This research was supported by the University Medicine Greifswald and the DZHK (German Centre for Cardiovascular Research; Grand No. D347000002). The authors wish to thank Christian Goeze, Stefanie Tobschall, and Clip Film- und Fernsehproduktion GmbH.

This protocol was approved by the ethics committee of the University Medicine Greifswald (number BB 076/18; June 2018).
1. Video construction and experimental design
<ol>
	<li>Select a publicly available or self-produced video based on the specific experimental question. The video should explain the terms used in the self-report questionnaire to support participants&#8217; understanding. The video used here contains explaining and visualizing symptoms as well as naming examples of light, moderate, and vigorous PA.
	<ol>
		<li>In the video, have a person on a treadmill in a fitness center give a general introduction to the different intensity levels of PA.</li>
		<li>Have the person explain differences in heart rate, breathing frequency, and capability to talk normally in accordance with the intensity levels. Have the person simultaneously demonstrate those symptoms while walking/running on a treadmill at the according pace.</li>
		<li>Have the person give examples of daily-life activities and emphasize individual differences in the evaluation of PA intensity levels. 
		NOTE: The video used here was produced in&#160;German based on a video clip from the Centers for Disease Control and Prevention (CDC)20. If participants are native English speakers, the original video may be used with emphasis on minutes 1:46 to 3:25. The person in the present video is an approximately fifty-year-old, normal-weight, white male in good physical shape. See Figure 1 for a visual depiction of video structure and contents.</li>
	</ol>
	</li>
	<li>Integrate the video into a self-administered tablet-computer survey to be presented directly before the PA questionnaire and make sure participants cannot skip the video. Randomize presentation of the video 1:1.
	<ol>
		<li>Integrate questions on sociodemographic and health related variables into the survey as desired for description of sample characteristics.</li>
		<li>In the present study, self-reported PA is assessed using a modified version of the International Physical Activity Questionnaire Short Form (IPAQ-SF)4, German version21, addressing the last seven days. Two items each address number of days and respective time spent in moderate and vigorous PA. The original items on walking are replaced with questions on light PA as walking may be performed on different intensity levels22 and walking is not equivalent to light PA measured by accelerometry. Sociodemographic and health variables included in the survey are sex, age, school education, employment, current living together with a partner, current smoking, and self-rated general health.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58997/58997fig1.jpg" /> 
Figure 1: Schematic structure of the video demonstration of different physical activity intensity levels. The main scenes of the video with according single shots, lengths, and summary of contents are depicted. The video was based on a video clip provided by the CDC20. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58997/58997fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Power calculation
<ol>
	<li>Conduct a power analysis using respective software in order to define the sample size necessary to obtain statistically conclusive results. Include an interim analysis to verify underlying assumptions and early stopping of the study.
	<ol>
		<li>Choose a statistical test appropriate for the research question.</li>
		<li>Based on the literature, set the assumed mean difference between questionnaire and accelerometer data in the control group, that is, the divergence between self-reported and directly measured PA without presentation of the video.</li>
		<li>Set the assumed mean difference between questionnaire and accelerometer data in the experimental group, that is, the divergence between self-reported and directly measured PA with inclusion of the video demonstration.</li>
		<li>Set the assumed standard deviation (SD) for both groups.</li>
		<li>Choose power and alpha-level as desired.</li>
	</ol>
	</li>
	<li>Based on the literature and considering the specific study design, decide on an assumed drop-out rate to retrieve the final number of participants to be recruited.</li>
	<li>The power analysis of the present study is based on a two-sample t-test assuming equal variance. Based on a comparable sample10, the assumed mean difference between questionnaire and accelerometer data in the control group is 90 min per day of MVPA. The assumed mean difference in the experimental group is 60 min per day (SD in both groups = 100 min per day). As it is hypothesized that the integration of the video reduces the gap between the two measures, a one-sided significance level of p = .05 is chosen (power = .80). Results of power calculation including interim analysis revealed that a total number of 314 participants is needed for demonstrating the experimental effect. Assuming a drop-out rate of about 10%, it is planned to recruit 350 participants (Figure 2).</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58997/58997fig2v2.jpg" /> 
Figure 2: Schematic depiction of the calculated participation flow. n = number of participants. All n refer to results of the power calculation. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58997/58997fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Participant recruitment and preparation for data collection
<ol>
	<li>Choose a recruitment setting that permits enough time to hand out the accelerometer and to prepare it for data collection (e.g., in a shopping mall or at the workplace) in order to keep efforts of participants low and to increase adherence to the study.
	<ol>
		<li>Recruit participants who have the ability to walk independently (e.g., no permanent use of a wheelchair) and who are physically and cognitively capable of completing a self-report questionnaire. Be sure to recruit a similar number of male and female participants of all ages within the desired age range.</li>
		<li>As an incentive for participation, point out that participants are going to receive a feedback letter on directly measured PA and sedentary time after completing the study. Use monetary incentives as desired.</li>
		<li>Obtain written informed consent from each person prior to their participation.</li>
	</ol>
	</li>
	<li>For objective measurement, use a three-axial accelerometer to be worn on the right hip. Alternative devices can be used but should have the memory capacity for data collection on seven consecutive days. In order to most accurately capture daily PA, follow the instructions of the specific device used.
	<ol>
		<li>Have accelerometers prepared with elastic belts of various sizes and select one that fits the participant comfortably.</li>
		<li>Give adequate information on how to handle the accelerometer according to instructions provided by the producer of the device. Instruct participants to start wearing the device on the next day. Ensure that participants wear the device during waking hours (i.e., every day after getting up until going to sleep).</li>
		<li>Initialize the accelerometer on a computer using the appropriate software. Make sure to set up the wearing period correctly. Select a sampling rate of 30 Hz23. If applicable, choose to fill in participant specific information as desired (e.g., body weight or date of birth for reasons of participant identification).</li>
		<li>Schedule each participant for an assessment session to obtain self-reported PA and somatometry. Ensure this session takes place one day after the last accelerometer wearing day. Hence, accelerometer and questionnaire data refer to the same period of time. If this is not possible for reasons of time, admit a maximum delay of two days.</li>
		<li>Dismiss the participant with encouragement to engage in normal daily activities and make sure the participant remembers to return the accelerometer when appearing for the session. 
		NOTE: This study is conducted in Greifswald, a city in Western Pomerania, a rural area in northeastern Germany. Persons from the general population aged between 40 and 75 years are recruited proactively at a shopping mall. Accelerometer feedback letters and shopping vouchers in the amount of 10 euros are used as incentives. Participants are instructed to wear the device for seven consecutive days and to remove it for any water-based activities (e.g., showering or swimming).</li>
	</ol>
	</li>
</ol>
4. Participant assessment session
NOTE: Conduct this session within three days after the last accelerometer wearing day.
<ol>
	<li>Collect the accelerometer from the participant.</li>
	<li>Set up a new participant in the tablet-computer survey and type in the individual study identification number of the participant.</li>
	<li>Hand over the tablet computer to the participant to answer the self-administrative questionnaire.</li>
	<li>When the participant has completed the questionnaire, collect the tablet computer and continue with measurement of somatometry.
	<ol>
		<li>Ask the participant to take off their shoes and to stand on calibrated scales for measurement of body weight. Type in the result into the tablet computer.</li>
		<li>Ask the participant to stand up straight in front of a mirror with toes at a mark on the ground for measurement of body height. Type in the result into the tablet computer.</li>
		<li>Ask the participant to remove upper layers of clothing for measurement of waist and hip circumference. Measure waist circumference midway between lowest rib and iliac crest. Measure hip circumference about two inches below iliac crest. Use the mirror to check accurate positioning of the tape. Type in the results into the tablet computer.</li>
	</ol>
	</li>
	<li>Thank and dismiss the participant.</li>
</ol>
5. Download of accelerometer data for processing and creation of feedback letters
<ol>
	<li>Download the data from the device using the appropriate software.
	<ol>
		<li>Select to use data from the vertical axis and choose an epoch length of 10 s.</li>
		<li>Export the data to an appropriate program for further processing. According to the output metric used, choose cut points to determine non-wear time and to differentiate between PA intensity levels24,25.
		<ol>
			<li>Define non-wear time as at least 60 min of consecutive zero counts, allowing for &#8804;2 min of counts between 0 and 10024.</li>
			<li>In an adult sample (ages 18 or older), classify values &#60;100 counts per min as sedentary time, values between 100 and 2019 counts per min as light PA, values between 2020 and 5998 as moderate PA and values of 5999 or more counts per min as vigorous PA24.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Import all relevant variables into a computer program appropriate for creating a computerized feedback letter using an algorithm to automatically integrate the individual data into a general template. The letter may contain a number of graphs visualizing accelerometer-based PA outcomes as well as sedentary time as desired. Have each graph accompanied by a paragraph of three&#160;to five sentences explaining the content of the figures and providing respective health recommendations.</li>
	<li>Deliver the feedback letter as soon as possible after the participant completed the study. 
	NOTE: Accelerometer feedback letters in the present study include three graphs. The first graph visualizes daily steps across the wearing period. The second graph shows amounts of time spent sedentary and in light, moderate, and vigorous PA on each wearing day. The third graph depicts all observed 10-min-bouts of sedentary time between 6 and 10 pm exemplified on a weekday and on a weekend day. Recommendations on PA are presented according to the PA guidelines of the World Health Organization for apparently healthy adults2. Recommendations on sedentary breaks are presented based on relevant studies26,27,28.</li>
</ol>
6 Statistical analysis
<ol>
	<li>Calculate descriptive statistics for all variables.</li>
	<li>Define a cut-off value for daily accelerometer wear time to avoid bias in accelerometer data.</li>
	<li>Create a variable that presents the gap between the two measures. Calculate the variable as self-reported minus accelerometer-derived min of moderate-to-vigorous PA which results in a difference score (delta, &#8710;). Use a two-sample t-test to determine the difference of deltas between experimental and control group.</li>
	<li>Create a graph to visualize the results of the main analysis as desired.</li>
</ol>

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The authors have nothing to disclose.

This report describes a methodology for testing the effect of a video demonstration on the gap between self-reported and accelerometer-based PA. If self-report assessment is preceded by a video demonstration of PA intensity levels, over-reporting of MVPA might be reduced. This protocol can be used to test the effect of any existing or self-produced information video on the gap between self-reported PA data derived from a computer-assisted assessment and directly measured PA.
The most important steps in the protocol include fundamental aspects of trial conduction that ensure the receipt of accurate data, such as correct accelerometer initialization and data download or making sure that the video may not be skipped by respondents. Further, there are more specific issues about the accelerometer wearing period and the daily wear time. First, the accelerometer wearing period and self-reported data should refer to the same time frame. To hand out accelerometers and agree on the date of the assessment session immediately after recruitment seems helpful to ensure participants&#39; adherence to the scheduled appointment. Second, participants may not always comply with the instructions for accelerometer wearing. The device may be worn for less than seven days and/or only a few hours per day, whereas subsequent self-reports refer to the complete wearing period. Thus, over-reporting of MVPA may be bound to occur. Moreover, if wear time substantially differs between study groups, results may be compromised due to biased accelerometer-based MVPA data. Inspection of interim descriptive statistics may uncover insufficient amounts of wear time. For example, among the participants who completed the study protocol (n = 142), only 115 participants wore the device at least 10 hours on each of the seven days. There were three participants with a wear time of 0 minutes on one or more days. Excluding outliers seems necessary to ensure that the data are representative for an entire day as well as the total assessment period. Although most studies on correlations between accelerometry and PA questionnaire data request a wear time of &#8805;10 hours per day on &#8805;4 days per week29, investigations on the gap between measures may require more conservative cut-off values. Thus, we decided to exclude participants from the analysis who did not wear the accelerometer for &#8805;10 hours per day on &#8805;6 days.
Further modifications of the protocol may be appropriate. Preliminary results of descriptive statistics shown in Table 1 indicate an unbalanced proportion of men and women in our total sample and between study groups. If the video affects self-reports differentially in men and women, overall video effects may be biased. Thus, basic variables (e.g., sex and age) may need to be considered in the randomization algorithm. Moreover, the main analysis model may need to include sociodemographic and health related variables as potential confounders using a linear regression model instead of a t-test.
The methodology described here aims at reducing the gap between self-reported and accelerometer-derived PA by using a video to address comprehension of PA intensity levels. However, specific characteristics inherent to each measure remain to affect this gap. First, self-reported PA data is susceptible to recall bias30 and may be affected by social desirability bias31,32. Second, bias in accelerometer data particularly origins in different motivation to wear the device. Third, hip-worn accelerometers may lack the ability to accurately capture cycling and swimming13. Finally, accelerometers capture absolute amounts of movement whereas self-reports account for relative physical exertion33,34,35. Considering these factors, the visualization of intensity levels may present only one of many options to reduce the gap between self-reported and directly measured PA.

Assessment of physical activity (PA) is commonly done by questionnaires because they are inexpensive and easy to administer. As positive associations between amounts of higher-intensity PA and cardiovascular health are well established1,2,3, many questionnaires inquire frequency and time spent in light, moderate, and vigorous PA presenting examples of respective activities4,5,6,7,8. However, they may be flawed by inaccuracy due to individually different understanding of PA intensity levels9. Further, specific activity examples may not hold true for individuals with different physical constitutions. For example, overweight or obese persons may feel more exerted than persons with normal weight when performing the exact same activity. Direct measures on the other hand (e.g., accelerometry) require considerable amounts of time and costs and possess limited validity due to reactivity bias10,11, sample selection bias12, and the lack of ability to accurately capture certain activities13. A broad range of studies showed only low to moderate agreements between self-reported and accelerometer-based PA14,15,16. Most findings indicate that respondents report more time spent in higher-intensity PA compared to directly measured data. Throughout the manuscript, the term &#34;gap&#34; is used to designate this lack of agreement between accelerometry and self-reported PA.
A video as part of a computer-assisted self-completed questionnaire might help to reconcile the two measures by increasing the accuracy of self-reports. A video demonstration provides an opportunity to show different intensity levels of PA that are hard to explain by written text only. Respondents receive a visual reference they may compare their performance levels with and thus, misclassification of light, moderate, and vigorous PA may be reduced. Up to now, videos to support assessments are available in the context of mobility and physical functioning validated for older adults17,18,19. To our knowledge, there are no video-supported assessments that provide a reference for light, moderate, and vigorous PA.
We developed a 3-minute video showing a middle-aged man on a treadmill in a fitness center who describes the terms light, moderate, and vigorous PA and simultaneously visualizes symptoms related to these intensity levels. The methodology described here is a randomized controlled trial to test the effect of the video demonstration on the gap between self-reported and accelerometer-based moderate-to-vigorous physical activity (MVPA). In addition, standardized assessment of somatometry (height, body weight, and waist and hip circumference) is conducted to investigate whether effects differ according to participants&#39; physical constitution.
The methodology is appropriate to test the effect of any video demonstration that is meant to support computer-assisted PA questionnaire assessment with the aim of reducing the gap between self-reported and directly measured PA. The methodology can be used in various populations and contexts as accurate measures are needed to evaluate current and changing PA levels, efficacy of PA interventions, and associations between PA and health outcomes.

<table><tbody><tr><td>Accelorometers</td><td>ActiGraph, LLC</td><td>ActiGraph Model GT3X+</td><td>This is the most common device on the market. Similar products are available from other vendors.</td></tr><tr><td>Access Software</td><td>Microsoft</td><td></td><td>The software ist used for creation of computerized feedback letters.</td></tr><tr><td>Actilife Software</td><td>ActiGraph, LLC</td><td></td><td>Software to prepare, initialize, download, and processing of data collected by the accelerometers.</td></tr><tr><td>Belts</td><td>ActiGraph, LLC</td><td>Elastic Belt</td><td>Elastic bands for accelerometer wearing on the hip.</td></tr><tr><td>Computational software</td><td>StataCorp</td><td></td><td>The software Stata ist used for statistical analysis.</td></tr><tr><td>Digital scales (height)</td><td>ADE GmbH &amp;amp; Co.</td><td>MZ 10020</td><td>The scales are used for body height measurement.</td></tr><tr><td>Digital scales (weight)</td><td>Soehnle Industrial solutions GmbH</td><td>SOEHNLE 7720</td><td>The scales are used for body weight measurement.</td></tr><tr><td>Excel Software</td><td>Microsoft</td><td></td><td>The software ist used for calculations on accelerometer-based data.</td></tr><tr><td>PASS Sample Size Software</td><td>NCSS</td><td>PASS Sample Size 16</td><td>The software is used for power calculations.</td></tr><tr><td>Tablet</td><td>Apple Inc.</td><td>iPad MC769FD/A</td><td>The tablet comupter ist used for the self-administered assessment.</td></tr><tr><td>USB cable</td><td>ActiGraph, LLC</td><td>USB cable</td><td>USB cable for device communication and charging of accelerometers.</td></tr></tbody></table>

visualization of intensity levels to reduce the gap between self-reported and directly measured physical activity

Physical activity (PA) assessment needs tools that are inexpensive and easy to administer. Common questionnaires inquire time spent in light, moderate, and vigorous PA. However, inaccuracies may occur due to individually different understanding of PA intensity levels. Alternatively used direct measures (e.g., accelerometers) are susceptible to reactivity bias and may lack the ability to capture certain activities. Compared to accelerometer measurement, respondents report more time spent in higher-intensity PA. A video that visualizes PA intensity levels might help to overcome this problem. This report describes the design of a randomized controlled trial as a methodology to investigate the effect of a video on the difference between self-reported and directly measured PA. It is hypothesized that the video reduces the mean difference between the two measures. Individuals from the general population are recruited. Hip-worn accelerometers are used to collect directly measured PA data on seven consecutive days. Afterwards, participants are randomly allocated to the experimental and the control group. The experimental group receives a video demonstration on PA intensity levels and subsequent PA assessment via self-administered computer-assisted questionnaire. The control group receives PA assessment only. Thereafter, the data are processed to compare the difference between self-reported and accelerometer-based moderate-to-vigorous physical activity (MVPA) between the study groups using a two-sample t-test. This methodology is appropriate for investigating the effect of any existing or self-produced video on the difference between the two measurement methods. It can be used not only for persons from the general population, but for a variety of other populations and contexts as accurate measures are needed to evaluate PA levels.

The methods detailed above describe a randomized controlled trial to test whether a video demonstration of PA intensity levels reduces the gap between self-reported and accelerometer-based MVPA. An interim analysis (n = 157) was planned to evaluate whether the estimated sample size of 314 participants is sufficient to test our hypothesis. Up to this point, 142 participants completed the study protocol. Participants who were too old (n = 1) or who did not wear the accelerometer for &#8805;10 hours per day on &#8805;6 days (n = 10) were excluded from the analysis. Thus, data analysis was carried out using a sample of 131 participants to give an example of representative results among individuals from the general population aged between 40 and 75 years.
Table 1 presents descriptive statistics of the analysis sample (n = 131). Of this sample, 68 participants (52%) were randomized to the experimental group and 63 participants (48%) were randomized to the control group. The experimental group received a video demonstration before completing the PA questionnaire, whereas the control group received PA assessment only. It was hypothesized that the video demonstration reduces the gap between self-reported and accelerometer-based PA. Preliminary results of interim analysis revealed a lower formal mean difference in the video group (M = 21.8, SD = 108.9) compared to controls (M = 41.0, SD = 117.4, t(129) = 0.97, p = .166, Figure 3 and Figure 4). The p-value lies between the signi&#64257;cance (p &#60; 0.010) and futility (p &#62; 0.269) boundaries of the test simulations. Thus, the study may continue as planned until the total sample size is reached.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>Total Sample</td>
			<td>Control group</td>
			<td>Video group</td>
		</tr>
		<tr>
			<td>N</td>
			<td>131</td>
			<td>63 (48%)</td>
			<td>68 (52%)</td>
		</tr>
		<tr>
			<td>Sex, women</td>
			<td>85 (65%)</td>
			<td>46 (73%)</td>
			<td>39 (57%)</td>
		</tr>
		<tr>
			<td>Age, years</td>
			<td>60.1 &#177; 8.9</td>
			<td>58.1 &#177; 9.6</td>
			<td>61.9 &#177; 7.9</td>
		</tr>
		<tr>
			<td>Current living together with a partner, yes</td>
			<td>102 (78%)</td>
			<td>51 (81%)</td>
			<td>51 (75%)</td>
		</tr>
		<tr>
			<td>School education</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>&#160; &#60; 10 years</td>
			<td>20 (16%)</td>
			<td>12 (19%)</td>
			<td>8 (12%)</td>
		</tr>
		<tr>
			<td>&#160; 10 years</td>
			<td>64 (50%)</td>
			<td>27 (44%)</td>
			<td>37 (56%)</td>
		</tr>
		<tr>
			<td>&#160; &#62; 10 years</td>
			<td>44 (34%)</td>
			<td>23 (37%)</td>
			<td>21 (32%)</td>
		</tr>
		<tr>
			<td>&#160; Not specified (n = 3)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Employment&#160;</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>&#160; Full-time or part-time</td>
			<td>55 (42%)</td>
			<td>33 (52%)</td>
			<td>22 (32%)</td>
		</tr>
		<tr>
			<td>&#160; Irregularely</td>
			<td>23 (18%)</td>
			<td>8 (13%)</td>
			<td>15 (22%)</td>
		</tr>
		<tr>
			<td>&#160; Not employed or retired</td>
			<td>53 (40%)</td>
			<td>22 (35%)</td>
			<td>31 (46%)</td>
		</tr>
		<tr>
			<td>Current smoker, yes</td>
			<td>22 (17%)</td>
			<td>12 (19%)</td>
			<td>10 (15%)</td>
		</tr>
		<tr>
			<td>Body mass index</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>&#160; &#60; 25 kg/m2</td>
			<td>34 (26%)</td>
			<td>23 (37%)</td>
			<td>11 (16%)</td>
		</tr>
		<tr>
			<td>&#160; &#8805; 25 kg/m2 and &#60; 30 kg/m2</td>
			<td>55 (42%)</td>
			<td>22 (35%)</td>
			<td>33 (49%)</td>
		</tr>
		<tr>
			<td>&#160; &#8805; 30 kg/m2</td>
			<td>42 (32)</td>
			<td>18 (29%)</td>
			<td>24 (35%)</td>
		</tr>
		<tr>
			<td>Self-reported general health</td>
			<td>2.8 &#177; 0.7</td>
			<td>2.8 &#177; 0.8</td>
			<td>2.8 &#177; 0.6</td>
		</tr>
		<tr>
			<td>Accelerometer wear time, min/day</td>
			<td>883.0 &#177; 82.8</td>
			<td>896.1 &#177; 74.4</td>
			<td>870.8 &#177; 88.7</td>
		</tr>
		<tr>
			<td>Accelerometer-based MVPA, min/day</td>
			<td>45.2 &#177; 27.7</td>
			<td>44.1 &#177; 24.3</td>
			<td>46.2 &#177; 30.7</td>
		</tr>
		<tr>
			<td>Self-reported MVPA, min/day</td>
			<td>77.2 &#177; 117.2</td>
			<td>85.2 &#177; 119.0</td>
			<td>68.0 &#177; 115.8</td>
		</tr>
	</tbody>
</table>
Table 1: Sample characteristics of participants included in the preliminary interim analysis. N = number of participants. MVPA = moderate-to-vigorous physical activity. Data are presented as mean &#177; standard deviation for continuous variables and as the number of participants (%) for categorical variables. Body mass index was calculated from objectively measured height and body weight at the participant assessment session. Self-reported general health was measured on a 5-point scale from 1 &#34;very good&#34; to 5 &#34;very bad&#34;. Self-reported and accelerometer-based MVPA as well as accelerometer wear time refer to average minutes per day across seven days.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58997/58997fig3v3.jpg" /> 
Figure 3: Mean difference between self-reported and accelerometer-based moderate-to-vigorous physical activity compared between study groups. &#916; = delta. MVPA = moderate-to-vigorous physical activity. min/day = minutes per day. The mean differences with according 95% confidence intervals of the control group (grey square) and the video group (blue diamond) are depicted. Mean differences were calculated as self-reported minus accelerometer-derived min of MVPA. The data refer to preliminary results of interim analysis (n = 131). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58997/58997fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58997/58997fig4.jpg" /> 
Figure 4: Bland Altman plots for visual depiction of the difference between self-reported and accelerometer-based moderate-to-vigorous physical activity in the control group (A) and in the video group (B).&#160;MVPA = moderate-to-vigorous physical activity. min/day = minutes per day. SD = standard deviation. Differences were calculated as self-reported minus accelerometer-derived min of MVPA. A perfect agreement between the measures would be present if all dots lied on a horizontal line at the value 0 of the y-axis (red line). The data refer to preliminary results of interim analysis (n = 131). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/58997/58997fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity at JoVE.com

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

This protocol describes a randomized controlled trial as a method to test the effect of a video demonstration on the intra-individual difference between self-reported and accelerometer-based moderate-to-vigorous physical activity.

Physical activity (PA) assessment needs tools that are inexpensive and easy to administer. Common questionnaires inquire time spent in light, moderate, ...

visualization-intensity-levels-to-reduce-gap-between-self-reported

Nanyang Technological University

Research

JoVE Journal

Behavior

6.7K Views.  University Medicine Greifswald. This protocol describes a randomized controlled trial as a method to test the effect of a video demonstration on the intra-individual difference between self-reported and accelerometer-based moderate-to-vigorous physical activity.

Video: Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

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 ...

Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation

Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to exercise training recommendations. The study of exercise adherence is thus a key step towards the optimization of PR programs. To date, mostly indirect measures, such as rates of participation, completion, and attendance, have been used to determine adherence to PR. The purpose of the present protocol is to describe how continuous data tracking technology can be used to measure adherence to a prescribed aerobic training intensity on a second-by-second basis.
In our investigations, adherence has been defined as the percent time spent within a specified target heart rate range. As such, using a combination of hardware and software, heart rate is measured, tracked, and recorded during cycling second-by-second for each participant, for each exercise session. Using statistical software, the data is subsequently extracted and analyzed. The same protocol can be applied to determine adherence to other measures of exercise intensity, such as time spent at a specified wattage, level, or speed on the cycle ergometer. Furthermore, the hardware and software is also available to measure adherence to other modes of training, such as the treadmill, elliptical, stepper, and arm ergometer. The present protocol, therefore, has a vast applicability to directly measure adherence to aerobic exercise.

Pulmonary rehabilitation is widely recognized in the management of respiratory diseases. A key component to successful pulmonary rehabilitation is adherence to the recommended exercise training. The purpose of the present protocol is to describe how continuous data tracking technology can be used to precisely measure adherence to a prescribed aerobic training intensity.

Pulmonary rehabilitation (PR) is an important component in the management of respiratory diseases. The effectiveness of PR is dependent upon adherence to ...

Medicine

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 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 ...

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom- measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field&#8217;s most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person&#8217;s stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision.&#160;

This protocol offers a digitization of portions of traditional clinical tasks commonly used to measure cognition and motor control in Parkinson&#8217;s disease. Clinical tasks are digitized while biophysical rhythms are co-registered from different functional levels of the nervous systems, ranging from voluntary, spontaneous, automatic to autonomic.

As Parkinson&#8217;s disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard ...

Measuring Engagement of Spectators of Social Digital Games

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of participants with motion tracking systems. In the context of games that are not confined within a screen, measuring the different dimensions of engagement such as physiological arousal can be challenging. The focus of the study is made on the spectators of the game and the differences in their engagement according to interactivity. Engagement is measured with physiological and self-reported arousal, as well as an engagement questionnaire at the end of the experiment. Physiological arousal is measured with electrodermal activity (EDA) sensors that record the data on a portable device (EDA box). Portability was essential because of the nature of the game, which is akin to a life-size pong and includes many participants that move. To have an overview of the events of the game, three cameras are used to film three angles of the playing field. To synchronize the EDA data with events happening in the game, boxes with digital numbers are used and put in the frames of cameras. Signals are sent from a sync box simultaneously to the EDA boxes and to light boxes. The light boxes show the synchronization numbers to the cameras, and the same numbers are also logged on the EDA data file. That way, it is possible to record EDA of many people that move freely in a large space and synchronize this data with events in the game. In our particular study, we were able to assess the differences in arousal for the different conditions of interactivity. One of the limitations of this method is that the signals cannot be sent farther than 20 meters away. This method is, therefore, appropriate for recording physiological data in games with an unlimited number of players but is restricted to a limited space.

We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of ...

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 ...

Home-Based Monitor for Gait and Activity Analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

Bioengineering

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. ...

Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise

The gold standard to assess the aerobic capacity in physically active subjects and athletes is the maximal oxygen consumption test (VO2&#8211;max), which involves analysis of exhaled&#8211;gases and cardiorespiratory variables obtained via the breath-by-breath method in an ergospirometer during an incremental exercise. However, this method cannot elucidate metabolic changes at the muscular level. Near-infrared spectroscopy (NIRS) has emerged as a valuable technology to evaluate local oxygen levels (Tissular Saturation Index, TSI) by quantifying the concentrations of oxygenated (O2-Hb) and deoxygenated (H-Hb) hemoglobin in the microvasculature of tissues. NIRS applications extend to respiratory and locomotor muscles, assessing metabolic changes associated with the cost of breathing (COB) and peripheral workload, respectively. Additionally, cerebral regions, such as the prefrontal cortex, have been explored with NIRS technology to assess physiological changes related to cognitive demand associated with planning or ideation of motor tasks linked to sports performance. Thus, by analyzing exercise-induced changes (D) in O2-Hb, H-Hb, and TSI, it is possible to identify central and peripheral exercise limitations, particularly when endurance training is the main component of physical fitness (e.g., running, cycling, triathlon, etc.). Addressing these factors is paramount for coaches and exercise physiologists to optimize athletic performance, incorporating training strategies focused on the primary exercise-limiting factors. This study outlines a protocol for utilizing wearables devices equipped with NIRS technology to analyze exercise changes in TSI, O2-Hb, and H-Hb, alongside cardiorespiratory variables typically registered in athletes during VO2&#8211;max tests. This approach offers a comprehensive method for identifying the primary systems involved in stopping exercise progression and sports performance improvement.

This protocol integrates near-infrared spectroscopy (NIRS) technology to assess localized hematological and oxygenation changes at the prefrontal cortex, respiratory (m.Intercostales), and locomotor (m.Vastus Lateralis) muscles during cardiopulmonary exercise testing, enabling the identification of central and peripheral limiting factors affecting exercise performance.

The gold standard to assess the aerobic capacity in physically active subjects and athletes is the maximal oxygen consumption test (VO2&#8211;max), which ...