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. Author manuscript; available in PMC: 2025 Oct 23.
Published in final edited form as: Mult Scler Relat Disord. 2025 Aug 30;102:106710. doi: 10.1016/j.msard.2025.106710

Understanding Use and Predictors of Compensatory Cognitive Strategies in People with Multiple Sclerosis

Jeeyeon Kim a, Susan L Murphy a,b, Libak Abou a, Kevin N Alschuler c,d, Dawn M Ehde c, Nora E Fritz e,f, Anna L Kratz a
PMCID: PMC12543356  NIHMSID: NIHMS2117414  PMID: 40907346

Abstract

Background:

Cognitive impairment is common in multiple sclerosis (MS) and affects daily functioning. Compensatory cognitive strategies can help mitigate these challenges, but their naturalistic use and predictors remain unclear. This study aimed to examine the frequency and types of strategies used and to determine whether demographics, objective cognitive function, or perceived cognitive function are independently associated with the use of compensatory strategies.

Methods:

A secondary analysis of baseline data from 268 people with MS was conducted. Compensatory cognitive strategy use was assessed via the Compensatory Cognitive Strategies Scale, perceived cognitive function via the PROMIS Cognitive Function Abilities Short Form, and objective cognitive function via the NIH Toolbox Cognitive Battery. Descriptive statistics characterized compensatory strategy use. Hierarchical linear regression analysis was performed to identify significant predictors of compensatory cognitive strategy use.

Results:

Participants (mean age = 50.8 ± 11.9 years, 77.2% female) most frequently used electronic reminders (86.6%), established routines (83.6%), and broke tasks into smaller steps (84.7%), while work-related strategies were the least utilized. Lower perceived cognitive function (β = −0.232, p < .01) and walking disability (β = 0.182, p < .01) were significantly associated with greater compensatory strategy use.

Conclusions:

Perceived cognitive function, rather than objective cognitive function, was associated with compensatory cognitive strategy use. Neuropsychological assessments should identify individuals who are cognitively impaired, especially those unaware of their deficits, to provide guidance on effective strategies. Future research should evaluate compensatory cognitive strategy training to improve long-term functional independence in MS.

Keywords: Multiple sclerosis, Cognitive function, Compensatory cognitive strategy, Neuropsychological tests

1. Introduction

Cognitive dysfunction is highly prevalent in people with multiple sclerosis (MS), affecting 44–65% of individuals with the condition (DiGiuseppe et al., 2018; Patti et al., 2015; Ruano et al., 2017). These cognitive deficits span multiple domains, including memory, attention, processing speed, and executive functioning (Branco et al., 2019; Grigsby et al., 1994; Rao et al., 1991). Such deficits significantly interfere with daily life, making it challenging for people with MS to maintain employment due to problems with memory (Yeandle et al., 2024), limiting their participation in social activities (Rao et al., 1991), and making everyday tasks like cooking more difficult to manage (Goverover et al., 2015). Moreover, individuals’ subjective perceptions of their cognitive dysfunction exacerbate the psychosocial burden of MS through heightened stress, depression, and a reduced quality of life (Grech et al., 2019). These combined challenges not only undermine the well-being and independence of individuals with MS but also place significant emotional and physical burdens on their caregivers (Giordano et al., 2016).

Cognitive rehabilitation interventions have emerged as promising approaches to support cognitive functioning and improve everyday outcomes in MS. These interventions broadly fall into two categories: restorative and compensatory approaches. Restorative strategies aim to improve impaired cognitive functions by leveraging neuroplasticity through repeated practice and progressively challenging tasks (Shoulson et al., 2012). In contrast, compensatory strategies do not attempt to restore lost function. Instead, they aim to bypass impairments and enhance functional independence by helping individuals adapt to their cognitive limitations using tools and behavioral techniques such as external aids, task simplification, and establishing routines (Weakley & Tomaszewski Farias, 2023). Recent clinical guidelines emphasize that these two approaches serve distinct but complementary purposes (Bergquist et al., 2023). Restorative interventions may be more appropriate when the goal is to retrain specific cognitive skills (Gamito et al., 2017), particularly in structured clinical settings. However, compensatory strategies are often more effective for addressing everyday functional challenges (Gitlin et al., 2017) and promoting long-term independence in real-world contexts (Shevil & Finlayson, 2010; Stuifbergen et al., 2018). For individuals with MS who often face progressive and fluctuating cognitive symptoms (Kratz et al., 2017), compensatory strategies offer practical, flexible solutions that can be immediately integrated into daily routines. Furthermore, cognitive strategy training programs that emphasize the development and use of compensatory techniques have been shown to improve self-efficacy in managing cognitive difficulties and increase life satisfaction while reducing depressive symptoms and perceived cognitive dysfunction (Huckans et al., 2010; Shevil & Finlayson, 2010).

Despite the recognized importance of compensatory strategies, research in this area remains limited within the MS population. Moreover, previous studies in MS samples often employ measures focused primarily on memory strategies and overlook other cognitive domains like attention, processing speed, and executive functioning (Johnson et al., 2009; Stuifbergen et al., 2018). This gap underscores the need for more comprehensive assessments that capture a broader range of compensatory strategies across various cognitive domains.

A recently developed instrument, the Compensatory Cognitive Strategies Scale (CCSS; Becker et al., 2019), addresses this gap by evaluating a broader spectrum of compensatory strategies. The CCSS assesses not only memory strategies but also those related to attention and executive functioning, such as avoiding multitasking and establishing routines. Furthermore, it incorporates modern technological aids like smartphones and online calendars, which are increasingly used to manage cognitive challenges. Although people with MS spontaneously adopt compensatory strategies to cope with their daily cognitive difficulties (Mc Auliffe & Hynes, 2019), research on naturalistic compensatory strategy use among people with MS is limited. Previous studies on older adults with mild cognitive impairment have shown that higher subjective memory complaints and lower working memory performance are associated with higher memory compensation strategy use, with subjective memory complaints emerging as the strongest predictor (Lin et al., 2020). However, these relationships have not been thoroughly investigated in people with MS using a comprehensive and validated instrument like the CCSS.

This study aims to fill these gaps by examining the compensatory cognitive strategies used by people with MS through the CCSS in a community-based sample. Furthermore, it seeks to determine whether demographics, objective cognitive function, or perceived cognitive function are independently associated with the use of the strategies. Given that perceived cognitive difficulties in MS are more closely linked to individuals’ ability to manage daily activities and maintain key life roles (Shevil & Finlayson, 2006), we hypothesized that worse perceived cognitive function rather than objective cognitive impairment would be more strongly associated with the use of compensatory strategies in everyday life.

2. Methods

2.1. Participants and data collection

The present study was a secondary analysis of baseline data from a longitudinal observational study, the Optimizing Detection and Prediction of Cognitive Function in Multiple Sclerosis (CogDetect-MS). Detailed descriptions of the CogDetect-MS protocol are provided elsewhere (Kratz et al., 2024). Inclusion criteria were: (1) 18 years of age or older, (2) fluent in English for both written and verbal communication, (3) a confirmed diagnosis of MS verified via medical record review, and (4) the ability to ambulate independently or with the use of a cane or walker. Participants were excluded if they (1) had experienced an MS relapse within the 30 days before enrollment or (2) were unable to use study data collection tools such as smartphone applications.

Participants were recruited through various sources, including existing participant registries, electronic health record queries, institution-specific recruitment websites, community outreach, clinic-based recruitment, flyers, and partnerships with local organizations such as National MS Society chapters. Initial eligibility was assessed through a telephone prescreening process. Those meeting the preliminary criteria were invited to a laboratory visit for the baseline data collection, during which full screening was conducted to confirm study eligibility.

Participant recruitment and data collection were conducted from three different sites: the University of Michigan, Ann Arbor, MI, Wayne State University, Detroit, MI, and the University of Washington, Seattle, WA. Each site adhered to standardized study procedures approved by the medical institutional review board at the University of Michigan. Informed consent was obtained before any data collection activities. Survey data were collected electronically via REDCap for demographics and self-reported measures. Performance-based neuropsychological tests were conducted via iPad App to assess objective cognitive function. All assessments were conducted in a private, quiet room by trained and certified research staff. For neuropsychological tests, standardized instructions and practice trials were presented before each task. Participants were encouraged to ask questions during the instruction phase, and staff monitored performance throughout testing to ensure comprehension and task adherence. No technical difficulties were reported during administration. The data was gathered from May 2022 to February 2024.

2.2. Measurements

Demographics and Disease-Specific Measures

Demographic variables including age, sex, education, race, marital status, employment status, and MS subtype (checked through medical record review) were collected. Disease severity and walking ability were assessed using the Patient-Determined Disease Steps (PDDS; Learmonth et al., 2013) scale, which is a patient-reported outcome measure. The PDDS is a single item ranging from 0 (normal) to 8 (bedridden).

Compensatory Cognitive Strategies

Compensatory cognitive strategies were measured using a 20-item version of the CCSS. Originally developed and validated for people with MS, the CCSS assesses the frequency of cognitive strategies employed to manage daily life and workplace tasks (Becker et al., 2019). Items reflect both external and internal strategies, including the use of technological aids (e.g., “Use reminder functions on a computer or smartphone”), routine establishment (e.g., “Establish a routine of always putting items in the same place in your house or workspace”), and environmental modifications (e.g., “Minimize distractions in your workspace by closing the door”). Response options include a 5-point Likert-type scale: 0 (never); 1 (rarely); 2 (sometimes); 3 (often); and 4 (all the time). The total scores range from 0 to 80. Higher scores indicate more frequent use of strategies. The CCSS has demonstrated strong psychometric properties in people with MS, including excellent internal consistency reliability (Cronbach’s alpha = 0.90) and established convergent validity (Becker et al., 2019). For missing data, values were imputed using the average of existing items if less than 15% of responses were missing. Participants with 15% or more missing data were excluded from the analysis (Zhang, 2016).

Perceived Cognitive Function

Perceived cognitive function was assessed using the PROMIS Cognitive Function Abilities Short Form (Cella et al., 2010). Participants rated their cognitive abilities over the past week using an 8-item, 5-point Likert-type scale (1 = not at all to 5 = very much). Total raw scores ranged from 8 to 40 and were converted to a T-score metric (mean = 50, standard deviation = 10 for the general population), with higher scores indicating better perceived cognitive function.

Neuropsychological Function

Cognitive performance was assessed using the National Institutes of Health Toolbox Cognition Battery (NIHTB-CB), a brief and psychometrically validated set of computerized tests administered via iPad. The battery includes seven tasks designed to measure diverse cognitive domains: Dimensional Change Card Sort Test (executive function), Flanker Inhibitory Control and Attention Test (executive function), List Sorting Working Memory Test (working memory), Oral Reading Recognition Test (language), Pattern Comparison Processing Speed Test (processing speed), Picture Sequence Test (episodic memory), and Picture Vocabulary Test (language). A total composite score was generated from these individual tests. The uncorrected total composite standard score was used as an indicator of objective cognitive function in this study. The NIHTB-CB was selected due to its shorter administration time and automated scoring system (Weintraub et al., 2013). Additionally, the NIHTB-CB is designed for digital administration and has demonstrated feasibility and validity, including those with neurological conditions (Carlozzi et al., 2017; Matuska et al., 2024). These features allowed for standardized administration across sites and reduced the potential for examiner bias.

2.3. Data Analysis

Statistical analyses were conducted using SPSS (version 28.0). The participants’ characteristics were described using mean, standard deviation (SD), frequency, and percentage. Descriptive statistics were used for individual CCSS items to identify the strategies most frequently used by people with MS. Since all variables were normally distributed, correlation analysis was performed to examine the bivariate associations between demographic variables, neuropsychological measures, perceived cognitive function, and CCSS scores.

Hierarchical linear regression analysis was performed to identify significant predictors of CCSS scores. Demographic variables - age, sex, education, and PDDS - were entered in step 1. Objective cognitive function was entered in step 2, followed by perceived cognitive function in step 3. This approach enabled the evaluation of the unique contribution of each variable set and the identification of significant predictors at each stage of the analysis. Assumptions of multiple regression, including linearity, multicollinearity, normality, and heteroscedasticity of residuals, were assessed. One identified outlier was retained in the analyses as it did not significantly influence the results. All statistical tests were two-sided, with the alpha level set at < .05.

3. Results

Of 274 enrolled people with MS, data from 268 participants with available CCSS scores were included in the study. A detailed demographic summary is presented in Table 1. The sample had a mean age of 50.8 (±11.9) years. Most participants were female (77.2%), White (73.1%), and diagnosed with relapsing-remitting MS (83.2%). The average years of education was 16 (±4.2), and the mean time since diagnosis was 15.4 (±10) years. Almost half of the participants (45.9%) were employed, with the majority working full-time. Nearly half (47.2%) reported walking disability per PDDS score. The sample was only mildly cognitively impaired: 28 participants scored 1 SD below, and 5 participants scored 2 SD below the average of 50 in the general population on the fully corrected total composite T score. A similar pattern of mild impairment was also observed across domain-specific cognitive measures, with most participants performing within or near the average range (See Appendix A).

Table 1.

Participant Characteristics (N = 268)

Characteristics N % Mean SD
Age (years) 268 50.79 11.87
Years diagnosed 267 15.4 9.98
Education (years) 268 15.98 4.22
Sex
    Female 207 77.2
    Male 61 22.8
Race
    White 196 73.1
    Black 52 19.4
    Others 20 7.5
Marital status
    Single 58 21.6
    Married/Domestic partnership 147 54.8
    Divorced/Separated 49 18.3
    Widowed 13 4.9
Employment
    Full-time 103 38.4
    Part-time 20 7.5
    Retired 44 16.4
    Unemployed due to disability 78 29.1
    Other 23 8.6
MS subtype
    Relapsing Remitting (RRMS) 223 83.2
    Primary Progressive (PPMS) 19 7.1
    Secondary Progressive (SPMS) 21 7.8
    Clinically Isolated Syndrome (CIS) 1 0.4
    Unknown 3 1.1
PPDS
    No walking disability (0-2) 140 52.2
    Walking disability (3+) 125 47.2
    Compensatory Cognitive Strategy Scale score 268 38.09 14.70
PROMIS Cognition 268 48.56 5.48
Total composite standard score 255 103.95 10.66
    a1SD below the mean (30-39) 28 11.1
    a2SD below the mean (<30) 5 2.0
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Note. PDDS: Patient Determined Disease Steps; PROMIS: Patient Reported Outcomes Measurement Information System.

a

Classification based on fully corrected NIHTB-CB total composite T scores (mean = 50, SD = 10).

3.1. Compensatory Cognitive Strategy Use

The most frequently used compensatory cognitive strategy was setting reminders on electronic devices, reported by 86.6% of participants, with a mean score of 3.0 (±1.2), indicating that this strategy was used “often.” Other commonly used strategies included establishing routines for placing items in the same location (83.6%; 2.7 ± 1.2) and breaking tasks into smaller steps (84.7%; 2.7 ± 1.1). The least frequently used strategies were requesting accommodations (38.06%; 1.3 ± 1.4), logging off email to limit distractions (37.7%; 1.2 ± 1.3), asking others to minimize interruptions when working (28.7%; 1.0 ± 1.2), and using a digital recorder (14.9%; 0.5 ± 1.0). The average total CCSS score was 38.1 (±14.7). Table 2 presents the mean scores and the number of participants using each strategy for each CCSS item, ranked by higher scores.

Table 2.

Mean scores for each CCSS item (N=268)

Items Mean SD n %
Use reminder functions on computer or personal electronic device (e.g., Smart Phone, tablets). 3.02 1.20 232 86.57
Establish a routine of always putting key items in the same place in house or workspace. 2.71 1.20 224 83.58
Break down complex tasks into smaller steps. 2.69 1.13 227 84.70
Take notes while listening to or viewing presentations to organize/reinforce information. 2.52 1.25 210 78.36
Prioritize to do list to aid time management and organization. 2.47 1.25 206 76.87
Organize to do the most challenging tasks at best (i.e., most energetic) time of day. 2.47 1.27 204 76.12
Recheck work after taking a break. 2.27 1.25 188 70.15
Do one thing at a time rather than multitasking. 2.22 1.21 187 69.78
Minimize distractions in your workspace by closing the door, etc. 2.05 1.36 172 64.18
Write out notes (in advance) to help verbally relay a sequence of events. 1.98 1.35 167 62.31
Confirm what others say by repeating the gist back to them. 1.91 1.22 168 62.69
Slow down; avoid situations that require fast reaction time. 1.86 1.32 157 58.58
Use personal electronic devices to record written documents. 1.57 1.48 131 48.88
Minimize the number of open tabs/windows when on the internet. 1.54 1.39 122 45.52
Request agenda and/or meeting notes. 1.48 1.33 126 47.01
Slow the pace of communication in meetings. 1.31 1.24 115 42.91
Request accommodations as needed (e.g., extra time to complete assignments). 1.25 1.35 102 38.06
Log off of email periodically to limit distractions. 1.22 1.36 101 37.69
Ask others to minimize the number of interruptions when working. 1.01 1.19 77 28.73
Use a digital recorder to make short reminder memos and review them later. 0.53 1.04 40 14.93
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Note. Responses are made on a 5-point Likert-type scale (0 = never; 1 = rarely; 2 = sometimes; 3 = often; and 4 = all the time).

3.2. Bivariate Correlational Analysis

Table 3 shows the correlations among CCSS scores, demographics, objective cognitive function, and perceived cognitive function. Pearson correlation analyses revealed that higher CCSS scores were correlated with lower perceived cognitive function (r = −0.29, p < .01), indicating people with MS employ more strategies when experiencing subjective cognitive deficits. However, there was no association between CCSS scores and objective cognitive function, age, and education.

Table 3.

Correlations between CCSS score and demographics and cognitive function (N=268)

1 2 3 4 5
1. CCSS —
2. Age −0.10 —
3. Education 0.03 0.16** —
4. PPDS 0.21** 0.24** −0.03 —
5. Objective Cognitive function (N=255) −0.12 −0.22** 0.30** −0.31** —
6. Perceived cognitive function −0.29** 0.27** 0.18** −0.16** 0.17**
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*

p < .05.

**

p < .01.

Note. Spearman’s correlation was used for PDDS.

3.3. Predictors of CCSS Scores

Hierarchical linear regression analysis predicting CCSS scores is summarized in Table 4. In step 1, demographic variables, including age, sex, education, and PDDS, accounted for 6.7% of the variance in compensatory cognitive strategy use with PDDS (β = 0.247, p < .01) emerging as significant. When objective cognitive function was added in step 2, an additional 2.0% of the variance was explained (ΔR2 = 0.02, F-change [1, 246] = 4.42, p < .05). Adding perceived cognitive function as a predictor in step 3 explained a further 5.0% of the variance (ΔR2 = 0.05, F-change [1, 245] = 13.15, p <.01). In this final model, which explained a total of 13% of the variance in strategy use, only education (β = 0.127, p = .048), PDDS (β = 0.182, p < .01) and perceived cognitive function (β = −0.232, p < .01) remained significant.

Table 4.

Hierarchical linear regression analysis predicting compensatory cognitive strategy use

Variable Step 1 (β) Step 2 (β) Step 3 (β)
Age −0.124 −0.158* −0.087
Sex (ref = female) −0.110 −0.119 −0.089
Education 0.073 0.120 0.127*
PDDS (ref = no disability in walking) 0.247** 0.221** 0.182**
Objective cognitive function −0.143* −0.101
Perceived cognitive function −0.232**
ΔR2 0.067** 0.016* 0.047**
R2 0.067 0.084 0.130
Adjusted R2 0.052 0.065 0.109
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Note. N= 252 in all models (3 participants had missing data for PDDS; 13 for objective cognitive function)

*

p < .05.

**

p < .01.

3.4. Post Hoc Analysis

We conducted post hoc analyses to examine how alignment between objective and perceived cognitive function relates to compensatory cognitive strategy use. Participants with objective cognitive impairment (fully corrected total composite T score < 40) were divided into two groups: those who also reported perceived cognitive difficulties (PROMIS T ≤ 50) and those who did not (PROMIS T > 50). Participants with both objective impairment and subjective awareness reported significantly greater use of compensatory strategies (45.7 ± 13.3) than those with objective impairment but without perceived difficulties (30.2 ± 11.6; t (31) = 3.2, p < .01).

Additionally, we conducted separate linear regression analyses for each of the seven domain-specific cognitive measures to assess their unique associations with compensatory strategy use. Only the Pattern Comparison Processing Speed Test showed a significant association (β = −0.14, p = .04), after adjusting for age, sex, education, PDDS, and perceived cognitive function (see Appendix B).

5. Discussion

This study aimed to examine naturalistic compensatory cognitive strategies used by people with MS in daily life and to identify factors independently associated with strategy use. Consistent with prior research in MS (Becker et al., 2019), the most frequently used strategy was utilizing reminder functions on electronic devices, with 86.6% of participants reporting they used this strategy. In contrast, work-related strategies, such as requesting agendas or meeting notes, requesting workplace accommodations, and slowing the communication pace during meetings, were underutilized. Importantly, our findings align with our hypothesis that worse perceived cognitive function, rather than objective cognitive function, is associated with greater strategy use. This suggests that individuals’ perceptions of their cognitive abilities drive the adoption of compensatory strategies, rather than their actual cognitive performance. Given this, neuropsychological assessments in MS may need to incorporate discussions of cognitive strategy use to ensure that individuals with cognitive impairment, especially those unaware of their deficits, receive appropriate guidance on implementing effective compensatory cognitive strategies.

The mean CCSS score in our sample was 38.1, within the possible range of 0–80, similar to prior reports in MS from different cities (Becker et al., 2019). In addition to memory aids (e.g., reminder functions), participants commonly employed executive functioning strategies, such as establishing routines and breaking down complex tasks. These findings highlight the importance of considering a broad range of compensatory strategies when designing research studies and cognitive rehabilitation interventions for people with MS. Mani et al. (2018) demonstrated the effectiveness of a compensatory cognitive rehabilitation program that trained individuals with a wide range of strategies. This program included the use of external memory aids (e.g., calendars, alarms, reminders, and medication boxes) as well as self-regulation techniques, such as organizing daily activities using 24-hour and 7-day charts to enhance executive functioning. The intervention led to significant improvements in cognitive function among people with MS, highlighting the potential benefits of structured cognitive strategy training.

Despite nearly half of the participants being employed, work-related strategies were among the least utilized, particularly those requiring external support. For example, only 43 out of 131 employed participants requested workplace accommodations (e.g., extra time for tasks), and even fewer (30 out of 131) asked colleagues to minimize workplace interruptions. This trend highlights a need for targeted interventions that empower people with MS to advocate for workplace accommodations and adopt strategies to navigate workplace challenges effectively. Prior research has demonstrated that MS symptoms negatively affect employment retention (Sweetland et al., 2012), and many individuals with MS leave the workforce without attempting to modify their work environment (McCabe et al., 2008). However, qualitative studies suggest that employed individuals with MS actively develop cognitive compensation techniques (Yorkston et al., 2003) and express a desire for individualized vocational support (Sweetland et al., 2007). Given the mixed effectiveness of existing vocational rehabilitation programs (Khan et al., 2009), future research should focus on developing evidence-based vocational rehabilitation programs that integrate compensatory cognitive strategies to enhance workplace retention and job performance.

Correlational analyses revealed that lower perceived cognitive function was associated with higher compensatory cognitive strategy use, a relationship that remained significant in the final regression model. Notably, while objective cognitive function was a significant predictor in the earlier regression step, it lost significance once perceived cognitive function was included in the model. This suggests that individuals’ perceptions of their cognitive abilities, rather than their actual cognitive performance, drive the strategy use. This conclusion is further supported by post hoc analysis, which revealed a significant difference between the two groups with objective cognitive impairment—those who perceived cognitive deficits reported significantly greater strategy use compared to those who did not perceive such deficits. This finding aligns with prior research demonstrating that individuals’ beliefs about their memory abilities (memory self-efficacy), rather than objective memory performance itself, drive compensatory strategy use in aging populations (Simon & Schmitter-Edgecombe, 2016). Similarly, Tomaszewski Farias et al. (2018) observed that older adults with mild cognitive impairment used compensatory strategies more frequently than those with dementia, further suggesting that objective cognitive deficits alone do not fully explain the adoption of such strategies. Based on these findings, people with MS who perceive greater cognitive challenges may be more proactive in adopting compensatory cognitive strategies, regardless of their actual cognitive performance. However, given that awareness of cognitive deficits does not always align with objective impairment in people with MS (Rosti-Otajärvi et al., 2014), those with significant cognitive impairments may not recognize their difficulties and, consequently, fail to adopt effective compensatory strategies. This underscores the importance of integrating regular neuropsychological assessments into care plans to identify individuals who could benefit from strategy training and to actively promote its use.

While global objective cognitive function was not significantly associated with compensatory cognitive strategy use in the final model, exploratory domain-specific analyses revealed a significant and modest association between slowed processing speed and increased use of compensatory strategies. This suggests that while perceived cognitive function is a consistent predictor of compensatory strategy use, processing speed specifically may also contribute to adopting compensatory approaches. Slowed processing speed can hinder the ability to manage complex tasks and increase the time required for routine activities (Owsley et al., 2002), potentially prompting individuals to rely on compensatory strategies. Future research should systematically examine the associations between specific cognitive domains and specific types of compensatory strategies to inform personalized cognitive rehabilitation interventions for people with MS.

Walking disability, as measured by PDDS, was another significant predictor of strategy use in the final regression model. Participants with walking disability reported using strategies more frequently, contrasting with earlier studies that used the clinician-rated Expanded Disability Status Scale (EDSS; Johnson et al., 2009). The PDDS, a self-reported measure, better captures individuals’ subjective perceptions of their limitations. This distinction underscores the importance of considering subjective experiences of disability, which may motivate greater use of strategies to overcome daily challenges.

Contrary to earlier research on people with MS (Johnson et al., 2009) that found demographic factors such as age and sex to be associated with memory strategy use, the current findings showed no significant associations between these factors and compensatory cognitive strategy use. This discrepancy may arise from differences in measurement tools; while earlier studies focused on memory-specific strategies, the CCSS captures a broader array of strategies encompassing multiple cognitive domains. This more comprehensive assessment may attenuate the apparent influence of demographic factors on strategy use. Notably, education level was selectively associated with greater use of strategies involving electronic devices, suggesting that demographic characteristics may influence the adoption of specific types of strategies. Although our findings did not reveal a significant effect of age, it remains important to consider the potential compounding influence of aging and MS on cognitive functioning. Cognitive decline is common with aging and may affect both the need for and capacity to implement compensatory cognitive strategies. In our sample, however, age was not a significant predictor in regression analyses, and exploratory comparisons across three age groups (young, middle-aged, and older adults) similarly revealed no significant differences in either overall or specific strategy use. These findings suggest that age alone may not sufficiently explain variability in compensatory strategy use, particularly when strategies extend beyond memory-specific approaches. Nevertheless, further research is warranted to explore how aging might interact with MS-related cognitive changes to influence the adoption, accessibility, or perceived utility of specific types of strategies, especially in older adults facing greater functional limitations.

Our findings contribute to understanding of how people with MS utilize compensatory cognitive strategies in daily life. Previous research in other populations has highlighted the beneficial effects of strategy use. For example, compensatory cognitive strategy use was associated with better everyday functioning, regardless of cognitive impairment severity, among older adults (Tomaszewski Farias et al., 2018). Also, cognitive strategy training programs improved life satisfaction and reduced psychological distress in veterans with mild cognitive disorders (Huckans et al., 2010). Future research should evaluate the efficacy of compensatory cognitive strategy training in MS, particularly in promoting both daily and long-term functional independence. Interventions should be tailored to individual differences, ensuring that those who do not recognize their cognitive deficits still receive adequate support. Healthcare providers should encourage people with MS to adopt tailored strategies, including advocating for workplace accommodations and using externally supported strategies to optimize job performance and quality of life.

4.1. Limitations

Several limitations should be considered when interpreting our findings. First, the cross-sectional design of the study prevents us from drawing causal conclusions. Second, our sample was only mildly cognitively impaired and predominantly middle-aged, which limited our ability to explore how more severe cognitive impairment or advanced age may impact the adoption or effectiveness of specific compensatory strategies. This may limit the generalizability of our findings to those with more severe cognitive impairment and older adults. Given that compensatory strategies may be particularly beneficial for individuals with significant cognitive challenges, future studies should examine MS populations with diverse cognitive and age profiles. As this study is part of an ongoing longitudinal investigation, it is possible that the cohort will exhibit greater cognitive decline over time. This would allow for future analyses to reexamine the predictors and utility of compensatory strategies as cognitive profiles evolve. Third, our final model explained only 13% of the variance in compensatory cognitive strategy use, suggesting that additional factors should be considered. Future research should incorporate variables such as fatigue, pain, and depression, which have been shown to influence cognitive function in MS (Brown et al., 2023; Yigit et al., 2021), to provide a more comprehensive understanding of the determinants of strategy use. Additionally, our findings may be influenced by a common-method bias, as both perceived cognitive function and strategy use were self-reported. Given that subjective measures tend to be more strongly related to each other than to objective measures, the stronger association between perceived cognitive function and strategy use compared to objective cognitive function may reflect this bias rather than a true underlying relationship. Future research should incorporate multi-method assessments, including objective cognitive testing and observer-reported strategy use, to strengthen the validity of findings. While this study provides valuable insights into the naturalistic use of compensatory cognitive strategies, intervention-based research is needed to determine whether increased strategy use directly improves long-term functioning. Identifying the most effective strategies for enhancing cognitive and functional outcomes in MS is also a critical next step. Future studies should focus on developing and testing structured cognitive rehabilitation programs that incorporate compensatory strategies, ensuring that people with MS receive the necessary support to maintain their functional independence and overall quality of life.

5. Conclusion

This study found that memory aids were the most commonly used compensatory cognitive strategies among people with MS, followed by executive functioning strategies. In contrast, work-related cognitive strategies were notably underutilized. Importantly, perceived cognitive function, rather than objective cognitive function, was a significant predictor of strategy use, with worse perceived cognitive function associated with greater strategy use. These findings highlight the need for neuropsychological assessments in MS to identify cognitively impaired individuals, particularly those unaware of their deficits, and to provide appropriate guidance on implementing effective strategies. Future research should evaluate the efficacy of compensatory cognitive strategy training in enhancing both daily and long-term functional independence to optimize health outcomes and quality of life for people with MS.

Supplementary Material

Supplementary Material

Funding

This work was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health (R01HD102337-01A1; MPI: Kratz/Fritz). Dr. Kim was supported by a Mentor-Based Postdoctoral Fellowship Program in Rehabilitation Research (MB-1706-27943; PI: Kratz) from the National Multiple Sclerosis Society.

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