Role of Mental Fatigue in Engagement in Cognitively Stimulating Activities in Community-Dwelling Older Adults with Cardiovascular Disease Risk Factors

2.50
Hdl Handle:
http://hdl.handle.net/10755/301691
Category:
Abstract
Type:
Research Study
Level of Evidence:
Quasi-Experimental Study, Other
Research Approach:
Pilot/Exploratory Study
Title:
Role of Mental Fatigue in Engagement in Cognitively Stimulating Activities in Community-Dwelling Older Adults with Cardiovascular Disease Risk Factors
Author(s):
Lin, Feng
Lead Author STTI Affiliation:
Beta Eta-at-Large
Author Details:
Feng Lin, PhD, RN; Faculty page: http://www.son.rochester.edu/faculty/detail/vlin (Please cut and paste link into a browser.)
Abstract:

1. Summary of project aims

Fatigue is the most common symptom complaint in community-dwelling older adults, and can have a devastating impact on older adults’ well-being. Fatigue is a multidimensional concept, which can be experienced as general tiredness (i.e., chronic, subjective fatigue) or as the expectation and experience of becoming tired in response to activities, and having difficulty maintaining activities at a desired level (i.e., acute fatigability). 1-4 Fatigue diminishes older adults’ capacity to maintain activities of daily living and reduces participation in leisure activities that protect cognitive, physical, and psychosocial well-being. 5-7 Perhaps surprisingly, older adults may not necessarily complain about more fatigue than their younger counterparts. 8 The factors underlying why older adults experience greater functional effects of fatigue are still unclear. It is important to note that subjective fatigue and fatigability are not necessarily correlated. In other words, even if an older adult may complain that he is “tired all the time,” he may still lead an active life and have near-normal functional capacity; while another person who has the same complaint of chronic fatigue may live in a physically and mentally restricted manner and be functionally impaired. The differences may be explained by their different levels of acutely fatigued in response to activities. 9 Recent work has begun to focus on the fatigability in relation to defined activities in order to answer the question of “When is fatigue a problem?” A more complete understanding of fatigability may help us understand how fatigue symptoms translate to poor function in old age.

Physical fatigability is measured by self-report or performance during a physical task that requires sustained energy. 3, 10 Compared to physical fatigability, mental fatigability, the failure to sustain in tasks or activities requiring sustained mental efforts, is problematic but rarely recognized by the medical community.10-12 Currently, mental fatigability is usually measured by self-report, but numerous issues (e.g., construct contamination, see discussion by Leavitt, et al.13) may affect the utility of self-report alone for mental fatigability. There is a need for a complementary objective measurement of perceived mental fatigability. Previous studies have attempted to meet this need, but are limited because they directly applied approach used in measuring objective physical fatigability (i.e., assessing decreased muscle movements over time). Unfortunately, results of these attempts have revealed inconsistent associations between self-report mental fatigability and performance during mental tasks. 13 One reason why these attempts have been unsuccessful is that they assessed cognitive performance using accuracy rate which can be greatly affected by education or childhood intelligence. Also, these approaches used time-consuming, but not necessarily mentally demanding tasks. 13 Two studies of fatigability in patients with multiple sclerosis identified a significant and consistent relationship between self-report fatigability and speed of processing assessed by cognitive tests requiring sustained mental efforts. 14, 15 A recent study found that accuracy rate in cognitive tasks was only significantly correlated to self-reported mental fatigability in
the group whose response time (RT) increased across the executive-attention demanding mental tasks. 16 Increased RT may be a more reliable index for performance-based mental fatigability; and sustained attention is important when designing a mental task that can produce fatigue. In sum, these results suggest that mental fatigability is likely influenced by multiple factors and these need to be taken into account when devising a fatigue manipulation task. Previous studies have shown that psychological, physiological, and situational factors influence both idiopathic (non-disease related) and disease-related fatigue. 17, 18 It is unclear, however, whether any of those factors influence mental fatigability, and further, whether different sets of factors influence self-reported versus performance-based measures of mental fatigability.

The objectives of the current study were two-fold. First, we examined the relationship between a novel, performance-based measure of mental fatigability that entailed consecutive assessment of RT to a cognitive task requiring sustained mental effort and self-reported mental fatigability. Second, we aimed to identify psychological, physiological, and situational predictors of mental fatigability. The target sample in this study was community-dwelling older adults 75 years of age and older with vascular risk. Type 2 diabetes, hypertension, dyslipidemia, and smoking are common risk factors for “poor cardiovascular health” in the newest American Heart Association guidelines. 19 Over 40 million Americans aged 60 years or older have one or more of these risk factors. 20 In older adults with vascular risk, fatigue is common and problematic. 21, 22 Fatigue may both cause and be caused by vascular risk. Fatigue often prompts a sedentary lifestyle that can enhance the risk of developing vascular risk 21. Once vascular risk is present, acute and severe fatigue related to metabolic disorders,21 physiological changes of cerebrovascular vessels, 23 endothelial dysfunction, 24 and side-effects of relevant medications (e.g., beta-blockers) 25 may directly influence functional health and interfere with daily activities. However, mental fatigability in older adults with vascular risk has yet to be characterized. 17, 18

2. Theoretical/conceptual framework

Psychological, physiological, and situational factors influence mental fatigabiliy. 17, 18

3. Methods, procedures and sampling

Design

The present study was a cross-sectional correlational study.

Participants and setting

A total of 58 study participants were recruited from local community senior centers in a northeast metropolitan city. Inclusion criteria were: English speaking, aged 65 years or older, capacity to provide informed consent, presence of self-reported vascular risk (hypertension, high cholesterol, smoking, or/and diabetes), as confirmed by relevant medications on the medication list if applicable, and adequate auditory and visual acuity for testing. Exclusion criteria included self- or clinician-reported clinically diagnosed dementia or mild cognitive impairment, treatment with any cholinesterase inhibitors or Memantine within three years, self-reported history of stroke, sleep disorder, or major depression, and skilled nursing home resident. The study was approved by the University of Rochester institutional review board.

Measurements

Mental fatigability

Fatigue-manipulation task (see Figure 1). A 20-minute fatigue-manipulation task was used to induce mental fatigue and to develop the two mental fatigability measures (performance vs. self-report). The fatigue-manipulation task included 20 sessions of 1-back computerized tasks. 26 The tasks used either visual or auditory stimuli. During a task with visual stimuli, participants were presented with a sequence of red squares, appearing in one of the eight different loci on the screen. Participants were required to press the “Visual” button on the touch screen whenever the currently presented stimulus was at the same location as the one 1 position back in the sequence. During a task with auditory stimuli, participants were presented with a sequence of letters spoken in a female voice. There were eight candidate letters, selected on the basis of their distinctiveness. Participants were required to press the “Audio” button if the current letter matched the letter back in the sequence. Positions of square locations and sequence of letters spoken in the tasks were determined randomly. The computer program used a locked periodic design in which the visual or auditory stimuli were presented to the participants during epochs of 60 seconds per session. Each stimulus lasted for 500 milliseconds, and the inter-stimulus interval lasted 2,500 milliseconds. No responses were required for non-targets. Two types of data were recorded: response accuracy and the time taken to respond to the stimulus (RT). Standardized instruction and 2-3 minute practices were provided to help participants fully understand the tasks before the 20-minute task period. The task was operated as an app on the iPad (Brian Williams, 2008); and relevant technical support was provided by the program developer. We failed to record one participant’s data during the task.

Performance-based/objective fatigability was calculated as the increase of RT over the fatigue-manipulation task. We averaged the time to make correct response to the stimuli within one session; a total of 20 response times were developed. This method has been validated in previous studies. 13, 16 We also computed the accuracy rate (the ratio of the total correct responses to the total required responses) per session for comparison. Self-report/perceived fatigability was calculated as the increased self-report fatigue before and after engaging in the fatigue-manipulation task 1, 3. Participants were presented with 18 items measuring varying aspects of fatigue (e.g., efficient, “concentrating is a tremendous chore”, etc.) and they indicated their response by marking on a 10-cm analogue rating line. 27 The length of the line between 0 and the place the participant indicated the level of fatigue was recorded for each item, and a mean score was developed for the 18 items; higher scores indicated a higher level of fatigue. The scale has been validated in adults with and without chronic illnesses across a wide range of ages. 28 In the present study, the internal consistency of the 18 items before and after fatigue-manipulation task was 0.88 and 0.94, respectively.

Associated predictors

Psychological predictors: Subjective chronic fatigue was measured by a mean score of the 20-item Multidimensional Fatigue Inventory. 29 This measurement captured five domains of chronic fatigue: mental fatigue, physical fatigue, general fatigue, reduced motivation, and reduced activities. Internal consistency of the 20 items was 0.89 in this study. Cognitive process was measured by three neuropsychological tests: Trail making test A and B, 30 Stroop word and color test, 31 and Digit span forward and backward. 32 These are commonly used cognitive tests for executive control and cognitive processes in elderly groups. 33 Seven performance scores were calculated: Trail making test A, Trail making test B, Stroop word, Stroop color, Stroop interference, Digit span forward, and Digit span backward, and then standardized separately. A composite score was developed by averaging the standardized scores. Depressive symptoms were measured by the 15-item Geriatric Depression Scale (GDS) 34. Participants responded to questions related to their depressive symptoms during the past week using “yes” or “no”. A total depressive symptom score was calculated as the total number of “yes” answers.

Physiological predictors: Vascular risk was calculated as the total number of vascular risk factors (hypertension, high cholesterol, smoking, or/and diabetes) reported. Sleepiness was measured by the 8-item Epworth scale 35. Participants responded to questions related to their sleepiness (in contrast to feeling just tired) under different situations (e.g., sitting and reading) using a scale ranging from “would never doze” to “high chance of dozing”. A mean score was computed with higher scores indicating more sleepiness. Internal consistency of the scale in this study was 0.68. Anti-inflammatory medication and beta-blocker use were extracted from a medication list the participant brought to the study.

Situational predictors: Mental energy was measured by multiplying duration and intensity of engaging in six mental activities: reading, doing word games, playing cards, attending lectures, writing, and using a computer.3 Duration spent on each activity was measured by asking “how long usually do you spend on this activity in the past?” and was assessed before the fatigue-manipulation task. Intensity was measured after the computerized fatigue manipulation task for each activity by asking “how intensely do you work on the activity every time?” using a 10-point numerical rating scale (0 = “very light” to 10 “as intense as the computer task I just did”). The questionnaire has been used in a study with a nationally representative sample, showing correlations with cognitive abilities. 36

Additionally, participants’ age, sex, and years of education were collected by self-report.

Procedure

During a laboratory visit, the participant was first asked to sit quietly and relax for 5 to 10 minutes in order to adapt to the environment. The participant then completed measures of mental energy (duration component) and subjective chronic fatigue, as well as the neuropsychological tests. The participant then engaged in the mental fatigue manipulation. Additional self-report measures were completed following the manipulation (Epworth scale, mental energy (intensity component), GDS), and information on anti-inflammatory medication and beta-blockers were obtained from their medical record.

Data analysis

IBM SPSS 19.0 was used in the data analysis. Descriptive analysis was first performed to describe the sample’s demographic and health characteristics.

We thereafter performed the following analyses to examine whether objective and perceived fatigability were inducible after the fatigue-manipulation task. To examine objective fatigability, a generalized estimating equation (GEE) with an unstructured working correlation matrix was used. 37 RT was the dependent variable, and age, sex, education, and the session (time) of fatigue-manipulation task as predictors. The model was: YRT= β0 + β1age + β2sex + β3education + β4time + ԑ. Any significant main effects of time would indicate a significant change of RT over the fatigue manipulation task; a significant increase of RT indicated objective fatigability. For comparison, we also performed the same analysis using accuracy rate as the dependent variable. Next, to examine perceived fatigability, repeated measure ANOVA was used with self-reported fatigue before and after the fatigue manipulation task as the dependent variables, and age, sex, and education as predictors. A significant main effect from the repeated measure indicated a significant change in self-report fatigue; an increase indicated perceived fatigability.

Next, we examined the correlation between objective and perceived fatigability. Because self-report fatigue was only assessed twice, the GEE model was tested first to develop a “predictive value of mean of response” for self-report fatigue by adjusting age, sex, education, and time of the fatigue-manipulation task. We then examined the correlation between the adjusted self-report fatigue and RT over the fatigue-manipulation task using GEE, taking the adjusted self-report fatigue as the predictor and adjusted RT over sessions as the dependent variable. The model was: Yadjusted RT= β0 + β1adjusted self-report fatigue + ԑ.

We also repeated all steps related to RT described above using accuracy rate (by dichotomizing into 100% vs. others).

Finally, to examine predictors of objective and perceived fatigability, psychological, physiological, and situational factors were transformed to normalize distributions. Subjective fatigue and cognitive performance were dichotomized based on the mean scores, and depressive symptom score was dichotomized as without (GDS score = 0) vs. with depressive symptoms (GDS score > 0). GEE and repeated ANOVA were used separately for RT and self-reported fatigue, taking each potential psychological, physiological, and situational factor as the predictor along with age, sex, education, and time of fatigue-manipulation task. For GEE model, the model was: YRT= β0 + β1age + β2sex + β3education + β4time + β5factor + β6session × factor + ԑ. A significant main effect of the factor indicated a significant difference in the RT by the factor. A significant interaction term involving task sessions would indicate different rates of change in RT over session as a function of the factor. Of note, only a significant interaction term indicated moderation by the particular factor on fatigability. Similar procedure in repeated ANOVA was used to identify significant factors associated with perceived fatigability. Alpha was set at .05.

4. Summary of findings

The average age of the sample was 82.95. All subjects had at least one vascular risk factors; 14 subjects had only one risk factor, 37 had two, and 4 had three. No one was an active smoker. There was significant increase in response time and self-reported fatigue to the fatigue manipulation task, indicating the occurrence of objective and perceived mental fatigability. The two measures of mental fatigability were significantly correlated, but predicted by different factors. Higher mental energy, cognitive process, and subjective chronic fatigue were related to lower objective mental fatigability. Higher levels of subjective chronic fatigue, depressive symptoms, and sleepiness were related to higher perceived fatigue before and after the fatigue-manipulation task, but they did not predict perceived fatigability in response to the tasks.

5. Recommendations

Two dimensions of mental fatigability - objective vs. perceived - were identified. Although highly correlated, these two dimensions were associated with different factors. After a large-scale validation study, the long-term goal will be to incorporate the mental fatigability measures into cognitive training interventions aimed at maintaining cognitive well-being in older adults.

Keywords:
aging; Fatigue classification; Cognition
Repository Posting Date:
16-Sep-2013
Date of Publication:
16-Sep-2013
Sponsors:
Sigma Theta Tau International
Description:
This pilot project provided valuable preliminary data in multiple ways: first, I was able to obtain a Career Award (KL2) started in July, 2013 focusing on cognition training in old age; second, I am planning to apply for a pilot intervention study examining the role of mental fatigability in effect of cognitive training in old age.
Note:
The Sigma Theta Tau International grant application that funded this research, in whole or in part, was completed by the applicant and peer-reviewed prior to the award of the STTI grant. No further peer-review has taken place upon the completion of the STTI grant final report and its appearance in this repository.; This is an abstract-only submission. If the author has submitted a full-text item based on this abstract, you may find it by browsing the Virginia Henderson Global Nursing e-Repository by author. If author contact information is available in this abstract, please feel free to contact him or her with your queries regarding this submission. Alternatively, please contact the conference host, journal, or publisher (according to the circumstance) for further details regarding this item.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.type.categoryAbstracten
dc.typeResearch Studyen
dc.evidence.levelQuasi-Experimental Study, Otheren
dc.research.approachPilot/Exploratory Studyen
dc.titleRole of Mental Fatigue in Engagement in Cognitively Stimulating Activities in Community-Dwelling Older Adults with Cardiovascular Disease Risk Factorsen_US
dc.contributor.authorLin, Feng-
dc.contributor.departmentBeta Eta-at-Largeen
dc.author.detailsFeng Lin, PhD, RN; Faculty page: http://www.son.rochester.edu/faculty/detail/vlin (Please cut and paste link into a browser.)en
dc.identifier.urihttp://hdl.handle.net/10755/301691-
dc.description.abstract<p><strong><span style="font-family: 'Arial','sans-serif';">1. Summary of project aims</span></strong></p> <p>Fatigue is the most common symptom complaint in community-dwelling older adults, and can have a devastating impact on older adults’ well-being. Fatigue is a multidimensional concept, which can be experienced as general tiredness (i.e., chronic, subjective fatigue) or as the expectation and experience of becoming tired in response to activities, and having difficulty maintaining activities at a desired level (i.e., acute fatigability). <sup>1-4</sup> Fatigue diminishes older adults’ capacity to maintain activities of daily living and reduces participation in leisure activities that protect cognitive, physical, and psychosocial well-being. <sup>5-7</sup> Perhaps surprisingly, older adults may not necessarily complain about more fatigue than their younger counterparts. <sup>8</sup> The factors underlying why older adults experience greater functional effects of fatigue are still unclear. It is important to note that subjective fatigue and fatigability are not necessarily correlated. In other words, even if an older adult may complain that he is “tired all the time,” he may still lead an active life and have near-normal functional capacity; while another person who has the same complaint of chronic fatigue may live in a physically and mentally restricted manner and be functionally impaired. The differences may be explained by their different levels of acutely fatigued in response to activities. <sup>9</sup> Recent work has begun to focus on the fatigability in relation to defined activities in order to answer the question of “When is fatigue a problem?” A more complete understanding of fatigability may help us understand how fatigue symptoms translate to poor function in old age.</p> <p>Physical fatigability is measured by self-report or performance during a physical task that requires sustained energy. <sup>3, 10</sup> Compared to physical fatigability, mental fatigability, the failure to sustain in tasks or activities requiring sustained mental efforts, is problematic but rarely recognized by the medical community.<sup>10-12</sup> Currently, mental fatigability is usually measured by self-report, but numerous issues (e.g., construct contamination, see discussion by Leavitt, et al.<sup>13</sup>) may affect the utility of self-report alone for mental fatigability. There is a need for a complementary objective measurement of perceived mental fatigability. Previous studies have attempted to meet this need, but are limited because they directly applied approach used in measuring objective physical fatigability (i.e., assessing decreased muscle movements over time). Unfortunately, results of these attempts have revealed inconsistent associations between self-report mental fatigability and performance during mental tasks. <sup>13</sup> One reason why these attempts have been unsuccessful is that they assessed cognitive performance using accuracy rate which can be greatly affected by education or childhood intelligence. Also, these approaches used time-consuming, but not necessarily mentally demanding tasks. <sup>13</sup> Two studies of fatigability in patients with multiple sclerosis identified a significant and consistent relationship between self-report fatigability and speed of processing assessed by cognitive tests requiring sustained mental efforts. <sup>14, 15</sup> A recent study found that accuracy rate in cognitive tasks was only significantly correlated to self-reported mental fatigability in<br />the group whose response time (RT) increased across the executive-attention demanding mental tasks. <sup>16</sup> Increased RT may be a more reliable index for performance-based mental fatigability; and sustained attention is important when designing a mental task that can produce fatigue. In sum, these results suggest that mental fatigability is likely influenced by multiple factors and these need to be taken into account when devising a fatigue manipulation task. Previous studies have shown that psychological, physiological, and situational factors influence both idiopathic (non-disease related) and disease-related fatigue. <sup>17, 18</sup> It is unclear, however, whether any of those factors influence mental fatigability, and further, whether different sets of factors influence self-reported versus performance-based measures of mental fatigability.</p> <p>The objectives of the current study were two-fold. First, we examined the relationship between a novel, performance-based measure of mental fatigability that entailed consecutive assessment of RT to a cognitive task requiring sustained mental effort and self-reported mental fatigability. Second, we aimed to identify psychological, physiological, and situational predictors of mental fatigability. The target sample in this study was community-dwelling older adults 75 years of age and older with vascular risk. Type 2 diabetes, hypertension, dyslipidemia, and smoking are common risk factors for “poor cardiovascular health” in the newest American Heart Association guidelines. <sup>19</sup> Over 40 million Americans aged 60 years or older have one or more of these risk factors. <sup>20</sup> In older adults with vascular risk, fatigue is common and problematic. <sup>21, 22</sup> Fatigue may both cause and be caused by vascular risk. Fatigue often prompts a sedentary lifestyle that can enhance the risk of developing vascular risk <sup>21</sup>. Once vascular risk is present, acute and severe fatigue related to metabolic disorders,<sup>21</sup> physiological changes of cerebrovascular vessels, <sup>23</sup> endothelial dysfunction, <sup>24</sup> and side-effects of relevant medications (e.g., beta-blockers) <sup>25</sup> may directly influence functional health and interfere with daily activities. However, mental fatigability in older adults with vascular risk has yet to be characterized. <sup>17, 18</sup></p> <p><strong><span style="font-family: 'Arial','sans-serif';">2. Theoretical/conceptual framework</span></strong></p> <p>Psychological, physiological, and situational factors influence mental fatigabiliy. <sup>17, 18</sup></p> <p><strong><span style="font-family: 'Arial','sans-serif';">3. Methods, procedures and sampling</span></strong></p> <p><strong><span style="font-family: 'Arial','sans-serif';">Design</span></strong></p> <p>The present study was a cross-sectional correlational study.</p> <p><strong><span style="font-family: 'Arial','sans-serif';">Participants and setting</span></strong></p> <p>A total of 58 study participants were recruited from local community senior centers in a northeast metropolitan city. Inclusion criteria were: English speaking, aged 65 years or older, capacity to provide informed consent, presence of self-reported vascular risk (hypertension, high cholesterol, smoking, or/and diabetes), as confirmed by relevant medications on the medication list if applicable, and adequate auditory and visual acuity for testing. Exclusion criteria included self- or clinician-reported clinically diagnosed dementia or mild cognitive impairment, treatment with any cholinesterase inhibitors or Memantine within three years, self-reported history of stroke, sleep disorder, or major depression, and skilled nursing home resident. The study was approved by the University of Rochester institutional review board.</p> <p><strong><span style="font-family: 'Arial','sans-serif';">Measurements</span></strong></p> <p><strong><span style="text-decoration: underline;"><span style="font-family: 'Arial','sans-serif';">Mental fatigability</span></span></strong></p> <p><span style="text-decoration: underline;">Fatigue-manipulation task (see Figure 1).</span> A 20-minute fatigue-manipulation task was used to induce mental fatigue and to develop the two mental fatigability measures (performance vs. self-report). The fatigue-manipulation task included 20 sessions of 1-back computerized tasks. <sup>26</sup> The tasks used either visual or auditory stimuli. During a task with visual stimuli, participants were presented with a sequence of red squares, appearing in one of the eight different loci on the screen. Participants were required to press the “Visual” button on the touch screen whenever the currently presented stimulus was at the same location as the one 1 position back in the sequence. During a task with auditory stimuli, participants were presented with a sequence of letters spoken in a female voice. There were eight candidate letters, selected on the basis of their distinctiveness. Participants were required to press the “Audio” button if the current letter matched the letter back in the sequence. Positions of square locations and sequence of letters spoken in the tasks were determined randomly. The computer program used a locked periodic design in which the visual or auditory stimuli were presented to the participants during epochs of 60 seconds per session. Each stimulus lasted for 500 milliseconds, and the inter-stimulus interval lasted 2,500 milliseconds. No responses were required for non-targets. Two types of data were recorded: response accuracy and the time taken to respond to the stimulus (RT). Standardized instruction and 2-3 minute practices were provided to help participants fully understand the tasks before the 20-minute task period. The task was operated as an app on the iPad (Brian Williams, 2008); and relevant technical support was provided by the program developer. We failed to record one participant’s data during the task.</p> <p><span style="text-decoration: underline;">Performance-based/objective fatigability</span> was calculated as the increase of RT over the fatigue-manipulation task. We averaged the time to make <em><span style="font-family: 'Arial','sans-serif';">correct </span></em>response to the stimuli within one session; a total of 20 response times were developed. This method has been validated in previous studies. <sup>13, 16</sup> We also computed the accuracy rate (the ratio of the total correct responses to the total required responses) per session for comparison. <span style="text-decoration: underline;">Self-report/perceived fatigability</span> was calculated as the increased self-report fatigue before and after engaging in the fatigue-manipulation task <sup>1, 3</sup>. Participants were presented with 18 items measuring varying aspects of fatigue (e.g., efficient, “concentrating is a tremendous chore”, etc.) and they indicated their response by marking on a 10-cm analogue rating line. <sup>27</sup> The length of the line between 0 and the place the participant indicated the level of fatigue was recorded for each item, and a mean score was developed for the 18 items; higher scores indicated a higher level of fatigue. The scale has been validated in adults with and without chronic illnesses across a wide range of ages. <sup>28</sup> In the present study, the internal consistency of the 18 items before and after fatigue-manipulation task was 0.88 and 0.94, respectively.</p> <p><strong><span style="text-decoration: underline;"><span style="font-family: 'Arial','sans-serif';">Associated predictors</span></span></strong></p> <p>Psychological predictors: <em><strong><span style="font-family: 'Arial','sans-serif';">Subjective chronic fatigue</span></strong></em> was measured by a mean score of the 20-item Multidimensional Fatigue Inventory. <sup>29</sup> This measurement captured five domains of chronic fatigue: mental fatigue, physical fatigue, general fatigue, reduced motivation, and reduced activities. Internal consistency of the 20 items was 0.89 in this study. <em><strong><span style="font-family: 'Arial','sans-serif';">Cognitive process </span></strong></em>was measured by three neuropsychological tests: Trail making test A and B, <sup>30</sup> Stroop word and color test, <sup>31</sup> and Digit span forward and backward. <sup>32</sup> These are commonly used cognitive tests for executive control and cognitive processes in elderly groups. <sup>33</sup> Seven performance scores were calculated: Trail making test A, Trail making test B, Stroop word, Stroop color, Stroop interference, Digit span forward, and Digit span backward, and then standardized separately. A composite score was developed by averaging the standardized scores.<em><strong><span style="font-family: 'Arial','sans-serif';"> Depressive symptoms </span></strong></em>were measured by the 15-item Geriatric Depression Scale (GDS) <sup>34</sup>. Participants responded to questions related to their depressive symptoms during the past week using “yes” or “no”. A total depressive symptom score was calculated as the total number of “yes” answers.</p> <p>Physiological predictors:<em><strong><span style="font-family: 'Arial','sans-serif';"> Vascular risk </span></strong></em>was calculated as the total number of vascular risk factors (hypertension, high cholesterol, smoking, or/and diabetes) reported. <em><strong><span style="font-family: 'Arial','sans-serif';">Sleepiness </span></strong></em>was measured by the 8-item Epworth scale <sup>35</sup>. Participants responded to questions related to their sleepiness (in contrast to feeling just tired) under different situations (e.g., sitting and reading) using a scale ranging from “would never doze” to “high chance of dozing”. A mean score was computed with higher scores indicating more sleepiness. Internal consistency of the scale in this study was 0.68.<em><strong><span style="font-family: 'Arial','sans-serif';"> Anti-inflammatory medication</span></strong></em> and <em><strong><span style="font-family: 'Arial','sans-serif';">beta-blocker use</span></strong></em> were extracted from a medication list the participant brought to the study.</p> <p>Situational predictors: <em><strong><span style="font-family: 'Arial','sans-serif';">Mental energy </span></strong></em>was measured by multiplying duration and intensity of engaging in six mental activities: reading, doing word games, playing cards, attending lectures, writing, and using a computer.<sup>3</sup> Duration spent on each activity was measured by asking “how long usually do you spend on this activity in the past?” and was assessed before the fatigue-manipulation task. Intensity was measured after the computerized fatigue manipulation task for each activity by asking “how intensely do you work on the activity every time?” using a 10-point numerical rating scale (0 = “very light” to 10 “as intense as the computer task I just did”). The questionnaire has been used in a study with a nationally representative sample, showing correlations with cognitive abilities. <sup>36</sup></p> <p>Additionally, participants’ age, sex, and years of education were collected by self-report.</p> <p><strong><span style="font-family: 'Arial','sans-serif';">Procedure</span></strong></p> <p>During a laboratory visit, the participant was first asked to sit quietly and relax for 5 to 10 minutes in order to adapt to the environment. The participant then completed measures of mental energy (duration component) and subjective chronic fatigue, as well as the neuropsychological tests. The participant then engaged in the mental fatigue manipulation. Additional self-report measures were completed following the manipulation (Epworth scale, mental energy (intensity component), GDS), and information on anti-inflammatory medication and beta-blockers were obtained from their medical record.</p> <p><strong><span style="font-family: 'Arial','sans-serif';">Data analysis</span></strong></p> <p>IBM SPSS 19.0 was used in the data analysis. Descriptive analysis was first performed to describe the sample’s demographic and health characteristics.</p> <p>We thereafter performed the following analyses to examine whether objective and perceived fatigability were inducible after the fatigue-manipulation task. To examine objective fatigability, a generalized estimating equation (GEE) with an unstructured working correlation matrix was used. <sup>37</sup> RT was the dependent variable, and age, sex, education, and the session (time) of fatigue-manipulation task as predictors. The model was: <em><span style="font-family: 'Arial','sans-serif';">Y<sub>RT</sub>= β0 + β1<sub>age</sub> + β2<sub>sex</sub> + β3<sub>education</sub> + β4<sub>time</sub> + ԑ</span></em>. Any significant main effects of time would indicate a significant change of RT over the fatigue manipulation task; a significant increase of RT indicated objective fatigability. For comparison, we also performed the same analysis using accuracy rate as the dependent variable. Next, to examine perceived fatigability, repeated measure ANOVA was used with self-reported fatigue before and after the fatigue manipulation task as the dependent variables, and age, sex, and education as predictors. A significant main effect from the repeated measure indicated a significant change in self-report fatigue; an increase indicated perceived fatigability.</p> <p>Next, we examined the correlation between objective and perceived fatigability. Because self-report fatigue was only assessed twice, the GEE model was tested first to develop a “predictive value of mean of response” for self-report fatigue by adjusting age, sex, education, and time of the fatigue-manipulation task. We then examined the correlation between the adjusted self-report fatigue and RT over the fatigue-manipulation task using GEE, taking the adjusted self-report fatigue as the predictor and adjusted RT over sessions as the dependent variable. The model was: <em><span style="font-family: 'Arial','sans-serif';">Y<sub>adjusted</sub> <sub>RT</sub>= β0 + β1<sub>adjusted</sub> <sub>self-report fatigue</sub> + ԑ</span></em>.</p> <p>We also repeated all steps related to RT described above using accuracy rate (by dichotomizing into 100% vs. others).</p> <p>Finally, to examine predictors of objective and perceived fatigability, psychological, physiological, and situational factors were transformed to normalize distributions. Subjective fatigue and cognitive performance were dichotomized based on the mean scores, and depressive symptom score was dichotomized as without (GDS score = 0) vs. with depressive symptoms (GDS score > 0). GEE and repeated ANOVA were used separately for RT and self-reported fatigue, taking each potential psychological, physiological, and situational factor as the predictor along with age, sex, education, and time of fatigue-manipulation task. For GEE model, the model was: <em><span style="font-family: 'Arial','sans-serif';">Y<sub>RT</sub>= β0 + β1<sub>age</sub> + β2<sub>sex</sub> + β3<sub>education</sub> + β4<sub>time</sub> + β5<sub>factor</sub> + β6<sub>session × factor</sub> + ԑ. </span></em>A significant main effect of the factor indicated a significant difference in the RT by the factor. A significant interaction term involving task sessions would indicate different rates of change in RT over session as a function of the factor. Of note, only a significant interaction term indicated moderation by the particular factor on fatigability. Similar procedure in repeated ANOVA was used to identify significant factors associated with perceived fatigability. Alpha was set at .05.</p> <p><strong><span style="font-family: 'Arial','sans-serif';">4. Summary of findings</span></strong></p> <p>The average age of the sample was 82.95. All subjects had at least one vascular risk factors; 14 subjects had only one risk factor, 37 had two, and 4 had three. No one was an active smoker. There was significant increase in response time and self-reported fatigue to the fatigue manipulation task, indicating the occurrence of objective and perceived mental fatigability. The two measures of mental fatigability were significantly correlated, but predicted by different factors. Higher mental energy, cognitive process, and subjective chronic fatigue were related to lower objective mental fatigability. Higher levels of subjective chronic fatigue, depressive symptoms, and sleepiness were related to higher perceived fatigue before and after the fatigue-manipulation task, but they did not predict perceived fatigability in response to the tasks.</p> <p><strong><span style="font-family: 'Arial','sans-serif';">5. Recommendations</span></strong></p> <p>Two dimensions of mental fatigability - objective vs. perceived - were identified. Although highly correlated, these two dimensions were associated with different factors. After a large-scale validation study, the long-term goal will be to incorporate the mental fatigability measures into cognitive training interventions aimed at maintaining cognitive well-being in older adults.</p>en_GB
dc.subjectagingen_GB
dc.subjectFatigue classificationen_GB
dc.subjectCognitionen_GB
dc.date.available2013-09-16T20:06:17Z-
dc.date.issued2013-09-16-
dc.date.accessioned2013-09-16T20:06:17Z-
dc.description.sponsorshipSigma Theta Tau Internationalen
dc.descriptionThis pilot project provided valuable preliminary data in multiple ways: first, I was able to obtain a Career Award (KL2) started in July, 2013 focusing on cognition training in old age; second, I am planning to apply for a pilot intervention study examining the role of mental fatigability in effect of cognitive training in old age.en_GB
dc.description.noteThe Sigma Theta Tau International grant application that funded this research, in whole or in part, was completed by the applicant and peer-reviewed prior to the award of the STTI grant. No further peer-review has taken place upon the completion of the STTI grant final report and its appearance in this repository.en
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