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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2017 Feb 18;74(1):59–68. doi: 10.1093/geronb/gbx012

Daily Memory Lapses in Adults: Characterization and Influence on Affect

Jacqueline Mogle 1,✉, Elizabeth Muñoz 2, Nikki L Hill 1, Joshua M Smyth 3, Martin J Sliwinski 4
PMCID: PMC6941205  PMID: 28329832

Abstract

Objective

The current analyses examined the impact of daily memory lapses on daily affect and whether this impact varied across age.

Method

One hundred sixty-six adults (ages 20–79) completed assessments of memory lapses and affect each day for 7 consecutive days. Assessments included retrospective and prospective memory lapses as well as the impact of these lapses (how irritating, interfering, and consequential). Affect was assessed using ratings of daily positive and negative affect.

Results

Participants reported memory lapses on 33.3% of days. Prospective lapses were consistently rated as more consequential. Regardless of age, participants had significantly lower in positive affect and significantly higher in negative affect on days with a prospective lapse. Effects of retrospective lapses depended on age: compared to older adults, younger adults reported lower positive affect on days with a retrospective lapse.

Discussion

Previous work on daily memory lapses has focused on prospective lapses. Although retrospective lapses occurred more frequently in this sample, prospective lapses appeared to have a greater impact on daily experiences regardless of age. By measuring daily memory lapses and affect over consecutive days, we can begin to understand how the experience of forgetting impacts individuals at a micro-level.

Keywords: Aging, Daily affect, Daily memory lapses, Subjective memory

Research on subjective memory (SM), an individual’s perception of their memory functioning (Mitchell, 2008), finds reports of memory problems common across the adult lifespan, particularly in older age (Reid & MacLullich, 2006). Researchers typically utilize global reports such as “Do you find you have trouble with your memory?” to assess SM (Rabin et al., 2012; Reid & MacLullich, 2006). Such questions require individuals to provide a broad judgment of their memory performance across an indefinite time period and without consideration of situational factors (e.g., current mood) that could introduce recall inaccuracies or heuristic biases. Alternatively, by asking individuals to report over a briefer period of time, daily diary approaches better approximate actual experiences and may avoid some of the inaccuracies and biases of global reports (Neupert, Almeida, Mroczek, & Spiro III, 2006; Neupert et al., 2006b; Whitbourne, Neupert, & Lachman, 2008).

At the micro-level (i.e., daily), SM is conceptualized as instances of forgetfulness during the day, or memory lapses or memory failures. In the current article, we use the term memory lapse rather than memory failure to differentiate subjective reports from traditional objective memory assessments where “failure” frequently refers to instances of forgetting that are independently observed and substantiated. Daily reports of memory lapses provide researchers with an ecologically valid assessment of the types of items or events that are forgotten most frequently within a specific, narrow timeframe. In addition, daily assessments can capture the impact of daily memory lapses on everyday routines and well-being. However, micro-level research has primarily focused on the predictors of daily memory lapses rather than their effects on daily experiences (Neupert et al., 2006, 2006b; Whitbourne et al., 2008). The current study extends previous daily diary work by describing the types and frequency of daily memory lapses experienced by younger and older adults, as well as the impact of these events on daily affect, in a sample of community-residing adults.

Measuring SM: Daily Diary Research

Daily diary studies examine the frequency of a pre-defined set of memory lapses that an individual may experience during the course of a single day (e.g., forgetting someone’s name). Assessing memory lapses on a daily basis provides an advantage over global SM assessments, which require respondents to make broad judgments about their memory as a whole over long (and usually unspecified) periods of time. Shifting assessments to focus on reports of events from the past day allows individuals to reflect on concrete instances of forgetting (Schwarz, 1999), thereby reducing tendencies to rely on broad self-schemas (Cavanaugh, Feldman, & Hertzog, 1998). That is, the individual will be more likely to report on a specific instance of forgetting a name rather than their subjective impression that they are “bad” at remembering names, thus reducing recall bias.

Another advantage of asking participants to report on daily events is the ability to obtain additional details about the context of the specific memory lapse. Previous work using event-contingent reporting (i.e., reporting on events as they occur) of memory lapses found contextual and affective predictors of these events. For example, researchers have been able to identify factors that can both increase (e.g., daily stressors; Neupert et al., 2006, 2006b) and decrease (e.g., daily physical activity; Whitbourne et al., 2008) reports of memory lapses on a day-to-day basis using daily diary approaches. Yamanaka (2003) also identified an effect of emotional states; individuals reported the most memory lapses when they were preoccupied or experiencing an emotion high in arousal, regardless of valence (e.g., excited or angry). These studies emphasize the importance of understanding the context of memory lapses in a given day, which would allow researchers to identify days when an individual is at risk for experiencing these events.

One limitation of previous daily diary studies is the lack of separation of prospective and retrospective memory (RM) lapses. Prospective memory (PM) refers to remembering to carry out a future task or intention. For example, “I have to remember to attend an appointment tomorrow.” This type of memory has been posited as an important and ubiquitous part of daily life (McDaniel & Einstein, 2007). In contrast, RM refers to remembering specific information such as names or where something was placed (Craik, 1994). The current study examines prospective and RM lapses to determine whether there are differences in the frequency of these experiences as well as differences in their impact on affect.

Impact of Memory Lapses

Perceptions of memory lapses are often a source of worry and may interfere with daily routines, particularly for older adults (Reese & Cherry, 2004). Differences in the types of memory lapses between younger and older adults, as well as individual interpretations of their cause(s), may influence the effects of these events on daily positive and negative affect. In one qualitative study, middle-aged adults (40 to 64 years) did not differ from older adults (65+ years) in the number of memory lapses reported, but middle-aged adults assigned significantly higher distress ratings to these lapses compared to older adults (65+ years; Burmester, Leathem, & Merrick, 2015). These studies, however, rely on a person’s recall of forgetting and forgetting-related levels of distress over periods of months or years, which may be biased as well as limited to the most salient events (Cavanaugh et al., 1998). Indeed, in the Burmester and colleagues study, participants were subsequently asked to complete a detailed questionnaire regarding types of memory difficulties experienced. Regardless of age, more difficulties were identified in the questionnaire than in an initial open-ended question, but the memory difficulties initially described in the interview had significantly higher distress ratings. This suggests a systematic bias toward peak effects in reports of memory lapses over long periods of time (cf. pain; Schneider, Stone, Schwartz, & Broderick, 2011) and implies a need for carefully considering the measurement of memory lapses on a daily basis.

Daily Impact of Memory Lapses

We extend the above literature to look at the impact of memory lapses on a daily basis. Smyth and Stone (2003) argued that the impact of an event has an effect on how that event is later recalled. This is particularly salient in memory lapses. Forgetting often occurs outside the awareness of the individual until it is brought to their notice (Rabbitt & Abson, 1990). For example, an individual may not realize they have forgotten an appointment with someone until it is pointed out by the other party. The affective and tangible consequences of forgetting are highly variable and largely unexplored at the level of the daily memory lapse.

In the current study, we explored the effect of daily memory lapses using an assessment model similar to that used to assess daily stressful events (Almeida, Wethington, & Kessler, 2002). After endorsing a specific memory lapse, participants rated the direct impact of that lapse (cf. severity ratings of daily stressors; Almeida et al., 2002). Next, participants reported on their positive and negative affect for an entire day. We examined whether these affect ratings systematically varied with memory lapses as an index of the indirect impact of these events on daily experiences. This approach to estimating day to day affective reactivity is consistent with that used in daily diary work on daily stressful events (cf. Charles, Piazza, Mogle, Sliwinski, & Almeida, 2013).

Aims of the Present Study

In an effort to address gaps in the literature and understanding of SM, we developed a daily diary assessment of memory lapses using mobile devices that incorporates reports of the direct impact of these events. Previous work suggests that these events occur relatively frequently throughout the day and week (Hahn & Lachman, 2015; Ossher, Flegal, & Lustig, 2013), but their direct and indirect impact is unknown. Based on previous work in this area we tested the following hypotheses:

  • 1.

    Older adults would report more daily memory lapses compared with younger adults and this would not depend on the type of memory lapse (retrospective vs. prospective).

  • 2.

    Older adults would report more distress in response to daily memory lapses using a direct assessment of daily impact (i.e., irritation, interference, and consequence).

  • 3.

    Older adults would report more distress in response to daily memory lapses using an indirect assessment of daily impact (i.e., daily positive and negative affect).

Methods

Recruitment

Recruitment and data collection began in 2010 and ended in 2012. Individuals responded to recruitment ads for a study of the daily experiences of adults ages 20 to 80. During an initial screening phone call, participants were informed on the commitment related to participating and the general goals of the research. Inclusion criteria required that participants: (a) be 20 to 80 years old and fluent in English, and (b) have a daily schedule of waking up after 4 a.m. but before 11 a.m. Of the 214 individuals screened, 22 were not eligible, 12 were not interested after an initial description of the study, 14 did not complete the study despite being eligible and interested, four did not return their mobile devices, and five completed the 7-day study but did not complete the primary measure of interest (the evening daily assessment).

Participants

The final sample included 166 participants, with an average age of 49.5 (SD = 16.81, range 20–79) and slightly more than a high school education (M = 13.6 years, SD = 2.83). Approximately half of the sample was women (52.4%), and 40.5% were employed at the time of participation. All participants were free of clinical depression. The ethnicity breakdown was similar to the population from which the sample was selected (central New York state), with the majority of the participants identifying as Caucasian (59.4%), followed by individuals identifying as black (30.3%). The remaining participants were of Hispanic (3.03%), or other ethnicities (7.3%). Table 1 includes demographics for each age decade in the current study.

Table 1.

Demographics Breakdown by Age Decade

20–29 30–39 40–49 50–59 60–69 70–79
Age decade M (SD) or % M (SD) or % M (SD) or % M (SD) or % M (SD) or % M (SD) or %
N 27 26 31 28 28 26
Age (years) 24.81 (2.63) 35.04 (2.92) 44.48 (2.69) 54.32 (2.93) 64.29 (3.1) 74.19 (3.06)
Education (years) 13.63 (3.27) 13.19 (2.37) 12.9 (2.04) 12.71 (2.37) 14.54 (3.29) 14.54 (3.17)
% Caucasian 44% 46% 54% 46% 71% 96%
% Women 55% 57% 54% 50% 42% 53%
% Employed 44% 42% 43% 32% 46% 33%
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Note: Means and SDs reported for continuous variables, all others reported as percentages.

Study Design and Procedures

Data were collected as part of a larger ecological momentary assessment project with the measure of memory lapses included once daily as part of the evening assessment. Participants completed two face-to-face sessions in a research laboratory and 7 days of daily assessments using a mobile device (palmtop computer with functionality limited to the study protocol). During the first in-lab session, participants received training with the mobile devices to ensure familiarity with the daily assessment protocol. Participants were asked to carry the mobile device with them for 7 consecutive days. Each day, participants completed an evening assessment (the target of the current analysis) as well as a morning assessment and five prompted assessments that occurred approximately 3 hr apart throughout the day that were not included in the current analysis.

In the current sample of 166 participants, there were 1,162 possible assessments (one assessment per day for 7 days × 166 participants). Of the possible assessments, participants completed 996 assessments. For the purposes of the current analyses, evening surveys were considered compliant if they were completed after 7 p.m. but before 3 a.m.; 932 (93.5%) met these criteria. Surveys completed after midnight (but prior to 3 a.m.) were determined to refer to the previous day. If participants completed more than one evening survey for a given day, the first completed survey was used (n = 29). Average participant compliance was 82.7% (~5.8 surveys).

Measures

Primary measures

Memory lapses checklist

This instrument was modeled on the daily inventory of stressful events (DISE; Almeida et al., 2002) and consisted of an 11-item checklist of types of memory lapses: five related to RM (memory for information learned in the past), five related to PM (memory for intended activities), and an “other” category. RM lapses included forgetting directions, someone’s name, where something was, information, or a personal date. PM lapses included medications, errands, household chores, appointments, and incomplete activities that they forgot to start, or started but forgot to finish.

Memory lapses: Direct impact

Following each memory lapse reported, participants indicated the negative impact of the event in three areas: level of irritation (“How much does forgetting [event] bother you now?”), the extent to which it interfered with their daily routine (“How much did forgetting [event] interfere with your schedule?”), and whether they believed that it would have consequences beyond that day (“Do you think forgetting [event] will have consequences beyond today?”). If no memory lapses were experienced that day, participants answered several questions about the strategies used for improving their memory (e.g., how often they wrote things down that day). This was included to encourage participants to report memory lapses, as not reporting events did not allow them to finish the questionnaire more quickly (Smyth & Stone, 2003). Intra-class correlations indicated that variance associated with differences between persons ranged from .31 (consequences) to .49 (interference); a majority of the variability was due to differences within individuals across memory lapses.

Although participants could report on more than one memory lapse at each evening assessment, participants reported more than one lapse at only 2% and 3% of occasions (prospective and RM lapses, respectively). Due to this low frequency of multiple lapse occasions, direct impact ratings were averaged at the few occasions when more than one lapse was reported.

Memory lapses: Indirect impact

At each evening assessment, participants rated how much each of eight adjectives characterized their emotions that day on a scale from 1 (i.e., not at all) to 7 (i.e., extremely). There were four items indicating positive affect (happy, enthusiastic, content, and excited) and four indicating negative affect (tense, sad, upset, and disappointed). Items were taken from the PANAS-X (Watson & Clark, 1994). Reliability for these scales was adequate: Rchange positive affect = 0.81; Rchange negative affect = 0.85 (Cranford et al., 2006).

Covariates

Demographics

Participants completed a questionnaire assessing general demographic characteristics including age, gender, education, work status, and ethnicity.

Health Survey—Short Form 36 (SF-36 McHorney, Ware Jr, & Raczek, 1993)

In the in-person session, participants completed a range of questions assessing the state of their health and any impairments they experienced in completing a range of physical and social activities in the past 4 weeks. For the current analyses, we used the general health item at the beginning of the questionnaire as a global measure of self-rated health: “In general, would you say your health is…?”, with the response options excellent, very good, good, fair, and poor.

Center for Epidemiological Studies Depression Scale (CESD)

Completed in the in-person session, this 20-item scale was developed to assess symptoms of depression in the general population (Radloff, 1977). Sample items included “I feel that I cannot shake the blues even with help from my family and friends” and “I feel that everything I do is an effort.” Items were rated for how the individual was feeling at the current time on a four-point scale ranging from not at all to very much; Cronbach’s alpha = .76.

Stressful events

Using a modified version of the daily inventory of stressful events (DISE; Almeida et al., 2002), individuals reported whether each of the following stressful events were experienced including arguments, avoided arguments, work, home, network, and other events. Total number of events experienced throughout the assessment period was divided by the total number of occasions for each person and included to control for other sources of daily hassles.

Neuroticism

Participants completed a measure of personality during the in-person session based on items from the International Personality Item Pool (Goldberg et al., 2006). The items in this pool were developed to tap the Big Five personality characteristics: neuroticism, extraversion, agreeableness, openness to experience, and conscientiousness. Only the neuroticism subscale was included as a covariate. Cronbach’s alpha for this scale was acceptable at .83.

Analytic Strategy

Analyses were conducted in SAS 9.4 using proc glimmix and proc mixed (SAS Institute, 2014). For analyses examining the frequency of memory lapses and whether these frequencies varied with age (hypothesis 1), we used generalized multilevel modeling (GMM). GMM with a Poisson distribution is appropriate for the analysis of count variables, particularly when events are considered relatively rare (i.e., there are a large proportion of non-event occasions; Hox, Moerbeek, & van de Schoot, 2010). For hypotheses 2 and 3 examining the ratings of direct and indirect daily impact, linear multilevel models were fit to the data. The multilevel component of the models accounted for events nested within persons across the duration of the study.

Age was entered as a continuous variable and when age was a significant predictor, estimates were generated for the average age (~50 years old) as well as one standard deviation above and below the average age to indicate younger (~30 years old) and older adults (~65 years old), respectively. All models included gender, ethnicity, employment status, and education as demographic covariates. The personality trait neuroticism was included because previous work has shown a relationship between neuroticism and frequency of reporting daily memory lapses (Neupert, Mroczek, & Spiro III, 2008). Additionally, self-rated physical health and depressive symptoms were included to account for individual differences in reports due to physical and psychological health. Models examining daily affect additionally included exposure to daily stressors to determine the effects of memory lapses above and beyond these events. All continuous variables were grand mean centered to allow for appropriate interpretation.

Estimates of effects from the multilevel models are reported as unstandardized bs. Pseudo-R2 was calculated to compare the proportion of variance accounted for based on the equation provided by Singer and Willett (2003).

Results

Frequency of Daily Memory Lapses

Participants reported memory lapses on 33.2% of assessments. Frequencies of individual memory lapses and the totals for RM and PM categories appear in Table 1. RM lapses occurred on 18% (Noccasions = 181) of occasions, whereas PM lapses occurred on 15% (Noccasions = 150). The results of the GMM indicated a marginal effect (b = 0.821, SE = 0.083, p = .052; 95% confidence interval [CI]: 0.673–1.002) comparing the frequency of memory lapses across categories; RM lapses were reported slightly more frequently than PM lapses. The most common RM lapse was forgetting where something was (10% of occasions), and the most common PM lapse was forgetting a medication (6% of occasions).

One concern was whether asking about memory lapses would increase noticing and reporting of events (i.e., assessment reactivity). Reporting of RM lapses did not change over the course of the study (p = .527) and a small but significant effect on PM lapses indicated participants were somewhat less likely to report PM lapses across the study (b = −0.121, SE = 0.039; 95% CI: −0.198 to −0.044). Daily assessments thus did not appear to induce participants to perceive and report more events; however, this may also be evidence of assessment fatigue.

Hypothesis 1: Older adults would report more memory lapses regardless of type

Age was significantly related to the frequency of memory lapses. Individuals who were older reported more memory lapses overall (b = 1.028, SE = 0.007, p < .001; 95% CI: 1.015–1.041), and this difference was larger for RM lapses (b = 0.969, SE = 0.006, p < .001; 95% CI: 0.956–0.981).

Direct Impact of Daily Memory Lapses

Negative impact ratings for each of the follow up items are presented in Table 2. We then examined whether the PM and RM lapses differed in their ratings of negative impact. RM and PM lapses did not differ on ratings of irritation or interference with daily routine (ps = .91 and .22, respectively). However, PM lapses were rated significantly higher in the expectation of future consequences, compared to RM lapses (MRM = 2.17 vs. MPM = 2.96, b = 0.793, SE = 0.220, p < .001; 95% CI: 0.349–1.236).

Table 2.

Frequency and Negative Impact Ratings for Categories and Individual Memory Lapses

Irritation Interference Consequences
N occasions M (SD) M (SD) M (SD)
Prospective memory 150 3.49 (1.72) 2.65 (1.66) 3.10 (1.69)
Medication 62 3.69 (1.94) 2.81 (1.85) 3.37 (1.88)
Errand 35 3.23 (1.56) 2.40 (1.59) 2.77 (1.50)
Chore 35 3.17 (1.84) 2.86 (1.82) 2.94 (1.89)
Appointment 18 4.44 (1.38) 2.89 (1.57) 3.61 (1.54)
Forgot to finish 11 2.82 (1.17) 3.00 (1.61) 3.27 (1.62)
Forgot to start 15 3.00 (1.51) 2.20 (1.57) 2.53 (1.85)
Retrospective memory 181 3.19 (1.80) 2.47 (1.62) 2.25 (1.63)
Where something was put 108 3.13 (1.73) 2.63 (1.58) 2.25 (1.54)
Important information 20 4.95 (2.11) 4.00 (2.20) 4.20 (2.33)
Personal date 10 3.90 (1.97) 2.70 (1.42) 3.00 (1.89)
Someone’s name 69 2.58 (1.40) 1.83 (1.20) 1.70 (0.98)
Directions 11 3.46 (1.92) 3.27 (1.95) 2.55 (1.70)
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Note: Total number of occasions = 996.

Hypothesis 2: Older adults would report more distress using direct measures of the impact of daily memory lapses

Older age significantly predicted lower ratings for all of the negative impact indicators. Figures 1 and 2 present the model estimated means for each follow up rating for younger, middle-aged, and older adults. For ratings of irritation, older adults reported less irritation in response to lapses compared with younger adults (b = −0.039, SE = 0.012, p = .001; 95% CI: −0.062 to −0.016). There was no significant interaction with lapse type; age differences were consistent across the two categories of lapses (p = .515). Interference ratings were also significantly lower for older adults compared with younger (b = −0.035, SE = 0.012, p = .003; 95% CI: −0.057 to −0.0123), and the age by type of lapse interaction was not significant (p = .562). Finally, for ratings of future consequences, older age was significantly related to lower expectations of consequences (b = −0.032, SE = 0.011, p = .005; 95% CI: −0.054 to −0.010) and there was no significant age by lapse type interaction (p = .182).

Figure 1.

Figure 1.

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Impact ratings for prospective memory lapses for younger (~age 30), middle-age (~age 50), and older (~age 65) adults.

Figure 2.

Figure 2.

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Impact ratings for retrospective memory lapses for younger (~age 30), middle-age (~age 50), and older (~age 65) adults.

Indirect Impact of Daily Memory Lapses on Daily Affect

To determine the effects of daily memory lapses on daily affect, we examined whether evening reports of negative and positive affect differed on days with and without memory lapses (Table 3, Model 1). PM lapses were significantly related to greater daily negative affect (b = 1.241, SE = 0.281, p < .001) although RM lapses were not (b = 0.245, SE = 0.269, p = .363) with the Pseudo-R2 = .022. With respect to positive affect, again only PM lapses were related to lower daily ratings (RM: b = −0.262, SE = 0.239, p = .274; PM: b = −0.801, SE = 0.250, p < .001; Pseudo-R2 = .010).

Table 3.

Multilevel Models Examining Daily Affect

Positive affect Negative affect
Model 1 Model 2 Model 1 Model 2
b (SE) b (SE) b (SE) b (SE)
Intercept 18.084 (0.296)* 18.049 (0.297)* 7.965 (0.296)* 7.982 (0.295)*
RM lapse −0.262 (0.239) −0.459 (0.261) 0.245 (0.269) 0.472 (0.293)
PM lapse −0.801 (0.25)* −0.704 (0.254)* 1.241 (0.281)* 1.171 (0.286)*
Age 0.048 (0.018)* 0.04 (0.019)* −0.061 (0.018)* −0.057 (0.019)*
RM × Age — 0.03 (0.014)* — −0.028 (0.016)
PM × Age — 0.005 (0.016) — 0.012 (0.018)
Employment status (ref = employed) −0.29 (0.608) −0.258 (0.609) 1.184 (0.604) 1.164 (0.602)
Depression −0.079 (0.031)* −0.08 (0.031)* 0.131 (0.031)* 0.132 (0.031)*
Ethnicity (ref = Caucasian) −0.735 (0.315)* −0.721 (0.316)* 0.886 (0.313)* 0.877 (0.312)
Education 0.21 (0.112) 0.205 (0.112) −0.034 (0.111) −0.028 (0.111)
Self-rated health 0.482 (0.335) 0.444 (0.336) 0.111 (0.333) 0.14 (0.332)
Stress exposure −0.481 (0.13)* −0.454 (0.131)* 1.182 (0.148)* 1.155 (0.149)*
Gender (ref = women) 0.004 (0.565) −0.022 (0.566) −0.431 (0.561) −0.408 (0.559)
Pseudo-R2 .006 .005 .022 .027
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Note: PM = Prospective memory; RM = Retrospective memory. All estimates reported as unstandardized bs. Model 1—main effects of memory lapses and age, Model 2—interactions of memory lapses and age.

*p < .05.

Hypothesis 3: Older adults would report more distress using indirect measures of the impact of daily memory lapses

To examine whether age moderated the effects of memory lapses on daily affect, the age by memory lapse interaction was added to models predicting evening negative and positive affect (see Table 3, Model 2). Age did not significantly moderate the effects of either type of memory lapses on daily negative affect (ps > .07). For daily positive affect, age did significantly moderate the effect of RM lapses with younger adults (compared to older adults) reporting higher positive affect on days when they also reported an RM lapse (younger MPM = 16.40, SE = 0.55; MNOPM = 17.38, SE = 0.42; older MPM = 18.78, SE = 0.48; MNOPM = 18.72, SE = 0.46). Age did not moderate the effect of PM lapses.

Supplemental analyses examining indirect impact using lagged effects of daily memory lapses on daily affect

Finally, we examined whether lagged effects existed for both types of memory lapses. That is, after controlling for concurrent memory lapses on dayt+1, is forgetting something on dayt related to affect on dayt+1? There was one significant lagged effect; a PM lapse on a given day was significantly related to an increase in next day negative affect (b = 0.599, SE = 0.305, p = .049). The effect of a current PM lapse remained relatively unchanged (b = 1.235, SE = 0.341, p < .001). The lagged effect for RM lapses was not significant for negative affect (b = 0.283, SE = 0.289, p = .327). Pseudo-R2 for the additional lagged effects was .006. Neither type of event significantly predicted positive affect (ps > .30; Pseudo-R2 = .005). Age did not moderate any of lagged effects (ps > .064).

Discussion

The current analyses examined the frequency and impact of daily memory lapses in a community-dwelling lifespan sample. We found that older adults reported more daily memory lapses, particularly RM lapses, compared to younger adults. In contrast to expectations, older adults reported less direct impact of daily memory lapses; they were less likely to report experiencing irritation, interference with their daily life, or future negative consequences related to a memory lapse. Similarly, older adults’ daily affect (both positive and negative) was not as affected by daily memory lapses compared to younger adults. Although retrospective lapses were more common, PM lapses were more strongly associated with both same day and next day affect regardless of age. We discuss each of these results in more detail below.

Frequency of Memory Lapses

We found that memory lapses related to previously learned information (i.e., RM) were more frequent in daily life relative to memory lapses related to completing an intended action (i.e., PM). Previous work has theorized that daily PM lapses would be more common relative to daily RM lapses due to the higher frequency of intentions throughout the day (McDaniel & Einstein, 2007).

Frequency of memory lapses increased with age in the current sample. However, other daily diary work has failed to find significant age effects. One explanation for this discrepancy is the greater number of possible events reported in the current study. Older adults reported more RM lapses which contributed to a greater frequency of memory lapses overall. Another possibility is the assessment model that included separation of event reports from their impact on daily experiences. Older adults may have felt more comfortable reporting a problem with their memory when they were also able to indicate that the memory lapse did not significantly impair other areas of their lives, consistent with research that suggests that loss of independence due to cognitive impairment is a significant worry for older adults (Reese & Cherry, 2004).

Impact of Daily Memory Lapses

RM and PM lapses did not differ in their ratings of irritation and interference. However, in support of previous theorizing on the importance of PM lapses (McDaniel & Einstein, 2007), these lapses were rated as higher in consequences compared to RM lapses for both younger and older adults. This suggests that, although these lapses may occur less frequently, they are viewed as having effects beyond the immediate experience of the event.

Both types of memory lapses were related to poorer daily affect even after accounting for other daily stressors. Specifically, prospective lapses were related to higher negative affect and lower positive affect whereas RM lapses were related to lower positive affect. These findings suggest that memory lapses represent a unique source of daily hassle that contribute to variations in daily affect. Previous work has shown that estimations of daily reactivity to events (e.g., higher negative affect in response to stressors) predicts physical and mental health 10 years later (Charles et al., 2013; Piazza, Charles, Sliwinski, Mogle, & Almeida, 2013). Memory lapses may represent an additional, typically unmeasured, source of events that cause daily emotional reactivity. Older adults were less affected by daily RM lapses compared with younger adults, which may reflect better emotional regulation in response to memory lapses in this age group (Charles, Mogle, Urban, & Almeida, 2016). An alternative explanation is that memory lapses represent normative events for older adults and therefore do not hold the same significance for this age group compared with younger adults (Diehl et al., 2014).

Exploring the mechanism by which memory lapses impair daily affect is a promising next step in understanding how SM impairment precedes serious impairments in affect such as clinical depression and anxiety (Hill et al., 2016). Affective (i.e., depressive and anxiety) symptoms are known to co-occur with reports of memory problems, and may result from negative reactions to the experience of SM impairment, such as depressive symptoms precipitated by concerns about memory decline (Kaup et al., 2016; Lugtenburg, Zuidersma, Oude Voshaar, & Schoevers, 2015). The Subjective Cognitive Decline Initiative (SCD-I) Working Group, convened to develop a common research concept for SM and other aspects to cognition, acknowledges the numerous potential causes of self-perceptions of cognitive impairment, including psychiatric disorders (Jessen et al., 2014). Although SM impairment is frequently accompanied by subthreshold depressive and anxiety symptoms, the temporality of these symptoms is as of yet unclear. Our findings support an association between reported memory lapses and affect at the daily level, which could contribute to a cumulative effect of affective symptom load over time. As this was only for younger adults in our sample; however, it is important to consider whether individual difference factors moderate this association in older adults.

Limitations and Future Directions

This study had several limitations. Our sample ranged in age from 20 to 80 years, and participants aged 65 or older only comprised 16% of that group. Thus, our ability to detect age differences is limited in this sample and it is critical to extend this work in larger community samples of older adults to determine the validity of current analyses in these age groups. Assessing SM has important implications for older adults who may be experiencing actual, although subtle, cognitive decline, including associations with psychological distress and poor self-rated health (Mewton, Sachdev, Anderson, Sunderland, & Andrews, 2014). In addition, although reports of memory lapses were made on a daily basis, drastically decreasing recall bias, these reports still involved a retrospective assessment across an entire day. Further complicating recall bias is that it may operate differently for younger, middle, and older adults. Lifestyle differences, the meaning ascribed to memory lapses, and other factors potentially play a role in whether and how memory lapses are recalled.

Future research should expand reports of daily memory lapses using open-ended reports or other methods for capturing more events throughout the day. Additionally, including measures of life routine or busyness (e.g., the Martin and Park Environmental Demands questionnaire) would strengthen future work in this area (Martin & Park, 2003). Finally, the current work was not experimental in nature and so other pathways may explain how memory lapses impacted daily affect. For example, not being able to find one’s keys or to take out the trash may have led to an argument with a significant other, and the argument in turn elevated negative affect. An important next step in this area is identifying the temporal relationships among memory lapses and changes in daily affect including the proximity of memory lapses and impairments in affect.

Conclusion

Daily memory lapses represent another method for understanding SM and the impact of cognitive functioning on daily experiences. Using sophisticated and intensive data collection methods, measures of SM can be expanded to assess the direct effects of memory lapses on an individual’s affect and whether this changes across the lifespan. Finding an effect of memory lapses on affect is an important step toward clarifying the relationship of SM and important clinical outcomes such as depression in older adults. Although documenting the experience of memory lapses is important, a better understanding of their emotional and functional impact in daily life will allow researchers to target interventions for those events most likely to lead to negative consequences. This represents a first step toward daily assessments that consider how forgetting impacts individuals in daily life.

Acknowledgments

J. Mogle, J. M. Smyth, and M. J. Sliwinski acknowledge support from the National Institute on Aging grant number R01-AG039409.

References

  1. Almeida D. M. Wethington E., & Kessler R. C (2002). The daily inventory of stressful events: An interview-based approach for measuring daily stressors. Assessment, 9, 41–55. doi:10.1177/1073191102009001006. [DOI] [PubMed] [Google Scholar]
  2. Burmester B. Leathem J., & Merrick P (2015). Assessing subjective memory complaints: A comparison of spontaneous reports and structured questionnaire methods. International Psychogeriatrics, 27, 61–77. doi:10.1017/S1041610214001161 [DOI] [PubMed] [Google Scholar]
  3. Cavanaugh J. C. Feldman J. M., & Hertzog C (1998). Memory beliefs as social cognition: A reconceptualization of what memory questionnaires assess. Review of General Psychology, 2, 48. doi:10.1037//1089-2680.2.1.48. [Google Scholar]
  4. Charles S. T. Mogle J. Urban E. J., & Almeida D. M (2016). Daily events are important for age differences in mean and duration for negative affect but not positive affect. Psychology and Aging, 31, 661–671. doi:10.1037/pag0000118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Charles S. T. Piazza J. R. Mogle J. Sliwinski M. J., & Almeida D. M (2013). The wear and tear of daily stressors on mental health. Psychological Science, 24, 733–741. doi:10.1177/0956797612462222 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Craik F. I. (1994). Memory changes in normal aging. Current Directions in Psychological Science, 3, 155–158. doi:10.1111/1467-8721.ep10770653. [Google Scholar]
  7. Cranford J. A. Shrout P. E. Iida M. Rafaeli E. Yip T., & Bolger N (2006). A procedure for evaluating sensitivity to within-person change: Can mood measures in diary studies detect change reliably?Personality & Social Psychology Bulletin, 32, 917–929. doi:10.1177/0146167206287721 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Diehl M. Wahl H. W. Barrett A. E. Brothers A. F. Miche M. Montepare J. M.,…Wurm S (2014). Awareness of aging: Theoretical considerations on an emerging concept. Developmental Review: DR, 34, 93–113. doi:10.1016/j.dr.2014.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Goldberg L. R. Johnson J. A. Eber H. W. Hogan R. Ashton M. C. Cloninger C. R., & Gough H. G (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40, 84–96. doi:10.1016/j.jrp.2005.08.007. [Google Scholar]
  10. Hahn E. A., & Lachman M. E (2015). Everyday experiences of memory problems and control: The adaptive role of selective optimization with compensation in the context of memory decline. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 22, 25–41. doi:10.1080/13825585.2014.888391 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hill N. L. Mogle J. Wion R. Munoz E. DePasquale N. Yevchak A. M., & Parisi J. M (2016). Subjective cognitive impairment and affective symptoms: A systematic review. The Gerontologist, 56, e109–e127. doi:10.1093/geront/gnw091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Hox J. J. Moerbeek M., & van de Schoot R (2010). Multilevel analysis: Techniques and applications. New York, NY: Routledge. [Google Scholar]
  13. Jessen F. Amariglio R. E. van Boxtel M. Breteler M. Ceccaldi M. Chételat G.,…Wagner M.; Subjective Cognitive Decline Initiative (SCD-I) Working Group. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 10, 844–852. doi:10.1016/j.jalz.2014.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kaup A. R. Byers A. L. Falvey C. Simonsick E. M. Satterfield S. Ayonayon H. N.,…Yaffe K (2016). Trajectories of depressive symptoms in older adults and risk of dementia. JAMA Psychiatry, 73, 525–531. doi:10.1001/jamapsychiatry.2016.0004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Lugtenburg A. Zuidersma M. Oude Voshaar R. C., & Schoevers R. A (2016). Symptom dimensions of depression and 3-year incidence of dementia: Results from the Amsterdam study of the elderly. Journal of Geriatric Psychiatry and Neurology, 29, 99–107. doi:10.1177/0891988715606235 [DOI] [PubMed] [Google Scholar]
  16. Martin M., Park D. (2003). The Martin and Park environmental demands (MPED) questionnaire: Psychometric properties of a brief instrument to measure self-reported environmental demands. Aging Clinical and Experimental Research, 15, 77–82. doi:10.1007/bf03324483. [DOI] [PubMed] [Google Scholar]
  17. McDaniel M. A., & Einstein G. O (2007). Prospective memory: An overview and synthesis of an emerging field: An overview and synthesis of an emerging field. Thousand Oaks, CA: Sage Publications. doi:doi.org/10.4135/9781452225913. [Google Scholar]
  18. McHorney C. A. Ware J. E. Jr., & Raczek A. E (1993). The MOS 36-Item Short-Form Health Survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Medical Care, 31, 247–263. doi:10.1097/00005650-199303000-00006. [DOI] [PubMed] [Google Scholar]
  19. Mewton L. Sachdev P. Anderson T. Sunderland M., & Andrews G (2014). Demographic, clinical, and lifestyle correlates of subjective memory complaints in the Australian population. The American Journal of Geriatric Psychiatry: official journal of the American Association for Geriatric Psychiatry, 22, 1222–1232. doi:10.1016/j.jagp.2013.04.004 [DOI] [PubMed] [Google Scholar]
  20. Mitchell A. J. (2008). The clinical significance of subjective memory complaints in the diagnosis of mild cognitive impairment and dementia: A meta-analysis. International Journal of Geriatric Psychiatry, 23, 1191–1202. doi:10.1002/gps.2053 [DOI] [PubMed] [Google Scholar]
  21. Neupert S. D. Almeida D. M. Mroczek D. K., & Spiro A. III (2006). Daily stressors and memory failures in a naturalistic setting: Findings from the VA Normative Aging Study. Psychology and Aging, 21, 424–429. doi:10.1037/0882-7974.21.2.424 [DOI] [PubMed] [Google Scholar]
  22. Neupert S. Almeida D. Mroczek D., & Spiro A (2006b). The effects of the Columbia shuttle disaster on the daily lives of older adults: findings from the VA Normative Aging Study. Aging & Mental Health, 10, 272–281. doi:10.1080/13607860500409682. [DOI] [PubMed] [Google Scholar]
  23. Neupert S. D. Mroczek D. K., & Spiro A (2008). Neuroticism moderates the daily relation between stressors and memory failures. Psychology and Aging, 23, 287–296. doi:10.1037/0882-7974.23.2.287 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ossher L. Flegal K. E., & Lustig C (2013). Everyday memory errors in older adults. Neuropsychology, Development, and Cognition. Section B, Aging, Neuropsychology and Cognition, 20, 220–242. doi:10.1080/13825585.2012.690365 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Piazza J. R. Charles S. T. Sliwinski M. J. Mogle J., & Almeida D. M (2013). Affective reactivity to daily stressors and long-term risk of reporting a chronic physical health condition. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine, 45, 110–120. doi:10.1007/s12160-012-9423-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Rabbitt P., & Abson V (1990). ‘Lost and found’: Some logical and methodological limitations of self-report questionnaires as tools to study cognitive ageing. British Journal of Psychology (London, England: 1953), 81, 1–16. doi: 10.1111/j.2044-8295.1990.tb02342.x. [DOI] [PubMed] [Google Scholar]
  27. Rabin L. A. Wang C. Katz M. J. Derby C. A. Buschke H., & Lipton R. B (2012). Predicting Alzheimer’s disease: Neuropsychological tests, self-reports, and informant reports of cognitive difficulties. Journal of the American Geriatrics Society, 60, 1128–1134. doi:10.1111/j.1532-5415.2012.03956.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Radloff L. S. (1977). The CES-D scale a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401. doi:10.1177/014662167700100306. [Google Scholar]
  29. Reese C. M., & Cherry K. E (2004). Practical memory concerns in adulthood. International Journal of Aging & Human Development, 59, 235–253. doi:10.2190/8pa5-8kvb-emnq-bfky. [DOI] [PubMed] [Google Scholar]
  30. Reid L. M., & Maclullich A. M (2006). Subjective memory complaints and cognitive impairment in older people. Dementia and Geriatric Cognitive Disorders, 22, 471–485. doi:10.1159/000096295 [DOI] [PubMed] [Google Scholar]
  31. SAS Institute. (2014). Base SAS 9.4 procedures guide. Cary, NC: Author. [Google Scholar]
  32. Schneider S. Stone A. A. Schwartz J. E., & Broderick J. E (2011). Peak and end effects in patients’ daily recall of pain and fatigue: A within-subjects analysis. The Journal of Pain: Official Journal of the American Pain Society, 12, 228–235. doi:10.1016/j.jpain.2010.07.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Schwarz N. (1999). Self-reports: How the questions shape the answers. American Psychologist, 54, 93. doi:10.1037//0003-066x.54.2.93. [Google Scholar]
  34. Singer J. D., & Willett J. B (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York, NY: Oxford University Press. doi:10.1093/acprof:oso/9780195152968.001.0001. [Google Scholar]
  35. Smyth J. M., & Stone A. A (2003). Ecological momentary assessment research in behavioral medicine. Journal of Happiness Studies, 4, 35–52. doi:10.1023/a:1023657221954. [Google Scholar]
  36. Watson D., & Clark L. A (1999). The PANAS-X: Manual for the positive and negative affect schedule-expanded form. [Google Scholar]
  37. Whitbourne S. B. Neupert S. D., & Lachman M. E (2008). Daily physical activity: Relation to everyday memory in adulthood. Journal of Applied Gerontology: The Official Journal of the Southern Gerontological Society, 27, 331–349. doi:10.1177/0733464807312175 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Yamanaka A. (2003). Relations of mood states with types of typical cognitive failure in every life: A diary study. Psychological Reports, 92, 153–160. doi:10.2466/pr0.2003.92.1.153 [DOI] [PubMed] [Google Scholar]

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