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/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/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 &#38; 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/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/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

Research

JoVE Journal

Behavior

6.6K 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

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning process. Here a method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process. This discovery-learning task uses an infant activated mobile that rotates and plays music based on specified leg action of infants. Supine infants activate the mobile by moving their feet vertically across a virtual threshold. This paradigm is unique in that as infants independently discover that their leg actions activate the mobile, the infants&#8217; leg movements are tracked using a motion capture system allowing for the quantification of the learning process. Specifically, learning is quantified in terms of the duration of mobile activation, the position variance of the end effectors (feet) that activate the mobile, changes in hip-knee coordination patterns, and changes in hip and knee muscle torque. This information describes infant exploration and exploitation at the interplay of person and environmental constraints that support task-specific action. Subsequent research using this method can investigate how specific impairments of different populations of infants at risk for movement disorders influence the discovery-learning process for task-specific action.

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.

Task-specific actions emerge from spontaneous movement during infancy. It has been proposed that task-specific actions emerge through a discovery-learning ...

A Mouse Model of Subchronic and Mild Social Defeat Stress for Understanding Stress-induced Behavioral and Physiological Deficits

Stressful life events often increase the incidence of depression in humans. To study the mechanisms of depression, the development of animal models of depression is essential. Because there are several types of depression, various animal models are needed for a deeper understanding of the disorder. Previously, a mouse model of subchronic and mild social defeat stress (sCSDS) using a modified chronic social defeat stress (CSDS) paradigm was established. In the paradigm, to reduce physical injuries from aggressors, the duration of physical contact between the aggressor and a subordinate was reduced compared to in the original CSDS paradigm. sCSDS mice showed increased body weight gain, food intake, and water intake during the stress period, and their social behaviors were suppressed after the stress period. In terms of the face validity of the stress-induced overeating and overdrinking following the increased body weight gain, the sCSDS mice may show some features related to atypical depression in humans. Thus, a mouse model of sCSDS may be useful for studying the pathogenic mechanisms underlying depression. This protocol will help establish the sCSDS mouse model, especially for studying the mechanisms underlying stress-induced weight gain and polydipsia- and hyperphagia-like symptoms.

Here, methods for developing a mouse model of subchronic and mild social defeat stress are described and used to investigate the pathogenic features of depression including hyperphagia- and polydipsia-like symptoms following increased body weight.

Stressful life events often increase the incidence of depression in humans. To study the mechanisms of depression, the development of animal models of ...

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion expressions, and multimodal (i.e., film clips) stimuli that included contextual cues. All stimuli were presented via computer. Participants were required to indicate how the person in each stimulus was feeling using a forced-choice format. Additionally, for the film clips, participants had to indicate how they felt in response to the stimulus, and the level of intensity with which they experienced that emotion.

This paper describes how to implement a battery of behavioral tasks to examine emotion recognition of isolated facial and vocal emotion expressions, and a novel task using commercial television and film clips to assess multimodal emotion recognition that includes contextual cues.

The current study presented 60 people with traumatic brain injury (TBI) and 60 controls with isolated facial emotion expressions, isolated vocal emotion ...

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One difficulty in proving this objectively is that the natures of brain and heart activities are quite different. In this paper, we propose a methodology that uses wavelet entropy to measure the chaotic levels of both electroencephalogram (EEG) and electrocardiogram (ECG) data and show how this may be used to explore the potential coordination between the mind and heart under different experimental conditions. Furthermore, Statistical Parametric Mapping (SPM) was used to identify the brain regions in which the EEG wavelet entropy was the most affected by the experimental conditions. As an illustration, the EEG and ECG were recorded under two different conditions (normal rest and mindful breathing) at the beginning of an 8-week standard Mindfulness-based Stress Reduction (MBSR) training course (pretest) and after the course (posttest). Using the proposed method, the results consistently showed that the wavelet entropy of the brain EEG decreased during the MBSR mindful breathing state as compared to that during the closed-eye resting state. Similarly, a lower wavelet entropy of heartrate was found during MBSR mindful breathing. However, no difference in wavelet entropy during MBSR mindful breathing was found between the pretest and posttest. No correlation was observed between the entropy of brain waves and the entropy of heartrate during normal rest in all participants, whereas a significant correlation was observed during MBSR mindful breathing. Additionally, the most well-correlated brain regions were located in the central areas of the brain. This study provides a methodology for the establishment of evidence that mindfulness practice (i.e., mindful breathing) may increase the coordination between mind and heart activities.

This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.

In both the East and West, traditional teachings say that the mind and heart are somehow closely correlated, especially during spiritual practice. One ...

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Decades of behavioral science research have documented functional shifts in attitudes and ideological adherence in response to various challenges, but little work to date has illuminated the neural mechanisms underlying these dynamics. This paper describes how continuous theta burst transcranial magnetic stimulation may be employed to experimentally assess the causal contribution of cortical regions to threat-related ideological shifts. In the example protocol provided here, participants are exposed to a threat prime-an explicit reminder of their own inevitable death and bodily decomposition-following a downregulation of the posterior medial frontal cortex (pMFC) or a sham stimulation. Next, disguised within a series of distracter tasks, participants' relative degree of ideological adherence is assessed-in the present example, with regard to coalitional prejudice and religious belief. Participants for whom the pMFC has been downregulated exhibit less coalitionally biased responses to an immigrant critical of the participants' national in-group, and less conviction in positive afterlife beliefs (i.e., God, angels, and heaven), despite having recently been reminded of death. These results complement prior findings that continuous theta burst stimulation of the pMFC influences social conformity and sharing and illustrate the feasibility of investigating the neural basis of high-level social cognitive shifts using transcranial magnetic stimulation.

Threats reliably evoke shifts in high-level ideological investment, but little work to date has explored the neural mechanisms underlying these dynamics. This paper describes how continuous theta burst transcranial magnetic stimulation may be employed to test the contribution of the posterior medial frontal cortex (and/or other regions) to threat-related ideological shifts.

Decades of behavioral science research have documented functional shifts in attitudes and ideological adherence in response to various challenges, but ...

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

Virtual reality (VR) experiments can be difficult to implement and require meticulous planning. This protocol describes a method for the design and implementation of VR experiments that collect physiological data from human participants. The Experiments in Virtual Environments (EVE) framework is employed to accelerate this process.

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and ...

Supramaximal Intensity Hypoxic Exercise and Vascular Function Assessment in Mice

Exercise training is an important strategy for maintaining health and preventing many chronic diseases. It is the first line of treatment recommended by international guidelines for patients suffering from cardiovascular diseases, more specifically, lower extremity artery diseases, where the patients' walking capacity is considerably altered, affecting their quality of life. 
Traditionally, both low continuous exercise and interval training have been used. Recently, supramaximal training has also been shown to improve athletes' performances via vascular adaptations, amongst other mechanisms. The combination of this type of training with hypoxia could bring an additional and/or synergic effect, which could be of interest for certain pathologies. Here, we describe how to perform supramaximal intensity training sessions in hypoxia on healthy mice at 150% of their maximal speed, using a motorized treadmill and a hypoxic box. We also show how to dissect the mouse in order to retrieve organs of interest, particularly the pulmonary artery, the abdominal aorta, and the iliac artery. Finally, we show how to perform ex vivo vascular function assessment on the retrieved vessels, using isometric tension studies.

High-intensity training in hypoxia is a protocol that has been proven to induce vascular adaptations potentially beneficial in some patients and to improve athletes&#39; repeated sprint ability. Here, we test the feasibility of training mice using that protocol and identify those vascular adaptations using ex vivo vascular function assessment.

Exercise training is an important strategy for maintaining health and preventing many chronic diseases. It is the first line of treatment recommended by ...

Measuring Biophysical and Psychological Stress Levels Following Visitation to Three Locations with Differing Levels of Nature

Visitation to natural environments has been linked to psychological stress reduction. Although most stress-related research has relied on self-report formats, a growing number of studies now incorporate biological stress-related hormones and catalysts, such as cortisol and &#945;-amylase, to measure levels of stress. Presented here is a protocol to examine the effects on levels of biophysical and psychological stress following visitation to three different locations with differing levels of nature. Biophysical and self-reported psychological stress levels are measured immediately upon entering the selected locations and just prior to the visitors leaving the site. Using a &#34;drool&#34;&#160;method, the biophysical measure consists of 1-2 mL samples of saliva provided by study subjects upon entry to one of three study locations. As prescribed by extant literature, the saliva is collected within a 45 minute time frame following the end of the visitor&#8217;s engagement at the location. Following saliva collection, the samples are labeled and transported to a biological lab. Cortisol is the biophysical variable of interest in this study and measured using an ELISA process with a TECAN plate reader. To measure self-reported stress, the Perceived Stress Questionnaire (PSQ), which reports levels of worry, tension, joy, and perceived demands. Data are collected at all three sites in the late afternoon through early evening. When compared across all three settings, stress levels, as measured by both the biological markers and self-reports, are significantly lower after visitation to the most natural setting.

The purpose of this paper is to identify changes in stress levels after visitation to three different settings and to describe the methods used in identifying stress levels based on measures of salivary cortisol, &#945;-amylase, and a psychological self-report instrument.&#160;

Visitation to natural environments has been linked to psychological stress reduction. Although most stress-related research has relied on self-report ...

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has been described. The exercise-induced cTnT response distorts the diagnostic role of the cTnT assay. Although high-intensity interval exercise (HIE) is growing in popularity and concerns remain about its safety, available data related to cTnT release after HIE is limited, which hampers the use of HIE as a health intervention. Here, we present three representative HIE protocols [traditional HIE (repeated 4 min cycling at 90% V&#775;O2max interspersed with 3 min rest, 200 kJ/session); sprint interval exercise (SIE, repeated 1 min cycling at 120% V&#775;O2max interspersed with 1.5 min rest, 200 kJ/session); and repeated sprint exercise (RSE, 40 x 6 s all-out sprints interspersed with 9 s rest)] and one representative MCE protocol (continuous cycling exercise at an intensity of 60% V&#775;O2max, 200 kJ/session). Forty-seven sedentary, overweight young women were randomly assigned to one of four groups (HIE, SIE, RSE, and MCE). Six bouts of respective exercise were performed by every single group, with each being 48 h apart. Meanwhile, for four groups, the duration of the entire testing period was identical, being 10 days. Before and after the first and final exercise bouts, an assessment was conducted of cTnT. The current study provides a frame of reference giving a clear picture of how a specific exercise session affects the circulating cTnT concentration at the early stage of training. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise, especially for HIE.

Here, we present protocols of high-intensity interval and moderate-intensity continuous exercise to observe the response of circulating cardiac troponin T (cTnT) concentration to acute exercise over 10 days. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise.

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has ...

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal  Phase  of 5km Treadmill Running

Running is beneficial for physical health, but it is also accompanied by many injuries. However, the main factors leading to running injury remain unexplained. This study investigated the effects of long running distance on lower-limb kinematic variables and the lower limb kinematic difference of between the initial (IR) and terminal phase (TR) of 5 km running was compared. Ten amateur runners ran on a treadmill at the speed of 10 km/h. Dynamic kinematic data was collected at the phase of IR (0.5 km) and TR (5 km), respectively. The peak angle, peak angular velocities, and range of motion were recorded in this experiment. The main results demonstrated the following: ankle eversion and knee abduction were increased at TR; ROMs of ankle and knee were increased in the frontal plane at TR than IR; a larger peak angular velocity of ankle dorsiflexion and hip interrotation were found in TR compared to IR. These changes during the long distance running may provide some specific details for exploring potential reasons of running injuries.

This study investigated the biomechanical characteristics of the lower extremity kinematic variables between the initial and terminal phase of 5 km treadmill running. The lower-limb kinematic data of 10 runners were collected using a three-dimensional motion capture system on a treadmill at the initial phase (0.5 km) and the terminal phase (5 km), respectively.