Name: using continuous data tracking technology to study exercise adherence in pulmonary rehabilitation
Uploaded: 2013-11-08T23:00:00
Duration: 9 min 42 s
Description: Watch this Scientific Journal Video about Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation at JoVE.com

Canadian Lung Association &ndash; Canadian Respiratory Health Professionals; Fonds de recherche du Qu&eacute;bec - Sant&eacute; (FRQS)

Once data is collected, a single file per subject per session of raw data is obtained. Using statistical software, all sessions per subject are combined into a single file. Subsequently, the target intensity must be calculated for each subject. The adherence rate to that target intensity can then be calculated per session per subject, for each session for all subjects combined, or per group.
1. Data Collection (carried out by personnel supervising the training session)
<ol>
	<li>Minimize electrical interference by turning off wireless devices (e.g. cell phones, Wi-Fi, etc.) and minimize crosstalk by ensuring the heart rate monitors and equipment are at least 1 meter apart. Refer to Figure 1 for placement of heart rate transmitter.</li>
	<li>Turn on the data tracking software. Press start on the aerobic equipment and train the participant at the target intensity. For example, in our studies, participants are asked to train within &#177; 5 beats/min of their target heart rate. Refer to Figure 2 for CardioMemory.</li>
	<li>Collect data second-by-second for each participant for each rehabilitation session. Collected data includes the following: subject ID, duration (hhmmss), level of intensity (1-30), workload (watts), pedaling speed (revolutions/minute), distance (km), pace (mm:ss/km), heart rate (beats/minute), estimated oxygen consumption (VO2, ml/min/kg), metabolic equivalent of physical effort (METs), estimated energy expenditure (kcal/hour), and estimated energy consumed (kcal). See Figure 3.</li>
	<li>Press stop on the aerobic equipment. Click &#34;save&#34; to upload the data to CardioMemory. Click &#34;export&#34; to save the document outside of CardioMemory. The document will be in .cvs format and will automatically include the date of the session.</li>
</ol>
2. Data Extraction
CardioMemory software does not allow for the distinction of various exercise-training phases. As such, the data obtained must be exported to a statistical software in order to eliminate the phases that are not of interest (e.g. warm-up and cool-down), merge the data files, and compare achieved against target intensity.
<ol>
	<li>Open statistical analysis software to import excel file. Procedure: File &#8594; Open &#8594; Data &#8594; In &#34;Open Data&#34; window, select All Files in the dropdown menu of &#34;Files of Types&#34; &#8594; Select Excel. xls file &#8594; Open &#8594; In &#34;Opening Excel Data Source&#34; window click OK.</li>
	<li>Save the data file in a statistical analysis software. See Figure 4 for a sample database.</li>
	<li>Eliminate the nontraining phases, i.e.&#160;warm-up and cool-down, if the interest is time spent at the target intensity during the training phase.
	<ol>
		<li>Eliminate warm-up phase (e.g. first 10 min):
		<ol>
			<li>To recode duration, create a variable to identify every second as 1. Procedure: 
Transform 
&#8594; 
Recode into Different variables&#8230; 
&#8594;
 In &#34;Recode into Different Variables&#34; window, select 
Duration_A 
img alt="Arrow" fo:content-width=".2in" src="/files/ftp_upload/50643/arrow.jpg" width="20px" />
 click arrow 
&#8594; Identify &#34;Output Variable Name&#34; (e.g. tempo) &#8594; Change &#8594; Click on Old and New Values &#8594; Under &#34;Old Value&#34;, select Value: and enter 0 &#8594; Under &#34;New Value&#34;, Select Value: and enter 0 &#8594; Add &#8594; Under &#34;Old Value&#34;, select All other values then click on Value:, under &#34;New Value&#34; and enter 1 &#8594; Add &#8594; Continue &#8594; OK.
			<ul>
				<li>RECODE Duration_A (0=0) (ELSE=1) INTO Tempo.</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
			<li>Create a second temporary variable. Procedure: Transform &#8594; Shift Values &#8594; Select tempo &#8594; Click arrow &#8594; Under &#34;Name:&#34; type temporary variable (e.g. tempo2) &#8594; Change &#8594; OK.
			<ul>
				<li>SHIFT VALUES VARIABLE=Tempo RESULT=Tempo2 LAG=1.</li>
			</ul>
			</li>
			<li>To start tempo2 at 0, it must be recoded. Procedure: Transform &#8594; Recode into Same Variables &#8594; Select tempo2 &#8594; Click arrow &#8594; Click Old and New Values &#8594; Under &#34;Old Value&#34;, select System-Missing &#8594; Under &#34;New Value&#34;, select Value: and enter 0 &#8594; Add &#8594; Continue &#8594; OK.
			<ul>
				<li>RECODE Tempo2 (SYSMIS=0).</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
			<li>Sum the seconds starting from zero. Procedure: Transform &#8594; Compute Variable &#8594; Under &#34;Target Variable:&#34; type tempo &#8594; Under &#34;Numeric Expression&#34; type lag (tempo) +1 &#8594; IF... &#8594; Select Include if case satisfies condition: &#8594; Type tempo2 &#62; 0 &#8594; Continue &#8594; OK.
			<ul>
				<li>IF (Tempo2 &#62; 0) Tempo=Lag (tempo) + 1.</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
			<li>To eliminate the first 10 min&#160;of warm-up, remove tempo data that precedes 599 seconds. Procedure: Data &#8594; Select cases&#8230; &#8594; In &#34;Select Cases&#34; window, under &#34;Select&#34;, choose &#34;If condition is satisfied&#34; &#8594; If&#8230; &#8594; In &#34;Select Cases: If&#34; window, insert equation tempo &#62; 599 &#8594; Continue &#8594; Under &#34;Output&#34;, choose Delete unselected cases &#8594; OK. See Figure 5.
			<ul>
				<li>FILTER OFF.</li>
				<li>USE ALL.</li>
				<li>SELECT IF (tempo &#62; 599).</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
		</ol>
		</li>
		<li>Eliminate cool-down phase (e.g. last 5 minutes):
		<ol>
			<li>Sort data in descending order for Duration_A to bring the cool-down phase to the top of the database, as SPSS removes data from the top of the file onwards. Procedure: Data &#8594; Sort Cases &#8594; In &#34;Sort Cases&#34; window, select Duration_A &#8594; click arrow &#8594; In &#34;Sort Order&#34; menu select Descending &#8594; OK.
			<ul>
				<li>SORT CASES BY Duration_A(D).</li>
			</ul>
			</li>
			<li>Recode Duration_A to identify every second as 1. Procedure: Transform &#8594; Recode Into Different variables&#8230; &#8594; In &#34;Recode into Different Variables&#34; window, select Duration_A &#8594; click arrow &#8594; Identify &#34;Output Variable Name&#34; (e.g. tempoA) &#8594; Change &#8594; Click Old and New Values &#8594; Under &#34;Old Value&#34;, select Value: and enter 0 &#8594; Under &#34;New Value&#34;, select Value: and enter 0 &#8594; Add &#8594; Select All other values under &#34;Old Value&#34;, then click on Value: under &#34;New Value&#34; and enter 1 &#8594; Add &#8594; Continue &#8594; OK.
			<ul>
				<li>RECODE Duration_A (0=0) (ELSE=1) INTO TempoA.</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
			<li>Create a second temporary variable. Procedure: Transform &#8594; Shift Values &#8594; Select tempoA &#8594; Click arrow &#8594; Under &#34;Name:&#34; type temporary variable (e.g. tempoA2) &#8594; Change &#8594; OK.
			<ul>
				<li>SHIFT VALUES VARIABLE=TempoA RESULT=TempoA2 LAG=1.</li>
			</ul>
			</li>
			<li>To start tempoA2 at 0, it must be recoded. Procedure: Transform &#8594; Recode into Same Variables &#8594; Select tempoA2 &#8594; Click arrow &#8594; Click Old and New Values &#8594; Under &#34;Old Value&#34;, select System-Missing &#8594; Under &#34;New Value&#34;, select Value: and enter 0 &#8594; Add &#8594; Continue &#8594; OK.
			<ul>
				<li>RECODE TempoA2 (SYSMIS=0).</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
			<li>Sum the seconds of the tempoA variable. Procedure: Transform &#8594; Compute Variable &#8594; Under &#34;Target Variable:&#34; type tempoA &#8594; Under &#34;Numeric Expression&#34; type lag (tempoA)+1 &#8594; IF... &#8594; Select Include if case satisfies condition: &#8594; Type tempoA2&#62; 0 &#8594; Continue &#8594; OK.
			<ul>
				<li>IF (TempoA2 &#62; 0) TempoA=Lag (tempoA) + 1.</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
			<li>To eliminate the cool-down phase (i.e. 5 min), remove tempo data that precedes 299 seconds. Procedure: Data &#8594; Select cases&#8230; &#8594; In &#34;Select Cases&#34; window, under &#34;Select&#34;, choose &#34;If condition is satisfied&#34; &#8594; If&#8230; &#8594; In &#34;Select Cases: If&#34; window, insert equation tempoA &#62; 299 &#8594; Continue &#8594; under &#34;Output&#34;, choose Delete unselected cases &#8594; OK. See Figure 6.
			<ul>
				<li>FILTER OFF.</li>
				<li>USE ALL.</li>
				<li>SELECT IF (tempoA &#62; 299).</li>
				<li>EXECUTE.</li>
			</ul>
			</li>
		</ol>
		</li>
		<li>Identify the session number (or date) associated with the dataset. Create and name a new variable (e.g. Session). Procedure: Transform &#8594; Compute Variable &#8594; In compute variable window under Target Variable, type Session &#8594; click Type &#38; Label to open &#34;Compute Variable: Type an...&#34; window &#8594; under &#34;Type&#34; select String &#8594; Continue &#8594; under String Expression type &#39;1&#39; &#8594; OK. See Figure 7.
		<ul>
			<li>STRING Session (A8).</li>
			<li>COMPUTE Session= &#39;1&#39;.</li>
			<li>EXECUTE.</li>
		</ul>
		</li>
		<li>Save the modified SPSS document in a new file (example: subjectID_session#).</li>
		<li>Repeat the above procedure for all remaining sessions for the same subject.</li>
	</ol>
	</li>
</ol>
3. Data Merging - Single Participant
<ol>
	<li>To merge all sessions into a single SPSS database, open participant&#39;s first session (i.e. subjectID_session1).</li>
	<li>Merge remaining sessions to the current file. Procedure: Data &#8594; Merge Files &#8594; Add Cases &#8594; in &#34;Add Cases to subjectID_session1.sav&#34; window, click Browse and choose file subjectID_session2 &#8594; Open &#8594; Continue &#8594; in the &#34;Add Cases from ...&#34; window click OK. Repeat for all remaining sessions. See Figure 8.
	<ul>
		<li>ADD FILES /FILE=*</li>
		<li>/FILE=&#39;SubjectAB001_Session1.sav&#39;.</li>
		<li>EXECUTE.</li>
	</ul>
	</li>
	<li>Add a column which contains the subject&#39;s ID number. Procedure: Transform &#8594; Compute Variable &#8594; In &#34;Compute Variable&#34; window under Target Variable, type SubjectID &#8594; click Type &#38; Label to open &#34;Compute Variable: Type an...&#34; window &#8594; under &#34;Type&#34; select String &#8594; Continue &#8594; under String Expression type &#39;SubjectID&#39; (e.g.&#160;&#39;AB001&#39;) &#8594; OK. See Figure 9.
	<ul>
		<li>STRING Subject_ID (A8).</li>
		<li>COMPUTE Subject_ID=&#39;AB001&#39;.</li>
		<li>EXECUTE.</li>
	</ul>
	</li>
	<li>Add a column that contains the subject&#8217;s target intensity (e.g. target heart rate [THR]). Procedure: Transform &#8594; Compute Variable &#8594; In &#34;Compute Variable&#34; window under Target Variable, type THR &#8594; click Type &#38; Label to open &#34;Compute Variable: Type an...&#34; window &#8594; under &#34;Type&#34; select Numeric &#8594; Continue &#8594; under Numeric Expression type THR (e.g. 110) &#8594; OK. See Figure 10.
	<ul>
		<li>STRING THR (A8).</li>
		<li>COMPUTE THR=&#39;110&#39;.</li>
		<li>EXECUTE.</li>
	</ul>
	</li>
	<li>Save database under a different file name (e.g. SubjectAB001_session1-36).</li>
	<li>Repeat for all remaining participants. At this point, each participant will have a database containing all sessions.</li>
</ol>
4. Data Merging - Grouping Participants
<ol>
	<li>To group several participants into a single database, open participant&#39;s file (i.e. subjectID_session1-36).</li>
	<li>Merge the remaining participants to the current file. Procedure: Data &#8594; Merge Files &#8594; Add Cases &#8594; in &#34;Add Cases to SubjectAB001_session1-36.sav&#34; window, click Browse and choose file SubjectCD002_session1-36 &#8594; Open &#8594; Continue &#8594; in the &#34;Add Cases from ...&#34; window click OK. Repeat for all participants that you wish to group. See Figure 11.
	<ul>
		<li>ADD FILES /FILE=*</li>
		<li>/RENAME (AB001=d0)</li>
		<li>/FILE=&#39;SubjectAB001_Session1-36.sav&#39;</li>
		<li>/RENAME (CD002=d1)</li>
		<li>/DROP=d0 d1.</li>
		<li>EXECUTE.</li>
	</ul>
	</li>
	<li>Save new database (e.g.Group01_Subjects001-010).</li>
</ol>
5. Identification of Target Intensity (e.g. THR Range)
<ol>
	<li>Identify a THR range; click Transform &#8594; Compute Variable &#8594; in the &#34;Compute Variable&#34; window under &#34;Target Variable&#34; enter a new variable name (e.g. Diff_HR_THR) &#8594; &#34;Type &#38; Label...&#34; &#8594; in the &#34;Compute Variable: Type an....&#34; select Numeric &#8594; Continue &#8594; Under &#34;Numeric Expression&#34; enter equation: HR - THR &#8594; OK. This provides us with a new variable.
	<ul>
		<li>COMPUTE Diff_HR_THR=HR - THR.</li>
		<li>EXECUTE.</li>
	</ul>
	</li>
	<li>Recode variables to identify whether the HR lies below, above, or within the THR range. Procedure:Transform &#8594; Recode Into Different Variables... &#8594; select Diff_HR_THR &#8594; click arrow &#8594; under &#34;Output Variable&#34; under &#34;Name&#34; type Diff_HR_THR _recoded &#8594; Change &#8594; Old and New Values... &#8594; in &#34;Recode Into Different Variables: Old and New Values&#34; window:</li>
</ol>
<table border="1">
	<tbody>
		<tr>
			<td align="center">Old Value</td>
			<td align="center">New Value</td>
			<td></td>
			<td align="center">Old--&#62;New:</td>
		</tr>
		<tr>
			<td align="center">Range: -5 through 5</td>
			<td align="center">1</td>
			<td align="center" rowspan="4">Add</td>
			<td align="center">-5 thru 5 --&#62; 1</td>
		</tr>
		<tr>
			<td align="center">Range, LOWEST through value: -5</td>
			<td align="center">0</td>
			<td align="center">Lowest thru -5 --&#62; 0</td>
		</tr>
		<tr>
			<td align="center">Range, value through HIGHEST: 5</td>
			<td align="center">0</td>
			<td align="center">5 thru Highest --&#62; 0</td>
		</tr>
		<tr>
			<td align="center">System-missing</td>
			<td align="center">System-missing</td>
			<td align="center">SYSMIS--&#62; SYSMIS</td>
		</tr>
	</tbody>
</table>
&#8594; Continue &#8594; OK. See Figure 12.
<ul style="margin-left: 80px;">
	<li>RECODE Diff_HR_THR (SYSMIS=SYSMIS) (-5 thru 5=1) (Lowest thru -5=0) (5 thru Highest=0) INTO</li>
	<li>Diff_HR_THR_Recoded.</li>
	<li>EXECUTE.</li>
</ul>
6. Calculation of Percent Adherence
<ol>
	<li>In Group01_Subjects001-010 file, calculate all seconds that patients were within the THR range by doing the following: Data &#8594; Aggregate &#8594; in &#34;Aggregate Data&#34; window, under &#34;Break Variable(s):&#34; select subjectID and session &#8594; click arrow &#8594; under &#34;Summaries of Variable(s):&#34; select Diff_HR_THR _recoded &#8594; click arrow &#8594; OK. A new variable is created with the name Diff_HR_THR _recoded_mean.
	<ul>
		<li>AGGREGATE</li>
		<li>/OUTFILE=* MODE=ADDVARIABLES</li>
		<li>/BREAK=Subject_ID Session</li>
		<li>/Diff_HR_THR_Recoded_mean=MEAN(Diff_HR_THR_Recoded).</li>
	</ul>
	</li>
	<li>Convert the obtained value into a percentage; click Transform &#8594; Compute Variable &#8594; under &#34;Target Variable&#34; enter variable name (e.g. Perc_THR) &#8594; under &#34;Numeric Expression&#34; select Diff_HR_THR _recoded_mean &#8594; click arrow &#8594; multiply the value by 100 (Diff_HR_THR _recoded_mean * 100) &#8594; OK. We then obtain adherence as a percentage of time spent within the THR for each subject for each session. See Figure 13
	<ul>
		<li>COMPUTE Perc_THR=Diff_HR_THR_Recoded_mean * 100.</li>
		<li>EXECUTE.</li>
	</ul>
	</li>
	<li>To obtain adherence for the percentage of time spent within the THR for each subject for all sessions combined, in &#34;Aggregate Data&#34; window, under &#34;Break Variable(s):&#34; substitute subjectID and session with only subjectID. See Figure 14.</li>
	<li>To obtain adherence for the percentage of time spent within the THR for each session for all subjects combined, in &#34;Aggregate Data&#34; window, under &#34;Break Variable(s):&#34; substitute subjectID and session with only session.</li>
	<li>Save the database under a different file name (e.g.&#160;Group01_Subjects001-010_Aggregate).</li>
</ol>

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Chest Dis. 54 (2), 189-192 (1999).</li><li>Foglio, K., Bianchi, L., Ambrosino, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Is+it+really+useful+to+repeat+outpatient+pulmonaryrehabilitation+programs+in+patients+with+chronic+airway+obstruction?+A+2-year+controlled+study.">Is it really useful to repeat outpatient pulmonaryrehabilitation programs in patients with chronic airway obstruction? A 2-year controlled study.</a> Chest. 119 (6), 1696-1704 (2001).</li><li>George, J., Kong, D. C. 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J., Smith, D. L., Hyland, M. E., Morgan, M. D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+short+outpatient+pulmonary+rehabilitation+programme:+immediate+and+longer+term+effects+on+exercise+performance+and+quality+of+life.">A short outpatient pulmonary rehabilitation programme: immediate and longer term effects on exercise performance and quality of life.</a> Respir. Med. 92, 1146-1154 (1998).</li><li>Young, P., Dewse, M., Fergusson, W., Kolbe, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Respiratory+rehabilitation+in+chronic+obstructive+pulmonary+disease:+predictors+of+nonadherence.">Respiratory rehabilitation in chronic obstructive pulmonary disease: predictors of nonadherence.</a> Eur. Respir. J. 13, 855-859 (1999).</li><li>Brooks, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+pulmonary+rehabilitation+programs+in+Canada+in.">Characterization of pulmonary rehabilitation programs in Canada in.</a> Can. Respir. J. 14 (2), 87-92 (2005).</li><li>Fischer, M. J., Scharloo, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drop-out+and+attendance+in+pulmonary+rehabilitation:+the+role+of+clinical+and+psychosocial+variables.">Drop-out and attendance in pulmonary rehabilitation: the role of clinical and psychosocial variables.</a> Respir. Med. 103 (10), 1564-1571 .</li><li>Sabit, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictors+of+poor+attendance+at+an+outpatient+pulmonary+rehabilitation+programme.">Predictors of poor attendance at an outpatient pulmonary rehabilitation programme.</a> Respir. Med. 102 (6), 819-824 .</li></ol>

The authors declare that they have no competing financial interests.

Continuous data tracking technology enables for a very precise measurement of exercise adherence. This procedure can be easily adapted to other definitions of adherence by replacing target heart rate range with target wattage, level, speed, or MET level. In the present example, the warm-up and cool-down phases were eliminated to isolate the exercise phase because of our specific research objective. Should the warm-up and cool-down phases be of interest to other researchers, step 2.3 (&#34;Eliminate the nontraining phases&#34;) can be eliminated from the protocol. 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.
When following the above protocol, certain simple steps are critical. First, the CardioMemory software must be started before the exercise equipment (e.g. cycle ergometer) for exercise data to be tracked and subsequently recorded. Should data be lost at this initial step, the data extraction protocol will need to be adjusted accordingly. Secondly, sources of interference must be minimized to reduce the risk of crosstalk and/or lost data. The heart rate monitors communicate wirelessly with the equipment and software. Thus, interference is especially detrimental if using target heart rate to calculate adherence. Finally, it is imperative to select statistical software for the database that has the capacity to permit for large quantities of data. For example, in a study with 10 participants completing 36 sessions at 40 min&#160;each, 864,000 rows of data points will be generated. Excel 2007 and later versions have the capacity to contain 1,048,576 rows in a worksheet23, whereas SAS24 and SPSS25 have no limit for the number of rows. Depending on the total number of data points expected for a given study, the software needs to be selected accordingly.
Despite the notable advantages of this technology, two main limitations exist. The first is data loss, which can result from equipment and/or software failure. As mentioned above, data loss can be due to electrical interference with wireless devices (i.e.&#160;cell phones or Wi-Fi), and more specifically interference with the wireless data transmission of heart rate. However, at times, data loss can also be due to unidentifiable causes. A second limitation is that the software does not provide the option of marking or splitting the exercise protocol systematically in order to differentiate/identify different phases. If this option were available, the extraction of the exercise phase of interest could be performed directly in the software, which would limit steps in the adherence calculation protocol. As well, the option of placing markers would be practical for the study of adherence to interval or intermittent training protocols as it would allow for the differentiation of the different phases (e.g. low versus high intensity).
For future perspectives, the use of continuous data tracking technology to precisely quantify adherence will enable researchers to investigate patterns of exercise response to different interventions, identify determinants of adherence, and characterize good and poor adherers. Ultimately, a better understanding of exercise adherence will allow for the optimization of exercise rehabilitation programs.

Pulmonary rehabilitation (PR) combines exercise training, patient education and psychosocial support, and is widely recognized as a cornerstone in the management of pulmonary disease1-5. The goals of PR are to reduce symptoms, optimize functional status, improve health-related quality of life, and reduce health care costs4,5. In a meta-analysis of 31 randomized controlled trials in chronic obstructive pulmonary disease (COPD), PR was shown to significantly improve exercise capacity, reduce dyspnea and fatigue, improve emotional function and enhance patients&#8217; sense of control over their condition6. Furthermore, evidence documents its effectiveness in reducing respiratory exacerbations7 and days spent in hospital8-13. Exercise training is considered the key to successful PR since it is responsible for much of the benefits associated with this intervention3-5. However, a major issue for several patients is adhering to the recommended amount or level of exercise. Nonadherence to recommended treatment may result in the failure of therapeutic interventions as well as inefficient use of health resources14.
According to the World Health Organization, the term &#39;&#39;adherence&#39;&#39; refers to the extent to which a person&#8217;s behavior coincides with recommendations given by a health care professional15. To date, adherence to exercise training in rehabilitation settings has been largely assessed as either the rate of participation (i.e registration to the program), the rate of completion (i.e. finishing the program), or the rate of attendance (i.e. number of exercise sessions attended)16-18. At present, no &#34;gold standard&#34; exists for measuring adherence15 and current methods do not allow for great precision. Furthermore, depending on the selected method, rates of adherence to PR have shown large variability16-19. For example, Hogg et al.16 measured adherence in COPD patients as the ratio between those who completed the program to those referred and found a low adherence of approximately 40%. However, other PR studies that have used attendance rates demonstrated, on average, a 90% adherence10,20,21. The lack of homogeneity in calculating adherence makes it difficult to compare results between studies. Another concern is the lack of precision with the existing calculation methods; attendance to an exercise training session does not guarantee adherence to the prescribed intensity. This gap in information led us to investigate how adherence could be calculated in a more precise way.
Recent advances in fitness equipment technology have allowed for continuous data tracking, which can be used to monitor adherence to a prescribed aerobic training intensity during individual exercise sessions in a PR context. More specifically, data tracking hardware and software permits for second-by-second recording of duration, speed, level, wattage, pace, heart rate, distance, calorie consumption, VO2, METS, and calories, and provides averages of all variables with the exception of level and VO2. The main advantage of this technology is the ability to record continuous detailed measures, which allows for the precise calculation of adherence to prescribed exercise versus previously reported general attendance or completion rates. This procedure can be of value for any study examining the impact of one or several aerobic exercise training programs. Using this technology, patient adherence to a prescribed intensity can be assessed by the percent time spent at a specified wattage, level, speed, or heart rate during the training phase of each session. For our investigations, adherence to an exercise training protocol has been defined as the percent time spent within a specified target heart rate range. Since heart rate response at a given submaximal workload decreases as cardiorespiratory fitness increases, this approach ensures that patients remain at the same relative (versus absolute) training intensity throughout the program. The present protocol describes in detail how continuous data tracking technology can be used to precisely measure adherence to a prescribed target heart rate range.

<table border="0" cellpadding="0" cellspacing="0" width="892">
<colgroup>
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</colgroup>
<tbody>
<tr height="21">
<td height="21" style="height:21px;width:195px;">Bike Excite Med 700</td>
<td style="width:239px;">Technogym - www.technogym.com</td>
<td style="width:164px;"> </td>
<td style="width:295px;">SCIFIT (http://scifit.com/)</td>
</tr>
<tr height="21">
<td height="21" style="height:21px;">CardioMemory software </td>
<td style="width:239px;">Technogym - www.technogym.com</td>
<td style="width:164px;">Version 1.0</td>
<td style="width:295px;">SCIFIT (http://scifit.com/)</td>
</tr>
<tr height="21">
<td height="21" style="height:21px;">Polar heart rate monitor</td>
<td style="width:239px;">Polar - www.polarca.com</td>
<td>T31 coded Transmitter</td>
<td style="width:295px;"> </td>
</tr>
<tr height="21">
<td height="21" style="height:21px;width:195px;">SPSS Statistical Software</td>
<td style="width:239px;">SPSS Inc. - www.spss.com/</td>
<td style="width:164px;">Version 16.0</td>
<td style="width:295px;">SAS/STAT software (http://www.sas.com/)</td>
</tr>
<tr height="21">
<td height="21" style="height:21px;width:195px;"> </td>
<td style="width:239px;"> </td>
<td style="width:164px;"> </td>
<td style="width:295px;"> </td>
</tr>
</tbody>
</table>

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.

When the protocol is performed correctly, an adherence rate is obtained for each subject for each session (Figure 13), for each subject for all sessions (Figure 14), and for each session for all subjects combined. An estimate of the time required to complete the above protocol for a single session of one subject is approximately 5 min. Results for adherence can range from 0-100%. Using this information, additional analyses can be performed to determine differences between subjects (i.e. sex differences, disease severity, etc.),to identify changes over time, and to reveal patterns in adherence. Moreover, the comparison of adherence between groups can be performed; for example, different exercise&#160;training programs can be compared. Finally, through further investigation, causes of nonadherence can be identified at specific time points during PR.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig1.jpg" width="600px" /> 
Figure 1. Heart rate transmitter placement. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig1highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig2.jpg" width="600px" /> 
Figure 2. Sample of data collected using data tracking software. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig2highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig3.jpg" width="600px" /> 
Figure 3. Sample of data tracking software output. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig3highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig4.jpg" width="600px" /> 
Figure 4. Sample database illustrating a sample of statistical software database. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig4highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig5.jpg" width="600px" /> 
Figure 5. Sample database illustrating the eliminated warm-up phase. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig5highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 6" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig6.jpg" width="600px" /> 
Figure 6. Sample database illustrating the eliminated cool-down phase. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig6highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 7" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig7.jpg" width="600px" /> 
Figure 7. Sample database illustrating a column added for session number. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig7highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 8" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig8.jpg" width="600px" /> 
Figure 8. Sample database illustrating the merged sessions for a single participant. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig8highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 9" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig9.jpg" width="600px" /> 
Figure 9. Sample database illustrating a column added for subject identification number. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig9highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 10" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig10.jpg" width="600px" /> 
Figure 10. Sample database illustrating a column added for target heart rate. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig10highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 11" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig11.jpg" width="600px" /> 
Figure 11. Sample database illustrating the merged participants&#39; files. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig11highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 12" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig12.jpg" width="600px" /> 
Figure 12. Sample database illustrating the recoded heart rate variables. <a href="https://www.jove.com/files/ftp_upload/50643/50643fig12highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 13" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig13.jpg" width="600px" /> 
Figure 13. Sample database illustrating adherence as a percentage of time spent within the target heart rate range for each subject for each session (horizontal red line highlights the change in adherence between sessions for the same subject). <a href="https://www.jove.com/files/ftp_upload/50643/50643fig13highres.jpg" target="_blank">Click here to view larger image</a>.
<img alt="Figure 14" fo:content-width="5in" src="/files/ftp_upload/50643/50643fig14.jpg" width="600px" /> 
Figure 14. Sample database illustrating adherence for the percentage of time spent within the target heart rate range for each subject for all sessions (horizontal red line highlights the difference between subjects). <a href="https://www.jove.com/files/ftp_upload/50643/50643fig14highres.jpg" target="_blank">Click here to view larger image</a>.

Watch this Scientific Journal Video about Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation at JoVE.com

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

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 ...

using-continuous-data-tracking-technology-to-study-exercise-adherence

Research

JoVE Journal

Medicine

Manual Muscle Testing:  A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients

Survivors of acute respiratory distress syndrome (ARDS) and other causes of critical illness often have generalized weakness, reduced exercise tolerance, and persistent nerve and muscle impairments after hospital discharge.1-6 Using an explicit protocol with a structured approach to training and quality assurance of research staff, manual muscle testing (MMT) is a highly reliable method for assessing strength, using a standardized clinical examination, for patients following ARDS, and can be completed with mechanically ventilated patients who can tolerate sitting upright in bed and are able to follow two-step commands. 7, 8 
This video demonstrates a protocol for MMT, which has been taught to &ge;43 research staff who have performed &gt;800 assessments on &gt;280 ARDS survivors. Modifications for the bedridden patient are included. Each muscle is tested with specific techniques for positioning, stabilization, resistance, and palpation for each score of the 6-point ordinal Medical Research Council scale.7,9-11 Three upper and three lower extremity muscles are graded in this protocol: shoulder abduction, elbow flexion, wrist extension, hip flexion, knee extension, and ankle dorsiflexion. These muscles were chosen based on the standard approach for evaluating patients for ICU-acquired weakness used in prior publications. 1,2.

Survivors of acute respiratory distress syndrome (ARDS) and critical illness frequently develop long-lasting muscle weakness. Manual muscle testing (MMT) is a standardized clinical examination commonly used to measure strength of peripheral skeletal muscle groups. This video demonstrates MMT using the 6-point Medical Research Council scale.

Survivors of acute respiratory distress syndrome (ARDS) and other causes of critical illness often have generalized weakness, reduced exercise tolerance, ...

Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device

Spinal cord injury (SCI) is a debilitating neurological condition characterized by somatic and autonomic dysfunctions. In particular, SCI above the mid-thoracic level can lead to a potentially life-threatening hypertensive condition called autonomic dysreflexia (AD) that is often triggered by noxious or non-noxious somatic or visceral stimuli below the level of injury. One of the most common triggers of AD is the distension of pelvic viscera, such as during bladder and bowel distension or evacuation. This protocol presents a novel pattern recognition algorithm developed for a JAVA platform software to study the fluctuations of cardiovascular parameters as well as the number, severity and duration of spontaneously occurring AD events. The software is able to apply a pattern recognition algorithm on hemodynamic data such as systolic blood pressure (SBP) and heart rate (HR) extracted from telemetry recordings of conscious and unrestrained animals before and after thoracic (T3) complete transection. With this software, hemodynamic parameters and episodes of AD are able to be detected and analyzed with minimal experimenter bias.

The catheter of a telemetry device is implanted into the abdominal aorta in order to continuously collect beat-by-beat hemodynamic data from animals pre and post-high thoracic spinal cord transection. A novel JAVA software was employed to analyze hemodynamic parameters as well as frequency and intensity of spontaneous episodes of autonomic dysreflexia.

Spinal cord injury (SCI) is a debilitating neurological condition characterized by somatic and autonomic dysfunctions. In particular, SCI above the ...

Assessment of Pulmonary Capillary Blood Volume, Membrane Diffusing Capacity, and Intrapulmonary Arteriovenous Anastomoses During Exercise

Exercise is a stress to the pulmonary vasculature. With incremental exercise, the pulmonary diffusing capacity (DLCO) must increase to meet the increased oxygen demand; otherwise, a diffusion limitation may occur. The increase in DLCO with exercise is due to increased capillary blood volume (Vc) and membrane diffusing capacity (Dm). Vc and Dm increase secondary to the recruitment and distension of pulmonary capillaries, increasing the surface area for gas exchange and decreasing pulmonary vascular resistance, thereby attenuating the increase in pulmonary arterial pressure. At the same time, the recruitment of intrapulmonary arteriovenous anastomoses (IPAVA) during exercise may contribute to gas exchange impairment and/or prevent large increases in pulmonary artery pressure.
We describe two techniques to evaluate pulmonary diffusion and circulation at rest and during exercise. The first technique uses multiple-fraction of inspired oxygen (FIO2) DLCO breath holds to determine Vc and Dm at rest and during exercise. Additionally, echocardiography with intravenous agitated saline contrast is used to assess IPAVAs recruitment.
Representative data showed that the DLCO, Vc, and Dm increased with exercise intensity. Echocardiographic data showed no IPAVA recruitment at rest, while contrast bubbles were seen in the left ventricle with exercise, suggesting exercise-induced IPAVA recruitment.
The evaluation of pulmonary capillary blood volume, membrane diffusing capacity, and IPAVA recruitment using echocardiographic methods is useful to characterize the ability of the lung vasculature to adapt to the stress of exercise in health as well as in diseased groups, such as those with pulmonary arterial hypertension and chronic obstructive pulmonary disease.

To assess the pulmonary diffusion and vasculature responses to exercise, we describe the multiple-inspired oxygen diffusion capacity technique to determine capillary blood volume and membrane diffusing capacity, as well as agitated saline contrast echocardiography to assess the recruitment of intrapulmonary arteriovenous anastomoses.

Exercise is a stress to the pulmonary vasculature. With incremental exercise, the pulmonary diffusing capacity (DLCO) must increase to meet the increased ...

Integration of Brain Tissue Saturation Monitoring in Cardiopulmonary Exercise Testing in Patients with Heart Failure

Cerebral hypo-oxygenation during rest or exercise negatively impacts the exercise capacity of patients with heart failure with reduced ejection fraction (HF). However, in clinical cardiopulmonary exercise testing (CPET), cerebral hemodynamics is not assessed. NIRS is used to measure cerebral tissue oxygen saturation (SctO2) in the frontal lobe. This method is reliable and valid and has been utilized in several studies. SctO2 is lower during both rest and peak exercise in patients with HF than in healthy controls (66.3 &#177; 13.3% and 63.4 &#177; 13.8% vs. 73.1 &#177; 2.8% and 72 &#177; 3.2%). SctO2 at rest is significantly linearly correlated with peak VO2 (r = 0.602), oxygen uptake efficiency slope (r = 0.501), and brain natriuretic peptide (r = -0.492), all of which are recognized prognostic and disease severity markers, indicating its potential prognostic value. SctO2 is determined mainly by end-tidal CO2 pressure, mean arterial pressure, and hemoglobin in the HF population. This article demonstrates a protocol that integrates SctO2 using NIRS into incremental CPET on a calibrated bicycle ergometer.

This protocol integrated near-infrared spectroscopy into conventional cardiopulmonary exercise testing to identify the involvement of the cerebral hemodynamic response in exercise intolerance in patients with heart failure.

Cerebral hypo-oxygenation during rest or exercise negatively impacts the exercise capacity of patients with heart failure with reduced ejection fraction ...

Providing Visual Biofeedback Using Brightness Mode Ultrasound During a Golf Swing

Using ultrasound biofeedback in conjunction with verbal cueing can increase muscle thickness more than verbal cueing alone and may augment traditional rehabilitation techniques in an athletic, physically active population. Brightness mode (B-mode) ultrasound can be applied using frame-by-frame analysis synchronized with video to understand muscle thickness changes during these dynamic tasks. Visual biofeedback with ultrasound has been established in static positions for the muscles of the lateral abdominal wall. However, by securing the transducer to the abdomen using an elastic belt and foam block, biofeedback can be applied during more specific tasks prevalent in lifetime sports, such as golf. To analyze muscle activity during a golf swing, muscle thickness changes can be compared. The thickness must increase throughout the task, indicating that the muscle is more active. This methodology allows clinicians to immediately replay ultrasound videos for patients as a visual tool to instruct proper activity of the muscles of interest. For example, ultrasound can be used to target the external and internal obliques, which play an important role in swinging a golf club or any other rotational sport or activity. This methodology aims to increase oblique muscle thickness during the golf swing. Additionally, the timing of muscle contraction can be targeted by instructing the patient to contract the abdominal muscles at specific time points, such as the beginning of the downswing, with the goal of improving muscle firing patterns during tasks.

Brightness mode ultrasound can be used to provide visual biofeedback of the muscles of the lateral abdominal wall during a golf swing. Post-swing visual and verbal instruction can increase the muscle activation and timing of the external and internal obliques.

Using ultrasound biofeedback in conjunction with verbal cueing can increase muscle thickness more than verbal cueing alone and may augment traditional ...

Muscle Function Obtained with Motion Mode Ultrasound and Surface Electromyography during Core Endurance Exercise

Motion mode (M-mode) ultrasound allows researchers and clinicians to measure the change of muscle thickness across time. Muscle thickness can be measured between fascial borders at a given time point during an exercise. This selected time point produces a one-dimensional image resulting in real-time, live observation of anatomy. Ultrasound used during functional movement can be referred to as dynamic ultrasound; this is feasible and reliable with the use of a linear transducer, elastic belt, and foam block to secure consistent transducer placement. The lateral abdominal wall is commonly investigated using ultrasound due to the overlapping nature of the muscles. Surface electromyography (sEMG) can complement M-mode ultrasound imaging because it measures the electrical representation of muscle activation. There is minimal evidence using M-mode ultrasound and sEMG simultaneously during core exercise. Exercises that challenge the core musculature involve both isometric holds (e.g., side plank), as well as oscillatory extremity movements (e.g., dead bug). In this study, both instruments will be used simultaneously to measure core muscle function during exercise. Ultrasound measurements will be obtained using a linear transducer and ultrasound unit, and sEMG measurements will be acquired from a wireless sEMG system. To make comparisons between participants and exercises, normalization methods using static, exercise starting positions for both instruments will be used. An activation ratio will be used for ultrasound and calculated by dividing the contracted thickness (thickness during a time point of exercise) by the rested (starting position) thickness. Muscle thickness will be measured in centimeters from the superior inferior fascial border to inferior superior fascial border. These methods aim to offer an innovative and practical measurement of muscle function with M-mode ultrasound and sEMG during core endurance exercises.

This protocol uses motion mode ultrasound and surface electromyography simultaneously to measure muscle function of the core. Muscle thickness and activation of the local stabilizers (e.g., transverse abdominis, internal oblique) and global movers (e.g., external oblique) is achievable during specific time points of the side plank and dead bug exercises.

Motion mode (M-mode) ultrasound allows researchers and clinicians to measure the change of muscle thickness across time. Muscle thickness can be measured ...

Assessing Strength Exercise Volume and Its Impact on Insulin Sensitivity in Obesity

Randomized Controlled Trial to Study the Acute Effects of Strength Exercise on Insulin Sensitivity in Obese Adults

An acute session of strength exercise (SE) ameliorates insulin sensitivity (IS) for several hours; however, the effects of SE volume (i.e., number of sets) have not been studied thoroughly. Although it is intuitive that some SE is better than none, and more is better than some for the improvement of IS, high-volume sessions might be challenging for diseased populations to complete, especially obese adults, for whom even a brisk walk can be challenging. This protocol details a randomized clinical trial to assess the acute effects of SE on IS in obese adults. The inclusion criteria are body mass index &#62;30 kg/m2, central obesity (waist circumference &#62;88 cm and &#62;102 cm for women and men, respectively), and age &#62;40 years. Participants will be familiarized with the SE (7 exercises targeting major muscle groups) and then will perform three sessions in a randomized order: session 1 - high-volume session (3 sets/exercise); session 2 - low-volume session (1 set/exercise); session 3 - control session (no exercise). Diet will be controlled the day before and on the day of the sessions. Sessions will be completed at night, and an oral glucose tolerance test will be performed the next morning, from which several indexes of IS will be derived, such as the area under the curve (AUC) of glucose and insulin, the Matsuda index, the Cederholm index, the muscle IS index, and the Gutt index. Based on pilot studies, we expect ~15% improvement in IS (insulin AUC, and Matsuda and Cederholm indexes) after the high-volume session, and ~8% improvement after the low-volume session compared to the control session. This study will benefit individuals who find high-volume SE sessions challenging but still aim to improve their IS by investing 1/3 of their time and effort.

Author Spotlight: Exploring the Impact of Reduced Resistance Exercise Volume on Metabolic Health 

This study describes a randomized controlled trial protocol aiming at assessing the acute effects of strength exercise volume on insulin sensitivity in obese individuals.

An acute session of strength exercise (SE) ameliorates insulin sensitivity (IS) for several hours; however, the effects of SE volume (i.e., number of ...

Enhancing Stroke Rehabilitation with Virtual Reality-Based Occupational Training

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the potential to meet these demands by providing engaging and motivating therapy options. The digital occupational training system is a VR application that utilizes cutting-edge technologies, including multi-touch screens, virtual reality, and human-computer interaction, to offer diverse training techniques for advanced cognitive capacity and hand-eye coordination abilities. The objective of this study was to assess the effectiveness of this program in enhancing cognitive function and upper extremity rehabilitation in stroke patients. The training and assessment consist of five cognitive modules covering perception, attention, memory, logical reasoning, and calculation, along with hand-eye coordination training. This research indicates that after eight weeks of training, the digital occupational training system can significantly improve cognitive function, daily living skills, attention, and self-care abilities in stroke patients. This software can be employed as a time-saving and clinically effective rehabilitation aid to complement traditional one-on-one occupational therapy sessions. In summary, the digital occupational training system shows promise and offers potential financial benefits as a tool to support the functional recovery of stroke patients.

Author Spotlight: Rehabilitation of Stroke Patients With a Digital Occupational Training System

The current protocol outlines how the VR-based digital occupational training system enhances the rehabilitation of patients with cognitive impairment and upper limb dysfunction following a stroke.

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the ...

Assessing Pulmonary Alveolar-Capillary Reserve During Exercise

Dual Test Gas Pulmonary Diffusing Capacity Measurement During Exercise in Humans Using the Single-Breath Method

The combined single-breath measurement of the diffusing capacity of carbon monoxide (DL,CO) and nitric oxide (DL,NO) is a useful technique to measure pulmonary alveolar-capillary reserve in both healthy and patient populations. The measurement provides an estimate of the participant&#39;s ability to recruit and distend pulmonary capillaries. The method has recently been reported to exhibit a high test-retest reliability in healthy volunteers during exercise of light to moderate intensity. Of note, this technique permits up to 12 repeated maneuvers and only requires a single breath with a relatively short breath-hold time of 5 s. Representative data are provided showing the gradual changes in DL,NO and DL,CO from rest to exercise at increasing intensities&#160;of up to 60% of maximal workload. The measurement of diffusing capacity and evaluation of alveolar-capillary reserve is a useful tool to evaluate the lung&#39;s ability to respond to exercise both in the healthy population as well as in patient populations such as those with chronic lung disease.

Author Spotlight: Integrating Alveolar-Capillary Reserve Measurements in Exercise Adaptation and Therapeutic Strategies

This protocol presents a method to assess pulmonary alveolar-capillary reserve measured by combined single-breath measurement of the diffusing capacity to carbon monoxide (DL,CO) and nitric oxide (DL,NO) during exercise. Assumptions and recommendations for using the technique during exercise form the foundation of this article.

The combined single-breath measurement of the diffusing capacity of carbon monoxide (DL,CO) and nitric oxide (DL,NO) is a useful technique to measure ...

Functional OT and Near-IR Spectroscopy to Study Brain Function Remodeling

Investigating the Effect of Different Types of Exercise on Upper Limb Functional Recovery in Patients with Right Hemisphere Damage Based on fNIRS

To investigate the effects of functional occupational therapy (FOT) combined with different types of exercise on upper limb motor function recovery and brain function remodeling in patients with right hemisphere damage (RHD) by analyzing functional near-infrared spectroscopy (fNIRS). Patients (n = 32) with RHD at Beijing Bo'ai Hospital were recruited and randomly allocated to receive either FOT combined with passive motion (N=16) or FOT combined with assisted active movement (N=16). The passive motion group (FOT-PM) received functional occupational therapy for 20 min and passive exercise for 10 min in each session, while the assisted active movement group (FOT-AAM) received functional occupational therapy for 20 min and assisted active exercise for 10 min. Both groups received conventional drug therapy and other rehabilitation therapy. Treatment was performed once a day, 5 times a week for 4 weeks. The recovery of motor function and activities of daily living (ADL) was assessed using Fugl-Meyer Assessment upper extremity (FMA-UE) and modified Barthel index (MBI) before and after treatment, and brain activation of the bilateral motor area was analyzed with fNIRS. The findings suggested that FOT combined with AAM was more effective than FOT combined with PM in improving the motor function of RHD patients' upper limbs and fingers, improving their ability to perform activities of daily living, and facilitating brain function remodeling of the motor area.

Author Spotlight: Advancing Upper Limb Rehabilitation in Patients with Right Hemisphere Damage Using Assisted Active Exercise

Here, we investigate the effect of functional occupational therapy combined with assisted active or passive motion on the upper limb function of patients with right hemisphere damage and explore the effect of functional near-infrared spectroscopy on brain function remodeling.

To investigate the effects of functional occupational therapy (FOT) combined with different types of exercise on upper limb motor function recovery and ...