Фрейлти и ее связь с психическим здоровьем и качеством жизни в множестве склерозов: перекрестное исследование.

Introduction

The median age of people with multiple sclerosis (MS) is rising, 1 , 2 introducing new clinical challenges 3 such as frailty—an age-related syndrome characterized by reduced physiological reserve and heightened vulnerability to adverse health outcomes. 4 While an increasing body of research has focused on the physical sequelae of frailty in MS, relatively little attention has been paid to the relationship between frailty and mental health. 5

Mental health conditions such as depression and anxiety are common in both MS and frail individuals. In MS, these conditions are linked with greater levels of pain, fatigue, sleep disturbances, and cognitive impairment, as well as reduced adherence to disease-modifying therapy and lower quality of life (QoL). 6 – 9 Mental health problems may exacerbate frailty, leading to increased healthcare utilization and reduced functional capacity. 10 Moreover, frailty may intensify feelings of isolation, sadness, and emotional distress. 11 , 12 Although there is a lack of evidence regarding the relationship between frailty and mental health in people with MS, indirect support emerges from studies examining broader well-being outcomes. For instance, Frau et al. 13 reported that individuals with MS living with frailty showed significantly lower QoL compared to their non-frail counterparts. However, whether this relationship was at least partially explained by poor mental health remains unknown.

The objective of the current study was to examine the relationship between frailty, mental health, and QoL in people with MS. We hypothesized that frailty would be significantly associated with higher levels of anxiety and depression and lower QoL.

Methods

Study design, setting, and participants

This study employed a cross-sectional design to explore the association between frailty, mental health, and QoL in a convenience sample of people with MS. Participants were recruited from the Center for MS Care at the University of Kansas Medical Center, and data were collected over 9 weeks, from October 2024 through January 2025, by a single researcher (N.A.W.). Eligibility criteria included MS patients aged 18 or older who received a confirmed MS diagnosis from their treating physician and had a proficient understanding of spoken and written English. Exclusion criteria encompassed a concurrent diagnosis of other neurological disorders (e.g., Parkinson’s disease) and cognitive impairment, evaluated by their treating physician. Subjects were identified as eligible during their routine clinic appointment. All eligible subjects on a given clinic day were approached regardless of their age, sex, ethnicity, or disability level.

All participants were provided with detailed information about the nature of the study, the purpose of their involvement, potential risks and benefits, available alternatives, and their rights as research participants. All participants had the mental capacity to understand the information relevant to their decision to participate in the study and provided written consent prior to participation.

Measurements

Sociodemographic and clinical characteristics

Sociodemographic information, such as age, gender, height, weight, marital status, race/ethnicity, and working hours, was collected using a standardized sociodemographic form ( Supplemental materials ). Clinical characteristics, such as the number of prescribed medications, disease duration, MS subtype, and the presence of other health conditions, such as anxiety and depression, were also recorded. Disability levels were assessed using the Patient-Determined Disease Steps (PDDS), 14 a validated scale that evaluates motor and ambulatory dysfunction on a nine-level scale, ranging from 0 (normal) to 8 (bedridden).

Hospital Anxiety and Depression Scale

The Hospital Anxiety and Depression Scale (HADS) 15 was used to assess depression and anxiety symptoms. The HADS questionnaire includes seven questions for each anxiety and depression subscale; the total score is calculated by summing the individual scores within each subscale, scored 0–21 each. 16 The HADS has high reliability and validity as a self-assessment scale for patients with MS. 16

The 54-item Multiple Sclerosis Quality of Life

Participants completed the 54-item Multiple Sclerosis Quality of Life (MSQoL-54), 17 a validated QoL questionnaire for MS. It includes 52 items divided into 12 subscales in addition to 2 single items. 17 The MSQoL-54 subscale scores range from 0 to 100, with higher values representing better outcomes. 17

Frailty assessments

Frailty is a multidimensional syndrome that affects physical, psychological, and social aspects of health. 18 To have a broader picture of frailty and to confirm the robustness of the findings across different frailty definitions, we used two self-report tools to assess frailty: the Tilburg Frailty Indicator (TFI) to classify participants as frail and non-frail (primary assessment), and the frailty index (FI) for sensitivity analyses. The TFI assesses multiple dimensions of frailty, offering a comprehensive view of an individual’s health status. It consists of two parts: Part A gathers sociodemographic data using 10 determinants, while Part B includes 15 components that measure the physical, psychological, and social aspects of frailty. Each item in Part B is scored, resulting in a total score ranging from 0 to 15; a person is considered frail if the TFI score is ⩾5. 19 The TFI has undergone extensive validation, showing good internal consistency, 20 and has been previously used to characterize frailty in MS populations. 13

The FI is a comprehensive tool used to assess individuals’ overall health and vulnerability by evaluating the presence of multiple health-related deficits. The evaluated deficits typically encompass comorbidities and proxies of reduced physical, psychological, and cognitive function. 21 At least 30 health-related deficit items are required to ensure a valid FI result. 22 The current study used the 33-item self-reported FI by Theou et al. 21 The FI score was calculated using a two-stage process per standard procedures. 22 First, we summed the health-related deficit items, expressed on a 0–1 scale to indicate the absence or presence of the deficit, respectively. Second, we divided the total sum of all scaled deficit items by the number of available deficit items. The FI score ranges from 0 to 1, with higher scores indicating higher levels of frailty.

Sample size considerations

Owing to the convenience sampling design of the study, no formal a priori sample size calculation was conducted. However, a post hoc power analysis was performed based on the observed odds ratios (ORs) for anxiety (OR = 1.45) and depression (OR = 1.58) predicting frailty status. Using an alpha level of 0.05 and the total sample size of 204 participants, the analysis indicated greater than 80% power to detect medium effect sizes. This suggests that the sample size was sufficient to detect significant associations between mental health variables and frailty in this sample.

Data analysis

The statistical analyses were conducted using SPSS, Version 29.0 (IBM, Inc., Armonk, NY, USA). The normality of the data was assessed using the Kolmogorov–Smirnov test, complemented by visual inspection of Q–Q plots and histograms. The Kolmogorov–Smirnov test indicated significant deviations from normality for several key variables, including the HADS anxiety and depression scores and MSQoL-54 subscales ( p < 0.05). Differences between frail and non-frail participants in demographics and clinical characteristics were analyzed using the Chi-squared test for categorical variables and by Mann–Whitney U tests for continuous variables. Differences in measures of mental health and QoL between frail and non-frail participants were also initially examined through Mann–Whitney U tests. In addition, multivariable logistic regression analyses were conducted to examine associations between the mental health measures (independent variables) and frailty status according to the TFI (dependent variable), adjusting for age, sex, and disability level (PDDS score). Similar analyses were performed to examine the association between the QoL measures and frailty. The mental health (anxiety and depression) and QoL measures were entered separately in each model. Prior to conducting the logistic regression analyses, we evaluated model assumptions. Multicollinearity among independent variables was assessed using variance inflation factors (VIF). The VIFs were ⩽1.9, suggesting no multicollinearity concerns. The assumption of linearity in the logit for continuous predictors was tested using the Box–Tidwell approach and found to be satisfied. Model fit was evaluated using the Hosmer–Lemeshow goodness-of-fit test, which indicated an acceptable fit ( p -values ⩾ 0.384). A sensitivity analysis was also conducted using separate linear regression analyses, with FI as the independent variable and mental health and QoL as the dependent variables (one variable per model). A significance level of p > 0.05 was used for the statistical interpretation of the study findings.

Results

Participants

A total of 210 people with MS agreed to participate and completed the survey. However, six individuals did not provide complete responses and were excluded from the study. Therefore, the data of 204 participants who provided complete responses to the survey were analyzed ( Table 1 ). The sample median age was 51 years (interquartile range = 18, 23–83 years), with women comprising 74.0% ( n = 151) of the sample. Based on the TFI scores, 103 participants (50.5%) met the criteria for frailty, while 101 (49.5%) were classified as non-frail. Comparisons of sociodemographic and clinical variables revealed that frail participants were older ( p = 0.018) and reported fewer working hours ( p < 0.001) than their non-frail counterparts.

Frail and non-frail participants did not differ in terms of race, sex, marital status, body mass index, and MS duration. However, the distribution of MS subtypes differed significantly between the frail and non-frail groups ( p < 0.001), as both primary and secondary progressive MS (SPMS) were more frequent among frail participants ( Table 1 ). Among the participants, 86 (42.6%) reported having a confirmed diagnosis of anxiety and/or depression, with 25 (24.8%) classified as non-frail and 61 (59.2%) as frail ( p < 0.001). Finally, 169 participants provided a list of their current prescribed medications. Of these, 65 reported taking anti-anxiety and/or antidepressant medications. A higher proportion of frail participants (66.2%) reported taking anti-anxiety and/or antidepressant medications compared to the non-frail (33.8%, p < 0.001).

Frailty, mental health, and QoL

As shown in Figure 1 , frail participants reported significantly higher levels of anxiety and depression symptoms than non-frail individuals ( p < 0.001). Moreover, across the tested domains of the MSQoL-54, frail participants had notably lower QoL scores ( p < 0.001, Figure 2 ). The logistic regression analysis ( Table 2 ) revealed that higher anxiety and depression scores were associated with higher odds of being frail, even after adjusting for age, sex, and disability level (PDDS). Each one-unit increase in anxiety score was associated with a 45.4% increase in the odds of frailty (OR = 1.45, 95% CI (1.28, 1.65), p < 0.001), while each additional point in the depression score increased the odds by 58.6% (OR = 1.58, 95% CI (1.37, 1.84), p < 0.001). In addition, to assess the independent contributions of anxiety and depression to frailty, we conducted an additional logistic regression analysis including both variables in the same model, while adjusting for age, sex, and disability. Both anxiety (OR = 1.27, 95% CI (1.11, 1.46), p < 0.001) and depression (OR = 1.39, 95% CI (1.18, 1.64), p < 0.001) remained significant predictors of frailty. Using the established HADS cut point of ⩾8, 16 , 23 – 25 a Chi-square analysis revealed that 58.3% of frail participants experienced anxiety, compared to only 14.9% of non-frail participants ( p < 0.001). Similarly, 41.7% of frail participants exhibited depressive symptoms, compared to only 5% of their non-frail counterparts ( p < 0.001).

Table 3 shows the association between QoL measures and frailty. Even after controlling for age, sex, and PDDS, lower scores in overall QoL were associated with higher odds of frailty (OR = 0.91, 95% CI (0.88, 0.94), p < 0.001). In other words, each one-unit decrease in overall QoL score was associated with a 9.9% increase in the odds of frailty. Similar trends were observed for both physical health composite scores (OR = 0.90, 95% CI (0.87, 0.93), p < 0.001) and mental health composite scores (OR = 0.90, 95% CI (0.88, 0.93), p < 0.001).

The sensitivity analyses ( Tables 4 and 5 ) further confirmed these associations. After adjusting for age, sex, and PDDS, higher frailty scores (FI) remained significantly associated with worse mental health outcomes ( p < 0.001). Higher FI scores were also associated with worse overall QoL (β = −0.60, p < 0.001), as well as lower physical (β = −0.64, p < 0.001) and mental (β = −0.72, p < 0.001) health composite scores.

Discussion

The current study aimed to explore the relationship between frailty, mental health, and QoL in people with MS. To our knowledge, this is the first investigation to specifically address the mental health characteristics of people with MS living with frailty while also providing a comprehensive assessment of their QoL. Our findings clearly demonstrate that frailty was associated with higher anxiety and depression symptoms, as well as reduced QoL in people with MS. Notably, a higher proportion of frail participants had either a diagnosis of anxiety or depression or exhibited anxiety and depressive symptoms using objective diagnostic criteria (HADS) compared to the non-frail people with MS.

In terms of generalizability of the current sample, the observed female-to-male ratio (2.8:1) aligns closely with well-documented epidemiological trends, indicating that women are more frequently affected by MS than men (3:1 ratio). 26 However, the fact that all participants were patients in a dedicated MS clinic could bias the population to a more disabled group than the general MS population. Approximately half of the participants met the criteria for frailty using the TFI. This proportion is toward the upper range of frailty prevalence reported in MS, which ranges from 17% to 66%. 27 In this regard, the actual prevalence of frailty in MS is still unknown and is likely dependent on the specific tool used to evaluate frailty.

Consistent with previous findings from Belvisi et al., 28 frail participants were significantly older than the non-frail. Although the effect of age on frailty is not surprising, it should be noted that the median age of frail participants was 54 years, which is much younger than the general population. This finding reinforces the notion that people with MS become frail at an earlier age, 27 compared to people without the disease, and that MS may represent a condition of accelerated aging. 3

It is also worth noting that relapsing-remitting MS (RRMS) was significantly more prevalent among non-frail participants, while primary progressive MS (PPMS) and SPMS were more common in frail participants, which is consistent with previous findings. 28 This could be partially explained by disability levels and age (people with PPMS and SPMS tend to be older and more disabled than those with RRMS).

This study showed that frail individuals with MS experience higher levels of anxiety and depression compared to the non-frail. This finding suggests that frailty may play a role in increasing the odds of mental health issues in people with MS and is aligned with findings from the geriatric literature. For instance, Tan et al. 29 reported that frail older adults were more than three times as likely to develop anxiety symptoms compared to robust older adults, while Soysal et al. 30 found that frailty conferred a four-fold increase in the likelihood of depression. In our study, we found that 58.3% and 41.7% of frail people with MS had HADS scores ⩾8 for anxiety and depression, respectively. These proportions were higher than those observed in non-frail participants—14.9% and 5%, respectively. This suggests that frail participants had 3.9 and 8.3 times higher odds of having anxiety and depressive symptoms, respectively. Mirroring these findings, 59.2% of frail participants had a confirmed diagnosis of depression and/or anxiety, compared to 24.8% of the non-frail participants. Conversely, our findings may also indicate that depression and anxiety could lead to a greater likelihood of developing frailty. Thus, the relationship between frailty and mental health is likely bidirectional. On the one hand, frailty can heighten psychological distress through reduced mobility, limiting physical function, and promoting social isolation, which contribute to susceptibility to mental health conditions. 31 , 32 On the other hand, anxiety and depression may exacerbate frailty through reducing motivation to physical activity, fostering unhealthy behaviors and lifestyles, and having poor personal relationships. 33 – 35

While our study focused on frailty and mental health in MS, other research shows that factors like socioeconomic disadvantages, such as low income, education, and material resources, may influence the development of frailty. 36 In addition, the presence of chronic disease and multimorbidity may also be involved in the etiology of frailty. This suggests that the association we observed between mental health and frailty may also be shaped by broader social inequalities and the presence of multiple health conditions.

Recent evidence indicates that personality traits are associated with the occurrence of anxiety and depression and may influence physical disability in people with MS. 37 Similarly, personality traits have been associated with fatigue in MS, which can contribute to anxiety and depression. 38 Although we did not assess personality traits in our study, they may represent an additional pathway of vulnerability that could indirectly influence frailty-related mental health outcomes in MS. Another factor that may expose the susceptibility of people with MS to frailty and mental health challenges is the COVID-19 pandemic. Research has reported heightened psychological stress stemming from fear of infection, disruptions in medical care, and social isolation, all of which have been linked to increased levels of anxiety and depression in people with MS. 39

Collectively, these observations suggest the existence of an intricate interplay between frailty and mental health, regardless of age or clinical context, and emphasize the need for integrated interventions that address both physical vulnerability and mental health concerns in people with MS. In this respect, multimodal exercise-based interventions such as treadmill walking augmented by virtual reality have shown promise in improving frailty and mental health-related QoL in ambulatory people with MS. 40 , 41 Importantly, these interventions could also be adapted to the non-ambulatory segment of the MS population. In addition, psychological approaches such as cognitive behavioral therapy, mindfulness, and supportive psychotherapy can play a role in reducing mental health symptoms in MS. 42

In parallel with the observed mental health findings, our results also indicate that individuals with MS who are frail have substantially lower QoL compared to the non-frail. Frau et al. 13 provided the first evidence that frailty is associated with lower QoL in people with MS. Our findings reinforced this notion, revealing that higher frailty levels corresponded with lower scores in the MSQoL-54 physical and mental health composites and overall QoL. In addition, although our study did not investigate the combined or interactive effects of frailty and mental health on QoL, these relationships remain an important area for future research to better understand their potential synergistic impact in people with MS.

This study is not without limitations. First, its cross-sectional design does not allow us to examine causality. While the relationship between frailty and mental health is likely bidirectional in nature, we cannot determine to what extent frailty may be responsible for worse mental health outcomes and/or whether mental health problems are implicated in the etiology of MS-related frailty. Further research with a longitudinal study design would be needed to elucidate such a relationship. Second, because frailty, mental health, and QoL were all assessed via self-report from the same respondents at a single time point, the observed associations may be inflated due to common method bias. Relatedly, owing to the self-reported nature of the included measures, there is also an increased risk of recall and reporting bias. Using two measurement tools to assess frailty strengthened the analysis and helped confirm that the associations between frailty and mental health were not dependent on a single measurement tool. However, given the complex and multidimensional nature of frailty, it is still possible that using different tools may yield different association estimates. Therefore, future research should incorporate more objective assessments of frailty to confirm our results. Third, the exclusion of participants with cognitive impairment may bias our sample toward individuals with less severe frailty, which could potentially affect the study’s results. In addition, although the sample size was adequate for statistical analysis, we recruited a convenience sample from a single MS clinic, which could introduce selection bias and limit the generalizability of findings. Consequently, our findings should be interpreted cautiously and confirmed in a broader MS population by using more diverse recruitment strategies. Lastly, the response rate was not systematically recorded, which limits our ability to assess participation bias and the representativeness of the sample.

Conclusion

This study revealed that frail people with MS have significantly higher anxiety and depression symptoms alongside lower QoL compared to the non-frail. The association between frailty, mental health, and QoL was independent of age, sex, and disability. Future research should investigate the directionality of these relationships and explore whether interventions targeting either mental health or physical frailty can improve overall functioning and QoL in this population.

Supplemental Material

sj-pdf-1-taj-10.1177_20406223251408288 – Supplemental material for Frailty and its association with mental health and quality of life in multiple sclerosis: A cross-sectional study

Supplemental material, sj-pdf-1-taj-10.1177_20406223251408288 for Frailty and its association with mental health and quality of life in multiple sclerosis: A cross-sectional study by Nida’ Al Worikat, Anna Zanotto, Sharon G. Lynch, Amanda J. Thuringer, Jacob J. Sosnoff and Tobia Zanotto in Sage Open Chronic Disease

Acknowledgments

We would like to thank all the participants who took part in this study.

Footnotes

ORCID iD: Tobia Zanotto https://orcid.org/0000-0002-6571-4763

Ethical considerations: This study adhered to the ethical principles for medical research involving human participants outlined in the World Medical Association Declaration of Helsinki and received ethical approval from the University of Kansas Medical Center Institutional Review Board (IRB; ID: STUDY00161073).

Consent to participate: All participants provided written informed consent prior to participating.

Consent for publication: Not applicable.

Author contributions: Nida’ Al Worikat: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Visualization; Writing—original draft.

Anna Zanotto: Conceptualization; Formal analysis; Methodology; Supervision; Writing—review & editing.

Sharon G. Lynch: Conceptualization; Resources; Supervision; Writing—review & editing.

Amanda J. Thuringer: Investigation; Resources; Writing—review & editing.

Jacob J. Sosnoff: Conceptualization; Resources; Supervision; Writing—review & editing.

Tobia Zanotto: Conceptualization; Formal analysis; Methodology; Project administration; Supervision; Writing—review & editing.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement: The data that support the findings of this study may be available from the corresponding author upon reasonable request.

Supplemental material: Supplemental material for this article is available online.

References

1. Hittle M, Culpepper WJ, Langer-Gould A, et al. Population-based estimates for the prevalence of multiple sclerosis in the United States by race, ethnicity, age, sex, and geographic region. JAMA Neurol 2023; 80(7): 693–701.

2. Vaughn CB, Jakimovski D, Kavak KS, et al. Epidemiology and treatment of multiple sclerosis in elderly populations. Nat Rev Neurol 2019; 15(6): 329–342.

3. Graves JS, Krysko KM, Hua LH, et al. Ageing and multiple sclerosis. Lancet Neurol 2023; 22(1): 66–77.

4. Clegg A, Bates C, Young J, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing 2016; 45(3): 353–360.

5. Al Worikat N, Zanotto A, Sosnoff JJ, et al. Mental health and frailty in people with multiple sclerosis: unraveling a complex relationship. Front Psychol 2024; 15: 1387618.

6. Amtmann D, Askew RL, Kim J, et al. Pain affects depression through anxiety, fatigue, and sleep in multiple sclerosis. Rehabil Psychol 2015; 60(1): 81–90.

7. Butler E, Matcham F, Chalder T. A systematic review of anxiety amongst people with multiple sclerosis. Mult Scler Relat Disord 2016; 10: 145–168.

8. Marrie RA, Walld R, Bolton JM, et al. Effect of mood and anxiety disorders on health care utilization in multiple sclerosis. Mult Scler J 2021; 27(9): 1411–1420.

9. Washington F, Langdon D. Factors affecting adherence to disease-modifying therapies in multiple sclerosis: systematic review. J Neurol 2022; 269(4): 1861–1872.

10. Mutz J, Choudhury U, Zhao J, et al. Frailty in individuals with depression, bipolar disorder and anxiety disorders: longitudinal analyses of all-cause mortality. BMC Med 2022; 20(1): 274.

11. Mehrabi F, Béland F. Effects of social isolation, loneliness and frailty on health outcomes and their possible mediators and moderators in community-dwelling older adults: a scoping review. Arch Gerontol Geriatr 2020; 90: 104119.

12. Ní Mhaoláin AM, Fan CW, Romero-Ortuno R, et al. Frailty, depression, and anxiety in later life. Int Psychogeriatr 2012; 24(8): 1265–1274.

13. Frau J, Mulasso A, Coghe G, et al. Multidimensional frailty and its association with quality of life and disability: a cross-sectional study in people with multiple sclerosis. Mult Scler Relat Disord 2023; 79: 105036.

14. Hohol MJ, Orav EJ, Weiner HL. Disease steps in multiple sclerosis. Neurology 1995; 45(2): 251–255.

15. Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 1983; 67(6): 361–370.

16. Jerković A, Proroković A, Matijaca M, et al. Psychometric properties of the HADS measure of anxiety and depression among multiple sclerosis patients in Croatia. Front Psychol 2021; 12: 794353.

17. Vickrey BG, Hays RD, Harooni R, et al. A health-related quality of life measure for multiple sclerosis. Qual Life Res 1995; 4(3): 187–206.

18. Wleklik M, Uchmanowicz I, Jankowska EA, et al. Multidimensional approach to frailty. Front Psychol 2020; 11: 564.

19. Gibson CB. Elaboration, generalization, triangulation, and interpretation: on enhancing the value of mixed method research. Organ Res Methods 2017; 20(2): 193–223.

20. Sutton JL, Gould RL, Daley S, et al. Psychometric properties of multicomponent tools designed to assess frailty in older adults: a systematic review. BMC Geriatr 2016; 16(1): 55.

21. Theou O, O‘Connell MDL, King-Kallimanis BL, et al. Measuring frailty using self-report and test-based health measures. Age Ageing 2015; 44(3): 471–477.

22. Searle SD, Mitnitski A, Gahbauer EA, et al. A standard procedure for creating a frailty index. BMC Geriatr 2008; 8(1): 24.

23. Litster B, Fiest KM, Patten SB, et al. Screening tools for anxiety in people with multiple sclerosis: a systematic review. Int J MS Care 2016; 18(6): 273–281.

24. Marrie RA, Zhang L, Lix LM, et al. The validity and reliability of screening measures for depression and anxiety disorders in multiple sclerosis. Mult Scler Relat Disord 2018; 20: 9–15.

25. Wu Y, Levis B, Sun Y, et al. Accuracy of the Hospital Anxiety and Depression Scale Depression subscale (HADS-D) to screen for major depression: systematic review and individual participant data meta-analysis. BMJ 2021; 373: n972.

26. Portaccio E, Magyari M, Havrdova EK, et al. Multiple sclerosis: emerging epidemiological trends and redefining the clinical course. Lancet Reg Health Eur 2024; 44: 100977.

27. Al Worikat N, Molaei F, Zanotto A, et al. Frailty in multiple sclerosis: a scoping review. Mult Scler Relat Disord 2024; 92: 106157.

28. Belvisi D, Canevelli M, Baione V, et al. Operationalization of a frailty index in patients with multiple sclerosis: a cross-sectional investigation. Mult Scler J 2021; 27(12): 1939–1947.

29. Tan M, Bhanu C, Frost R. The association between frailty and anxiety: a systematic review. Int J Geriatr Psychiatry 2023; 38(5): e5918.

30. Soysal P, Veronese N, Thompson T, et al. Relationship between depression and frailty in older adults: a systematic review and meta-analysis. Ageing Res Rev 2017; 36: 78–87.

31. Benraad CEM, Disselhorst L, Laurenssen NCW, et al. Frailty, multimorbidity and functional status as predictors for health outcomes of acute psychiatric hospitalisation in older adults. Aging Ment Health 2020; 24(1): 119–128.

32. Sciacchitano S, Carola V, Nicolais G, et al. To be frail or not to be frail: this is the question—a critical narrative review of frailty. J Clin Med 2024; 13(3): 721.

33. Ploughman M, Wallack EM, Chatterjee T, et al. Under-treated depression negatively impacts lifestyle behaviors, participation and health-related quality of life among older people with multiple sclerosis. Mult Scler Relat Disord 2020; 40: 101919.

34. Mayo A, O’Brien MW, Godin J, et al. Can an active lifestyle offset the relationship that poor lifestyle behaviours have on frailty? Arch Gerontol Geriatr 2024; 127: 105556.

35. Gascoyne CR, Simpson S, Jr, Chen J, et al. Modifiable factors associated with depression and anxiety in multiple sclerosis. Acta Neurol Scand 2019; 140(3): 204–211.

36. Wang J, Hulme C. Frailty and socioeconomic status: a systematic review. J Public Health Res 2021; 10(3): 2036.

37. Vaheb S, Mokary Y, Yazdan Panah M, et al. Multiple sclerosis and personality traits: associations with depression and anxiety. Eur J Med Res 2024; 29(1): 171.

38. Ciancio A, Moretti MC, Natale A, et al. Personality traits and fatigue in multiple sclerosis: a narrative review. J Clin Med 2023; 12(13): 4518.

39. Heidler F, Hecker M, Frahm N, et al. Trauma burden affected people with multiple sclerosis during SARS-CoV-2 pandemic. J Clin Med 2025; 14(8): 2665.

40. Zanotto T, Galperin I, Pradeep Kumar D, et al. Effects of a 6-week treadmill training with and without virtual reality on frailty in people with multiple sclerosis. Arch Phys Med Rehabil 2025; 106(2): 187–194.

41. Zanotto T, Kumar DP, Tabatabaei A, et al. Multimodal exercise training to reduce frailty in people with multiple sclerosis: study protocol for a pilot randomized controlled trial. Pilot Feasibility Stud 2024; 10(1): 65.

42. Wang C, Si H, Bian Y, et al. Effectiveness of subjective support-focused cognitive behavioral therapy on depressive symptoms among (pre)frail community-dwelling older adults: a randomized controlled trial. J Affect Disord 2024; 347: 92–100.

Associated Data

Supplementary Materials

sj-pdf-1-taj-10.1177_20406223251408288 – Supplemental material for Frailty and its association with mental health and quality of life in multiple sclerosis: A cross-sectional study

Supplemental material, sj-pdf-1-taj-10.1177_20406223251408288 for Frailty and its association with mental health and quality of life in multiple sclerosis: A cross-sectional study by Nida’ Al Worikat, Anna Zanotto, Sharon G. Lynch, Amanda J. Thuringer, Jacob J. Sosnoff and Tobia Zanotto in Sage Open Chronic Disease