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Research Article| Volume 56, 103215, November 2021

Cognitive Fatigability, not Fatigue predicts employment status in patients with MS three months after rehabilitation

Published:August 19, 2021DOI:https://doi.org/10.1016/j.msard.2021.103215

      Highlights

      • Discrimination between fatigue as subjective sensation and fatigability as change of performance is crucial.
      • Fatigue is determined by questionnaires; while cognitive fatigability can be measured by tonic alertness.
      • 15 of 17 patients working fulltime had severe fatigue according to the Fatigue Scale for Motor and Cognition (FSMC).
      • Cognitive fatigability (tonic alertness) is a better predictor of employment status three months later.
      • Alertness1 in the morning was the strongest predictor.

      Abstract

      Background

      : Fatigue is potentially the most important factor causing unemployment in people with Multiple Sclerosis (PwMS). Widely accepted is a discrimination between fatigue as subjective sensation and fatigability as objective measure of change in performance. The aim of this study was to identify, whether cognitive fatigue or cognitive fatigability is a better predictor for employment status three months after discharge from a neurological rehabilitation center.

      Methods

      : 64 PwMS (mean age 48.9, 43 females, mean time since diagnosis 14.7 years, median Expanded Disability Status Scale (EDSS) 3.8), complaining of fatigue and reporting difficulties with their working capacity, participated in a cognitive loading task during inpatient rehabilitation. Reaction time performance was measured using a standardized alertness test (TAP-M). Tonic alertness was measured at 8 a.m., 11 a.m. and 2 p.m. Patients worked on a standardized test battery during the morning and after lunch to induce fatigability. All of them completed the Fatigue Scale for Motor and Cognition (FSMC), a standardized questionnaire to rate the trait component of cognitive and motor fatigue. Their employment status was rated within a standardized interview by phone three months after discharge from the clinic.

      Results

      : Mean cognitive fatigue according to the FSMC was 38.9 ± 7.4 and mean motor fatigue 41.0 ± 5.6, indicating severe cognitive and motor fatigue. 15 (88%) of 17 patients working fulltime had severe fatigue according to the FSMC. The cognitive subscale of the FSMC (“FSMC cognition”) did not correlate (rs = -.084, p = .512) and the motor subscale of the FSMC (“FSMC motor”) correlated rather weakly but not significantly (rs= -.220, p = .080) with the employment status. In contrast, there was a significant and medium correlation between alertness at 8 a.m. (alertness1) and employment status (rs = -.304, p = .014). Ordered logistic regression revealed that only alertness1 and the alertness difference between afternoon and noon (alertness difference32) predicted significantly the employment status. The FSMC motor and cognition subscales had no predictive value for employment.

      Conclusion

      : Cognitive fatigability (tonic alertness at 8 a.m. or increase of reaction time during the afternoon) is more adequate to predict employment status in PwMS three months after discharge from the clinic than the subjective sensation of fatigue as determined by the FSMC.

      Key words

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      References

        • Strober LB.
        Cognition and employment in multiple sclerosis.
        in: DeLuca J. Sandroff B.M. Cognition and Behavior in Multiple Sclerosis. American Psychological Association, Washington, DC2018: 191-206
        • Atlas of MS
        MS International Federation.
        2016 (Available at: https://www.msif. org/)
        • García-Domínguez JM
        • Maurino J
        • Martínez-Ginés ML
        • Carmona O
        • Caminero AB
        • Medrano N
        • Ruíz-Beato E
        W-IMPACT Clinical Investigators. Economic burden of multiple sclerosis in a population with low physical disability.
        BMC Public Health. 2019; 19 (May 20Erratum in: BMC Public Health. 2019 Jul 8;19(1):909): 609https://doi.org/10.1186/s12889-019-6907-x
        • Kurtzke JF
        Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS).
        Neurology. 1983; 33 (Nov): 1444-1452
        • DeLuca J
        • Chiaravalloti ND
        • Sandroff BM
        Treatment and management of cognitive dysfunction in patients with multiple sclerosis.
        Nat. Rev. Neurol. 2020;
        • Strober LB
        • Chiaravalloti N
        • DeLuca J.
        Should I stay or should I go? A prospective investigation examining individual factors impacting employment status among individuals with multiple sclerosis (MS).
        Work. 2018; 59: 39-47
        • Penner IK
        • Paul F.
        Fatigue as a symptom or comorbidity of neurological diseases.
        Nat. Rev. Neurol. 2017; 13: 662-675
        • Kluger BM
        • Krupp LB
        • Enoka RM.
        Fatigue and fatigability in neurologic illnesses: proposal for a unified taxonomy.
        Neurology. 2013; 80: 409-416
        • Sehle A
        • Vieten M
        • Sailer S
        • Mundermann A
        • Dettmers C.
        Objective assessment of motor fatigue in multiple sclerosis: the Fatigue index Kliniken Schmieder (FKS).
        J. Neurol. 2014; 261: 1752-1762
        • Enoka RM
        • Duchateau J.
        Translating Fatigue to Human Performance.
        Med. Sci. Sports Exerc. 2016; 48: 2228-2238
        • Bellgrove MA
        • Hester R
        • Garavan H.
        The functional neuroanatomical correlates of response variability: evidence from a response inhibition task.
        Neuropsychologia. 2004; 42: 1910-1916https://doi.org/10.1016/j.neuropsychologia.2004.05.007
        • Calabrese M.
        • Pitteri M.
        Cognition and fatigue in multiple sclerosis.
        in: DeLuca J. Sandroff B.M. Cognition and Behavior in Multiple Sclerosis. American Psychological Association, 2018: 127-148
        • Sander C
        • Voelter H-U
        • Schlake H-P
        • Eling P
        • Hildebrandt H.
        Diagnostik der fatigue bei multipler sklerose.
        Aktuelle Neurol. 2017; 44
        • Neumann M
        • Sterr A
        • Claros-Salinas D
        • Gütler R
        • Ulrich R
        • Dettmers C
        Modulation of alertness by sustained cognitive demand in MS as surrogate measure of fatigue and fatigability.
        J. Neurol. Sci. 2014; 340: 178-182
        • Weinges-Evers N
        • Brandt AU
        • Bock M
        • Pfueller CF
        • Dörr J
        • Bellmann-Strobl J
        • Scherer P
        • Urbanek C
        • Boers C
        • Ohlraun S
        • Zipp F
        • Paul F.
        Correlation of self-assessed fatigue and alertness in multiple sclerosis.
        Mult. Scler. 2010; 16: 1134-1140
        • Greim B
        • Benecke R
        • Zettl UK
        Qualitative and quantitative assessment of fatigue in multiple sclerosis (MS).
        J Neurol. 2007; 254 (II58-64)
        • Heesen C
        • Schulz KH
        • Fiehler J
        • Von der Mark U
        • Otte C
        • Jung R
        • Poettgen J
        • Krieger T
        • Gold SM
        Correlates of cognitive dysfunction in multiple sclerosis.
        Brain Behav. Immun. 2010; 24: 1148-1155
        • Claros-Salinas D
        • Dittmer N
        • Neumann M
        • Sehle A
        • Spiteri S
        • Willmes K
        • Schoenfeld MA
        • Dettmers C.
        Induction of cognitive fatigue in MS patients through cognitive and physical load.
        Neuropsychol. Rehabil. 2013; 23: 182-201
        • DeLuca J
        Fatigue, cognition and mental effort.
        Fatigue as a Window to the Brain. 2005; : 37-58
        • Seamon BA
        • Harris-Love MO
        Clinical assessment of fatigability in multiple sclerosis: a shift from perception to performance.
        Front Neurol. 2016; 7: 194
        • DeLuca J.
        Fatigue: Its definition, its study, and its future.
        in: DeLuca J Fatigue as a Window to the Brain. MIT, Cambridge, MA2005: 319-325
        • Pust GEA
        • Dettmers C
        • Randerath J
        • Rahn AC
        • Heesen C
        • Schmidt R
        • et al.
        Fatigue in multiple sclerosis is associated with childhood adversities.
        Front. Psychiatry. 2020; 11: 811
        • Pust GEA
        • Randerath J
        • Goetzmann L
        • Weierstall R
        • Korzinski M
        • Gold SM
        • Dettmers C
        • Ruettner B
        • Schmidt R.
        Association of fatigue severity with maladaptive coping in multiple sclerosis: a data-driven psychodynamic perspective.
        Front. Neurol. 2021; 12652177
        • Penner IK
        • Raselli C
        • Stocklin M
        • Opwis K
        • Kappos L
        • Calabrese P.
        The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue.
        Mult. Scler. 2009; 15: 1509-1517
        • Polman CH
        • Reingold SC
        • Banwell B
        • Clanet M
        • Cohen JA
        • Filippi M
        • et al.
        Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria.
        Ann. Neurol. 2011; 69: 292-302
        • Widder B.
        Begutachtung und sozialmedizinische Beurteilung.
        in: Penner IK Fatigue bei Multipler Sklerose. 2nd edition. Hippocampus Verlag, Bad Honnef2021
        • Zimmermann P
        • Fimm B.
        TAP-M Testbatterie zur Aufmerksamkeitsprüfung Version 2.3.1. 2017: Vera Fimm.
        Psychol. Testsyst. 2017;
        • Claros-Salinas D
        • Bratzke D
        • Greitemann G
        • Nickisch N
        • Ochs L
        • Schroter H.
        Fatigue-related diurnal variations of cognitive performance in multiple sclerosis and stroke patients.
        J. Neurol. Sci. 2010; 295: 75-81
      1. Statistisches Bundesamt (2021). Klassifikationen.www.destatis.de/Europa/DE/Methoden Metadaten/Klassifikationen/Klassifikationen_Uebersicht.html. (Last access: 4th of June,2021).

        • Learmonth YC
        • Dlugonski D
        • Pilutti LA
        • Sandroff BM
        • Klaren R
        • Motl RW.
        Psychometric properties of the Fatigue Severity Scale and the Modified Fatigue Impact Scale.
        J. Neurol. Sci. 2013; 331: 102-107
        • Amtmann D
        • Bamer AM
        • Noonan V
        • Lang N
        • Kim J
        • Cook KF
        Comparison of the psychometric properties of two fatigue scales in multiple sclerosis.
        Rehabil. Psychol. 2012; 57: 159-166
        • Hubacher M
        • Calabrese P
        • Bassetti C
        • Carota A
        • Stöcklin M
        • Penner IK.
        Assessment of post-stroke fatigue: the fatigue scale for motor and cognitive functions.
        Eur. Neurol. 2012; 67: 377-384
        • Wolff W
        • Schüler J
        • Hofstetter J
        • Baumann L
        • Wolf L
        • Dettmers C.
        Trait self-control outperforms trait fatigue in predicting ms patients' cortical and perceptual responses to an exhaustive task.
        Neural Plast. 2019; 20198527203