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Mechanisms of change underlying the efficacy of cognitive behaviour therapy for chronic fatigue syndrome in a specialist clinic: a mediation analysis

Published online by Cambridge University Press:  12 August 2013

D. Stahl*
Affiliation:
Department of Biostatistics, Institute of Psychiatry, King's College London, UK
K. A. Rimes
Affiliation:
Department of Psychology, University of Bath, UK
T. Chalder
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, King's College London, UK
*
*Address for correspondence: D. Stahl, Ph.D., Department of Biostatistics, Institute of Psychiatry, King's College London, UK (Email: daniel.r.stahl@kcl.ac.uk)

Abstract

Background

Several randomized controlled trials (RCTs) have shown that cognitive behavioural psychotherapy (CBT) is an efficacious treatment for chronic fatigue syndrome (CFS). However, little is known about the mechanisms by which the treatment has its effect. The aim of this study was to investigate potential mechanisms of change underlying the efficacy of CBT for CFS. We applied path analysis and introduce novel model comparison approaches to assess a theoretical CBT model that suggests that fearful cognitions will mediate the relationship between avoidance behaviour and illness outcomes (fatigue and social adjustment).

Method

Data from 389 patients with CFS who received CBT in a specialist service in the UK were collected at baseline, at discharge from treatment, and at 3-, 6- and 12-month follow-ups. Path analyses were used to assess possible mediating effects. Model selection using information criteria was used to compare support for competing mediational models.

Results

Path analyses were consistent with the hypothesized model in which fear avoidance beliefs at the 3-month follow-up partially mediate the relationship between avoidance behaviour at discharge and fatigue and social adjustment respectively at 6 months.

Conclusions

The results strengthen the validity of a theoretical model of CBT by confirming the role of cognitive and behavioural factors in CFS.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2013 

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References

Akaike, H (1973). Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory (ed. Petrov, B. N. and Csaki, F.), pp. 267281. Akademiai Kiado: Budapest.Google Scholar
Antoni, MH, Brickman, A, Lutgendorf, S, Klimas, N, Imia-Fins, A, Ironson, G, Quillian, R, Miguez, MJ, van Riel, F, Morgan, R, Patarca, R, Fletcher, MA (1994). Psychosocial correlates of illness burden in chronic fatigue syndrome. Clinical Infectious Diseases 18 (Suppl. 1), S73S78.Google Scholar
Arbuckle, JL (2006). Amos (Version 7.0). SPSS: Chicago, IL.Google Scholar
Baron, RM, Kenny, DA (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 11731182.CrossRefGoogle ScholarPubMed
Brown, H, Prescott, R (2006). Applied Mixed Models in Medicine. John Wiley & Sons Ltd: New York.Google Scholar
Burgess, M, Manoharan, A, Chalder, T (2011). Cognitive behaviour therapy for chronic fatigue syndrome in adults: Face to face versus telephone treatment; a randomised controlled trial. Behavioural and Cognitive Psychotherapy 20, 117.Google Scholar
Burnham, KP, Anderson, DR (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretical Approach. Springer: New York.Google Scholar
Carey, KB, Henson, JM, Carey, MP, Maisto, SA (2010). Perceived norms mediate effects of a brief motivational intervention for sanctioned college drinkers. Clinical Psychology: Science and Practice 17, 5871.Google Scholar
Cella, M, Chalder, T (2010). Measuring fatigue in clinical and community settings. Journal of Psychosomatic Research 69, 1722.CrossRefGoogle ScholarPubMed
Cella, M, Sharpe, M, Chalder, T (2011 a). Measuring disability in patients with chronic fatigue syndrome: reliability and validity of the Work and Social Adjustment Scale. Journal of Psychosomatic Research 71, 124128.CrossRefGoogle ScholarPubMed
Cella, M, Stahl, D, Reme, SE, Chalder, T (2011 b). Therapist effects in routine psychotherapy practice: an account from chronic fatigue syndrome. Psychotherapy Research 21, 168178.Google Scholar
Chalder, T, Berelowitz, G, Pawlikowska, T, Watts, L, Wessely, S, Wright, D, Wallace, EP (1993). Development of a fatigue scale. Journal of Psychosomatic Research 37, 147153.CrossRefGoogle ScholarPubMed
Chalder, T, Power, MJ, Wessely, S (1996). Chronic fatigue in the community: ‘a question of attribution’. Psychological Medicine 26, 791800.CrossRefGoogle ScholarPubMed
Chambers, D, Bagnall, AM, Hempel, S, Forbes, C (2006). Interventions for the treatment, management and rehabilitation of patients with chronic fatigue syndrome/myalgic encephalomyelitis: an updated systematic review. Journal of the Royal Society of Medicine 99, 506520.Google ScholarPubMed
Cheong, J, MacKinnon, DP, Khoo, ST (2003). Investigation of mediational processes using parallel process latent growth curve modeling. Structural Equation Modeling 10, 238262.CrossRefGoogle ScholarPubMed
Claeskens, G, Lid, N (2008). Model Selection and Model Averaging. Cambridge University Press: Cambridge, UK.Google Scholar
Cutter, GR, Baier, ML, Rudick, RA, Cookfair, DL, Fischer, JS, Petkau, J, Syndulko, K, Weinshenker, BG, Antel, JP, Confavreux, C, Ellison, GW, Lublin, F, Miller, AE, Rao, SM, Reingold, S, Thompson, A, Willoughby, E (1999). Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain 122, 871882.Google Scholar
Deale, A, Chalder, T, Marks, I, Wessely, S (1997). Cognitive behavior therapy for chronic fatigue syndrome: a randomized controlled trial. American Journal of Psychiatry 154, 408414.Google Scholar
Deale, A, Chalder, T, Wessely, S (1998). Illness beliefs and treatment outcome in chronic fatigue syndrome. Journal of Psychosomatic Research 45, 7783.Google Scholar
Duncan, TE, Duncan, SC, Strycker, LA (2006). An Introduction to Latent Variable Growth Curve modeling: Concepts, Issues, and Applications. Lawrence Erlbaum Associates: Mahwah, NJ.Google Scholar
Emsley, R, Dunn, G, White, IR (2010). Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research 19, 237270.CrossRefGoogle ScholarPubMed
Fan, X, Thompson, B, Wang, L (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling 6, 5683.Google Scholar
Fukuda, K, Straus, SE, Hickie, I, Sharpe, MC, Dobbins, JG, Komaroff, A (1994). The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group. Annals of Internal Medicine 121, 953959.Google Scholar
Judd, CM, Kenny, DA (1981). Process analysis: estimating mediation in treatment evaluations. Evaluation Review 5, 602619.Google Scholar
Kline, RB (2004). Principles and Practice of Structural Equation Modeling. Guilford Press: New York.Google Scholar
Knoop, H, van der Meer, JW, Bleijenberg, G (2008). Guided self-instructions for people with chronic fatigue syndrome: randomised controlled trial. British Journal of Psychiatry 193, 340341.Google Scholar
Knudsen, AK, Henderson, M, Harvey, SB, Chalder, T (2011). Long-term sickness absence among patients with chronic fatigue syndrome. British Journal of Psychiatry 199, 430431.Google Scholar
Kraemer, HC, Wilson, GT, Fairburn, CG, Agras, WS (2002). Mediators and moderators of treatment effects in randomized clinical trials. Archives of General Psychiatry 59, 877883.Google Scholar
Laurenceau, J-P, Hayes, AM, Feldman, GC (2007). Some methodological and statistical issues in the study of change processes in psychotherapy. Clinical Psychology Review 27, 682695.Google Scholar
Little, RJA, Rubin, DB (2002). Statistical Analysis with Missing Data. Wiley: New York.Google Scholar
MacKinnon, DP (2008). Introduction to Statistical Mediation Analysis. Erlbaum: Mahwah, NJ.Google Scholar
MacKinnon, DP, Luecken, LJ (2008). How and for whom? Mediation and moderation in health psychology. Health Psychology 27, S99S100.CrossRefGoogle ScholarPubMed
Mundt, JC, Marks, IM, Shear, MK, Greist, JH (2002). The Work and Social Adjustment Scale: a simple measure of impairment in functioning. British Journal of Psychiatry 180, 461464.Google Scholar
Nijs, J, De Meirleir, K, Duquet, W (2004). Kinesiophobia in chronic fatigue syndrome: assessment and associations with disability. Archives of Physical Medicine and Rehabilitation 85, 15861592.Google Scholar
Petrie, K, Moss-Morris, R, Weinman, J (1995). The impact of catastrophic beliefs on functioning in chronic fatigue syndrome. Journal of Psychosomatic Research 39, 3137.Google Scholar
Preacher, KJ, Hayes, AF (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers 36, 717731.Google Scholar
Price, JR, Mitchell, E, Tidy, E, Hunot, V (2008). Cognitive behaviour therapy for chronic fatigue syndrome in adults. Cochrane Database Systematic Review 3, CD001027.Google Scholar
Prins, JB, Bleijenberg, G, Bazelmans, E, Elving, LD, de Boo, TM, Severens, JL, van der Wilt, GJ, Spinhoven, P, van der Meer, JW (2001). Cognitive behaviour therapy for chronic fatigue syndrome: a multicentre randomised controlled trial. Lancet 357, 841847.Google Scholar
Quarmby, L, Rimes, KA, Deale, A, Wessely, S, Chalder, T (2007). Cognitive-behaviour therapy for chronic fatigue syndrome: comparison of outcomes within and outside the confines of a randomised controlled trial. Behaviour Research and Therapy 45, 10851094.CrossRefGoogle ScholarPubMed
Ray, C, Jefferies, S, Weir, WR (1995). Coping with chronic fatigue syndrome: illness responses and their relationship with fatigue, functional impairment and emotional status. Psychological Medicine 25, 937945.Google Scholar
Rosseel, Y (2012). lavaan: an R-package for structural equation modeling. Journal of Statistical Software 48, 136.CrossRefGoogle Scholar
Royston, P (2005). Multiple imputation of missing values: update of ice. Stata Journal 5, 527536.Google Scholar
Sharpe, M, Chalder, T (1994). Management of the chronic fatigue syndrome. In Neurological Rehabilitation (ed. Ellis, L. S.), pp. 282294. Blackwell Scientific Publications: Oxford.Google Scholar
Sharpe, M, Hawton, K, Simkin, S, Surawy, C, Hackmann, A, Klimes, I, Peto, T, Warrell, D, Seagroatt, V (1996). Cognitive behaviour therapy for the chronic fatigue syndrome: a randomized controlled trial. British Medical Journal 312, 2226.Google Scholar
Sharpe, MC, Archard, LC, Banatvala, JE, Borysiewicz, LK, Clare, AW, David, A, Edwards, RH, Hawton, KE, Lambert, HP, Lane, RJ (1991). A report – chronic fatigue syndrome: guidelines for research. Journal of the Royal Society of Medicine 84, 118121.Google Scholar
Shipley, B (2003). From biological hypotheses to structural equation models: the imperfection of causal translation. In Structural Equation Modeling: Applications in Ecological and Evolutionary Biology (ed. Pugesek, B. H., Tomer, A. and von Eye, A.), pp. 194211. Cambridge University Press: Cambridge, UK.Google Scholar
Silver, A, Haeney, M, Vijayadurai, P, Wilks, D, Pattrick, M, Main, CJ (2002). The role of fear of physical movement and activity in chronic fatigue syndrome. Journal of Psychosomatic Research 52, 485493.Google Scholar
Skerrett, TN, 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, 587593.Google Scholar
StataCorp (2007). Stata Statistical Software: Release 10. StataCorp LP: College Station, TX.Google Scholar
Stulemeijer, M, de Jong, LW, Fiselier, TJ, Hoogveld, SW, Bleijenberg, G (2005). Cognitive behaviour therapy for adolescents with chronic fatigue syndrome: randomised controlled trial. British Medical Journal 330, 14.Google Scholar
Wessely, S, David, A, Butler, S, Chalder, T (1989). Management of chronic (post-viral) fatigue syndrome. Journal of the Royal College of General Practitioners 39, 2629.Google ScholarPubMed
White, PD, Goldsmith, KA, Johnson, AL, Potts, L, Walwyn, R, DeCesare, JC, Baber, HL, Burgess, M, Clark, LV, Cox, DL, Bavinton, J, Angus, BJ, Murphy, G, Murphy, M, O'Dowd, H, Wilks, D, McCrone, P, Chalder, T, Sharpe, M (2011). Comparison of adaptive pacing therapy, cognitive behaviour therapy, graded exercise therapy, and specialist medical care for chronic fatigue syndrome (PACE): a randomised trial. Lancet 377, 823836.Google Scholar
Wiborg, JF, Knoop, H, Prins, JB, Bleijenberg, G (2011). Does a decrease in avoidance behavior and focusing on fatigue mediate the effect of cognitive behavior therapy for chronic fatigue syndrome? Journal of Psychosomatic Research 70, 306310.Google Scholar
Wiborg, JF, Knoop, H, Stulemeijer, M, Prins, JB, Bleijenberg, G (2010). How does cognitive behaviour therapy reduce fatigue in patients with chronic fatigue syndrome? The role of physical activity. Psychological Medicine 40, 12811287.Google Scholar
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