Psychology and Aging 2014, Vol. 29, No. 3, 744 –755

© 2014 American Psychological Association 0882-7974/14/$12.00 http://dx.doi.org/10.1037/a0037181

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Improving Everyday Prospective Memory Performance in Older Adults: Comparing Cognitive Process and Strategy Training Sarah Susanne Brom

Matthias Kliegel

Technische Universität Dresden

University of Geneva

Considering the importance of prospective memory for independence in old age recently, research has started to examine interventions to reduce prospective memory errors. Two general approaches can be proposed: (a) process training of executive control associated with prospective memory functioning, and/or (b) strategy training to reduce executive task demands. The present study was the first to combine and compare both training methods in a sample of 62 community-dwelling older adults (60 – 86 years) and to explore their effects on an ecologically valid everyday life prospective memory task (here: regular blood pressure monitoring). Even though the training of executive control was successful in enhancing the trained ability, clear transfer effects on prospective memory performance could only be found for the strategy training. However, participants with low executive abilities benefited particularly from the implementation intention strategy. Conceptually, this supports models suggesting interactions between task demands and individual differences in executive control in explaining individual differences in prospective memory performance. Keywords: prospective memory, old age, implementation intentions, executive control, medical adherence behavior

Remembering to implement intended actions in the future (prospective memory) is particularly crucial for maintaining independence and autonomy in old age (Elvevåg, Maylor, & Gilbert, 2003; Kliegel, Mackinlay, & Jäger, 2008; Shum, Leung, Ungvari, & Tang, 2001). Prospective memory tasks, such as remembering to take medication on time, are central to the increased health needs of older adults (McDaniel, Einstein, & Rendell, 2008; Steinhagen-Thiessen & Borchelt, 1999). Here, prospective memory errors can have severe (negative) consequences. Importantly, laboratory findings consistently report decreases in prospective memory performance with age (for meta-analytic reviews see Henry, MacLeod, Phillips, & Crawford, 2004; Ihle, Hering, Mahy, Bisiacchi, & Kliegel, 2013; Kliegel, Jäger, & Phillips, 2008). Taken together, these considerations strongly call for research on whether and how prospective memory can be improved in older adults. The present study was aimed at this question.

Two Ways of Improving Prospective Memory in Older Adults To theoretically predict efficient ways to improve prospective memory performance in old age, the current study rests on conceptual models that disentangle the cognitive processes involved in prospective remembering and that link age-related prospective memory decline to decline in those processes. In details, prospective remembering has early been separated into several distinct phases (e.g., Ellis, 1996; Kliegel, Martin, McDaniel, & Einstein, 2002): (a) intention formation— here, the prospective intention is formed and encoded; (b) intention retention—a delay period which is typically filled with an “ongoing” task (Ellis & Kvavilashvili, 2000) that prevents continuous rehearsal of the intended action in working memory; (c) intention initiation—the point in time at which the execution of the intention is (or should be) initiated; and (d) intention execution— here, the intention is successfully executed according to the previous intention. More recently, this general framework has been used to further pinpoint specific cognitive processes to the different phases of prospective memory performance: planning (to intention formation), episodic memory (to intention retention), and executive control such as inhibition of the ongoing task and switching from the ongoing to the prospective memory task (to intention initiation and intention execution; see Kliegel, Altgassen, Hering, & Rose, 2011; Kliegel, Mackinlay, & Jäger, 2008). Importantly for present purposes, Kliegel, Altgassen, Hering, and Rose (2011) have also used this framework to predict age-related and clinical deficits in prospective memory. Here, it is postulated that, prospective memory is not affected by age per se or fully determined by task characteristics (such saliency or cue focality) but that possible deficits in prospective memory depend on the interplay of individual differences in cognitive resources involved in each phase and task characteristics

Sarah Susanne Brom, Department of Psychology, Technische Universität Dresden; Matthias Kliegel, Department of Psychology, University of Geneva. We thank the participants of the study for their time and effort. Our gratitude goes to Anne Ueck and Kristin Kissmann for their help in collecting the data. Additionally, we thank Jutta Kray for providing the task on which the training and transfer task are based. Preparation of the article was partially funded by the Deutsche Forschungsgemeinschaft (DFG). Correspondence concerning this article should be addressed to Sarah Susanne Brom, Department of Psychology, Technische Universität Dresden, 01062 Dresden, Germany. E-mail: [email protected] 744

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PROCESS AND STRATEGY TRAINING IN PROSPECTIVE MEMORY

determining the level of cognitive resources required for a given task. Accordingly, impaired performance is assumed to be mediated by a mismatch between the cognitive resources that are required to perform the specific prospective memory task at hand and individual differences in those required cognitive processes such as episodic memory or executive control. In other words, it is assumed that age differences in prospective memory should vary as a function of this mismatch. Only if available resources are insufficient for the specific prospective memory task age effects should occur (even though some older adults have reduced executive resources, they may still be sufficient for a routine prospective memory task with low executive control requirements and vice versa). Resting on this conceptual rationale, from an intervention perspective, two starting points can be suggested to reduce this mismatch and thereby improve prospective memory performance in old age: (a) directly enhancing those available cognitive resources which are required for most prospective memory tasks and which are known to decline with normal aging, such as taskswitching; or (b) reducing prospective memory task demands with task specific strategies known to be effective in older adults. Therefore, the present study used a fully crossed experimental design to directly compare and combine the following two intervention approaches: (a) process training of individual taskswitching resources which are vital to initiate and execute a prospective intention (in phases c and d), and/or (b) a strategy training aiming to generally reduce the executive task demands of switching from the ongoing task to the prospective memory task (again in phases c and d; note that in relation to the prospective memory model, the strategy intervention took place in the intention formation phase but was expected to reduce task demands for later realization; see below for more details). So far, these approaches have never been tested against each other; in fact, the literature on both, the effects of process training on prospective memory in general and the combination of the two training regimes in particular, is virtually nonexisting. For further conceptual and evidence-based justification of the specific intervention methods chosen in the present study, in the next sections we briefly review evidence showing that executive control, especially task-switching, is involved in age-related prospective memory performance and that executive control, especially taskswitching, may be improved in older adults via process-based training regimes.

The Role of Cognitive Control in Age-Related Prospective Memory The conceptually assumed importance of executive control for age-related prospective memory has been empirically confirmed in an increasing body of literature mostly using correlational designs. Several studies found strong evidence for a relationship between executive control and prospective memory (e.g., Salthouse, Berish, & Siedlecki, 2004; Schnitzspahn, Stahl, Zeintl, Kaller, & Kliegel, 2013; West, Scolaro, & Bailey, 2011). Specifically, a recent study conducted by Schnitzspahn, Stahl, Zeintl, Kaller, and Kliegel (2013) found both task-switching (measured by mixing costs) as well as inhibition to be strong predictors for age-related prospective memory performance. Following up on these recent correlational findings, training interventions for older adults aiming to

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enhance the individual level of executive control should be especially beneficial for prospective memory transfer effects; particularly when targeting inhibition or task-switching. To our knowledge, to date this prediction has never been tested.

Process Training of Executive Control in Older Adults Importantly, when deciding on which executive component (inhibition or task-switching) should be trained best, the few training studies using inhibitory tasks show rather inconsistent findings concerning transfer effects to other cognitive domains (e.g., Dowsett & Livesey, 2000; Enge et al., 2014; Thorell, Lindqvist, Nutley, Bohlin, & Klingberg, 2009). In contrast, training studies targeting task-switching abilities found relatively robust transfer effects to other executive control domains and higher cognitive processes (e.g., Minear & Shah, 2008; Kray, Lucenet, & Blaye, 2010), especially the findings by Karbach and Kray (2009) seem promising. Three different age groups (8 –10; 18 –26; 62–76 years of age) performed a self-paced task-switching task, which required them to switch between two tasks on every second stimuli. In only four training sessions, Karbach and Kray (2009) found substantial training gains in the task-switching training task and transfer effects to a structurally similar task-switching task as well as inhibition and fluid intelligence measures. Particularly the group of older adults showed enhanced training and transfer effects (see also Karbach, Mang, & Kray, 2010; or for effects in adolescence, Zinke, Einert, Pfennig, & Kliegel, 2012). For this reason, the current study used a task-switching training closely modeled after the regime published by Karbach and Kray (2009). Extending the literature, one aim of the current study was to explore the efficiency of the task-switching training when implemented into daily routine of older adults and investigate the possible transfer effect to everyday prospective memory performance.

Reducing Prospective Memory Task Demands With Strategy Interventions In the next section, we review the available evidence on taskspecific strategies to generally lower executive demands of prospective memory tasks. Training research strongly supports the assumption that strategy training can improve memory performance (for a brief summary see Morrison & Chein, 2011). With respect to prospective memory, one strategy that has been shown to successfully lower executive task demands and to help realizing delayed intention over a great variety of goal-directed behavior is the so-called implementation intention strategy (IMP; for a metaanalytic review see Gollwitzer & Sheeran, 2006). By forming specific if(situation: when and where)–then(behavior: what) plans (“If situation X arises, then I will initiate behavior Y”), implementation intentions specify where, when, and how a person will initiate and execute the intended action (Gollwitzer, 1999; Gollwitzer & Sheeran, 2006). Through specific if–then planning in the intention formation phase (phase a of the prospective memory process), the intended behavior is directly linked to explicit situational cues that will signal the initiation of the intended action in the future situation (i.e., in the intention initiation phase). Conceptually important for present purposes, Gollwitzer and colleagues argue that the so formed cue-behavior link automatically activates

BROM AND KLIEGEL

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the intended behavior without the (or with much less) need for effortful switching between the ongoing activity and the to-beexecuted behavior. Therefore, participants with lower executive control abilities, that are insufficient to match the demands of the prospective memory task should benefit especially from the implementation intention strategy. Initial support for this assumption comes from different studies targeting clinical populations with action control problems, such as frontal brain lesions (Lengfelder & Gollwitzer, 2001), addicts in withdrawal (Brandstätter, Lengfelder, & Gollwitzer, 2001), and schizophrenic patients (Brandstätter et al., 2001, Study 2). In older adults, so far, the effect of the implementation intention strategy has only been investigated by a handful of studies and these studies showed somewhat mixed results. For some tasks an increase in performance was found, while for other tasks no effect or even a decrease for the implementation intention strategy was revealed (e.g., Bélanger-Gravel, Godin, Bilodeau, & Poirier, 2013; Chasteen, Park, & Schwarz, 2001; Liu & Park, 2004; Schnitzspahn & Kliegel, 2009).

the effect of both training methods on a naturalistic prospective memory task on participants aging above 60. From a conceptual perspective, in line with the mismatch prediction of the process framework, older adults with lower controlled cognitive resources should benefit particularly from the reduced executive task demands by the implementation intention strategy. We aimed to answer the research questions in an applied context to ensure real-life implications. Therefore, we chose blood pressure monitoring as an everyday prospective memory task with high ecological validity. Because cardiovascular diseases are one of the primary causes of death (US: No. 1, National Center for Health Statistics, 2013; U.K.: No. 2, Office of National Statistic, 2012; Germany: No. 1, Statistisches Bundesamt [Federal Statistical Office], 2012), blood pressure monitoring as a prevention activity should be of high importance and closely resemble a common everyday life prospective memory task to older adults.

Method Participants and Design

Interaction Between Cognitive Resources and Implementation Intentions In trying to explain these mixed results, so far only two studies focused on investigating the interplay between implementation intention strategy and individual differences in executive control. McFarland and Glisky (2011) tested in their study 32 older adults (aged 65 or older) on a laboratory prospective memory task. Results showed that participants with high frontal lobe functions outperformed participants in the low frontal lobe group. However, both groups profited significantly from the implementation intention strategy. This finding suggests that individual differences in frontal lobe function do not influence the effectiveness of the implementation intention strategy. In a recent study by Brom et al. (2013), 39 older adults (mean age of 69 years) performed a health-related prospective memory task in everyday life. Participants with higher processing speed outperformed participants with lower processing speed under the control condition as well. However, both participants with higher and lower processing speed performed close to ceiling (Merror ⫽ 0.63 out of 15 possible errors) when applying the implementation intention strategy. Moderation analysis revealed that only participants with limited processing speed profited from the implementation intention strategy. Following up on these studies and extending the literature on aging and implementation intentions, another aim of the present study was therefore to further investigate the effect of the implementation intentions strategy on prospective memory performance in old age. Importantly, based on the mismatch assumption of the process model, we focused on exploring the role of controlled cognitive processes (task-switching) on the effect of the implementation intention strategy.

Summary: The Present Study So far, no study has combined a process training of executive control to enhance individual cognitive resources and the implementation intentions strategy to lower executive task demands to improve prospective memory performance in old age. Therefore, the core aim of the current study was to investigate and compare

The sample comprised of 62 community-dwelling older adults (31 females) aged 60 to 86 years (mean age ⫽ 68.76 years, SD ⫽ 5.23).1 They were recruited via local newspaper and presentations in different senior clubs in Dresden, Germany. To control for possible confounding variables, any experience with regular monitoring of blood pressure, as well as unfamiliarity with computers, were exclusion criteria. No participant reported any neurological, psychological disease or color blindness. All participants reported strong interest in the topic of regular blood pressure monitoring and received a gratuity of 15 Euro for completing the study. The study followed a 2 ⫻ 2 factorial design with two betweensubjects factors, process training (yes vs. no) and implementation intentions instruction (yes vs. no). Prospective memory performance served as dependent variable. After assessing exclusion criteria, participants were randomly allocated to one of four groups: combined group (Group 1: process training/strategy training), process training group (Group 2: process training/no strategy training), implementation intentions group (Group 3: no process training/strategy training), or simple intention group (Group 4: no process training/no strategy training). The four groups did not differ significantly in age, gender, process speed, or pretest task-switching performance (see Table 1). However, groups varied in their average short-term memory capacity, with participants in Group 3 showing the lowest score. Thus, individual differences in those variables were additional controlled for (see below).

Procedure All older adults participated in three laboratory sessions at the university, which took place over the course of the 15-day study period (for study design see Figure 1). Sessions 1 and 2 served as pre- and posttest assessment for the task-switching 1 Sample size was based on effect sizes from previous single intervention studies using either process training (e.g., Karbach & Kray, 2009; Zinke et al., 2012) or strategy training (Brom et al., 2013).

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Group 1: Combined (process training/IMP) (N ⫽ 16)

Group 2: Process training (N ⫽ 15)

Group 3: Implementation intension (IMP) (N ⫽ 15)

Group 4: Simple intention (N ⫽ 16)

F⫺/␹2 value

8 68.69 (5.03) 11.40 (1.06)

7 68.33 (3.92) 11.43 (.94)

7 69.80 (6.30) 10.50 (1.17)

9 68.07 (5.89) 11.13 (1.13)

.38 .27 2.06

58.81 (10.55) 7.25 (1.44) 502.04 (174.57) 560.46 (277.89)

57.00 (13.16) 7.60 (.91) 442.36 (203.80) 476.15 (255.78)

52.53 (7.95) 6.47 (1.64) 515.50 (200.25) 667.80 (303.63)

57.87 (13.42) 8.31 (1.85) 582.94 (202.75) 596.63 (199.45)

.89 4.01ⴱ 1.35 1.40

2.94 (.85) 2.75 (.80)

3.07 (.80) 3.02 (.55)

3.80 (1.01) 3.07 (.57)

3.13 (.96) 3.01 (.54)

2.75 .83

Sex (male) Age (years) Education (years) Cognitive variables Speed Short term memory capacity Switching costs Mixing costs Individual variables Computer competence (self-report) Stability of lifestyle

Note. Speed measured by correct responses in the digit symbol task, short-term memory capacity measured by correct responses in the digit span forward task. ⴱ p ⬍ .05.

training to assess performance in transfer tasks. In both sessions all participants (Groups 1 to 4) completed a battery of cognitive tests. The sequence of tasks was held constant throughout all assessments. Testing started with the processing speed task, followed by the short-term memory capacity task and the taskswitching task. Sessions 1 and 2 were scheduled at Day 1 and Day 7, respectively. On Days 2 to 6 participants in the process training groups (Groups 1 and 2) performed one task-switching training session daily at home, which lasted about 15–25 min. Participants in Groups 3 (implementation intentions group) and 4 (simple intention group) did not have any training sessions at home. In Session 2 (Day 7), in addition to performing the cognitive tasks, all participants were introduced to the prospective memory task blood pressure monitoring. All participants learned how to regularly test their blood pressure with a common blood

telephone interview (exclusion criteria) randomization group 1: combined

group 2: process training

group 3: implementation intension (IMP)

group 4: simple intention

5 days

Session 1 (pre test assessment)

Task-switching training

Task-switching training

Session 2 (post test assessment)

7 days

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Table 1 Participants’ Mean Scores and Standard Deviations on Demographic and Individual Variables Group Comparison

implementation intention strategy

simple intention

implementation intention strategy

Session 3 (debriefing)

Figure 1.

Study design outline.

simple intention

pressure monitor for the lower arm (“Sanitas SBM 03”). Following the procedure of Brom et al. (2013), participants practiced using the monitor themselves until they successfully recorded a blood pressure test and felt confident to do so. Thereafter, the prospective adherence task was introduced: Participants were instructed to test their blood pressure at home three times daily at fixed times for the next 7 days. Participants were explicitly told to remember, as best as they could, to take their regular blood pressure readings and to not invoke any external assistance (e.g., asking their partner to remind them or setting a clock). In addition, participants were informed that the blood pressure monitor stores the exact times and dates electronically whenever it is used. Following the common implementation intention procedure (e.g., Liu & Park, 2004; McFarland & Glisky, 2011), participants in the strategy training groups (Groups 1 and 3) were instructed to specify when and where they wanted to perform their blood pressure tests the following days (e.g., “Please consider which times would be most suitable for you to perform the blood pressure measurement”). Thereafter, the information concerning time and place were combined to form the if-then implementation intention statement (e.g., “If I am at the kitchen table the next 7 days at 8 a.m., 12 p.m., and 6 p.m., then I will check my blood pressure”). Participants formed the implementation intention statement on a worksheet and were required to (a) speak the sentence three times aloud, (b) paste the statement twice on a sheet of paper and finally, (c) memorize the sentence and repeat it once to the experimenter. In line with previous studies (e.g., Liu & Park, 2004; McFarland & Glisky, 2011), participants in the “no strategy training” condition (Groups 2 and 4) were asked to only state the times when they wanted to perform the blood pressure tests and were instructed to write a “simple” intention statement on a sheet of paper (e.g., “In the next 7 days at 8 a.m., 12 p.m., and 6 p.m. I am going to check my blood pressure”). To ensure equal exposure und rehearsal, participants in the simple intention groups were also required to (a) speak the sentence three times aloud, (b) paste the intention twice on a sheet of paper, and (c) tell the memorized sentence

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BROM AND KLIEGEL

once to the experimenter. Sessions 1 and 2 lasted between 90 and 120 min for each participant. After the 7-day interval, participants returned their blood pressure monitor in Session 3 (Day 15, debriefing session), filled in a questionnaire concerning their monitoring and were informed about the aim of the study. Blood pressure readings were obtained from the monitor and scored as incorrect, if the blood pressure task was not completed within a 10-min window around the individual goal time (see Brom et al., 2013; Liu & Park, 2004).

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Materials and Tasks Task-switching training. A self-paced task-switching training (adapted from Karbach & Kray, 2009) was used to train executive control. Participants were instructed to switch as fast and as accurately as possible between two tasks. Presented stimuli were judged whether to be an upper- or lower-case letter (Task A, “letter” task) or to be in red or black font (Task B, “color” task), respectively. The stimuli set only comprised of letters that figuratively differed as an upper- and lower-case letter; each upper- and lower-case letter was presented in red or black. Letters were presented in the middle of the screen in a fixed size (front: Arial 200). For both tasks participants indicated their response with the same two keys. Each training session comprised of two short practice blocks (eight trials each) and 24 mixed blocks, each block consisting of 17 trials. Stimuli presentation started with a fixationcross (1,400 ms) followed by the target, until a response was made. Following a 25 ms delay after the key press, the next fixation-cross appeared. Participants were told to switch between tasks on every second trial, that is, letter task twice, then color task twice, then letter task twice, and so on. At the beginning of each block, the sequence started anew and was introduced by a short reminder of the task order. After each block, participants received performance feedback (correct responses, average time per trial). The training program stored performance data on each trial and can be used on every Microsoft Windows based personal computer. Main dependent variables were switching costs (difference in mean performance between switch and nonswitch trials) for reaction time (RT) and accuracy. Transfer tasks (pre- and posttest assessments). To assess near and far transfer of task-switching training to related and other cognitive domains, a set of cognitive tasks was used. For near transfer, a computer-based task-switching task was used that was structurally similar to the training task but included different stimuli and tasks. The stimuli set varied as it comprised pictures of fruit and vegetables (“food” task, Task A) which were presented either in small or large size (“size” task, Task B; see Karbach & Kray, 2009). Stimuli were presented in blocks of 17 trials each, and the timing and process of presentation equaled the task-switching training task. The assessment comprised single blocks for each task, where participants performed only one task (Task A or Task B) and mixed blocks, where participants had to switch on every second trial (Task A and B). The sequence in mixed blocks resembled switching in the task-switching training tasks (see above; food task twice, size task twice, food task twice, . . . ). At the beginning, participants performed four practice blocks (Task A only, Task B only, two mixed blocks) before starting with the 16 experimental blocks (eight single, eight mixed). Two types of task-switching costs were analyzed for RT and accuracy:

switching costs as the difference in mean performance between switch and nonswitch trials within mixed blocks and mixing costs as the difference in mean performance between mixed and single task blocks. For far transfer the digit symbol subtask of the Wechsler Adult Intelligence Scale (WAIS-III, German rev. version by Aster, Neubauer, & Horn, 2006) was used as an indicator for processing speed, the digit span forward task from the German revised version of the Wechsler Memory scale (WMS-R, Härting et al., 2000) to assess short-term memory capacity. For both tasks correct responses served as dependent variables. To control for possible confounding variables with regular health behavior and to ensure equal stability of contextual cues stability of lifestyle was assessed via The Martin and Park Environmental Demands Questionnaire (MPED; Martin & Park, 2003, four items).

Results We first investigated whether task-switching training enhanced individual task-switching abilities, and other related cognitive domains, relevant for prospective memory performance in old age. Data was collapsed across both process training groups (Groups 1 and 2, N ⫽ 31) and both nonprocess training groups (Groups 3 and 4, N ⫽ 31).

Task-Switching Training2 Comparing first and fifth (last) training sessions, paired t tests revealed large-sized training effects for mean RT per trial and accuracy, t(30) ⫽ 12.00, p ⬍ .001, d ⫽ 1.21 and t(30) ⫽ ⫺3.93, p ⬍ .001, d ⫽ .63, respectively. RT switching costs were also reduced from first to the last training session, t(30) ⫽ 5.78, p ⬍ .001, d ⫽ .76. No significant effect was found for switching costs in accuracy, t(30) ⫽ ⫺1.22, p ⫽ .23, d ⫽ .19. There was no difference in training effects for the two process training groups (Groups 1 and 2) for all four variables, F(1, 29) ⫽ .01–1.26, p ⫽ .27–.91, ␩p2 ⫽ .00 –.04. This indicates that participants in both process training groups improved in their ability to switch correctly between the two training tasks. Mean RT per trial, accuracy and switching costs for all five task-switching training sessions are displayed in Table 2 for both process training groups (Groups 1 and 2).

Switching Transfer Tasks For the process training groups collapsed, paired t tests showed significant performance gains in the structurally similar taskswitching task between pre- and postassessment session, t(30) ⫽ 2.53–9.18, p ⬍ .001–.05, d ⫽ .55–1.32. Compared with the collapsed nonprocess training groups, repeated measure ANOVAs revealed significant medium-sized interaction effects (process training ⫻ time) for RT switching and mixing costs, F(1, 60) ⫽ 8.48, p ⬍ .01, ␩p2 ⫽ .12 and F(1, 60) ⫽ 9.79, p ⬍ .01, ␩p2 ⫽ .14, respectively. However, no interaction effect was found for accu2 For task-switching task and training, practice blocks and the first trials in each block were not included (following Karbach & Kray, 2009). For task-switching task RTs ⬎ 4,000 ms were excluded from the analyses (pretest and posttest: 1.55%).

PROCESS AND STRATEGY TRAINING IN PROSPECTIVE MEMORY

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Table 2 Mean RT and Accuracy Data for the Task-Switching Training Task Over All Five Training Sessions Presented Separately for the Combined and Process Training Groups Training session 1 M (SD)

Training session 2 M (SD)

Training session 3 M (SD)

Training session 4 M (SD)

Training session 5 M (SD)

Group 1: Combined 1256 (340) 361 (204)

1034 (271) 250 (143)

929 (214) 196 (113)

880 (204) 183 (124)

801 (176) 178 (132)

91.3 (6.9) ⫺1.9 (2.6)

93.1 (9.9) ⫺2.6 (2.2)

95.4 (6.3) ⫺2.3 (2.2)

96.1 (6.2) ⫺1.9 (1.5)

96.0 (6.4) ⫺1.5 (1.7)

Group 2: Process training RT in ms Total Switching costs Accuracy rate in % Total Switching costs Note.

1253 (384) 348 (234)

1033 (341) 250 (188)

946 (289) 243 (195)

887 (296) 204 (203)

837 (272) 199 (208)

85.6 (12.7) ⫺1.2 (3.2)

89.8 (10.4) ⫺1.4 (3.7)

92 (9.2) ⫺2.1 (3.2)

92 (10.6) ⫺0.8 (2.5)

94.0 (9.4) ⫺0.6 (2.1)

RT ⫽ reaction time.

racy switching and mixing costs, F(1, 60) ⫽ 2.51, p ⫽ .12, ␩p2 ⫽ .04 and F(1, 60) ⫽ 0.04, p ⫽ .84, ␩p2 ⫽ .00, respectively. Comparing performance gains for the collapsed process training and nonprocess training groups between pre- and posttest assessments, there was a trend toward an interaction effect (i.e., transfer effect) in the digit symbol task, F(1, 60) ⫽ 3.98, p ⫽ .05, ␩p2 ⫽ .06. For the digit span forward task, process training and nonprocess training groups’ performance did not significantly differ between preand posttest assessment session, F(1, 60) ⫽ .44, p ⫽ .51, ␩p2 ⫽ .01.

Comparing Process and Strategy Training on Prospective Memory Performance To examine whether process and/or strategy training improve prospective memory performance, we calculated a 2 ⫻ 2 ANOVA with the two between-subjects factors: process training (task-switching training vs. no task-switching training) and strategy training (implementation intentions vs. simple inten-

8 Number of forgotten blood pressure tests

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RT in ms Total Switching costs Accuracy rate in % Total Switching costs

tion instruction). No significant effects were found for process training and the interaction between process training and strategy training, F(1, 58) ⫽ .46, p ⫽ .50, ␩p2 ⫽ .01 and F(1, 58) ⫽ .84, p ⫽ .36, ␩p2 ⫽ .01, respectively. However, a significant main effect for strategy training was revealed, F(1, 58) ⫽ 4.85, p ⫽ .03, ␩p2 ⫽ .08. As shown in Figure 2, participants who applied the implementation intention strategy remembered to test their blood pressure significantly more often than participants forming simple intentions. For exploratory reasons, we conducted a post hoc t test to compare both single training methods directly. Analyses showed a significant large-sized difference between Groups 2 and 3, t(19.41) ⫽ ⫺2.21, p ⬍ .05, d ⫽ .81, suggesting that participants in the implementation intentions group (Group 3, Merror ⫽ 2.60, SD ⫽ 2.30) performed the blood pressure reading more accurate than participants in the process training group (Group 2, Merror ⫽ 5.80, SD ⫽ 5.12). Results were unchanged when the individualdifferences variables age, education, short-term memory capacity, and speed were entered as covariables.3

Exploring the Interplay of Individual Executive Control and Reduced Task Demands

7 6 5 4

process training

3 no process training

2

In addition, to determine the effect of individual differences in cognitive control on participants’ ability to use implementation intentions as strategy to improve prospective memory, a moderator analysis with posttest assessment task-switching ability as moderator was conducted. Due to their relevance for prospective memory performance (see Schnitzspahn et al., 2013), we chose RT mixing costs as index. The significant total model, R2 ⫽ .15, F(3, 58) ⫽ 3.46,

1 0 no strategy training

strategy training

Figure 2. Average number of forgotten blood pressure tests as a function of training group. Error bars represent the standard error (SE). Errors distributed equally across weekends and weekdays, t(61) ⫽ 1.25, p ⫽ .22, d ⫽ .10.

3 ANCOVA results also displayed a main effect for strategy training, F(1, 54) ⫽ 5.33, p ⬍ .05, ␩2p ⫽ .09, but not effects for process training and the interaction between process and strategy training, F(1, 54) ⫽ .60, p ⫽ .44, ␩2p ⫽ .01 and F(1, 54) ⫽ 1.46, p ⫽ .23, ␩2p ⫽ .03, respectively. Neither the effects for age, education, short-term memory capacity, nor speed had a significant influence on blood pressure monitoring, F(1, 54) ⫽ .07–1.68, p ⫽ .20 –.78, ␩2p ⫽ .00 –.03.

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Number of forgotten blood pressure tests

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Process Training of Executive Control and Age-Related Prospective Memory Performance

9 8 7 6 Low mixing costs

5 4

High mixing costs

3 2 1

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0 no strategy training

strategy training

Figure 3. Average number of forgotten blood pressure tests as a function of strategy training group displayed separately for participants with high and low mixing costs.

p ⬍ .05, showed a main effect for strategy training, ␤ ⫽ ⫺0.29, t(58) ⫽ ⫺2.35, p ⬍ .05, and a significant interaction effect of RT mixing costs and strategy training, ␤ ⫽ ⫺0.25, t(58) ⫽ ⫺2.06, p ⬍ .05 (see Figure 3). The interaction leads to an increased variance (⌬R2 ⫽ .06). Post hoc extreme group analysis (⫾ 0.5 SD) revealed that implementation intention strategy significantly improved accurate blood pressure testing for participants with high mixing costs (low task-switching ability), t(14) ⫽ 3.03, p ⬍ .01, d ⫽ 1.44. No significant difference was found between the two strategy training conditions for participants with low mixing costs (high task-switching ability), t(18) ⫽ ⫺0.61, p ⫽ .55, d ⫽ .28. Effects for strategy training and interaction between mixing costs and strategy training stayed significant when adding age, education, short-term memory capacity, and speed as possible confounding variables, ␤ ⫽ ⫺0.32, t(54) ⫽ ⫺2.44, p ⬍ .05; ␤ ⫽ ⫺0.29, t(54) ⫽ ⫺2.30, p ⬍ .05.

Discussion The present study was the first to combine process training of executive control and strategy training to investigate and compare their effect on everyday prospective memory performance in old age. Although analyses showed training gains in the trained taskswitching and a similar near transfer task, no transfer effect was found for the real-life prospective memory task blood pressure monitoring. In strong contrast, a substantial and reliable effect on prospective memory performance was found for the implementation intention strategy. Conceptually important, as revealed by the moderation effect, the implementation intention strategy only enhanced prospective memory performance for participants with high mixing costs (i.e., low switching abilities). Participants with high switching abilities performed as well in the simple intention instruction as in the implementation intention condition. In conclusion, supporting our theoretical predictions, findings suggest two paths to reduce failures in real-life prospective memory in old age: high executive control abilities and specific planning strategies to reduce executive task demands. We first discuss the conceptual, methodological and practical implications for both training methods individually and then compare both interventions directly.

Cognitive plasticity in old age has been well established through various studies (see, e.g., Morrison & Chein, 2011; Verhaeghen, Marcoen, & Goossens, 1992). Hitherto, almost all of the training studies with healthy older adults targeting specific domains or processes have been realized in the laboratory. Aiming to explore the efficiency of a task-switching training in older adults that was implemented into their daily routine, the current study revealed a significant decrease in overall RT and RT switching costs as well as a higher accuracy rate between the first and fifth training session. This implies a successful improvement in the training as such, even when it was applied self-controlled at home. In addition, for the process training groups, we found significant pre/ posttest reduction of mixing and switching costs in RT as well as accuracy to an untrained, but structurally similar, switching task. The reduction was generally more pronounced on the level of latencies (d ⫽ 1.23 and 1.32) than on the level of accuracy (d ⫽ .55 and .87). Indicated by similar effect sizes (mixing costs d ⫽ 1.4, switching costs d ⫽ 1.0, Karbach, Mang, & Kray, 2010; mean d ⫽ 1.1; Karbach & Kray, 20094), the reduction of mixing and switching costs on the level of RT in this study is consistent with previous studies using a similar switching paradigm (e.g., Karbach & Kray, 2009; Karbach, Mang, & Kray, 2010; Minear & Shah, 2008; Zinke et al., 2012). In contrast to Karbach and Kray (2009) and other previous studies, participants in the present study also decreased significantly in both mixing and switching costs on the level of accuracy. One reason could be the amount of as well as the time between training sessions in the present study. Participants were instructed to train 5-times on a daily basis compared with two (Karbach et al., 2010) or four training sessions (Karbach & Kray, 2009; Zinke et al., 2012) on a weekly basis, implicating higher training intensity and thus, stronger increase in the trained ability for the present sample. Most importantly, we found significant medium-sized effects for pre-posttest switching and mixing costs (RT) in comparison with the nontraining groups (Groups 3 and 4), which indicate that improved task-switching abilities in the process training groups (Groups 1 and 2) are acquired due to training. The trend toward a transfer effect for speed but not for short-term memory capacity is also in line with previous results. Studies so far found improvements in tasks measures for fluid mechanics or executive abilities but not for capacities measures (Karbach & Kray, 2009; Karbach et al., 2010; Zinke et al., 2012). In sum, along with previous lab-based studies, our results suggest that substantial training gains in the executive control ability task-switching are possible in old age. This may be even more impressive given the fact that participants trained individually at home and not under controlled laboratory conditions, which demonstrates that computer-based training can also be successful if carried out in everyday life. However, importantly for present purposes, the improvement in the task-switching ability did not lead to a subsequent increase in prospective memory performance measured by omission errors in a blood pressure monitoring task. Thus, there is no transfer effect for the task-switching training groups to a 4

Reported in Karbach, Mang, and Kray (2010).

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PROCESS AND STRATEGY TRAINING IN PROSPECTIVE MEMORY

more complex everyday life prospective memory task in the current sample. Nevertheless, it must be considered that effects are generally smaller for transfer to dissimilar and complex tasks than for closely related tasks. Additionally, even though five training sessions speak of a higher intensity of training than used in previous task-switching studies, both switching and mixing costs still suggest potential for further improvement. Thus, although the short but intense intervention may have been beneficial for near transfer, for far transfer and possibly even more so for real-life transfer, a more distributed training may be more appropriate. In fact, several studies finding large transfer effects to more complex tasks often trained over a longer time period. For example, participants in the study by Jaeggi, Buschkuehl, Jonides, and Perrig (2008) trained daily over a period of 19 consecutive days and showed substantial transfer effects, for example, to a fluid intelligence measure. Nevertheless, it should be noted that the original study of Karbach and Kray (2009), whose training program we adapted, and who trained even less often, also observed far transfer. Thus, the relation between characteristics of the training regime (intensity, length, massive, distributed, etc.) appears to be complex, domain-specific and may in fact be nonlinear. Disentangling those interactions will be an important issue for future training research.

Reducing Prospective Memory Task Demands: Strategy Training The implementation intention strategy appears to be one of the most effective strategies to enhance specific intended behavior (for a meta-analytic review see Gollwitzer & Sheeran, 2006). Aiming to investigate the effect of the implementation intentions strategy in older adults, we found better prospective memory performance for participants receiving the implementation intention strategy than for participants using the simple intention instructions. With an error rate of 28.9%, participants in the simple intention group (Group 4) forgot to test their blood pressure on average more than two times more often (factor 2.3) than participants in the implementation intentions group (Group 3, 12.4%). Comparing our results with previous studies suggests the representativeness of the current sample. Prospective memory performance in the simple intention group can be compared with similar studies using real-life prospective memory tasks in old age (27.3%, Brom et al., 2013; 32%, Liu & Park, 2004). Moreover, relating our findings to the meta-analytic result for implementation intentions on health behavior by Gollwitzer and Sheeran (2006; d ⫽ .59 [k ⫽ 23]), our effect displays a similar strength. This is only the fourth study to investigate and reveal this effect of the implementation intention strategy on a real-life prospective memory task in old age, and therefore in concert with the previous studies (Bélanger-Gravel et al., 2013; Brom et al., 2013; Liu & Park, 2004) allowing a more generalized conclusion on the efficiency of the implementation intention strategy for older adults. Conceptually more important, the present study tested the prediction of an interaction between reduced task demands through strategy training and individual differences in specific executive resource levels. Resting on the mismatch assumption from the prospective memory framework (Kliegel et al., 2011), especially or only participants with cognitive resources that are insufficient for the specific prospective memory task should benefit from the lower task demands through the strategy training. As predicted, the

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interaction effect revealed in the current study showed that only participants with lower task-switching abilities profited from the implementation intention strategy. Results demonstrated that participants with lower task-switching abilities showed a significantly higher error rate in the simple intention condition than in the implementation intention condition; whereas participants with high task-switching abilities performed as well in both intention conditions (implementation intention strategy/simple intention instruction). This finding underlines recent theoretical assumptions put forward by prospective memory models and adds important conceptual insights to implementation intention strategy, prospective memory as well as adherence behavior literature. First, the discovered interaction effect supports the conceptual assumption by Gollwitzer (1999) that the implementation intentions strategy relies rather on automatic processing than effortful control processes. In conclusion, participants with lower cognitive process abilities should benefit especially by using the implementation intention strategy. Initially expecting the same results, in a labbased task McFarland and Glisky (2011) found that both participants with high and low frontal lobe functions profited from the formation of implementation intentions, resulting in high frontal lobe participants performing better under both conditions (readonly and implementation intentions). The rather artificial task (pressing a key) as well as the established result that older adults perform better in naturalistic than lab-based settings (Henry et al., 2004; Schnitzspahn et al., 2013) could have led to the fact that both frontal lobe groups did not activate their full cognitive resources. This could account for the improvement of both groups through the implementation intention strategy. Furthermore, the present study focused directly on specific cognitive processes that are relevant to prospective memory performance. McFarland and Glisky (2011), however, globally assessed the frontal lobe function using a score combining different process abilities (e.g., working memory and executive control) and therefore possibly blurring effects of specific controlled resources (see, e.g., Schnitzspahn et al. 2013, suggesting that not all cognitive control abilities mediated by frontal networks play an equally vital role in age-related prospective memory performance). In this context, this finding also adds to current research on the relationship between prospective memory performance and cognitive control in general, experimentally singling out task-switching abilities as key for everyday life prospective memory performance. Finally, from a more applied perspective, in regard to medical adherence behavior, our results suggest that action control is strongly required to perform regular health tasks efficiently and that performance in old age may be error prone but that those errors can be substantially reduced. Thus, for older adults or other populations with limited executive resources strategy training aiming to reduce executive task demands can provide a good solution to foster adherence of new regular health behavior and minimize risks of nonadherence.

Comparing Process and Strategy Training In addition to investigating each training approach individually and extending the literature, a further aim of the study was to compare the efficiency of a process and strategy training. Tested directly, analyses revealed that older adults using the strategy training outperformed the process training only group (Group 2 vs. Group 3, d ⫽ .81). Contrasting both approaches, our study sug-

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gests that forming a specific plan in the intention formation phase that fosters intention retrieval encountering the specific situation (when the intention should be performed) is more successful than strengthening relevant task-switching processes (at least in the present regime). Interestingly, in our study older adults who used both training approaches showed a reduced implementation intention effect (Merror ⫽ 4.38, SD ⫽ 4.10 vs. Merror ⫽ 2.60, SD ⫽ 2.30, see Table A2). This result could suggest that metacognitive or motivational changes may have occurred throughout or after the 5 days of task-switching training at home while potentially not seeing (immediate) benefits; this may lead to questioning the intervention program as such and subsequently to lower commitment in forming the implementation intentions as well as performing the blood pressure monitoring task (high motivation throughout goal striving has been shown to be an important factor for the effectiveness of the implementation intention strategy; Gollwitzer, 2006). We acknowledge that this has to remain speculative at this point and that future studies are required to test this hypothesis. However, these questions seem to raise issues of broader importance for the cognitive training literature as more and more programs are suggested where older adults are asked to train cognitive control or working memory (at home or in the lab) possibly without immediate performance gains in everyday relevant performance domains. The current results appear to suggest that training regimes with this kind of process interventions (that do lead to measurable training but to no transfer effects) could not only have no transfer effects but could also result in reduced efficiency of otherwise successful strategy interventions (due to reduced motivation or self-efficiency). Again, further research will be necessary to explore these questions directly.

Limitations We acknowledge that our sample size can be seen as a possible limitation. Especially in regard to the process training approach, the possibility that small transfer effects stayed undetected cannot be ruled out in the present study. However, the current sample size was calculated after effect sizes reported in the respective single intervention studies and is comparable or even larger than reference studies for each approach (e.g., Karbach & Kray, 2009 or Karbach et al., 2010; Liu & Park, 2004 or McFarland & Glisky, 2011). In addition, post hoc power analyses showed that for the nonsignificant effects observed in our study a sample size of 787 participants would be necessary to have sufficient power (power of .80) to detect those effects.5 No published training study in the cognitive aging literature has ever included a sample of this size. Thus, we are confident that it can be concluded that—at least—the applied process training procedure (which was based on a published training regime) has not resulted in substantial transfer effects on the ecologically valid prospective memory task used in the present study (notably in contrast to the strategy intervention, which showed an effect ceteris paribus). In this context, given that one aim of the study was to examine for the first time ecologically valid transfer effects of both types of interventions on a real-life prospective memory task, we also argue that training methods only leading to small-sized effects (even when detected in large scale studies) may involve costs (e.g., training time, loss of motivation, see above) which are potentially too high for the individual older adult compared with the encountered performance gain.

One other possible limitation is that the implementation intention groups were asked to specify the location of performance as it has been standard in previous studies using this strategy. We cannot exclude that this additional cueing element may have been more instrumental to the strategy effect than the way of phrasing the intention. Thus, future studies will have to disentangle the “if–then” wording of the strategy from the “where and when” specifications (e.g., by assessing whether the locations were actually used in later performance). We can further not completely rule out that participants used additional aids to enhance their performance in task-switching training or blood pressure monitoring. However, this seems rather unlikely due to the fact that we explicitly instructed participants how to perform both tasks and that it is vital for the experiment to perform them without outside assistance. Furthermore, after each task experimenters assessed how participants performed the task, if problems had occurred and if they used any aids or strategies to enhance their performance. Participants did not mention anything that could have influenced the results.

Conclusions In sum, the current study clearly identifies two prospects to reduce prospective memory failures in old age. First, in real-life high process abilities relevant to prospective memory performance (here: task-switching) seem to act as a buffer against prospective memory failures in old age. Hence, although no transfer effect was found in the current study, intensive process training to ensure high cognitive abilities and good general prospective memory performance in old age can be suggested based on the moderator analysis. Second, learning the simple implementation intention strategy enhanced prospective memory performance substantially for participants with low task-switching ability. Thus, for specific prospective memory performance that can be planned in advanced (e.g., medical adherence), integrating this strategy can be recommended. Conceptually, age effects in prospective memory seem to stem from a mismatch of executive task demands and available controlled attentional resources, especially task-switching. Thus, reducing task demands via strategy training appears to be a promising option for interventions, but only for a specific subgroup of older adults. Taken together, the current study fills an important research gap in combining process and strategy training for the first time, and contributes important insights into the conceptual and theoretical framework of the underlying process of prospective memory and the implementation intention strategy in discovering task-switching abilities as a moderator variable.

5

Calculated with GⴱPower (Faul, Erdfelder, Lang, & Buchner, 2007).

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Appendix

Table A1 Mean Performance on Task-Switching Task and Far Transfer Variables as a Function of Session (Pretest/Posttest Assessment), and Group (Combined/Process Training/Implementation Intention/Simple Intention) Training group

Group 1: Combined (training/IMP)

Group 2: Process training

Group 3: Implementation intension (IMP)

Group 4: Simple intention

M (SD)

M (SD)

M (SD)

M (SD)

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Pretest (Session 1) Near transfer: Task-switching task Reaction time, in ms Total—single blocks Total—mixed blocks Switching costs Mixing costs Accuracy rate in % Total—single blocks Total—mixed blocks Switching costs Mixing costs Far transfer tasks Speed Short-term memory capacity

976 (172) 1536 (320) 502 (176) 560 (278)

951 (220) 1509 (406) 494 (180) 559 (288)

941 (142) 1609 (419) 515 (200) 668 (304)

962 (193) 1558 (283) 583 (203) 597 (199)

95.9 (2.6) 87.8 (8.7) ⫺7.8 (4.7) ⫺8.1 (7.3)

95.9 (5.0) 91.1 (6.9) ⫺4.8 (5.0) ⫺4.8 (6.4)

93.1 (5.5) 84.5 (10.8) ⫺6.9 (6.5) ⫺8.7 (8.8)

96.4 (2.3) 89.8 (9.2) ⫺5.4 (4.6) ⫺6.6 (9.3)

58.8 (10.6) 7.25 (1.4)

57.0 (13.2) 7.6 (0.9)

52.5 (8.0) 6.5 (1.6)

57.9 (13.4) 8.3 (1.9)

Posttest (Session 2) Near transfer: Task-switching task Reaction time, in ms Total—single blocks Total—mixed blocks Switching costs Mixing costs Accuracy rate in % Total—single blocks Total—mixed blocks Switching costs Mixing costs Far transfer tasks Speed Short-term memory capacity

832 (145) 1073 (230) 284 (167) 242 (178)

794 (159) 983 (181) 250 (115) 189 (111)

870 (155) 1331(339) 494 (200) 461 (273)

845 (154) 1265 (331) 422 (188) 420 (257)

95.2 (5.3) 92.9 (6.9) ⫺2.9 (4.9) ⫺2.3 (5.1)

98.2 (1.5) 97.3 (1.8) ⫺1.6 (2.4) ⫺0.9 (2.3)

96.6 (2.2) 93.6 (6.3) ⫺3.8 (6.5) ⫺2.9 (6.6)

97.4 (3.4) 94.5 (5.3) ⫺3.5 (5.9) ⫺2.8 (6.2)

68.3 (11.3) 6.8 (1.8)

63.8 (15.9) 6.7 (1.9)

59.5 (8.5) 6.1 (1.5)

61.9 (13.6) 7.9 (1.7)

Table A2 Groups’ Mean Scores and Standard Deviations for Error Rates in the Real-Life Prospective Memory Task Including Effect Sizes for Single Group Comparisons Strategy training

Process training No process training Effect size (d) Note.

No strategy training

M

SD

M

SD

Effect size (d)

(Group 1) 4.38 (Group 3) 2.60 .54

4.10 2.30

(Group 2) 5.80 (Group 4) 6.06 .05

5.12 5.26

.31 .85

Effect sizes for cross group comparisons are d ⫽ .81 (Group 2 vs. 3) and d ⫽ .36 (Group 1 vs. 4).

Received September 24, 2013 Revision received April 16, 2014 Accepted April 28, 2014 䡲

Improving everyday prospective memory performance in older adults: comparing cognitive process and strategy training.

Considering the importance of prospective memory for independence in old age recently, research has started to examine interventions to reduce prospec...
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