Development and Validation of the Norfolk Quality of Life Fatigue Tool (QOL-F): A New Measure of Perception of Fatigue

JAMDA xxx (2019) 1e6

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Original Study

Development and Validation of the Norfolk Quality of Life Fatigue Tool (QOL-F): A New Measure of Perception of Fatigue Etta J. Vinik MA(Ed) a, *, Aaron I. Vinik MD, PhD a, Serina A. Neumann PhD b, Rajan Lamichhane PhD c, Steven Morrison PhD d, Sheri R. Colberg PhD e, Ying-Chuen Lai MD f, James Paulson PhD g, Richard Handel PhD b, Carolina Casellini MD a, Kim Hodges PharmD a, Joshua Edwards MPH a, Henri K. Parson PhD a a

Department of Internal Medicine, Division of Endocrinology and Metabolism, Eastern Virginia Medical School, Norfolk, VA Department of Psychiatry and Behavioral Sciences, Eastern Virginia Medical School, Norfolk, VA c School of Health Professions, Eastern Virginia Medical School, Norfolk, VA d School of Physical Therapy and Athletic Training, Old Dominion University, Norfolk, VA e Human Movement Sciences Department, Old Dominion University, Norfolk, VA f Department of Internal Medicine, National Taiwan University Hospital, Yun Lin Branch, Taiwan g Department of Psychology, Old Dominion University, Norfolk, VA b

a b s t r a c t Keywords: Quality of Life (QOL) Fatigue Norfolk QOL-F PROMIS aging cognitive physical

Objectives: To design a questionnaire to evaluate and distinguish between cognitive and physical aspects of fatigue in different age groups of “nondiseased” people and guide appropriate prevention and interventions for the impact of frailty occurring in normative aging. Study design and participants: The Norfolk QOL-Fatigue (QOL-F) with items of cognitive and physical fatigue, anxiety, and depression from validated questionnaires including items from the Patient-Reported Outcomes Measure Information System (PROMIS) databank was developed. The preliminary QOL-F was administered to 409 healthy multiethnic local participants (30-80 years old) in 5 age groups. Methods: The authors distilled the item pool using exploratory (EFA) and confirmatory factor analysis (CFA). EFA identified 5 latent groups as possible factors related to problems due to fatigue, subjective fatigue, reduced activities, impaired activities of daily living (ADL), and depression. Results: CFA demonstrated good overall fit [c2(172) ¼ 1094.23, P < .001; Tucker-Lewis index ¼ 0.978; root mean square error of approximation ¼ 0.049] with factor loadings >0.617 and strong interfactor correlations (0.69-0.83), suggesting that fatigue in each domain is closely related to other domains and to the overall scale except for ADL. The 5-factor solution displayed good internal consistency (Cronbach a ¼ 0.78-0.94). Total and domain scores were fairly equivalent in all age groups except for the 40 to 49-year-old group with better overall scores. In addition, 70 to 79-year-olds had better ADL scores. In item response analysis, factor scores in different age groups were similar, so age may not be a significant driver of fatigue scores. Fatigue scores were significantly higher in females than in males (P < .05). Conclusions and clinical implications: The developed Norfolk QOL-F tool demonstrated fatigue as a perceived cognitive phenomenon rather than an objective physical measure, suggesting mandatory inclusion of cognitive as well as physical measures in the evaluation of people as they age. QOL-F is able to distinguish QOL-F domain scores unique to different age groups, proposing clinical benefits from physical, balance, and cognitive interventions tailored to impact frailty occurring in normative aging. Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.

This study was supported by an NIH Grant: 1R21AG037123e01A1 (principal investigator: A.I. Vinik). The authors declare no conflicts of interest.

https://doi.org/10.1016/j.jamda.2019.10.021 1525-8610/Ó 2019 AMDA e The Society for Post-Acute and Long-Term Care Medicine.

* Address correspondence to Etta J. Vinik, MA(Ed), Department of Internal Medicine, Division of Endocrinology and Metabolism, Eastern Virginia Medical School, Norfolk, VA, USA. E-mail address: [email protected] (E.J. Vinik).

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Chronic fatigue occurs in 15% to 45% of the American population.1,2 Fatigue can affect all aspects of quality of life (QOL) from mood to physical functioning and activities of daily living (ADL). Particularly for older individuals, fatigue has the potential to restrict their physical activity, which can negatively impact health and well-being. Reduced activity is often a precursor to increased falls, declining muscle function, general inanition, depression, and apathy. For each individual, the level of fatigue is determined not only by the availability, utilization, and restoration of resources needed to perform activities3 but also by the demands of those activities. Fatigue in the community has been previously measured through questionnaires and surveys. However, there is no gold standard with which to compare fatigue instruments, nor has there been a uniform approach to obtain consensus on the concept and its measurement. Some approaches assess the symptomatic complaints of distress or functional impact of fatigue.4,5 These tools are oriented to health-related quality of life and are useful for screening as well as diagnosing fatigue, along with evaluating patients with medical conditions.6e8 However, none of the current tools are sensitive to aging and its complications, including the impact of depression, somatic and autonomic nerve dysfunction, sarcopenia, and frailty, that occur in older, otherwise nondiseased populations. In addition, no existing tools assess cognitive vs physical fatigue. This study was, therefore, undertaken to design a new tool to evaluate both cognitive and physical aspects of fatigue in different age groups. Methods

QOL-F. Similarly, we sought to correlate the depression questions in the Norfolk QOL-F with those in the Center for Epidemiologic StudieseDepression scale and the physical functioning questions with the Modified Falls Efficacy Scale. We used the Delphi method to review the original item pool containing more than 100 questions. The Delphi panel of experts was composed of physicians, nurses, psychologists, education specialists, and exercise physiologists. After general consensus (unweighted), a draft questionnaire was created with responses on a 5-point Likert-type scale. A focus group of 10 patients representative of the intended study population assessed readability and content as well as question redundancy and suggestions for additional questions. After group discussion, and agreed changes, the preliminary item pool comprising 42 items was arranged so that each item related to a priori subscales of Symptoms of General Fatigue (lack of energy or tiredness), General Health, ADL, Physical Functioning, Cognitive Scale, and Feelings Scale. During this development process, we explored another psychometric methodology, item response theory, and computerized adaptive testing, the PROMIS (Patient Reported Outcomes Measurement Information System) 96-item inventory of fatigue questions.11,12 Then, using the Delphi technique with our panel of experts, we chose 75 items for combination with 42 in the preliminary QOL-F, giving a total of 117 questions as a basis for the Norfolk/PROMIS QOL-F tool. We believed that addition of the items from PROMIS would strengthen these correlations and improve the structure of the ultimate tool. This sequence of events is shown diagrammatically in Supplementary Figure 1. All 117 questions were answered by all participants.

Study Population Item-Level Diagnostics Development and validation of the new fatigue tool was planned in a multiethnic population of 409 participants, divided into 5 age groups, each with 80 participants, ranging from 30 to 79 years. The patient population was recruited from southeastern Virginia and northeastern North Carolina, areas that included a city and suburban community with African American, Hispanic, Caucasian, and Asian groups. There was diversity in ethnicity, education levels, literacy abilities, and socioeconomic status. The language was predominantly English, including a small number of bilingual Asians. Participants were recruited locally by written invitation, newspaper advertisements, and flyers placed within retirement homes, with the assistance of the local center for geriatrics. Potential participants with ocular or systemic disease (including diabetes), recent or recurrent history of musculoskeletal injury, neurologic conditions, history of vertigo, use of an aid while walking, difficulty standing upright, visible tremor, and uncorrected visual deficits were excluded. A total of 400 participants were determined to be sufficient to reach covariance matrix stability and consistent with the 10 participants per item rule of thumb. The protocol was approved by the institutional review board and performed in accordance with the Declaration of Helsinki, with written informed consent obtained from all participants. Item Selection To develop a comprehensive fatigue questionnaire, we examined pre-existing, validated, generic, health-related quality of life questionnaires and included items associated with cognitive, mental, and physical fatigue; anxiety; fear; stress; depression; and perceived health status. We added these to existing items on physical functioning, ADL, and depression scales from our previously validated neuropathy and neuroendocrine tools, Norfolk QOL-DN and Norfolk QOL-NET,9,10 to create a preliminary Quality of Life Fatigue questionnaire (QOL-F). We also explored the relationship between the 9 fatigue items in the Fatigue Severity Scale (FSS) and the Norfolk

All items in the initial pool were examined for distribution, skewness, and kurtosis to identify floor and ceiling effects, invariance, and other potential problems for interfering with the planned factor analysis. Some items measuring impairment in ADL showed expected low base rates, but these items were judged to have sufficient variance and content importance to be carried forward to the next stage of analysis. Identifying Dimensionality Between Items From PROMIS and Norfolk QOL-F Exploratory factor analysis (EFA) was performed on the data from the full sample (n ¼ 409) to identify dimensionality among items from PROMIS and Norfolk QOL-F. To assess factor structure, principal axis factor analysis with a rotation method of Promax with Kaiser Normalization was used. Parallel analysis was used to select the number of factors to extract. We used an oblique rotation strategy because we hypothesized that the underlying dimensions in all factor analyses would be correlated. Because of the large number of combined PROMIS and Norfolk-QOL items relative to our sample size, we first sought to reduce the size of the overall combined item pool. Accordingly, we examined the factor structure of the 42 preliminary Norfolk QOL-F items and 75 PROMIS items separately using an SPSS program developed by O’Connor to evaluate the number of factors to retain.13,14 For each parallel analysis, we generated 1000 random data sets based on permutations of our raw data. We initially retained factors for the PROMIS and Norfolk-QOL analyses if their eigenvalues were greater than the 95th percentile of the randomly generated data. Within each instrument, items that were identified as having low communalities (below 0.30), pattern coefficients below 0.40, or high cross loadings were dropped. This yielded a tool with refined items. The post hoc test was performed for additional exploration of the differences among average factor scores. It was needed to provide specific information on which average factor scores were significantly

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different from each other. We used Tukey’s honestly significant difference test to control the experiment-wise error rate in our multiple comparisons. A confirmatory factor analysis (CFA) model was fit in SAS, version 9.4, using the 5 scales and 35 items that were derived via EFA. This analysis was fit using a conservative fit algorithm, weight measured as least squares with mean and variance adjustment. Results

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accounts for the ordinal response format of items. This model demonstrated good overall fit [c2(172) ¼ 1094.23, P < .001; TuckerLewis index ¼ 0.978, root mean square error of approximation ¼ 0.049], with standardized factor loadings for all factors being 0.617 or higher. Strong interfactor correlations were observed, ranging from 0.690 to 0.830, suggesting that experience of fatigue in one domain is closely related to experience of fatigue in other domains and overall. See Figure 1 for CFA model loadings and Table 2 for factor intercorrelations.

Exploratory Factor Analysis Reliability Analysis From the EFA procedure, a total of 56 items were retained for further analysis. Because Norfolk-QOL and PROMIS scales were developed independently, the combined item pool was examined for duplicative item content. Here, EFA was again conducted and the lowest-loading item of a pair with similar content was dropped. This step resulted in a reduced pool of 42 items. We subsequently conducted EFA on the actual data and a parallel analysis using 1000 randomly generated data sets based on permutations of the actual data. The parallel analysis suggested 5 factors. Table 1 shows item loadings into the 5 factors. All items with loading scores <0.4 were dropped. Confirmatory Factor Analysis A CFA model was fit in SAS version 9.4 using the 5 scales and 42 items that were derived via EFA. This CFA model was fit using weighted least squares with mean and variance adjustment, which better

We calculated internal consistency for each of the 5 resulting scales using Cronbach alpha. Scales 1 to 4 showed good internal consistency (Cronbach a ¼ 0.939, 0.932, 0.872, and 0.890, respectively), and scale 5 showed acceptable internal consistency (Cronbach a ¼ 0.783). The CFA showed good overall fit (Tucker-Lewis index ¼ 0.978, root mean square error of approximation ¼ 0.049), with standardized factor loading for all factors being 0.617 or higher. Items that loaded clearly on each of the 5 factors were designated as constituents of preliminary subscales. Each item set was examined for internal consistency using item-total correlations and scale-level Cronbach alpha.  Scale 1 (Subjective Fatigue): composed of 9 items with good internal consistency (Cronbach a ¼ 0.939); corrected item-total correlations range from 0.680 to 0.802.

Table 1 Exploratory Factor Analysis: Item Loadings Factor 1 1. How often did you feel run-down?* 4. How often were you sluggish?* n2. Lack energyy n1. Tired during the dayy 13. How often did you find yourself getting tired easily?* 5. How often did you run out of energy?* n3. Sleepy during dayy 7. How often were you bothered by your fatigue?* 6. How often were you physically drained?* 34. How often was it an effort to carry on a conversation because of your fatigue?* 31. How often were you too tired to take a bath or shower?* 32. How often did your fatigue make it difficult to organize your thoughts when doing things at home?* 37. How often were you too tired to think clearly?* 43. I am too tired to eat?* 36. How often were you too tired to leave the house?* 44. I need help doing my usual activities.* 46. I have to limit my social activity because I am tired.* 27. How often did your fatigue make it difficult to make decisions?* 69. To what degree did your fatigue interfere with your physical functioning?* 40. How often were you too tired to take a short walk?* n28. Could not shake off the bluesz n30. Tired, depressed, cryingz n37. Easily annoyedz n31. Lonely when other people were aroundz n26. Bothered by things that don’t usually botherz n27. Over-/undereatingz n29. Trouble keeping mind on what doingz n10. Limited in work or activitiesx n11. Difficulty performing workx n9. Accomplished lessx n8. Cut down on time work and activitiesx n16. Getting on and off toiletk n14. Dressingk n17. Getting out of chairk n13. Bathing and showeringk

2

3

4

5

0.836 0.787 0.783 0.781 0.769 0.764 0.739 0.656 0.617 0.843 0.818 0.707 0.669 0.643 0.607 0.598 0.574 0.552 0.551 0.414

0.319

0.738 0.706 0.685 0.640 0.608 0.531 0.524 0.825 0.729 0.699 0.695

0.324

0.795 0.701 0.635 0.546

The letter “n” in the item numbers represents items derived from the Norfolk scales, the remainder are derived from PROMIS. Symbols matches the 5 factors that emerge after confirmatory factor analysis in Figure 2: *Problems Due to Fatigue ySubjective Fatigue zDysphoria (Depression) xReduced Activities kImpaired Activities of Daily Living

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Fig. 1. Confirmatory factor analysis: model loadings. Note: All paths shown have associated P < .05. Correlations between factors are shown in Table 2.

 Scale 2 (Problems Due to Fatigue): composed of 11 items with good internal consistency (Cronbach a ¼ 0.932); corrected item-total correlations range from 0.608 to 0.797.  Scale 3 (Dysphoria): composed of 7 items with good internal consistency (Cronbach a ¼ 0.872); corrected item-total correlations range from 0.579 to 0.699.  Scale 4 (Reduced Activities): composed of 4 items with good internal consistency (Cronbach a ¼ 0.890); corrected item-total correlations range from 0.744 to 0.767.  Scale 5 (Activities of Daily Living): composed of 4 items with acceptable internal consistency (Cronbach a ¼ 0.783); corrected item-total correlations range from 0.556 to 0.655. Item Response Theory Analysis EFA identified 5 latent groups as possible factors related to problems due to fatigue, subjective fatigue, reduced activities, impaired ADL, and dysphoria. After determining the latent groups from EFA, we performed CFA using an item response theory model to estimate the factor scores related to each latent group. Factor scores for the general model and other latent groups are presented in Table 1. As the responses were ordinal, ranging from 0 to 4, we implemented the graded response model and marginal maximum likelihood method to

calculate these factor scores. Low factor scores indicate lower latent response, whereas higher scores indicate higher latent scores. Table 3 shows scores for the Total QOL-F and effects of aging in all 5 groups and all domains. It also graphically displays the scores in the subset of 40-49-year-olds shown to have better quality of life and

Table 2 Correlations Between Confirmed Factors Factor

1

2

3

4

5

1. 2. 3. 4. 5.

d d d d d

0.83 d d d d

0.77 0.83 d d d

0.77 0.78 0.69 d d

0.64 0.70 0.61 0.75 d

Subjective Fatigue Problems Due to Fatigue Dysphoria Reduced Activities Activities of Daily Living

Presents the final scale names and factor correlations for each scale of the Norfolk QOL-F. Items that loaded clearly on each of 5 factors were designated as constituents of 5 scales: (1) Subjective Fatigue, (2) Problems Due to Fatigue, (3) Dysphoria, (4) Reduced Activities, and (5) Activities of Daily Living. Strong interfactor correlations were observed, ranging from 0.690 to 0.830, suggesting that experience of fatigue in one area is closely related to experience of fatigue in other areas and overall. “Activities of Daily Living” is an exception and does not correlate with the other factors but is the major driver of fatigue when analyzed using item response analysis (see below).

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Table 3 Scores for the Total QOL-F Fatigue Score and the 5 Domains for all Age Groups Entire Group Total fatigue score Factor 1: Subjective Fatigue Factor 2: Problems Due to Fatigue Factor 3: Dysphoria Factor 4: Reduced Activities Factor 5: ADL

26.44 10.08 7.21 5.66 3.03 0.44

     

2.27 0.80 0.78 0.48 0.32 0.14

Group 1 (30-39 y) 33.41 12.29 9.88 7.24 3.11 0.88

     

6.18 2.04 2.05 1.40 0.71 0.36

Group 2 (40-49 y) 20.66 8.66 5.50 4.06 2.27 0.16

     

3.51* 1.50 1.20* 0.80* 0.55 0.12

Group 3 (50-59 y) 27.40 10.07 7.53 6.13 3.26 0.40

     

4.61 1.60 1.62 1.05 0.69 0.33

Group 4 (60-69 y) 26.43 9.81 6.75 6.06 3.18 0.63

     

5.91 2.04 1.98 1.27 0.80 0.50

Group 5 (70-79 y) 24.56 9.63 6.44 4.94 3.44 0.13

     

4.80 1.77 1.80 0.62 0.86 0.13y

Data are presented as mean  standard error of the mean; comparisons between groups were analyzed using Wilcoxon rank sum test. *P < .05, group 1 vs group 2. y P < .05, group 1 vs group 5.

less fatigue and depression than the other groups, except for the 70-79-year age group, which surprisingly displayed the least fatigue and best quality of life related to the ADL domain. A summary of all factor scores related to each latent group is presented in Table 4. Noticeably, the average scores of all but 1 related to impaired ADL are not different from zero; thus, any negative score can be interpreted as less than average and positive scores can be considered a more than average score. Also, Shapiro-Wilk test shows that scores for the general fatigue model is normally distributed (P ¼ .09), whereas factor scores for other latent groups are not normally distributed (P < .05). The differences among age groups on factor scores are compared by using 1-way analysis of variance, with Tukey adjustment for the general fatigue score and pairwise Kruskal-Wallis test for the other latent groups. The results of multiple comparisons are presented in Supplementary Table 1. In most of the cases, there were no significant differences between the different age groups on the average factor scores of all latent outcomes. We also included ethnicity and gender in the model to assess whether they have an impact on the general fatigue score. Although ethnicity was not a significant factor in predicting the general fatigue score, the average fatigue score of females was significantly higher than the average fatigue score of males (P value < .05) (Supplementary Table 1, “Gender”).

Discussion We developed the Norfolk QOL-F tool (shown in Supplementary Material 1) based on the factor structure of the Norfolk QOL-F items distilled from 100 questions in a Delphi iterative process and 75 PROMIS items. After EFA and CFA, the tool was further distilled to 35 items with Cronbach a loading of >0.74 resulting in 5 scales: scale 1 (Subjective Fatigue) with 9 items, scale 2 (Problems Due to Fatigue) with 11 items, scale 3 (Dysphoria) with 7 items, scale 4 (Reduced Activities) with 4 items, and scale 5 (Activities of Daily Living) with 4 items. Four hundred nine healthy subjects, aged 30 to 79 years, completed the questionnaire and their resulting scores in the 5 domains were not significantly different, except for those in the 40 to

49-year age group who had better total fatigue scores than all the other groups (as shown in Table 3) and also better scores in all other domains than the other groups. The 70 to 79-year age group had the best ADL scores. The Norfolk QOL-F tool has been shown here to quantify cognitive fatigue, the impact of depression, problems due to fatigue, and ADL in those aged 30 to 79 years. The literature reveals that there have been several attempts to relate fatigue to the quantified perception of an individual’s response to normal activities. The Dutch Exertion Fatigue Scale has measured exertion fatigue by specifically describing fatigue-inducing situations, such as walking, shopping, or visiting,5 whereas the Dutch Fatigue Scale measures patients’ general fatigue. Both of these instruments targeted patients with heart disease, postpartum women, and patients living in homes for older adults.15 However, they do not examine the impact of increasing duration of time nor do the tools contain items that address the impact of cognitive activity on fatigue. The Chalder Fatigue Questionnaire16 is used to measure physical and mental fatigue in patients with chronic fatigue syndrome. More recently, Yang and Wu3 constructed a 13-point subjective rating scale referred to as the Situational Fatigue Scale to determine fatigue, with questions specifically addressing physical and mental fatigue. Principal component analysis revealed 2 underlying constructs of physical and cognitive activity with good internal consistency for the total scale as well as these 2 domains. They examined the relationship with the Fatigue Assessment Instrument and found weak correlations, mainly because the Fatigue Assessment Instrument targets were pathological fatigue, whereas the Situational Fatigue Scale was designed to examine normative fatigue. None of the above-mentioned tools are designed or are appropriate for examining normative fatigue of aging and the conditions to which the target population is exposed that the Norfolk QOL-F fulfills. The strong interfactor correlations, ranging from 0.690 to 0.830, suggest that experience of fatigue in one domain is closely related to experience of fatigue in other domains and in the overall scale except for ADL. In a later stress paradigm study, we assessed the short-term effects of performing fatiguing walking activity (on a treadmill) on falls risk, balance, and general physiological function on 75 healthy adults randomly drawn from this larger cohort of 409 subjects. Significant age-related differences were

Table 4 Summary of Latent Groups Latent Groups

General fatigue factor Problems Due to Fatigue Subjective Fatigue Dysphoria Reduced Activities Impaired Activities of Daily Living

Age Groups, y 39 (n ¼ 80)

40-49 (n ¼ 83)

50-59 (n ¼ 88)

60-69 (n ¼ 84)

70 (n ¼ 74)

0.23 0.24 0.36 0.17 0.07 0.08

0.07 0.06 0.02 0.03 0.12 0.04

0.07 0.07 0 0.16 0.09 0.19

0.05 0.05 0.07 0.11 0.18 0.31

0.14 0.12 0.25 0.13 0.07 0.3

(1.01) (0.97) (0.97) (0.98) (0.87) (0.49)

(0.9) (0.88) (0.89) (0.89) (0.78) (0.4)

(0.99) (1.00) (0.86) (0.84) (0.85) (0.57)

(1.09) (1.04) (0.93) (1) (0.89) (0.76)

All data are presented as mean (standard deviation). P value is for testing the null hypothesis that average score is 0. *P value based on nonparametric Wilcoxon signed rank test.

(0.79) (0.76) (0.75) (0.71) (0.86) (0.68)

All (Pooled) (n ¼ 409)

P value (H0: Mean ¼ 0)

0.01 0.02 0.02 0.03 0.06 0.18

.84 .89* .98* .84* .63* <.001*

(0.97) (0.94) (0.90) (0.9) (0.85) (0.6)

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observed before the walking activity. Increasing age was associated with declines in gait speed, lower limb strength, slower reaction times, and increases in overall falls risk. Following the treadmill task, older adults (60-79 years old) exhibited increased sway, worse postural coordination, and declines in lower limb strength. However, significant decreases in reaction times and increases in overall falls risk were only seen in the oldest group (aged 70-79 years). For all other persons (aged 30-69 years), changes resulting from the treadmill-walking task did not lead to a significant increase in falls risk.17 Paradoxically, the oldest group had significantly better ADL than all the younger subjects. It seems that aging in a younger group (aged 40-49 years) is compatible with a reduction in fatigue until one reaches the age of >70 years when cognitive fatigue decreases dramatically, yet aspects of physical function deteriorate, separating cognitive from physical components of fatigue.17 It is possible that many of the older adults aged >70 years have transitioned into a retirement phase of life when no longer engaging in the same types of cognitively effortful tasks compared with when working and managing their families, which could account for the decreased cognitive fatigue during this age period. However, we must continue to consider that if this age group is cognitively taxed chronically, then they may be at higher risk for adverse consequences such as cognitive decline, reduced independent living skills function, and decreased socialization.18 Thus, it is mandatory that cognitive as well as physical measures be included in evaluating people as they age, and the 2 may not be parallel. Because these quantitative instrument development procedures relied on a single modest size sample (n ¼ 409), to fully assess the structural validity of the 5 selected factors, a replication using a large independent sample will be needed. Additionally, the approach to identify the 5 selected factors used primarily empirical methods starting with item pools from the Norfolk fatigue measures and PROMIS. Although this approach can produce meaningful and replicable factors, it may have neglected fatigue areas related to sleep and other medical conditions. We have developed the QOL-F tool to be sensitive to aging, gender, cognitive and emotional function, and to be able to evaluate the impact of health on fatigue. Our preliminary observations indicate that cognitive function plays an important role in fatigue. In fact, we suggest that fatigue is a cognitive rather than a physical phenomenon, or possibly that cognitive fatigue influences the experience of physical fatigue.19 QOL-F also displays sensitivity to the effects of aging in all 5 groups and all domains and is capable of distinguishing a subset of participants 40-49 years old with better quality of life and less fatigue and depression than even their younger counterparts (30-39 years) and 70 to 79-yearolds with best ADL. These unique findings warrant further exploration. Conclusions and Clinical Implications The developed Norfolk QOL-F tool demonstrated fatigue as a perceived cognitive phenomenon rather than an objective physical measure, suggesting mandatory inclusion of cognitive as well as physical measures in the evaluation of people as they age. QOL-F is

able to distinguish QOL-F domain scores unique to different age groups, proposing clinical benefits from physical, balance, and cognitive interventions tailored to impact frailty occurring in normative age groups. Acknowledgments We thank Dr David Cella, principal investigator on the PROMIS Statistical Coordinating Center, for his encouragement to engage with the PROMIS database on this project. Supplementary Data Supplementary data related to this article can be found online at https://doi.org/10.1016/j.jamda.2019.10.021. References 1. Ranjith G. Epidemiology of chronic fatigue syndrome. Occup Med (Lond) 2005; 55:13e19. 2. Junghaenel DU, Christodoulou C, Lai JS, Stone AA. Demographic correlates of fatigue in the US general population: Results from the Patient-Reported Outcomes Measurement Information System (PROMIS) initiative. J Psychosom Res 2011;71:117e123. 3. Yang CM, Wu CH. The situational fatigue scale: A different approach to measuring fatigue. Qual Life Res 2005;14:1357e1362. 4. Shapiro CM, Flanigan M, Fleming JA, et al. Development of an adjective checklist to measure five FACES of fatigue and sleepiness. Data from a national survey of insomniacs. J Psychosom Res 2002;52:467e473. 5. Tiesinga LJ, Dassen TW, Halfens RJ. DUFS and DEFS: Development, reliability and validity of the Dutch Fatigue Scale and the Dutch Exertion Fatigue Scale. Int J Nurs Stud 1998;35:115e123. 6. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The Fatigue Severity Scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989;46:1121e1123. 7. Piper BF, Dibble SL, Dodd MJ, et al. The revised Piper Fatigue Scale: Psychometric evaluation in women with breast cancer. Oncol Nurs Forum 1998;25:677e684. 8. Schwartz JE, Jandorf L, Krupp LB. The measurement of fatigue: A new instrument. J Psychosom Res 1993;37:753e762. 9. Vinik EJ, Hayes RP, Oglesby A, et al. The development and validation of the Norfolk QOL-DN, a new measure of patients’ perception of the effects of diabetes and diabetic neuropathy. Diabetes Technol Ther 2005;7:497e508. 10. Vinik E, Carlton CA, Silva MP, Vinik AI. Development of the Norfolk Quality of Life tool for assessing patients with neuroendocrine tumors. Pancreas 2009;38: e87ee95. 11. Cella D, Yount S, Rothrock N, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH Roadmap Cooperative Group during its first two years. Med Care 2007;45:S3eS11. 12. Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol 2010;63:1179e1194. 13. Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika 1965;30:179e185. 14. O’Connor BP. SPSS and SAS programs for determining the number of components using parallel analysis and velicer’s MAP test. Behav Res Methods Instrum Comput 2000;32:396e402. 15. Tiesinga LJ, Dassen T, Halfens R, van den Heuvel W. Validation and application of the Dutch Fatigue Scale and the Dutch Exertion Fatigue Scale. Int J Nurs Terminol Classif 2003;14:59e62. 16. Chalder T, Berelowitz G, Pawlikowska T, et al. Development of a fatigue scale. J Psychosom Res 1993;37:147e153. 17. Morrison S, Colberg SR, Parson HK, et al. Walking-induced fatigue leads to increased falls risk in older adults. J Am Med Dir Assoc 2016;17:402e409. 18. Leavitt VM, DeLuca J. Central fatigue: Issues related to cognition, mood and behavior, and psychiatric diagnoses. PM R 2010;2:332e337. 19. Ren P, Anderson AJ, McDermott K, et al. Cognitive fatigue and cortical-striatal network in old age. Aging (Albany NY) 2019;11:2312e2326.

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Supplementary Fig. 1. Item Development. Norfolk QOL-DN, Norfolk quality of life patient-reported outcome measures for diabetic neuropathy; SF-36, (Short Form 36) self-reported functional and well-being measures; CES-D, Center for Epidemiologic StudieseDepression Scale; Norfolk QOL-NET, Norfolk quality of life patient-reported outcome measures for neuroendocrine tumors; PROMIS, (Patient Reported Measurement Information System) Item bank for measuring quality of life with an item-response theory approach; N, number of items.

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Supplementary Table 1 Multiple Comparisons of Factor Scores of Different Latent Groups by Age Group Latent Group

Age Groups, y

Average Difference

Standard Error

P value Unadjusted

P value (Adjusted)

General fatigue

39 39 39 39 40-49 40-49 40-49 50-59 50-59 60-69 Gender M) 39 39 39 39 40-49 40-49 40-49 50-59 50-59 60-69 39 39 39 39 40-49 40-49 40-49 50-59 50-59 60-69 39 39 39 39 40-49 40-49 40-49 50-59 50-59 60-69 39 39 39 39 40-49 40-49 40-49 50-59 50-59 60-69 39 39 39 39 40-49 40-49 40-49 50-59 50-59 60-69

40-49 50-59 60-69 70 50-59 60-69 70 60-69 70 70 (F vs

0.30 0.16 0.28 0.37 0.14 0.02 0.07 0.12 0.21 0.08 0.37

0.15 0.16 0.16 0.15 0.14 0.15 0.13 0.16 0.14 0.15 0.09

.04 .25 .08 .007 .36 .90 .55 .48 .13 .51 <.001

.23 .77 .39 .06 .89 >.99 .97 .96 .56 .96 <.001

40-49 50-59 60-69 70 50-59 60-69 70 60-69 70 70 40-49 50-59 60-69 70 50-59 60-69 70 60-69 70 70 40-49 50-59 60-69 70 50-59 60-69 70 60-69 70 70 40-49 50-59 60-69 70 50-59 60-69 70 60-69 70 70 40-49 50-59 60-69 70 50-59 60-69 70 60-69 70 70

0.30 0.17 0.29 0.36 0.13 0.01 0.06 0.12 0.20 0.07 0.34 0.36 0.43 0.61 0.02 0.09 0.27 0.07 0.25 0.18 0.14 0.01 0.28 0.30 0.13 0.14 0.15 0.27 0.29 0.01 0.18 0.03 0.11 0.00 0.21 0.30 0.18 0.09 0.03 0.11 0.04 0.12 0.24 0.22 0.16 0.28 0.26 0.12 0.10 0.02

0.15 0.15 0.16 0.14 0.14 0.15 0.13 0.16 0.14 0.14 0.15 0.14 0.15 0.14 0.13 0.14 0.13 0.14 0.13 0.13 0.15 0.14 0.15 0.14 0.13 0.15 0.13 0.14 0.12 0.14 0.13 0.13 0.14 0.14 0.12 0.13 0.13 0.13 0.13 0.14 0.07 0.08 0.10 0.10 0.08 0.09 0.09 0.10 0.10 0.11

.041 .27 .07 .010 .36 .95 .63 .43 .16 .61 .02 .011 .004 <.001 .87 .53 .04 .63 .05 .17 .33 .92 .07 .03 .33 .34 .23 .06 .02 .92 .16 .83 .41 .99 .09 .023 .16 .52 .84 .42 .56 .15 .018 .023 .035 .003 .004 .25 .31 .88

.24 .81 .35 .08 .89 >.99 .99 .94 .62 .99 .13 .08 .03 <.001 >.99 .97 .24 .99 .29 .65 .86 >.99 .35 .19 .86 .88 .75 .31 .14 >.99 .62 >.99 .92 >.99 .44 .15 .63 .97 >.99 .93 .98 .61 .12 .15 .22 .027 0.033 .78 .85 >.99

Problems from fatigue

Subjective Fatigue

Dysphoria

Reduced activities

Impaired ADL

P value < .05 is considered significant.