2.50
Hdl Handle:
http://hdl.handle.net/10755/158235
Type:
Presentation
Title:
Methodological Issues Associated with Symptom Self-Management
Abstract:
Methodological Issues Associated with Symptom Self-Management
Conference Sponsor:Western Institute of Nursing
Conference Year:2001
Author:Meek, Paula, PhD
P.I. Institution Name:University of Arizona
Title:Assistant Professor
Contact Address:College of Nursing, 1305 North Martin Street, PO Box 210203, Tucson, AZ, 85721-0203, USA
Contact Telephone:520.626.3233
Specific Aims: This paper presents the methodological issues related to analysis of daily measures of the symptoms of breathing distress and breathing effort. Rationale and Background: When measuring symptom levels, multiple, rather than single measures of a symptom are more reflective of an individual's overall state of symptom management. Multiple measures of a symptom present significant methodological challenges related to the highly variable nature of symptom levels over time, decisions regarding the best statistical technique to capture this variation. Methods: Individual regression analysis (IRA), repeated measures analysis of variance (ANOVA), and values for breathing effort and distress were assessed for appropriateness as a symptom management indicator. Sample: The sample for this analysis consisted of 156 individuals, 37 who were healthy, 38 who had asthma, and 81 who had chronic obstructive pulmonary disease. The sample was 54% male, had a mean age of 65 years (standard deviation of 11.1) and a mean length of time since diagnosis of 141 months (standard deviation of 178.5). Measurement: The symptoms of breathing effort and breathing distress were self-reported daily for 14 days through the use of a visual analogue scale. Results: The highly variable nature of daily levels of these symptoms precluded fitting of a linear or curvilinear model to the individual regression models. Even with the best fit model the IRA models resulted in slope values that basically were zero indicating no change and failing to capture the variability. Despite the fact that repeated measures ANOVA demonstrated significant differences between groups (F=114.031, p=.000) which was also problematic. The ANOVA technique measured changes over time in the symptom and differences between the groups, but is limited to treating differences in breathing distress and effort as dependent variables, constraining the questions that can be asked of the data and the number of measurement points used. Breathing effort and distress computed scores such as means, standard deviations (SD), variance, minimum, and maximum scores for the 14 day period were examined to determine if they might capture the variability and also be used as variables in other analysis and depending on the one used can capture either the variance or overall intensity levels. Forced Expiratory Volume (FEV1) % was related to mean breathing effort (r=-.46, p=.000), breathing effort SD (r=-.33, p=.000), maximum breathing effort (r=-.47, p=.000), minimum breathing effort (r=-.28, p=.002), and breathing effort variance (r=-.22, p=.012) and with mean breathing distress (r=-.44, p=.000), breathing distress SD (r=-.33, p=.000), maximum breathing distress (r=-.42, p=.000), minimum breathing distress (r=-.25, p=.005), and breathing distress variance (r=-.25, p=.005). FEV1% correlated with both breathing effort (range -.47 to -.22) and breathing distress (range -.44 to -.25). Conclusion: Final result correlations provide evidence of the usefulness of these variables that could be used in different types of analysis such as model testing. Depending on the purpose in obtaining the symptom measures, computed variables may provide more flexibility.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Western Institute of Nursing

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleMethodological Issues Associated with Symptom Self-Managementen_GB
dc.identifier.urihttp://hdl.handle.net/10755/158235-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Methodological Issues Associated with Symptom Self-Management</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Western Institute of Nursing</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2001</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Meek, Paula, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University of Arizona</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Assistant Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">College of Nursing, 1305 North Martin Street, PO Box 210203, Tucson, AZ, 85721-0203, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">520.626.3233</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">pmeek@nursing.arizona.edu</td></tr><tr><td colspan="2" class="item-abstract">Specific Aims: This paper presents the methodological issues related to analysis of daily measures of the symptoms of breathing distress and breathing effort. Rationale and Background: When measuring symptom levels, multiple, rather than single measures of a symptom are more reflective of an individual's overall state of symptom management. Multiple measures of a symptom present significant methodological challenges related to the highly variable nature of symptom levels over time, decisions regarding the best statistical technique to capture this variation. Methods: Individual regression analysis (IRA), repeated measures analysis of variance (ANOVA), and values for breathing effort and distress were assessed for appropriateness as a symptom management indicator. Sample: The sample for this analysis consisted of 156 individuals, 37 who were healthy, 38 who had asthma, and 81 who had chronic obstructive pulmonary disease. The sample was 54% male, had a mean age of 65 years (standard deviation of 11.1) and a mean length of time since diagnosis of 141 months (standard deviation of 178.5). Measurement: The symptoms of breathing effort and breathing distress were self-reported daily for 14 days through the use of a visual analogue scale. Results: The highly variable nature of daily levels of these symptoms precluded fitting of a linear or curvilinear model to the individual regression models. Even with the best fit model the IRA models resulted in slope values that basically were zero indicating no change and failing to capture the variability. Despite the fact that repeated measures ANOVA demonstrated significant differences between groups (F=114.031, p=.000) which was also problematic. The ANOVA technique measured changes over time in the symptom and differences between the groups, but is limited to treating differences in breathing distress and effort as dependent variables, constraining the questions that can be asked of the data and the number of measurement points used. Breathing effort and distress computed scores such as means, standard deviations (SD), variance, minimum, and maximum scores for the 14 day period were examined to determine if they might capture the variability and also be used as variables in other analysis and depending on the one used can capture either the variance or overall intensity levels. Forced Expiratory Volume (FEV1) % was related to mean breathing effort (r=-.46, p=.000), breathing effort SD (r=-.33, p=.000), maximum breathing effort (r=-.47, p=.000), minimum breathing effort (r=-.28, p=.002), and breathing effort variance (r=-.22, p=.012) and with mean breathing distress (r=-.44, p=.000), breathing distress SD (r=-.33, p=.000), maximum breathing distress (r=-.42, p=.000), minimum breathing distress (r=-.25, p=.005), and breathing distress variance (r=-.25, p=.005). FEV1% correlated with both breathing effort (range -.47 to -.22) and breathing distress (range -.44 to -.25). Conclusion: Final result correlations provide evidence of the usefulness of these variables that could be used in different types of analysis such as model testing. Depending on the purpose in obtaining the symptom measures, computed variables may provide more flexibility.</td></tr></table>en_GB
dc.date.available2011-10-26T20:38:40Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:38:40Z-
dc.description.sponsorshipWestern Institute of Nursingen_GB
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