Abstract

Individuals with substance use disorders (SUDs) commonly report lapses in prospective memory (PM) in their daily lives; however, our understanding of the profile and predictors of laboratory-based PM deficits in SUDs and their associations with everyday PM failures is still very preliminary. The current study examined these important questions using well-validated measures of self-report and laboratory-based PM in a mixed cohort of 53 SUD individuals at treatment entry and 44 healthy adults. Consistent with prior research, the SUD group endorsed significantly more self-cued and environmentally based PM failures in their daily lives. Moreover, the SUD group demonstrated significantly lower time-based PM performance, driven largely by cue detection errors. The effect of SUDs on PM was particularly strong among participants with fewer years of education. Within the SUD cohort, time-based PM was correlated with clinical measures assessing executive functions, retrospective memory, and psychomotor speed. Importantly, time-based PM was uniquely associated with elevated PM failures in daily lives of the SUD participants, independent of current affective distress and other neurocognitive deficits. Findings suggest that individuals with SUD are vulnerable to deficits in PM, which may in turn increase their risk for poorer everyday functioning outcomes (e.g., treatment non-compliance).

Introduction

Substance use disorders (SUDs) represent a major source of morbidity and mortality among adolescents and adults and are associated with poorer psychosocial (e.g., Cooke, Kelley, Fals-Stewart, & Golden, 2004), medical (e.g., Huckans, Balckwell, Harms, Indest, & Hauser, 2005), and psychiatric (e.g., Marshall & Werb, 2010) outcomes. SUDs also confer an increased risk of injury to brain structure and function, including neurocognitive impairment (e.g., Fernández-Serrano, Pérez-García, & Verdejo-García, 2011; Sullivan, 2007). Broadly, mild-to-moderate deficits in a range of cognitive functions can occur in persons who abuse alcohol (e.g., Oscar-Berman & Marinković, 2007), marijuana (e.g., Grant, Gonzalez, Carey, Natarajan, & Wolfson, 2003), opioids (e.g., Pau, Lee, & Chan, 2002), and stimulants, including cocaine (Jovanovski, Erb, & Zakzanis, 2005) and methamphetamine (Scott et al., 2007). Across these substances of abuse, deficits are most commonly observed on measures of episodic memory, executive functions, information processing speed, and spatial cognition (e.g., Scott et al., 2007). Importantly, cognitive deficits can increase the risk of poorer everyday functioning outcomes among substance abusers (e.g., Henry, Minassian, & Perry, 2010), including treatment non-compliance (e.g., Grohman, Fals-Stewart, & Donnelly, 2006; Reinhard et al., 2007).

Only recently, however, have investigators begun to specifically examine the effects of SUDs on prospective memory (PM). PM, often referred to as “remembering to remember,” describes the cognitive capacity to accurately execute a planned intention at a specified future time or in response to a particular environmental cue. The clinical relevance of PM is immediately apparent, as this cognitive ability is ubiquitous in daily activities critical to successful independent living (e.g., household management, healthcare compliance, and vocational functioning). PM is a complex cognitive operation that is commonly parsed into stages of “encoding” (e.g., planning), “delay interval” (during which other activities are ongoing, although one may periodically monitor the environment for cues), “cue detection” (a cardinal feature of PM that requires a shift from the ongoing activity), “retrieval” (of the content of the intention from retrospective memory), and “intention execution” (e.g., Kliegel, Jager, Altgasen, & Sum, 2008). The inherent complexity of PM operations necessitates the involvement of retrospective memory, working memory, and executive functions, but it is also separable from these related constructs at the cognitive (e.g., Gupta, Woods, Weber, Dawson, & Grant, 2010) and neurobiological (e.g., Woods et al., 2006) levels. In fact, when compared with the standard clinical tests of cognition, PM may be a particularly sensitive indicator of everyday functioning; for example, in individuals living with HIV infection, deficits in PM show incremental validity in predicting declines in instrumental activities of daily living (Woods et al., 2008), unemployment (Woods et al., 2011), and healthcare compliance (Contardo, Black, Beauvais, Dieckhaus, & Rosen, 2009; Woods et al., 2009; Zogg et al., 2010).

It is now fairly well established that many substance-abusing populations report experiencing higher levels of PM failures in their daily lives, including heavy users of alcohol (e.g., Heffernan, Moss, & Ling, 2002), marijuana (e.g., McHale & Hunt, 2008), nicotine (e.g., Heffernan et al., 2005), and “ecstasy” (e.g., Heffernan, Jarvis, Rodgers, Scholey, & Ling, 2001). However, only a handful of studies to date have used performance-based laboratory paradigms to examine the effects of substances of abuse on PM ability. In non-clinical samples, cigarette smoking (e.g., Heffernan, Clark, Bartholomew, Ling, & Stephens, 2010) and acute administration of alcohol (e.g., Leitz, Morgan, Bisby, Rendell, & Curran, 2009) and benzodiazepines (Rich, Svoboda, & Brown, 2006) are associated with lower PM. Among young adults, binge-drinking (Heffernan et al., 2010) and use of cannabis (Bartholomew, Holroyd, & Heffernan, 2010) and ecstasy (Bedi & Redman, 2008a; cf. Montgomery, Hatton, Fisk, Ogden, & Jansari, 2010; Rendell, Gray, Henry, Tolan, 2007) have been linked to worse performance on naturalistic tasks of PM. Finally, two recent clinical studies have shown evidence of deficits in both time- (Iudicello et al., 2011) and event-based (Rendell, Mazur, & Henry, 2009) PM in methamphetamine-dependent individuals, which may be driven by errors of cue detection and executive dyscontrol.

The current study seeks to enhance the clinical applicability of this literature by examining the profile and correlates of PM deficits in a mixed cohort of substance users (SUs) at treatment entry. Whereas prior studies have used rigorous inclusion and exclusion criteria to examine the mechanisms of specific substances of abuse on PM (e.g., Rendell et al., 2009), we adopted a more ecologically grounded approach that more closely parallels the modal clinic experience (i.e., a consecutive series of patients assessed with a well-validated, proprietary test of PM). It was hypothesized that SUs would demonstrate moderate deficits (i.e., broadly medium effect sizes) in both time- and event-based PM as measured by self-report and performance-based tasks. Given the prevalence of lower educational achievement among substance abusers (e.g., SAMHSA, 2009), its possible role in neurocognitive impairment (e.g., Sabia et al., 2010), and the known associations between PM and education (e.g., Woods et al., 2008), we were also interested in the possible role of education in the expression of PM deficits in this population. Next, considering the typical cognitive profile of poly-SUDs (e.g., Fernández-Serrano et al., 2011) and the architecture of PM (e.g., Kliegel et al., 2008), it was hypothesized that PM deficits would correlate with clinical measures of episodic memory, executive functions, psychomotor speed, and attention/working memory.

Finally, we were interested in determining the strength and uniqueness of the association between PM deficits in the laboratory and self-reported memory problems in everyday life. The few prior studies that have examined this question in substance using populations have produced mixed results. Bedi and Redman (2008b) found weak and inconsistent associations between naturalistic PM tasks and self-reported daily failures in ecstasy users, whereas Heffernan and colleagues (2010) observed no significant associations among binge-drinking teenagers. In contrast, a recent paper by Hadjiefthyvoulou, Fisk, Montgomery, and Bridges (2011a) reports modest correlations in a group of ecstasy users, such that more self-reported PM failures in daily life corresponded to worse performance on standardized laboratory-based PM tasks. However, these findings are generally restricted to non-clinical samples, in which there may be limited variability in everyday functioning outcomes. Another important issue in extending these PM studies to a clinical sample is consideration of mood, particularly in interpreting elevated PM complaints in daily life (e.g., Woods et al., 2007). Finally, our understanding of whether the association between PM and everyday memory failures among SUs is specific to PM, or might also be driven by other, related cognitive deficits (e.g., executive dysfunction) remains quite limited (e.g., Hadjiefthyvoulou, Fisk, Montgomery, & Bridges, 2011b).

Methods

Participants

Fifty-three consecutive, treatment-seeking participants were recruited from a drug and alcohol rehabilitation center in Perth, Australia. Individuals with histories of severe psychiatric (e.g., schizophrenia) or neurological (e.g., multiple sclerosis, stroke, epilepsy, or closed head injury with loss of consciousness >30 min) disorders were excluded. We also excluded participants who scored below published cut points on the Test of Memory Malingering or tested positive for recent use of drugs (using a saliva test, the Cozart RapiScan® device) or alcohol (using the Alcotech AR 1005 Breathalyser). A sample of 44 healthy adults (HAs) without histories of SUDs was recruited using flyers asking for participation from non-using family members at the treatment agency, as well as flyers posted in a variety of community locations (e.g., grocery stores, laundromats, etc.). Participants were reimbursed $25.00 AUD for travel expenses. Approval for this project was obtained from the University of Western Australia Human Ethics Research Committee and the St John of God Committee for the Drug and Alcohol Office.

The demographic and psychiatric characteristics of the SUs and HA study groups can be viewed in Table 1. Participants did not significantly differ in age, sex, or ethnic identity (ps > .10); however, the HA reported having obtained significantly more years of education (p < .001). In addition, the SU group endorsed higher levels of current affective distress as measured by the Depression Anxiety Stress Scales-21 (DASS-21; Lovibond & Lovibond, 1995; ps < .001).

Table 1.

Demographic and psychiatric characteristics of the study samples

Variable Substance users (n = 53) HAs (n = 44) p-value 
Demographic characteristics 
 Age (years) 39.9 (11.8) 42.1 (14.2) .587 
 Education (years) 11.2 (2.0) 14.4 (2.9) <.001 
 Sex (% men) 57 41 .124 
 Ethnicity (% Caucasian) 98 93 .296 
Psychiatric characteristics 
 DASS-21 Total 58.0 (28.6) 10.9 (10.7) <.001 
  Depression 21.2 (12.1) 3.1 (4.1) <.001 
  Anxiety 15.1 (9.4) 2.3 (2.9) <.001 
  Stress 21.7 (10.5) 5.5 (5.5) <.001 
Variable Substance users (n = 53) HAs (n = 44) p-value 
Demographic characteristics 
 Age (years) 39.9 (11.8) 42.1 (14.2) .587 
 Education (years) 11.2 (2.0) 14.4 (2.9) <.001 
 Sex (% men) 57 41 .124 
 Ethnicity (% Caucasian) 98 93 .296 
Psychiatric characteristics 
 DASS-21 Total 58.0 (28.6) 10.9 (10.7) <.001 
  Depression 21.2 (12.1) 3.1 (4.1) <.001 
  Anxiety 15.1 (9.4) 2.3 (2.9) <.001 
  Stress 21.7 (10.5) 5.5 (5.5) <.001 

Note: DASS-21 = Depression Anxiety Stress Scales-21; HA = healthy adult.

In order to quantify the clinical characteristics of the SU cohort, participants were interviewed on their substance history and administered the Opiate Treatment Index (OTI; Darke, Ward, Hall, Heather, & Wodak, 1991). The OTI is a structured interview designed to collect information on past and present (within the last month) use of substances of abuse (e.g., alcohol, cannabis, amphetamines). Descriptive data are displayed in Table 2. Overall, alcohol (52%) and heroin (23%) were most likely to be endorsed as the primary drug of choice by the entire SU sample (N = 53), followed by amphetamines (14%), cannabis (8%), and others (3%). Fifty-three percent of the SU sample reported smoking cigarettes on a daily basis.

Table 2.

Substance use characteristics of the mixed clinical sample at treatment entry

Substance Descriptive statistics (N = 53) 
Any lifetime alcohol use (%) 89 
 Primary drug of choice (%)a 57 
 Frequency (% daily) 53 
 Age at first use (years) 15.0 (13.0–16.0) 
 Total duration of use (years) 6.0 (1.0–18.0) 
Any lifetime cannabis use (%) 66 
 Primary drug of choice (%)a 11 
 Frequency (% daily) 60 
 Age at first use (years) 16.0 (14.0–17.0) 
 Total duration of use (years) 10.0 (4.25–13.75) 
Any lifetime amphetamine use (%) 64 
 Primary drug of choice (%)a 21 
 Frequency (% daily) 21 
 Age at first use (years) 18.0 (15.0–21.5) 
 Total duration of use (years) 4.0 (2.0–9.75) 
Any lifetime heroin use (%) 38 
 Primary drug of choice (%)a 45 
 Frequency (% daily) 15 
 Age at first use (years) 19.5 (17.0–27.0) 
 Total duration of use (years) 6.0 (1.5–14.5) 
Any lifetime use of other drugs (%)b 100 
 Primary drug of choice (%) 10 
Substance Descriptive statistics (N = 53) 
Any lifetime alcohol use (%) 89 
 Primary drug of choice (%)a 57 
 Frequency (% daily) 53 
 Age at first use (years) 15.0 (13.0–16.0) 
 Total duration of use (years) 6.0 (1.0–18.0) 
Any lifetime cannabis use (%) 66 
 Primary drug of choice (%)a 11 
 Frequency (% daily) 60 
 Age at first use (years) 16.0 (14.0–17.0) 
 Total duration of use (years) 10.0 (4.25–13.75) 
Any lifetime amphetamine use (%) 64 
 Primary drug of choice (%)a 21 
 Frequency (% daily) 21 
 Age at first use (years) 18.0 (15.0–21.5) 
 Total duration of use (years) 4.0 (2.0–9.75) 
Any lifetime heroin use (%) 38 
 Primary drug of choice (%)a 45 
 Frequency (% daily) 15 
 Age at first use (years) 19.5 (17.0–27.0) 
 Total duration of use (years) 6.0 (1.5–14.5) 
Any lifetime use of other drugs (%)b 100 
 Primary drug of choice (%) 10 

aPercentage of those who reported any lifetime use.

bOther drugs reported are benzodiazepines (n = 28), opiates (n = 13), hallucinogens (n = 12), and ecstasy (n = 7).

Procedure

All participants were administered the research version (Woods et al., 2008) of the Memory for Intentions Screening Test (MIST; Raskin, Buckheit, & Sherrod, 2010), which is a well-validated performance-based test of PM. During this 30 min test, participants are asked to complete a word search, which serves as the ongoing task for eight PM trials (i.e., four time- and four event-based). The MIST generates a summary score (range = 0–48), time- and event-based scales (range = 0–48), ongoing task total, and a series of error scores. Error codes are classified by: (i) no response (i.e., omissions); (ii) task substitutions (i.e., performing either an incorrect written or verbal response at the correct time); (iii) loss of content (i.e., knowing a task should be performed, but failing to recall which task); (iv) loss of time (performing the correct task, but at the incorrect time). After completing the MIST, participants are administered an 8-item, 3-choice questionnaire to assess recognition of the PM trials (range = 0–8). A 24 h trial is also administered in which participants are instructed to call the examiner the day after the evaluation and report how many hours they slept (range = 0–2).

Self-report of PM failures in everyday life was measured using the PM scale of the Prospective and Retrospective Memory Questionnaire (PRMQ; Smith, Della Sala, Logie, & Maylor, 2000). The PRMQ is a 16-item, self-report inventory that measures the frequency with which perceived memory difficulties occur in everyday life on a 5-point Likert-type scale that ranges from 1 (“never”) to 5 (“very often”). The PRMQ includes eight PM complaints, which are separated into four self-cued (e.g., “How often do you forget appointments if you are not prompted by someone else or by a reminder, such as a diary or a calendar?”) and four environmentally cued (e.g., “How often do you forget to buy something you planned to buy, like a birthday card, even when you see the shop?”) PM complaints.

All participants also received a comprehensive clinical neuropsychological battery to assess the cognitive domains that are mostly closely related to PM. Consistent with prior research (e.g., Zogg et al., 2011) and in an effort to minimize Type I error, we elected to group these tests on an a priori basis by domain according to conventional definitions of their putative construct. It is important to note that all of these clinical tests are highly multifactorial and do not necessarily directly correspond to a single cognitive domain. The broad domains of interest were: (i) retrospective memory as measured by: the long delayed recall trials of the Rey Auditory Verbal Learning Test (Schmidt, 1996) and the Rey Complex Figure Test (Meyers & Meyers, 1995); (ii) executive functions as measured by: Trail Making Test (TMT) Part B (Reitan & Wolfson, 1985), perseverative responses of Wisconsin Card Sorting Test (Kongs, Thompson, Iverson, & Heaton, 2000), total from Iowa Gambling Test (Bechara, 2007), and Stroop Color-Word (Golden & Freshwater, 2002); (iii) information processing speed as measured by: TMT Part A (Reitan & Wolfson, 1985) and the Symbol Search and Digit Symbol subtests from the Wechsler Adult Intelligence Scale-Third Edition (Psychological Corporation, 1997); (iv) attention as measured by: Digit Span and Letter-Number Sequencing subtests from the Weschler Adult Intelligence Scale (Psychological Corporation, 1997); and (v) verbal fluency as measured by: Controlled Oral Word Association Test (CFL), Animals (Benton, Hamsher, & Sivan, 1994), and Actions (Woods et al., 2005). As these data were intended to examine the cognitive correlates of PM in the SU sample, raw scores were converted to population-based z-scores derived from the SU group, which were averaged to create a composite z-score for each cognitive domain assessed.

Results

Descriptive PM data for the SU and HA groups are displayed in Table 3. Given the between-group differences on education reported above, we conducted a series of multiple regression analyses predicting PM on the MIST and PRMQ from study group, education level, and their interaction. Results of these analyses are shown in Table 3. Of primary interest, we observed a significant interaction between study group and education on the MIST Summary Score, Time-based Scale, and No Response errors (p < .05). Note that, these findings did not change when the DASS was included in the regression model. Post hoc analyses using a median split of education at 12 years showed that there was a larger effect of SU on these MIST scores at lower levels of education (Figure 1). Specifically, there was a large effect for SU on the MIST Summary Score in the individuals with 12 or fewer years of education (SU, n = 42; HA, n = 15; p = .01, d = 0.76), whereas there was a more modest effect among persons with more than 12 years of education (SU, n = 11; HA, n = 29; p = .04, d = 0.63). Main effects for SU were evident for Task Substitution errors (p < .05), whereas main effects of education emerged on event-based Loss of Content errors (p < .05).

Table 3.

PM scores in SUs and HAs

PM SUs (n = 53) HAs (n = 44) t-ratio
 
Adj. R2 
Drug Education Drug × Education 
MIST Summary Score 39.0 (33.0, 42.0) 45.0 (42.0, 48.0) −2.6* 2.2* 2.3* .21*** 
 Time-based Cues 6.0 (5.0, 7.0) 7.0 (6.0, 8.0) −2.4* 1.4 2.0* .14*** 
 Event-based Cues 7.0 (7.0, 8.0) 8.0 (8.0, 8.0) −1.6 2.4* 1.4 .14*** 
Errors 
 No Response 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 2.3* −1.3 −2.3* .13** 
 Task Substitution 1.0 (0.0, 2.0) 0.0 (0.0, 1.0) 2.4* −0.6 −1.7 .10** 
 Loss of Content 1.0 (0.0, 1.0) 0.0 (0.0, 1.0) −1.4 −2.7* 0.8 .06* 
 Loss of Time 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 1.6 0.6 0.2 <.01 
Recognition 8.0 (7.0, 8.0) 8.0 (7.3, 8.0) −0.5 2.7** 1.7 .10** 
Word Search 15.0 (12.0, 18.5) 17.5 (14.0, 25.0) −1.3 1.4 −0.1 .06* 
24 h Delay (% Complete) 0.0 (0.0, 1.0) 0.0 (0.0, 2.0) −0.7 0.1 −0.4 — 
PRMQ Total 43.0 (36.0, 50.8) 31.0 (26.0, 38.8) 3.4*** −1.7 −0.7 .23*** 
 Retrospective 20.0 (16.3, 24.0) 14.0 (11.0, 17.0) 2.9** −1.1 −0.1 .29*** 
 Prospective 22.0 (19.0, 26.8) 17.5 (13.0, 22.0) 3.7*** −2.3* −1.2 .15*** 
  Self-cued 11.0 (10.0, 14.0) 9.0 (7.0, 11.0) 3.1** −0.5 −0.1 .13** 
  Environment-cued 11.0 (9.0, 13.0) 8.0 (7.0, 11.0) 2.4* −1.5 −0.1 .14*** 
PM SUs (n = 53) HAs (n = 44) t-ratio
 
Adj. R2 
Drug Education Drug × Education 
MIST Summary Score 39.0 (33.0, 42.0) 45.0 (42.0, 48.0) −2.6* 2.2* 2.3* .21*** 
 Time-based Cues 6.0 (5.0, 7.0) 7.0 (6.0, 8.0) −2.4* 1.4 2.0* .14*** 
 Event-based Cues 7.0 (7.0, 8.0) 8.0 (8.0, 8.0) −1.6 2.4* 1.4 .14*** 
Errors 
 No Response 0.0 (0.0, 1.0) 0.0 (0.0, 0.0) 2.3* −1.3 −2.3* .13** 
 Task Substitution 1.0 (0.0, 2.0) 0.0 (0.0, 1.0) 2.4* −0.6 −1.7 .10** 
 Loss of Content 1.0 (0.0, 1.0) 0.0 (0.0, 1.0) −1.4 −2.7* 0.8 .06* 
 Loss of Time 0.0 (0.0, 0.0) 0.0 (0.0, 0.0) 1.6 0.6 0.2 <.01 
Recognition 8.0 (7.0, 8.0) 8.0 (7.3, 8.0) −0.5 2.7** 1.7 .10** 
Word Search 15.0 (12.0, 18.5) 17.5 (14.0, 25.0) −1.3 1.4 −0.1 .06* 
24 h Delay (% Complete) 0.0 (0.0, 1.0) 0.0 (0.0, 2.0) −0.7 0.1 −0.4 — 
PRMQ Total 43.0 (36.0, 50.8) 31.0 (26.0, 38.8) 3.4*** −1.7 −0.7 .23*** 
 Retrospective 20.0 (16.3, 24.0) 14.0 (11.0, 17.0) 2.9** −1.1 −0.1 .29*** 
 Prospective 22.0 (19.0, 26.8) 17.5 (13.0, 22.0) 3.7*** −2.3* −1.2 .15*** 
  Self-cued 11.0 (10.0, 14.0) 9.0 (7.0, 11.0) 3.1** −0.5 −0.1 .13** 
  Environment-cued 11.0 (9.0, 13.0) 8.0 (7.0, 11.0) 2.4* −1.5 −0.1 .14*** 

Notes: Data are presented as median values with the interquartile range in parenthesis unless otherwise indicated. PM = prospective memory; HA = healthy adult; SU = substance user; MIST = Memory for Intentions Screening Test; PRMQ = Prospective and Retrospective Memory Questionnaire.

*p < .05.

**p < .01.

***p < .001.

Fig. 1.

Mean performances of HAs and SUs on the MIST based on level of education. Error bars reflect the standard deviations. *p = .04, d = −0.63; **p = .02, d = −0.76.

Fig. 1.

Mean performances of HAs and SUs on the MIST based on level of education. Error bars reflect the standard deviations. *p = .04, d = −0.63; **p = .02, d = −0.76.

Analysis of the PRMQ revealed significant main effects of the SU group across the PM total, self-cued, and environmentally cued scales (ps < .05). Education showed a main effect on the PRMQ PM total scale (p < .05), but there were no other main effects of education nor any interactions between study group and education (all ps > .10). Importantly, however, the effects of the SU group on the PRMQ did not persist when the DASS-21 Total was included in the statistical model (ps > .10).

Correlation analyses were conducted within the SU group to examine the association between the MIST and other cognitive measures. Significance testing was restricted to the composite scores, but all individual test effect sizes are reported for descriptive purposes in Table 4. Results showed that the MIST Summary Score and Time-based Scale were significantly associated with composite scores of retrospective memory, executive functions, information processing speed, and verbal fluency (ps < .05), but not attention and working memory (p > .10). The magnitude of the correlations fell broadly in the medium effect size range for both the Summary (M = 0.35, SD = 0.11, range = 0.20–0.50) and Time-based (M = 0.31, SD = 0.10, range = 0.20–0.50) Scales. In contrast, significant correlations with the Event-based Scale were only found for measures of executive functions and information processing speed (ps < .05), and effect sizes were generally smaller (M = 0.26, SD = 0.14, range = 0.09–0.42).

Table 4.

Cognitive correlates of the MIST in substance users (N = 53)

Cognitive variable MIST
 
Summary Time-based Event-based 
Retrospective Memory .28* .28* .09 
 RAVLT Delay .36 .32 .16 
 RCFT Delay .12 .12 .03 
Executive Functions .36** .29* .42** 
 TMT Part B .28 .22 .25 
 WCST Perseverative Responses .21 .12 .28 
 IGT Total .29 .27 .36 
 Stroop Incongruent .36 .29 .21 
Information Processing Speed .50*** .43** .40** 
 TMT Part A .35 .28 .26 
 WAIS-III Symbol Search .47 .47 .31 
 WAIS-III Digit Symbol .52 .45 .42 
Attention/Working Memory .20 .16 .22 
 WAIS-III Digit Span .09 .10 .05 
 WAIS-III Letter-Number Sequencing .36 .25 .41 
Verbal Fluency .39** .37** .19 
 Letters (CFL) .22 .22 .05 
 Actions .40 .36 .18 
 Animals .39 .35 .25 
Cognitive variable MIST
 
Summary Time-based Event-based 
Retrospective Memory .28* .28* .09 
 RAVLT Delay .36 .32 .16 
 RCFT Delay .12 .12 .03 
Executive Functions .36** .29* .42** 
 TMT Part B .28 .22 .25 
 WCST Perseverative Responses .21 .12 .28 
 IGT Total .29 .27 .36 
 Stroop Incongruent .36 .29 .21 
Information Processing Speed .50*** .43** .40** 
 TMT Part A .35 .28 .26 
 WAIS-III Symbol Search .47 .47 .31 
 WAIS-III Digit Symbol .52 .45 .42 
Attention/Working Memory .20 .16 .22 
 WAIS-III Digit Span .09 .10 .05 
 WAIS-III Letter-Number Sequencing .36 .25 .41 
Verbal Fluency .39** .37** .19 
 Letters (CFL) .22 .22 .05 
 Actions .40 .36 .18 
 Animals .39 .35 .25 

Notes: MIST = Memory for Intentions Screening Test; RAVLT = Rey Auditory Verbal Learning Test; RCFT = Rey Complex Figure Test; WCST = Wisconsin Card Sorting Test; IGT = Iowa Gambling Test; TMT = Trail Making Test; WAIS-III = Wechsler Adult Intelligence Scale-third edition.

*p < .05.

**p < .01.

***p < .001.

Finally, we wished to examine the association between performance-based PM as measured by the MIST and complaints of everyday PM failures on the PRMQ. As above, significance testing was restricted to the composite scores. Table 5 shows that the MIST Summary, Time-based, and Event-based Scales all correlated with the PRMQ PM Self-cued scale (ps < .05). The MIST Time-based Scale also correlated with the PRMQ PM Total and Self-cued Scales (ps < .05). Importantly, a follow-up regression showed that this association remained significant when the DASS-21 was included in the model (ps < .05). There was no association between the PRMQ PM Scales and any of the other cognitive domains (ps > .10).

Table 5.

Cognitive correlates of the PRMQ PM Scale in substance users (N = 53)

Cognitive variable PRMQ PM Scale
 
Total Self-cued Environmentally cued 
MIST Summary −.27 −.35* −.14 
 Time −.30* −.36** −.12 
 Event −.24 −.30* −.18 
Retrospective Memory −.16 −.20 −.09 
 RAVLT Delay −.12 −.17 −.03 
 RCFT Delay −.21 −.18 −.21 
Executive Functions −.26 −.24 −.18 
 TMT Part B −.04 −.08 .05 
 WCST Perseverative Responses −.12 −.15 −.07 
 IGT Total −.24 −.21 −.22 
 Stroop Incongruent −.24 −.22 −.15 
Information Processing Speed −.17 −.25 −.04 
 TMT Part A −.11 −.21 −.08 
 WAIS-III Symbol Search −.24 −.30 −.06 
 WAIS-III Digit Symbol −.14 −.19 .03 
Attention/Working Memory −.03 .01 −.01 
 WAIS-III Digit Span −.01 .05 .04 
 WAIS-III Letter-Number Sequencing −.11 −.14 −.12 
Verbal Fluency −.11 −.13 −.10 
 Letters (CFL) −.05 −.04 −.07 
 Actions −.09 −.15 −.10 
 Animals −.13 −.15 −.07 
Cognitive variable PRMQ PM Scale
 
Total Self-cued Environmentally cued 
MIST Summary −.27 −.35* −.14 
 Time −.30* −.36** −.12 
 Event −.24 −.30* −.18 
Retrospective Memory −.16 −.20 −.09 
 RAVLT Delay −.12 −.17 −.03 
 RCFT Delay −.21 −.18 −.21 
Executive Functions −.26 −.24 −.18 
 TMT Part B −.04 −.08 .05 
 WCST Perseverative Responses −.12 −.15 −.07 
 IGT Total −.24 −.21 −.22 
 Stroop Incongruent −.24 −.22 −.15 
Information Processing Speed −.17 −.25 −.04 
 TMT Part A −.11 −.21 −.08 
 WAIS-III Symbol Search −.24 −.30 −.06 
 WAIS-III Digit Symbol −.14 −.19 .03 
Attention/Working Memory −.03 .01 −.01 
 WAIS-III Digit Span −.01 .05 .04 
 WAIS-III Letter-Number Sequencing −.11 −.14 −.12 
Verbal Fluency −.11 −.13 −.10 
 Letters (CFL) −.05 −.04 −.07 
 Actions −.09 −.15 −.10 
 Animals −.13 −.15 −.07 

Notes: PRMQ = Prospective and Retrospective Memory Questionnaire;

MIST = Memory for Intentions Screening Test; RAVLT = Rey Auditory Verbal Learning Test; RCFT = Rey Complex Figure Test; TMT = Trail Making Test; WCST = Wisconsin Card Sorting Test; IGT = Iowa Gambling Test; WAIS-III = Wechsler Adult Intelligence Scale-third edition.

*p < .05.

**p < .01.

Discussion

Although it has been widely reported that substance use is associated with elevations in PM complaints among young adults (e.g., Heffernan et al., 2001), relatively little is known about profile and correlates of PM complaints and deficits in substance abusers more likely to be encountered in clinical settings (cf. Iudicello et al., 2011; Rendell et al., 2009). In the present study, a mixed sample of substance abusers assessed at treatment entry reported significantly more frequent PM complaints in their everyday lives when compared with HAs. Elevations in complaints were apparent across both self-based and environmentally based PM daily tasks, suggesting that substance abusers' perceived PM difficulties were not differentially affected by the salience of the cue. These findings were associated with large effect sizes and were independent of demographics, including the notable education differences between the substance-abusing cohort and the HAs. However, the apparent elevation in PM complaints among SUs appears to have been mediated by the current level of affective distress. Specifically, post hoc analyses showed that higher DASS-21 scores were associated with more frequent PM complaints as measured by the PRMQ; in fact, the inclusion of the DASS-21 (along with education) in the multiple regression predicting PRMQ eliminated the previously significant effect of the substance use grouping variable. This observation echoes similar findings regarding the association between affective distress and self-reported PM in other populations, including recreational ecstasy polydrug users (Bedi & Redman, 2008b) and persons living with HIV infection (Woods et al., 2007). These data suggest that researchers and clinicians would be well advised to consider the possible role of affective distress when evaluating PM complaints.

By way of contrast, affective distress did not mediate the PM deficits substance abusers evidenced in this study on a well-validated laboratory test of PM (i.e., the MIST). The substance abuse group performed significantly lower on overall PM, but did not differ from the HAs on the ongoing word search task. The overall PM effect appears to have been primarily driven by difficulties with time-based, rather than event-based cues. Analysis of PM error types revealed that the substance abusers demonstrated increased errors of omission (i.e., no response to a cue) and task substitutions (i.e., intrusions and perseverations). When considered in the context of the absence of group differences in loss of content errors and the post-test recognition trial, this PM profile indicates that substance abusers may have experienced problems with the more cognitively demanding self-directed monitoring and signal detection aspects of PM. Substance abusers struggled to balance the competing demands of the ongoing task and strategic (i.e., time) monitoring, thereby increasing their risk of omission errors and impulsive/perseverative responding. This is consistent with recent data from individuals with methamphetamine dependence (i.e., Iudicello et al., 2011) and ecstasy (Weinborn, Woods, Nulsen, & Park, in press), who exhibit impairment in the strategic aspects of PM (i.e., executive dyscontrol of the encoding, monitoring, and/or retrieval of future intentions). Future studies would benefit from the inclusion of direct measures of time monitoring, estimation, and production, which prior research suggests are linked to time-based PM performance (e.g., Costa, Peppe, Caltagirone, & Carlesimo, 2008). Another direction for future research is elucidation of the role of ongoing task delay interval (i.e., the length of time in between the prescription of the intention and the presentation of the cue), as our group recently showed that ecstasy users may be particularly susceptible to deficits in strategic target monitoring and maintenance of cue-intention pairings over longer PM delays (Weinborn et al., in press). Finally, this study may inform future experimental manipulations of the strategic aspects of event-based PM, for example, cue focality (Kliegel et al., 2008), cognitive load, or the semantic relatedness of the cue-intention pairing (e.g., Woods et al., 2010).

Taken together, the profile of PM deficits observed in this mixed sample of substance abusers is suggestive of executive dyscontrol over the realization of future intentions. In support of this contention, PM was significantly correlated with clinical measures of executive functions in the substance-abusing sample. These moderate associations were observed with standard clinical measures of cognitive flexibility, verbal fluency, and decision-making and were generally comparable in magnitude across those specific executive functions. Yet, the specificity of this association may be called into question by correlations between PM and composite indices of information processing speed and retrospective memory (i.e., delayed verbal and visual recall). This pattern of correlations across multiple high-level cognitive domains is consistent with some prior PM research in substance abusers (i.e., Rendell et al., 2009; cf. Weinborn et al., in press) and HIV infection (Carey et al., 2006; Zogg et al., 2011). One possibility is that these associations reflect the multifaceted nature of PM, which is a complex cognitive process that places demands on several different components of cognition, including working and retrospective memory (Kliegel et al., 2008). Another, although not mutually exclusive, possibility is that these data reflect the association between PM and the strategic aspect of these other domains, which although not traditionally classified as “executive functions” certainly require elements of cognitive control (e.g., semantic clustering during verbal list learning; Carey et al., 2006). This latter interpretation falls more in line with recent clinical studies suggesting that although PM correlates with a variety of higher-level cognitive domains, executive dysfunction may be the primary driving mechanism (e.g., Iudicello et al., 2010; Zogg et al., 2011).

Importantly, the effects of substance abuse on PM were most apparent at lower levels of education (i.e., <12 years), suggesting a possible influence of cognitive reserve. Although the impact of substance abuse on PM was significant at all levels of educational achievement, the effect size in the lower educated sample was somewhat larger. Thus, it may be that individuals with lower cognitive reserve may be more vulnerable to the expression of PM deficits stemming from the neural injury associated with substance abuse. Of course, this interpretation requires replication in independent studies, which would ideally include a multifaceted index of cognitive reserve (e.g., word reading, occupational attainment) and direct measures of brain reserve. Nevertheless, this interpretation is consistent with some prior studies of cognitive reserve in substance using populations, including alcohol (e.g., Sabia et al., 2010) and cocaine (Di Sclafani et al., 1998). It has been previously shown that PM correlates with education in HAs (e.g., Woods et al., 2008), but to our knowledge, this is the first study to examine PM and education as a proxy for cognitive reserve in substance abusers (or any other clinical population). The specificity of this finding to PM remains to be determined by studies examining the possible role of cognitive and brain reserve in the expression of other specific cognitive ability areas among substance abusers.

Speaking to the ecological relevance of this study, lower PM in the laboratory was associated with more frequent self-reported PM failures in the substance abusers' daily lives. As noted above, it has been widely hypothesized that PM may capture critical aspects of everyday functioning not assayed by other cognitive abilities. Prior research suggests that PM may possess incremental ecological validity in predicting declines in instrumental activities of daily living, such as medication adherence (e.g., Woods et al., 2009). Indeed, the association with PM in daily life was unique among cognitive abilities measured in this study; that is, no other cognitive domain score was significantly associated with PM complaints. Moreover, this relationship could not be better explained by affective distress (in this case, as measured by the DASS-21), which as noted above was independently predictive of PM complaints. This finding extends prior research in SUs (e.g., Bedi & Redman, 2008b; Hadjiefthyvoulou et al., 2011a; cf. Heffernan et al., 2010) to a mixed clinical sample at treatment entry. These data indicate that SU with impaired PM are at higher risk for everyday PM failures, perhaps to include compliance with healthcare instructions (Iudicello et al., 2011; Zogg et al., 2010) and substance abuse treatment (e.g., Grohman et al., 2006). We are presently gathering data to address the latter question, but future research is needed to examine the ecological relevance of PM in substance abusers as concerns a range of everyday functioning outcomes, such as employment (Woods et al., 2011) and financial management. The possible associations between PM and engagement in risk behaviors among substance abusers may also warrant closer examination; for example, PM deficits have been associated with riskier decision-making in the laboratory among ecstasy users (Weinborn et al., in press) and more frequent HIV transmission risk behaviors in the daily lives of polysubstance abusers (Martin et al., 2007).

Naturally, there are several limitations to the study methods and generalizability that should be noted. First, the characterization of SUDs was suboptimal in its omission of structured clinical interviews for DSM-IV diagnoses and more detailed substance use quantification (e.g., duration, route). Moreover, although the use of a mixed clinical group of substance abusers has ecological advantages, the incorporation of such liberal inclusion criteria (cf. Rendell et al., 2009) precludes us from drawing inferences about the effects of specific drugs of abuse on PM. As such, we cannot confidently hypothesize about the neural systems involved in the PM deficits in this mixed clinical group, which are questions better suited for functional and structural neuroimaging studies of more specifically selected SUs (e.g., Chang, Alicata, Ernst, & Volkow, 2007). Another limitation to this study was our exclusive use of a self-report measure of PM as an everyday functioning outcome. Given the oft-described problems with self-report of functional status (e.g., Marcotte & Grant, 2010), future studies should incorporate performance-based measures (e.g., medication management, vocational skills) and independent behavioral outcomes, such as treatment compliance and employment status.

Funding

This research was supported by grants from the Raine Medical Research Foundation to M.W. and the National Institute of Mental Health (MH073419) to S.P.W.

Conflict of Interest

None declared.

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Are time- and event-based prospective memory comparably affected in HIV infection?
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