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. 2018 Jun 11;34(3):403–417. doi: 10.1093/arclin/acy049

Is Episodic Future Thinking Important for Instrumental Activities of Daily Living? A Study in Neurological Patients and Healthy Older Adults

Amanda M Brunette 1,✉, Matthew Calamia 2, Jenah Black 1, Daniel Tranel 1,3
PMCID: PMC6454851  PMID: 29893785

Abstract

Objective

Episodic future thinking is the ability to mentally project oneself into the future. This construct has been explored extensively in cognitive neuroscience and may be relevant for adaptive functioning. However, it has not been determined whether the measurement of episodic future thinking might be valuable in a clinical neuropsychological setting. The current study investigated (1) the relationship between episodic future thinking and instrumental activities of daily living (IADLs); and (2) whether episodic future thinking is related to IADLs over and above standard measures of cognition.

Method

Sixty-one older adults with heterogeneous neurological conditions and 41 healthy older adults completed a future thinking task (the adapted Autobiographical Interview), a performance-based measure of instrumental activities of daily living (the Independent Living Scales), and standard clinical measures of memory and executive functioning.

Results

Episodic future thinking significantly predicted IADLs after accounting for age, education, gender, and depression (increase in R2 = .050, p = .010). Episodic future thinking significantly predicted IADLs over and above executive functioning (increase in R2 = .025, p = .030), but was not predictive of IADLs over and above memory (p = .157).

Conclusions

This study suggests that episodic future thinking is significantly associated with IADLs, beyond what can be accounted for by executive functioning. However, episodic future thinking did not predict IADLs over and above memory. Overall, there is limited evidence for the clinical utility of episodic future thinking. The findings suggest that an episodic future thinking task does not provide enough valuable information about IADLs to justify its inclusion in a clinical neuropsychological setting.

Keywords: Assessment, Aging, Everyday functioning, Executive functions, Learning and memory

Introduction

Neuropsychological assessment aims to address the following: identify cognitive, behavioral, or mood dysfunction, assess changes in these areas over time, estimate the appropriate level of care, and identify suitable treatments (Hebben & Milberg, 2009; Lezak, Howieson, Bigler, & Tranel, 2012). However, improvements are needed in neuropsychological assessment to be able to understand patients’ functioning in everyday life and recommend treatments to address everyday functional deficits (Larrabee, 2014; Ruff, 2003). Throughout the history of neuropsychology, novel measures have been developed to improve the information gained from our assessments so we can better address patient needs (Burgess et al., 2006).

A cognitive domain not included in standard clinical neuropsychological assessment is episodic future thinking. Episodic future thinking is defined as the ability to mentally project oneself into the future into a specific time and place (Atance & O’Neill, 2001). In an episodic future thinking task (Addis, Wong, & Schacter, 2008), participants are asked to describe a specific future event in as much detail as possible. Episodic future thinking performance is scored based on the amount and type of details included in a future event description, such as details tied to a specific time and place (i.e., internal details) and general knowledge and facts (i.e., semantic details) (Addis et al., 2008; Levine, Svoboda, Hay, Winocur, & Moscovitch, 2002; Palombo, Keane, & Verfaellie, 2015).

Episodic future thinking is a construct that has been studied in cognitive neuroscience research. Impairment in episodic future thinking has been found across various neurological populations, including patients with dementia (Addis, Sacchetti, Ally, Budson, & Schacter, 2009b; de Vito et al., 2012; Irish, Addis, Hodges, & Piguet, 2012a; Irish, Hodges, & Piguet, 2013), mild cognitive impairment (Gamboz et al., 2010), amnesia (Hassabis, Kumaran, Vann, & Maguire, 2007; Race, Keane, & Verfaellie, 2011), damage to the prefrontal cortex (PFC) (Berryhill, Picasso, Arnold, Drowos, & Olson, 2010; Kurczek et al., 2015), damage to the posterior parietal cortex (Berryhill et al., 2010), traumatic brain injury (Rasmussen & Berntsen, 2014), among others. Most of the literature suggests that patients with neurological conditions provide fewer internal details in their future descriptions. Schacter and colleagues proposed the constructive episodic simulation hypothesis, which states that the flexible recombination of details from memory allows one to engage in future thinking (Schacter & Addis, 2007a, 2007b; Schacter, Addis, & Buckner, 2007). This hypothesis is supported by neuroimaging research, which has found a core neural network involved in memory and episodic future thinking, including the medial temporal lobe, medial PFC, retrosplenial cortex, posterior parietal cortex, anterior and lateral temporal lobe, cingulate cortex, and precuneus (Addis, Pan, Vu, Laiser, & Schacter, 2009a; Buckner & Carroll, 2007; Schacter & Addis, 2007a; Schacter et al., 2007, 2012; Schacter, Addis, & Buckner, 2008; Szpunar, 2010). Other cognitive processes have been found to be involved in future thinking as well, such as semantic memory, imagery, executive functioning, and self-referential processes (Table 1; for in-depth discussion see reviews by Irish & Piolino, 2016; Schacter et al., 2008, 2012; Szpunar, 2010; Ward, 2016).

Table 1.

Overview of research on episodic future thinking

Areas of episodic future thinking research Findings from each research area
Core neural network of episodic future thinking Medial temporal lobe
Medial prefrontal cortex
Retrosplenial cortex
Posterior parietal cortex
Anterior and lateral temporal cortex
Cingulate cortex
Precuneus
Cognitive processes involved in episodic future thinking Episodic memory
Semantic memory
Executive functioning
Self-referential processing
Imagery
Domains of adaptive functioning related to episodic future thinking Decision-making
Problem solving
Goal processing
Coping
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Although there is overlap between thinking in the past and future, there is neuroimaging and behavioral evidence that memory and future thinking are at least partially distinct constructs. Neural regions have been found to be more active during thinking in the future, which may be due to greater mental construction required while thinking of novel future events (Schacter et al., 2008, 2012). Research suggests that increased activity occurs in certain neural regions, including the left dorsolateral PFC, posterior inferior parietal lobe, regions of the default network, and regions of the fronto-parietal control network (Benoit & Schacter, 2015). Participants rate future simulation as more difficult than recalling the past, and individuals report more sensory-perceptual details for the past than the future (Schacter et al., 2012). Episodic future thinking has been examined in neuroscience research, but it remains unclear whether it would be helpful to measure in a clinical neuropsychological setting.

The Relationship between Standard Measures of Cognition and IADLs in Clinical Neuropsychology

Instrumental activities of daily living (IADLs) are abilities that allow one to adapt to the environment in order to live independently, such as managing medications and finances, preparing meals, completing household duties, and travel (Lawton & Brody, 1969). Patients who are neurologically compromised often have impairments in their IADLs (Hariz & Forsgren, 2011; Marshall et al., 2011; Mioshi et al., 2007; Stephens et al., 2005). Understanding the ability to complete IADLs can help determine the level of care needed (e.g., whether a person needs full time care in a nursing home) (Andel, Hyer, & Slack, 2007; Gaugler, Duval, Anderson, & Kane, 2007; Wattmo, Wallin, Londos, & Minthon, 2011) and can help predict future decline (Pérès et al., 2008; Sikkes et al., 2011; Tabert et al., 2002).

Studies in neuropsychology have examined the relationship between a range of cognitive tasks used in clinical neuropsychological assessment and IADLs (Desai, Grossberg, & Sheth, 2004; Gold, 2012; Royall et al., 2007). Executive functioning and memory have been found to be significant predictors of IADLs (Desai et al., 2004; Gold, 2012; Mcalister, Schmitter-Edgecombe, & Lamb, 2016; Royall et al., 2007). However, research is variable in terms of the significance of these relationships and magnitude of the effects (Mcalister et al., 2016; Royall et al., 2007). Standard measures of cognition tend to have low ecological validity (Rabin, Paolillo, & Barr, 2016), leaving room for improvements in our predictions of functional abilities in clinical neuropsychology.

The Relationship between Episodic Future Thinking and Adaptive Functioning

As reviewed by Schacter (2012), Schacter et al. (2012),Schacter, Benoit, and Szpunar (2017), Szpunar (2010), and Ward (2016), there is some evidence to suggest that episodic future thinking has a relationship with adaptive functioning, particularly decision-making, goal processing, problem solving, and coping (Table 1) (Benoit, Gilbert, & Burgess, 2011; Lin & Epstein, 2014; Liu, Feng, Chen, & Li, 2013; Madore & Schacter, 2014; Palombo et al., 2015; Peters & Büchel, 2010; Taylor & Schneider, 1989; Taylor, Pham, Rivkin, & Armor, 1998). However, there are some inconsistencies in findings (Kwan et al., 2012, 2015; Kwan, Craver, Green, Myerson, & Rosenbaum, 2013; Palombo et al., 2015). Studies that objectively measured episodic future thinking found that internal details are related to adaptive functioning (Madore & Schacter, 2014; Palombo et al., 2015). Other studies conclude that prompting individuals to vividly think in the future improves adaptive functioning (Benoit et al., 2011; Brown, Macleod, Tata, & Goddard, 2002; Lin & Epstein, 2014; Liu et al., 2013; Peters & Büchel, 2010; Pham & Taylor, 1999; Taylor & Schneider, 1989; Taylor et al., 1998). Episodic future thinking, especially internal details, may provide information about IADLs due to its connection to decision-making, goal processing, problem solving, and coping.

Adaptive functions (i.e., decision-making, coping, problem-solving, and goal processing) may be helpful in completing IADLs. For instance, individuals who engaged in coping, such as implementing compensatory strategies, were more successful in completing IADLs (Chaytor & Schmitter-Edgecombe, 2003). Emotional coping also improves the ability to successfully complete IADLs (Sveen, Thommessen, Bautz-Holter, Wyller, & Laake, 2004). Decision-making has been associated with IADLs in individuals with traumatic brain injury (Goverover & Hinojosa, 2002) and older adults (Lindbergh, Puente, Gray, MacKillop, & Miller, 2014). Individuals who had damage to neural regions important for decision-making (i.e., ventromedial PFC) were at a higher risk for mismanagement of their finances (Asp et al., 2012). Individuals with impaired goal setting have difficulties performing IADLs (Bottari, Gosselin, Guillemette, Lamoureux, & Ptito, 2011). Interventions that address goal-directed behavior result in improved IADLs (Grant, Ponsford, & Bennett, 2012; Levine et al., 2000, 2007; Novakovic-Agopian et al., 2011; Tornås et al., 2016). Furthermore, research has found that problem-solving is predictive of functional abilities (Bell‐McGinty, Podell, Franzen, Baird, & Williams, 2002; Jefferson, Paul, Ozonoff, & Cohen, 2006; Kimbler, 2013; Razani et al., 2007; Ziv, Roitman, & Katz, 1999). These findings suggest that there may be an association between future thinking and IADLs, although this had not yet been tested empirically.

The Current Study

Research is necessary to determine if episodic future thinking can be informative regarding IADLs in clinical neuropsychology. Mentally placing oneself into a vivid future scenario could potentially aid in anticipating and overcoming obstacles. For example, this may be especially relevant for an individual with a neurological condition who is attempting to live independently. Without being able to think ahead vividly, they may fail to perform daily tasks or not complete them as successfully. The association between episodic future thinking and optimal functional abilities seems intuitive, but episodic future thinking has not been objectively measured with respect to IADLs. When neuropsychologists rely on standard tests such as measures of memory and executive functioning to determine an individual’s functional abilities, they may make incorrect assumptions about real-world functioning. The relationship between a neuropsychological measure and functional abilities should be considered when setting a criterion for inclusion in a clinical battery (Larrabee, 2014). Improvement is needed in the prediction of functional abilities. The goal of this study is to investigate the relationship between episodic future thinking and IADLs. The relationship between episodic future thinking and IADLs was investigated to clarify if episodic future thinking is a mechanism that aids in the successful completion of IADLs. By determining this relationship, the findings also clarify if episodic future thinking is worth measuring in a clinical neuropsychological setting.

Aims and Hypotheses

The first aim of our study was to investigate the relationship between episodic future thinking and instrumental activities of daily living. It was hypothesized that there would be a significant, positive relationship between episodic future thinking (as measured by internal details) and IADLs. The second aim was to investigate the relationship between episodic future thinking and IADLs over and above standard neuropsychological assessments. It was hypothesized that episodic future thinking (as measured by internal details) would have a significant, positive prediction of IADLs in an older adult heterogeneous neurological population and healthy older adults, over and above (1) episodic memory and (2) executive functioning. These two domains of cognition were examined given they are the most widely associated with IADLs in aging populations. These domains of cognition were examined separately to help understand the unique contribution of episodic future thinking over and above each of these constructs.

The aims were addressed in an older adult heterogeneous neurological population that ranged from normal to moderate cognitive impairment and healthy older adults. This is because functional deficits are common within an older adult neurological population, and can also be challenging for healthy older adults. Furthermore, previous research suggests the relationship between episodic future thinking and adaptive functioning is relevant for both healthy older adults and patients with neurological conditions (Benoit et al., 2011; Lin & Epstein, 2014; Liu et al., 2013; Madore & Schacter, 2014; Palombo et al., 2015; Peters & Büchel, 2010; Taylor & Schneider, 1989; Taylor et al., 1998). Examining these populations also allowed for more variability in the measures of interest. The relationship between episodic future thinking and IADLs was examined in a heterogeneous neurological population because a robust relationship between these two constructs must be established to determine if episodic future thinking is useful in a clinical setting.

Methods

Participants

There were 107 participants in this study. All participants were age 55 or older. Sixty-five older adult patients with neurological disease and 42 healthy older adult participants were enrolled in this cross-sectional study. Table 2 provides an outline of the neurological conditions included in the study. All work was conducted with the formal approval of the University of Iowa Institutional Review Board. Exclusion criteria consisted of the following: intellectual disability, history of a learning disability, a neurodevelopmental disorder, any psychiatric condition besides a current diagnosis of major depression or anxiety that is treated and stable, a history of being an inpatient for drug or alcohol abuse, impaired and uncorrected vision or hearing, severe dementia as indicated by an MMSE score <10 (Perneczky et al., 2006), being involved in litigation, and age less than 55. We excluded patients with severe dementia because our preliminary work indicated that such patients are not capable of completing the main experimental task (the Autobiographical Interview, see the following) in a valid manner. The same exclusion criteria applied to healthy older adults, with the addition of a history of a neurological condition. Eligibility was determined through patient and caregiver interviews and review of the medical record. The Mini-Mental Status Exam (MMSE) was administered in person during data collection. The older adult patients with neurological disease were recruited through the Benton Neuropsychology Clinic in the University of Iowa Hospitals and Clinics. The healthy older adult participants were recruited through the Cognitive Neuroscience Registry for Normative Data and mass e-mails at the University of Iowa. All participants were tested at the Benton Neuropsychology Clinic.

Table 2.

Types of neurological conditions included in the study

Patients with neurological conditions Frequency
Parkinson’s disease 16
Mild cognitive impairment 15
Stroke 11
Traumatic brain injury 5
Normal pressure hydrocephalus 3
Tumor with resection 2
Mixed dementia 1
Lewy body dementia 1
Behavioral variant frontotemporal dementia 1
Alzheimer’s disease 1
Vascular dementia 1
Primary progressive aphasia 1
Multiple sclerosis 1
Epilepsy with resection 1
Normal pressure hydrocephalus and Lewy body dementia 1
Total 61
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The two groups were matched on mean age and education. All participants gave written consent or assent to the research study. A standardized assessment (DeRenzo, Conley, & Love, 1998) was used to determine whether patients with neurological conditions could consent to the study. If patients with neurological conditions could not consent, their caregivers provided informed consent and the participants provided assent.

There were five participants who were initially enrolled in the study who had to be excluded. One participant did not complete the visit and four participants did not report an ineligible past history during the initial screening (one patient had a history of inpatient psychiatry admissions due to alcohol use, one participant had macular degeneration which interfered with the ability to complete cognitive tasks, and two participants had bipolar disorder). Thus, the final sample included 61 patients with a neurological condition and 41 healthy older adults.

Measures

Demographics and mood

Patients or their caregivers filled out a demographics questionnaire. The Beck Depression Inventory-II (Beck, Steer, & Brown, 1996) was administered as a measure of depression. The Beck Anxiety Inventory (Beck & Steer, 1993) was administered as a measure of anxiety.

Mini-Mental State Exam (MMSE)

The MMSE is a standardized assessment to measure global cognitive functioning (Folstein, Folstein, & McHugh, 1975). An MMSE score of less than 26 indicates cognitive impairment (Perneczky et al., 2006). The MMSE can be categorized into the following cutoff scores: mild (21–25), moderate (11–20), and severe cognitive impairment (0–10) (Perneczky et al., 2006). Patients with severe cognitive impairment based on the MMSE were excluded from the study.

Adapted autobiographical interview

The task used to measure episodic future thinking was the adapted Autobiographical Interview (AI) (Addis et al., 2008; Levine et al., 2002). In the adapted AI task, participants were instructed to describe five specific events in as much detail as possible in response to verbal cues. Participants were asked to respond with a novel and reasonable event that did not last longer than a day and could occur three to five months from the current date. They were given 3 min to describe each event once they had chosen an event. Participants were not required to include the verbal cue in their description. The following verbal cues were used: going to a sporting event, being visited by someone, going on a vacation, giving assistance to someone, and making a large purchase. These verbal cues have been used in previous studies on autobiographical memory (Levine et al., 2002). Prompts would be provided if the participant was silent for 30 s or if the participant provided an off-topic or vague response. A total of four prompts could be given during each future description. Two prompts could be provided for off-topic or vague responses. Two prompts could be provided for silence. This task took about around 30 –60 min to administer.

The results of the adapted AI task were scored by the scoring method proposed by Palombo and colleagues (2015), which was slightly modified from Addis and colleagues (2008). In this scoring method, a central event is chosen based on the most fundamental event described. Within the central event, internal and semantic details are identified. Internal details signify episodic details. Internal details were categorized into the following: event, place, time, perceptual, or thought/emotion. Semantic details are general knowledge and facts, ongoing events, and extended states of being, as defined by Palombo and colleagues (2015). Semantic details were categorized into general semantic, semantic autobiographical, semantic time, and semantic place details. The remaining details were in a category called “other” which included details that were unrelated to the main event described by the participant, repetitions and metacognitive statements. Internal details and semantic details were scored based on the mean number of details. To account for the length of future descriptions, the proportion of internal to total details was also calculated (Cole, Morrison, & Conway, 2013; Kurczek et al., 2015; Sheldon, McAndrews, & Moscovitch, 2011). Two raters coded episodic future thinking performance from a random subset of participants (10 healthy older adults and 12 patients with neurological disease) to establish inter-rater reliability. The two raters scored the transcripts in a reliable manner as indicated by intraclass correlation coefficients (internal details = .91, semantic details = .82).

Independent Living Scales

The Independent Living Scales (ILS) is a performance-based measure of IADLs (Loeb, 1996). It requires participants “to do problem solving, to demonstrate knowledge, or to perform a task” relevant to IADLs (Loeb, 1996). The scale assesses various functional abilities including memory/orientation, managing money, managing home and transportation, health and safety, and social adjustment. The ILS was examined using the total score. The maximum total score on the ILS is 140. The ILS takes around 45 min to administer. Evidence suggests the ILS has adequate internal consistency, test re-test reliability, interrater reliability, content validity, concurrent validity, criterion validity, and construct validity (Loeb, 1996).

Neuropsychological assessment

Two standard clinical domains of neuropsychological functioning were investigated, namely episodic memory and executive functioning. A composite memory score was created from the following memory measures: the Rey Auditory Verbal Learning Test (AVLT; long delay recall) (Rey, 1964) and the Logical Memory II Test (delayed free recall) from the Wechsler Memory Scale Third Edition (Wechsler, 1997). A composite executive functioning score was created from the following measures: Trail-making B minus A (time in seconds) (Reitan & Wolfson, 1985), Controlled Oral Word Association test (Benton, Hamsher, & Sivan, 1994), and the Color Word condition of the Stroop Color and Word Task (Golden & Freshwater, 2002). To create composite scores, z-scores were calculated based on the raw memory and executive functioning tests from the entire sample. Then the z-scores were averaged for each domain to create a composite memory score and composite executive functioning score. The approach of computing z-scores across both healthy and clinical groups to examine a range of cognitive abilities has been used in prior studies (Daffner et al., 2013; Elias, Elias, Sullivan, Wolf, & D’agostino, 2003; Manschot et al., 2006; Vogels et al., 2007).

Correlations were conducted across all participants on the absolute values of the neuropsychological tests to provide further evidence that these measures were related and assessing the same construct. For the memory composite score, AVLT long delay recall and the Logical Memory II subtest were strongly associated (r = .64). For the executive composite score, all variables were moderately to strongly associated (the correlation between Stroop Color and Word Task and COWA was .46; the correlation between the Stroop Color and Word Task and Trails B minus A was .54; the correlation between COWA and Trails B minus A was .45).

Retrospective neuropsychological data from the Benton Neuropsychological Clinic were used if the patients were assessed within 6 months of the research study. Most of the neuropsychological data were collected for the neuropsychological populations because the AVLT, Logical Memory Test, COWA, Trails, and Stroop are commonly administered within the Benton Neuropsychology Clinic. If these measures were not included in the Benton Neuropsychology Clinic, they were collected in the context of this research project. The neuropsychological data were collected for the healthy older adult comparisons within the context of the current study.

Statistical Approach and Data Analysis

Demographics and cognition

Demographic and cognitive variables were compared between individuals with and without neurological disease. T-tests were used for continuous variables that were normally distributed and Mann–Whitney U tests were used for continuous variables that were not normally distributed (e.g., depression, anxiety, and MMSE). A Chi-square test was used to compare gender between groups. Episodic future thinking internal details and semantic details were compared for the older adult neurological group and healthy comparisons using a 2 (Detail: Internal vs. semantic) by 2 (Group: Neurological vs. healthy older adults) mixed factorial ANOVA. This approach has been used in the previous literature on episodic future thinking (Addis et al., 2009b; de Vito et al., 2012; Irish et al., 2013). Detail was included as a within-subjects factor and group was included as a between-subjects factor. Internal and semantic details were examined using the mean raw number of details. All statistical assumptions were met for these analyses and subsequent analyses.

Aim 1: A hierarchical regression model was used to determine if episodic future thinking (internal details) was associated with IADLs. All participants (patients with neurological conditions and healthy older adults) were included in this analysis so we could examine a wide range of performance on episodic future thinking and IADL tasks. Age and education were selected as a priori covariates due to their relationship with IADLs. Gender (t(92) = −2.208, p = .030) and depression (r = −.30, p = .002) were included as covariates because they were significantly associated with IADLs. Episodic future thinking was examined using the mean proportion of internal-to-total details to account for the length of descriptions. A correlation matrix between variables was included to clarify the relationship among episodic future thinking, specific cognitive variables, and the ILS.

Aim 2: Hierarchical multiple regression models were used to determine if episodic future thinking (internal details) was significantly associated with IADLs over and above standard domains of cognition. The two cognitive domains of interest were episodic memory and executive functioning. A total of two hierarchical regression models were conducted for this aim to account for each domain of cognition. The first model was conducted to examine the relationship between episodic future thinking and IADLs over and above executive functioning and the second model was conducted to examine the relationship between episodic future thinking and IADLs over and above episodic memory. Similar to Aim 1, all participants were included in this analysis. Age and education were selected as a priori covariates, and gender and depression were included as covariates because they were significantly associated with IADLs (as indicated earlier). Episodic future thinking was examined using the mean proportion of internal-to-total details.

With all regression analyses, collinearity was assessed using the VIF values. No variables exceeded the standard cutoff of VIF = 10 (Field, 2013).

Results

Differences between Older Adult Patients with Neurological Disease and Healthy Older Adults in Demographic Variables and Cognition

Demographic and cognitive performances are presented in Table 3. Individuals with a neurological condition performed significantly lower on the MMSE (p = .002). About 27.9% of the neurological participants performed in the mildly impaired range on the MMSE and 8.1% of the neurological participants performed in the moderately impaired range on the MMSE. Individuals with a neurological condition reported significantly higher depression (p = .006) and anxiety (p = .002). There were no significant differences in age (p = .786), education (p = .250), or gender (p = .067). Individuals with a neurological condition performed significantly worse on the standard memory tasks, including the composite memory score (p < .001), AVLT long delay recall (p < .001), and the Logical Memory II subtest (p < .001). Individuals with a neurological condition performed significantly worse on executive functioning tasks, including the composite executive functioning score (p < .001), Stroop Color Word Task (p < .001), COWA (p < .001), and Trails B minus A (p < .001). Individuals with a neurological condition performed worse on IADLs (p < .001).

Table 3.

Demographic, cognitive, and functional variables

All participants,
n = 102		 Neurological disease,
n = 61		 Healthy older adults,
n = 41
Mean (SD) (n) % Mean (SD) (n) % Mean (SD) (n) % p Effect size
Demographic and clinical variables
Age 69.3 (8.5) 69.1 (7.7) 69.6 (9.8) .786 0.06
Sex (n/% female) (46) 45.1 (23) 37.7 (23) 56.1 .067 0.18
Education 15.7 (2.8) 15.4 (2.9) 16.1 (2.7) .250 0.25
Mini Mental State Exam (MMSE) 26.8 (3.2) 26.0 (3.6) 28.0 (2.2) .002 0.67
Depression (BDI) 6.6 (6.5) 7.7 (6.4) 4.9 (6.2) .006 0.44
Anxiety (BAI) 4.5 (5.2) 5.6 (6.0) 2.8 (3.2) .002 0.58
Neuropsychological tests
AVLT long delay recall (raw score) 7.3 (3.8) 5.8 (3.7) 9.6 (2.5) <.001 1.20
Logical Memory II (raw score) 20.2 (9.8) 16.4 (9.8) 26.2 (6.0) <.001 1.21
Composite memory score (z-score) 0.01 (0.90) −0.39 (0.90) 0.60 (0.49) <.001 1.37
Stroop color word condition (raw score) 31.8 (11.5) 27.9 (11.4) 37.5 (9.2) <.001 0.93
Trails B minus A (raw score) 53.0 (38.0) 66.6 (42.1) 35.6 (22.3) <.001 0.92
COWA (raw score) 38.2 (13.3) 34.5 (12.8) 43.8 (12.1) <.001 0.75
Composite executive functioning score (z-score) −0.03 (0.82) −0.35 (0.79) 0.45 (0.59) <.001 1.15
Episodic future thinking
Internal details (mean raw score) 26.8 (10.8) 25.2 (11.9) 29.2 (8.7) .137 0.38
Semantic details (mean raw score) 4.3 (2.9) 3.9 (2.5) 5.0 (3.2) .137 0.38
Other details (mean raw score) 8.6 (4.2) 9.0 (4.4) 8.0 (3.9) – –
Total details (mean internal and semantic) 31.1 (12.3) 29.0 (13.4) 34.1 (10.0) .042 0.43
Proportion of internal details (mean) 0.68 (0.12) 0.66 (0.13) 0.70 (0.11) .451 0.33
Proportion of semantic details (mean) 0.10 (0.06) 0.10 (0.05) 0.11 (0.06) .451 0.18
Proportion of other details 0.22 (0.10) 0.24 (0.11) 0.19 (0.08) – –
Proportion of total details (mean internal and semantic) 0.79 (0.11) .77 (0.12) 0.81 (0.08) .012 0.39
Instrumental activities of daily living
Independent living scales (total raw score) 124.8 (12.4) 121.3 (14.2) 129.9 (6.5) <.001 0.78
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Notes: Groups were compared using t-tests, Mann–Whitney U tests, or Chi-squared tests for all variables except episodic future thinking variables. Episodic future thinking variables (internal and semantic details) were compared using mixed factorial ANOVA. The p value included in Table 3 for the episodic future thinking variables were based on the interaction between detail type and group. The effect sizes included in the table were Cohen’s D for continuous variables and Phi for the dichotomous variable. The range of possible scores on the Independent Living Scales is 0–140. AVLT = Auditory Verbal Learning Test; COWA = Controlled Oral Word Association test.

Episodic future thinking performance was examined between the older adult neurological group and healthy comparisons using a mixed factorial ANOVA. There was not a statistically significant interaction between detail (internal vs. semantic) and group (neurological vs. healthy older adults) (p = .137). There was a main effect of group, F(1,100) = 4.26, p = .042, partial η2 = .041, suggesting that participants with a neurological condition (mean = 29.0,SD = 13.4) provided less overall details in their future descriptions than healthy older adults (mean = 34.1, SD = 10.0). There was a main effect of detail, F(1,100) = 513.78, p = <.001, partial η2 = .837, suggesting that overall more internal details (mean = 26.8, SD = 10.8) were reported than semantic details (mean = 4.3, SD = 2.9).

To verify that the difference between groups was not due to differences in the length of future descriptions, a follow-up analysis was conducted with proportion of internal details and the proportion of semantic details. Overall, we found the same results using the mean raw details and mean proportion of details when comparing groups on the episodic future thinking task. There was not a statistically significant interaction between proportion of details (internal vs. semantic) and group (neurological vs. healthy older adults) (p = .451). There was a main effect of group, F(1,100) = 6.49, p = .012, partial η2 = .061, suggesting that participants with a neurological condition (mean = .77, SD = 0.12) provided a lower proportion of total details in their future descriptions than healthy older adults (mean = 0.81, SD = 0.08). There was a main effect of detail, F(1,100) = 1235.75, p = <.001, partial η2 = .925, suggesting that an overall higher proportion of internal details (mean = 0.68, SD = 0.12) was reported than the proportion of semantic details (mean = 0.10, SD = 0.06).

Relationship between Episodic Future Thinking and Instrumental Activities of Daily Living

A correlation matrix is included in Table 4 to provide information about individual relationships between episodic future thinking, cognitive tests, and IADLs. Hierarchical multiple regression models were used to investigate the relationship between episodic future thinking (proportion of internal details; mean = 0.68; SD = 0.12; range = (.33–.97)) and IADLs over and above relevant covariates. Age, education, gender, and depression were included as covariates. The model was significant with age, education, gender, and depression entered into the first block, F(4,95) = 8.506, p < .001. The addition of the mean proportion of internal details to the prediction of IADLs led to a statistically significant increase in R2 of .050, F(1,94) = 6.881, p = .010 (Table 5).

Table 4.

Correlations between episodic future thinking, standard neuropsychological measures, and IADLs

Variable 1 2
1. Proportion of internal details – –
2. ILS total raw score .27** –
3. AVLT .38*** .52***
4. Logical Memory II .30** .51***
5. Stroop color and word task .30** .54***
6. COWAT .17 .43***
7. Trails B minus A −.34** −.49***
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*p < .05, **p < .01, and ***p < .001.

Table 5.

Hierarchical multiple regression: prediction of IADLs (ILS) from episodic future thinking

IADLs (ILS)
Model 1 Model 2
Variable B β p B β p
Constant 131.860 <.001** 115.529 <.001**
Age −0.245 −.217 .020* −0.174 −.154 .098
Education 0.797 .228 .015* 0.728 .208 .023*
Gender 4.819 .248 .010* 4.379 .226 .017*
Depression −0.559 −.374 <.001** −0.615 −.411 <.001**
Mean proportion of internal details 19.300 .238 .010*
F 8.506 <.001** 8.603 <.001**
R2 .264 .314
ΔF 8.506 <.001** 6.881 .010*
ΔR2 .264 .050
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*p < .05 and **p < .001.

Relationship between Episodic Future Thinking and Instrumental Activities of Daily Living Over and Above Standard Measures of Cognition

Hierarchical multiple regression models were used to investigate the relationship between episodic future thinking and IADLs over and above standard neuropsychological assessments. Two hierarchical regression models were conducted to account for each domain of cognition (e.g., memory and executive functioning). Age, education, gender, and depression were included as covariates.

We examined the relationship between the mean proportion of internal details and IADLs over and above executive functioning. The model was significant with age, education, gender, and depression entered into the first block, F(4,96) = 7.775, p < .001. In the second block, the addition of executive functioning to the prediction of IADLs led to a statistically significant increase in R2 of .251, F(1,95) = 47.281, p < .001. The addition of the mean proportion of internal details to the prediction of IADLs led to a statistically significant increase in R2 of .025, F(1,94) = 4.866, p = .030 (Table 6).

Table 6.

Hierarchical multiple regression: prediction of IADLs (ILS) from episodic future thinking over and above executive functioning

Variable IADLs (ILS)
Model 1 Model 2 Model 3
B β p B β p B β p
Constant 127.984 <.001*** 121.038 <.001*** 108.916 <.001***
Age −0.270 −.214 .023* 0.059 .047 .584 0.090 .072 .397
Education 1.078 .278 .003* 0.092 .024 .780 0.095 .025 .768
Gender 5.786 .268 .006* 4.604 .213 .008** 4.272 .198 .013*
Depression −0.511 −.306 .001** −0.473 −.283 <.001*** −0.524 −.314 <.001***
Executive functioning 7.995 .604 <.001*** 7.339 .554 <.001***
Mean proportion of internal details 15.368 .174 .030*
F 7.775 <.001*** 18.675 <.001*** 17.007 <.001***
R2 .245 .496 .521
ΔF 7.775 <.001*** 47.281 <.001*** 4.866 .030*
ΔR2 .245 .251 .025
Open in a new tab

*p < .05, **p < .01, and ***p < .001.

We examined the relationship between the mean proportion of internal details and IADLs over and above memory. The model was significant with age, education, gender, and depression entered into the first block, F(4,95) = 8.506, p < .001. In the second block, the addition of memory to the prediction of IADLs led to a statistically significant increase in R2 of .146, F(1,94) = 23.188, p < .001. In the third block, the addition of the mean proportion of internal details to the prediction of IADLs did not lead to a statistically significant increase in R2 of .013 (p = .157).

Discussion

The present study examined episodic future thinking in an older adult heterogeneous neurological population and healthy older adults. We examined the relationship between episodic future thinking and IADLs. Further, we investigated the relationship between episodic future thinking and IADLs over and above memory and executive functioning, because they have been shown to be related to IADLs (Desai et al., 2004; Gold, 2012; Royall et al., 2007). This study examined whether episodic future thinking could provide useful information about functional abilities and examined the incremental validity of episodic future thinking over and above standard neuropsychological measures. These research questions are especially relevant for a clinical neuropsychological setting.

We found a significant relationship between episodic future thinking and IADLs. This supports previous literature suggesting a relationship between episodic future thinking and adaptive functioning (Benoit et al., 2011; Lin & Epstein, 2014; Liu et al., 2013; Madore & Schacter, 2014; Palombo et al., 2015; Peters & Büchel, 2010; Taylor & Schneider, 1989; Taylor et al., 1998). This finding supports our hypothesis that episodic future thinking could be relevant to IADLs and may even be a mechanism of daily functioning. Episodic future thinking may be a domain of cognition that aids individuals in anticipating the steps necessary to successfully complete IADLs, such as handling finances or completing errands.

Although there was a significant relationship between episodic future thinking and IADLs, episodic future thinking explained a small amount of variance (5%) in IADLs while accounting for age, education, gender, and depression (Table 5). In our sample, while accounting for the same covariates, executive functioning explained a large amount of variance in IADLs (25.1%; Table 6) and memory explained a medium amount of variance in IADLs (14.6%). These findings support previous research that suggests memory and executive functioning are significantly related to IADLs (Mcalister et al., 2016). Our data suggest that episodic future thinking does not provide substantial information about IADLs. These results provide little evidence for the clinical utility of an episodic future thinking task. Episodic future thinking may not be a domain of cognition that clinical neuropsychologists should attend to when thinking about areas of cognition that may influence a patient’s ability to successfully complete daily tasks.

We then investigated the incremental validity of episodic future thinking. We found a significant relationship between episodic future thinking and IADLs over and above executive functioning. Though findings suggest that episodic future thinking provides information about daily functioning abilities beyond standard executive functioning tasks, the amount of variance explained over and above executive functioning was small (2.5%). Executive functioning tasks are standard in neuropsychological assessment and have the strongest relationship to IADLs among clinical neuropsychological assessments (Gold, 2012). These findings suggest that assessing episodic future thinking would not substantially improve neuropsychologists’ predictions about patients’ abilities.

We also examined the relationship between episodic future thinking and IADLs over and above episodic memory. We found that episodic future thinking did not significantly predict IADLs when accounting for episodic memory. This finding suggests that episodic future thinking may provide similar information as episodic memory abilities when considering their relationship to functional abilities. Given that episodic memory measures are a staple in neuropsychological practice (Marshall & Gurd, 2012), this finding challenges the notion of incorporating episodic future thinking into the clinical neuropsychological setting. Further, reliable and valid measures of episodic memory, such as the AVLT (Rey, 1964) and the Wechsler Memory Scale (Wechsler, 1997), are used extensively and can be efficiently administered and scored, unlike currently available episodic future thinking measures. Thus, if episodic future thinking does not provide unique information to neuropsychologists, it may not be a construct worth measuring.

One explanation for the non-significant relationship between episodic future thinking and IADLs over and above memory could be that episodic future thinking was largely overlapping with episodic memory. Previous research suggests episodic future thinking and memory are significantly related (Schacter & Addis, 2007a; Ward, 2016). Episodic future thinking and memory were moderately correlated in our sample (r = .36, p < .001). Therefore, our findings suggest that episodic future thinking involves cognitive processes unique from episodic memory, but the unique elements of episodic future thinking may not be important for predicting IADLs. Notably, previous research has found a moderate to strong relationship between internal details across episodic future thinking and autobiographical memory (Ward, 2016), though autobiographical memory was not examined in the current study. The authors chose to examine standard episodic memory tasks because they are regularly assessed in clinical neuropsychological assessment.

We found that patients with neurological conditions provided less overall details in their future descriptions, suggesting impaired episodic future thinking performance. This finding supports previous literature suggesting that patients with neurological conditions provide less overall details in their future descriptions (Addis, et al., 2009b; Berryhill et al., 2010; de Vito et al., 2012; Hassabis et al., 2007; Irish et al., 2013; Zeman, Beschin, Dewar, & Della Sala, 2013). When examining internal details, there were no differences between the heterogeneous neurological population and healthy older adults. It is unclear why our heterogeneous neurological population did not perform more poorly on internal details than the healthy older adult populations as has occurred in previous studies (de Vito et al., 2012; Gamboz et al., 2010; Irish et al., 2013; Irish, Addis, Hodges, & Piguet, 2012b; Race et al., 2011). Our heterogeneous neurological population was impaired in IADLs when compared to healthy older adults, which is consistent with previous literature (Aretouli & Brandt, 2010; Hariz & Forsgren, 2011; Marshall et al., 2011; Mioshi et al., 2007; Pérès et al., 2006; Reppermund et al., 2013; Stephens et al., 2005).

Based on our results, episodic future thinking may be related to functional abilities in older adult neurological patients and healthy older adults. However, our study does not support episodic future thinking as a more useful measure than standard neuropsychological tasks to indicate functional impairment in a clinical setting. There are also logistical issues with the measurement of episodic future thinking. The measurement, as is, would not plausibly fit into a clinical setting given the extensive administration and scoring time required (Ward, 2016). The practical implication of our findings is that incorporating an episodic future thinking task may not provide enough additional information to justify its use in a clinical setting given the length of time required to assess it. If future research determines there is clinical utility in using an episodic future thinking task, a more efficient measure would need to be developed.

There are limitations in our study. The sample was all Caucasian, and relatively highly educated, which may limit the generalizability of the results to different socioeconomic, racial, and ethnic groups. Also, we examined the relationship between episodic future thinking and functional abilities over and above standard measures of cognition using a limited sample for this research question. Future research could examine this research question with a larger sample size. Another limitation to this study is that we examined a heterogeneous neurological population, and it is possible that the relationship between episodic future thinking and IADLs differs by neurological condition (e.g., dementia versus TBI). Future research could examine the relationship between episodic future thinking and functional abilities in specific neurological populations to aid in understanding whether this relationship is particularly relevant for certain neurological conditions. However, it was helpful to first examine this relationship in a heterogeneous neurological population, because episodic future thinking does need to provide information about functional abilities across neurological populations in order to be valuable in a clinical setting. It may also be helpful to separately examine the relationship within only healthy older adults. Another limitation is that some of the neurological patients in our study completed the experimental tasks as much as 6 months differently in time relative to their clinical neuropsychological testing. We chose this 6-month outer limit as reasonable, given the general fact that patients with mild to moderate dementia tend not to change dramatically within this timeframe. On average, patients with neurological conditions were tested about 4 months after their clinical neuropsychological exam. We acknowledge that this could add some variability to the relations we found between the experimental and neuropsychological data. This could limit the internal validity of the study. However, our findings were generally consistent with a previous meta-analysis examining the relationship between standard areas of cognition (e.g., memory and executive functioning) and IADLs (Mcalister et al., 2016).

A limitation of the current study is that the novelty of the patients’ descriptions could not be assessed. The patients could have been describing events that have happened to them in the past, rather than describing novel future events. Future work could use alternate episodic future thinking tasks which may better capture the ability to think in the future. For example, Addis, Musicaro, Pan, and Schacter (2010) used an “experimental recombination task” where participants were provided with a person, place, and object and asked to combine these details into a novel, plausible personal future experience. An additional limitation is that the episodic future thinking task is language-dependent, similar to many neuropsychological tasks, and it would not be valid for patients with severe language disturbances.

Future research could examine the utility of episodic future thinking in predicting specific functional domains, such as medication use, financial decision-making, and navigation abilities, over and above standard measures of cognition. We did not conduct these analyses due to concerns about having adequate power to examine the relationship between episodic future thinking and specific domains of IADLs above standard cognitive measures, given that there is less variability in domain IADL scores than the total IADL score.

The current study examined internal details because this is the episodic future thinking domain that is most commonly compromised in neurological populations (Addis et al., 2009b; Gamboz et al., 2010; Irish et al., 2012a, 2012b, 2013; Kurczek et al., 2015; Race et al., 2011; Rasmussen & Berntsen, 2014) and is related to adaptive functioning (Madore & Schacter, 2014; Palombo et al., 2015). The relationship between semantic details and IADLs was not examined in this study. There was limited variability in semantic details in our sample which restricted our ability to examine the relationship between semantic details and IADLs. This study used an updated method for separating out semantic details (Palombo et al., 2015) given that previous research provided evidence that semantic details are an important component of episodic future thinking (Irish & Piguet, 2013; Irish et al., 2012a). Previous research examined “external details,” which included semantic details, details unrelated to the main event, repetitions, and metacognitive statements (Addis, et al., 2009b; de Vito et al., 2012; Gamboz et al., 2010). However, the amount of semantic details in the current study was consistent with other research that reported semantic details separately (Race et al., 2011). Future research could examine the relationship between semantic details and IADLs using a task that directly assesses semantic future thinking (Race, Keane, & Verfaellie, 2013).

In conclusion, our findings suggest that episodic future thinking does not provide sufficient valuable information about everyday functioning over and above other standard neuropsychological tasks to justify its inclusion in a clinical neuropsychological setting. It is important to note that the current study explored one aim of neuropsychological assessment. Future research is needed to determine if episodic future thinking could be useful for other purposes in clinical neuropsychological assessment. Episodic future thinking has been explored mainly in neuroscientific research, and more recently it has been in clinical neuropsychology (Irish & Piolino, 2016; Ward, 2016). Although these findings do not provide sufficient justification for incorporating episodic future thinking into a clinical neuropsychological setting, it is important for neuropsychologists to continue to pursue research to improve upon current assessments. Deriving neuropsychological predictors of functional abilities is a fundamental basis of our clinical practice. Neuropsychologists make critical judgments about patients’ functional abilities, such as their ability to take medications, drive, handle finances, and live independently. Neuropsychologists commonly rely on tests that were developed in the infancy of the field of neuropsychology. Though these tests may provide adequate information, we must not become complacent in our test selection and development. By improving cognitive testing, neuropsychologists can make more accurate predictions to aid in treatment planning (Ruff, 2003). It is our responsibility as neuropsychology researchers to create assessments that are objective, reliable, valid, and have clinical utility. Although this is a challenge, it has important implications for neuropsychologists’ ability to provide useful information to patients, their caretakers, and their healthcare providers.

Funding

This work was supported by Kiwanis International, the James S. McDonnell Foundation (Grant no. McDonnell UHC-Collab 220020387), Fraternal Order of Eagles under (Grant no. 18184200 (BR01)), and the National Institute of Mental Health (Grant no. 2P50MH094258).

Acknowledgements

The authors thank members of the Benton Neuropsychology Laboratory for help with recruitment and testing. We also thank Kiwanis International, McDonnell Foundation, Fraternal Order of Eagles, and NIMH.

Conflict of interest

None declared.

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