Demographic correlates of fatigue in the US general population: Results from the patient-reported outcomes measurement information system (PROMIS) initiative

https://doi.org/10.1016/j.jpsychores.2011.04.007Get rights and content

Abstract

Objective

To investigate demographic correlates of fatigue in the US general population using a new instrument developed by the Patient-Reported Outcome Measurement Information System (PROMIS). First, we examined correlations between the new PROMIS instrument and the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) and the SF-36v2 Vitality subscale. Based on prior findings, we further examined several demographic correlates of fatigue: whether women would report higher levels of fatigue compared to men, and whether married people would experience lower levels of fatigue compared to unmarried people. We also explored the relationship between age, education, and fatigue.

Methods

Analyses were based on fatigue ratings by 666 individuals from the general population. Fatigue was assessed with the new PROMIS instrument, the FACIT-F, and the SF-36v2 Vitality subscale. Differences in fatigue were examined with independent samples t-tests and univariate ANOVAs.

Results

The three fatigue instruments were highly intercorrelated. Confirming prior reports, women reported higher levels of fatigue than men. Married participants reported significantly less fatigue than their unmarried counterparts. Univariate ANOVAs yielded a main effect for participants' age; younger participants gave significantly higher fatigue ratings. We also found a main effect for participants' education. Participants with a masters or doctoral degree had significantly lower ratings of fatigue than participants with some college education and education up to high school.

Conclusion

Female gender, not being married, younger age and lower educational attainment were each associated with increased fatigue in the general population and the three fatigue instruments performed equally well in detecting the observed associations.

Introduction

Fatigue is a common reason for seeking medical care and a source of considerable economic burden [1], [2]. The prevalence of fatigue in the general population has been reported to range from 7% to 45% [see [1], [2]]; a recent study found that 38% of US workers reported being fatigued [2]. Adequate treatment of fatigue has proven challenging and it is often overlooked by healthcare providers due to its diagnostically non-specific nature [3]. Fatigue is a common pathological feature of various medical conditions including chronic heart disease, cancer, multiple sclerosis, chronic insomnia, and depression [4] and chronic fatigue syndrome [5].

Fatigue can be broadly characterized as either a subjective feeling or a decrement in a person's ability to perform up to a certain standard [6]. At pathological levels, fatigue can be overwhelming, debilitating, and lead to a sustained sense of exhaustion [7], [8], [9], [10], [11]. Non-pathological fatigue has lower intensity, shorter duration, and less disabling effects on functional activities.

Fatigue has been extensively studied in medical conditions. Numerous fatigue instruments have been developed for disease-specific use, such as for rheumatoid arthritis [12], cancer [13], [14], [15], [16], multiple sclerosis [17], [18], [19], chronic fatigue syndrome [20], and myasthenia gravis [21]. Research has also sought to differentiate fatigue experienced by clinical samples from that experienced by the community [22]; the development of tools to capture this difference has been initiated [23], [24]. Little is known, however, about the utility of these questionnaires across medical conditions and their applicability to healthy populations [25].

Recently, an effort has been made to advance fatigue measurement across healthy and medical populations as part of the Patient-Reported Outcomes Measurement Information System (PROMIS), funded within the National Institutes of Health (NIH) Roadmap for Medical Research Initiative. PROMIS is a multi-center, collaborative project to improve the measurement of clinically important symptoms and outcomes (e.g., fatigue, pain, emotional distress, physical functioning). Its goal is to develop and standardize a set of item banks that allow the assessment of key symptoms and health concepts across a wide range of populations [30], [26], [27]. The PROMIS measurement tools are being developed using a standardized step-wise series of methods including qualitative item review and sophisticated quantitative methods of advanced psychometric modeling [26], [27], [28]. The following phases of item development were completed and yielded a core pool of items for each of the PROMIS domains: identification of extant items, classification and selection, review of items and item revision, focus groups on appropriate domain coverage, cognitive interviews for item refinement, and final revisions before field testing (described in 10).

The PROMIS item banks were developed using item response theory (IRT). In IRT, an individual's true score is defined on the latent trait or construct of interest as compared to the test score, as is the case in classical test theory. A major concept of the IRT approach is the “item characteristic curve”, which describes the association between an individual's level on the trait/construct (for example, fatigue) and the probability that the individual will select a particular response option on a particular item [see 29]. The IRT-calibrated PROMIS item banks consist of an exhaustive set of carefully calibrated questions that define and quantify a common trait/construct [see 30]. IRT has distinct advantages over classical test theory approaches: 1) One does not need the full set of items to appropriately capture the construct. Assessments may be accomplished with fewer items compared to static instruments; 2) Items can be filtered from the bank that may be particularly relevant for one specific disease (for example, cognitive fatigue aspects for multiple sclerosis); 3) All PROMIS item banks are normed to match the 2000 United States Census by gender, age, and education, allowing comparison across domains and people with different conditions; 4) The banks can be administered in multiple formats including dynamic computerized adaptive testing (CAT) to allow individually tailored assessment. CAT is a specific type of computer-based assessment, similar in approach to clinical interviewing, that selects the most informative questions about the individual based on his/her previous response choices [see 29]. As such, PROMIS provides a sound, yet flexible, opportunity to stimulate and standardize the assessment of patient-reported outcomes (PROs) and to assist clinicians in evaluating treatment response [27].

Studies using more traditional measures have tried to shed light on the etiology of fatigue by identifying demographic correlates. Research in medical settings has demonstrated a consistent relationship between gender and fatigue. Women generally experience more fatigue than men with a ratio as high as 3:1 [31], [32], [33]. Evidence also suggests that married people experience less fatigue compared to unmarried people [31]. Results for age and educational status, however, are less consistent. Some studies suggest that older individuals and those who have less education experience less fatigue [32], while others observe no relationship [33].

Demographic correlates have also been investigated in the general population with similar findings for gender and marital status [2], [34], [35], [36], [37], however, more population-based research is needed. In the US, fatigue has risen to become the main reason for approximately 5 to 10% of visits to primary care settings, and is a secondary issue for an additional 10 to 20% of visits [1], [2]. American workers with fatigue are estimated to cost employers more than 136 billion dollars annually in health-related lost productive time, over 101 billion dollars more than non-fatigued workers [2]. Insight into the prevalence of fatigue in the general population can facilitate a better understanding of who is more or less likely to seek healthcare and why. This, in turn, has the potential to inform tailored treatment plans and more cost-effective utilization of healthcare resources.

The present study had three goals. The first was to examine correlations between the newly developed PROMIS instrument and two widely used fatigue legacy scales, the FACIT-F and the Vitality subscale of the SF-36v2. The second goal was to test the PROMIS instrument in a large US population study to confirm previously observed associations between fatigue, gender, and marital status. We expected that women would report higher levels of fatigue than men and that people who are married would experience lower levels of fatigue than unmarried people. Finally, we explored the relationship between age, educational status, and fatigue.

Section snippets

Sample

Data collection was conducted from July 2006 to March 2007. Institutional Review Board (IRB) approval was obtained for the study conduction. The present study employed secondary analysis of de-identified data. A detailed overview of the sampling strategy used for the PROMIS data collection is described elsewhere [27]. In brief, data could be classified into either full-bank testing (i.e., sample completed all items included in the testing forms) or block-testing (i.e., sample completed some

Demographic and medical characteristics

Table 3 displays the demographic characteristics. The present sample conformed adequately to the PROMIS target distributions. In terms of medical characteristics, Table 2 shows how many participants were ever told by a physician/health professional that they had a specific condition and how many participants were currently limited by this condition.

Distributions, intercorrelations, and mean scores for the fatigue measures

Table 4 presents the correlations among the three fatigue instruments. The PROMIS fatigue bank correlated highly with the FACIT-F (r = .95, p < .001)

Discussion

We administered the newly developed PROMIS fatigue scale and two established fatigue instruments to investigate the association between fatigue and demographic characteristics in a US general population sample. Our first goal was to examine correlations between the new PROMIS instrument and two widely used fatigue legacy scales, the FACIT-F and the Vitality subscale of the SF-36v2. The overall pattern of results demonstrates the similarity between the measures. The three scales correlated

Acknowledgments

The Patient-Reported Outcomes Measurement Information System (PROMIS) is a National Institutes of Health (NIH) Roadmap initiative to develop a computerized system measuring patient-reported outcomes in respondents with a wide range of chronic diseases and demographic characteristics. PROMIS was funded by cooperative agreements to a Statistical Coordinating Center (Evanston Northwestern Healthcare, PI: David Cella, PhD, U01AR52177) and six Primary Research Sites (Duke University, PI: Kevin

References (57)

  • LJ Tiesinga et al.

    Factors related to fatigue; priority of interventions to reduce or eliminate fatigue and the exploration of a multidisciplinary research model for further study of fatigue

    Int J Nurs Stud

    (1999)
  • PM Averill et al.

    Correlates of depression in chronic pain patients: a comprehensive examination

    Pain

    (1996)
  • L Manzoli et al.

    Marital status and mortality in the elderly: a systematic review and meta-analysis

    Social science & medicine

    (2007)
  • G Ranjith

    Epidemiology of chronic fatigue syndrome

    Occup Med Oxf

    (2005)
  • JA Ricci et al.

    Fatigue in the U.S. workforce: prevalence and implications for lost productive work time

    J Occup Environ Med

    (2007)
  • K Fukuda et al.

    The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group

    Ann Intern Med

    (1994)
  • C Christodoulou

    The assessment and measurement of fatigue

  • AL Stewart et al.
  • NorthAmericanNursingDiagnosisAssociation. Nursing Diagnoses: Definition and Classification, 1997–1998. Philadelphia,...
  • A Glaus

    Fatigue in patients with cancer: analysis and assessment

    (1998)
  • DA DeWalt et al.

    Evaluation of item candidates: the PROMIS qualitative item review

    Med Care

    (2007)
  • C Christodoulou et al.

    Cognitive interviewing in the evaluation of fatigue items: results from the patient-reported outcomes measurement information system (PROMIS)

    Qual Life Res

    (2008)
  • BL Belza

    Comparison of self-reported fatigue in rheumatoid arthritis and controls

    J Rheumatol

    (1995)
  • DM Hann et al.

    Measurement of fatigue in cancer patients: further validation of the Fatigue Symptom Inventory

    Qual Life Res

    (2000)
  • BF Piper et al.

    The revised Piper Fatigue Scale: psychometric evaluation in women with breast cancer

    Oncol Nurs Forum

    (1998)
  • KD Stein et al.

    A multidimensional measure of fatigue for use with cancer patients

    Cancer Pract

    (1998)
  • J Iriarte et al.

    The Fatigue Descriptive Scale (FDS): a useful tool to evaluate fatigue in multiple sclerosis

    Mult Scler

    (1999)
  • Cited by (87)

    • Approach to Fatigue: Best Practice

      2021, Medical Clinics of North America
    • How Pediatric Sleep Disordered Breathing Impacts Parental Fatigue

      2024, Annals of Otology, Rhinology and Laryngology
    View all citing articles on Scopus
    View full text