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A systematic review of anxiety amongst people with Multiple Sclerosis

Multiple Sclerosis and Related Disorders, Volume 10, November 2016, Pages 145 - 168

Abstract

Background

Multiple Sclerosis (MS) is a chronic neurological disease, which poses significant psychological challenges. The purpose of this systematic review was to identify factors that are associated with anxiety in people with MS (PwMS). It aimed to examine the strength of evidence for factors associated with anxiety symptoms and identify limitations of existing studies.

Method and results

One hundred and thirty one studies met inclusion criteria and were included in the review. A narrative synthesis was then conducted. Anxiety was found to be associated with a variety of demographic, physical, psychological, cognitive and social factors. A consistent finding was that anxiety was strongly associated with both high level of disability and low quality of life. A strong association between anxiety and depression was also found.

Conclusion

Implications for these results are discussed and a preliminary model of understanding anxiety in the context of MS is outlined. Given the overlap between anxiety and depression, a transdiagnostic treatment approach is suggested. In light of the shortcomings of extant studies, suggestions for future research are offered.

Highlights

  • There is a high prevalence of anxiety amongst PwMS.
  • It is associated with high levels of depression and disability.
  • Anxiety should be treated alongside depression to improve quality of life.

Abbreviations: MS - Multiple Sclerosis, PwMS - People with Multiple Sclerosis.

Keywords: Anxiety, Multiple Sclerosis, Systematic review.

1. Introduction

Multiple Sclerosis (MS) is a chronic inflammatory neurological condition of the central nervous system (DeLuca et al., 2008). The illness occurs when the protective myelin sheath of nerve fibers in the brain and spinal cord become damaged, disrupting the transfer of messages from the brain to other parts of the body (National MS Society, 2012). There are various types of disease courses including: a benign course, a relapsing-remitting course (RR), which often progresses into a secondary progressive course (SP); and a type that is progressive from its onset (PP)(NHS Choices, 2013). Due to the unpredictability of the course of the illness, and the lack of treatments available, the main goals of intervention are usually to delay progression, relieve symptoms and treat any mental health issues that arise (Burks et al., 2009). Mental health problems, such as depression and anxiety, have been found to be particularly common among people with Multiple Sclerosis (PwMS) (Janssens et al., 2003) and to date most of the literature has focused on depression in PwMS. This study therefore aims to broaden current knowledge by focusing exclusively on the role of anxiety in PwMS.

Anxiety can be defined as an excessive feeling of unease and worry which individuals find difficult to control, and consequently interferes with everyday functioning (American Psychiatric Association, 2000). For PwMS, anxiety may be severe and prolonged due to the individual's concern about the uncertain outcome of future episodes and the potential seriousness of the symptoms (Cecile et al., 2004). Worldwide estimates of the proportion of the population who are likely to suffer from anxiety in their lifetime range between 0.8% and 6.4%(Kessler and Wang, 2008). However, for PwMS, anxiety has been found to affect between 15.8% and 57% of the population (Feinstein et al., 1999; Garfield et al., 2012). Anxiety is amongst the most important factors to investigate for PwMS as, if untreated, it can significantly impact quality of life, treatment adherence and symptoms (Mohr and Cox, 2001). Therefore, it is widely agreed that identification of symptoms and treatment should be introduced at the earliest opportunity (Dahl et al., 2009). However, current knowledge of anxiety and its treatment in PwMS is limited by the lack of research on why PwMS experience anxiety.

The purpose of the current review is to systematically identify existing literature that focused on anxiety among PwMS. The aims are a) to gain an overview of the strength of evidence for factors associated with anxiety in the context of MS b) to identify methodological problems, gaps within the literature and directions for future research. A reliable overview of the field of research is likely to help clinicians improve the wellbeing of PwMS and to gain a greater understanding of factors that are linked to anxiety so that these could be targeted within interventions.

2. Method

2.1. Search strategy and selection criteria

The systematic review protocol and data extraction forms were designed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)(Moher et al., 2009). Electronic databases (MEDLINE, EMBASE, Web of Science, BIOSIS, KCI, SciELO, CINAHL and Psychinfo) were searched for studies between January 1980 and September 2016 (see Appendix A for search terms). The screening process then took place which involved: removing duplicates; title screening; excluding irrelevant titles; abstract screening; then full-text assessment against eligibility criteria.

2.2. Inclusion and exclusion criteria

Cross-sectional and prospective studies were included that were published empirical quantitative research reports examining anxiety in PwMS. Intervention studies were not included in this review as data collected in randomised trials may not be applicable to the wider population of patients because trials use such rigorous inclusion criteria to get an artificially homogenous group of patients (Rothwell, 2005). The review included all available studies that identified clinically relevant anxiety, i.e. anxiety severe enough to warrant clinical intervention that was measured by clinical judgment or a validated, appropriate multi-item measure. Single item measures were excluded due to their vulnerability to random measurement errors and lack of internal consistency reliability (Hoeppner et al., 2011). The factors associated with anxiety were examined. Prevalence estimates were also obtained for Generalised Anxiety Disorder (GAD), Obsessive Compulsive Disorder (OCD), Post-traumatic Stress Disorder (PTSD) and Social Anxiety. Ambiguities as to whether a study met the inclusion criteria were resolved through discussion between the authors (EB and TC).

2.3. Data extraction

Study titles were screened first, followed by abstracts by a research assistant in collaboration with the primary researcher (EB). The full texts were then screened by EB and all ineligible papers were excluded. Information for each eligible study was extracted and tabulated. Extracted data included sample characteristics, type of analysis, relevant measures, main findings, demographics and information for quality assessment. The extraction process was completed independently by EB and any disagreements were resolved through discussion of the study with TC. It was decided that abstracts without full articles would be included given that many met inclusion criteria and appeared relevant to the research question.

2.4. Synthesis

A narrative synthesis was conducted in line with previously described methods (Popay et al., 2006). Full reference information for included studies can be found in Table 1 and Table 2 and Appendix C. Factors relating to anxiety were grouped into overarching conceptually or thematically related categories. The importance of each factor was accounted for by combining a count of the studies that identified or did not identify significant relationships while considering their methodological quality. Patterns in the data were examined and potential sources of heterogeneity between studies were explored including moderators of results such as sample size. Discrepancies, uncertainties and unanswered questions within the studies were also analysed.

Table 1

Cross Sectional Studies.

 

Authors, Year Multi-Centre Type of MS Measure of anxiety Other measures used Gender/Age Full paper Eligibility Criteria Specified Powered Recruitment strategy
Akbar et al. (2011) no Definite MS HADS MSNQ, NEO-FFI, 81 females/27 males av. 45 years yes yes no Convenience
Al-Asmi et al. (2013) no Definite MS (McDonald criteria) HADS EDSS 41 females/16 males. av. 31.4 years yes yes no Consecutive
Aloulou et al. (2011) no Definite MS HADS TAS-20 18 females/13 males av. 39 years. no yes n/s n/s
Anhoque et al. (2011) no 14 RRMS, 3 SPMS, 2 PPMS BAI BDI 14 females/5 males. av. 37 years. yes yes no Consecutive
Askari et al. (2014) no 142 RRMS, 38 SPMS BAI BDI, EDSS 151 females/29 males, av. 32.4 years yes yes no Consecutive
Bamer et al. (2008) yes Definite MS HADS PHQ-9 1034 females/237 males. av. 50.9 years no n/s n/s n/s
Beier et al. (2013) n/s Definite MS PHQ-A None Specified 421 females/92 males female no n/s n/s n/s
Beiske et al. (2008) yes Clinically Definite MS (without severe cognitive impairment, psychiatric impairment or other serious disabling disease) HSCL-25 PASAT, MS-FS 94 females/46 males av. 30.9 years yes yes no Consecutive
Bogart (2015) yes Self-report diagnosis MS HADS Disability Personal Identity Scale, ADL 48 females, 58 males av.=58.3 years Yes Yes No Convenience
Brajkovic et al. (2009) no Definite MS HADS MSQLI, COPE 51 females/17 males no n/s n/s n/s
Bruce and Arnett (2009) yes RRMS, SPMS STAI PSWQ, CMDI, EDSS, FIS, BPI, SILS, SRT, SDMT, Visual Elevator subtest. 42 females/8 males av. 46.2 years yes yes no n/s
Bruce et al. (2009) no RRMS, SPMS STAI Memory questionnaire, DES-11, NEO-FFI, BDI-FS, neuropsychological tests 71 females/8 males. av. 45.4 years yes yes no n/s
Bruce and Lynch (2011) no MSRR, MSSP, MSPP STAI NEO-FFI, MINI, BDI-FS, MFIS, EDSS 74 females/11 males. av. 47.12 years yes yes no n/s
Chalfant et al. (2004) yes MSRR, MSSP, MSPP Clinician Administered PTSD scale GDS, DSQ 45 females/13 males av. 49.6 years yes no no n/s
Chalk (2007) yes Definite MS BAI EDSS, CMSS, CAHS, SSQ, SWLS, CES-D, BAI-PC, MHI 266 females/63 males yes yes no Randomised
Chylova et al. (2009) n/s Definite MS HADS SF-36, EDSS 150 female/73 males. av. 38.9 years no n/s n/s n/s
Cihelkova and Bojar (2009) n/s Definite MS BAI BDI II, SCL-90, MMSE, EDSS 25 females/15 males no n/s n/s n/s
Counsell et al. (2013) yes Definite MS − 61.1% RRMS, 17.5% SPMS, 7.9% PPMS, 3.2% PPMS, 3.2% benign type, 4.8% were unsure of type. HADS; PTSD Checklist MSIS-29 99 females/27 males. av. 45.5 years yes yes no Randomised
Curral et al. (2011) no Definite MS HADS MMSE, Raven, MOS SF-36, SCL-90, EDSS 35 females/13 males no n/s n/s n/s
Da Silva et al. (2011) no 80.4% RRMS, 9.9% SPMS, 9.6% PPMS HADS EDSS, MSSS 214 females/111 males av. 39.5 years yes yes no Consecutive
Dahl et al. (2009) yes Definite MS HADS FSS 111 females/61 males av. 50.1 years yes yes no Convenience
Dubayova et al. (2013) yes Definite MS HADS PCS, MCS, EDSS, UDPRS 154 females/44 males. av. 67.6 year yes yes no Randomised
Espinola-Nadurille et al. (2010) no Definite MS (McDonald criteria) − 75.6% RRMS, 18.9% SPMS, 5.4% PPMS HADS DSM, MADRS 24 females/13 males av. 36.3years yes yes no Consecutive
Etesam et al. (2016) Yes n/s HADS DDI, EDSS n/s no no no n/s
Farrell et al. (2011) no Definite MS HADS MSNQ n/s no n/s n/s n/s
Feinstein et al. (1999) no Definite MS (Posars criteria) HADS GHQ-28 107 females/45 males av. 44.98 years yes yes no Consecutive
Fisk et al. (2014) yes Definite MS HADS EDSS, HUI, HRQoL, D-FIS 714 females/235 males av. 48.6 years no n/s n/s n/s
Foroughipour et al. (2012) no Definite MS Y-BOCS EDSS DSM 84 females/28 males. av. 31.9 years yes yes no Consecutive
Fruhwald et al. (2001) no Definite MS ZARS EDSS, MMSE, ZDRS 57 females/17 males. av. 39.8 years yes (German) yes no n/s
Garfield and Lincoln (2012) yes Definite MS- 35 MSRR, 10 MSPP, 20 MSSP, 3 Benign. HADS EDSS, GHQ-12, GNDS, MHLC, MSSS, 41 females/27 males av. 50 years yes yes no Randomised
Glanz et al. (2012). no RRMS STAI MSQOL-54, CESD, MFIS, STAI, SDMT 287 females/90 males. av. 45.4 years yes yes no Consecutive
Goretti et al. (2014). yes RRMS STAI BRB, BDI 140 females/50 males av. 37.5 years yes yes no Consecutive
Grech et al. (2015) no RR or SPMS (McDonald criteria) STAI BDI-II, MSQOL-54, Daily Hassles Scale, measures of cognition 83 females, 24 men av.=48.8 years yes yes No Convenience
Hakim et al. (2000) yes Definite MS HADS EDSS, MI Male: female ratio- 1:2.1. av. 48.3 years yes yes no Consecutive
Harding et al. (2012) n/s 50 RRMS, 39 SPMS. GHQ30 EDSS, GHQ30 80 females/22 males no n/s n/s n/s
Ionescu et al. (2012) n/s Definite MS HAM-A HAM-D, EDSS, QOL, IADL n/s no n/s n/s n/s
Iriarte et al. (2000) no Definite MS HAM-A FDS, FSS, HRSD EDSS 105 females/50 males. av. 32.6 years yes yes no Consecutive
Janssens et al. (2003) yes Definite MS HADS SF-36, IES 71 females/30 males. av. 37.5 years yes yes no n/s
Janssens et al. (2004) yes Definite MS HADS EDSS, IES 71 females/30 males. av. 37.5 years yes yes no n/s
Jones et al. (2012) yes 14.4% PPMS, 61.7% RRMS, 9.4% SPMS, 14.5% unsure HADS None specified 2941 females/1237 males. av. 50.9 years yes yes no Convenience
Jones et al. (2013) yes Definite MS HADS MSIS-29, EQ5D n/s no n/s n/s n/s
Jones and Amtmann (2014) yes Definite MS − 58.8% RR, 11.1% PP, 20% SP, 1%PR PROMIS Neuro-QOL, EDSS 280 females/125 males av. 52.68 years yes yes no Convenience
Jones et al. (2014) yes Definite MS- 14.8% PPMS, 62.1% RRMS, 8.1% SPMS, 14.9% unsure HADS MSIS-29 3211 females/1305 males. av. 50.7 years yes yes no Convenience
Jopson and Moss-Morris (2003) yes Definite MS HADS SIP-68, FSS, RSES, IPQ-R 131 females/37 males. av. 56.2 years yes yes no Convenience
Julian and Arnett (2009) yes Definite or probable MS (Posar criteria) STAI Neuropsychological measures of executive functioning, CMDI 61 females/16 males. av. 46.58 years, yes yes no Convenience
Karadayi et al. (2014) no Definite MS (McDonald criteria) HAM-A GAF, FSS, EDSS MMSE, Cognitive measures 21 females/10 males av. 38.3 years yes yes yes n/s
Kehle and Hadjistavropoulos (2009) yes Definite MS HADS CHIP, GNDS 201 females/45 males. av. 41.82 years yes yes no Convenience
Kikuchi et al. (2013) yes Definite MS NAS-J EDSS, FAMS 118 females/45 males av. 31.9 years yes yes no n/s
Korostil and Feinstein (2007) no Definite MS HADS BSS, SSSI, Neuropsychological Screening Battery 104 females/36 males. av. 43.9 years yes yes no Consecutive
Kostaras et al. (2008) n/s Definite MS STAI SDMT, BDI n/s no n/s n/s n/s
Kraft et al. (2012) no Definite MS PROMIS Quality of life indicator n/s no n/s n/s n/s
Krokavcova et al. (2010) yes Definite MS HADS SF-36, EDSS 122 females/62 males. av. 40.5 years yes yes no Convenience
Labuz-Roszak et al. (2007) no n/s conference paper HADS FSS, ESS, AIS, MADRS 87 females/35 males av. 37.7 years no n/s n/s n/s
Leonavicius and Adomaitiene (2013) no Definite MS. Progressive- 182, RRMS- 130 HADS EDSS, ICD-10 196 females/116 males. av. 42.01 years yes yes no n/s
Leonavicius and Adomaitiene (2011) yes MSRR, Progressive MS HADS EDSS, ICD-10 187 females/83 males av. 42.42 years no n/s n/s n/s
Leonavicius and Adomaitiene (2014) no RRMS HADS MOSS, HRQoL 86 females/51 males. av. 42.01years yes yes no n/s
Lester et al. (2007) no MSRR, MSPP, MSSP HADS EDSS, MSIS-29, Self-efficacy for managing chronic disease, Neuropsych screening questionnaire 62 females/18 males. av. 44 years yes yes no n/s
Levinthal and Bielefeldt (2014) no Definite MS HADS MSIS-29, PAGI-SYM, PAGI-QOL, PHQ-15 n/s no n/s n/s n/s
Liu et al. (2009) no Definite MS SCL-90 LES, EPQ, SSRS 26 females/15 males. av. 37.44 years yes yes no Consecutive
Lopes et al. (2012) n/s Definite MS HADS EDSS, FSS 88 females/31 males. av. 42 years no n/s n/s n/s
Maia et al. (2011) n/s Definite MS HADS LNNB, FACE-111 25 females no n/s n/s n/s
Marrie et al. (2013) yes Definite MS ICD Diagnosis. ATC 3006 females/1186 males yes yes no Consecutive
Medin et al. (2014) n/s Definite MS Determined by a psychiatrist None Specified n/s no n/s n/s n/s
Middleton et al. (2006) no Definite MS (McDonald criteria) SAS Cognitive tests, CFQ, FFS, CES-D, EDSS 163 females/8 males av. 44.8 years yes yes no Consecutive
Milanlioglu et al. (2013) n/s RRMS and SPMS POMS EDSS, COPE 36 females/14 males yes yes no Consecutive
Milinis et al. (2014) yes Definite MS HADS WHOQOL-BREF, LMSQOL, WHODAS, MSSS-88, NRS 0–10, NFI-MS, NPS n/s no n/s n/s n/s
Miljatovic (2013) no Definite MS HAM-A HAMD-17, EDSS n/s no n/s n/s n/s
Mills and Young (2011) yes Definite MS HADS NFI-MS, ESS, MSIS-29 451 females/184 males av. 46.6 years yes yes no n/s
Montel and Bungener (2007) no Definite MS (Posars criteria) HAM-A MINI, MADRS, EHD, FABWCC, CHIP, SEP59 89 females/46 males. av. 44 years yes yes no n/s
Morrow et al. (2014) no Newly diagnosed HADS FSS, BDIFS 70 females/26 males. av. 36.9 years no n/s n/s n/s
Nicholl et al. (2001) no Definite MS- 14% MSRR, 45% MSPP, 19% MSPP HADS GHQ, EADL, CORE, BSI, GNDS, BDI 70 females/26 males. av. 48.97 years yes yes no Consecutive
Niino et al. (2012) n/s Definite MS NAS-J EDSS, FAMS n/s no n/s n/s n/s
Noy et al. (1995) yes RRMS HAM-A HRSD, Hackett-Cassem Denial Scale, MAACL-R, EDSS 15 females/5 males. av. 41.8 years yes yes no Consecutive
Ostacoli et al. (2013) no Definite MS- 90.8% MSRR, 3.02% MSPP, 6.9% MSSP HADS IES-R, DSM, FSS 164 females/68 males av. 41.29 years yes yes no Consecutive
Paredes and Kirchner Nebot (2012) yes Definite MS SCL-90-R (Spanish) SCL-90-R 62 females/28 males. av. 38.58 years no n/s n/s n/s
Pfaff et al. (2014) n/s RRMS HAM-A EHD, TAS 20 16 females no n/s n/s n/s
Pieper et al. (2012) yes Definite MS DSM-IV UNDS, LOT-R n/s no n/s n/s n/s
Poder et al. (2009) no Definite MS SPIN, HADS HUI, EDSS 201 females/44 males. av. 46.1 years yes yes no Consecutive
Poder et al. (2007) no Definite MS SPIN, EDSS, HUI 216 females/49 males no n/s n/s n/s
Reade et al. (2012) yes 66% RRMS, 12% PPMS, 9% SPMS, 13% other MHI MSQOL-54, MOS 112 females/33 males yes yes no Randomised
Roy Bellina et al. (2010) n/s Definite MS HAM-A IPQ-R, BDI, TAS-20 26 females/6 males. av. 40 years no n/s n/s n/s
Roy-Bellina et al. (2009) n/s Definite MS STAI EDSS, MHKC, SSQ, WCC, IPQ-R, BDI-11 34 females/11 males av. 45 years no n/s n/s n/s
Sarisoy et al. (2013) no Definite MS STAI SCL-90-R, BDI, PSQI, Padua Inventory, SES, EATS-26, EDSS 56 females/20 males av. 37.84 years yes yes no Convenience
Schwartz et al. (1996) no Definite MS Anxiety subscale on AIMS MAF, EDSS, SIP, Rao cognitive battery, the Trailmaking Test, depression and social activity limitations subscales from AIMS, Ryff Happiness Scale. 101 females/38 males av. 43.06 years yes yes no Inclusive
Shabani et al. (2007) yes Definite MS DSM interview None Specified 45 females/40 males av. 47. 6 years yes yes no Randomised
Sidorenko et al. (2010) no Definite MS HADS EDSS n/s no n/s n/s n/s
Silva et al. (2009) n/s 171 RRMS,25 SPMS, 24 PPMS HADS EDSS, MMSE 150 females/81 males. av. 39.94 years no n/s n/s n/s
Silva et al. (2011) n/s Definite MS (minimum 10 years disease duration) HADS EDSS, FAMS 103 females/56 males av. 44.45 years no n/s n/s n/s
Simpson et al. (2014) yes Definite MS Clinician recording None Specified 2766 females/1060 males. av. 53.4 years yes yes no Consecutive
Smith and Young (2000) no Definite MS HADS BDI, MRS, two visual analogue scales. 61 females/88 males av. 46 years yes yes no Consecutive
Spain et al. (2007) yes Definite MS (Posars criteria)− 54% MSRR, 30% MSSP, 10% MSPP, 6% intermediate subtype. HADS EDSS, SDMT, IPQ, SF-36 458 females/122 males. av. 46.7 years yes yes no Convenience
Stenager et al. (1994) no Definite MS (Posars criteria) STAI EDSS, measures of cognitive impairment. 52 females/42 males av. 42. 6 years yes yes no n/s
Suh et al. (2010) yes Definite MS HADS PDDS 62 females/34 males av. 48.6 years yes yes no Convenience
Szilasiova et al. (2011) no Definite MS- 77.2% MSRR, 18.4% MSSP, 4.4% MSPP HADS FSS, SF-36, EDSS 82 females/32 males. av. 36.1 years yes yes no Consecutive
Tadic and Dajic (2013) no Definite MS (McDonald Criteria) HAM-A EDSS, MSQoL-54, HDRS 28 females/22 males. av. 41.2 years yes yes no n/s
Tan-Kristanto et al. (2015) Yes Definite MS (McDonald criteria) DASS EDSS, RSA, MSSE, Brief COPE 117 women, 12 men av=38.41 years yes yes No Convenience
Theaudin et al. (2016) Yes Definite MS (Posar and McDonald criteria) HADS EDSS 489 females/222 males av. 44.8 years yes yes no Consecutive
Thornton et al. (2006) no Definite MS HADS WQMS, PSQW, SESMS 27 females/12 males av. 48.3 years yes yes no Randomised
Tsivgoulis et al. (2007) yes RRMS STAI BDI, EDSS 56 females/30 males. av. 39 years yes yes no Consecutive
Uca et al. (2016) No RRMS (McDonald criteria) SCID-I/CV EDSS, SCID-II/CV 96 females, 15 males av. 35.1 years yes yes no n/s
Uguz et al. (2008) no RRMS (42 in exacerbation phase, 32 in remission phase) DSM EDSS 50 females/24 males av. 34.57 years yes yes no Consecutive
Van Der Hiele et al. (2014) yes RRMS patients HADS SF-36, BADS DEX, FIS 39 females/5 males. av. 37.2 years yes yes no Consecutive
Van Der Hiele et al. (2010) yes Benign, MSRR, MSSP, MSPP HADS BADS-DEX, SCWT, WCST, EDSS n/s no n/s n/s n/s
Van der Hiele et al. (2012) yes Benign, MSRR, MSSP, MSPP HADS SF-36, FIS, EPCL 392 females/138 males. av. 48.3 years yes yes no Convenience
Van der Hiele et al. (2012) yes Benign, MSRR, MSSP, MSPP HADS BADS, EPCL, UCL, BAD DEX, cognitive and neuropsych assessments 86 females/28 males av. 50.2 years yes yes no Convenience
Visser et al. (2009) n/s Definite MS HADS BADS-DEX, DIP, CERQ, FIS, AMS, UCL, SCL-R-90 n/s no n/s n/s n/s
Voiticovschi-Iosob and Moldovanu (2013) n/s Definite MS STAI BDI, SF-36, Structured pain questionnaire n/s no n/s n/s n/s
Vuger-Kovacic et al. (2007) no Definite MS CCEI Locus of control Inventory n/s yes yes no n/s
Weisbrot et al. (2012) n/s Definite MS K-SADS PedsQL, CGAS 29 females/16 males. av. 15.7 years no n/s n/s n/s
White et al. (2008) yes Definite MS 66% RRMS, 12% PPMS, 9% SPMS, 13% unsure of type MHI MFIS, MHI 75 females/26 males. av. 50 years yes yes no Randomised
Ziegler et al. (2010) n/s Definite MS DSM-IV CAR n/s no n/s n/s n/s
Zorzon et al. (2001) no Definite MS HADS DSM 62 females/33 males av. 39.5 years yes yes no Consecutive

Table 2

Prospective studies.

 

Authors, Year Multi-Centre Type of MS Measure of anxiety Other measures used Gender/Age Full paper Eligibility Criteria Specified Powered Recruitment strategy
Bianchi et al. (2014) no RRMS HAM-A WCQ, HAM-D 30 females/9 males av. 28.8 years yes yes no Consecutive
Brown et al. (2009) yes Definite MS STAI EDSS, SEFCI, LEDS, FIS, BDI, WOCQ, LOT, MHLC, SSQ, PSQI 81 females/101 males av. 42.6 years yes yes no n/s
Burns et al. (2013) yes MSRR, MSSP HADS CES-D, MINI 82 females/17 males. av. 42.7 years yes yes no Randomised
Christodoulou et al. (2009) yes 28 MSRR, 10 MSSP STAI CESD, CMDI, PANAS, FSS, AES, MS cognitive batteries, EDSS 22 females/16 males. av. 45.5 years yes yes no n/s
Dalos et al. (1983) no Definite MS GHQ-28 None 66 females/34 males av. 40.4 years yes yes no Consecutive
Diaz-Olavarrieta et al. (1999) no RRMS and Chronic Progressive NPI EDSS, MMSE 30 females/14 males av. 33 years yes yes no Convenience
Giordano et al. (2011) yes Definite MS HADS EDSS 56 females/26 males. av. 35.2 years yes yes no Randomised
Hartoonian et al. (2015) yes Self-reported diagnosis MS HADS-A EDSS, NRS, FSS, PHQ-9 421 females, 92 males Av. 51 years yes yes no Convenience
Hoang et al. (2016) Yes Definite MS ICD-10 diagnosis None n/s yes yes no Consecutive
Janssens et al. (2006) yes Recently Diagnosed HADS IES, EDSS 71 females/30 males. av. 37.5 years yes yes no Consecutive
Johnson et al. (2012) yes Definite MS PROMIS Neuro-QoL n/s no n/s n/s n/s
McCabe (2005) yes Definite MS POMS WHOGOL-100, WOCQ 163 females/0 males av. 44.86 years yes yes no Randomised
McKay et al. (2016) Yes Definite MS HADS CAGE, EDSS 714 females/235 males Av. 48.6 years yes yes no Consecutive
Olivares et al. (2012) n/s Definite MS HADS BRB, MSNQ-S, FFS, MS-QOL 40 females/10 males av. 36.8 years no n/s n/s n/s
Pakenham and Samios (2013) yes Definite MS DASS-21 AAQ, MAAS 54 females/15 males av. 42.12 years yes yes no Randomised
Pekmezovic et al. (2012) n/s Definite MS HAM-A HDRS, MSQoL, EDSS n/s no n/s n/s n/s
Potagas et al. (2008) no Definite MS HAM-A SRRS 37 females. av. 32.8 years yes yes no n/s
Solari et al. (2010) n/s 92% RRMS HADS EDSS 56 females/26 males av. 35 years no n/s n/s n/s
Wood et al. (2013) yes Definite MS (McDonald criteria) HADS FSS, EDSS, MSSS 125 females/73 males. av. 48.2 years yes yes no Consecutive

A box-score method was used to quantify the relationships between the factors associated with anxiety among PwMS (Matcham et al., 2015). The method involved tabulating each factor and its relationship with anxiety, in terms of significance and direction: a positive sign (+) was given for a positive significant association between variables: a negative sign (−) for a negative significant association between variables and a naught (0) for no association between variables (Green and Hall, 1984). This table (see Table 3) also included study design (cross-sectional or prospective) and level of analysis (bivariate or multivariate). Thus tabulated associations could be synthesized alongside indicators of study quality. Data from all studies were included in the box-score table except those that focused uniquely on prevalence rates and those that focused on subtypes of anxiety such as OCD, Social Anxiety or PTSD. These excluded studies will be outlined in detail below. Data from both bivariate and multivariate models were included in order to retain as much data for comparison as possible.

Table 3

Box analysis of factors associated with anxiety for PwMS.

 

Category Factor CS Bivariate CS Multivariate PS Bivariate PS Multivariate
Demographics Gender (female) ++++++++00 + +0 ++0
Age (younger) ++++++ ++ 0 +
Cognitive Dysfunction ++++00 +++++000 +
Cognitive change +
Perception of cognitive dysfunction +++ +
Self-reported memory problems 0
Illness intrusiveness +
Illness representation (illness identity, cyclical timeline and illness coherence) 0 +
Perception of risk +
Neuropsychological functioning 00 0 +
Mood Related Depression +++++++++++ ++++++++ + ++++++
Suicidal thoughts +
Alexithymia +
Low Self-efficacy + ++
Low emotional well-being +
Low general well-being +
Psychological Distress +
Difficulty managing mood +
Worry +
Stress ++ +++
Health worry/anxiety ++0
Personality Extroversion +
Neuroticism + +
Conscientiousness +
Low optimism +
Personality disorder (SCID-II) +
Coping Avoidance coping ++ 0
Accepting Responsibilities ±
Mindfulness +
Resilience
Distress disclosure +
Planning +
Positive interpretation
Use of social support
Emotion focused coping ++ +++ 0
Problem focused coping 0
No relaxation training +
Unhealthy behaviours (eg. drinking to excess, smoking) + ++
Physical functioning Sleep disturbances 0+ ++
Better health status ±(Young)
Long Duration +00000−− 00−− 0 +
Wheelchair bound 0
Level of disability ++++++++++00–00 +++++++++++0 00 0
Daily functioning +
Physical activity 0
Symptoms Number of symptoms +
Pain ++++0 ++
Fatigue ++++0 +++0 ++
Exacerbations/course of illness 000 ++0 ++ +
Dyspepsia +
Spasticity +
Speech difficulties +
Biological Other physical health problem +(Epilepsy)
Frontotemporal changes 0
Lesion loads 0
Brain Volume 0
Medical Poor Adherence of medication +
Immunotherapy status +
Treatment with disease modifying drug (DMD) +
Number of hospitalisations +
Social Factors Lack of Education ± 0
Negative life events + +
Problems in family life +
Few MS group activities +
Lack of information on MS +
Unemployment 0 ++++
Quality of life +++ +++++ ++
Presenteeism at work +
Lower social activity/support + 0 +
Type MS Relapsing Remitting Multiple Sclerosis (RRMS) 0++(Women) +
Secondary Progressive Multiple Sclerosis (SPMS) ++

+ Positive association with anxiety (p<0.05); − negative association with anxiety (p>0.05); 0 no association with anxiety; CS (Cross-Sectional Study); PS (Prospective Study).

3. Results

3.1. Overview

The literature search yielded 6494 relevant articles (Fig. 1). Removal of duplicates and screening of titles and abstracts left 216 articles for full-text screening. Full versions of these articles were obtained and reviewed against inclusion criteria. Eighty-five of these did not meet the eligibility criteria. The most common reason for exclusion was not having measured or reported anxiety. One hundred and thirty-one studies were deemed eligible for inclusion in the narrative synthesis. Fifty-two percent of the studies used the Hospital Anxiety and Depression Scale (HADS). In the remaining studies, 20 other previously validated questionnaires were used or anxiety was measured by assessment with a clinician (see Appendix B).

Fig. 1

Fig. 1

PRISMA flow diagram.

 

The sample size of studies (N=131) ranged from 19 to 5084 participants. However, the majority of studies had sample sizes of between 50 and 200 participants. Most of the studies in the review included both men and women. However, within these studies women represented the majority of participants. Three studies only included women. Although not all studies reported the age of participants, in those that did, the average range was between 15.7 years and 50.9 years. Most studies focused on prevalence rates, cognitive processes, physical, social or psychological factors. An overview of all study characteristics for both cross-sectional (N=112) and prospective studies (N=19) was carried out (see Appendix C). Data were typically analysed using correlations or regression.

Elements of study quality were also examined by applying a previously used quality-assessment tool which was appropriate for both cross-sectional and prospective study designs (Matcham et al., 2013) (see Table 4). The tool was adjusted slightly and included: whether a full paper was available rather than just an abstract; whether anxiety was measured using a validated tool; whether the recruitment strategy was randomised or consecutive; whether the participants were recruited from multiple centres; whether there was a control group; whether eligibility criteria were specified and whether the study was adequately powered. In the case where studies did not report anything for a particular eligibility indicator, they were allocated to the no category.

Table 4

Quality assessment of studies including conference abstracts.

 

Study Type Full paper available Validated measure of anxiety Randomised/consecutive recruitment strategy Multi-centre Control Group Eligibility criteria specified Adequately powered
Cross Sectional-studies 69.57% 94.78% 32.76% 39.13% 14.81% 65.21% 0.93%
Prospective studies 78.95% 100% 52.63% 63.15% 47.47% 78.95% 0%

The remainder of the results section summarises and synthesizes findings regarding the factors associated with anxiety (grouped into thematically or conceptually related categories). Given the large number of studies reviewed, thorough discussion of individual studies is beyond the scope of this report. The focus is therefore on providing a broad overview of the evidence available, including the conceptual backgrounds of the research. The strength of associations are later outlined (see Appendix C). The results are considered under the following sections: Prevalence of Anxiety and Subtypes; Demographics; Cognition; Physical Functioning; Mood Related Factors; Personality; Social Factors and Coping.

3.2. Prevalence of anxiety and subtypes

Fifty-two cross-sectional and six prospective studies in the review included prevalence rates. Although rates for anxiety tended to differ across studies, all studies reported higher rates of anxiety among PwMS in comparison to control groups when present. The prevalence of anxiety generally ranged between 4% and 57% (Montel et al., 2007; Garfield et al., 2012). In a prospective study, the prevalence of anxiety was 25.4% at baseline, however, this decreased over time after diagnosis (Wood et al., 2013).

Other studies examined prevalence rates of anxiety and anxiety subtypes for PwMS; 11.8% was reported for GAD, 1.2% for panic disorder, 7.1% for specific phobias and 11.8% for OCD (Shabani et al., 2007). Another study reported 18.9% for GAD, 18.9% for specific phobia and 14.9% for OCD (Uguz et al., 2008). Furthermore, the same study found that OCD was significantly more common among patients experiencing a relapse compared to patients in the remission phase (Uguz et al., 2008). Similar to the previously mentioned studies, a prevalence rate of 16.1% for OCD was also reported (Foroughipour et al., 2012). In this study, OCD was significantly correlated with a higher level of disability and long duration of disease. Furthermore, biological factors such as cranial, cerebellar, autonomic, sensory and motor nerve involvement were also associated with OCD (Foroughipour et al., 2012).

A prevalence rate of 16% was reported for PTSD in PwMS (Chalfant et al., 2004). Higher level of disability and having another health condition were both significantly related to these symptoms (Counsell et al., 2013). Furthermore, a prevalence rate of 24% was reported for PTSD that was associated with MS diagnosis. These patients showed significantly lower self-efficacy and social support (Ziegler et al., 2010). Level of education, GAD and depression were also reported as significant determinants of PTSD (Ostacoli et al., 2013).

Two studies that specifically focused on clinically significant social anxiety reported prevalence rates of 29.8% and 30.6% respectively for PwMS (Podor et al., 2014, 2009). Half of these patients with social anxiety had GAD and a quarter had depression. Severity of social anxiety symptoms was associated with reduced health-related quality of life and was not related to neurological disability.

3.3. Demographics

The eleven cross-sectional and six prospective studies that focused on gender differences associated with anxiety varied. Many studies found females were significantly more anxious than males at bivariate level (Dahl et al., 2009; Feinstein et al., 1999; Da Silva et al., 2011) and multivariate level (Theaudin et al., 2016; Wood et al., 2013; Chalk, 2007; Solari et al., 2010). Similarly, it was found that being female was an independent predictor of anxiety at multivariate level (Giordano et al., 2011). However, other bivariate studies found no gender differences for those who reported high levels of anxiety (Anhoque et al., 2011; Diaz-Olavarrieta, 1999; Hakim et al., 2000; Paredes et al., 2012). One study found slightly higher anxiety levels amongst men (31.1%) in comparison to women with MS (29.7%) (Dahl et al., 2012).

Seven cross-sectional and two prospective studies in this review investigated the relationship between anxiety and age. Many studies found younger age was associated with anxiety symptoms (Beiske et al, 2008, Hakim et al, 2000, Leonavicius and Adomaitiene, 2013, and Wood et al, 2013). However, three studies found no evidence for increased levels of anxiety among younger PwMS (Diaz-Olavarrieta et al, 1999, Espinola-Nadurille et al, 2010, and Harding et al, 2012).

3.4. Cognition

Many studies found that cognitive dysfunction (deficits in thinking, remembering, and reasoning) was significantly correlated with increased anxiety (Akbar et al, 2011, Bamer et al, 2008, Bruce and Arnett, 2009, Farrell et al, 2011, and Visser et al, 2009). Anxiety was also negatively correlated with self-awareness of cognitive dysfunction (Grech et al, 2015, Van Der Hiele et al, 2010, and Van Der Hiele et al, 2012). Although, self-reported memory problems did not predict anxiety (Bruce et al., 2010), anxiety was found to independently predict cognitive performance (Julian et al., 2009). Another study found that anxiety predicted perceptions of global cognitive functioning but not objective cognitive performance (Middleton et al., 2006). A prospective study found that anxiety was a significant predictor of cognitive change over time (Christodoulou et al., 2009). Despite these findings, other studies found no association between anxiety and cognitive dysfunction (Goretti et al, 2014, Kostaras et al, 2008, and Middleton et al, 2006). Furthermore, anxiety was found to have no association with perception of disability (Smith et al., 2000). However, others found patients’ illness representations were a significant predictor of anxiety (Jopson et al., 2003) and higher perception of two-year risk of wheelchair dependence was significantly related with high levels of anxiety (Janssens et al., 2004).

Neuropsychological performance, which examines cognitive, motor, behavioural, linguistic, and executive functioning, failed to reveal significant correlations with anxiety in both cross-sectional and prospective studies (Bruce and Arnett, 2009, Maia et al, 2011, and Olivares et al, 2012).

3.5. Physical functioning

Twenty-eight studies investigated the association between level of disability and anxiety symptoms; eighteen of these reported a significant relationship between high level of disability and high level of anxiety at either bivariate or multivariate level (Anhoque et al, 2011, Askari et al, 2014, Curral et al, 2011, Ionescu et al, 2012, Sarisoy et al, 2013, and Tan-Kristanto and Kiropoulos, 2015). Multiple linear regression analysis showed that depression and disability level were independent predictors of anxiety (Askari et al., 2014). A strong correlation was found between the number of symptoms inherent to the disease and anxiety (Roy-Bellina et al., 2010). Bogart (2015) also reported that disability identity; affirming one's status as a person with a disability, was a predictor of lower anxiety among PwMS.

In total, fourteen studies investigated the relationship between duration of illness and anxiety and in general findings were inconsistent. However, a recent study with a cohort of 5084 MS patients found that PwMS have an increased risk of anxiety during both the pre-diagnostic and post-diagnostic period compared to controls (Hoang et al., 2016). Furthermore, a prospective study with 513 PwMS reported that anxiety at baseline predicted anxiety four months later while controlling for demographic and disease related variables (Hartoonian et al., 2015).

The seven studies that investigated the relationship between pain and anxiety found a significant relationship between high levels of pain and anxiety at bivariate and multivariate level, with the exception of one cross-sectional bivariate study. Similarly, ten studies examined the relationship between anxiety and fatigue; all of these reported significant results either at bivariate or multivariate levels with the exception of two cross-sectional studies. Four studies examined the relationship between sleep disturbance and anxiety. Two multivariate studies and one bivariate study found strong correlations (Bamer et al, 2008 and Leonavicius and Adomaitiene, 2014). However, one bivariate study found no association between anxiety and sleep disturbance (Bruce et al., 2009).

Eight studies examined the relationship between course of illness or relapses and anxiety. The four prospective studies showed that number of relapses over time could significantly increase anxiety levels (Brown et al, 2009, Dalos et al, 1983, McCabe, 2005, and Potagas et al, 2008).

A range of biological factors was investigated in relation to anxiety. Anxiety did not correlate significantly with any of the measures of regional and total lesion loads and brain volume (Zorzon et al., 2002). Similarly, anxiety had no significant association with frontotemporal changes measured by MRI (Diaz-Olavarrieta, 1999).

3.5.2. Medical factors and treatment

Adherence is often a key issue for those who are chronically ill. One study reported a significant relationship between high level of anxiety and low level of adherence at bivariate level (Sidorenko et al., 2010). Anxiety levels were also significantly associated with lower levels of social support (Reade et al., 2012). Knowledge of disease information was found to improve anxiety levels (Niino et al., 2012).

Overall, nineteen cross-sectional and eight prospective studies investigated the relationship between anxiety and depression. Strong positive correlations were consistently found between anxiety and depression in the cross-sectional studies (Aloulou et al, 2011, Anhoque et al, 2011, Dahl et al, 2009, Espinola-Nadurille et al, 2010, Garfield and Lincoln, 2012, Karadayi et al, 2014, Leonavicius and Adomaitiene, 2011, Smith and Young, 2000, and White et al, 2008). Furthermore, multiple linear regression analysis showed that depression was an independent predictor of anxiety in patients (Anhoque et al, 2011, Bamer et al, 2008, and Giordano et al, 2011). Prospective studies have further shown a strong association between depression and anxiety (Brown et al, 2009, Pakenham and Samios, 2013, and Solari et al, 2010).

3.7. Personality

Both extroversion and neuroticism were strongly correlated with anxiety symptoms among PwMS (Liu et al., 2009). Furthermore, anxiety accounted for unique variance in conscientiousness (R2=0.23, F=25.37, P<0.001) and neuroticism (R2=0.65, F=153.09, P<0.001) at the multivariate level (Bruce et al., 2011). A prospective study found anxiety was strongly correlated to low dispositional optimism at baseline (Brown et al., 2009). Recently, a study also found that PwMS and a personality disorder had a significantly higher frequency of any anxiety disorder including PTSD (Uca et al., 2016).

3.8. Social factors

Seven studies focused on the role anxiety had on employment and job productivity. Presenteeism, the act of attending work when sick, was significantly associated with elevated levels of anxiety (Glanz et al., 2012). Changes in employment status after MS onset, also had a negative impact on anxiety levels at multivariate but not bivariate level (Kikuchi et al, 2013 and Niino et al, 2012). Patients without anxiety were significantly more likely to be employed (Krokavcova et al, 2010 and Tan-Kristanto and Kiropoulos, 2015). However, one study found no relationship between employment and anxiety (Van der Hiele et al., 2014).

Significant associations were found between anxiety and negative life events, problems in family life and social functioning (Liu et al, 2009 and Potagas et al, 2008). Mixed results were found for the relationship between anxiety and level of education as both low level of education and having a university education were significantly associated with high levels of anxiety (Da Silva et al, 2011 and Shabani et al, 2007). Quality of life (QoL) was examined in eight cross-sectional and two prospective studies. Anxiety was strongly associated with lower QoL (Fisk et al, 2014, Fruhwald et al, 2001, Olivares et al, 2012, Pekmezovic et al, 2012, Spain et al, 2007, Szilasiova et al, 2011, and Tadic and Dajic, 2013), at all levels of illness severity (Ionescu et al., 2012).

3.9. Coping

High levels of anxiety were consistently associated with emotional preoccupation coping (focusing on the emotional consequences of MS) (Kehler and Hadjistavropoulos, 2009, Montel and Bungener, 2007, Roy-Bellina et al, 2010, Roy-Bellina et al, 2009, and Tan-Kristanto and Kiropoulos, 2015), avoidance coping (denial) (Tan-Kristanto et al., 2015), drinking to excess (Dahl et al., 2009) and substance abuse (Milanlioglu et al., 2013). A recent, large, representative study found alcohol dependence and smoking were associated with anxiety (McKay et al., 2016). Prospective studies found that smoking and no previous training in relaxation exercises were predictors of anxiety (Brown et al., 2009). Somewhat surprisingly planning, acceptance and focus on emotional ventilation worsened symptoms (Brajkovic et al., 2009).

Research examining predictors of anxiety found positive reinterpretation, social emotional support and humour predicted an improvement of anxiety symptoms. “Problem focused coping” (targeted at reducing the stressor) was also negatively correlated with anxiety (Roy-Bellina et al., 2009). A prospective study found that patients who accessed social support to cope had a reduction in anxiety scores in contrast to the population norm (Johnson et al., 2012). Anxiety was also significantly associated with low levels of acceptance and mindfulness (Pakenham et al., 2013).

4. Discussion

4.1. Summary of main findings

The results of this review suggest that anxiety is associated with a variety of physical, psychological, cognitive and social factors, some of which are amenable to change, as highlighted in Table 3. Overall, the high prevalence of anxiety that was reported across majority of the studies for PwMS contrasted strongly to a prevalence rate for anxiety of 5.1% for the general population (Aarsland and Figved, 2007). Many studies found significant associations between high levels of anxiety and depression in PwMS. In one study, depression was found to be the most significant factor associated with anxiety (Garfield et al., 2012). The significance of other factors such as self-efficacy, level of disability and stress were diluted due to their influence in multivariate analysis. Although prospective studies were limited in number, one prospective study carried out over two years revealed that depression strongly predicted anxiety, and anxiety strongly predicted later depression (Brown et al., 2009). In many of the studies women represented the majority of participants. This gender imbalance is likely due to the higher prevalence of MS among women. It has been suggested that a gender ratio approaching 4:1 exists which is similar to other autoimmune diseases such as rheumatoid arthritis (Orton et al., 2006).

Anxiety and depression were predicted by a combination of unhealthy lifestyle behaviours (e.g. drug use, smoking) (Brown et al, 2009 and McKay et al, 2016). Several studies suggested that various other types of coping strategies were linked to anxiety symptoms. PwMS were less likely to use positive and problem-focused strategies in comparison with the general population, and used avoidance and emotional preoccupation strategies more frequently (Goretti et al., 2009).

Although low level of social support was associated with higher level of anxiety it is important to acknowledge the complex nature of social support in relation to health (Matcham et al., 2015): those with anxiety may have distorted perceptions of the availability of social support. Furthermore, personality factors such as neuroticism may confound associations between social support and health, and there are genuine cultural differences in perceptions of “adequate” social support (Thoits, 2011).

4.2. Methodological critique of reviewed studies

Overall, despite the large number of studies relevant to this review, the quality of the research was disappointing. In some cases, the strengths of associations between anxiety-related factors were not reported, for example when only the abstract of the study was available.

The fundamental limitation of the reviewed research was that most studies (112 out of 131) were cross-sectional; without prospective evidence causal relationships cannot be established. Nevertheless, understanding the factors associated with anxiety provides an insight into issues that could be tackled through psychological interventions, leading to better outcomes.

Another problem was that some studies used small sample sizes and were therefore underpowered to detect relationships, or prevented the application of positive findings to larger populations (Kothari, 2004). Inadequate reporting of participant characteristics made it difficult to interpret study findings or make assumptions about generalisability. Some studies failed to report important demographic data or recruitment strategies which prevented the reporting of descriptive statistics for demographic data and information for quality assessment.

Other significant weaknesses were linked to the measurement of anxiety and factors associated with anxiety. The studies relied primarily on participant self-report measures. This may mean that results were influenced by shared-method variance which could threaten the validity of conclusions made regarding the relationships between measures (Huang et al., 1998). In some studies a rating scale for anxiety was included alongside many other psychometric measures, therefore formal Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V) diagnoses could not be made. The use of scales can cause difficulty with interpreting data and making accurate assumptions. Screening tools for anxiety disorders in comparison to depression can be particularly problematic because different kinds of anxiety disorders generally have more heterogeneous symptoms than different types of depressive disorders. In addition to this, normal expressions of anxiety in clinical samples generally exhibit a greater overlap with anxiety symptoms shown by patients that are diagnosed with an anxiety disorder (Rose and Devine, 2014). It is important to consider that in some studies anxiety was not the primary focus of research and therefore it was difficult to interpret findings. However, we felt that it was important to include these studies to ensure this paper gave a comprehensive overview of the literature to date.

Some studies also failed to report results in conventional formats (e.g. a regression analysis table including beta coefficients and p-values) (American Psychological Association, 2009). This prevented the interpretation of results about the relative importance of factors associated with anxiety.

4.3. Models of understanding anxiety in the context of MS

A number of theoretical models of anxiety have previously been proposed, including the Avoidance Model of Worry and GAD (Borkovec, 1994), the Intolerance of Uncertainty Model (Dugas, et al., 1995), the Metacognitive Model (Wells, 1995), the Emotion Dysregulation Model (Mennin et al., 2002) and the Acceptance-based Model of GAD (Roemer and Orsillo, 2002). These offer valuable insights into the basic nature of GAD and the necessary steps for successful treatment (Behar et al., 2009). They share a common emphasis on the central importance of avoidance of internal experiences. However, given their general nature they do not specifically account for the unique worries associated with MS. The Working Model of Adjustment to Multiple Sclerosis (Dennison et al., 2009) has been proposed as a provisional working model of adjustment to MS that incorporates many specific concerns associated with MS. Beck's model of emotional disorders (Beck, 2011) can usefully be applied to people with anxiety in the context of MS (see Fig. 2). The model assumes that anxiety can be triggered by critical events. These events may include developing the disease, fear of disability or other life stressors. Subsequently, avoidance coping, low mood or stress and unhelpful thinking maintains the symptoms.

Fig. 2

Fig. 2

A working conceptual model of anxiety for PwMS.

 

4.4. Clinical implications of key findings

The findings of this review have many clinical implications. Firstly, although anxiety is commonly reported in PwMS, it is often not treated (Beiske et al., 2008). It is unknown whether this is due to the failure of clinicians to diagnose mental health problems or whether PwMS decline treatment. We think the latter unlikely. Nonetheless, this review suggests the importance of detecting and treating anxiety when patients are first diagnosed with MS. They should then be monitored throughout the course of the illness.

Few studies to date have specifically targeted anxiety within an MS population. It is difficult to know why this is the case but it may be that the more overt physical consequences of MS have been prioritized or it may be due to an over-focus on depression and cognition. One small study of individual cognitive behavioural therapy vs. psychotherapy found a reduction in anxiety levels in people who received CBT (Foley et al., 1987). Similarly, a small study of guided imagery and relaxation found reduced anxiety scores in PwMS (Maguire, 1996).

Comorbid anxiety and depression can have important consequences for patients in terms of their MS, their ability to engage in treatment and their quality of life (Garfield et al., 2012). Although interventions targeted at comorbid depression and anxiety have been neglected within the MS literature, treatments such as trans-diagnostic interventions that use the same underlying treatment principles across mental disorders, have been beneficial for general adult populations with comorbid anxiety and depression (McEvoy et al., 2009). These interventions apply core CBT-based treatment principles and techniques such as graded exposure and cognitive restructuring which target the common processes underlying anxiety and depression, rather than targeting the symptoms of specific disorders. Furthermore, a systematic review of existing literature on these types of treatments for depression and anxiety disorders in adults found large and significant reductions in both anxiety and depression, and moderate improvements in quality of life (Newby et al., 2015). Previous research indicates that PwMS benefit from strategies that enhance self-efficacy, a transdiagnostic process (Fraser and Polito, 2007 and Nodhturft et al, 2000). Future research should therefore investigate the efficacy of psychological interventions for PwMS.

From a pharmacological perspective serotonin reuptake inhibitors are considered first-line treatment in depression comorbid with a spectrum of anxiety disorders (Coplan et al., 2015). It has further been recommended that combining benzodiazepines with an SSRI can lead to more rapid control of anxiety and improved control of episodic or situational anxiety (Dunlop and Davis, 2008). There is little evidence to date on the pharmacological treatment of depression and anxiety specifically for PwMS. A Cochrane review on pharmacologic treatment of depression for PwMS reported only two randomised controlled trials, which both had significant problems within their methodology including loss of follow-up data (Koch et al., 2011) A recent review indicated high antidepressant use among PwMS and suggested that these drugs are possibly helpful in idiopathic major depressive disorder or based on patient and doctor beliefs that they are beneficial (Koch et al., 2015). Overall, it appears that large, well-controlled trials are required for pharmacological treatments in MS in order to establish a firm evidence base on their efficacy for both depression and anxiety.

4.5. Limitations of the review

A number of limitations of this review should be considered. Firstly, due to the large number of studies available, only studies published in peer-reviewed journals were considered. It was beyond the scope of the review to examine unpublished studies and the “grey literature”. Unfortunately this decision may result in bias since unpublished studies are more likely to demonstrate no relationships.

Secondly, due to the large number of studies, the review included only quantitative research (including one mixed methods study); qualitative studies were excluded. Although qualitative research does not generally determine whether one variable can influence another or conclusively establish relationships, qualitative research can offer rich and detailed understandings on the complex associations of interest.

Finally, there has been no previous systematic review conducted to date on anxiety among PwMS. In this study, because the inclusion criteria were broad, the studies were vastly heterogeneous, and so not conducive to an overall synthesis. Furthermore, the broad inclusion criteria resulted in studies that had weak methodology and did not provide strong evidence of associations, particularly causal relationships. This review aimed to draw attention to the dearth of high quality evidence, improve the quality of future research and highlight areas in which future studies should focus on.

4.6. Suggestions for future research

Specifically, prospective studies are needed to determine which factors precede or predict anxiety. Ideally, studies need to employ prospective designs and assess large samples of participants when MS is first diagnosed and thereafter. It is important that recruitment strategies are specified and power calculations are stated to improve the overall quality of this literature.

Future studies should also examine the neuroimaging correlates of anxiety as has been previously researched for depression and cognition in MS (e.g. (Malkki, 2015 and Mrabet et al, 2014). This may enhance our understanding of how potential biomarkers such as structural brain changes relate to anxiety in PwMS.

5. Conclusion

This review has shown that high levels of anxiety exist among PwMS, highlighting the need for early intervention and treatment of anxiety throughout the course of MS. Anxiety in MS is associated with a number of physical, cognitive, social and psychological factors which have been conceptualized in a model of anxiety. Due to the high levels of comorbidity with depression, many PwMS are likely to benefit from psychological and pharmacological interventions targeting both anxiety and depression.

Conflict of interest/role of funding source

TC receives salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the NHS. EB and FM declare that they have no conflicts of interest.

Appendix A. Search terms

A search was conducted for articles published between January 1980 and September 2016 which examined MS-related anxiety. Search terms were customized to each database and combined with key word searches e.g. “anxiety”, “anxiety disorder”, “anxious” and terms such as “Multiple Sclerosis”, “MS”, “determine*”, “predict*”, “correlate*”, “relationship*”, “prevalence”, “intervention*”, “association*” and “outcome*”.

Appendix B. Anxiety Measures used in studies (N=122)

Anxiety measure Number of studies
Hospital Anxiety and Depression Scale (HADS) 65
Beck Anxiety Inventory (BAI) 4
Patient Health Questionnaire (PHQ) 1
Hopkins Symptom Checklist 1
Hamilton Anxiety Scale (HAM-A) 12
Spielberger State Trait Anxiety Inventory (STAI) 15
General Health Questionnaire (GHQ-28) 1
General Health Questionnaire (GHQ-30) 1
Yale-Brown Obsessive Compulsive Scale 1
Neuropsychology Inventory (NPI) 1
Health Anxiety Inventory 2
Profile of Mood States (POMS) 2
Mental Health Inventory (MHI) 2
Social Phobia Inventory (SPIN) 2
Depression Anxiety Stress Scale (DASS) 2
Crown-Crisp Experiential Index (CCEI) 1
Nottingham Adjustment Scale (NAS) 2
Zung Self-Rating Anxiety Scale (SAS) 1
Zung Anxiety Rating Scale (ZARS) (German) 1
Symptom Checklist-90 (SCL-90) (adapted to Spanish) 1
PROMIS 3
DSM 3
ICD 2
K-SADS 1
Determined by Psychiatrist 2
Clinician administered PTSD scale 1

Appendix C. Results and strengths of associations for cross-sectional and prospective studies

Cross-sectional studies

Authors, Year No. Patients No. Controls Factor/s investigated Type analysis Results
Akbar et al. (2011) 108 0 Cognitive dysfunction Stepwise linear regression model Anxiety was a predictor for cognitive dysfunction (R2=0.108, F=12.4, p=0.001) (measure completed by informant of the patient) (R2=0.272, F=38.93, p<0.001) (measure completed by patient).
Al-Asmi et al. (2013) 57 53 Prevalence Chi-Square; ANOVA; Wilcoxon Rank test Prevalence=50.8%. Multiple Sclerosis was associated with substantial risks of symptoms of anxiety (OR=2.43; 95% CI 1.13, 6.82; P=0.03). HADS scores on anxiety among MS patients were significantly higher (8.3 vs. 4.9) than that among the control group (P=0.04).
Aloulou et al. (2011) 31 0 Disability; Age; Alexithymia; Depression Descriptive statistics and correlation analysis Prevalence=52%. Anxiety was correlated with the level of disability and age of disease onset. A positive correlation was also found between anxiety and alexithymia and anxiety and depression. (Only abstract available)
Anhoque et al. (2011) 19 0 Prevalence; Disability Correlation analysis MS patients had a high prevalence of anxiety. The association between anxiety and level of disability was significant (p=0.02) (no strength specified).
Askari et al. (2014) 180 0 Depression; Level of disability Multiple Regression Analysis Depression (b=0.61, 95% CI (0.53, 0.8) p=0.001) and level of disability (b=0.38, 95% CI (1, −3.3) p<0.001) were significant predictors of anxiety in PwMS.
Bamer et al. (2008) 1271 0 Prevalence; Clinical variables Multiple Logistic Regression Prevalence=25%. Regression analysis showed that anxiety is associated with difficulties in thinking, higher depressive symptoms, more pain, increased level of stress, sleep problems, not using a wheel chair and with better health. (Only abstract available)
Beier et al. (2013) 513 0 Measure of anxiety and related factors EFA/CFA and Correlations The reliability and validity of the PHQ-A as a measure for PwMS was supported for PwMS. The PHQ-A was highly correlated with anxiety(r=0.70). Age (r=−0.12) and duration of MS (r=−0.14) were both negatively associated with anxiety (both p<0.005). Women endorsed significantly higher anxiety than men (t (508)=2.13, p=0.03). There were also significant differences in anxiety between MS types; PwMS with SPMS endorsed the most anxiety symptoms (F (3, 496)=55.4, p=0.001). Individuals higher in anxiety also endorsed more depressive symptoms (0.70, p<0.001), reported higher pain severity (r=0.40) and more pain interference (r=0.42), (all p<0.001). (Only abstract available)
Beiske et al. (2008) 140 0 Pain; Fatigue; Age Multiple Regression Analysis Pain (OR=5.120, 95%CI (1.087–24.122), p=0.010), fatigue (OR=0.993, 95% CI (0.882–0.986), p=0.039) and younger age at onset (OR=0.93, 95% CI 0.88–0.99) p=0.014) were significant predictors of anxiety. OR=Odds Reduction.
Bogart et al. (2015) 106 0 Disability identity, depression, anxiety, MS duration, demographics Hierarchical regressions 37% had likely cases of anxiety. Stronger disability identity was associated with significantly lower anxiety and explained a significant 4% increase in variance. The significant predictors of anxiety in the final model were age, disability identity, and ADL. The final model explained 19% of the variance in participants’ anxiety.
Brajkovic et al. (2009) 68 0 Prevalence, Coping mechanisms Multiple Regression Analysis Prevalence=63.2% (symptoms of anxiety) Predictors improving anxiety were positive reinterpretation, social emotional support and humour; Predictors worsening anxiety were planning, acceptance focus on emotional ventilation and denial. (Only abstract available)
Bruce and Arnett (2009) 50 45 Depression, Level of disability, Fatigue, Sleep Disturbances Correlations High level of anxiety was correlated with higher levels of depression (r=0.73, p<0.001). No significant relationships were found between anxiety and disability level, overall fatigue, sleep disturbance, pain, and neuropsychological functioning.
Bruce et al. (2009) 79 0 Cognitive impairment and Perceived memory failures. Stepwise Linear Regression PwMS reported more anxiety than controls (32.75+6.54; t (96)=2.59, p<0.05). Self-reported memory problems (r=0.50, p<0.001) and higher total dissociation (disruption to cognitive processes) (r=0.59, p<0.001) were correlated with high level of anxiety. Anxiety did not account for unique variance in self-reported memory in the regression model.
Bruce and Lynch (2011) 85 20 Personality Stepwise Regression Stepwise regression revealed that anxiety accounted for unique variance in neuroticism (R2=0.65, F=153.09, p<0.001) and in conscientiousness (R2=0.23, F=25.37, p<0.001).
Chalfant et al. (2004) 58 0 Prevalence PTSD Descriptive Statistics Nine PwMS (16%) met symptom criteria for PTSD.
Chalk (2007) 329 0 Gender; RRMS type MANCOVA Females (F (1,312)=5.20, p<0.05) and patients with RRMS had higher levels of anxiety (F (1,312)=5.08, p<0.05).
Chylova et al. (2009) 223 0 Age; Health status Multiple Regression analysis Anxiety was related to worse physical and mental health status in younger MS patients, but not in the older ones (Only abstract available)
Cihelkova and Bojar (2009) 40 0 Prevalence Descriptive Analysis A high prevalence of anxiety was detected by BAI II of 65% and by SCL-90 of 62% in PwMS (Only abstract available)
Counsell et al. (2013) 126 0 PTSD Multiple Regression analysis Higher MS-related disability (b=0.15, p=0.007), having another health condition (b=2.48, p=0.005) and anxiety (b=1.35, p=0.000) were significant predictors of PTSD symptoms (R2=0.51).
Curral et al. (2011) 48 0 Prevalence Descriptive Statistics PwMS did not have significantly high levels of anxiety (Only abstract available)
Da Silva et al. (2011) 325 183 Prevalence; Gender; Education. Correlation analysis; MANCOVA Prevalence=51%. Anxiety scores were significantly higher for women (t=4.213, p<0.001). Negative correlations were found between number of years of education and level of anxiety (r=− 0.215 and p<0.001). For MANCOVA no statistically significant differences were found between anxiety and course of illness.
Dahl et al. (2009) 172 56,000 Gender; Duration; Disability Descriptive statistics and correlation analysis Among men the prevalence rate of anxiety was 31.1% vs. 12.1% for controls (p=0.002). For women, the prevalence of anxiety was 29.7% vs. 17.4% for controls (p<0.001). Anxiety was not correlated with duration of disease or disability.
Dubayova et al. (2013) 198 142 Quality of life Multiple Regression Analysis Anxiety was a significant predictor for lower scores for quality of life in the regression model (b=0.38, p<0.001, R2=0.33)
Espinola-Nadurille et al. (2010) 37 37 Prevalence of anxiety; Depression Descriptive Statistics Prevalence=28.6%. Levels of anxiety were significantly higher in patients with depressive disorders compared to those without (21.2 SD=9.91 vs. 9.33 SD=8.54, p=.001). No significant differences concerning correlations between age, number of relapses, duration of illness, disability and anxiety were found.
Etesam et al. (2016) 112 0 Self-disclosure; Depression Pearson Correlation coefficient
Multiple Regression analysis
There was a positive and significant correlation among disease variables (level of disability and the number of times hospitalized) and anxiety (p<0.01.) Higher scores of anxiety were negatively associated with distress disclosure (p<0.01.). Multiple linear regression analysis revealed that frequency of hospitalization and disability level, predicted anxiety (p<0.05.) Controlling disease variables demonstrated distress disclosure as an independent factor to predict anxiety in the participants (p<0.05.)
Farrell et al. (2011) 101 0 Prevalence; Cognitive Difficulties; Depression Correlation analysis Prevalence=32.1%. There were significant correlations between anxiety and reported cognitive difficulties and anxiety and depression. (Only abstract available)
Feinstein et al. (1999) 152 0 Prevalence; Gender Descriptive Statistics Prevalence=15.8%. Anxious patients were statistically more likely to be female (22/24 were female).
Fisk et al. (2014) 949 0 Quality of life Multiple Regression Analysis Regression analyses showed that anxiety was associated with lower quality of life (Only abstract available)
Foroughipour et al. (2012) 112 0 OCD Chi-Square tests Prevalence of OCD in patients with MS was 16.1%. OCD was significantly associated with a higher level of disability (X2=86.515, p=0.0001), duration of disease (X2=9.135, p=0.033), phenotypic subgroup (X2=8.970, p=0.029), cranial nerve involvement (X2=6.531, p=0.011), cerebellar nerve involvement (X2=19.390, p=0.0001), autonomic nerve involvement (X2=18.587, p=0.0001), sensory nerve involvement (X2=11.593, p=0.001) and motor nerve involvement (X2=25.652, p=0.0001).
Fruhwald et al. (2001) 74 74 Quality of life Mann Whitney-U tests There was a significant relationship between anxiety and quality of Life (Only abstract available in English).
Garfield and Lincoln (2012) 157 0 Prevalence; Difficulty with mood; Distress; Depression WALD Chi-square test Logistic Regression Prevalence=57%. Experiencing difficulty with mood (Wald's X2 (1)=11, b=1.18, p=0.001), psychological distress (Wald's X2 (1)=6.3, b=0.21, p=0.01) and depression (Wald's X2 (1)=5.05, b=0.28, p=0.03) were significant predictors of clinically significant levels of anxiety (R2=0.46).
Glanz et al. (2012) 377 0 Presenteeism Correlation Analysis Presenteeism was correlated with anxiety (r=0.39, p<0.05).
Goretti et al. (2014) 190 0 Cognitive Dysfunction Multiple Regression Analysis In the multivariate analysis, anxiety was associated with failure on the Symbol Digit Modalities Test (OR=2.07, 95% CI 1.01–4.41, p=0.05), There was no relationship between anxiety and failure on other neuropsychological tasks nor overall cognitive dysfunction.
Grech et al. (2015) 107 0 Cognitive Dysfunction; stress, depression; Quality of life Hierarchical regression analysis Self- and independent-report variables significantly predicted trait anxiety (DEX–S: B=0.72, p=0.008, OR=2.06; DEX–I: B=0.41, p=0.04, OR=1.51). They were entered into the final model jointly at Step 2, where they significantly predicted trait anxiety, although only the DEX–S individually reached significance, B=0.64, p=0.03, OR=1.89. The total model accounted for 29.0% of the variability in trait anxiety.
Hakim et al. (2000) 305 0 Prevalence; Gender; Age; Duration; Severity Standardised Prevalence Ratios Prevalence=16%. Anxiety was not related to disease severity, duration or gender. Anxiety was present in 37% of patients who were less than 30 years old compared to 17% of those in the age group 30–44 years.
Harding et al. (2012) 102 0 Age Correlation analysis There was no evidence for increased levels of anxiety in the younger onset MS group (Only abstract available)
Ionescu et al. (2012) 112 0 Level of disability; Depression; Daily functioning; Quality of life; Pearson and Kendall methods. Level of disability, depression, daily functioning and quality of life were all strongly correlated with anxiety (p=0.0001) (Only abstract available).
Iriarte et al. (2000) 155 0 Fatigue Kruskal Wallis H test Anxiety was associated with fatigue (Kruskal H=7.59, P<0.005).
Janssens et al. (2003) 101 101 Prevalence of anxiety ANOVA Prevalence=34% (eight months after diagnosis)
Janssens et al. (2004) 101 0 Prevalence; Level of disability; Risk perception Multiple Regression Analysis Prevalence=34%. Higher level of disability reported by PwMS was a significant predictor for anxiety symptoms (b=0.24, p=0.002). Perceived 2-year risk was also a significant predictor for anxiety (b=0.78, p<0.001).
Jones et al. (2012) 4178 0 Prevalence of anxiety; Age; Time since diagnosis; Depression; MS type Kruskal-Wallis, ANOVA Prevalence=54%. There was a weak negative relationship between age and anxiety score (rho=20.18, p<0.001) and between time since diagnosis and anxiety score (rho=20.10, p<0.001). Depression and anxiety scores were positively correlated (rho=0.565, p<0.001). Anxiety was most frequent among people with RRMS (56.5%). Anxiety scores were higher in women (p<0.001, N=4287). There was no significant difference in anxiety scores between the genders for SPMS (not sig, N=396). Kruskall-Wallis tests showed that there was no significant difference in men's anxiety scores for different types of MS (not sig, N=1226). However, women's anxiety scores differed with type of MS (p=0.017, N=2998), with the highest levels in RRMS.
Jones et al. (2013) 4500 0 Prevalence Descriptive Analysis There was a high prevalence of anxiety in PwMS (Only abstract available)
Jones and Amtmann (2014) 405 0 Healthcare worry Multiple Regression Analysis Total level of health care worry was found to be a significant predictor for anxiety (b=0.363, p<0.01, R2=0.184)
Jones et al. (2014) 4 516 0 Level of disability; Gender; Durations; Age Multiple Regression analysis Level of disability (b=0.398), male gender (b=20.076), PPMS (b=20.062), disease duration (b=20.080), and age (b=20.218) (all p<0.001) were all significant predictors of anxiety. This indicates that level of disability is the major contributor to the increase in the anxiety score, and that the other factors actually reduce the effect, with age being the most important of these (R2=0.184; Durbin-Watson 1.984; ANOVA F=161.508, df=5, p<0.001).
Jopson and Moss-Morris (2003) 168 0 Illness representations Hierarchical Multiple Regression Analysis Illness identity (b=0.20. p<0.05), cyclical timeline (b=0.23, p<0.05) and illness coherence (b=0.18, p<0.05) (all types of illness representations) were all significant predictors of anxiety (R2=0.23).
Julian and Arnett (2009) 77 0 Cognitive functioning Multiple Regression Analysis Cognitive functioning in MS was a significant predictor for anxiety in the regression model F (1,68)=4.53, p<0.05 (R2=0.35).
Karadayi et al. (2014) 31 31 Disability; Fatigue; Depression; Cognitive Impairment Correlations Anxiety was significantly correlated with level of disability (r=0.69, p<0.01), depression (r=0.43, p<0.01) and fatigue (r=0.49, p<0.01). Anxiety was not correlated with cognitive impairment.
Kehler and Hadjistavropoulos (2009) 246 0 Health anxiety and coping Logistic and Hierarchical Regression Prevalence=28%. Anxiety did not significantly differ among individuals with different types of MS (F (2, 210)=1.10, ns). Emotional Preoccupation coping was found to be a significant predictor for anxiety (R2=0.40, F(1,227)=152.90, p<0.001). Participants with high health anxiety were 1.38 times more likely to have high generalized anxiety. Health anxiety and generalized anxiety demonstrated a strong positive correlation (r (231)=0.67, p<0.001).
Kikuchi et al. (2013) 163 0 Employment Structural Equation Modeling Changes in employment status after MS onset were negatively associated with anxiety (b=−0.25, p<0.05)
Korostil and Feinstein (2007) 140 0 Prevalence, Gender, Depression, Drinking; Social stress; Suicide T-tests and Discriminative Function Analysis Lifetime prevalence of any anxiety disorder=35.7%. Prev. panic disorder=10%, OCD=8.6%, and GAD=18.6%. Subjects with an anxiety disorder were more likely to be female (t (1)=7.7, p=0.06), have a history of depression (t (1)=22.0, p=0.001), drink to excess (t (1)=4.7, p=0.03), report higher social stress (t (1)=3.6, p=0.001) and have contemplated suicide (t (138)=3.6, p=0.04).
Kostaras et al. (2008) 60 0 Cognitive deficits Correlations No correlation was established between anxiety and cognitive dysfunction (Only abstract available)
Kraft et al. (2012) 1,543 0 Prevalence Descriptive Statistics There was a higher prevalence of anxiety in PwMS in comparison to healthy controls (p<0.001) (Only abstract available)
Krokavcova et al. (2010) 184 0 Employment Stepwise Logistic Regression Patients without anxiety were 2.64 times more likely to be employed. Not suffering from anxiety was a significant predictor of employment status (b=0.972, 95%CI: 1.23–5.67, p=0.012).
Labuz-Roszak et al. (2007) 122 0 Prevalence; Fatigue Correlation Analysis Prevalence=26.2%. An association was found between anxiety and fatigue (Only abstract available)
Leonavicius and Adomaitiene (2013) 312 0 Prevalence; Gender; Age, Duration, Depression; level of social activity. Logistic Regression Prevalence=20.2%. Anxiety was found to be higher among females (22.4% vs. 16.4%). Younger age (b=2.357, p<0.05), shorter MS duration (b=11.904, p<0.05), depression (b=17.283, p<0.05) and few MS group activities (b=8.222, p<0.005) were significant predictors of anxiety. Being socially active was not a significant predictor of anxiety among PwMS.
Leonavicius and Adomaitiene (2011) 270 0 Prevalence; Duration Depression Odds Ratios Prevalence=17%. Patients with shorter MS duration (<10 years) had 2.52 times higher odds ratio to be diagnosed with anxiety. Patients with depression had 3.03 times higher odds ratio to be diagnosed with anxiety (Only abstract available).
Leonavicius and Adomaitiene (2014) 137 0 Prevalence; Sleep disturbances Multivariate Linear Regression Prevalence=19.7%. Sleep disturbances were found to be a significant predictor anxiety (b=3.362, CI 95% (1.113, 5.885)
Lester et al. (2007) 82 0 Level of disability; Cognitive impairment; Hierarchical Regression Analysis Level of disability (b=0.46) and cognitive impairment (b=0.46) were both significant predictors of anxiety (p<0.005, R2=0.39)
Levinthal and Bielefeldt (2014) 71 0 Dyspeptic symptoms F-tests, Fisher Exact Tests PwMS with moderate to severe dyspepsia had higher anxiety compared to controls (62.5% vs. 27.3%) (Only abstract available)
Liu et al. (2009) 41 0 Negative life events; Personality Correlation analysis There were higher levels of anxiety in PwMS than controls (p<0.001). Significant correlations were found between anxiety and negative life events (r=0.258), problems of family life (r=0.254) (both p<0.01), social functioning (r=0.247, p<0.05), extroversion (r=-−0.296) and neuroticism (r=0.380) (both p<0.001).
Lopes et al. (2012) 119 0 Prevalence; Fatigue Correlation analysis Prevalence=44.5%. Fatigue was associated with anxiety (OR 8.33, 95%CI 2.33–29.75, p<0.001) (Only abstract available)
Maia et al. (2011) 25 0 Neuropsychological functioning Correlation analysis There was no significant relationship between neuropsychological functioning and anxiety (Only abstract available)
Marrie et al. (2013) 4192 20,940 Prevalence of anxiety Descriptive Analysis Prevalence=35.6%
Medin et al. (2014) 13 0 Epilepsy Correlation analysis Of those diagnosed with MS and epilepsy, 77% had clinically significant levels of anxiety (Only abstract available)
Middleton, et al. (2006) 221 0 Perception global functioning Multiple Regression Analysis Anxiety was found to significantly predict perceptions of global cognitive functioning (b=0.55; t=8.16; p<0.001) but not objective cognitive performance.
Milanlioglu et al. (2013) 50 30 Coping strategies Correlation analysis There was a positive correlation with substance use and non-functional coping strategies for anxiety (no strength specified in paper).
Milinis et al. (2014) 260 0 Spasticity Correlation analysis There was a weak correlation between anxiety and spasticity (r=0.23,p<0.05). (Only abstract available)
Miljatovic (2013) 98 0 Level of disability Correlation analysis A strong correlation was found between anxiety and level of disability (p=0.0001). (Only abstract available)
Mills and Young (2011) 635 0 Fatigue Linear regression High levels of anxiety were associated with greater fatigue (rho=0.426, mean difference 1.30 logits, 95% CI 0.99–1.61, p<0.001, R2<0.3).
Montel and Bungener (2007) 135 0 Coping; Pain; Distress; Sleep ANOVA Prevalence anxiety=4%. Anxiety had a significant effect on emotional coping strategies (WCC: p=0.005; CHIP: P<0.001). In the AVOVA, anxiety was negatively related to pain (−0.44), emotional well-being (−0.52), distress (−0.39), cognitive functions (−0.42), general well-being (−0.35), and sleep (−0.31). WCC=Ways of Coping Checklist. CHIP=Coping with Health Injuries and Problems.
Morrow et al. (2014) 96 0 Prevalence Descriptive statistics Prevalence=24% (Only abstract available)
Nicholl et al. (2001) 96 0 Prevalence; Disability Correlation analysis Prevalence=31%. There was a correlation between level of disability and anxiety (r=0.49, p<0.01)
Niino et al. (2012) 163 0 Employment; Knowledge; Disability Structural Equation Modeling Level of disability was not associated with anxiety. Changes in employment status after onset of MS increased anxiety. Knowledge of disease information improved level of anxiety (Only abstract available).
Noy et al. (1995) 20 0 Exacerbations; Duration Correlations Anxiety level was positively correlated with number of exacerbations (r=0.39 p=0.08) but not with disease duration.
Ostacoli et al. (2013) 232 0 PTSD Multiple Linear Regression Prevalence=5.17% for PTSD. Levels of education (0R=0.672, 95% p=0.019) anxiety (0R=1.474, p=0.016) and depression (0R=1.398, 95% p=0.022) were significant determinants of the presence of PTSD.
Paredes and Kirchner Nebot (2012) 90 0 Gender; Time since diagnosis Correlation analysis High levels of anxiety were present in both genders. There was no relationship between anxiety and time since diagnosis (Only abstract available).
Pfaff et al. (2014) 16 16 Level of disability n/s (bivariate) Level of disability was associated with anxiety (p=0.03) (Only abstract available)
Pieper et al. (2012) 50 0 Prevalence; Disability n/s (bivariate) Lifetime prevalence of any anxiety disorder=53.2% Anxiety was not associated with level of disability (Only abstract available)
Poder et al. (2009) 245 0 Social Anxiety Correlation analysis Prevalence of social anxiety=30.6% (SPIN). There was a strong correlation between social anxiety and generalized anxiety (r=0.59, p<0.01) and social anxiety and depression (r=0.56, p<0.01). Severity of social anxiety symptoms was correlated with reduced health-related quality of life (r=−0.40, p<0.01) and not related to neurological disability
Poder et al. (2007) 265 0 Social Anxiety Multivariate Models Prevalence of social anxiety=29.8% (SPIN) and 22.4% (MINI SPIN). Gender, age, EDSS and use of disease modifying drugs did not differ between groups with and without social anxiety. Those with social anxiety had lower health related quality of life scores (p<0.001) (Only abstract available).
Reade et al. (2012) 145 0 Stress; Social support ANOVA Anxiety levels were significantly associated with high level of stress and lower levels of social support (F (3, 125)=14.23, p<0.001).
Roy Bellina et al. (2010) 32 0 Number of symptoms Correlation analysis A positive correlation was found between “the number of symptoms inherent to the disease” and anxiety (r=0.59, p=0.0004) (Only abstract available)
Roy-Bellina et al. (2009) 45 0 Coping strategies Correlation analysis Problem-focused coping was negatively correlated with anxiety (state: r=−0.410, p=0.0048; feature: r=−0.458, p=0.0013). Emotion-focused coping was positively correlated with anxiety (feature: r=0.554, p=0.0001) (Only abstract available).
Sarisoy et al. (2013) 76 76 Prevalence; Disability Correlation analysis There was a high prevalence of anxiety in PwMS compared to the healthy controls. Level of disability and high level of anxiety were correlated (r=0.355, p=0.002).
Schwartz et al. (1996) 139 0 Fatigue Multiple Regression Analysis Anxiety was not a predictor for fatigue in the regression model.
Shabani et al. (2007) 85 0 Prevalence Chi Square tests Prevalence of all anxiety disorders=22.4%. Prevalence of OCD was significantly higher compared to controls (p<0.05). Prevalence of GAD=11.8%, panic disorder=1.2%, specific phobias=7.1% and OCD=11.8%. There was a significant association between anxiety and university education (p<0.05, df=1, F2=13.99).
Sidorenko et al. (2010) 148 0 Prevalence; Adherence n/s (bivariate analysis) PwMS had higher levels of anxiety than controls (38,5% vs. 19,5% respectively; p<0.05). Low adherence was associated with high anxiety (Only abstract available).
Silva et al. (2009) 231 0 Course of illness n/s (bivariate analysis) Level of anxiety was significantly higher in patients than controls (p<0.001). No significant differences for anxiety were found during course of illness (Only abstract available).
Silva et al. (2011) 159 0 Quality of life ANCOVA QoL was predicted by anxiety (Benign MS: F=4.39, p=0.045; Non-Benign MS: F=12.704, p=0.001) (Only abstract available).
Simpson et al. (2014) 3826 1,268,859 Prevalence of anxiety Odds ratios Anxiety was the 2nd highest comorbid mental health condition for PwMS (OR=3.18).
Smith and Young (2000) 88 0 Prevalence; Depression; Perception of disability Descriptive statistics Prevalence=34%. Depression was associated with anxiety in two-thirds of depressed patients. There was no association between anxiety and perception of disability.
Spain et al. (2007) 580 0 Prevalence; Quality of life Regression Analysis Prevalence=34%. Anxiety was found to be a significant predictor for quality of life as indicated under the following QoL sub-scales: anxiety predicted physical functioning (b=0.0.19, p<0.05), bodily pain (b=−0.19,p<0.01), general health (b=−0.13, p<0.05), vitality (b=−0.12, p<0.05), social function (b=−0.12, p<0.05), emotional functioning (b=−0.42, p<0.01) and mental health (b=0.5, p<0.001).
Stenager et al. (1994) 94 0 Cognitive Dysfunction; Course of illness Correlation analysis Level of disability was correlated with anxiety (F-Test=2.61, p<0.05; df. 1.92). Trail Making (cognitive functioning) was also correlated with anxiety (no strength specified). No significant correlation was found between the course of illness and State anxiety (F=1.73; p=0.18, d.f. 1.92) and Trait anxiety (F=0.18; p=0.84, df. 1.92).
Suh et al. (2010) 96 0 Prevalence; Physical Activity Correlation analysis Prevalence=41%. Physical activity was not correlated with anxiety (r=−0.021; p>0.05).
Szilasiova et al. (2011) 114 0 Prevalence; Quality of LIfe Multiple Linear Regression Prevalence=27%. Anxiety was found to be a significant predictor of QoL (b=−0.51, p<0.001, R2=0.613)
Tadic and Dajic (2013) 50 0 Quality of life Correlation Analysis A strong correlation was found between high anxiety and overall QoL (r=−0.674, p<0.001)
Tan-Kristanto et al. (2015) 129 0 Resilience, self-efficacy, coping styles; depression Pearson's correlation; hierarchical regres- sion analyses Denial (b=0.29, p<0.001), level of disability (b=0.23, p<0.05) and lower levels of personal competence (b=−0.26, p<0.05) were independent significant predictors of the anxiety score (R2=0.364)
Theaudin et al. (2016) 711 0 Depression; Gender Linear regression analysis. Higher HADS anxiety scores in females (t=−4.555, p<0.001),52.1% of females and 37.8% of males were deemed clinically anxious (HADS anxiety⩾8; χ2=12.532, p<0.001). A gender comparison of depression together with anxiety revealed higher frequencies of both these symptoms in women than in men (32.3% of females depressed and anxious vs. 23.4% of males, χ2=5.795, p=0.02).
Thornton et al. (2006) 39 40 Self-efficacy; Worry Correlation analysis Anxiety was significantly correlated with self-efficacy and worry(r=0.59-WQMS) (r=0.77-PSWQ)(both p<0.01).
Tsivgoulis et al. (2007) 86 0 Level of disability; Level of education Multiple Linear Regression Analyses Level of disability (b=+0.430, p<0.001) and level of education (b=−0.235, P<0.041) were significant predictors for anxiety. These accounted for 18.5% (R2=0.185) and 5.5% (R2=0.055) of the variance in anxiety. Initial association between education and anxiety failed to retain statistical significance in the multiple linear regression analyses performed both with the backward and forward procedure (unstandardized linear regression coefficient: −0.471; 95% CI −1.170 to 0.228; b=−0.143; p=0.184).
Uca et al. (2016) 55 56 Prevalence; Personality disorders; Depression t test; Mann- Whitney U test c2test/Fisher's exact test 38% had an anxiety disorder. MS patients with any personality disorder had significantly higher frequency of GAD (p=0.008), any anxiety disorder (p=0.005), and post-traumatic stress disorder (p=0.037),
Uguz et al. (2008) 74 0 Anxiety subtypes; Exacerbation; Disease duration Logistic Regression Analysis Prevalence rate for GAD=18.9%, specific phobia=18.9% and OCD=14.9%. The predictors of any anxiety disorder were presence of the exacerbation phase of MS (B=−1.233, Wald x2=5.603, df 1, p=0.018) and shorter disease duration (B=−0.137, Wald x2=4.376, df 1, p=0.036).
Van Der Hiele et al. (2014) 44 0 Employment Correlation analysis There was no correlation between employment and anxiety symptoms.
Van Der Hiele et al. (2010) 128 0 Subjective cognitive functioning Correlation analysis Participants with subjective executive problems had higher anxiety scores (p<0.001) than participants without subjective executive problems. Objective executive performance was not correlated with anxiety (Only abstract available).
Van der Hiele et al. (2012) 715 0 Psychosocial stress Logistic Regression Higher psychosocial stress was found to be a significant predictor of higher anxiety scores (b=0.21, CI 95% (1.16, 1.31) p<0.001) R2=0.22 (Cox and Snell), 0.30 (Nagelkerke); model χ2(6)=151.3.
Van der Hiele et al. (2012) 114 0 Executive cognitive performance ANOVA Patients with MS underestimating their executive cognitive performance had higher levels of anxiety (F2,105=7.4, p=0.001).
Visser et al. (2009) 708 0 Cognitive complaints Correlation analysis PwMS with cognitive complaints had higher levels of anxiety compared to PwMS without cognitive complaints (p<0.001) (Only abstract available).
Voiticovschi-Iosob and Moldovanu (2013) 54 0 Pain Descriptive Statistics Anxiety was associated with chronic pain in 87.8% of PwMS (Only abstract available).
Vuger-Kovacic et al. (2007) 457 0 Duration Correlation analysis Anxiety did not significantly increase with longer duration of MS.
Weisbrot et al. (2012) 45 0 Prevalence Descriptive Analysis Prevalence=44.4% (Only abstract available).
White et al. (2008) 145 0 Speech difficulties; Pain; Fatigue; Depression; Health distress Correlation and Regression Analyses Anxiety was correlated with speech difficulties (r=0.32, p<0.001), pain (r=0.34, p<0.001), fatigue (p=0.30, p<0.001) and depression (r=0.74, p<0.001). It was not predictive of health distress in the regression model with other factors.
Ziegler et al. (2010) 50 0 PTSD ANOVAS. 12% of the PwMS had a PTSD lifetime diagnosis lifetime and 24% had a PTSD associated with MS-diagnosis. MS-patients with PTSD showed statistically significant lower self-efficacy, sense of coherence and social support (Only abstract available).
Zorzon et al. (2001) 97 110 Biological correlates Correlation analysis The anxiety did not correlate significantly with any of the measures of regional and total lesion loads and brain volume.

Results and strengths of associations for prospective studies

Authors, Year No. Patients No. Controls Factor/s investigated Type analysis Results
Bianchi et al. (2014) 39 39 Prevalence and Coping Multivariable linear regression analyses Prevalence=53.8%. There were higher scores for anxiety (p<0.001) in patients compared with controls. ‘Accepting responsibility’ coping (b=0.53; p<0.001) and ‘Seeking social support’ coping (b=0.22; p=0.02) were both predictors of anxiety (R2=0.29). Over the 24-month follow-up, there was a significant reduction in anxiety (p<0.001). At the end of follow-up, changes in depression and anxiety scores were strongly correlated with eachother (r=0.70, p<0.001). This decrease was also correlated with a decrease in ‘Accepting responsibilities’ coping scores for anxiety (r=0.43; p=0.018).
Brown et al. (2009) 101 0 Depression, Immunotherapy; Coping; Optimism Regression models Depression (b=1.07, p<0.0001) and baseline immunotherapy status (b=−4.85, p<0.0001) best predicted later anxiety levels, followed by smoking (b=2.5, p<0.003), no relaxation training (b=−2.56, p<0.0011), and low dispositional optimism (b=−0.35, p<0.0017).
Burns et al. (2013) 121 0 Exacerbations Logistic regression Pseudo-exacerbations were associated anxiety symptoms, t (1072)=3.19, p=0.001. There was a significant main effect of confirmed exacerbations on anxiety symptoms (t (1072)=2.93, p=0.004). Baseline anxiety symptoms were not significantly associated with risk for pseudo- or confirmed exacerbations (ps>0.14). Increases in anxiety symptoms relative to baseline predicted subsequent onset of new pseudo-exacerbations (Z=2.30, p=0.02).
Christodoulou et al. (2009) 38 38 Cognitive change; Cognitive Performance Multiple Regression and Pearson Correlations Anxiety as a significant predictor of cognitive change (b=−0.569, p<0.01, R2=0.458). A correlation was also found for anxiety and cognitive performance (r=−0.523, p<0.01).
Dalos et al. (1983) 73 23 (Spinal Chord patients) Relapse T-tests There were significant levels of anxiety in PwMS experiencing relapse (p<0.001) compared to controls (no strength of association specified)
Diaz-Olavarrieta et al. (1999) 44 25 Prevalence; Frontotemporal changes The Kruskal-Wallis test Prevalence=37%. Anxiety had no significant association with frontotemporal changes measured by the MRI. No associations were found between anxiety and level of disability, gender or age.
Giordano et al. (2011) 197 0 Gender; Depression; Duration Multivariate Linear mixed model analysis There was a statistically significant decrease in anxiety score at the six-month follow-up. Multivariate linear mixed model analysis showed that the decrease of anxiety scores over time remained significant (F=4.96, DF(2, 97), p=0.008) after controlling for clinical variables (disability, depression, duration) and demographic variables (sex, age, education). Of these, female sex (F=15.80, DF (1, 114), p<0.001) and depression (F=24.80, DF (1, 23), p<0.001) each had an independent influence on anxiety.
Hartoonian et al. (2015) 513 0 Depression; Time since onset of MS; EDSS; pain; fatigue Hierarchical regression model Anxiety (β<0.001), employment (β=0.07) and non-somatic depressive symptoms (β=0.10) at baseline significantly predicted anxiety at time 2, ps<0.05
Hoang et al. (2016) 5084 24771 Depression; antidepressant and anxiolytic prescriptions, Logistic regression analyses In the pre-diagnostic period, the OR for having a diagnosis of depression and anxiety is 1.4 (95% confidence interval (CI)=1.05–1.88). In the post-diagnostic period the OR is 1.23 (CI=0.92–1.64) for depression and anxiety diagnosis.
Janssens et al. (2006) 101 76 Disability; Time since diagnosis SAS Proc Mixed Prevalence=34% at baseline, 30% at follow-up. Mean anxiety scores of patients remained higher than controls at all times (all p<0.05). Higher level of disability was associated with higher levels of anxiety in patients (all P<0.001). Time since diagnosis was not associated with levels of anxiety. No association was found between anxiety and changes in disability status. Anxiety at baseline correctly identified 55% of the participants with high anxiety at follow-up (sensitivity=55%), and 85% of those who will not develop high levels of anxiety during follow-up.
Johnson et al. (2012) 613 0 Social support T-tests The high social support group had a reduction in anxiety scores in contrast to the population norm (p<0.001) (Only abstract available).
McCabe (2005) 243 184 Gender, Coping MANOVA and Regression No significant sex or sex-group interaction demonstrated significant differences between the groups in levels of anxiety ((F (2,421)=8.92, P<0.001). With all of the variables in the regression equation, anxiety at Time 1 was the only unique predictor for anxiety 18 months later (F (1,95)=45.47, P<0.001, R2=0.32). In the non-exacerbation group, coping variables did not explain variance of anxiety. With all of the variables in the regression equation, anxiety at Time 1 was the only unique predictor for anxiety 18 months later (F(1,149)=73.44, P<0.001, R2=0.33).
McKay et al. (2016) 949 0 Depression; Smoking; Alcohol Dependence; Disability Pearson χ2 test/Fisher's exact test, logistic regression. Alcohol dependence was associated with increased odds of anxiety (OR: 1.88; 95% CI: 1.37–2.57). The association persisted after adjusting for age, sex, EDSS, and smoking status (OR: 1.84; 95% CI: 1.32–2.58) Smoking was associated with increased odds of anxiety (unadjusted OR: 1.32; 95% CI: 1.05–1.65). When adjusted for age, sex, EDSS, and alcohol dependence, the relationship persisted (OR: 1.29; 95% CI: 1.02–1.63).
Olivares et al. (2012) 50 0 Cognitive and Clinical variables Pearson Correlations At baseline there was a correlation between neuropsychological functioning, (r=0.442, p<0.05), QoL (r=−0.627, p<0.001) and years since onset (r=−0.611, p<0.001) with anxiety. Anxiety symptoms were markedly related with low disease duration. Follow-up measures found fatigue (r=0.472, p<0.001), QoL mental (r=−0.727, p<0.001) and QoL physical (r=−0.518, p<0.001) were correlated with anxiety. Years since onset and neuropsychological performance were not correlated with anxiety at follow-up (Only abstract available).
Pakenham and Samios (2013) 69 69 Coping Multilevel Modeling Anxiety was significantly associated with low acceptance (−0.41) and mindfulness (0.40) and high depression (0.68) (all p<0.001).
Pekmezovic et al. (2012) 109 0 Quality of life T-tests Baseline scores for quality of life were associated with anxiety (Only abstract available).
Potagas et al. (2008) 37 0 Stressful life events; Relapse Linear Mixed Model Zero stressful events are associated with a mean anxiety score of 11.7 (95% CI [10.2–13.1]), one event with an anxiety score of 14.1 (95% CI [12.7–15.4]), two events with an anxiety score of 16.1 (95% CI [14.5–17.8]), three with a anxiety score of 20.3 (95% CI [18.7–21.9]), and four or five events with an anxiety score of 22.2 (95% CI: [20.2–24.1]). In the univariate analysis the risk of a relapse was associated with high level of anxiety (2.9, 95% CI: (1.3–6.4) P=0.008).
Solari et al. (2010) 121 0 Prevalence; Depression; Gender Multivariate linear regression Prevalence=43%. Female gender (p=0.02) and depression (p<0.001) were predictors of anxiety (R2=0.54; p<0.001). Anxiety decreased (p<0.001) between 1 and 6-months (no influence of the intervention). (Only abstract available)
Wood et al. (2013) 198 0 Age; Duration of illness; Level of disability; Depression; Fatigue Regression analysis At cohort entry, prevalence=25.4% Older age at cohort entry was associated with lower prevalence of anxiety (RR=0.88). Disease duration at cohort entry was not associated with risk of prevalent anxiety (p=0.33). A higher disability score was not associated with anxiety (0.75). Anxiety and depression (r=0.51) and anxiety and fatigue (r=0.25), were correlated. Prevalence of anxiety decreased by 8.1% per year of cohort observation (RR=0.92 (95%CI 0.86–0.98), p=0.009). However this effect was driven by a strong decrease of 14.6% per year among females (RR 0.85 (95%CI 0.79–0.93), p<0.001), with no significant change over time in males (RR 1.03 (95%CI 0.90–1.17), p=0.77).

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Footnotes

Department of Psychological Medicine, Weston Education Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE59RJ, United Kingdom

Corresponding author.


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    Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College, Cornell...
  • Dr Claire S. Riley

    Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia University,...
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    Rebecca Farber, MD is an attending neurologist and assistant professor of neurology at the Neurological Institute, Columbia University, in New...

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