2.3 until all peaks have been checked and/or corrected. 
		NOTE: Some files have limited variability in waveform amplitude and are quicker to check, as seen in Figure 4 while others are more variable and may require closer zoom to accurately locate peaks during visual inspection.</li>
	</ol>
	</li>
	<li>Obtain HRV measure calculations.
	<ol>
		<li>Save the original plot generated from Peak_Detection.m for later reference.</li>
		<li>Run HRV_Measures.m to generate the correctly labeled plot. A sample of corrected data is shown in Figure 6.
		<ol>
			<li>Change the plot title by using Insert | Title on the plot window and changing it to the desired title.</li>
			<li>Check the window for output, the program will notify the user of the location incorrectly entered data if any exists.</li>
		</ol>
		</li>
		<li>Save the variable named interval.</li>
		<li>Open the variable entitled HRV from the Workspace window to view Mean RR (ms), Average HR (bpm), RMSSD (ms), SDNN (ms), NN50 (count), pNN50 (%), low frequency (LF)/ high frequency (HF) (ECG), LF/HF RR, Low Frequency Power RR, and High Frequency Power (RR)). Save the h values of these variable to a table such as the one shown in Table 4.</li>
		<li>Repeat Sections 3.2 - 3.4 for all other segments, sessions, and subjects that need analysis.</li>
	</ol>
	</li>
</ol>

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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preprocessing+RR+interval+time+series+for+heart+rate+variability+analysis+and+estimates+of+standard+deviation+of+RR+intervals.">Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals.</a> Computer Methods and Programs in Biomedicine. 83 (1), 78-82 (2006).</li><li>Alamili, M., Rosenberg, J., G&#246;genur, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Day-night+variation+in+heart+rate+variability+changes+induced+by+endotoxaemia+in+healthy+volunteers.">Day-night variation in heart rate variability changes induced by endotoxaemia in healthy volunteers.</a> Acta Anaesthesiologica Scandinavica. 59 (4), 457-464 (2015).</li><li>Pal, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preference+for+salt+contributes+to+sympathovagal+imbalance+in+the+genesis+of+prehypertension.">Preference for salt contributes to sympathovagal imbalance in the genesis of prehypertension.</a> European Journal of Clinical Nutrition. 67 (6), 586-591 (2013).</li><li>Telles, S., Raghavendra, B. 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Doctoral thesis. , (2009).</li><li>Murai, K., Hayashi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+mental+workload+for+ship+handling+using+physiological+indices.">Evaluation of mental workload for ship handling using physiological indices.</a> , 604-608 (2009).</li><li>Taelman, J., Vandeput, S., Spaepen, A., Van Huffel, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+mental+stress+on+heart+rate+and+heart+rate+variability.">Influence of mental stress on heart rate and heart rate variability.</a> Heart. 29 (1), 1366-1369 (2009).</li><li>Durantin, G., Gagnon, J. F., Tremblay, S., Dehais, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+near+infrared+spectroscopy+and+heart+rate+variability+to+detect+mental+overload.">Using near infrared spectroscopy and heart rate variability to detect mental overload.</a> Behavioural Brain Research. 259, 16-23 (2014).</li><li>Buchheit, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+training+status+with+HR+measures:+Do+all+roads+lead+to+Rome?.">Monitoring training status with HR measures: Do all roads lead to Rome?.</a> Frontiers in Physiology. 5, (2014).</li><li>Achten, J., Jeukendrup, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+monitoring:+Applications+and+limitations.">Heart rate monitoring: Applications and limitations.</a> Sports Medicine. 33 (8), 517-538 (2012).</li><li>Amichai, T., Katz-Leurer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+variability+with+cerebral+palsy:+Review+of+literature+and+meta-analysis.">Heart rate variability with cerebral palsy: Review of literature and meta-analysis.</a> NeuroRehabilitation. 35, 113-122 (2014).</li><li>Billman, G., Haikuri, H., Sacha, J., Trimmel, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+introduction+to+heart+rate+variability:+Methodological+considerations+and+clinical+applications.">An introduction to heart rate variability: Methodological considerations and clinical applications.</a> Frontiers in Physiology. 6, (2015).</li><li>Beffara, B., Bret, A., Vermeulen, N., Mermillod, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resting+high+frequency+heart+rate+variability+selectively+predicts+cooperative+behavior.">Resting high frequency heart rate variability selectively predicts cooperative behavior.</a> Physiology &amp; Behavior. 164, 417-428 (2016).</li><li>Fogt, D., Cooper, P., Freeman, C., Kalns, J., Cooke, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+variability+to+assess+combat+readiness.">Heart rate variability to assess combat readiness.</a> Military Medicine. 174, 491-495 (2009).</li><li>Kerppers, I. L., Arisawa, E. A. L., Oliveira, L. V. F., Sarmpaio, L. M. M., Oliverira, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heart+rate+variability+in+individual+with+cerebral+palsy.">Heart rate variability in individual with cerebral palsy.</a> Archives of Medical Science. 5, 45-50 (2009).</li><li>Giggins, O. M., Persson, U. M., Caulfield, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biofeedback+in+Rehabilitation.">Biofeedback in Rehabilitation.</a> Journal of Neuroengineering and Rehabilitation. 10, (2013).</li><li>Shaffer, F., Ginsberg, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+overview+of+heart+rate+variability+metrics+and+norms.">An overview of heart rate variability metrics and norms.</a> Frontiers in Public Health. 5, 258 (2017).</li><li>Shaffer, F., McCarty, R., Zeir, C. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+healthy+heart+is+not+a+metronome:+an+integrative+review+of+the+heart&#8217;s+anatomy+and+heart+rate+variability.">A healthy heart is not a metronome: an integrative review of the heart&#8217;s anatomy and heart rate variability.</a> Frontiers in Psychology. 5, 1040 (2014).</li></ol>

At this time, the authors (CL and PAS) have nothing to disclose. Dr. O&#39;Neil is a co-founder of enAbleGames, LLC and Kollect is one of the games offered by this web-based company. enAbleGames is in game development phase and is not a public company at this time (www.enAbleGames.com).&#160;

Ten youth with CP participated in this study (mean + SD) [ age (yrs) = 15.53 &#177; 3.57 ; height (cm) 154.8 &#177; 12.6; weight (kg) 50.69 &#177; 11.1; body mass index (BMI) 50.46 &#177; 29.2; mHR 9 bpm) = 186.8 &#177; 12.4]. Please see Table 5 for patient demographics.
There are some considerations for use of HR monitors and the associated measures of HR and HRV which relate to modifications and troubleshooting. Two issues that are apparent, regardless of the technology empl...

Cerebral palsy (CP) is the most common physical disability of childhood1. CP is caused by a neurologic insult to the developing brain and is associated with motor impairments such as muscle weakness, spasticity, deconditioning, and decreased motor control and balance2,3. CP is a non-progressive condition but with age, children become less physically active and more sedentary compared to their peers with typical development (TD) mostly because of the increased demands of growth on their compromised neuromuscular and musculoskeletal systems4.
Youth with CP usually receive physical therapy (PT) services to improve functional mobility and promote physical activity and fitness (e.g. aerobic and muscular endurance)2. Oftentimes, there is limited access to PT services and community resources to achieve and sustain these PT goals5,6.Active video games (AVGs) may be a feasible strategy in activity-based PT interventions in clinic, home or community settings7,8. Commercial AVGs have limited flexibility to adapt game play and meet the specific needs and PT goals for youth with CP9. However, customized AVGs provide flexible gaming parameters to challenge youth with CP while promoting physical activity and fitness10.
Our team has developed a customized AVG (called KOLLECT) to examine youth exercise responses (e.g., physical activity and aerobic fitness). The game uses a motion sensor to track youth motion during game play. The goal of the game is to &#39;collect&#39; as many objects as possible for a high score and to avoid the hazards to avoid losing points. Objects may be collected with hand and/or feet icons as determined by the therapist in the flexible game parameters.
Designing activity-based PT interventions that dose physical activity intensity to promote aerobic fitness is critical for youth with CP11. Custom AVGs may be an effective strategy to dose intensity and engage youth in physical activity to promote fitness10. Heart rate (HR) monitors are often used in clinical PT practice to determine aerobic performance and activity intensity. Therefore, HR monitors will help determine feasibility of AVGs in dosing physical activity intensity to promote aerobic fitness9.&#160;ECG data generated from a HR monitor can be used to calculate heart rate variability (HRV). Analytic methods were used to generate HRV from ECG data to examine aerobic workload. Recent applications of HRV indicate that short-term measurements (5 min bouts) are appropriate and that HRV biofeedback may help improve symptoms and the quality of life in a variety of health conditions32,33,34. The application of short-term HRV measures is an appropriate means of assessing cardiovascular function during AVG sessions. Given that HRV is derived from the R-R interval of an ECG, we used selected time-domain and frequency-domain measures. Time-domain measure of HRV quantify the amount of variablility in the interbeat intervals which represents the time between successive heartbeats. We used the AVNN (average NN interval), RMSSD (root mean square of successive differences), SDNN (standard deviation of NN interval), NN50 (number of NN intervals &#62;50 ms) and PNN50 (percentage of NN intervals). Frequency domain measures estimate the distributionof absolute or relative power into possibly four frequency bands, we specifically addressed on two bands, low frequency (LF) power and high frequency (HF) power along with the LF/HF ratio. Although HR is a well-accepted clinical measure, HRV may be useful because it provides information about autonomic system function, recovery, adaptation, and provides an estimate of aerobic workload during an AVG session28.
The purpose of this study was to examine the feasibility of using AVG strategies to promote physical activity and fitness. A second purpose was to present the AVG data collection protocol and the methodology to calculate HRV from ECG data obtained via a HR monitor. These measures and this protocol may prove relevant to clinicians to monitor and dose PT intervention sessions.

<table><tbody><tr><td>BioHarness Bluetooth Module (Electronics sensor)&amp;nbsp;</td><td>Zephyr</td><td>9800.0189</td><td>Detects Heart Rate, Resiration Rate, Posture, and Skin Temperature.</td></tr><tr><td>BioHarness Chest Strap</td><td>Zephyr</td><td>9600.0189, 9600.0190</td><td>Sizes Small XS-M, Large M-XL</td></tr><tr><td>BioHarness Charge Cradle &amp;amp; USB Cable</td><td>Zephyr</td><td>9600.0257</td><td>Used to Transfer Data from the Module to a Computer for Analysis.</td></tr><tr><td>BioHarness Echo Gateway</td><td>Zephyr</td><td>9600.0254</td><td>Allows for Realtime Viewing of Subject&#039;s Heart Rate.</td></tr><tr><td>MATLAB R2016a</td><td>Mathworks</td><td>1.7.0_.60</td><td>Used for All Programming.</td></tr></tbody></table>

calculating heart rate variability from ecg data from youth with cerebral palsy during active video game sessions

The aim of this study was to generate a method for calculating heart rate variability (HRV) from electrocardiogram (ECG) waveforms. The waveforms were recorded by a HR monitor that participants (youth with cerebral palsy (CP)) wore during active video game (AVG) sessions. The AVG sessions were designed to promote physical activity and fitness (aerobic performance) in participants. The goal was to evaluate the feasibility of AVGs as a physical therapy (PT) intervention strategy. The maximum HR (mHR) was determined for each participant and the Target Heart Rate Zone (THRZ) was calculated for each of three exercise phases in the 20 min AVG session: (warm-up at 40-60% mHR, conditioning at 60-80% mHR, and cool down at 40-60% mHR). Each participant played three 20 min games during the AVG session. All games were played while sitting on a bench because many youth with CP cannot stand for extended periods of time. Each game condition differed with participants using hand icons only, hand and feet icons together or feet icons only to collect objects. The objective of the game (called KOLLECT) is to collect objects to gain points and avoid hazards to not lose points. Hazards were used in the warm-up and cool down phases only to promote slower, controlled movement to maintain HR in the target heart rate zone (THRZ). There were no hazards in the conditioning phase to promote higher levels and more intense physical activity. Analytic methods were used to generate HRV (selected time-domain and frequency-domain measures) from ECG data to examine aerobic workload. Recent applications of HRV indicate that short-term measurements (5 min bouts) are appropriate and that HRV biofeedback may help improve symptoms and the quality of life in a variety of health conditions. Although HR is a well-accepted clinical measure to examine aerobic performance and intensity in PT interventions, HRV may provide information of the autonomic system functions, recovery and adaptation during AVG sessions.

This method provides data for use in analyzing the effect that a newly developed method has on the subject&#39;s Heart Rate Variability (HRV). It does this by locating the R portion of the QRS waveform of a subject&#39;s ECG data, as shown in Figure 6, and by calculating various HRV values from it. If the HR monitor is making proper contact with the subject, the data will be uniform, substantially reducing the need for corrections (as seen in <strong class="x...

Watch this Scientific Journal Video about Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions at JoVE.com

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

This protocol describes a method for calculating Heart Rate Variability (HRV) from electrocardiogram (ECG) waveforms. Waveforms from continuous heart rate (HR) recordings during active video game (AVG) sessions were used to measure the aerobic performance of youth with cerebral palsy (CP).

The aim of this study was to generate a method for calculating heart rate variability (HRV) from electrocardiogram (ECG) waveforms. The waveforms were ...

calculating-heart-rate-variability-from-ecg-data-from-youth-with

Universidad de Cartagena UDEC

Research

JoVE Journal

Behavior

19.7K Views.  Drexel University. This protocol describes a method for calculating Heart Rate Variability (HRV) from electrocardiogram (ECG) waveforms. Waveforms from continuous heart rate (HR) recordings during active video game (AVG) sessions were used to measure the aerobic performance of youth with cerebral palsy (CP).

Video: Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Simultaneous Video-EEG-ECG Monitoring to Identify Neurocardiac Dysfunction in Mouse Models of Epilepsy

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can potentially increase risk of sudden unexpected death in epilepsy (SUDEP). Electroencephalography (EEG) and electrocardiography (ECG) are widely used clinical diagnostic tools to monitor for abnormal brain and cardiac rhythms in patients. Here, a technique to simultaneously record video, EEG, and ECG in mice to measure behavior, brain, and cardiac activities, respectively, is described. The technique described herein utilizes a tethered (i.e., wired) recording configuration in which the implanted electrode on the head of the mouse is hard-wired to the recording equipment. Compared to wireless telemetry recording systems, the tethered arrangement possesses several technical advantages such as a greater possible number of channels for recording EEG or other biopotentials; lower electrode costs; and greater frequency bandwidth (i.e., sampling rate) of recordings. The basics of this technique can also be easily modified to accommodate recording other biosignals, such as electromyography (EMG) or plethysmography for assessment of muscle and respiratory activity, respectively. In addition to describing how to perform the EEG-ECG recordings, we also detail methods to quantify the resulting data for seizures, EEG spectral power, cardiac function, and heart rate variability, which we demonstrate in an example experiment using a mouse with epilepsy due to Kcna1 gene deletion. Video-EEG-ECG monitoring in mouse models of epilepsy or other neurological disease provides a powerful tool to identify dysfunction at the level of the brain, heart, or brain-heart interactions.

Here, we present a protocol to record brain and heart bio signals in mice using simultaneous video, electroencephalography (EEG), and electrocardiography (ECG). We also describe methods to analyze the resulting EEG-ECG recordings for seizures, EEG spectral power, cardiac function, and heart rate variability.

In epilepsy, seizures can evoke cardiac rhythm disturbances such as heart rate changes, conduction blocks, asystoles, and arrhythmias, which can ...

Genetics

Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood

Effective regulation of emotion is one of the most important skills that develops in childhood. Research interest in this area is expanding, but empirical work has been limited by predominantly correlational investigations of children's skills. Relatedly, a key conceptual challenge for emotion scientists is to distinguish between emotion responding and emotion regulatory processes. This paper presents a novel method to address these conceptual and methodological issues in child samples. An experimental paradigm that assesses the effectiveness with which children regulate emotion is described. Children are randomly assigned to use specific emotion regulation strategies, negative emotions are elicited with film clips, and changes in subsequent psychophysiology index the extent to which emotion regulation is effective. Children are instructed to simply watch the emotion-eliciting film (control), distract themselves from negative emotions (cognitive distraction), or reframe the situation in a way that downplays the importance of the emotional event (cognitive reappraisal). Cardiac physiology, continuously acquired before and during the emotional task, serves as an objective measure of children's unfolding emotional responding while viewing evocative films. Key comparisons in patterns of obtained physiological reactivity are between the control and emotion regulation strategy conditions. Representative results from this approach are described, and discussion focuses on the contribution of this methodological approach to developmental science.

A detailed protocol for an experimental paradigm to assess the effectiveness with which children regulate emotion is described. Children are randomly assigned to use specific emotion regulation strategies, negative emotions are elicited with film clips, and changes in subsequent psychophysiology index the extent to which emotion regulation is effective.

Effective regulation of emotion is one of the most important skills that develops in childhood. Research interest in this area is expanding, but empirical ...

Measuring Cardiac Autonomic Nervous System (ANS) Activity in Children

The autonomic nervous system (ANS) controls mainly automatic bodily functions that are engaged in homeostasis, like heart rate, digestion, respiratory rate, salivation, perspiration and renal function. The ANS has two main branches: the sympathetic nervous system, preparing the human body for action in times of danger and stress, and the parasympathetic nervous system, which regulates the resting state of the body.
ANS activity can be measured invasively, for instance by radiotracer techniques or microelectrode recording from superficial nerves, or it can be measured non-invasively by using changes in an organ's response as a proxy for changes in ANS activity, for instance of the sweat glands or the heart. Invasive measurements have the highest validity but are very poorly feasible in large scale samples where non-invasive measures are the preferred approach. Autonomic effects on the heart can be reliably quantified by the recording of the electrocardiogram (ECG) in combination with the impedance cardiogram (ICG), which reflects the changes in thorax impedance in response to respiration and the ejection of blood from the ventricle into the aorta. From the respiration and ECG signals, respiratory sinus arrhythmia can be extracted as a measure of cardiac parasympathetic control. From the ECG and the left ventricular ejection signals, the preejection period can be extracted as a measure of cardiac sympathetic control. ECG and ICG recording is mostly done in laboratory settings. However, having the subjects report to a laboratory greatly reduces ecological validity, is not always doable in large scale epidemiological studies, and can be intimidating for young children. An ambulatory device for ECG and ICG simultaneously resolves these three problems.
Here, we present a study design for a minimally invasive and rapid assessment of cardiac autonomic control in children, using a validated ambulatory device 1-5, the VU University Ambulatory Monitoring System (VU-AMS, Amsterdam, the Netherlands,<a href="http://www.vu-ams.nl" target="_blank"> www.vu-ams.nl</a>).

Measuring Cardiac Autonomic Nervous System ANS Activity in Children

Measurement of autonomic nervous system activity usually confines the researcher and participant to the laboratory, which may provide an intimidating environment to children. The VU University Ambulatory Monitoring System (VU-AMS) device can record cardiac autonomic control in any setting. The VU-AMS proved very amenable to testing in children.

The autonomic nervous system (ANS) controls mainly automatic bodily functions that are engaged in homeostasis, like heart rate, digestion, respiratory ...

Medicine

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to join existing programs at cardiac rehabilitation centers. The unique remote patient monitoring system presented here enables healthcare providers to monitor CR patients at home in real-time and at low cost. The system combines mobile technology, artificial intelligence, and supportive services, expanding the delivery of medical care to the patient&#39;s home. The primary aim of the study is to increase the long-term adherence to physical activity in patients who participate in CR via the addition of a home-based digitally monitored CR component to the standard CR program in patients with ischemic heart disease (IHD), with the idea of forming new habitual health behaviors and increasing the long-term motivation for physical exercise (PE) habits at home. Secondary aims are to assess the program&#39;s impact on the physical activity level measured by average steps per day, minutes of exercise per week, blood pressure, metabolic parameters, body mass index, and waist-to-hip ratio, as well as a quality-of-life (QoL) questionnaire.The study has two arms: (1) home-based monitored exercise using a smart digital garment and wristband, in addition to motivation and reinforcement via text messages; (2) standard CR facility-based exercise. The study design is a randomized, controlled trial comparing standard CR to a home-based monitoring and reinforcement program. The study program is designed for 12 weeks.Clinical tests and anthropometric measurements are performed before and after the study, measuring height, weight, waist circumference, visceral fat and body mass index (BMI), blood pressure, and HbA1c and lipid profile. Patients have to complete a baseline survey including socio-demographic characteristics and QoL questionnaire SF-36. At the end of the study, patients complete a survey regarding the use of the smart digital garment&#39;s benefits and usability. The study is currently underway.

The aim is to promote a new approach to cardiac rehabilitation (CR), using a unique remote patient monitoring system that will enable healthcare providers to monitor CR patients at home at low cost, with the intention of making CR services more accessible and improving compliance. The study is currently underway.

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to ...

Physical Activity Measurement in Children Accepting Table Tennis Training

An increasing body of evidence now shows that the majority of children in China experience lower levels of physical activity (PA) than the recommended guideline. Table tennis is a compound and technically difficult game that is popular in China; undertaking table tennis training in clubs can help children to elevate their levels of PA. Given that children cannot complete self-evaluated questionnaires themselves and caregiver-based observations are not suitable for children, we hypothesized that an actigraphy-based method can be an objective method to measure PA. In the present study, we describe a procedure that can be used to evaluate PA levels using an actigraphic device and software. Furthermore, since hip-worn devices are known to reduce compliance, we attempted to assess the agreement between hip-worn and wrist-worn device data. Collectively, our results indicate that these devices are suitable for measuring PA and leisure time physical activity (LTPA) levels. Together with subjective questionnaires, both hip-worn and wrist-worn devices are highly suitable for evaluating PA in Chinese children undergoing table tennis training in clubs.

This study proposes an accelerometer-based method to objectively measure physical activity (PA) and leisure time physical activity (LTPA) in Chinese children accepting table tennis training in clubs.

An increasing body of evidence now shows that the majority of children in China experience lower levels of physical activity (PA) than the recommended ...

Measuring Cardiac Autonomic Nervous System (ANS) Activity in Toddlers - Resting and Developmental Challenges

The autonomic nervous system (ANS) consists of two branches, the parasympathetic and sympathetic nervous systems, and controls the function of internal organs (e.g., heart rate, respiration, digestion) and responds to everyday and adverse experiences 1. ANS measures in children have been found to be related to behavior problems, emotion regulation, and health 2-7. Therefore, understanding the factors that affect ANS development during early childhood is important. Both branches of the ANS affect young children&#8217;s cardiovascular responses to stimuli and have been measured noninvasively, via external monitoring equipment, using valid and reliable measures of physiological change 8-11. However, there are few studies of very young children with simultaneous measures of the parasympathetic and sympathetic nervous systems, which limits understanding of the integrated functioning of the two systems. In addition, the majority of existing studies of young children report on infants&#8217; resting ANS measures or their reactivity to commonly used mother-child interaction paradigms, and less is known about ANS reactivity to other challenging conditions. We present a study design and standardized protocol for a non-invasive and rapid assessment of cardiac autonomic control in 18 month old children. We describe methods for continuous monitoring of the parasympathetic and sympathetic branches of the ANS under resting and challenge conditions during a home or laboratory visit and provide descriptive findings from our sample of 140 ethnically diverse toddlers using validated equipment and scoring software. Results revealed that this protocol can produce a range of&#160;physiological responses to both resting and developmentally challenging conditions, as indicated by changes in heart rate and indices of parasympathetic and sympathetic activity. Individuals demonstrated&#160;variability in resting levels, responses to challenges, and challenge reactivity, which provides additional evidence that this protocol is useful for the examination of ANS individual differences for toddlers.

Measuring Cardiac Autonomic Nervous System ANS Activity in Toddlers - Resting and Developmental Challenges

We describe the methods for continuous monitoring of the autonomic nervous system under resting and challenge conditions with 18 month old children. Results revealed that this protocol can produce meaningful physiological responses in both branches of the autonomic nervous system and elicit significant individual variability in patterns of responses.

The autonomic nervous system (ANS) consists of two branches, the parasympathetic and sympathetic nervous systems, and controls the function of internal ...

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A key reason for referral to rehabilitation services after stroke and other neurological conditions is to improve one's ability to function in daily life. It has become important to measure a person's activities in daily life, and not just measure their capacity for activity in the structured environment of a clinic or laboratory. A wearable sensor that is now enabling measurement of daily movement is the accelerometer. Accelerometers are commercially-available devices resembling large wrist watches that can be worn throughout the day. Data from accelerometers can quantify how the limbs are engaged to perform activities in peoples' homes and communities. This report describes a methodology to collect accelerometry data and turn it into clinically-relevant information. First, data are collected by having the participant wear two accelerometers (one on each wrist) for 24 h or longer. The accelerometry data are then downloaded and processed to produce four different variables that describe key aspects of upper limb activity in daily life: hours of use, use ratio, magnitude ratio, and the bilateral magnitude. Density plots can be constructed that visually represent the data from the 24 h wearing period. The variables and their resultant density plots are highly consistent in neurologically-intact, community-dwelling adults. This striking consistency makes them a useful tool for determining if upper limb daily performance is different from normal. This methodology is appropriate for research studies investigating upper limb dysfunction and interventions designed to improve upper limb performance in daily life in people with stroke and other patient populations. Because of its relative simplicity, it may not be long before it is also incorporated in clinical neurorehabilitation practice.

This protocol describes a method to quantify upper limb performance in daily life using wrist-worn accelerometers.

A key reason for referral to rehabilitation services after stroke and other neurological conditions is to improve one's ability to function in daily life. ...

Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology

Participation in organized sports makes a significant contribution to youth development, but places youth at a higher risk for sustaining a concussion. To date, return-to-activity decision-making has been anchored in the monitoring of self-reported concussion symptoms and neurocognitive testing. However, multi-modal assessments that corroborate objective physiological measures with traditional subjective symptom reporting are needed and can be valuable. Heart rate variability (HRV) is a non-invasive physiological indicator of the autonomic nervous system, capturing the reciprocal interplay between the sympathetic and parasympathetic nervous systems. There is a dearth of literature exploring the effect of concussion on HRV in youth athletes, and developmental differences preclude the application of adult findings to a pediatric population. Further, the current state of HRV methodology has primarily included short-term (5-15 min) recordings, by using resting state or short-term physical exertion testing to elucidate changes following concussion. The novelty in utilizing a 24 h&#160;recording methodology is that it has the potential to capture natural variation in autonomic function, directly related to the activities a youth athlete performs on a regular basis. Within a prospective, longitudinal research setting, this novel approach to quantifying autonomic function can provide important information regarding the recovery trajectory, alongside traditional self-report symptom measures. Our objectives regarding a 24 h recording methodology were to (1) evaluate the physiological effects of a concussion in youth athletes, and (2) describe the trajectory of physiological change, while considering the resolution of self-reported post-concussion symptoms. To achieve these objectives, non-invasive sensor technology was implemented. The raw beat-to-beat time intervals captured can be transformed to derive time domain and frequency domain measures, which reflect an individual&#39;s ability to adapt and be flexible to their ever-changing environment. By using non-invasive heart rate technology, autonomic function can be quantified outside of a traditional controlled research setting.

We demonstrate a 24 h heart rate recording methodology to evaluate the influence of concussion across the recovery trajectory in youth athletes, within an ecologically valid context.

Participation in organized sports makes a significant contribution to youth development, but places youth at a higher risk for sustaining a concussion. To ...

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. However, in certain cardiac diseases such as inherited arrhythmia syndromes, e.g., arrhythmogenic cardiomyopathy (ACM) or myocarditis, physical exercise may have negative effects on the heart leading to a proarrhythmogenic substrate production. Currently, the underlying molecular mechanisms of exercise-related proarrhythmogenic remodeling are largely unknown, thus it remains unclear which frequency, duration, and intensity of exercise can be considered safe in the context of disease(s).
The proposed method allows to study proarrhythmic/antiarrhythmic effects of physical exercise by combining treadmill training with real-time monitoring of the ECG. Implantable telemetry devices are used to continuously record the ECG of freely moving mice over a period of up to 3 months both at rest and during treadmill training. Data acquisition software with its analysis modules is used to analyze basic ECG parameters such as heart rate, P wave duration, PR interval, QRS interval, or QT duration at rest, during and after training. Furthermore, heart rate variability (HRV) parameters and occurrence of arrhythmias are evaluated. In brief, this manuscript describes a step-by-step approach to experimentally explore exercise induced effects on cardiac electrophysiology, including potential proarrhythmogenic remodeling in mouse models.

Electrocardiogram (ECG) is the key variable to understanding cardiac electrophysiology. Physical exercise has beneficial effects but may also be harmful in the context of cardiovascular diseases. This manuscript provides a method of recording real-time ECG during exercise, which can serve to investigate its effects on cardiac electrophysiology in mice.

Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. ...

Unveiling Brain-Heart Interplay with EEG and Cardio Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human physiology and improve health outcomes. However, the multimodal analysis of these signals is challenging due to the lack of guidelines, standardized signal processing and statistical tools, graphical user interfaces (GUIs), and automation for processing large datasets or increasing reproducibility. A further void exists in standardized EEG and heart-rate variability (HRV) feature extraction methods, undermining clinical diagnostics or the robustness of machine learning (ML) models. In response to these limitations, we introduce the BrainBeats toolbox. Implemented as an open-source EEGLAB plugin, BrainBeats integrates three main protocols: 1) Heartbeat-evoked potentials (HEP) and oscillations (HEO) for assessing time-locked brain-heart interplay at the millisecond accuracy; 2) EEG and HRV feature extraction for examining associations/differences between various brain and heart metrics or for building robust feature-based ML models; 3) Automated extraction of heart artifacts from EEG signals to remove any potential cardiovascular contamination while conducting EEG analysis. We provide a step-by-step tutorial for applying these three methods to an open-source dataset containing simultaneous 64-channel EEG, ECG, and PPG signals. Users can easily fine-tune parameters to tailor their unique research needs using the graphical user interface (GUI) or the command line. BrainBeats should make brain-heart interplay research more accessible and reproducible.

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

The BrainBeats toolbox is an open-source EEGLAB plugin designed to jointly analyze EEG and cardiovascular (ECG/PPG) signals. It includes heartbeat-evoked potentials (HEP) assessment, feature-based analysis, and heart artifact extraction from EEG signals. The protocol will aid in studying brain-heart interplay through two lenses (HEP and features), enhancing reproducibility and accessibility.

The interplay between the brain and the cardiovascular systems is garnering increased attention for its potential to advance our understanding of human ...