fatigue is the most frequent symptom in multiple sclerosis ( ms).1 the multiple sclerosis council defines ms related fatigue as a  subjective lack of physical and/or mental energy that is perceived by the individual or caregiver to interfere with usual and desired activities,2 and has recommended evaluation with the 21-item modified fatigue impact scale ( mfis ) to measure fatigue,2 which is a smaller version of the 40-item fatigue impact scale ( fis).3 krupp et al,4 in 1989 , published the unidimensional fatigue severity scale ( fss ) , which is widely used not only in ms , but also in stroke,5 parkinson s disease,5 lung diseases,6 myotonic dystrophy,7 neuroborreliosis,8 hiv infection,9 and cancer,10 and in sleep disorders such as insomnia,11 shift working,12 restless legs syndrome ( rls),13,14 and obstructive sleep apnea.15 the international classification of sleep disorders second edition ( icsd-2 ) mentions  exhaustion  as a minimal criterion of sleep apnea,16 but in general there are no objective measurements to differentiate fatigue from exhaustion . complaints of fatigue may be as important as those of sleepiness are to obstructive sleep apnea syndrome ( osas ) patients,17 and consequently , continuous positive airway pressure ( cpap ) improves fatigue substantially in osas patients.18 ( interestingly , women suffering from osas appear to have complaints of fatigue more frequently than men with osas).17 hossain et al19 investigated 283 patients by polysomnography in a sleep laboratory . sixty - four percent of referred patients reported pathological fatigue without overlap of sleepiness ( mean fss 5.1 ; mean epworth sleepiness scale [ ess]20 6.9 ) , 19% were highly fatigued and sleepy ( mean fss 5.4 ; mean ess 17.2 ) , 13% did not feel either fatigue or sleepiness ( mean fss 2.1 ; mean ess 5.4 ) , and only 4% reported pathological sleepiness without overlap of fatigue ( mean fss 1.9 ; mean ess 14.5 ) . in summary , fatigue and sleepiness can occur together , but fatigue and sleepiness can be independent manifestations of a sleep disorder;21 there are many patients suffering from sleep disorders with fatigue but without sleepiness . in all likelihood this underemphasis may contribute to the overlooking of treatable sleep disorders , with enormous consequences for misdiagnosed patients . veauthier et al22 performed the first cross - sectional polysomnographic trial in the context of ms - related fatigue . they found a significant relationship between relevant sleep disorders ( including sleep - related breathing disorders [ srbds ] , rls , periodic limb movement disorder [ plmd ] , insomnia , and others ) on the one hand and fatigue measured with the mfis and the fss on the other hand ( mean mfis in fatigued ms patients 42.8 [ standard deviation { sd } 18.3 ] and 20.5 [ sd 17 ] in non - fatigued patients [ p < 0.001 ] ) . after performing a receiver operating characteristic analysis,23 they recommended polysomnographic investigations in all fatigued ms patients , when mfis > 34 or pittsburgh sleep quality index ( psqi)24 > 5 , so as not to overlook treatable sleep disorders.23 two different sleep studies have shown a significant relationship between ms - related fatigue and sleep disorders,22,25 and , in the corresponding follow - up trials , medical sleep treatment led to an improvement of ms - related fatigue.26,27 aside from the abovementioned studies,10,11,19,21 there are only a few polysomnographic studies investigating fatigue measured with the fss,28,29 and there are no other polysomnographic studies investigating fatigue measured with the mfis in the general population . hossain et al described fss mean values from 4.4 ( sd 1.6 ) to 5.1 ( sd 1.0 ) in osas , rls , plmd , depression , narcolepsy , parasomnia , delayed sleep phase syndrome , and insomnia,19 but as there have been , to date , no pathophysiological explanations for fatigue caused by sleep disorders , the question remains : is it caused by reduced sleep efficacy , disrupted sleep , suppressed deep sleep , intensified desaturation , or an increased number of hypopnea / apnea episodes ? the aims of this study were : to describe the mfis values in patients admitted to a sleep laboratory and to investigate which polysomnographic parameters can predict fatigue measured with the mfis and the fss . in this retrospective observational study , all consecutive patients who were admitted to the sleep laboratory of the hanse - klinikum , stralsund , germany between february 1 , 2011 and january 31 , 2012 were included . all patients admitted to the sleep laboratory completed the mfis , fss , ess , and psqi . these whole - night measurements were made after an impedance test and a biosignal test over a period of 8 hours , comprising : c3/c4 electroencephalograph ( eeg ) electrodes to the contralateral mastoid electrode ; ground electrode ; electrooculogram on the ipsilateral mastoid electrode ; bipolar chin electromyogram ( emg ) of the muscle mentalis or muscle submentalis ( according to biosignal testing and anatomical conditions ) ; nasal airflow ( pressure sensor ) ; thoracic and abdominal breathing ( piezoelectric measurement ) ; position sensor ; snoring signal ; pulse oximetry ; pulse , electrocardiogram ; and bipolar two - point emg electrodes on both anterior tibial muscles . the polysomnograph readings were scored according to the criteria of rechtschaffen and kales,30 using 30-sec - ond epochs . the following polysomnographic parameters were included in the analysis : apnea / hypopnea index ( ahi ) per hour of sleep , according to the american academy of sleep medicine;31 minimal desaturation ( spo2% min ) ; sleep efficacy ( ratio total sleep time / time spent in bed ) ; deep sleep ( ratio non - rapid eye movement sleep stage 3 and 4 [ nrem-3/4]/time spent in bed ) ; number of awakenings ; arousal index per hour of sleep ; and sleep latency ( time to first sleep epoch in minutes ) . sleep diagnoses were made according to the icsd-2.16 patients were divided for statistical reasons into different subgroups : ( 1 ) untreated srbds ; ( 2 ) treated srbds ( patients were treated with cpap or bilevel therapy ) ; ( 3 ) plmd and rls ; ( 4 ) insomnia ; ( 5 ) parasomnia ; ( 6 ) other neurological diseases such as narcolepsy , hypersomnia , and ms ( due to a low number of patients and small sample sizes , these patients were classified into one subgroup even though these diseases are quite different ) ; and ( 7 ) patients admitted to the sleep laboratory for tiredness or sleepiness but without pathological findings in the medical sleep investigations ( exclusion of a sleep disorder ) . the results were expressed as mean , sd , and range . following an exploratory analysis of the data and after a ( negative ) check for normality of the underlying distributions , differences between subgroups with respect to mfis , ess , psqi , and fss were ( univariately ) analyzed using the mann spearman correlation coefficients were calculated to assess the relationship between questionnaire results , and polysomnographic parameters and age , and scatter plots were performed to demonstrate these relationships . we defined spearman s rank correlations ( spearman s rho ) as follows : for values between 0.9 and 1 , the correlation is very strong ; for values between 0.7 and 0.89 , the correlation is strong ; values between 0.5 and 0.69 show a moderate correlation ; for values between 0.3 and 0.49 , the correlation is moderate to low ; and for values < 0.3 , the correlation is weak and not important . patients were classified into subgroups by mfis ( mfis greater versus less than or equal to mfis mean ) and fss ( fss greater versus less than or equal to fss mean ) values . afterwards , a logistic regression was performed in order to predict the outcome of these two dependent variables ( greater versus less than or equal to fss mean / mfis mean ) with age , sex , and polysomnographic parameters , including ahi , spo2% min , sleep efficacy , nrem-3/4 , number of awakenings , arousal index , and sleep latency , as predictor variables . this analysis was performed in two steps : first , a univariate analysis with all predictor variables ; subsequently , all significant variables remained in the final model of multiple logistic . all tests should be understood as constituting exploratory data analysis , such that neither previous power calculations nor subsequent adjustments for multiple testing have been made . analysis was performed with spss software ( v19 ; ibm corporation , armonk , ny , usa ) . in this retrospective observational study , all consecutive patients who were admitted to the sleep laboratory of the hanse - klinikum , stralsund , germany between february 1 , 2011 and january 31 , 2012 were included . all patients admitted to the sleep laboratory completed the mfis , fss , ess , and psqi . these whole - night measurements were made after an impedance test and a biosignal test over a period of 8 hours , comprising : c3/c4 electroencephalograph ( eeg ) electrodes to the contralateral mastoid electrode ; ground electrode ; electrooculogram on the ipsilateral mastoid electrode ; bipolar chin electromyogram ( emg ) of the muscle mentalis or muscle submentalis ( according to biosignal testing and anatomical conditions ) ; nasal airflow ( pressure sensor ) ; thoracic and abdominal breathing ( piezoelectric measurement ) ; position sensor ; snoring signal ; pulse oximetry ; pulse , electrocardiogram ; and bipolar two - point emg electrodes on both anterior tibial muscles . the polysomnograph readings were scored according to the criteria of rechtschaffen and kales,30 using 30-sec - ond epochs . the following polysomnographic parameters were included in the analysis : apnea / hypopnea index ( ahi ) per hour of sleep , according to the american academy of sleep medicine;31 minimal desaturation ( spo2% min ) ; sleep efficacy ( ratio total sleep time / time spent in bed ) ; deep sleep ( ratio non - rapid eye movement sleep stage 3 and 4 [ nrem-3/4]/time spent in bed ) ; number of awakenings ; arousal index per hour of sleep ; and sleep latency ( time to first sleep epoch in minutes ) . sleep diagnoses were made according to the icsd-2.16 patients were divided for statistical reasons into different subgroups : ( 1 ) untreated srbds ; ( 2 ) treated srbds ( patients were treated with cpap or bilevel therapy ) ; ( 3 ) plmd and rls ; ( 4 ) insomnia ; ( 5 ) parasomnia ; ( 6 ) other neurological diseases such as narcolepsy , hypersomnia , and ms ( due to a low number of patients and small sample sizes , these patients were classified into one subgroup even though these diseases are quite different ) ; and ( 7 ) patients admitted to the sleep laboratory for tiredness or sleepiness but without pathological findings in the medical sleep investigations ( exclusion of a sleep disorder ) . the results were expressed as mean , sd , and range . following an exploratory analysis of the data and after a ( negative ) check for normality of the underlying distributions , differences between subgroups with respect to mfis , ess , psqi , and fss were ( univariately ) analyzed using the mann spearman correlation coefficients were calculated to assess the relationship between questionnaire results , and polysomnographic parameters and age , and scatter plots were performed to demonstrate these relationships . we defined spearman s rank correlations ( spearman s rho ) as follows : for values between 0.9 and 1 , the correlation is very strong ; for values between 0.7 and 0.89 , the correlation is strong ; values between 0.5 and 0.69 show a moderate correlation ; for values between 0.3 and 0.49 , the correlation is moderate to low ; and for values < 0.3 , the correlation is weak and not important . patients were classified into subgroups by mfis ( mfis greater versus less than or equal to mfis mean ) and fss ( fss greater versus less than or equal to fss mean ) values . afterwards , a logistic regression was performed in order to predict the outcome of these two dependent variables ( greater versus less than or equal to fss mean / mfis mean ) with age , sex , and polysomnographic parameters , including ahi , spo2% min , sleep efficacy , nrem-3/4 , number of awakenings , arousal index , and sleep latency , as predictor variables . this analysis was performed in two steps : first , a univariate analysis with all predictor variables ; subsequently , all significant variables remained in the final model of multiple logistic . all tests should be understood as constituting exploratory data analysis , such that neither previous power calculations nor subsequent adjustments for multiple testing have been made . analysis was performed with spss software ( v19 ; ibm corporation , armonk , ny , usa ) . in sum , 410 patients 20 to 86 years ( 290 men and 120 women ) were investigated . table 1 displays the demographic and polysomnographic data and table 2 shows the questionnaire results . there was a moderate to strong correlation between mfis and fss values in the whole cohort and in all subgroups except for the plmd / rls subgroup . there was a moderate correlation between mfis and psqi in the whole cohort and in srbd patients ( treated and untreated ) and in patients without sleep disorders or with narcolepsy / hypersomnia / ms ( but not in plmd / rls patients nor insomnia patients ) . there was a moderate correlation between fss and psqi in srbd patients ( treated and untreated ) . there was a low correlation between ess values and mfis in untreated srbd patients and a moderate correlation between ess values and fss in patients without sleep disorders . higher age was related to increased psqi values in plmd / rls patients and with increased ess values in patients with narcolepsy / hypersomnia / ms ( table 3 ) . with respect to polysomnographic data , a low correlation could be found between increased ahi and lower minimal desaturation on the one hand and increased psqi values on the other in treated srbd patients . furthermore , there was a low correlation between the number of awakenings and psqi in all patients and untreated srbd patients . similarly , there was a low correlation between an increased arousal index and higher psqi values in treated srbd and insomnia patients . figures 1 and 2 show the relationships between mfis / fss values and polysomnographic parameters by scatter plots . whereas the graphical visualization did not show any correlation between mfis / fss values and ahi , spo2% min , sleep efficacy , sleep latency , or deep sleep , the scatter plots did visualize a correlation between mfis values and the number of awakenings and between fss values and a high arousal index . in the subgroup without sleep disorders , we did find a moderate nonparametric correlation between increased deep sleep ( nrem-3/4 ) and higher mfis and fss values and , similarly , between deep sleep and mfis in plmd / rls patients . in plmd / rls patients , lower sleep latency was correlated with increased ess values . in insomnia patients , higher fss values we introduced the dichotomous variable  mfis higher than mfis mean ( 34.2 )  ( yes or no ) and the dichotomous variable  fss higher than fss mean ( 4.0 ) the univariate logistic regression showed no significant relationship with spo2% min , sleep efficacy , sleep latency , or deep sleep . a significant relationship could be found with age , sex , and number of awakenings for mfis as well as fss ( table 4 ) . furthermore , there was a significant relationship with arousal index and ahi only for fss . the multiple logistic regression ( table 4 ) investigating the fss , including age , sex , arousal index , number of awakenings , and ahi , demonstrated a significant relationship with age , sex , and arousal index , whereas ahi and number of awakenings were not significant . younger age , female sex , and high arousal index were highly predictive of increased fss scores . in regards to mfis , the multiple logistic regression ( table 4 ) investigating the mfis , including age , sex , and number of awakenings , confirmed the significant relationship with age , sex , and number of awakenings . younger age , female sex , and high numbers of awakenings predicted high mfis values . in sum , 410 patients 20 to 86 years ( 290 men and 120 women ) were investigated . table 1 displays the demographic and polysomnographic data and table 2 shows the questionnaire results . there was a moderate to strong correlation between mfis and fss values in the whole cohort and in all subgroups except for the plmd / rls subgroup . there was a moderate correlation between mfis and psqi in the whole cohort and in srbd patients ( treated and untreated ) and in patients without sleep disorders or with narcolepsy / hypersomnia / ms ( but not in plmd / rls patients nor insomnia patients ) . there was a moderate correlation between fss and psqi in srbd patients ( treated and untreated ) . there was a low correlation between ess values and mfis in untreated srbd patients and a moderate correlation between ess values and fss in patients without sleep disorders . higher age was related to increased psqi values in plmd / rls patients and with increased ess values in patients with narcolepsy / hypersomnia / ms ( table 3 ) . with respect to polysomnographic data , a low correlation could be found between increased ahi and lower minimal desaturation on the one hand and increased psqi values on the other in treated srbd patients . furthermore , there was a low correlation between the number of awakenings and psqi in all patients and untreated srbd patients . similarly , there was a low correlation between an increased arousal index and higher psqi values in treated srbd and insomnia patients . figures 1 and 2 show the relationships between mfis / fss values and polysomnographic parameters by scatter plots . whereas the graphical visualization did not show any correlation between mfis / fss values and ahi , spo2% min , sleep efficacy , sleep latency , or deep sleep , the scatter plots did visualize a correlation between mfis values and the number of awakenings and between fss values and a high arousal index . in the subgroup without sleep disorders , we did find a moderate nonparametric correlation between increased deep sleep ( nrem-3/4 ) and higher mfis and fss values and , similarly , between deep sleep and mfis in plmd / rls patients . in plmd / rls patients , lower sleep latency was correlated with increased ess values . in insomnia patients , higher fss values we introduced the dichotomous variable  mfis higher than mfis mean ( 34.2 )  ( yes or no ) and the dichotomous variable  fss higher than fss mean ( 4.0 ) the univariate logistic regression showed no significant relationship with spo2% min , sleep efficacy , sleep latency , or deep sleep . a significant relationship could be found with age , sex , and number of awakenings for mfis as well as fss ( table 4 ) . furthermore , there was a significant relationship with arousal index and ahi only for fss . the multiple logistic regression ( table 4 ) investigating the fss , including age , sex , arousal index , number of awakenings , and ahi , demonstrated a significant relationship with age , sex , and arousal index , whereas ahi and number of awakenings were not significant . younger age , female sex , and high arousal index were highly predictive of increased fss scores . in regards to mfis , the multiple logistic regression ( table 4 ) investigating the mfis , including age , sex , and number of awakenings , confirmed the significant relationship with age , sex , and number of awakenings . younger age , female sex , and high numbers of awakenings predicted high mfis values . this is the first study , to our knowledge , to investigate the relationship between sleep disorders and mfis values , and we observed a number of important findings . firstly , this study clearly shows that sleep disorders can lead to high mfis values in sleep - disordered patients who are not suffering from ms . secondly , our results show both high mfis and fss values in sleep - disordered patients without ms and , above all , a strong correlation between mfis and fss for all patients and all subgroups except for plmd / rls patients . ( the fact that this correlation could not be demonstrated in plmd / rls patients could be due to small sample size ) . thirdly , the correlation between psqi and mfis values in all patients ( except for plmd / rls and insomnia patients ) and between psqi and fss in srbd patients , suggests causation of fatigue by sleep disorders ( the lack of correlation in some subgroups may be caused by small sample size ) . this could be confirmed by multiple logistic regressions , showing that a high number of awakenings were in line with a higher risk of developing fatigue measured with the mfis and that a high arousal index increased the risk of developing fatigue measured with the fss . this possible causative role of awakenings and arousals in the pathophysiology of fatigue is of particular interest , and further studies should be performed to elucidate its etiology . guilleminault et al used cyclic alternating pattern ( cap ) to investigate the relationship between sleep instability and fatigue in patients with upper airway resistance syndrome ( uars ) compared with healthy controls.32 they found higher arousal indexes and sleep disturbances measured with cap analysis in patients with uars that correlated with sleepiness and fatigue . feige et al summarized the hyperarousal concept in their review about sleep microstructural differences in chronic insomnia.33 they recommended spectral analysis of the sleep eeg , microarousal , and cap analysis as a research instrument of sleep in insomnia . it would be promising to investigate fatigue as well with these methods in patients without known sleep disorders , in order to find out what drives fatigue . on the other hand , little is known about the impact of arousal on fatigue in sleep disorders . yue et al34 found a significant relationship between emotional fatigue measured with the multidimensional fatigue symptom inventory short form35 and spontaneous movement arousals in untreated osas patients . it is worth noting that previous studies have not shown a significant association between the severity of osas ( ahi , spo2% min ) and fatigue scores.36,37 in our study , there was also no significant relationship between fatigue and ahi or spo2% min after multiple logistic regressions . previous polysomnographic investigations of consecutive ms patients showed a significant association of sleep disorders with fatigue ( fss / mfis),22 but not with sleepiness ( ess ) . supporting this finding , the works by hossain et al19 and aguillard et al21 showed that , in sleep - disordered patients without ms , fatigue and sleepiness can occur independently as well as together . in the present study , there was not a significant relationship between mfis values and ess values ( only untreated srbd patients showed a low correlation between ess values and mfis ) . the fact that we found a strong correlation between ess values and fss values in patients without sleep disorders remains without explanation . we have to note that these patients were not healthy controls , but rather fatigued and sleepy patients without pathological findings either in the primary health care setting or in the sleep laboratory . overall , as we performed multiple logistic regressions only for mfis and fss values , we can not interpret the correlations between ess and psqi values and age or sex or polysomnographic parameters . our study was focused on the relationship between sleep disorders and fss and mfis . in our study , women had a higher risk of suffering from fatigue than men . our results are in line with a previous study,17 suggesting that , in srbd patients , fatigue may be particularly correlated with female sex : in our study , female sex was predictive of high fss and mfis values . currently , we have no explanation for this relationship and further studies are needed to investigate the pathophysiological mechanisms . similarly , we have no explanation for the fact that younger age is predictive of fatigue and further studies are needed to understand this relationship as well . the present study has some methodological limitations : due to the retrospective design and owing to the lack of exclusion criteria , we can not exclude that some patients have other reasons for fatigue as a possible confounder . on the other hand , this study reflects the daily clinical praxis with consecutive patients over 1 year . younger age , female sex , and high number of awakenings and arousals are predictive of fatigue in sleep - disordered patients . this study proves that high mfis and fss values can occur in sleep - disordered patients .