LEARNING OBJECTIVES Enhance understanding of how common disease - - PDF document

HOW ANXIETY AND FATIGUE INTERACT TO INTERFERE W ITH MS PATIENT’S PROCESSING SPEED

CAROLIN E ALTARAS, MA1 ,JENNIFER MILLER, MA1 ,NICHOLAS VISSICHIO, MA1AND FRED FOLEY , PHD1,2

CMSC 2019 PLATFORM PRESENTATION

(1) RKAUF GRADUATE SCHOOL OF PSYCHOLOGY, YESHIVA UNIVERSITY, BRONX, NY; (2) HOLY NAME MEDICAL CENTER, TEANECK, NJ

LEARNING OBJECTIVES

 Enhance understanding of how common disease factors in MS interact to impact

cognition

 Evaluate considerations for interventions that may indirectly target and improve

cognition

INFORMATION PROCESSING SPEED

 Definition:

The rate an individual can process information

 Most commonly impaired cognitive domain in MS1



Underlies functioning of other cognitive abilities (e.g., memory, executive functioning, working memory) 3–6



Mediates the adverse effect of depression on cognition 7  Reduced processing speed associated with: 8



Increased risk on unemployment



Worse quality of life (QOL)  Symbol Digit Modality

Test (SDMT) is commonly used in MS to capture PS



One of the most sensitive measures of cognitive impairment in MS and best predictors of unemployment 9,10

ANXIETY

 Anxiety can be defined as “anticipation of future threat” associated with increased vigilance in

preparation of potential threat (see DSM-5)

 Highly prevalent in MS, affecting 15- 57% of patients



More prevalent than depression in early stages



Linked to psychosocial implications of MS (e.g., uncertainty of the disease, adjustment to disability)  Anxiety has been shown to negatively impact cognition, specifically processing speed, executive

functioning, episodic memory, as well as patient’s perception of cognitive impairment 2,26–30



Findings have been inconsistent



Less attention has been paid to the impact of anxiety on cognition

Attentional Control

Top-Down Processing

Goal Driven

Bottom-Up Processing

Stimulus Driven

Blob Woman Saxophone player

Attentional Control

Top-Down Processing

Goal Driven

Bottom-Up Processing

Stimulus Driven

Attentional Control

Top-Down Processing

Goal Driven

Bottom-Up Processing

Stimulus Driven

Executive Control:

• Inhibition
• Shifting
• Updating

Processing Efficiency Processing Effectiveness

Attentional Control

Top-Down Processing

Goal Driven

Bottom-Up Processing

Stimulus Driven

Executive Control:

• Inhibition
• Shifting
• Updating

Anxiety

Processing Efficiency Processing Effectiveness

FATIGUE



One of the most common and debilitating symptoms of MS- 90% prevalence 13



MS fatigue is defined as a subjective lack of phys ical and/or mental energy, out of proportion to amount of exerted effort, which interfere[s ] with the ability to engage in desired activities



Negatively impacts multiple domains of functioning 14–18



Quality of life



Employment



Social engagement



Sense of well-being

FATIGUE

 Classified by…12,20–23



Origin

 Central– originating in the central nervous system; can be motor or cognitive  Peripheral– originating in the peripheral nervous system (muscles and related tissues)



Affected domain

 Motor– “physical fatigue” reduction in ability to perform sustained physical activities  Cognitive– “mental fatigue” inability to sustain attention/concentration or endure mental activity



Method of meas urement

 Fatigue-“experienced” fatigue, measured subjectively

 Excessive tiredness, exhaustion, lack of energy

 Fatigability-“performance” fatigability, measured objectively

 Decrement of performance on cognitive (e.g., processing speed) and motor tasks (e.g., gait velocity)

MECHANISMS OF MS FATIGUE

 Multifactorial:

 Biological

 Primary disease factors: demyelination, axonal injury/loss, inflammation  Secondary disease factors: sleep disturbance, spasticity, weakness,

mood disorders  Psychological: depression, anxiety, stress, sleep disturbance  Cognitive:

Thoughts and beliefs (e.g., helplessness, catastrophizing, low sense of control)

 Physical/behavioral patterns: “All or nothing” behavior and constant rest

 Associated with reduced motor activity, as well as cognitive performance (i.e., slower

processing speed) 24

FATIGUE AND INFORMATION PROCESSING SPEED



Reliance on Cognitive Process 25



Fatigue is a feeling that distracts from cognitive processing and is seen behaviorally only when relying on specific process



Any additional factor that interferes with attention will exacerbate performance decrement  Interaction of anxiety and fatigue on processing speed has not yet been investigated in MS, despite high

prevalence of both symptoms



Hypothesis: Fatigue will serve as a moderator, further exacerbating the negative impact of anxiety on processing speed

METHODS AND DEMOGRAPHICS



Participants recruited from ongoing study at the MS Center at Holy Name Hospital in Teaneck, NJ



Underwent neuropsychological testing



Data analysis: N= 533



Statistical Analysis- SPSS 25.0



Three multivariate general linear models were run: 1) Total Fatigue; 2) Motor Fatigue; 3) Cognitive Fatigue



Controlled for age, gender, and education

Characteristics M SD Age 47.12 12.37 Years of Education 14.74 2.90 ISS Total Score 13.18 6.92 Gender Female Male 69.% 22.7% Race/Ethnicity Caucasian Black Hispanic 69.4% 7.8% 12.8% Employment Status Unemployed Employed 40.0% 22.8%

INSTRUMENTS



Symbol Digit Modalities Test (SDMT): Oral Administration 36,37



Most commonly used test for processing speed in MS



Patient is given a key that pairs 9 symbols with numbers 1-9; each symbol has it’s own number.



The subject must then match an array of symbols with their corresponding digits either orally or written.



90s timed tasked



Fatigue Scale for Motor and Cognitive Functions (FSMC) 38



Subscales: 1) Motor Fatigue; 2) Cognitive Fatigue



20 item, 5 point scale



Cut-off= M ild:

43-52, M oderate: 53-62, and Severe: ≥63



Hospital Anxiety and Depression Scale (HADS) 39



14 item, 4 point scale



Subscales: 1) Anxiety; 2) Depression



Cut-off (per subscale)= M ild= 8-10, M oderate= 11-14, Severe= 15-21

MODEL1: TOTAL FATIGUE

SDMT Total B SE B 95% CI Intercept 56.088 5.623 (45.043, 67.134) HADSA (Anxiety) .673 .496 (-.300, 1.650) FSMCT (Total Fatigue)

• .004

.059 (-.120, .112) HADSA x FSMCT

• .013

.006 (-.026, -.001) Gender 2.504 1.216 (.115, 4.893) Y ears of Education .408 .172 (.070, -2.363) Age

• .321

.043 (-.407, -.236) R2= .138, F(6, 533)=14.177, p<.001

MODEL 2: MOTOR FATIGUE

SDMT Total B SE B 95% CI Intercept 55.900 5.665 (44.771, 67.029) HADSA (Anxiety) .392 .492 (-.575, 1.360) FSMCM (Motor Fatigue)

• .019

.119 (-.253, .214) HADSA x FSMCM

• .021

.012 (-.045, .004) Gender 2.563 1.241 (.126, 5.001) Y ears of Education .439 .174 (.098, .781) Age

• .313

.043 (-.398, -.228) R2= .129, F(6, 533)=13.187, p<.001

MODEL 3: COGNITIVE FATIGUE

SDMT Total B SE B 95% CI Intercept 55.571 5.558 (44.652, 66.490) HADSA (Anxiety) .930 .490 (-.033, 1.892) FSMCC (Cognitive Fatigue) .032 .117 (-.199, .263) HADSA x FSMCC

• .033

.013 (-.058, -.009) Gender 2.357 1.203 (-.006, 4.720) Y ears of Education .370 .171 (.033, .707) Age

• .330

.043 (-.415, -.245) R2= .144, F(6, 533)=14.952, p<.001

44 45 46 47 48 49 50 51 52 53 54 55 1 SDMT Total No Cognitive Fatigue Cognitive Fatigue

Interaction of Anxiety and Cognitive Fatigue

• n Proces

s ing Efficiency

No Anxiety Anxiety

CONCLUSION AND FUTURE DIRECTIONS

 Anxiety interacts with cognitive fatigue to adversely impact processing efficiency in MS.



MS patients with high levels of cognitive fatigue and anxiety demonstrate substantially reduced efficiency in processing information. That is, cognitive fatigue moderates the relationship between anxiety and processing efficiency by significantly slowing processing speed at high levels of anxiety.



Supports the attentional control theory and reliance on cognitive process theory of cognitive fatigue.



This is interaction does not occur the motor fatigue.  Treatment implications on use of psychotherapeutic intervention addressing anxiety and cognitive

fatigue in “at risk” patients to improve processing speed.

 Substantial limitation is the correlational nature of this study, limiting interpretation of a casual

relationship.



Future studies may aim to assess trait vs. state anxiety, as well as ”real-time” fatigue.

REFERENCES

1. DeLuca J, Chelune GJ, Tulsky DS, LengenfelderJ, ChiaravallotiND. Is Speed of Processing or Working Memory the Primary Information Processing Deficit in Multiple Sclerosis? J

• urnal of Clinical and Experimental N europsychology. 2004;26(4):550-562. doi:10.1080/13803390490496641

2. Whitehouse CE, Fisk JD, Bernstein CN, et al. Comorbid anxiety, depression, and cognition in MS and other immune-mediated disorders. N eurology. January 2019:10.1212/WNL.0000000000006854. doi:10.1212/WNL.0000000000006854 3. Denney DR, Lynch SG. The impact of multiple sclerosis on patients’ performance on the Stroop T est: Processing speed versus interference. J

• urnal of the

International N europsychological Society. 2009;15(3):451-458. doi:10.1017/S1355617709090730

4. ChiaravallotiND, Stojanovic-Radic J, DeLuca J. The role of speed versus working memory in predicting learning new information in multiple sclerosis. J

• urnal
• f Clinical and Experimental N europsychology. 2013;35(2):180-191. doi:10.1080/13803395.2012.760537

5. Genova HM, LengenfelderJ, ChiaravallotiND, Moore NB, DeLuca J. Processing Speed Versus Working Memory: Contributions to an Information-Processing Task in Multiple Sclerosis. Applied N europsychology:

• Adult. 2012;19(2):132-140. doi:10.1080/09084282.2011.643951

6. Genova HM, DeLuca J, ChiaravallotiN, Wylie G. The relationship between executive functioning, processing speed, and white matter integrity in multiple

• sclerosis. J

Clin Exp N europsychol. 2013;35(6):631-641. doi:10.1080/13803395.2013.806649

7. Blair M, Gill S, Gutmanis I, Smolewska K, Warriner E, Morrow SA. The mediating role of processing speed in the relationship between depressive symptoms and cognitive function in multiple sclerosis. J

Clin Exp N europsychol. 2016;38(7):782-794. doi:10.1080/13803395.2016.1164124

8. Strober LB, Christodoulou C, Benedict RHB, et al. Unemployment in multiple sclerosis: the contribution of personality and disease. M ult Scler. 2012;18(5):647-653. doi:10.1177/1352458511426735 9. Parmenter BA, Weinstock-Guttman B, Garg N, MunschauerF, Benedict RH. Screening for cognitive impairment in multiple sclerosis using the Symbol Digit Modalities Test. M ultiple Sclerosis J

• urnal. 2007;13(1):52-57. doi:10.1177/1352458506070750

10. Campbell J, Rashid W, CercignaniM, Langdon D. Cognitive impairment among patients with multiple sclerosis: associations with employment and quality of

• life. Postgraduate M edical J
• urnal. 2017;93(1097):143-147. doi:10.1136/postgradmedj-2016-134071

REFERENCES

11. Kobelt G. Costs and quality of life of patients with multiple sclerosis in Europe. J

• urnal of N eurology,

N eurosurgery & Psychiatry. 2006;77(8):918-926.

doi:10.1136/jnnp.2006.090365 12. Cadden MH. Comprehensive examination of factors contributing to employment status in multiple sclerosis.. :49. 13. Kister I, Bacon TE, ChamotE, et al. Natural History of Multiple Sclerosis Symptoms. Int J

M S Care. 2013;15(3):146-156. doi:10.7224/1537-2073.2012-053

14. Hemmett L, Holmes J, Barnes M, Russell N. What drives quality of life in multiple sclerosis? QJ

M . 2004;97(10):671-676. doi:10.1093/qjmed/hch105

15. Patti F. Cognitive impairment in multiple sclerosis. M ultiple Sclerosis J

• urnal. 2009;15(1):2-8. doi:10.1177/1352458508096684

16. Opara JA, Jaracz K, Brola W. Quality of life in multiple sclerosis. J

M ed Life. 2010;3(4):352-358.

17. Kos D, Kerckhofs E, Nagels G, D’hooghe MB, Ilsbroukx S. Origin of Fatigue in Multiple Sclerosis: Review of the Literature. N eurorehabilitation and N eural

• Repair. 2008;22(1):91-100. doi:10.1177/1545968306298934

18. Brañas P, Jordan RCK, Fry-Smith A, Burls A, Hyde C. Treatments for fatigue in multiple sclerosis: a rapid and systematic review. H ealth technology assessment. 2000;4(27):1-61. 19. Mills RJ, Young CA. The relationship between fatigue and other clinical features of multiple sclerosis. M ult Scler. 2011;17(5):604-612. doi:10.1177/1352458510392262 20. Vucic S, Burke D, Kiernan MC. Fatigue in multiple sclerosis: mechanisms and management. Clin N europhysiol. 2010;121(6):809-817. doi:10.1016/j.clinph.2009.12.013

REFERENCES

21. VeauthierC, Hasselmann H, Gold SM, Paul F. The Berlin Treatment Algorithm: recommendations for tailored innovative therapeutic strategies for multiple sclerosis-related fatigue. EPM A J

• urnal. 2016;7(1):25. doi:10.1186/s13167-016-0073-3

22. Rudroff T, Kindred JH, Ketelhut NB. Fatigue in Multiple Sclerosis: Misconceptions and Future Research Directions. Front N eurol. 2016;7. doi:10.3389/fneur.2016.00122 23. Chaudhuri A, Behan PO. Fatigue in neurological disorders. The Lancet. 2004;363(9413):978-988. doi:10.1016/S0140-6736(04)15794-2 24. Andreasen AK, Spliid PE, Andersen H, Jakobsen J. Fatigue and processing speed are related in multiple sclerosis. European J

• urnal of N eurology.

2010;17(2):212-218. doi:10.1111/j.1468-1331.2009.02776.x 25. Hanken K, Eling P, Hildebrandt H. Is there a cognitive signature for MS-related fatigue? M ult Scler. 2015;21(4):376-381. doi:10.1177/1352458514549567 26. Morrow SA, Rosehart H, Pantazopoulos K. Anxiety and Depressive Symptoms Are Associated With Worse Performance on Objective Cognitive Tests in MS.

The J

• urnal of N europsychiatry and Clinical N eurosciences. 2016;28(2):118-123. doi:10.1176/appi.neuropsych.15070167

27. GorettiB, Viterbo RG, Portaccio E, et al. Anxiety state affects information processing speed in patients with multiple sclerosis. N eurol Sci. 2014;35(4):559-

• 563. doi:10.1007/s10072-013-1544-0

28. Simioni S, Ruffieux C, Bruggimann L. Cognition, mood and fatigue in patients in the early stage of multiple sclerosis. S

W I S S M E D W K LY.:7.

29. Middleton LS, Denney DR, Lynch SG, Parmenter B. The relationship between perceived and objective cognitive functioning in multiple sclerosis. Arch Clin

N europsychol. 2006;21(5):487-494. doi:10.1016/j.acn.2006.06.008

30. Akbar N, Honarmand K, Feinstein A. Self-assessment of cognition in Multiple Sclerosis: the role of personality and anxiety. Cogn BehavN eurol. 2011;24(3):115-121. doi:10.1097/WNN.0b013e31822a20ae

REFERENCES

31. Derakshan N, Eysenck MW. Anxiety, processing efficiency, and cognitive performance: New developments from attentional control theory. European

• Psychologist. 20090525;14(2):168. doi:10.1027/1016-9040.14.2.168

32. Sarason IG. Anxiety, self-preoccupation and attention. Anxiety Research. 1988;1(1):3-7. doi:10.1080/10615808808248215 33. Baddeley A. Working M emory. New York, NY, US: Clarendon Press/Oxford University Press; 1986. 34. Schoen CB, HoltzerR. Differential relationships of somatic and cognitive anxiety with measures of processing speed in older adults. Aging,

N europsychology, and Cognition. 2017;24(5):481-495. doi:10.1080/13825585.2016.1226247

35. Sussman TJ, Jin J, Mohanty A. Top-down and bottom-up factors in threat-related perception and attention in anxiety. Biological Psychology. 2016;121:160-172. doi:10.1016/j.biopsycho.2016.08.006 36. Benedict RH, DeLuca J, Phillips G, LaRocca N, Hudson LD, Rudick R. Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis. M ult Scler. 2017;23(5):721-733. doi:10.1177/1352458517690821 37. Costa SL, Genova HM, DeLuca J, ChiaravallotiND. Information processing speed in multiple sclerosis: Past, present, and future. M ultiple Sclerosis J

• urnal.

2017;23(6):772-789. doi:10.1177/1352458516645869 38. Penner I, Raselli C, Stöcklin 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. M ultiple Sclerosis J

• urnal. 2009;15(12):1509-1517. doi:10.1177/1352458509348519

39. Bjelland I, Dahl AA, Haug TT, NeckelmannD. The validity of the Hospital Anxiety and Depression Scale An updated literature review. J

• urnal of Psychosomatic
• Research. 2002:9.