Future Time Perspective and Awareness of Age-Related Change: Examining their Role in Predicting Psychological Well-Being

Abstract

This study examined how two distinct facets of perceived personal lifetime – future time perspective (FTP) and awareness of age-related change (AARC) – are associated with one another, and how they may interact to predict psychological well-being. To better understand associations among subjective perceptions of lifetime, aging and well-being, we tested a series of models to investigate questions of directionality, indirect effects, and conditional processes among FTP, AARC-Gains, AARC-Losses, and psychological well-being. In all models, we tested for differences between middle-aged and older adults, and between adults from the U.S. and Germany. Analyses were conducted within a structural equation modeling framework on a cross-national, 2.5-year longitudinal sample of 537 community-residing adults (age 40–98 years). Awareness of age-related losses (AARC-Losses) at Time 1 predicted FTP at Time 2, but FTP did not predict AARC-Gains or AARC-Losses. Furthermore, future time perspective mediated the association between AARC-Losses and well-being. Moderation analyses revealed a buffering effect of awareness of age-related gains (AARC-Gains) in which perceptions of more age-related gains diminished the negative effect of a limited future time perspective on well-being. Effects were robust across age groups and countries. Taken together, these findings suggest that perceived age-related loss experiences may sensitize individuals to perceive a more limited future lifetime which may then lead to lower psychological well-being. In contrast, perceived age-related gains may function as a resource to preserve psychological well-being, in particular when time is perceived as running out.

Relying on chronological age to understand developmental processes has been criticized because it reduces the multidimensional meaning of time and aging to just one constitutive dimension which neglects “the personal experiences of aging as living in time” (Baars, 2010, p. 374). Instead, psychological aging research has acknowledged the importance of subjective representations of lifetime and aging (e.g. Birren & Cunningham, 1985), and their utility in predicting developmental outcomes over and above chronological age (Kotter-Grühn, Kleinspehn-Ammerlahn, Gerstorf, & Smith, 2009; Westerhof et al., 2014). Based on evidence suggesting that multiple time scales are needed to understand human development and phenomena of psychological aging (Notthoff & Gerstorf, 2015; Ram, Gerstorf, Fauth, Zarit, & Malmberg, 2010), this paper focuses on individuals’ subjective understanding of lifetime and aging or, as Baars (2010) put it, the “personal dimension of time” (p. 367) during the second half of life. Both the reflections of philosophers and life-span developmental psychologists about the human experience of time suggest that subjective lifetime is inseparably tied to perceptions of the past and the future that together motivate human action and decision making in the present (Baars, 1997; Brandtstädter & Rothermund, 2003; Lewin, 1951). Therefore, we argue that perceptions of lifetime and perceptions of personal aging together shape the daily lives of individuals and have meaningful implications for developmental functioning.

Associations between Future Time Perspective and Awareness of Age-Related Change

For the present study, we examine subjective perceptions of lifetime using the construct of Future Time Perspective (FTP), as conceptualized by Lang and Carstensen (1996). FTP captures the extent to which an individual views the future in a positive or negative light. Specifically, FTP conceptualizes remaining personal lifetime on a continuum ranging from an “expansive” view in which the future is open and full of opportunities, to a “limited” view in which time is running out. A vast body of research has shown that FTP is related to social-emotional processes, such as social partner preference and the optimization of positive affect (Carstensen, 2006; Carstensen, Isaacowitz, & Charles, 1999).

To represent a person’s subjective perceptions of aging, we use the construct awareness of age-related change (AARC). AARC is concerned with a person’s self-perceptions and resulting awareness that behaviors and experiences have changed due to having grown older (Diehl & Wahl, 2010). AARC captures both positive and negative age-related changes across five behavioral domains, representing two latent dimensions: Perceived positive age-related changes (AARC-Gains) and perceived negative age-related changes (AARC-Losses; Brothers, Miche, Wahl, & Diehl, 2015). Because adults’ self-perceived age-related gains and losses are assessed with an explicit reference to their chronological age, the dimensions of AARC-Gains and AARC-Losses represent another facet of perceived personal lifetime and, hence, complement future time perspective.

We build upon the understanding that FTP is a fundamental motivational force for human goal strivings (Carstensen, 2006; Carstensen & Fredrickson, 1998; Lang & Carstensen, 2002) by examining associations between FTP with AARC-Gains and AARC-Losses. Theoretical reasoning suggests a connection between perceptions of lifetime and aging, given that future plans and decisions are not made independently of the self-perceived gains and losses experienced in the past or the present (Brandtstädter & Rothermund, 2003). Furthermore, it is plausible to expect that the same events that remind individuals of moving closer to the end of their lives also serve as cues for having grown older. As Carstensen et al. (1999) put it, “The monitoring of time occurs regularly at an unconscious level and is also primed acutely on a periodic basis by discrete events that mark time, such as a child’s wedding or a friend’s death” (p.168).

With regard to the specific nature of the association between FTP and AARC, we expected that a more expansive FTP would be associated with a greater awareness of age-related gains and that a less expansive FTP would be associated with a greater awareness of age-related losses. However, the empirical evidence to date provides no support yet regarding the directionality of such a relationship: On the one hand, there are reasons to suggest that AARC may precede FTP. Supporting this argument, Weiss and Lang (2009) showed in an experiment that an older age identity (one form of subjective aging) predicted a less expansive future time perspective. The cognitive processes underlying this effect may involve that perceived age-related losses are expected to persist in the future, hence restricting an individuals’ expectations and perceptions with regard to developmental opportunities in the future. On the other hand, FTP can also be conceived as a precursor of AARC-Gains and AARC-Losses, such that a sense that time is running out might trigger a greater awareness of age-related changes. An upcoming birthday or increasing health problems may serve as subtle reminders that the future is not infinite, and thus trigger awareness that changes in one’s life are linked to getting older. This would imply that a less expansive future time perspective may increase a person’s awareness for age-related losses. At the same time, perceiving one’s future as limited may also trigger socio-emotional processes that prompt individuals to focus on positive experiences and might hence lead to a more positive appreciation of age-related change. Starting from what is known to date about subjective perceptions of lifetime and aging, this paper conceptually and empirically links FTP with AARC in order to examine their associations with one another as well as their role regarding psychological well-being.

Future Time Perspective and Awareness of Age-Related Change as Predictors of Psychological Well-Being

The socioemotional selectivity theory (Carstensen et al., 1999) was the first life-span developmental theory to consider the central role of FTP with regard to well-being in later life, and several empirical studies have investigated the proposed theoretical link. For instance, Demiray and Bluck (2014) found that a less expansive FTP in young and middle-aged adults predicted lower psychological well-being. Similarly, Kotter-Grühn and Smith (2011) showed that, among participants age 70–104 from the Berlin Aging Study, a shortening in future time perception predicted a decrease in well-being at the next time point. A more expansive FTP has also been shown to predict higher well-being in the workplace, in terms of work-related aspirations and engagement (Kooij, de Lange, Jansen, & Dikkers, 2013). Taken together, the available evidence suggests a positive association such that a less expansive FTP tends to be associated with lower well-being, whereas a more expansive FTP is associated with greater psychological well-being. Furthermore, FTP seems to function as an antecedent of well-being in later life, rather than a consequence (Coudin & Lima, 2011; Hoppmann, Infurna, Ram, & Gerstorf, 2015). The body of research on FTP and well-being is paralleled by a growing number of studies which show that the subjective experience of aging is also a correlate of well-being. In particular, positive experiences of aging have been shown to predict higher levels of well-being (e.g., Mock & Eibach, 2011; Westerhof & Barrett, 2005). And recent work has also found that AARC, namely greater awareness of age-related gains and lower awareness of age-related losses, is able to predict well-being (e.g., Brothers et al., 2015).

Whereas previous work regarding FTP and subjective aging has drawn largely from a hedonic approach to well-being, focusing simply on the presence of positive affect or the absence of depressive symptoms or classic life satisfaction ratings (for an exception, see Demiray & Bluck, 2014), we instead selected a multifaceted conceptualization of well-being that addresses essential features of life reflection and what it means to “be well” (Ryff, 2014). Adopting this eudaimonic approach, we operationalized well-being by using the Scales of Psychological Well-Being (SPWB; Ryff, 1989). Rooted in life-span developmental and humanistic conceptions of well-being, the SPWB assesses six different dimensions: Autonomy, Environmental Mastery, Personal Growth, Purpose in Life, Positive Relations with Others, and Self-Acceptance. The domains have been studied extensively in the empirical literature and have been consistently shown to represent unique, yet related, facets of well-being (Ryff, 1989; Ryff & Keyes, 1995).

Although there is sufficient evidence to suggest that FTP, AARC, and psychological well-being are conceptually related, there have been no empirical investigations so far to illuminate the nature of the associations among these constructs. We have therefore identified several specific unanswered questions regarding the interplay of this set of constructs. As noted above, the directionality between FTP and AARC remains unexplored, although theoretical considerations suggest that FTP might be regarded both as a predictor as well as an outcome of AARC-Gains and AARC-Losses.

Another unresolved question is the nature of the relationship, such as indirect associations (mediating effects) or interactions (moderating effects) between FTP and AARC as they relate to psychological well-being. For instance, an indirect pathway by which a more limited FTP leads to lower psychological well-being via the perception of more AARC-Losses would be important to understand so that such a cascade of events could be interrupted in order to preserve well-being in later life. Furthermore, it is not yet known if and to what extent FTP and AARC may interact to influence well-being. For instance, based on the literature showing that positive views on aging act as a psychological resource throughout adulthood (Levy, Slade, Kunkel, & Kasl, 2002; Sargent-Cox, Anstey, & Luszcz, 2014), one might expect a buffering effect of AARC-Gains such that a more limited FTP exerts less of a negative effect on well-being in the presence of more perceived age-related gains.

Further, taking a broader perspective, it is also unknown if and to what extent the described associations may differ as a function of chronological age. From a developmental perspective, we regard it as important to study the role of chronological age as it may influence the associations among FTP, AARC-Gains, AARC-Losses, and well-being. Specifically, some first experiences of aging start to surface already in midlife and may lead to large variation in attitudes toward and experiences of the aging process (Lachman, 2004; Levy, 2009; Miche, Elsässer, Schilling, & Wahl, 2014). Furthermore, a reframing of lifetime typically happens in midlife. In particular, at this point in the life course, individuals increasingly conceive time as time left to live instead of time since birth (Neugarten, 1979). Hence, both FTP and AARC may assume their predictive role for personal development in midlife. However, there is no evidence to date regarding how the associations among subjective perceptions of FTP, AARC and well-being may differ as a function of chronological age.

Finally, we were also in a position to explore if and to what extent the associations among FTP, AARC, and well-being might differ between two Western countries. Future time perspective, subjective aging and well-being have been studied in many parts of the world. Across cultures, perceptions of lifetime and aging both play a fundamental role in shaping developmental motivations and outcomes (Barak, 2009; Gehlen, 1988; Löckenhoff et al., 2009). With regard to the U.S. and Germany, questions about aging perceptions and well-being (but not FTP) have been addressed, showing overall more similarities than differences (Staudinger, 2015; Westerhof, Barrett, & Steverink, 2003). Thus, we extended the existing research by also exploring the associations of FTP with perceptions of aging and well-being in a cross-national comparison.

The Present Study

Given these yet unanswered questions, the primary goal of this study was to investigate specific predictive associations, pathways, and conditional processes among FTP, AARC, and psychological well-being. In particular, the study had three primary aims.

• Study Aim 1: To investigate the directionality of the relationship between FTP and AARC by testing two competing pathways. The first pathway tested the assumption that AARC-Gains and AARC-Losses predict FTP. Conversely, the second analysis tested the pathway that FTP predicts AARC-Gains and AARC-Losses.

• Study Aim 2: To examine two plausible mediating pathways in order to uncover indirect effects among the variables of interest Specifically, we examined a) that fewer AARC-Gains and more AARC-Losses predict a more limited FTP which, in turn, is associated with lower well-being; and b) that a limited FTP predicts more AARC-Losses and fewer AARC-Gains, which, in turn, predicts lower well-being.

• Study Aim 3: To test the role of AARC-Gains and AARC-Losses as potential moderators between FTP and well-being to find out to what extent the effect of FTP on well-being might be dependent on AARC.

As secondary aims, we examined the extent to which the above associations differed by age group and country. That is, we tested Aims 1 – 3 across middle-aged and older adults, as well as between U.S. and German participants. We expected that the fundamental associations would be similar and robust across age groups and across countries.

Methods

Participants and Procedures

Participants were individuals for whom two waves of data were available from a survey about the subjective experience of aging. The total sample at Time 1 was comprised of 819 community-residing adults, ages 40 – 98 (M = 64.13 years, SD = 12.85 years), 396 of whom were from the U.S. and 423 from Germany. Participants were recruited by posting study announcements in public locations and by word of mouth. Participants’ primary language (i.e., English or German).

Upon enrollment into the study, participants completed a one-time self-report questionnaire packet that included different measures of subjective aging, future time perspective health- and well-being related questionnaires, and measures of personality. The order of the questionnaires was counter-balanced such that half of the respondents completed a version in which the AARC questionnaire was answered before the FTP scale; the other half of the respondents answered the FTP scale before the AARC questionnaire. Answering the questionnaire package took approximately 1 to 1½ hours. All participants provided informed consent as required by institutional policies at the respective universities.

Two and a half years later, participants were contacted again and asked to complete a similar set of questionnaires. Reminders were given two to four weeks after the questionnaires were sent out. Longitudinal data were available from 537 participants (U.S. n = 182; Germany n = 353). The response rate was 46% in the U.S. sample and 84% in the German sample. We attribute the large discrepancy in response rate to differences in administration procedures (e.g., reminder calls were made in Germany whereas only invitation letters were mailed in the U.S.; small compensation was provided in Germany, and the chance for a raffle drawing was provided in the U.S.). Despite the difference in response rate, we found that reasons for drop-outwere similar in each country. Drop-out analyses revealed that those participants who took part at both waves of the data collection were more likely to be women, χ²(1) = 7.35, p < .01, and better educated, χ² (2) = 11.90, p < .01, than those who dropped out. There were no differences in terms of age, t(817) = 0.87, marital status, χ² (4) = 8.72, employment status, χ² (1) = 3.44, or self-rated health, t(816) = 1.17, all p’s > .05. With regard to the main study variables, no significant differences between returners and non-returners were found with regard to AARC-Gains, t(817) = 0.97, AARC-Losses, t(817) = 0.16, or psychological well-being, t(817) = 1.10, all p’s > .05. However, returnees reported a slightly more limited future time perspective, M = 37.4, SD = 12.7, compared to non-returnees, M = 42.2, SD = 14.1, t(808) = 4.99, p < .001; the effect size of this difference was in the small to medium range (d = .37). Table 1 displays the demographic characteristics for the longitudinal sample as a whole, as well as by age group and country. In general, study participants reported above-average education and income.

Table 1.

Demographic Variables of the Longitudinal Sample, Reported by Age Group and Country

	< 65 years (n = 288) M (SD) or %	≥ 65 years (n = 249) M (SD) or %	U.S. (n = 182) M (SD) or %	Germany (n = 355) M (SD) or %	Full Sample (N = 537) M (SD) or %
Age at Time 1a,b	55.42 (6.56)	74.81 (7.79)	69.08 (11.94)	62.02 (11.38)	64.41 (12.04)
Sexa,b
Women	68.4%	57.4%	59.9%	65.1%	63.3%
Marital statusa,b
Single	14.4 %	8.1%	7.7%	13.4%	11.5%
Married/partnered	68.1%	51.0%	58.0%	61.3%	60.2%
Separated/divorced	14.7%	20.6%	19.3%	16.5%	17.5%
Widowed	2.8%	20.2%	14.9%	8.8%	16.5%
Retirement statusa,b	21.5% retired	87.3% retired	65.2% retired	44.3% retired	51.7% retired
Educationb
Low	14.9%	20.9%	17.6%	17.7%	17.7%
Medium	43.1%	30.9%	41.2%	35.5%	37.4%
High	42.0%	48.2%	41.2 %	46.8%	44.9%
Median annual household income	-	-	$70,000 – $79,000	30,000€ – 36,000€	-
Self-rated health at Time 2a (1 = poor, 5 = excellent)	3.08 (0.96)	3.01 (0.96)	2.90 (1.20)	3.13 (0.80)	3.05 (0.96)

Notes. Sample means or percentages differ at p < .05 between ^acountries, ^bage groups. Coding procedures for low, medium, and high education levels are provided in the Measures section.

Measures

To ensure equivalence of measurement across countries, the questionnaire packet was formatted identically in the U.S. and in Germany. In the German sample, we measured FTP and psychological well-being with existing German versions that have been used successfully in the literature (Carstensen & Lang, 1996; Staudinger, Lopez, & Baltes, 1997). AARC was assessed with a questionnaire that was developed in parallel in the U.S. and Germany. In this process, items were generated, tested, and refined in multiple samples that included both U.S. and German participants. All AARC items underwent a rigorous translation/back-translation that included three bilingual individuals who each carried out independent translations (Brothers, Gabrian, Wahl, & Diehl, 2016).

Future Time Perspective

We assessed FTP in terms of a unidimensional construct with the measure developed by Carstensen and Lang (1996). This 10-item questionnaire is the most widely used measure of FTP. Each item describes expectations of opportunities and limitations with regard to a person’s future life. An example item is “Many opportunities await me in the future.” Respondents rated items on a scale from 1 (very untrue) to 7 (very true). Answers for negatively-phrased items were reversed and all items were summed so that higher scores represented a more expansive FTP. For the structural equation analyses, we created observed indicators of FTP using a parceling approach. This approach ensured that items with similar means, standard deviations, and factor loadings were distributed equally across parcels (Little, Cunningham, Shahar, & Widaman, 2002).

Awareness of Age-Related Change

AARC was measured with a 50-item questionnaire (Brothers et al., 2016). Items cover conceptual content across five behavioral domains (i.e. Health and Physical Functioning; Cognitive Functioning; Interpersonal Relations; Social-Emotional/Social-Cognitive Functioning; Lifestyle and Engagement). The item stem “With my increasing age, I notice that…” preceded either an age-related gain or age-related loss statement. Example items include: “…my physical ability is not what it used to be” (Health and Physical Functioning – Loss); “…I am slower in my thinking” (Cognitive Functioning – Loss); “…I appreciate relationships and people much more” (Interpersonal Relations – Gain); “… I have a better sense of what is important to me” (Social-Emotional/Social-Cognitive – Gain); “…I have more time for the things I enjoy” (Lifestyle and Engagement – Gain). The response options ranged from 1 (not at all) to 5 (very much). The questions were distributed throughout the questionnaire in a pre-selected pattern of alternating domains and valences. Results of a counterbalancing approach in the present sample showed that response tendencies for AARC-Gains and AARC-Losses did neither differ depending on the order of the items in the questionnaire nor depending on the position of the AARC questionnaire within the questionnaire package as a whole. Sum scores for AARC-Gains and AARC-Losses were computed for the analyses reported in this paper, in accordance with the 2-factor structure supported by previous psychometric work (Diehl et al., 2013). For latent modeling, the domain sum scores served as the observed indicators for the AARC-Gains and AARC-Losses factors.

Psychological Well-Being

A short form of the SPWB (Ryff, 1989) was used to assess the six domains of eudaimonic well-being. The SPWB-SF Scale comprises 43 items, such as “In general, I feel confident and positive about myself.” Respondents rated each item on a scale ranging from 1 (strongly agree) to 6 (strongly disagree). Negatively-phrased items were reversed and sum scores were calculated for each subscale so that higher scores indicated greater well-being. Reliabilities of the scales were satisfactory. Scale reliabilities were satisfactory for all constructs. Reliability coefficients (i.e., Cronbach’s α) are reported in the main diagonal of Table 2. For the structural equation analyses, the six well-being scales were used as the observed indicators of a single latent factor representing overall psychological well-being, similar to what Springer and Hauser (2006) did in their study.

Table 2.

Bivariate Correlations between the Primary Variables at Time 1 and Time 2 for the Full Sample (N = 537)

Variable Name	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.
1. Future Time Perspective (T1)	.91
2. Future Time Perspective (T2)	.75***	.90
3. AARC-Gains (T1)	.10*	.06	.92
4. AARC-Gains (T2)	.09*	.09	.68***	.88
5. AARC-Losses (T1)	−.49***	−.46***	.26***	.21***	.92
6. AARC-Losses (T2)	−.39***	−.45***	.13**	.26***	.72***	.86
7. SPWB (T2 Sum Score)	.38***	.48***	.26***	.22***	−.36***	−.45***	.85
8. Chronological Age (T1)	−.32***	−.23***	.15***	.05	.22***	.18***	.13**	---
9. Education level (T1)	.03	.05	−.07	−.14**	−.10*	−.10**	.09*	−.08	---
10. Self-rated health (T2)	.02	.08	−.07	−.12**	−.20***	−.21***	.12	−.002	.05	---

Notes. AARC = Awareness of Age-Related Change; SPWB = Scales for Psychological Well-Being. Cronbach’s α for all scales are reported in the diagonal

Control Variables

Education and self-rated health were used as control variables in all analyses in order to take into account potentially important differences that have been shown in subjective aging research (Barrett, 2003). To examine age-group differences between middle-aged and older adults, we split the sample into two halves using the age of 65 as cut-off. Age 65 was selected as a societally accepted chronological indicator at which point individuals have traditionally reached retirement age and transitioned into a different developmental period.

We used a standard Personal Data Form, which asked for basic demographic information including age, gender, marital status, employment status, education, and income. To allow for comparable classifications across cultures, education was classified into three categories based on the highest degree that was received: low education (U.S.: high school degree or equivalent or below; GER: secondary school or below); medium education (U.S.: Associate’s or Bachelor’s degree; GER: vocational training or associate’s degree); and high education (U.S.: Master’s, Doctorate, Medical/Dental, or Law degree; GER: academic degree). Similar to other large-scale surveys (Sargent-Cox, Anstey, & Luszcz, 2010), self-rated health was assessed with a single-item question: “Compared to other people my age, I believe my health is…” Responses were provided on a 5-point scale ranging from 1 (poor) to 5 (excellent).

Statistical Approach

Bivariate associations, means, and standard deviations were calculated using SPSS 22.0. Mediation and moderation models were tested in a structural equation modeling framework using Version 7 of MPlus. The measurement model included seven latent variables, all observed indicators, error terms, and the factor covariance between AARC-Gains and AARC-Losses. Based on inspection of modification indices, model fit was further optimized if the suggested modifications made substantive sense to arrive at a baseline measurement model.

Measurement invariance

The measurement model was tested across age group, country, and measurement occasion using a step-wise approach in which increasing constraints were imposed on factor loadings, manifest variable intercepts, and residuals, in order to test the assumptions of weak, strong, and strict invariance, respectively (Horn & McArdle, 1992). If full invariance did not hold, partial invariance was tested by again relaxing the respective constraints (Byrne, Shavelson, & Muthén, 1989). Because a change in Comparative Fit Index (CFI) is more powerful in detecting lack of invariance in large sample sizes (Meade, Johnson, & Braddy, 2008), a cut-off value of ≤ .002 change in CFI was applied to evaluate progressively stricter models; a change in CFI of .002 or greater indicated a significant worsening of model fit (Cheung & Rensvold 2002). We used the minimum requirement to establish (partial) weak measurement invariance in order to proceed with structural equation modeling (Horn & McArdle, 1992).

Structural models

Study Aims 1 and 2 were addressed simultaneously in one model in which we tested for causal effects between future time perspective and awareness of age-related change (Study Aim 1) as well as their role as predictors and mediators of psychological well-being (Study Aim 2). Structural paths included three autocorrelations between FTP, AARC-Gains, and AARC-Losses, six cross-lagged effects between Time 1 predictors and Time 2 mediators, and six direct effects of the Time 1 predictors and Time 2 mediators on psychological well-being (see Figure 1). Time 2 mediators and the outcome were controlled for education and self-rated health. Using a multi-group model approach, the structural paths were first unconstrained between age groups or countries, and were then set equal in order to test if mediation effects differed by age group or country. A significant change in chi-square from the unconstrained to the constrained model indicated a significantly poorer model fit. Mediation effects were tested through bias-corrected bootstrapping using 1,000 random bootstrap samples, and mediation was determined to exist if the 95% confidence interval of the indirect effect did not include zero (Hayes & Scharkow, 2013).

Figure 1.

Structural equation model examining the causal relationship between future time perspective (FTP) and awareness of age-related change (AARC) and their indirect effects on the Scales of Psychological Well-Being (SPWB). Correlations among the residuals, predictors, and mediators were omitted in the figure for reasons of parsimony. FTP1 – FTP3 = item parcels to indicate future time perspective; PP – LN = AARC-Gains and Losses sum scores for the five AARC domains: Health and physical functioning, cognitive functioning, interpersonal relations, social-emotional/social-cognitive functioning, lifestyle and engagement. AARC abbreviations indicate valence as follows: PP = physical positive; PN = physical negative, etc. AUT = Autonomy; EM = Environmental Mastery; PG = Personal Growth; PUR = Purpose in Life; PR = Positive Relations with Others; SA = Self-Acceptance. e = manifest variable residuals.

To investigate Study Aim 3, the moderating role of AARC was tested using a latent variable interaction model in which psychological well-being was regressed on the main effects of FTP, AARC-Gains, and AARC-Losses, and the FTP×AARC-Gains and FTP×AARC-Losses interactions (Little, Card, Bovaird, Preacher, & Crandall, 2007). We tested the moderation model separately by age group and country. AIC and BIC indices were used for model comparisons, in which a model without the interaction terms was compared to a model with the interaction terms (Muthén & Asparouhov, 2015). Lower AIC and BIC terms indicate a better model fit as follows: 0–2 points = weak improvement; 2–6 points = positive improvement; 6–10 points = strong improvement; ≥10 = very strong improvement (Singer & Willett, 2003). With regard to interpretation, the interaction term represents the predicted difference in the effect of future time perspective on psychological well-being for each 1-unit increase in AARC. Specifically, a positive (negative) interaction term would indicate that the effect of future time perspective on psychological well-being becomes more (less) pronounced as more age-related gains or losses are perceived.

Results

Descriptive Statistics and Evaluation of Measurement Invariance

Prior to addressing Study Aims 1–3, we examined descriptive statistics for the primary study variables. Means and standard deviations of the primary variables are reported separately by age group and country in Table 3. With regard to age-group differences, older participants reported a more limited future time perspective, as well as more perceived age-related gains and losses. Older adults reported greater well-being for three of the six dimensions. With regard to country differences, a general pattern emerged such that the U.S. participants responded more positively than German participants on the primary variables; that is, they reported a significantly more open sense of the future, more perceived age-related gains, and higher psychological well-being (all p’s < .05). There was no significant difference in the number of perceived age-related losses reported between the two countries. The mean differences on primary variables between age groups and between countries were in the range of small to medium effect sizes (d = .20–.50; Cohen, 1988).

Table 3.

Means and Standard Deviations for Key Variables by Age Group and Country

Variable Name (possible range)	< 65 years (n = 288)	≥ 65 years (n = 249)	U.S. (n = 182)	German (n = 355)	Full Sample (N = 537)
	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)
Time 1
Future Time Perspective (10–70)	40.54 (11.62)b	33.63 (12.85)b	42.15 (13.32)b	34.87 (11.57)b	37.37 (12.67)
AARC-Gains (25–125)	77.70 (17.14)b	83.14 (15.93)b	85.67 (15.84)b	77.43 (16.61)b	80.22 (16.80)
AARC-Losses (25–125)	53.18 (15.05)b	58.49 (14.76)b	55.35 (14.75)	55.79 (15.35)	55.64 (15.14)
Time 2
Future Time Perspective (10–70)	39.01 (11.63)b	34.56 (12.73)b	41.65 (12.77)b	34.54 (11.41)b	36.95 (12.35)
AARC-Gains (25–125)	74.07 (17.76)a	76.33 (18.99)a	81.06 (18.67)b	72.15 (17.48)b	75.11 (18.35)
AARC-Losses (25–125)	50.31 (14.66)a	54.29 (15.18)a	52.20 (15.34)	52.12 (14.87)	52.15 (15.02)
SPWB-Autonomy (7–42)	30.15 (5.20)b	32.18 (4.86)b	33.08 (4.80)b	30.06 (5.01)b	31.08 (5.14)
SPWB-Environmental Mastery (7–42)	32.36 (5.76)a	34.04 (5.24)a	34.55 (5.95)b	32.41 (5.24)b	33.14 (5.58)
SPWB-Personal Growth (7–42)	33.30 (4.99)	33.47 (5.02)	35.20 (4.90)b	32.43 (4.79)b	33.38 (5.00)
SPWB-Purpose in Life (8–48)	36.50 (5.48)	37.15 (5.76)	38.43 (6.20)b	35.95 (5.10)b	36.80 (5.62)
SPWB-Positive Relations (7–42)	32.76 (5.12)	32.95 (5.75)	34.57 (5.58)b	31.96 (5.10)b	32.85 (5.41)
SPWB-Self-Acceptance (7–42)	31.98 (5.96)a	33.37(5.41)a	33.75 (6.16)a	32.03 (5.44)a	32.62 (5.75)

Notes.

^aSample means between country or between age group differ at p < .05 with small effect size (d = .20) or with

^bmedium effect size (d = .50).

FTP = Future Time Perspective; AARC = Awareness of Age-Related Change; SPWB = Scales for Psychological Well-Being

Correlations among the key variables at both measurement occasions are reported in Table 2. Overall, the pattern of correlations between the predictors and the outcome variable was in the expected direction, such that a more expansive FTP, more AARC-Gains, and fewer AARC-Losses were all associated with more positive well-being. FTP was also associated with AARC in the expected direction; that is, a more open sense of the future was associated with the awareness of fewer age-related losses and more age-related gains. AARC and FTP at Time 1 and Time 2 showed a high degree of rank-order stability (r’s > .68, p < .001).

Next, we tested for measurement invariance in order to proceed with comparisons between age groups, countries, and measurement occasions. The measurement model that was first fitted to the full sample did not yield satisfactory model fit, CFI = .862, TLI = .845, RMSEA = .075, SRMR = .056. However, based on inspection of modification indices, we added correlated error terms between the matching latent factor indicators at T1 and T2 (e.g. AARC-Gains at T1 and AARC-Gains at T2). This revised baseline measurement model fit the data well, CFI = .947, TLI = .938, RMSEA = .048, SRMR = .049, and was selected as the final measurement model to be used as the basis for measurement invariance testing.

Measurement invariance testing across age groups met criteria for weak, ΔCFI = .002, and partial strong invariance, ΔCFI = .002. Partial strong invariance was met once the following parameters were allowed to differ between age groups: Manifest variable means of AARC-Gains and AARC-Losses in the domains of ‘social-cognitive and social-emotional functioning’ and ‘lifestyle and engagement’ both at Time 1 and Time 2, and one parcel of future time perspective at Time 2. The highest form of measurement invariance that was achieved across countries was partial weak invariance with all loadings constrained equal across the German and U.S. sample except for one of the future time perspective parcels at Time 1. This model did not result in a loss of fit, ΔCFI = .002, compared to the baseline unconstrained multi-group model by country. Measurement invariance testing across measurement occasions provided evidence for weak, ΔCFI = .001, and partial strong invariance, ΔCFI = .001, with equality constraints being relaxed for all AARG-Gains subdomains and four of the AARC-Losses subdomains (i.e., Health and Physical Functioning; Cognitive Functioning; Interpersonal Relations; Lifestyle and Engagement).

Study Aim 1: Directionality of Effects

The structural model was used to test the directionality of effects between AARC and FTP (Study Aim 1) as well as their role as a mediator in the prediction of well-being (Study Aim 2). To investigate Study Aim 1, we examined the autocorrelations and cross-lagged paths of the structural model. All three autocorrelations (cf. Table 4) were found to be significant with standardized effects ranging from .69 (AARC-Gains) to .78 (AARC-Losses), thus suggesting relative temporal stability of the latent constructs. In terms of the directionality of the association between AARC and FTP, the cross-lagged effects (cf. Table 4) suggested a predictive effect of AARC-Losses on FTP, with the perception of more age-related losses being associated with a more limited perception of the future. In terms of the reverse order, FTP did not significantly predict perceptions of age-related gains or losses across the 2.5-year study period.

Table 4.

Structural Equation Model Results for the Final Mediation Model for the Full Sample

				95% Confidence Intervals
T1	T2	T2	Estimate	Lower	Upper
Autocorrelations
FTP	→ FTP		.67	.59	.75
AARC-Gains	→ AARC-Gains		.77	.64	.89
AARC-Losses	→ AARC-Losses		.80	.68	.93
Cross-Lagged Effects
FTP	→ AARC-Gains		.14	−.08	.37
FTP	→ AARC-Losses		−.03	−.31	.25
AARC-Gains	→ FTP		.02	−.01	.05
AARC-Gains	→ AARC-Losses		−.10	−.19	−.002
AARC-Losses	→ FTP		−.05	−.08	−.02
AARC-Losses	→ AARC-Gains		.03	−.06	.13
Direct Effects
FTP		→ SPWB	−.26	−.53	.02
AARC-Gains		→ SPWB	.18	.06	.32
AARC-Losses		→ SPWB	−.06	−.19	.06
	FTP	→ SPWB	.64	.34	.95
	AARC-Gains	→ SPWB	.15	.06	.28
	AARC-Losses	→ SPWB	−.32	−.47	−.19
Indirect Effects
FTP	→ AARC-Gains	→ SPWB	.02	−.003	.08
FTP	→ AARC-Losses	→ SPWB	.01	−.08	.10
AARC-Gains	→ FTP	→ SPWB	.01	−.008	.04
AARC-Gains	→ AARC-Losses	→ SPWB	.03	.01	.07
AARC-Losses	→ FTP	→ SPWB	−.03	−.06	−.01
AARC-Losses	→ AARC-Gains	→ SPWB	.005	−.007	.03
Control Variables
SRH (T2)	→ FTP		.06	−.02	.13
SRH (T2)	→ AARC-Gains		−.17	−.42	.09
SRH (T2)	→ AARC-Losses		−.28	−.49	−.05
SRH (T2)		→ SPWB	−.16	−.34	.02
EDU	→ FTP		.007	−.08	.09
EDU	→ AARC-Gains		−.39	−.68	−.15
EDU	→ AARC-Losses		−.18	−.46	.12
EDU		→ SPWB	.26	.04	.52
R2
	FTP		.64
	AARC-Gains		.52
	AARC-Losses		.59
		SPWB	.50

Notes. Unstandardized path coefficients are reported. Significant effects are printed in bold. FTP = Future Time Perspective; AARC = Awareness of Age-Related Change; SPWB = Scales for Psychological Well-Being

Study Aim 2: Examining Mediation Pathways

With regard to Study Aim 2, we first examined the individual structural paths of interest and then tested for the indirect effects of the mediating pathways that remained statistically significant (Table 4). The Time 2 measurements of FTP, AARC-Gains and AARC-Losses were all significant predictors of well-being in our model. Longitudinally, however, only AARC-Gains at Time 1 exerted a significant direct effect on well-being at Time 2. Based on the fact that FTP at Time 1 predicted neither AARC nor SPWB at Time 2, the hypothesized mediating pathway b (FTP → AARC → SPWB) was not supported. Therefore, the only plausible mediation pathways were from AARC-Losses → FTP → SPWB (hypothesized mediating pathway a) and from AARC-Gains → AARC-Losses → SPWB (not a hypothesized pathway).

Examining the indirect effects, we found evidence for the mediating role of FTP on the relationship between AARC-Losses and psychological well-being (AARC-Losses → FTP → well-being, Table 4). Path coefficients estimated from the full sample yielded an acceptable model fit, CFI = .938, TLI = .928, RMSEA = .055, SRMR = .055. Thus, our mediation model provided partial support for the hypothesis that aging perceptions influenced psychological well-being via perceptions of future time, as the finding was only present for AARC-Losses but not for AARC-Gains. The nature of the associations was such that higher perceived age-related losses may influence a more limited perception of the future, which then predicts lower levels of psychological well-being.

Study Aim 3: Evaluating the Moderating Role of AARC

In Study Aim 3, we tested the hypothesized role of the AARC dimensions as potential moderators between FTP and psychological well-being. Significant main effects were found in the full sample for FTP, AARC-Gains, and AARC-Losses. Furthermore, as hypothesized, the FTP x AARC-Gains interaction was significant. However, the interaction term involving FTP and AARC-Losses was not statistically significant. The parameters for the structural paths and the model fit statistics are reported in Table 5. The negative FTP x AARC-Gains interaction term indicated that for individuals reporting higher levels of positive age-related changes, there was an attenuated effect of limited future time perspective on psychological well-being. This confirmed the hypothesized “buffering effect”: Although a limited time perspective, in general, predicted lower psychological well-being, this relationship was offset in the presence of more perceived positive age-related changes. Analyses were conducted separately by country and by age-group. Again, a consistent pattern emerged in which AARC-Gains moderated the association between FTP and well-being (Table 5).

Table 5.

Structural Equation Model Results for the Moderation Analyses to Predict Well-Being, by Age Group and Country

	< 65	≥ 65	U.S.	German	Full Sample
	B (se)	B (se)	B (SE)	B (se)	B (se)
FTP (Time 1)	.50 (.18)**	.24 (.09)**	.16 (.09)	.02 (.15)	.23 (.09)*
AARC-Gains (Time 2)	.28 (.07)***	.25 (.06)***	.17 (.06)**	.35 (.07)***	.29 (.05)***
AARC-Losses (Time 2)	−.43 (.09)***	−.35 (.09)***	−.21 (.08)**	−.56 (.10)***	−.41 (.06)***
FTP x AARC-Gains	−.12 (.06)*	−.06 (.02)**	−.08 (.03)**	−.14 (.07)*	−.10 (.03)**
FTP x AARC-Losses	.04 (.04)	.02 (.03)	−.028 (.02)	.01 (.05)	.03 (.03)
Model Fit (AIC)
Model without interaction terms					48798.70
Model with interaction terms added					48787.98
ΔAIC					10.72
Model Fit (BIC)
Model without interaction terms					49076.19
Model with interaction terms added					49074.01
ΔBIC					2.18

Notes.

^*<.05,

^**<.01,

^***<.001.

Unstandardized coefficients are reported. FTP = future time perspective; AARC = awareness of age-related change.

Secondary Study Aims

In order to test for differences between middle-aged and older adults and between the U.S. and German samples, we imposed equality constraints on the structural paths between groups. Compared to the unconstrained model, there was no worsening of model fit between age groups, Δ χ² (15) = 12.02, p = .68, or between countries, Δ χ² (15) = 22.44, p = .10. Therefore, it can be assumed that the effects of the structural model were equivalent between middle-aged and older participants as well as between German and U.S. adults.

Discussion

Humans have a tendency to interpret and reflect upon their personal lifetime, and this evaluative process of constructing and reconstructing the personal life course has important implications for well-being later in life (Frazier, Hooker, Johnson, & Kaus, 2000). In light of this tendency, this paper represents a first empirical examination of the association of future time perspective (FTP) and awareness of age-related change (AARC) with eudaimonic psychological well-being. The inclusion of a 2.5-year longitudinal dataset allowed us to evaluate rivaling assumptions about how FTP and AARC influence each other, while the inclusion of a sample of adults ranging in age from 40–98 years offered the ability to examine these patterns across a wide age range and across two measurement occasions. We developed a series of empirical models to test associations, indirect pathways, and conditional processes among these three variables of interest. In sum, better psychological well-being was associated with a more expansive view of the future, with more perceived positive age-related changes, and with fewer perceived negative age-related changes. Furthermore, these associations remained robust regardless of whether participants were younger or older than 65 years of age, or whether they were from the U.S. or Germany. Specific findings with regard to each of the study aims are summarized and discussed in the following paragraphs.

Directionality of Associations Among the Key Constructs

With regard to Study Aim 1, in which we examined the question of directionality of effects, AARC-Losses emerged as a precursor of FTP. That is, age-related loss experiences seemed to shape the way in which individuals perceived future opportunities. The issue of directionality between different conceptions of lifetime and aging is of particular importance for gaining a better understanding of how subjective time perceptions may influence motivational processes and human development. Our findings suggest that expectations and cognitions about the future may represent another route through which the perception of negative age-related chances influences developmental outcomes. Hence, if older adults associate primarily negative experiences with their own aging they may be less likely to make plans for the future and pursue these plans. The finding that FTP, AARC-Gains and AARC-Losses were all associated with well-being cross-sectionally was as expected. However, we also found that awareness of positive age-related changes was the only longitudinal predictor of well-being at Time 2. Moreover, a mediating pathway emerged, such that more AARC-Gains predicted fewer AARC-Losses leading to higher well-being. These findings suggest that preserving the ability to perceive aging also as a time for gains and growth will also serve to promote enhanced well-being for individuals throughout adulthood. Given rampant misconceptions in the popular culture that aging is a time of crisis, loss and decline, many individuals simply do not expect that positive age-related changes may be a possibility (Lindland, Fond, Haydon, & Kendall-Taylor, 2015).

FTP as a Mediator Between AARC and Psychological Well-Being

With regard to Study Aim 2, we found support for the mediating role of FTP on the association between AARC-Losses and psychological well-being. Specifically, we found that greater awareness of age-related losses was associated with a perception of a more limited future lifetime, which, in turn, was associated with lower psychological well-being. This finding suggests that loss-related experiences that underlie individuals’ awareness of age-related change may sensitize them with regard to perceiving a more limited future lifetime which may then lead to lower psychological well-being. This finding adds to a growing body of research on the consequences of future time perspective and well-being (e.g. Coudin & Lima, 2011; Demiray & Bluck, 2014; Kooij et al., 2013; Kotter-Grühn & Smith, 2011). In line with these studies, our results suggest that when lifetime is perceived as turning to an end, it becomes more difficult for individuals to maintain high levels of subjective well-being. However, awareness of age-related changes may be an important variable impacting on such a gain-loss balancing process. Specifically, the knowledge of AARC-Losses as a precursor of limited FTP illuminates another step of the pathway that can be utilized in the promotion or preservation of high psychological well-being in later life. Viewed in this way, AARC complements the concept of personal awareness of lifetime in the SST framework by adding the aspect that time awareness is informed, at least to some extent, by awareness of age-related changes (see also Diehl et al., 2014).

The Buffering Role of Awareness of Age-Related Change

With regard to Study Aim 3, a consistent pattern emerged across both age groups and countries in which AARC-Gains (but not AARC-Losses) served as a moderator between future time perspective and psychological well-being. That is, AARC-Gains attenuated the negative influence of a limited FTP on psychological well-being. Our study extends previous literature investigating the bivariate associations between FTP and psychological well-being (e.g. Hoppman et al., 2015) by illustrating a scenario in which the association becomes less severe. Evidence of this buffering effect suggests that the perception of age-related gains as measured by the AARC-Gains scale can serve as a protective factor which can help to offset the negative psychological effects that may otherwise emerge in the face of a perceived limited future. To this end, our finding complements previous research documenting the protective effects of positive perceptions of aging for various outcomes, including better physical functioning (Sargent-Cox, Anstey & Luszcz, 2012) and longer survival (Levy et al., 2002). This finding also provides first evidence to support theoretical ideas about the potential protective effects of positive AARC experiences for well-being put forth by Diehl and Wahl (2010). In this context, it is also important to note that perceived age-related gains and perceived age-related losses operate as conceptually distinct dimensions. This also supports the notion that multidimensional conceptualizations of subjective aging can enrich our knowledge base above and beyond the traditional unidimensional approaches.

Based on growing evidence of the protective role of positive perceptions of aging, efforts seem to be warranted to promote a more positive view of aging among the general public. To be clear, we are not advocating an unrealistically positive view of aging, but rather, advocate in favor of a more balanced way of viewing aging as a time for new opportunities and possible gains and growth, in addition to challenges and declines. Promoting more positive perceptions and expectations of aging in this way may help to counteract the prevailing negative age stereotypes, as individuals may then be less likely to subscribe to a completely negative and deterministic view of aging.

Examination of Findings by Age Group and Country

Because of the wide age range included in the study design, we were able to show that the documented time-related processes are not only relevant in very old age but also serve a meaningful function in middle-aged individuals. Although midlife is a somewhat neglected period of adult development (Lachman, 2015), these findings suggest that perceptions about time and aging begin to influence psychological well-being when they emerge on the ‘developmental agenda’ in midlife.

The empirical patterns were also examined and largely substantiated in a cross-cultural sample of two Western countries, providing further support for an overall robust connection of FTP and AARC with well-being in the U.S. and Germany. As would be expected, we found some slight differences between countries with regard to the mean response pattern. However, our expectation for an overall similar pattern of associations was supported by the data. For example, consistent with previous research (Westerhof & Barrett, 2005), we found that American individuals had an overall more optimistic response pattern on FTP, AARC, and well-being compared to Germans. Moreover, our results were in line with the finding that perceptions of aging are related to positive aspects of psychological well-being. However, country-specific differences may exist in the association between subjective aging and negative aspects of well-being, such as negative affect or indicators of mental health (Westerhof & Barrett, 2005).

Limitations and Future Directions

Although the present study makes several new contributions to the literature, the interpretation and generalizability of the findings require some caution. First, we have used the traditional conceptualization of FTP as a unidimensional construct, although more recent research supports the idea that open and limited future may be two distinct dimensions that function mostly independently (Cate & John, 2007). In addition, work by Brothers, Chui and Diehl (2014) showed that a third dimension (i.e, future as being ambiguous) adds further utility with regard to assessing future time perspective in a more comprehensive way. Thus, future work will need to address if the analyses and findings presented here also hold when multiple dimensions of FTP are considered. For example it is possible that an expansive FTP is more closely associated with AARC-Gains, whereas limitations of FTP are more closely connected to AARC-Losses.

Second, the dichotomous grouping of young-old compared to old-old has some limitations. We acknowledge that social conventions have changed somewhat in recent years, especially in the U.S., such that the retirement age has become less stringently tied to the age of 65. However, for the purposes of a meaningful dichotomy, this dividing point is well-established in the literature and in cultural/societal norms as a relevant distinction—at least for the generations studied in the current sample. Future work will be needed to further examine the role of age – as well as the role of historical context and generation – with regard to subjective experiences of time, aging, and well-being.

Conclusion

Examining the combined effects of FTP and AARC represents an extension of previous approaches, and provides insight into how these constructs are related to psychological well-being. A more detailed understanding of the role of different facets of perceptions of lifetime has been advocated for quite some time across multiple disciplines (Baars, 2010; Gehlen, 1988; Nuttin, 1964). In order to advance the understanding regarding the fundamental role of time for human well-being across the life span, it seems promising to establish additional theoretical and empirical connections among different scales that tap into the subjective perception of lifetime and aging (Notthoff & Gerstorf, 2015; Ram & Diehl, 2015). We expect that the study of human development will benefit substantially from attempts to strengthen the connections between the ongoing discussion of time in gerontology and geropsychology (Baars, 2010; Gehlen, 1988) with subjective aging research (Diehl & Wahl, 2015). In a sense, the empirical research is “catching up” to key theoretical ideas that have been put forth for more than half a century.