Objective

Individuals with multiple sclerosis (MS) often report cognitive dysfunction, although neuropsychological evaluation findings may not correlate with subjective concerns. One factor that may explain this lack of correspondence is the controlled testing environment, which differs from busier settings where cognitive lapses are noted to occur. This study used a novel environmental manipulation to determine whether individuals with MS who report cognitive dysfunction are more vulnerable to the effects of auditory distraction during neuropsychological testing.

Method

Twenty-four individuals with clinically definite MS or clinically isolated syndrome were administered a cognitive battery during two counterbalanced auditory conditions: quiet/standard condition, and distraction condition with random office background noise. Participants were divided into high versus low cognitive complaint groups using a median split analysis of Perceived Deficits Questionnaire responses.

Results

Participants with more cognitive complaints showed a decrement in performance on the oral Symbol Digit Modalities Test during the distraction condition while those with fewer cognitive complaints demonstrated stable performance across conditions. These findings remained significant after controlling for education, premorbid intellect, fatigue, and depressed mood.

Conclusions

These results suggest that individuals with MS with more cognitive complaints are vulnerable to environmental distraction, particularly regarding processing speed. Incorporating random environmental noise or other distraction conditions during selected measures may enhance the ecological validity of neuropsychological evaluation results in MS.

Introduction

Multiple sclerosis (MS) is a neurological condition characterized by physical, emotional, and cognitive symptoms. Approximately 45%–65% of individuals with MS show at least mild cognitive dysfunction (Julian, 2011). Individuals with MS also tend to report cognitive problems in daily life (Kinsinger, Lattie, & Mohr, 2010). While subjective cognitive concerns in MS have been found to correlate with objective neuropsychological testing (Krch, Sumowski, DeLuca, & Chiaravalloti, 2011; Marrie, Chelune, Miller, & Cohen, 2005; Randolph, Arnett, & Freske, 2004), this relationship is inconsistent and can be influenced by depression, fatigue, and other factors (Bruce & Arnett, 2004). In general, the discrepancy between subjective cognitive dysfunction and cognitive performance in MS remains inadequately understood.

One possible explanation for the variable correspondence between subjective and objective cognition in MS is the controlled testing environment, in which extraneous noise and other stimuli are usually minimized (Lezak, Howieson, Bigler, & Tranel, 2012). While this has long been considered an ideal environment for neuropsychological testing to occur, it differs significantly from busier settings where cognitive lapses are typically reported. Certainly, it would not be appropriate to modify the testing environment in its entirety, but incorporating a subset of measures with modified testing conditions may prove fruitful in creating a laboratory analog of daily functioning. Such a modification, in turn, may increase the correlation between subjective cognitive complaints and objective neuropsychological performance, particularly in situations when secondary factors (e.g., depression, litigation) are less relevant. Indeed, by selectively incorporating testing modifications that better represent the stimuli encountered in daily life, reported cognitive dysfunction may be more apparent on cognitive measures.

Prior research with neuropsychiatric samples indicates that individuals with schizophrenia and brain injury are more negatively affected by distractions during cognitive test performance than controls (Barrow, Collins, & Britt, 2006; Cellard, Tremblay, Lehoux, & Roy, 2007; Knight, Titov, & Crawford, 2006; Schnabel & Kydd, 2012). One study in an MS sample found that auditory distraction consisting of babble and word repetition caused subtle deficits on reaction time measures (LaPointe et al., 2005). MS participants have also shown impairment on a computerized version of the Symbol Digit Modalities Test (SDMT) with built-in distractors (Patel, Zambrana, Walker, Herrmann, & Fainstein, 2016). D'Esposito and colleagues (1996) showed that performing one task (humming or reciting the alphabet) negatively affected MS patients’ performance on another task (judgment of line orientation) relative to controls. The authors proposed that these results provided evidence of a breakdown in working memory and the “central executive system” proposed by Baddeley (1986, 2012). It remains unclear, however, whether MS participants’ performance on traditional neuropsychological measures would be affected by an auditory environmental distraction condition that resembles distractions in daily life.

In a pilot study with 40 healthy controls, participants showed mild but not statistically significant decrements in auditory working memory, verbal episodic memory, and divided visual attention when a novel random environmental noise (REN) condition consisting of office noises was applied during cognitive testing (Higginson & Randolph, 2008). Given its subtle effect in cognitively intact adults on standard neuropsychological tests, we reasoned the experimental manipulation showed potential to demonstrate a measurable effect in individuals with MS, particularly in light of the evidence of vulnerability to distraction in MS patients as noted above.

This study sought to determine whether auditory distraction in the testing setting would serve to partially recreate the distractions in daily life and lead to better correspondence between subjective complaints and objective test performance in MS. We used the same REN manipulation to determine whether distraction would impact MS patients’ performance globally, and whether individuals with MS who report cognitive dysfunction are more vulnerable to the effects of auditory distraction during neuropsychological testing. Given the known effects of auditory distraction on general information processing efficiency (Hughes & Jones, 2003), multiple cognitive domains were evaluated in the REN and standard testing conditions to determine the relative burden of distraction on cognition in MS.

Materials and Methods

Participants

Inclusion criteria for the study were: diagnosed with clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), or secondary progressive MS (SPMS) by a treating neurologist and, for RRMS and SPMS, verification of diagnosis through medical record review and the application of the 2010 Revised McDonald Criteria (Polman et al., 2011), age 18–65, and minimum of a high school education or equivalent (e.g., General Education Development test). Potential participants were excluded if they had a history of: neurological condition(s) other than MS (e.g., epilepsy, brain cancer, stroke, head injury with loss of consciousness exceeding 5 min), medical condition(s) with potential effects on cognition, such as insulin-dependent diabetes, hypertension, or hypothyroidism (hypertension, hypothyroidism, and non-insulin-dependent diabetes were not an exclusion if the potential participant reported the condition as stable and under medical management), learning, attention, or other cognitive disorder of developmental origin, sensory-motor functioning deficits impeding testability, or current drug or alcohol dependence. An initial depression screen was conducted during the enrollment screening telephone interview, by asking both whether, over the past 2 weeks on most days, the potential participant felt depressed or sad, or felt decreased enjoyment or pleasure in things that normally gave them enjoyment or pleasure. Patients endorsing current depressive symptoms, defined as a “yes” to either of those questions, were not invited to participate but were offered contact information for local mental health resources if not currently in treatment for management of their mood. Participants endorsing highly elevated symptoms of depressed mood on the Chicago Multiscale Depression Inventory (CMDI) mood scale (T-score > 70) during the study were also excluded from analyses. Furthermore, participants were also excluded if they were experiencing an exacerbation of MS symptoms. Individuals with a recent exacerbation of symptoms were included if they were at least 1 month post exacerbation and related steroid treatment, if applicable.

Potential study participants were recruited from the New England area of the USA through a variety of methods including informational booths at MS-related conferences and events, brochures distributed to area neurologists, a database of previous research participants, and social media postings. Thirty-three individuals expressed interest in participating in the study and completed a telephone interview to determine study eligibility. Of these, 31 passed the telephone screen and 28 enrolled in the study.

All procedures related to the study were reviewed and approved by the Committee for the Protection of Human Subjects of Dartmouth College, and all enrolled participants provided written informed consent. Review of available medical records for diagnostic verification resulted in the exclusion of four enrolled patients due to insufficient information to confirm diagnosis by the application of the 2010 Revised McDonald Criteria (Polman et al., 2011).

Cognitive and Self-Report Measures

Participants were administered a battery of neuropsychological measures assessing cognitive functions known to be impacted by MS (Arnett & Strober, 2011; Wishart & Sharpe, 1997), including attention and executive functioning (Symbol Digit Modalities Test-Oral [SDMT-O; Smith, 1982]; Paced Auditory Serial Addition Test 3 Second Trial [PASAT, Rao, Leo, Haughton, Aubin-Faubert, & Bernardin, 1989], Delis–Kaplan Executive Function System, Trail Making Test-Number Letter Trial, Color-Word Interference - Inhibition Trial, [DKEFS, Delis, Kramer, & Kaplan, 2001]), and verbal episodic memory (California Verbal Learning Test-II, Total Learning [CVLT-II, Delis, Kramer, Kaplan, & Ober, 2000]). All participants completed measures of estimated baseline intellectual functioning (Test of Premorbid Functioning [TOPF, Pearson, 2009]) and performance validity (CVLT-II, Forced Choice trial) during quiet testing conditions.

Participants also completed measures of subjective cognitive dysfunction (Perceived Deficits Questionnaire [PDQ; Ritvo et al., 1997]) and fatigue severity (Fatigue Severity Scale [FSS; Krupp, LaRocca, Muir-Nash, & Steinberg, 1989]). Mood was assessed using the CMDI, a measure that minimizes the overlap between neurovegetative symptoms of depression and somatic symptoms of MS (Nyenhuis et al., 1995). Participants completed the Patient Determined Disease Steps (PDDS; Hohol, Orav, & Weiner, 1995) to serve as a measure of perceived disability in order to characterize the sample. The PDDS has been found to correlate well with the Expanded Disability Status Scale (Hohol, Orav, & Weiner, 1999; Learmonth, Motl, Sandroff, Pula, & Cadavid, 2013). At the end of the testing session, participants completed a brief questionnaire assessing perceived effects of the distraction condition on cognition.

REN Procedure

Background noise was created using “Thriving Office” (Thriving Office, 2006), an audio CD of the sounds of a large, busy office. The CD was played in the background on a portable radio at a volume of 65 decibels (set using a decibel meter), which reflects the typical noise level in a business office. This level did not interfere with communication between the test administrator and participant.

Study Design and Analysis Plan

Study participants were randomized to either a quiet first/noisy second testing sequence group, or noisy first/quiet second testing sequence group. A brief 10- to 15-min rest break occurred between testing sessions. Participants received the full battery of neuropsychological tests under each condition, and alternate test forms were used across experimental conditions for the CVLT-II, PASAT, and SDMT in an attempt to minimize practice effects of repeat administration. A blocked randomization scheme with a block size of four was employed to ensure equally sized groups by testing environment sequence and test form sequence.

Statistical analyses were performed using IBM SPSS Version 23 for Windows. Data were assessed for outliers and significant violations of normality. A repeated measures multivariate analysis of variance (MANOVA) was employed to determine the effect of testing environment (noisy vs. quiet) on cognitive performance on the sample as a whole, after first assessing correlations among the cognitive measures to assess suitability for inclusion as dependent variables. Subsequent to this analysis, participants were divided into high versus low cognitive complaint groups using a median split analysis of PDQ responses. Analysis of covariance (ANCOVA) was then employed to compare high versus low complaint groups on mean difference scores for cognitive performance during quiet versus noisy conditions. We used education, estimated baseline intellectual ability, depression, and fatigue as covariates, given known effects of depression and fatigue on subjective and objective cognition in MS (Arnett, Barwick, & Beeney, 2008; Kinsinger et al., 2010; Rogers & Panegyres, 2007).

Results

One participant in the sample was excluded from further analyses due to performing greater than three standard deviations below the sample mean on one cognitive measure (DKEFS Trail Making Test - Number Letter Trial), and for high endorsement of depressed mood items on the CMDI. The cognitive measures of the remaining sample were examined for normality using the Shapiro–Wilk test. Although CVLT-II Total Learning under the noisy condition, Trail Making Test - Number Letter Trial under the quiet condition, and PASAT 3 Second Trial under both the noisy and quiet conditions evidenced mild non-normality (Shapiro–Wilk p < .05), we did not favor attempting transformation of all the data given the robustness of MANOVA to such minor deviations.

Participant demographic data for the final sample of 23 are presented in Table 1. The majority of participants had RRMS. A 2:1 female:male subject ratio was obtained, consistent with other MS samples in the literature (e.g., Arnett, Higginson, & Randolph, 2001; Drake et al., 2010). On average, participants were relatively well educated, of average intellectual ability, and reported experiencing MS symptoms for over 16 years. Based on the median participant-endorsed PDDS score, our sample showed relatively mild disability (Marrie, Cutter, Tyry, Vollmer, & Campagnolo, 2006). Participants also reported significant fatigue and minimally depressed mood. As an initial indication of the face validity of the REN condition, participants reported moderate levels of distraction during the distraction condition (scale range = 0–10; mean = 5.0, standard deviation (SD) = 2.0). All participants showed intact performance validity on the CVLT-II Forced Choice trial.

Table 1.

Participant characteristics

Variable Raw/mean SD 
Sex (female/male) 16/7 — 
MS subtype (RRMS/SPMS/CIS) 19/2/2 — 
Age (years) 48.3 9.8 
Education (years) 15.2 2.1 
Disease duration since symptom onseta (years) 16.3 10.4 
Disease duration since diagnosis (years) 9.3 8.2 
PDDS (median score) 2.0 — 
TOPF (standard score) 107.2 11.7 
FSS (z-score) −3.4 2.1 
CMDI-mood subscale (T-score) 44.5 6.8 
Variable Raw/mean SD 
Sex (female/male) 16/7 — 
MS subtype (RRMS/SPMS/CIS) 19/2/2 — 
Age (years) 48.3 9.8 
Education (years) 15.2 2.1 
Disease duration since symptom onseta (years) 16.3 10.4 
Disease duration since diagnosis (years) 9.3 8.2 
PDDS (median score) 2.0 — 
TOPF (standard score) 107.2 11.7 
FSS (z-score) −3.4 2.1 
CMDI-mood subscale (T-score) 44.5 6.8 

Note: RRMS = relapsing-remitting MS; SPMS = secondary progressive MS; CIS = clinically isolated syndrome; PDDS = Patient Determined Disease Steps; TOPF = Test of Premorbid Functioning; FSS = Fatigue Severity Scale; CMDI = Chicago Multiscale Depression Inventory.

an = 22.

Table 2 summarizes cognitive test performance on measures predicted to be sensitive to distraction effects by condition order groupings. Pearson correlations among the cognitive variables were all r < .8 indicating no serious multicollinearity, so all measures were retained for inclusion in the MANOVA to determine the effect of testing environment (noisy vs. quiet) on cognitive performance, with the between-subjects factor of order of conditions. The MANOVA did not reveal a statistically significant effect of environment (quiet vs. noisy) on cognitive performance for the group as a whole (F(5, 17) = 1.79, p > .10), nor were there significant effects of order (F(5, 17) = 0.80, p > .10). We conducted an additional MANOVA to assess for practice effects by examining performance on first test administration (regardless of environment) compared to second test administration (regardless of environment). This finding was not significant but showed a statistical trend for better performance on the second administration (F(5, 17) = 2.51, p = .07).

Table 2.

Test performance in quiet and random environmental noise (REN) conditions, by condition order group

TEST Quiet 1st, REN 2nd Group N = 12 REN 1st, Quiet 2nd Group N = 11 
Time 1 Time 2 Time 1 Time 2 
CVLT-II, Total Learning, # correct 50.8 (13.7) 44.0 (13.5) 55.3 (7.7) 55.7 (8.5) 
SDMT-O, # correct 56.6 (19.0) 53.8 (13.6) 64.4 (12.5) 66.0 (11.9) 
DK Trails Number Letter, seconds 76.7 (27.2) 69.7 (22.5) 60.8 (37.6) 52.4 (17.3) 
PASAT 3 second trial, # correct 46.9 (7.3) 48.1 (9.5) 47.6 (11.9) 52.0 (10.7) 
DK Color-Word Interference, seconds 58.0 (8.7) 53.9 (9.0) 50.2 (11.5) 48.5 (12.1) 
TEST Quiet 1st, REN 2nd Group N = 12 REN 1st, Quiet 2nd Group N = 11 
Time 1 Time 2 Time 1 Time 2 
CVLT-II, Total Learning, # correct 50.8 (13.7) 44.0 (13.5) 55.3 (7.7) 55.7 (8.5) 
SDMT-O, # correct 56.6 (19.0) 53.8 (13.6) 64.4 (12.5) 66.0 (11.9) 
DK Trails Number Letter, seconds 76.7 (27.2) 69.7 (22.5) 60.8 (37.6) 52.4 (17.3) 
PASAT 3 second trial, # correct 46.9 (7.3) 48.1 (9.5) 47.6 (11.9) 52.0 (10.7) 
DK Color-Word Interference, seconds 58.0 (8.7) 53.9 (9.0) 50.2 (11.5) 48.5 (12.1) 

Note: N = 23. Values indicate means (standard deviations). REN = random environmental noise; CVLT-II = California Verbal Learning Test, Second Edition; SDMT-O = Symbol Digit Modalities Test - Oral version; DK Trails Number Letter = Delis–Kaplan Executive Function System Trail Making Test-Number Letter Trial; PASAT = Paced Auditory Serial Addition Test, Rao version; DK Color Word Interference = Delis–Kaplan Executive Function System Color-Word Interference - Inhibition Trial.

Participants were divided into high versus low cognitive complaint groups using a median split analysis of PDQ responses. Twenty-two participants with available PDQ data were included in this analysis, with 11 scoring less than 27 on the PDQ and 11 scoring 27 or higher on the measure. This cut-off score of 27 on the PDQ is equal to a score 1.5 standard deviations higher than the mean of a sample of 66 adult healthy controls previously collected in research in our laboratory. In each of the two groups, six participants received the quiet testing condition first, and five received the noisy condition first. Table 3 summarizes demographic information for the two complaint groups. The groups did not differ significantly on age, sex distribution, disease subtype distribution, symptom duration, time since diagnosis, estimated premorbid intellectual functioning, fatigue, or depressed mood (p > .10), although individuals with higher cognitive complaints were found to be less educated than those with fewer complaints (mean education = 13.9 vs. 16.2; p < .01). As planned, education was used as a covariate in subsequent analyses.

Table 3.

Participant characteristics, by cognitive complaint group

Variable Fewer complaints (PDQ < 27; N = 11) More complaints (PDQ ≥ 27; N = 11) 
(Raw/mean, SD(Raw/mean, SD
Sex (female/male) 7/4 8/3 
MS subtype (RRMS/SPMS/CIS) 9/2/0 9/0/2 
Age (years) 47.5 (10.1) 48.7 (10.4) 
Education (years) 16.2 (1.5) 13.9 (2.0)* 
Disease duration since symptom onset (years) 17.1 (10.4)a 15.6 (11.4) 
Disease duration since diagnosis (years) 11.6 (10.0) 6.8 (5.9) 
PDDS (median score) 2.3 (2.0) 1.9 (1.6) 
TOPF (standard score) 109.2 (13.9) 104.7 (9.9) 
FSS (z-score) −3.2 (2.4) −3.6 (1.9) 
CMDI-mood subscale (T-score) 47.0 (9.0) 42.2 (2.4) 
Variable Fewer complaints (PDQ < 27; N = 11) More complaints (PDQ ≥ 27; N = 11) 
(Raw/mean, SD(Raw/mean, SD
Sex (female/male) 7/4 8/3 
MS subtype (RRMS/SPMS/CIS) 9/2/0 9/0/2 
Age (years) 47.5 (10.1) 48.7 (10.4) 
Education (years) 16.2 (1.5) 13.9 (2.0)* 
Disease duration since symptom onset (years) 17.1 (10.4)a 15.6 (11.4) 
Disease duration since diagnosis (years) 11.6 (10.0) 6.8 (5.9) 
PDDS (median score) 2.3 (2.0) 1.9 (1.6) 
TOPF (standard score) 109.2 (13.9) 104.7 (9.9) 
FSS (z-score) −3.2 (2.4) −3.6 (1.9) 
CMDI-mood subscale (T-score) 47.0 (9.0) 42.2 (2.4) 

Note: PDQ = Perceived Deficits Questionnaire; RRMS = relapsing-remitting MS; SPMS = secondary progressive MS; CIS = clinically isolated syndrome; PDDS = Patient Determined Disease Steps; TOPF = Test of Premorbid Functioning; FSS = Fatigue Severity Scale; CMDI = Chicago Multiscale Depression Inventory.*p < .01.

an = 10.

Table 4 summarizes test performance under quiet and noisy conditions for the cognitive complaint groups. Complaint groups did not differ on cognitive measures during the quiet condition (independent t-test analyses all p > .10). Difference scores, subtracting the performance score under the quiet condition from the score under the noisy condition for each test measure, were calculated to reflect how much performance changed when the distracting REN was introduced. ANCOVAs were used to compare difference scores of the two groups on the five cognitive measures, while adjusting for education, fatigue, depression, and premorbid intellect. After adjustment and using Bonferroni-adjusted alpha levels of .01 per test (.05/5), there was a statistically significant difference in SDMT-O change score between participants with fewer and more cognitive complaints (F(1,16) = 9.507, p = .007, partial η2 = .373). Participants with more cognitive complaints showed a significant decrement in performance on the SDMT-O during the noisy condition (average of six fewer completed items), whereas those with fewer cognitive complaints demonstrated stable performance across conditions (average difference of <1 item; Fig. 1). Complaint group comparisons for other measures were not statistically significant.

Fig. 1.

Symbol Digit Modalities Test - Oral, by complaint group.

Fig. 1.

Symbol Digit Modalities Test - Oral, by complaint group.

Table 4.

Test performance in quiet and random environmental noise (REN) conditions, by cognitive complaint group

TEST Fewer complaints (PDQ < 27; N = 11) More complaints (PDQ ≥ 27; N = 11) 
Quiet (M, SDNoisy (M, SDDifference (M, SDQuiet (M, SDNoisy (M, SDDifference (M, SD
CVLT-II, Total Learning, # correct 50.6 (8.6) 47.1 (13.5) −3.5 (10.5) 55.4 (14.3) 51.3 (11.9) −4.1 (5.5) 
SDMT-O, # correct 57.4 (17.2) 58.3 (14.3) 0.9 (6.5) 65.0 (16.1) 59.0 (14.6) −6.0 (7.7)* 
DK Trails Number Letter, seconds 68.0 (31.9) 67.1 (22.4) −0.9 (18.6) 61.7 (20.4) 69.0 (34.2) 7.3 (25.6) 
PASAT 3 second trial, # correct 51.7 (7.4) 49.8 (8.7) −1.9 (8.3) 46.8 (10.9) 45.4 (12.2) −1.5 (5.8) 
DK Color-Word Interference, seconds 49.8 (9.7) 49.4 (7.0) −0.5 (5.0) 56.2 (12.4) 53.8 (12.5) −2.4 (5.5) 
TEST Fewer complaints (PDQ < 27; N = 11) More complaints (PDQ ≥ 27; N = 11) 
Quiet (M, SDNoisy (M, SDDifference (M, SDQuiet (M, SDNoisy (M, SDDifference (M, SD
CVLT-II, Total Learning, # correct 50.6 (8.6) 47.1 (13.5) −3.5 (10.5) 55.4 (14.3) 51.3 (11.9) −4.1 (5.5) 
SDMT-O, # correct 57.4 (17.2) 58.3 (14.3) 0.9 (6.5) 65.0 (16.1) 59.0 (14.6) −6.0 (7.7)* 
DK Trails Number Letter, seconds 68.0 (31.9) 67.1 (22.4) −0.9 (18.6) 61.7 (20.4) 69.0 (34.2) 7.3 (25.6) 
PASAT 3 second trial, # correct 51.7 (7.4) 49.8 (8.7) −1.9 (8.3) 46.8 (10.9) 45.4 (12.2) −1.5 (5.8) 
DK Color-Word Interference, seconds 49.8 (9.7) 49.4 (7.0) −0.5 (5.0) 56.2 (12.4) 53.8 (12.5) −2.4 (5.5) 

Note: N = 22. Values indicate means (standard deviations). REN = random environmental noise; PDQ = Perceived Deficits Questionnaire; CVLT-II = California Verbal Learning Test, Second Edition; SDMT-O = Symbol Digit Modalities Test - Oral version; DK Trails Number Letter = Delis–Kaplan Executive Function System Trail Making Test - Number Letter Trial; PASAT = Paced Auditory Serial Addition Test, Rao version; DK Color-Word Interference = Delis–Kaplan Executive Function System Color-Word Interference - Inhibition Trial.*p < .01.

Discussion

This study sought to determine whether an environmental manipulation would impact cognitive performance in an MS sample, particularly among those with cognitive complaints. We found that use of an auditory distraction condition—random office noises—affected processing speed in participants with reported cognitive dysfunction. Our results are complementary to earlier work in this area (LaPointe et al., 2005; Patel et al., 2016), and suggest a particular vulnerability to reduced processing speed in MS when distraction is present. It is also interesting to note that examining groups with high or low cognitive complaints was more effective in clarifying effects of distraction on cognition in MS than examining the sample as a whole; only those with more cognitive complaints showed a performance decrement due to distraction.

One of the primary interests in pursuing the present line of research was to determine factors that could be incorporated in neuropsychological testing to predict real-world functioning more effectively. While there is some evidence that so-called ecologically valid measures are more predictive of real-world functioning, traditional neuropsychological measures have also been found to be effective in this regard (Higginson, Arnett, & Voss, 2000). Comparing the relative effects of distraction on standard and more recently designed, ecologically valid measures may prove useful in future research. Furthermore, while we found that artificially manipulating the assessment setting can impact cognitive performance, virtual reality technology also has potential in examining performance in environments resembling daily life, including virtual office settings (Matheis et al., 2007). Ultimately, there may be value in examining performance in both quiet and distracting testing environments to determine how cognitive functioning differs across environmental conditions, particularly in those reporting cognitive changes in daily life.

This study examined only one type of auditory distraction. It is possible that considering other distracting noises would have had different effects on cognitive performance; indeed, other forms of auditory distraction, such as overheard cell-phone conversations, have been found to reduce cognitive performance (Emberson, Lupyan, Goldstein, & Spivey, 2010). Familiarity with certain types of sounds may also influence distraction effects. For example, it is possible that individuals accustomed to noisy office environments may not have been as affected by the distracting auditory condition as others. Furthermore, distraction impacting other senses, such as visual distraction, may have negative effects on cognition (Feinstein, Lapshin, & O'Connor, 2012). Future studies could employ both auditory and visual distractions to determine relative effects on cognition. In addition, manipulating the magnitude of distraction (e.g., ambient noise level) may lead to differential distraction effects. In a related vein, some work has found that creativity is positively impacted by moderate versus high noise (Mehta, Zhu, & Cheema, 2012).

We found incidentally that participants with more cognitive complaints tended to be somewhat less educated than those with fewer complaints. While education did not impact subsequent complaint group comparisons, it is notable that Marrie and colleagues (2005) also found that less educated MS participants tended to have more cognitive complaints than better educated participants. Future work may wish to consider further the relationship between education and cognitive complaints in MS, for example, whether more education is associated with a higher threshold for classifying a cognitive lapse as a problem or “complaint,” or whether less education is associated with a lower threshold.

Some limitations and characteristics of this study should be acknowledged. The lack of distraction effects beyond visual processing speed could be due to multiple factors. First, our sample size, while adequate to detect a large effect across the entire sample related to the impact of a distracting environment on cognitive performance, was relatively small, with reduced statistical power to reveal differences in the high versus low complaint groups. Furthermore, the quiet and noisy testing conditions were administered with a very brief (10–15 min) break between them, and there was potential for practice effects on some measures. While an analysis indicated that practice effects were not statistically significant, findings did trend in this direction. Multiple participants also indicated that the second administration of the CVLT-II was confusing, given the proximity to the first administration and multiple lists for the participant to reference (i.e., primary and distracter lists for each of two CVLT-II versions). Interestingly, the short test-retest interval did not seem to impact SDMT performance or produce demonstrable practice effects for that measure, which resembles findings from a recent study with rapid re-administration of the SDMT where alternate forms were also employed (Pereira, Costa, & Cerqueira, 2015). In future work, we plan to introduce a significantly longer delay between presentations of cognitive measures to minimize these issues.

MS participants in our sample did not exhibit significant depressive symptoms. While this allowed us to minimize potential effects of depression, our findings may not generalize to those with MS who exhibit more mood disturbance. In addition, MS participants in the cognitive complaints group reported moderate levels of cognitive difficulty. Some work using the PDQ has included MS participants with more severe cognitive complaints than our participants reported (e.g., Lovera et al., 2006), and future research may help clarify the relationship between distraction effects and severity of cognitive complaints. Furthermore, participants in our sample were relatively preserved cognitively, and it will be important for future work to explore distraction effects based on greater and lesser degrees of cognitive impairment. Future studies should also examine distraction effects by MS subtype, given potential differences (Julian, 2011).

In summary, we found that a simple environmental modification—auditory distraction in the form of random office noises—impacted processing speed in MS. Use of similar procedures may be an inexpensive and pragmatic method to obtain more ecologically valid neuropsychological findings in clinical and other settings. More generally, our findings appear to support the notion that environmental modifications of the testing environment during selected tests, including comparisons of processing speed during distracting and nondistracting conditions, may serve to improve the correspondence between MS patients’ self-reported cognitive dysfunction in daily life and neuropsychological performance. If so, the perceived relevance of cognitive evaluations for MS and other patients may be enhanced, adding value to the role and services of clinical neuropsychologists.

Funding

Supported by Pilot Grant number PP2079 from the National Multiple Sclerosis Society.

Acknowledgements

The authors thank Mirranda Boshart for assistance on this project. We gratefully acknowledge the participants in our study who contributed their time and energy to help us better understand the nature of MS. Portions of this study were presented at the 44th Annual Meeting of the International Neuropsychological Society. The PDDS is provided for use by the NARCOMS Registry. NARCOMS is supported in part by the Consortium of Multiple Sclerosis Centers (CMSC) and the CMSC Foundation.

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