Prevalence of Fatigue in Indian Urban Population
and its relation to nutritional & lifestyle factors

by: Adrian Kennedy PhD, Savita Menon PhD, E. Suneetha PhD.


Background: Fatigue is a common occurrence and has been associated with many factors. Scant research on fatigue in India, in relation to assessment, symptoms and severity has typically been restricted to either anemic women or chronic fatigue.

Objectives: A multi-centric health survey was planned to: 1) Estimate the prevalence of self-reported tiredness/ fatigue. 2) Identify and examine the symptoms and risk factors associated with the etiology of fatigue.

Methods: Fatigue was assessed using the Chalder’s Fatigue Scale (CFQ) with supporting data from a self administered Health and Lifestyle Questionnaire (HALS) in a purposive sample of healthy adults coming for Master Health Check in seven cities of India. Both 4-point Likert scale and bimodal scores were used to evaluate responses on the CFQ to assess the presence of fatigue. Appropriate statistical analysis was done to study and establish associations of various factors with fatigue.

Results: The survey was completed with data from 1309 subjects, with 63% of men and 37 % of females. The mean age was 41.49±10.48 years across 25-65 yrs olds. The percent prevalence of self-reported fatigue in the study population was 27.7 %.  Fatigue was found to be strongly associated with gender, BMI, hemoglobin and HDL. In addition low protein intake; poor intakes of raw fruit, vegetables and water; and high junk food intake were identified as nutritional factors associated with fatigue.

Conclusions: Fatigue is continuously distributed in normal healthy Indian Urban adults and is closely associated with nutrition factors.


Fatigue is a ubiquitous symptom and difficult to define. It is defined in literature as extreme and persistent tiredness, weakness or exhaustion either mental, physical or both [Dittner et al 2004]. In most cases, fatigue lacks a clear somatic cause and appears to be both, a hallmark of illness and a normal physiological consequence of exertion, inadequate rest or inadequate diet [Sharpe 2002]. Fatigue can therefore, best be understood as a continuum; ranging from mild complaints frequently seen in the community to severe disabling fatigue that can have high social and economic costs [Lewis and Wessely 1992].

1.1. Occurrence & Epidemiology of fatigue
The prevalence rates for fatigue range from 7% to 42% and is probably because there is no agreed definition upon what comprises a fatigue case [Lewis and Wessely 1992].The high prevalence rates clearly highlight that fatigue is a significant problem. In literature, there are paradoxical results on the associations of various factors like gender, age, social position with fatigue from different studies indicating that it is far from understood [Chen 1986, Loge et al 1998]. Data on the relationship between lifestyle and fatigue is also sparse. In India, to our knowledge, there is no published literature on the epidemiology of fatigue in normal, health-seeking urban adults. While there is a growing awareness of lifestyle diseases, symptoms/signs of fatigue/ tiredness are often unnoticed/ unreported. Given the changing lifestyle scenario and changing dietary habits in India, it is likely that the incidence of fatigue may also be high. Comprehensive evaluation of lifestyle behaviours along with fatigue may elicit interesting information.

1.2. Evaluation of fatigue
There is a growing consensus that fatigue is multi-dimensional in nature and should be measured in a multi-dimensional manner [Piper et al 1998]. The Chalder’s Fatigue Questionnaire (CFQ), Checklist Individual Strength (CIS) and, the Energy and Fatigue subscale of the World Health Organization Quality of Life assessment instrument (WHOQoL-EF) are some of the commonly used multi-dimensional questionnaires for evaluating fatigue [Michielsen et al 2003].

Keeping the above literature in mind, an exploratory study was planned with the aim of evaluating fatigue/ tiredness in normal healthy Indian adults from seven cities using the CFQ with supporting data from the Health & Lifestyle questionnaire. To achieve this objective, an exploratory survey was carried out in two phases:

Phase 1: Standardization and validation of the Chalder Fatigue Scale (CFQ) in an Indian population
Phase 2: Evaluate the prevalence and factors associated with fatigue in a healthy Indian urban Population.


2.1 Phase 1:
Standardization and validation of CFQ in Indian population

An initial pilot survey was carried out in a random sample of 40 normal healthy adult subjects coming for the Master Health Check-Up to identify suitable fatigue questionnaire for the Indian population. From literature, the CFQ and CIS were short listed as appropriate questionnaires for fatigue evaluation. The CFQ is an 11-item questionnaire scored on a 4-point scale, while the CIS is a 20-item questionnaire scored on a 7-point scale. Observations from the pilot study revealed that from the 20 subjects administered CIS, most of them had difficulty comprehending the options and found it confusing and time-consuming. In contrast, almost all the subjects showed a higher comprehension for the CFQ and found it convenient to answer. Based on these observations, it was decided to proceed with the CFQ for the exploratory study.

Reliability and validity of the CFQ in Indian population

A total of 149 normal healthy adult subjects, with approximately 54% males and 46% females, completed the validation study. The age range of the subjects was 30-60 years. The homogeneity and/ or internal consistency of the CFQ in the Indian context was checked by two methods. First, Cronbach’s alpha was calculated for all items. Second, variation in proportions was calculated at different time points. Data from the pilot study showed a high degree of internal consistency with a Cronbach’s alpha of 0.82. Comparison of the internal consistency of the pilot study with the Chalder et al [1993] study and Jin Cho et al [2007] studies is given in Table 1.

Table 1: Comparison of internal consistency of the pilot study with literature
  Validation Study Chalder et al Cho et al
N 149 100 204
Cronbach's aplha 0.82 0.89 0.86
Items included 11 11 11

The proportion of fatigue cases at three different time points was also evaluated to assess the homogeneity in comprehending and administering the questionnaire. The proportion of fatigue cases, using a cut-off of > 4 obtained on the bimodal response system is given in Table 2.

Table 2: Proportion of Fatigue Cases
Time Point N Fatigued (%)
1 50 24.0
2 50 28.0
3 49 24.4

The overall proportion for fatigue cases was 25.5% in the 149 participants. The prevalence is in line with the published data [Chen, 1986, Loge et al, 1998] suggesting that the CFQ has good reliability and validity in an Indian population.

In conclusion, the CFQ had good reliability and validity for the Indian population and hence, was chosen as a fatigue evaluation tool for the Phase II.

2.2. Phase 2:
Evaluate the prevalence of fatigue and associated factors

A multi-centric health survey was planned to evaluate fatigue in a 25-65 y normal healthy adult population coming for Master Health Check from a total of seven major cities of India.

Study Design:
Subjects deemed normal by medical history and/ or self-reporting and clinical laboratory tests were considered eligible to participate in the survey (Fig.1). In the survey, two questionnaires - the CFQ for fatigue and the Health and Lifestyle questionnaire (HALS) were administered to all the consented subjects. The Central Institutional Ethics Committee approved this survey.

Sample Size Calculation:
Assuming a 95% confidence interval, 10% relative precision and 10% non-responders, (based on prevalence levels of fatigue from pilot study) it was calculated that approximately 1250 subjects were to be screened in order to get 1122 subjects to complete the survey. However the present study could actually include1309 subjects. Eligibility for participation in the survey was based on the inclusion and exclusion criteria.

Fatigue Evaluation:
The CFQ was used to measure fatigue as a high internal consistency (Cronbach’s alpha = 0.82) was seen in the pilot study. The CFQ covers the following two sub-scales: physical fatigue (seven items) and mental fatigue (four items). The responses were scored on both, Likert (0,1,2,3) and dichotomized (0,0,1,1) scales. A total dichotomized score of > 4 was used to identify fatigue cases  Loge et al, 1998; Jin Cho et al 2007]. Total fatigue, physical fatigue and mental fatigue were calculated as sums of the Likert scores for the whole scale or the physical and mental subscales, respectively. A composite CFQ total score, ranging from 0 to 33, was constructed by adding the individual’s scores on the two factors. Higher scores indicated a higher degree of fatigue.

Health & Lifestyle Questionnaire [HALS]:

This self-administered 70 item questionnaire requires Yes/No responses along with medical and biochemical details. The questionnaire and tests were designed to assess the subjects’ nutrition and lifestyle habits.

Statistical analysis:

The SPSS-PC package (15 version) was used for the statistical analysis of the data. Descriptive statistics such as means and standard deviations were calculated for the continuous variables. Chi-Square tests were done to understand bi-variate associations. Multivariate regression analysis was carried out to know the differential influences and association between dependent and independent variables. The criteria for statistical significance was p<0.05.


The multi dimensional health survey was conducted between October-November 2007 to evaluate fatigue in healthy 25-65y adults attending Master Health Check at seven major cities in India. A total of 7144 subjects were screened for eligibility and finally, data from 1309 subjects was included in the final analysis.

Population Profile:
Of the 1309 subjects, 63% were males (822) and 37 % were females (488). The mean age of the subjects was 41.4910.48 years. The mean BMI of the total population was 25.114.17 kg/m2 suggesting that overweight/ obesity were common in this group.

Prevalence of Fatigue:
The percent prevalence of self-reported fatigue, calculated using the bi-modal score, in the study population (n=1309) was 27.7 %. Gender was significantly associated with fatigue (X2 = 4.745, P

Table 3: Prevalence of Self Reported Fatigue
Feature N Fatigued [N(%)] Non-Fatigued [N(%)] Total Fatigued [Mean±SD] Physical Fatigued [Mean±SD] Mental Fatigued [Mean±SD]
Total 1309 362 (27.7) 947 (72.3) 11.34 ± 5.26 7.57 ± 3.79 3.77 ± 1.97
Males 821 210 (25.6) 611 (74.4) 11.14 ± 5.25 7.40 ± 3.81 3.77 ± 1.94
Females 488 152 (31.1) 336 (68.9) 11.69 ± 5.26 7.86 ± 3.73 3.84 ± 2.02
F-Value       3.429 4.493*** 0.753
*** P<0.01

BMI and Fatigue Prevalence

The subjects were divided into three groups based on Asian cut-off levels [Steering Committee- Asia-Pacific Perspective 2000] for BMI, as underweight, (23 kg/m2). Both underweight and overweight groups reported higher prevalence of fatigue (39% and 27.7%, respectively) in comparison to normal group (25.6%). The mean fatigue scores were significantly different between different BMI groups [Table 4], with underweight and overweight/obese groups having significantly (P<0.05) higher total, physical and mental fatigue as compared to normal group [Table 5]. Similar trend was observed in males. However, in females, the underweight group had a higher proportion of fatigue cases. Underweight female subjects had higher mean scores (P<0.05) for total and mental fatigue while overweight subjects had significantly (P<0.05) higher mean scores for mental fatigue only. 

Table 4: Fatigue Prevalence in relation to BMI categories
Feature N Fatigued [N(%)] Non-Fatigued [N(%)] Total Fatigued [Mean±SD] Physical Fatigued [Mean±SD] Mental Fatigued [Mean±SD]
Total Population 1309     11.34 ± 5.26 7.57 ± 3.79 3.77 ± 1.97
Underweight 59 23 (39.0) 36 (61.0) 12.25 ± 4.59 8.46 ± 3.74 3.80 ± 1.68
Normal 344 88 (25.6) 256 (74.4) 10.67 ± 5.61 7.21 ± 4.06 3.46 ± 2.03
Overweight/ Obese 906 251 (27.7)  655 (72.3)  11.54 ± 5.14 7.65 ± 3.67 3.89 ± 1.96
F-Value       4.376* 3.416* 5.952*
Males 821     11.14 ± 5.25 7.40 ± 3.81 3.74 ± 1.94
Underweight 32 11 (34.4) 21 (65.6) 11.31 ± 4.74 7.94 ± 3.51 3.38 ± 1.64
Normal 205 45 (22.0) 160 (78.0) 10.35 ± 5.45 6.89 ± 3.90 3.46 ± 2.10
Overweight/ Obese 584 154 (26.4)  430 (73.6)  11.40 ± 5.19 7.55 ± 3.79 3.85 ± 1.93
F-Value       3.072* 2.620 3.675*
Females 488     11.69 ± 5.26 7.86 ± 3.73 3.84 ± 2.03
Underweight 27 12 (44.4) 15 (55.6) 13.37 ± 4.22 9.07 ± 3.97 4.30 ± 1.61
Normal 139 43 (30.9) 96 (69.1) 11.14 ± 5.59 7.68 ± 4.27 3.46 ± 2.07
Overweight/ Obese 322 97 (30.1)  225 (69.9)  11.79 ± 4.94 7.83 ± 3.45 3.96 ± 2.02
F-Value       2.220 1.609 3.730*
* P<0.05
Table 5: Mean Differences in Fatigue Scores in relation to BMI
  BMI Mean Difference in Fatigue
Total Physical Mental
Total Population Underweight v/s Normal 1.586(*) 1.251(*) 0.334
Overweight/ Obese v/s Normal 0.872(*) 0.443 0.430 (*)
Males Underweight v/s Normal 0.961 1.050 -0.088
Overweight/ Obese v/s Normal 1.051(*) 0.660(*) 0.391(*)
Females Underweight v/s Normal 2.234(*) 1.398 0.836
Overweight/ Obese v/s Normal - 0.655 - 0.156 0.499 (*)
* P<0.05

Haemoglobin and Fatigue Prevalence

The prevalence of fatigue was substantially higher in males with Hb < 13 mg/dL (31.9 %) as compared to those with Hb >13 mg/dl (24.8 %). Male subjects with Hb <13 mg/dL had significantly (P<0.01) higher mean total, physical and mental fatigue scores. Interestingly, in females the fatigue prevalence did not differ much between subjects with Hb < 12mg/dL (32.5%) and those with Hb > 12mg/dL (30.6%) [Table 6].

Table 6: Haemoglobin & Fatigue Prevalence
Feature N Fatigued [N(%)] Non-Fatigued [N(%)] Total Fatigued [Mean±SD] Physical Fatigued [Mean±SD] Mental Fatigued [Mean±SD]
Hb<13mg/dl 91 29 (31.9) 62 (68.1) 12.62±5.36 8.45±3.82 4.16±1.99
Hb>=13mg/dl 730 181 (24.8) 549 (75.2) 10.95±5.21 7.27±3.79 3.68±1.93
T-Test       2.860** 2.805** 2.226**
Hb<12mg/dl 151 49 (32.5) 102 (67.5) 11.77±5.20 7.95±3.69 3.82±2.17
Hb>=12mg/dl 337 103 (30.6) 234 (69.4) 11.67±5.08 7.81±3.74 3.86±1.94
T-Test       0.181 0.363 -0.200
** P <0.01

In women, further associations were explored between Hb<12mg/dL and individual questions of CFQ. Low hemoglobin levels were found to be significantly associated with the four signs of physical fatigue i.e. ‘problems with tiredness (P<0.01)’, ‘need for more rest’ (P<0.05), ‘lacking in energy’(P<0.05), ‘feeling weak’(P<0.001) and one sign of mental fatigue i.e. ‘difficulty in memory’(P<0.05).


The fatigue scores were related to the HDL-C values. Subjects with HDL-C <40 mg/dL had significantly (P<0.001) higher mean total, physical and mental fatigue scores as compared to subjects with Hb >40 mg/dL. Low HDL-C was significantly associated with higher fatigue prevalence (X2 =8.811, P<0.01).

Table 7: HDL & Mean Fatigue Scores
Feature N Fatigued [N(%)] Non-Fatigued [N(%)] Total Fatigued [Mean±SD] Physical Fatigued [Mean±SD] Mental Fatigued [Mean±SD]
HDL 1309          
<40 533 171 (32.1) 362 (67.9) 12.09±5.248 8.09±3.873 4.00±1.919
>40 776 191 (24.6) 549 (75.2) 10.83±5.211 7.21±3.688 3.62±1.998
F-Value       18.152** 16.970** 11.812**
** P <0.001

Other Nutritional Factors and Fatique Prevalence

Junk food intake like eating out frequently (twice a week or more) at restaurants, commercial food centers, or frequently consuming commercially-prepared foods etc was significantly associated (X2 =5.763, P<0.05) with fatigue. Approximately 34% of people who consumed junk food reported feeling fatigue as compared to 26% who consumed low junk food [Table 8]. A weak trend for higher fatigue prevalence was also seen in vegetarian subjects and with low consumption of raw fruits and vegetables. 

Table 8: Fatigue Prevalence in relation to Nutrition Profile [ N (%)]
  N Fatigued [N(%)] Non-Fatigued [N(%)]
Junk Food Intake      
High 272 91 (33.5) 181 (66.5)
Low 1033 270 (26.1) 763 (73.9)
Food Habits      
Vegetarians 379 115 (30.3) 264 (69.7)
Non - Vegetarians 925 246 (26.6) 679 (73.4)
Raw Fruits & Vegatables      
High 793 291 (26.8) 793 (73.2)
Low 148 68 (31.5) 216 (68.5)

When individual questions from the CFQ were evaluated for associations with nutrition profile, multiple significant associations were obtained; Table 9 presents these associations. Subjects with low protein intake were at 1.7 and 1.5 times higher risk for feeling lack of energy and less strength in muscles, respectively. Low raw fruits and vegetable intake was significantly associated with feeling less strength in muscles, weak and difficulty in concentration and memory. There was a 1.4 to 1.7 time’s higher risk of experiencing the above symptoms with low intake of raw fruits and vegetable intake. 

Table 9: Associations between Fatigue Symptoms & Nutrition Profile
  Odds Ratio 95% Confidence Interval Pearson's X2 Value
Lower Limit Upper Limit
Protein Intake vs Feeling Lack of Energy 1.685 1.132 2.508 6.735**
Protein Intake vs Feeling Low Strength in Muscles 1.531 1.011 2.320 4.088**
Raw Fruits & Veg vs Low Strength in Muscles 1.415 1.008 1.985 4.051**
Raw Fruits & Veg vs Feeling Weak 1.458 1.061 2.004 5.446**
Raw Fruits & Veg vs Difficulty in Concentration 1.758 1.246 2.479 10.531**
Raw Fruits & Veg vs Difficulty in Memory 1.586 1.112 1.263 6.550**
* P <0.05, ** P <0.01

Regression Analysis:

Multiple linear regression analysis was used for predicting biochemical and anthropometric risk factors for fatigue. Results indicated that HDL-C and hemoglobin accounted for 15% variance in the study population for fatigue [Table 10].

Table 10: Regression analsysis for Total Fatigue and BMI and Biochemical Variables
Model   UnStandardized
Coefficients Beta
R2 F - Value
(P < 0.01)
1 (Constant) 13.927 0.103 14.068**
HDL -0.057
2 (Constant) 19.068 0.152 15.388**
HDL -0.069
Hemoglobin -0.0334


This study supports that generalized fatigue is distributed continuously in a normal, healthy Indian urban population. The prevalence of fatigue in this study (27.7%) is in line with the published literature. Studies by Loge et al (1998) and Pawlikowska et al (1994) using the CFQ as the assessment tool have reported 22% prevalence in the general Norwegian population and 38% in a UK communitysurvey. Higher proportion of women were fatigued than men, and this finding is in agreement with other studies [Song et al 2002, Cho and Wessely, 2007]. 

Body mass index may affect the health status and may be regarded as possible risk factors for fatigue [Vgontzas et al 2006]. Strong associations between body mass index and fatigue were also seen in this study. This finding is of particular relevance since overweight/ obesity is emerging as a major concern in the urban Indian population [Ramachandran, 2004] and this trend was evident even in the adults surveyed in this study. Interestingly, associations were seen with high body mass index for men and low for women suggesting different effects of body mass index for men and women.

Nutritional factors are known to have a role in optimizing health but evidence of association with fatigue, while largely accepted, is under studied. The most commonly studied association is between hemoglobin and fatigue in women [Bhatia and Cleland, 1995, Patterson et al 2000, Verdon et al 2003]. In this study, low hemoglobin was found to be associated with fatigue only in males. It is however, noteworthy that the prevalence of fatigue in females was high irrespective of the hemoglobin levels suggesting that some other factors may be at play in causing fatigue. Patel et al [2005] have reported similar observations albeit in women belonging to lower socio-economic strata. This study also made an interesting discovery of the relation between HDL-C and Fatigue. Further studies are required to understand this relation.

With the rapid urbanization, hectic schedules and changing lifestyles, there is an increased consumption of processed and ready-to-eat foods with a potential adverse impact on health outcomes. This study finds a significant association between junk food consumption. Moreover, significant association of fatigue symptoms such as difficulty in memory and concentration was seen with low intakes of fruits and vegetables. These findings suggest a plausible cause-effect relationship.

Fatigue is increasingly identified as a complex, multi-factorial disorder with physical and psychological dimensions. However, the systematic study of prevalence and severity of fatigue is gaining importance only recently. The higher prevalence of fatigue even in a general population highlights the need for more such studies to understand the emotional, social, physical,and economic impact of fatigue. Subjects suffering from fatigue were found to have a higher risk for low productivity and efficiency as they were having problems starting things, problems with concentration, thinking and memory.  Low productivity and low efficiency in-turn cause stressful family and work atmosphere and impacts overall quality of life. 

In conclusion, self-perceived fatigue, extensively studied in the western world, is highly prevalent in the urbanIndiaand multiple factors are associated with this phenomenon. Findings from this study point towards the need for more detailed and quantitative research in the epidemiology of fatigue.


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