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Gepubliceerd in:

18-08-2022

Characterizing fatigue phenotypes with other symptoms and clinically relevant outcomes among people with multiple sclerosis

Auteurs: Matthew Plow, Douglas D. Gunzler, Julia H. C. Chang

Gepubliceerd in: Quality of Life Research | Uitgave 1/2023

Abstract

Purpose

Fatigue is a common symptom of multiple sclerosis (MS) and can adversely affect all aspect of quality of life. The etiology of fatigue remains unclear, and its treatments are suboptimal. Characterizing the phenotypes of fatigued persons with MS may help advance research on fatigue’s etiology and identify ways to personalize fatigue interventions to improve quality of life. The purpose of this study was to identify fatigue phenotypes; examine phenotype stability overtime; and characterize phenotypes by health and function, social and environmental determinants, psychosocial factors, and engagement in healthy behaviors.

Methods

We conducted a longitudinal study over a 3-month period with 289 fatigued participants with MS. To identify fatigue phenotypes and determine transition probabilities, we used latent profile and transition analyses with valid self-report measures of mental and physical fatigue severity, the mental and physical impact of fatigue, depression, anxiety, and sleep quality. We used ANOVAs and effect sizes to characterize differences among phenotypes.

Results

The best fitting model included six subgroups of participants: Mild Phenotype, Mild-to-Moderate Phenotype, Moderate-to-Severe Phenotype, Severe Phenotype, Fatigue-dominant Phenotype, and Mental Health-dominant Phenotype. The transition analysis indicated that phenotypic membership was highly stable. Variables with a large eta squared effect size included environmental barriers, self-efficacy, and fatigue catastrophizing.

Conclusion

These results indicate that the magnitude of fatigue experienced may be more important to consider than the type of fatigue when characterizing fatigue phenotypes. Future research should explore whether tailoring interventions to environmental barriers, self-efficacy, and fatigue catastrophizing reduce the likelihood of transitioning to a more severe phenotype.

vorige artikel One-year follow-up of depression, anxiety, and quality of life of Peruvian patients who survived COVID-19

volgende artikel The impact of stigma on engaged living and life satisfaction among people with mental illness in Hong Kong

Rooney, S., Wood, L., Moffat, F., & Paul, L. (2019). Prevalence of fatigue and its association with clinical features in progressive and non-progressive forms of multiple sclerosis. Multiple Sclerosis and Related Disorders, 28, 276–282. https://doi.org/10.1016/j.msard.2019.01.011CrossRef

Olsson, M., Lexell, J., & Soderberg, S. (2005). The meaning of fatigue for women with multiple sclerosis. Journal of Advanced Nursing, 49(1), 7–15. https://doi.org/10.1111/j.1365-2648.2004.03258.xCrossRef

Fisk, J., Pontefract, A., Ritvo, P., Archibald, C., & Murray, T. (1994). The impact of fatigue on patients with multiple sclerosis. Canadian Journal of Neurological Sciences, 21(1), 9–14.CrossRef

Smith, M. M., & Arnett, P. A. (2005). Factors related to employment status changes in individuals with multiple sclerosis. Multiple Sclerosis Journal, 11(5), 602–609. https://doi.org/10.1191/1352458505ms1204oaCrossRef

Skerrett, T. N., & Moss-Morris, R. (2006). Fatigue and social impairment in multiple sclerosis: The role of patients’ cognitive and behavioral responses to their symptoms. Journal of Psychosomatic Research, 61(5), 587–593. https://doi.org/10.1016/j.jpsychores.2006.04.018CrossRef

Hersche, R., Roser, K., Weise, A., Michel, G., & Barbero, M. (2021). Fatigue self-management education in persons with disease-related fatigue: A comprehensive review of the effectiveness on fatigue and quality of life. Patient Education and Counseling. https://doi.org/10.1016/j.pec.2021.09.016CrossRef

Nourbakhsh, B., Revirajan, N., Morris, B., Cordano, C., Creasman, J., Manguinao, M., Krysko, K., Rutatangwa, A., Auvray, C., & Aljarallah, S. (2021). Safety and efficacy of amantadine, modafinil, and methylphenidate for fatigue in multiple sclerosis: A randomised, placebo-controlled, crossover, double-blind trial. Lancet Neurology, 20(1), 38–48. https://doi.org/10.1016/S1474-4422(20)30354-9CrossRef

Moss-Morris, R., Harrison, A. M., Safari, R., Norton, S., van der Linden, M. L., Picariello, F., Thomas, S., White, C., & Mercer, T. (2021). Which behavioural and exercise interventions targeting fatigue show the most promise in multiple sclerosis? A systematic review with narrative synthesis and meta-analysis. Behaviour Research and Therapy, 137, 103464. https://doi.org/10.1016/j.brat.2019.103464CrossRef

Salomè, A., SassoD’Elia, T., Franchini, G., Santilli, V., & Paolucci, T. (2019). Occupational therapy in fatigue management in multiple sclerosis: an umbrella review. Multiple Sclerosis International, 2019, 2027947. https://doi.org/10.1155/2019/2027947CrossRef

10.

Chalah, M. A., & Ayache, S. S. (2018). Cognitive behavioral therapies and multiple sclerosis fatigue: A review of literature. Journal of Clinical Neuroscience, 52, 1–4. https://doi.org/10.1016/j.jocn.2018.03.024CrossRef

11.

Miller, P., & Soundy, A. (2017). The pharmacological and non-pharmacological interventions for the management of fatigue related multiple sclerosis. Journal of the Neurological Sciences, 381, 41–54. https://doi.org/10.1016/j.jns.2017.08.012CrossRef

12.

Phyo, A. Z. Z., Demaneuf, T., De Livera, A. M., Jelinek, G. A., Brown, C. R., Marck, C. H., Neate, S. L., Taylor, K. L., Mills, T., O’Kearney, E., Karahalios, A., & Weiland, T. J. (2018). The efficacy of psychological interventions for managing fatigue in people with multiple sclerosis: A systematic review and meta-analysis. Frontiers in Neurology. https://doi.org/10.3389/fneur.2018.00149CrossRef

13.

Manjaly, Z. M., Harrison, N. A., Critchley, H. D., Do, C. T., Stefanics, G., Wenderoth, N., Lutterotti, A., Muller, A., & Stephan, K. E. (2019). Pathophysiological and cognitive mechanisms of fatigue in multiple sclerosis. Journal of Neurology, Neurosurgery, and Psychiatry, 90(6), 642–651. https://doi.org/10.1136/jnnp-2018-320050CrossRef

14.

Plow, M., Motl, R. W., Finlayson, M., & Bethoux, F. (2020). Response heterogeneity in a randomized controlled trial of telerehabilitation interventions among adults with multiple sclerosis. Journal of Telemedicine and Telecare. https://doi.org/10.1177/1357633x20964693CrossRef

15.

Finlayson, M., Preissner, K., & Cho, C. (2012). Outcome moderators of a fatigue management program for people with multiple sclerosis. American Journal of Occupational Therapy, 66(2), 187–197. https://doi.org/10.5014/ajot.2012.003160CrossRef

16.

Ehde, D. M., Arewasikporn, A., Alschuler, K. N., Hughes, A. J., & Turner, A. P. (2018). Moderators of treatment outcomes after telehealth self-management and education in adults with multiple sclerosis: A secondary analysis of a randomized controlled trial. Archives of Physical Medicine and Rehabilitation, 99(7), 1265–1272. https://doi.org/10.1016/j.apmr.2017.12.012CrossRef

17.

Plow, M., & Nicka, A. (2018). Fatigue in multiple sclerosis. In F. Robert, A. Rae-Grant, & F. Bethoux (Eds.), Multiple sclerosis and related disorders: Clinical guide to diagnosis, medical management, and rehabilitation. Springer Publishing Company.

18.

Enoka, R. M., Almuklass, A. M., Alenazy, M., Alvarez, E., & Duchateau, J. (2021). Distinguishing between fatigue and fatigability in multiple sclerosis. Neurorehabilitation & Neural Repair, 35(11), 960–973. https://doi.org/10.1177/15459683211046257CrossRef

19.

Lyon, D. E., McCain, N. L., Pickler, R. H., Munro, C., & Elswick, R. K., Jr. (2011). Advancing the biobehavioral research of fatigue with genetics and genomics. Journal of Nursing Scholarship, 43(3), 274–281. https://doi.org/10.1111/j.1547-5069.2011.01406.xCrossRef

20.

Fisk, J. D., Ritvo, P. G., Ross, L., Haase, D. A., Marrie, T. J., & Schlech, W. F. (1994). Measuring the functional impact of fatigue: Initial validation of the fatigue impact scale. Clinical Infectious Diseases, 18(Suppl 1), S79–S83.CrossRef

21.

Miaskowski, C., Barsevick, A., Berger, A., Casagrande, R., Grady, P. A., Jacobsen, P., Kutner, J., Patrick, D., Zimmerman, L., Xiao, C., Matocha, M., & Marden, S. (2017). Advancing symptom science through symptom cluster research: Expert panel proceedings and recommendations. Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djw253CrossRef

22.

Shahrbanian, S., Duquette, P., Kuspinar, A., & Mayo, N. E. (2015). Contribution of symptom clusters to multiple sclerosis consequences. Quality of Life Research, 24(3), 617–629. https://doi.org/10.1007/s11136-014-0804-7CrossRef

23.

Kwekkeboom, K. L., Tostrud, L., Costanzo, E., Coe, C. L., Serlin, R. C., Ward, S. E., & Zhang, Y. (2018). The role of inflammation in the pain, fatigue, and sleep disturbance symptom cluster in advanced cancer. Journal of Pain and Symptom Management, 55(5), 1286–1295. https://doi.org/10.1016/j.jpainsymman.2018.01.008CrossRef

24.

DeVon, H. A., Vuckovic, K., Ryan, C. J., Barnason, S., Zerwic, J. J., Pozehl, B., Schulz, P., Seo, Y., & Zimmerman, L. (2017). Systematic review of symptom clusters in cardiovascular disease. European Journal of Cardiovascular Nursing, 16(1), 6–17. https://doi.org/10.1177/1474515116642594CrossRef

25.

Jenkins, B. A., Athilingam, P., & Jenkins, R. A. (2019). Symptom clusters in chronic obstructive pulmonary disease: A systematic review. Applied Nursing Research, 45, 23–29. https://doi.org/10.1016/j.apnr.2018.11.003CrossRef

26.

Motl, R. W., Suh, Y., & Weikert, M. (2010). Symptom cluster and quality of life in multiple sclerosis. Journal of Pain and Symptom Management, 39(6), 1025–1032. https://doi.org/10.1016/j.jpainsymman.2009.11.312CrossRef

27.

De Meo, E., Portaccio, E., Giorgio, A., Ruano, L., Goretti, B., Niccolai, C., Patti, F., Chisari, C. G., Gallo, P., Grossi, P., Ghezzi, A., Roscio, M., Mattioli, F., Stampatori, C., Simone, M., Viterbo, R. G., Bonacchi, R., Rocca, M. A., De Stefano, N., … Amato, M. P. (2021). Identifying the distinct cognitive phenotypes in multiple sclerosis. JAMA Neurology, 78(4), 414–425. https://doi.org/10.1001/jamaneurol.2020.4920CrossRef

28.

Leavitt, V. M., Tosto, G., & Riley, C. S. (2018). Cognitive phenotypes in multiple sclerosis. Journal of Neurology, 265(3), 562–566. https://doi.org/10.1007/s00415-018-8747-5CrossRef

29.

Kim, M. T., Radhakrishnan, K., Heitkemper, E. M., Choi, E., & Burgermaster, M. (2021). Psychosocial phenotyping as a personalization strategy for chronic disease self-management interventions. American Journal of Translational Research, 13(3), 1617.

30.

Wojczynski, M. K., & Tiwari, H. K. (2008). Definition of phenotype. Advances in genetics, 60, 75–105.CrossRef

31.

Wang, E., Chang, J. H. C., & Plow, M. (2022). Predictors of fatigue self-management behaviors in adults with multiple sclerosis. NeuroRehabilitation, 50(1), 75–87. https://doi.org/10.3233/nre-210179CrossRef

32.

Plow, M., & Gunzler, D. D. (2022). Disentangling self-reported fatigue, depression, and cognitive impairment in people with multiple sclerosis. Multiple Sclerosis and Related Disorders, 61, 103736. https://doi.org/10.1016/j.msard.2022.103736CrossRef

33.

Cella, D., Lai, J. S., Nowinski, C. J., Victorson, D., Peterman, A., Miller, D., Bethoux, F., Heinemann, A., Rubin, S., Cavazos, J. E., Reder, A. T., Sufit, R., Simuni, T., Holmes, G. L., Siderowf, A., Wojna, V., Bode, R., McKinney, N., Podrabsky, T., … Moy, C. (2012). Neuro-QOL: Brief measures of health-related quality of life for clinical research in neurology. Neurology, 78(23), 1860–1867. https://doi.org/10.1212/WNL.0b013e318258f744CrossRef

34.

Bowen, J., Gibbons, L., Gianas, A., & Kraft, G. H. (2001). Self-administered expanded disability status scale with functional system scores correlates well with a physician-administered test. Multiple Sclerosis Journal, 7, 201–206. https://doi.org/10.1177/135245850100700311CrossRef

35.

Katzman, R., Brown, T., Fuld, P., Peck, A., Schechter, R., & Schimmel, H. (1983). Validation of a short orientation-memory-concentration test of cognitive impairment. American Journal of Psychiatry, 140(6), 734–739.CrossRef

36.

Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D., & Wallace, E. P. (1993). Development of a fatigue scale. Journal of Psychosomatic Research, 37(2), 147–153. https://doi.org/10.1016/0022-3999(93)90081-pCrossRef

37.

Penner, I., Raselli, C., Stöcklin, M., Opwis, K., Kappos, L., & Calabrese, P. (2009). The fatigue scale for motor and cognitive functions (FSMC): Validation of a new instrument to assess multiple sclerosis-related fatigue. Multiple Sclerosis, 15(12), 1509–1517. https://doi.org/10.1177/1352458509348519CrossRef

38.

Miller, D. M., Bethoux, F., Victorson, D., Nowinski, C. J., Buono, S., Lai, J. S., Wortman, K., Burns, J. L., Moy, C., & Cella, D. (2016). Validating Neuro-QoL short forms and targeted scales with people who have multiple sclerosis. Multiple Sclerosis, 22(6), 830–841. https://doi.org/10.1177/1352458515599450CrossRef

39.

Ritvo, P., Fischer, J. S., Miller, D. M., Andrews, H., Paty, D., & LaRocca, N. (1997). Multiple Sclerosis Quality of Life Inventory: A user’s manual (pp. 1–65). New York: National Multiple Sclerosis Society.

40.

Rudick, R., Antel, J., Confavreux, C., Cutter, G., Ellison, G., Fischer, J., Lublin, F., Miller, A., Petkau, J., Rao, S., Reingold, S., Syndulko, K., Thompson, A., Wallenberg, J., Weinshenker, B., & Willoughby, E. (1996). Clinical outcomes assessment in multiple sclerosis. Annals of Neurology, 40(3), 469–479. https://doi.org/10.1002/ana.410400321CrossRef

41.

Buysse, D. J., Reynolds, C. F., 3rd., Monk, T. H., Berman, S. R., & Kupfer, D. J. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. https://doi.org/10.1016/0165-1781(89)90047-4CrossRef

42.

Hammel, J., Magasi, S., Heinemann, A., Whiteneck, G., Bogner, J., & Rodriguez, E. (2008). What does participation mean? An insider perspective from people with disabilities. Disability and Rehabilitation, 30(19), 1445–1460. https://doi.org/10.1080/09638280701625534CrossRef

43.

Heinemann, A. W., Lai, J. S., Magasi, S., Hammel, J., Corrigan, J. D., Bogner, J. A., & Whiteneck, G. G. (2011). Measuring participation enfranchisement. Archives of Physical Medicine and Rehabilitation, 92(4), 564–571. https://doi.org/10.1016/j.apmr.2010.07.220CrossRef

44.

Heinemann, A. W., Magasi, S., Bode, R. K., Hammel, J., Whiteneck, G. G., Bogner, J., & Corrigan, J. D. (2013). Measuring enfranchisement: Importance of and control over participation by people with disabilities. Archives of Physical Medicine and Rehabilitation, 94(11), 2157–2165. https://doi.org/10.1016/j.apmr.2013.05.017CrossRef

45.

Plow, M. A., Finlayson, M., Gunzler, D., & Heinemann, A. W. (2015). Correlates of participation in meaningful activities among people with multiple sclerosis. Journal of Rehabilitation Medicine. https://doi.org/10.2340/16501977-1948CrossRef

46.

Learmonth, Y. C., Motl, R. W., Sandroff, B. M., Pula, J. H., & Cadavid, D. (2013). Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurology, 13(1), 37. https://doi.org/10.1186/1471-2377-13-37CrossRef

47.

Hohol, M. J., Orav, E. J., & Weiner, H. L. (1999). Disease steps in multiple sclerosis: A longitudinal study comparing disease steps and EDSS to evaluate disease progression. Multiple Sclerosis, 5(5), 349–354. https://doi.org/10.1177/135245859900500508CrossRef

48.

Horton, M., Rudick, R. A., Hara-Cleaver, C., & Marrie, R. A. (2010). Validation of a self-report comorbidity questionnaire for multiple sclerosis. Neuroepidemiology, 35(2), 83–90. https://doi.org/10.1159/000311013CrossRef

49.

Whiteneck, G. G., Harrison-Felix, C. L., Mellick, D. C., Brooks, C. A., Charlifue, S. B., & Gerhart, K. A. (2004). Quantifying environmental factors: A measure of physical, attitudinal, service, productivity, and policy barriers. Archives of Physical Medicine and Rehabilitation, 85(8), 1324–1335. https://doi.org/10.1016/j.apmr.2003.09.027CrossRef

50.

Finlayson, M., Plow, M., & Cho, C. (2010). Use of physical therapy services among middle-aged and older adults with multiple sclerosis. Physical Therapy, 90(11), 1607–1618. https://doi.org/10.2522/ptj.20100072CrossRef

51.

Finlayson, M., Garcia, J. D., & Cho, C. (2008). Occupational therapy service use among people aging with multiple sclerosis. American Journal of Occupational Therapy, 62(3), 320–328. https://doi.org/10.5014/ajot.62.3.320CrossRef

52.

Plow, M., Cho, C., & Finlayson, M. (2010). Utilization of health promotion and wellness services among middle-aged and older adults with multiple sclerosis in the mid-west US. Health Promotion International, 25(3), 318–330. https://doi.org/10.1093/heapro/daq023CrossRef

53.

Lorig, K. (1996). Outcome measures for health education and other health care interventions. Sage Publications.CrossRef

54.

Jacobsen, P. B., Azzarello, L. M., & Hann, D. M. (2004). Relation of catastrophizing to fatigue severity in women with breast cancer. Journal of Consulting and Clinical Psychology, 72(2), 355–361. https://doi.org/10.1037/0022-006X.72.2.355CrossRef

55.

Godin, G. (2011). The Godin-Shephard leisure-time physical activity questionnaire. The Health & Fitness Journal of Canada, 4(1), 18–22.

56.

Godin, G., & Shephard, R. J. (1985). A simple method to assess exercise behavior in the community. Canadian Journal of Applied Sport Sciences, 10(3), 141–146.

57.

Mochari, H., Gao, Q., & Mosca, L. (2008). Validation of the MEDFICTS dietary assessment questionnaire in a diverse population. Journal of the American Dietetic Association, 108(5), 817–822. https://doi.org/10.1016/j.jada.2008.02.021CrossRef

58.

Mallik, P. S., Finlayson, M., Mathiowetz, V., & Fogg, L. (2005). Psychometric evaluation of the energy conservation strategies survey. Clinical Rehabilitation, 19(5), 538–543. https://doi.org/10.1191/0269215505cr789oaCrossRef

59.

Gunzler, D. D., Perzynski, A. T., & Carle, A. C. (2021). Structural equation modeling for health and medicine. Chapman and Hall/CRC.CrossRef

60.

Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/10.1016/j.jvb.2020.103445CrossRef

61.

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRef

62.

Muthén, B., & Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55(2), 463–469.CrossRef

63.

Muthén, L. K., & Muthén, B. (2017). Mplus user’s guide: Statistical analysis with latent variables, user’s guide. Muthén & Muthén.

64.

Adams, M. A., & Conway, T. L. (2014). Eta squared. In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research (pp. 1965–1966). Springer, Netherlands.CrossRef

65.

Muthén, L. K., & Muthén, B. O. (2021). Mplus: The comprehensive modeling program for applied researchers: User’s guide. Muthén & Muthén.

66.

Venables, W. N., Smith, D. M., & Team, R. D. C. (2009). An introduction to R. Citeseer.

67.

Hallquist, M. N., & Wiley, J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in M plus. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 621–638.CrossRef

68.

Walton, C., King, R., Rechtman, L., Kaye, W., Leray, E., Marrie, R. A., Robertson, N., La Rocca, N., Uitdehaag, B., & van der Mei, I. (2020). Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS. Multiple Sclerosis Journal, 26(14), 1816–1821. https://doi.org/10.1177/1352458520970841CrossRef

69.

Gilmour, H., Ramage-Morin, P. L., & Wong, S. L. (2018). Multiple sclerosis: Prevalence and impact. Health Reports, 29(1), 3–8.

70.

Pust, G. E. A., Pottgen, J., Randerath, J., Lau, S., Heesen, C., Gold, S. M., & Penner, I. K. (2019). In search of distinct MS-related fatigue subtypes: Results from a multi-cohort analysis in 1.403 MS patients. Journal of Neurology, 266(7), 1663–1673. https://doi.org/10.1007/s00415-019-09311-2CrossRef

71.

Chilcot, J., Norton, S., Kelly, M. E., & Moss-Morris, R. (2016). The Chalder fatigue questionnaire is a valid and reliable measure of perceived fatigue severity in multiple sclerosis. Multiple Sclerosis Journal, 22(5), 677–684. https://doi.org/10.1177/1352458515598019CrossRef

72.

Gunzler, D. D., Perzynski, A., Morris, N., Bermel, R., Lewis, S., & Miller, D. (2015). Disentangling multiple sclerosis and depression: An adjusted depression screening score for patient-centered care. Journal of Behavioral Medicine, 38(2), 237–250. https://doi.org/10.1007/s10865-014-9574-5CrossRef

73.

Kratz, A. L., Fritz, N. E., Braley, T. J., Scott, E. L., Foxen-Craft, E., & Murphy, S. L. (2018). Daily temporal associations between physical activity and symptoms in multiple sclerosis. Annals of Behavioral Medicine, 53(1), 98–108. https://doi.org/10.1093/abm/kay018CrossRef

74.

Salter, A., Fox, R. J., Tyry, T., Cutter, G., & Marrie, R. A. (2019). The association of fatigue and social participation in multiple sclerosis as assessed using two different instruments. Multiple Sclerosis and Related Disorders, 31, 165–172. https://doi.org/10.1016/j.msard.2019.04.014CrossRef

75.

Biernacki, T., Sandi, D., Kincses, Z. T., Füvesi, J., Rózsa, C., Mátyás, K., Vécsei, L., & Bencsik, K. (2019). Contributing factors to health-related quality of life in multiple sclerosis. Brain and behavior, 9(12), e01466. https://doi.org/10.1002/brb3.1466CrossRef

76.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

77.

Moore, S. M., Jones, L., & Alemi, F. (2016). Family self-tailoring: Applying a systems approach to improving family healthy living behaviors. Nursing Outlook, 64(4), 306–311. https://doi.org/10.1016/j.outlook.2016.05.006CrossRef

78.

Plow, M., Moore, S., Chang, J., & Bachhal, F. (2020). Randomized controlled trial of SystemCHANGE weight management intervention in neurological conditions. Patient Education and Counseling, 103(1), 112–119. https://doi.org/10.1016/j.pec.2019.08.007CrossRef

79.

Lukkahatai, N., & Saligan, L. N. (2013). Association of catastrophizing and fatigue: A systematic review. Journal of Psychosomatic Research, 74(2), 100–109. https://doi.org/10.1016/j.jpsychores.2012.11.006CrossRef

Titel: Characterizing fatigue phenotypes with other symptoms and clinically relevant outcomes among people with multiple sclerosis
Auteurs: Matthew Plow
Douglas D. Gunzler
Julia H. C. Chang
Publicatiedatum: 18-08-2022
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 1/2023
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-022-03204-0

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