Activate Now
 
ProHealth health Vitamin and Natural Supplement Store and Health
Home  |  Log In  |  My Account  |  View Cart  View Your ProHealth Vitamin and Supplement Shopping Cart
800-366-6056  |  Contact Us  |  Help
Facebook Google Plus
Fibromyalgia  Chronic Fatigue Syndrome & M.E.  Lyme Disease  Natural Wellness  Supplement News  Forums  Our Story
Store     Brands   |   A-Z Index   |   Best Sellers   |   New Products   |   Deals & Specials   |   Under $10   |   SmartSavings Club

Trending News

Patient Insights into the Design of Technology to Support a Strengths-Based Approach to Health Care.

Greater intake of dietary omega-3 fatty acids associated with lower risk of diabetic retinopathy

SURVEY: Weight Management & Chronic Illness

Japanese green tea consumers have reduced risk of dementia

Do Nothing, Accomplish Everything! The Connection Between Breathing and Healing

Best Herbs to Help With Insomnia

Nature Heals

Meet the ProHealth Editors

Choline: Why You Should Eat Your Egg Yolks and Take Krill

Calcium, vitamin D supplementation associated improved stroke recovery

 
Print Page
Email Article

Abstract: Prognosis of Chronic Fatigue [Syndrome] in a Community-Based Sample

  [ 18 votes ]   [ Discuss This Article ]
www.ProHealth.com • March 28, 2002



Journal: Psychosom Med 2002 Mar-Apr;64(2):319-27

Authors: Taylor RR, Jason LA, Curie CJ.

Affiliation: Department of Psychology, DePaul University, Chicago, IL.

NLM Citation: PMID: 11914449

OBJECTIVE: This study examined predictors of fatigue severity and predictors of continued chronic fatigue status at wave 2 follow-up within a random, community-based sample of individuals previously evaluated in a wave 1 prevalence study of chronic fatigue and chronic fatigue syndrome that originally took place between 1995 and 1997.

METHODS: Wave 1 data were from a larger community-based prevalence study of chronic fatigue syndrome. In the present study, a second wave of data were collected by randomly selecting a sample of participants from the wave 1 sample of 18,675 adults and readministering a telephone screening questionnaire designed to assess symptoms of chronic fatigue syndrome.

RESULTS: Findings revealed that wave 1 fatigue severity was a predictor of fatigue severity at wave 2 in the overall sample of individuals with and without chronic fatigue. In the smaller sample of individuals with chronic fatigue, wave 1 fatigue severity, worsening of fatigue with physical exertion, and feeling worse for 24 hours or more after exercise significantly predicted continued chronic fatigue status (vs.
improvement) at wave 2 follow-up.

CONCLUSIONS: These findings underscore the prognostic validity of postexertional malaise in predicting long-term chronic fatigue and also highlight the importance of using population-based, representative random samples when attempting to identify long-term predictors of chronic fatigue at follow-up.

Discussion:

The present study examined predictors of fatigue severity and continued CF status at wave 2 follow-up within a random community-based sample of individuals previously evaluated in a prevalence study (6, 11). Fifty percent of control subjects and 76.1% of individuals with CF randomly selected for interview from the wave 1 study (6, 11) were contacted and administered a follow-up telephone screening questionnaire assessing changes in fatigue severity, fatigue-related symptomatology, and emotional distress. These completion rates are comparable with rates from other community-based follow-up studies (36–38). To identify wave 1 predictors of fatigue severity at wave 2 follow-up, a series of simple univariate analyses were conducted comparing individuals with and without CF. Findings revealed significant differences between the CF and control groups with respect to wave 1 fatigue severity (23), age, marital status, disability status, and effects of fatigue on usual daily activities.

However, when the effects of each of these variables over and above wave 1 fatigue severity were tested separately as potential predictors of fatigue severity at wave 2 follow-up, none emerged as significant predictors. Thus, wave 1 fatigue severity was the only variable that significantly predicted fatigue severity at wave 2 in the overall sample of individuals with CF and control subjects. In part, these results are consistent with findings from prior studies of mixed populations, which have identified markers of more severe illness, such as fatigue severity and more severe disability, as predictors of poorer outcome (8). However, in contrast to prior reports of increasing age and changing or leaving employment as predictors of poorer outcome (8, 19), age and unemployment status did not emerge as significant predictors of fatigue severity at the wave 2 follow-up in the present study.

A number of variables were associated with significant differences between individuals with CF who remained fatigued at follow-up (fatigued group) and those who no longer had CF (improved group). These included work status, wave 1 fatigue severity (23), worsening of fatigue with physical exertion, past-month functional capacity, GHQ index of emotional distress, the frequency of eight minor CFS symptoms (7), postexertional malaise for 24 hours or more, joint pain, and unrefreshing sleep. However, when the effects of these variables on wave 2 fatigue status were examined, not in isolation but in relation to wave 1 fatigue severity, results of logistic regression analysis indicated that wave 1 fatigue severity, worsening of fatigue with physical exertion, and postexertional malaise for 24 hours or more were the only significant predictors of continued CF status at the wave 2 follow-up. With the exception of wave 1 fatigue severity predicting continued CF status, these results contrast with those of prior studies, which highlight the relationships between age, illness duration, illness attributions, and fatigue at follow-up (8).

In part, differences observed between this study and prior studies may stem from the use of different sampling methodologies between studies. Our sample was generated using randomized, population-based sampling methods, whereas a number of previous studies have used samples of convenience, such as tertiary care clinic samples. In contrast to clinic samples, which tend to be biased in terms of ethnic homogeneity, use of medical care, age (typically over 30), and tendency toward medical attribution for symptoms (18, 22), the random community population we sampled was more heterogeneous in terms of these variables and likely more representative of the general population. In addition, although most other studies have used correlational analyses (19) or have reported basic proportional and percentage data (39), the present study analyzed multivariate relationships in predicting outcome at follow-up.

In the present study, the contrast in the abundance of significant findings when simple univariate analyses were used as compared with fewer findings when regression analyses were used underscores the importance of considering multivariate relationships when generating conclusions about predictors of outcome in investigations of CF.

Findings for the significance of postexertional malaise in predicting continued fatigue status at follow-up can be interpreted as supporting the prognostic validity of this symptom in predicting long-term CF and should be considered in the future design of measures to assess characteristics of illnesses involving CF. In a recent study, Jason and Taylor (40) found that postexertional malaise was the single most important factor in discriminating CFS and medically explained CF from idiopathic CF.

In addition, postexertional malaise also discriminated medically explained CF from psychiatrically explained CF (40). Thus, fatigue resulting from physical exertion not only seems to be a key discriminator of more severe fatigue-related illnesses, but also seems to be an important prognostic indicator of poorer outcome at follow-up.

This study had a number of limitations. One central limitation was the small sample size, which required us to conduct a series of univariate analyses to select variables for the predictive model, thereby limiting the extent to which multivariate statistics could be used. Another limitation that, in part, contributed to the small sample size was the lower follow-up completion rate of 50% for the control sample. Although most follow-up studies of CF are characterized by small sample size (8) and our 50% rate was higher than that found in other large-scale, community-based follow-up studies (36–38), it was lower than the rate we achieved for the CF sample (76.1%).

It is possible that individuals in the control sample were less likely to participate because of lack of interest in the subject matter of fatigue. This was a preliminary investigation, and as such the initial follow-up sample targeted for interview was small. It would be important to test and replicate these findings in a future study involving the entire wave 1 sample of 18,675 individuals. In addition, measures of some variables found to be predictors of fatigue in previous studies, such as categorical measures of the presence vs. absence of psychiatric disorder, were not included in the present study.

In summary, results from the present investigation support the validity of fatigue severity (23) both as a predictor of fatigue severity at follow-up within a mixed sample of individuals with and without CF and also as a predictor of continued CF status among a sample of individuals with CF only. In contrast to findings from prior studies (8), findings from the present investigation also underscore the validity of postexertional malaise as a predictor of continued CF within a sample of individuals with CF. Differences in findings between the present study and previous studies not only highlight the importance of using random community-based sampling procedures when conducting epidemiological research, but also emphasize the utility of multivariate and logistic regression procedures in accurately testing and identifying predictors of outcome. (41–44)




Post a Comment

Featured Products From the ProHealth Store
Energy NADH™ 12.5mg Ultra EPA  - Fish Oil Optimized Curcumin Longvida®

Looking for Vitamins, Herbs and Supplements?
Search the ProHealth Store for Hundreds of Natural Health Products


Article Comments



Be the first to comment on this article!

Post a Comment


 
Natural Pain Relief Supplements

Featured Products

Mitochondria Ignite™ with NT Factor® Mitochondria Ignite™ with NT Factor®
Reduce Fatigue up to 45%
Energy NADH™ 12.5mg Energy NADH™ 12.5mg
Improve Energy & Cognitive Function
Ultra ATP+, Double Strength Ultra ATP+, Double Strength
Get energized with malic acid & magnesium
Optimized Curcumin Longvida® Optimized Curcumin Longvida®
Supports Cognition, Memory & Overall Health
Ultra EPA  - Fish Oil Ultra EPA - Fish Oil
Ultra concentrated source of essential fish oils

Natural Remedies

Fighting Fatigue with Ground-breaking French Oak Wood Extract Fighting Fatigue with Ground-breaking French Oak Wood Extract
Looking for Energy? Turn to Plants. Looking for Energy? Turn to Plants.
Itching to Find Dry Skin Relief? Itching to Find Dry Skin Relief?
Improve Cardiovascular and Metabolic Health with Omega-7 Improve Cardiovascular and Metabolic Health with Omega-7
Soothe, Heal and Regulate Your Digestive System with Nutrient-Rich Aloe Vera Soothe, Heal and Regulate Your Digestive System with Nutrient-Rich Aloe Vera

CONTACT US
ProHealth, Inc.
555 Maple Ave
Carpinteria, CA 93013
(800) 366-6056  |  Email

· Become a Wholesaler
· Vendor Inquiries
· Affiliate Program
SHOP WITH CONFIDENCE
Credit Card Processing
SUBSCRIBE AND SAVE 15% NOW*
Be the first to know about new products, special discounts and the latest health news. *New subscribers only

CONNECT WITH US ProHealth on Facebook  ProHealth on Twitter  ProHealth on Pinterest  ProHealth on Google Plus

© 2016 ProHealth, Inc. All rights reserved. Pain Tracker App  |  Store  |  Customer Service  |  Guarantee  |  Privacy  |  Contact Us  |  Library  |  RSS  |  Site Map