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Time attitudes profile stability and transitions: An exploratory study of adolescent health behaviours among high school students
Journal of Adolescence 69 (2018) 44–51
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Journal of Adolescence journal homepage: www.elsevier.com/locate/adolescence
Time attitudes profile stability and transitions: An exploratory study on adolescent health behaviours among high school students
T
Michael T. McKaya,∗, James R. Andrettab, Jon C. Colea, Svenja Konowalczykc, Kevin E. Wellsd, Frank C. Worrelle a
Department of Psychological Sciences, University of Liverpool, UK Child Guidance Clinic, Superior Court of the District of Columbia, USA c University of Dortmund, Germany d Baylor University, USA e University of California, Berkeley, USA b
A R T IC LE I N F O
ABS TRA CT
Keywords: Time attitudes Mover-Stayer model Physical exercise Smoking Cannabis
Purpose: Time attitudes refer to individuals' feelings about the past, present, and future, and an increasing number of cross-sectional studies have demonstrated that positive time attitudes are significantly related to better health and well-being. We investigated time attitude profile membership and associated transitions longitudinally in United Kingdom-based adolescents, and assessed the relationship between time attitude profile development on health behaviours at + 21 months after the data collection involving time attitudes. Methods: Participants were high school students (N = 1306; 41.8% female, Mage 12.5–14.5 years). The Adolescent and Adult Time Inventory – Time Attitudes Scale was employed to identify profiles, and a mover-stayer latent transition analysis was employed to examine developmental changes. Data were also gathered on sensation seeking, and a range of health indicators were assessed: Past week frequency of physical exercise, self-rated health, subjective life expectancy, lifetime cannabis and smoking, and dental attendance. Results: Staying in a positive time attitude profile was related to higher subjective life expectancy, and less frequent use of cannabis and cigarettes (1.00 ≤ d ≤ 4.00). Further, moving to a positive profile predicted healthier outcomes for most health measures used. Conclusions: Notwithstanding the limitation that health outcomes in the present study were distal, the present study bolstered a developing cross-sectional literature supporting the association between positive time attitudes and better health and well-being outcomes. Future longitudinal studies which assess measures concurrently are required.
Research into the ways in which an individual's time perspective influences health behaviours has grown considerably in recent years. Across studies, time perspective has been accounted for using both broad and narrowed methods. Zimbardo and Boyd (1999) assessed time perspective with the Zimbardo Time Perspective Inventory (ZTPI) using cognitive, affective, and behavioural items on five dimensions: (a) past negative, (b) past positive, (c) present hedonistic, (d) present fatalistic, and (e) future. Consideration of Future Consequences (CFC; Strathman, Gleicher, Boninger, & Edwards, 1994) is another widely-used construct which assesses both the degree to which individuals consider the future implications of current behaviour, and the degree to which that consideration influences present behaviour. The CFC scale (Strathman et al., 1994) also includes cognitive and behavioural items. Third, temporal ∗
Corresponding author. Department of Psychological Sciences, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK. E-mail address: [email protected] (M.T. McKay).
https://doi.org/10.1016/j.adolescence.2018.09.002 Received 28 March 2018; Received in revised form 31 July 2018; Accepted 8 September 2018 0140-1971/ © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
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focus concerns cognitions and describes the extent to which people characteristically devote their attention to perceptions of the past, present, and future (Bluedorn, 2002). Last, and the focus of the present study, time attitudes are the affective dimension of time perspective: The emotional and evaluative feelings toward the three time periods (Andretta, Worrell, Mello, Dixson, & Baik, 2013). With the exception of the CFC scale, all of these assess various aspects of temporal psychology (cognitive, affective, behavioural, and indeed, in some cases [ZTPI] an amalgam of all three) with reference to the past, present, and future. The CFC scale assesses consideration of only the present and future. However, while it might seem intuitive to believe that the aforementioned time perspective constructs measure temporal psychology similarly, a recent study (Authors, blinded) in University students reported only one moderate-sized correlation between scores on the combined 16 factors of the four scales. In terms of the broad time perspective literature, studies have demonstrated that a variety of temporal constructs are significantly related to health outcomes. Time perspective as conceptualised in the ZTPI (Zimbardo & Boyd, 1999) has been shown to be significantly related to cannabis use (Apostolidis, Fieulaine, Simonin, & Rolland, 2006; Fieulaine, 2017) and the frequency of physical exercise (Griva, Tseferidi, & Anagnostopoulos, 2015; Guthrie, Butler, Lessl, Ochi, & Ward, 2014). The extant literature in terms of smoking is more complex. Whereas some have reported no association between time perspective (assessed by the ZTPI) and smoking (e.g., Griva et al., 2015; Guthrie et al., 2014), Pozolotina and Olsen (2018) reported one significant but small correlation between ZTPI present fatalistic scores and smoking behaviour (r = 0.20); in this study, correlations between smoking and other ZTPI subscales were close to zero. Pozolotina and Olsenalos also reported statistically significant but small correlations between smoking and CFCimmediate (r = 0.17) and CFC-future (r = −0.10) scores. In a study on both smoking behaviour and frequency of physical exercise, Adams and Nettle (2009) reported that ZTPI future and CFC scores were significantly associated with current smoking status, and these results remained significant when socio-demographic factors were taken into account. Regarding frequency of physical exercise, only CFC was significantly associated with frequency of moderate intensity physical activity; however, this relationship did not remain significant after controlling for socio-demographic factors and five-factor personality constructs (Adams & Nettle, 2009). 1. Changes in time attitude profiles during early adolescence The present study was solely focused on time attitudes during early adolescence for several reasons. Beginning with age, early adolescence is marked by a surge in developmental changes. In fact, the development of synapses in the brain between the ages of 11 and 12 is so rapid that early adolescents have been shown to suffer affective setbacks (e.g., ability to recognize emotions in faces and words; McGirven, Andersen, Byrd, Mutter, & Reilly, 2002). Time attitudes have also been shown to change during early adolescence, where transitions to unfavourable profiles led to poor health behaviour outcomes (Morgan, Wells, Andretta, & Mckay, 2017). To provide an example, Morgan et al. (2017) reported that 12 and 13-year olds who transitioned from being relatively positive about all three time periods to being characterized by negative attitudes toward the future reported a concomitant increase in sensation seeking behaviour (d = 0.56). Turning to the narrowed scope on time attitudes, the construct was ideal because it is not encumbered by other constructs: Whereas the ZTPI uses an amalgam of cognitive, affective, and behavioural items, time attitudes are exclusively affective items. Further still, several studies using the Adolescent and Adult Time Inventory – Time Attitudes Scale (AATI-TA; Mello & Worrell, 2007) have yielded significant and meaningful associations between AATI-TA scores and alcohol-related behaviours and attitudes, subjective life expectancy (SLE), and psychiatric symptomatology (Authors, blinded I, II, III). Indeed, Knepple Carney and Patrick (2017), using the AATI-TA in an adult sample, reported that present positive, future positive, and future negative time attitudes were all significantly related to health intentions, albeit with small effect sizes. Although time attitudes might comprise just one dimension of time perspective, the construct is multivariate. In point of fact, individuals' have been shown hold attitudes towards the past, present, and future both simultaneously and to varying matters of degree (e.g., Andretta et al., 2013). For that reason, researchers have begun to account for heterogeneity in time attitudes using latent profile analysis (LPA), and the longitudinal extension of latent profile analysis, latent transition analysis (LTA; Morgan et al., 2017). LPA is one of many person-centered methods, where individual differences in time attitudes are appraised by grouping participants into categories based on both similarities and differences in attitudes towards the three time periods. Only after the identification of categories, or in this case time attitude profiles, are associations with covariates or distal outcomes assessed (Andretta et al., 2013). By contrast, in variable-centered analyses, such as correlation and regression, only the average associations between or among time attitudes and other constructs are captured in the sample. Across the extant person-centered studies, individuals with positive time attitude profiles have been found to have the best mental health and alcohol-related outcomes, with the reverse largely true for those with negative profiles (e.g., Andretta et al., 2013). However, and in keeping with the reality that adolescence is a period of intense biological, physical, and psychological change (e.g., Moksnes, Moljord, Espnes, & Byrne, 2010), previous longitudinal studies have reported relative instability in profile membership across time. For example, in a two-wave (+12 months) study, Authors (blinded) reported that 55.9% of the sample (n = 1100) were identified as movers, and 44.1% (n = 868) participants were identified as stayers. In an additional study using three waves of data (+24 months) based on a similar sample, Authors (blinded) reported that 91.2% (n = 1521) of participants were identified as movers, and only 8.8% (n = 146) of participants were identified as stayers (stayed in the same profile across all three waves). 2. The present study Because it remains unclear if this prospective relationship extends to a broader range of health behaviours, we examined the effect 45
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of time attitudes across the first three years of high school on self-rated health, frequency of physical exercise in the past week, subjective life expectancy (SLE), lifetime use of cannabis, lifetime smoking, and frequency of dental visits at a later point in time. These health behaviours have previously been examined in relation to other temporal constructs (e.g., time perspective using the ZTPI). We hypothesised that maintaining or transitioning to a positive time attitude profile would predict better health outcomes at +21 months after the collection of time attitude data. 3. Method 3.1. Participants Participants were 1306 school children (41.8% female) attending high schools in Northern Ireland (n = 992, 76.0%) and Scotland (n = 314, 24.0%). Data were collected in three waves (Wave 1: 12.5 years old, Wave 2: +12 months, and Wave 3: +24 months). Data on the health-related outcomes examined herein were gathered 21 months after Wave 3. Therefore, this study covered five years of adolescence. 3.2. Measures The AATI-TA (Mello & Worrell, 2007) is a 30-item instrument with six 5-item subscales assessing Past Positive, Past Negative, Present Positive, Present Negative, Future Positive, and Future Negative attitudes. A 5-point Likert-type scale with verbal and numerical anchors from 1(totally disagree) to 5 (totally agree) was used. Scores for each of the six subscales were computed by summing the responses to the five items and dividing by five. Evidence for the psychometric properties of AATI-TA scores has been shown in studies of adolescent participants in several countries, including the United Kingdom (e.g., Worrell, McKay, & Andretta, 2018; Worrell, Mello, & Buhl, 2013). Time attitudes data were gathered in Waves 1–3. Data on sensation seeking were also gathered as part of a broader study and used here as a covariate. Sensation seeking was measured using the four-item Brief Sensation Seeking Scale (BSSS-4; Stephenson, Hoyle, Palmgreen, & Slater, 2003). Responses to the four items were given on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Sensation seeking scores were computed by summing the responses to the four items and dividing by five. Scores in the present study were found to be internally consistent (α = 0.79). Health-related information was gathered at Wave 4 (+45 months from Wave 1, +21 months from Wave 3). SLE was assessed using a single question concerning participants' subjective probability of expecting to live to age 75. Participants were asked, “On a scale of 0 to 100, where 0 equals no chance, and 100 equals definitely, how likely do you think that it is that you will live to be 75 years old?” Integer options of “5s” (0, 5, 10, 15, 20, etc.) were available between 0 and 100. Age 75 was chosen both because it has been used elsewhere (Adams & Nettle, 2009; Authors, blinded), and because it is slightly below the actual average life expectancy in both Scotland (77.1 and 81.1 years for males and females, respectively; National Records of Scotland, 2016) and Northern Ireland (78.1 and 82.4 years for males and females, respectively; Northern Ireland Statistics and Research Agency, 2015). Participants were asked to indicate if they had smoked a cigarette or used cannabis, with the following response options: Never (coded 0); Yes, over a year ago (1); Yes, in the past year (2); Yes, in the past month (3); and Yes, in the past week (4). Physical activity in the past week was assessed using the following question: “Over the past seven days, on how many days were you physically active for a total of at least 60 min?” Response options ranged from 0 (no days) through 7 (indicating physical activity on each day). Participants were further asked to rate their own health using a Likert-type response where Excellent = 5 and Poor = 1. Finally, participants were asked to indicate how frequently they had attended the dentist in the past year. Data were also gathered on gender, country (Northern Ireland versus Scotland), and free school meals entitlement (FSM; 19.0% yes), an imperfect proxy for socio-economic status (Hobbs & Vignoles, 2007). Descriptive data for the health outcomes are displayed in Table 1. 3.3. Procedure LPA is a procedure used to examine the number of underlying subgroups, or latent profiles, of respondents who share similar patterns of latent factor scores. The goal of LPA is to identify the optimal number of profiles such that respondents within the same latent profile are as similar as possible while respondents in different latent profiles are as different as possible. For each time point, a Table 1 Descriptive Data for Health Outcomes.
Past week frequency of Physical Exercise Self-Rated Health Subjective Probability of living to 75 years old Lifetime Smoking Lifetime Cannabis use Frequency of past year Dental attendance
Range
M
SD
0–7 1–5 0–100 0–4
3.28 3.43 71.36 0.66 0.28 2.22
2.07 0.95 19.45 1.27 0.88 1.23
0–5
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Table 2 Log Odds Coefficients with Demographics and Sensation Seeking as Covariates.
Profile Time 1a Positives
Negatives
Moderately-Negatives
Profile Time 2a Positives
Negatives
Moderately-Negatives
Profile Time 3a Positives
Negatives
Moderately-Negatives
Effect
Logit
S.E.
t
Odds Ratio
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1
0.354 −0.135 0.320 0.462 0.460 0.427 0.275 0.404 0.173 0.409 0.163 0.184
0.210 0.181 0.230 0.239 0.232 0.200 0.247 0.242 0.195 0.174 0.222 0.180
1.689 −0.744 1.389 1.933 1.982 2.118 1.112 1.666 0.887 2.348 0.735 1.020
1.425 0.874 1.377 0.630 1.584 1.533 1.317 1.498 1.189 1.505 1.177 1.202
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T2 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T2 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T2
−0.010 −0.476 0.247 0.027 0.731* 0.802 0.329 0.663 0.178 0.002 0.392 0.198
0.219 0.193 0.230 0.172 0.226 0.211 0.264 0.238 0.201 0.175 0.217 0.153
−0.044 −2.467 1.072 0.155 3.239 0.391 1.245 2.793 0.889 0.010 1.810 1.291
0.990 0.622 1.280 1.027 2.077 1.086 1.389 1.941 1.195 1.002 1.480 1.219
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T3 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T3 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T3
−0.196 −0.221 0.070 0.601 −0.090 0.306 0.381 0.661 −0.448 −0.096 0.403 0.110
0.242 0.186 0.251 0.212 0.265 0.228 0.273 0.230 0.207 0.168 0.205 0.163
−0.813 −1.185 0.281 2.833 −0.341 1.343 1.398 2.868 −2.163 −0.574 1.969 0.676
0.822 0.802 1.073 1.824 0.914 1.358 1.464 1.936 0.639 0.908 1.497 1.117
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1 Sensation Seeking T2 Sensation Seeking T3
−0.058 −0.024 2.119 1.262 1.004 0.551
0.490 0.368 2.312 0.726 0.461 0.509
−0.119 −0.065 0.916 1.739 2.176 1.083
0.943 0.976 8.320 3.532 2.729 1.736
Mover-Stayerb
Note. FSM = Free School Meals Entitlement; T1 = Time 1; T2 = Time 2; T3 = Time 3. *p ≤ .001. a Ambivalents are the Comparison Group for Profile Comparisons. b Stayers are the Comparison Group for the Mover-Stayer Comparisons.
series of LPA models was conducted using six latent factor scores from the CFA. Altogether, we examined LPA models with between two and seven profiles, using nine different specifications of the covariance matrix, which allowed for various degrees of model complexity (compare, Authors, 2016). Considering various fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted BIC (aBIC), Integrated Likelihood Criterion with Bayesian-type Approximation (ICL-BIC), LoMendell-Rubin Likelihood Ratio Test (LMR), and the Bootstrapped Likelihood Ratio Test (BLRT), as well as previous literature, the solution with four profiles fitted best. Based on the parametrization, the covariances between the past positive and negative, present positive and negative, and future positive and negative time attitudes were allowed to differ across latent profiles. In contrast to Authors (2016), variances for each of the six time attitude scores were not allowed to differ across latent profiles. Initially, 1667 participants responded to the AATI-TA at the first three data collection points, and confirmatory factor analyses (CFAs) supported the six-factor structure of AATI-TA scores. Latent profile analyses (LPA) as well as latent transition analyses (LTA) were conducted using the six factor scores from the CFAs to explore time attitude profiles at Waves 1–3 (see Authors, blinded). At Wave 4, 1306 responded to the distal health-related outcome variables, which were the focus of the present study. Thus, in a first step, a mover-stayer LTA was conducted to explore latent transition patterns of these participants across profiles and time. In a second
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Table 3 Correlations Among AATI-TA and Distal Outcome Variables. Subscale Time 1 Past Positive Past Negative Present Positive Present Negative Future Positive Future Negative Time 2 Past Positive Past Negative Present Positive Present Negative Future Positive Future Negative Time 3 Past Positive Past Negative Present Positive Present Negative Future Positive Future Negative
Exercise
Health
Live 75
Smoke
Cannabis
Dentist
0.071 −0.065 0.069 −0.034 0.066 −0.063
0.140 −0.108 0.138 −0.106 0.089 −0.100
0.149 −0.088 0.179 −0.134 0.106 −0.097
−0.122 0.133 −0.165 0.176 −0.073 0.086
−0.070 0.042 −0.074 0.066 −0.048 0.020
0.015 0.011 0.007 −0.005 0.025 0.020
0.105 −0.053 0.087 −0.063 0.100 −0.112
0.194 −0.171 0.190 −0.166 0.157 −0.164
0.171 −0.130 0.194 −0.160 0.183 −0.167
−0.191 0.195 −0.197 0.204 −0.136 0.157
−0.090 0.096 −0.094 0.084 −0.059 0.041
0.045 0.026 0.086 −0.051 0.093 −0.031
0.093 −0.073 0.126 −0.127 0.149 −0.132
0.215 −0.180 0.266 −0.249 0.233 −0.231
0.177 −0.147 0.217 −0.211 0.219 −0.214
−0.175 0.180 −0.171 0.181 −0.117 0.166
−0.097 0.072 −0.093 0.080 −0.090 0.089
0.021 0.023 −0.008 −0.019 0.034 −0.002
Note. AATI-TA = Adolescent and Adult Time Attitudes Scale; Exercise = physical exercise in the past week; Health = self-rated health; Live = subjective life expectancy; Smoke = lifetime use of cigarettes; Cannabis = lifetime use of cannabis, Dentist = frequency of dentist's visits.
step, time-invariant (i.e., gender, country, FSM) and time-variant (i.e., sensation seeking) covariates were controlled for and added to the model (Table 2). Lastly, health-related distal outcome variables were correlated to AATI-TA (Table 3) and included in the final model. As can be seen in Tables 2 and 3, the effects of the covariates were generally small, as were the bivariate correlations between time attitudes and health outcomes.
4. Results Fig. 1 shows the profile characteristics at Time 1, 2, and 3. Overall, 104 (8.0%) stayed in the Positives profile, 23 (1.8%) in the Negatives profile, 70 (5.4%) in the Moderately-Negatives profile, and 70 (5.4%) in the Ambivalents profile. Means and standard deviations for distal outcomes are shown in Table 1, and the detailed profile transition pattern across waves one to three is available in Supplementary Table S1. Further, Supplementary Table S2 illustrates that gender, FSM, and country had a negligible effect on
Fig. 1. Latent profile means and frequencies at Time 1, 2, and 3 (LTA). PaP = Past Positive; PaN = Past Negative; PrP = Present Positive; PrN = Present Negative; FuP = Future Positive; FuN = Future Negative. 48
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profile transition. 4.1. Distal outcomes Means of distal outcome variables were calculated for each profile at Time 4. To explore differences between each of the profiles on the distal outcome variables, several Wald chi-square tests of parameter equalities were conducted. The p value, the associated mean difference, and Cohen's d are provided below. Cohen's d was calculated by dividing the reported mean difference by the standard deviation of the respective distal outcome. Results were adjusted for sensation seeking, FSM, gender, and country. Staying in the Negative profile was associated with a higher frequency of physical exercise in the past week than staying in the Moderately-Negative (M = 3.12, p ≤ .001, d = 1.51) or Ambivalent (M = 2.36, p ≤ .01, d = 1.14) profiles. Further, moving to Positive profile across time was associated with more frequent past-week physical exercise than moving to the Ambivalent profile, but this effect was modest (M = 0.57, p ≤ .05, d = 0.28). Among stayers, there was no significant association across time with self-rated health. However, moving to Positive was associated with a significant increase in self-rated health compared to moving to Negative (M = 0.37, p < .01, d = 0.39), Moderately-Negative (M = 0.29, p < .001, d = 0.31), or Ambivalent (M = 0.30, p < .001, d = 0.32). Results indicated that for SLE, there were significant results for staying Positive across time compared to staying Negative (M = 27.40, p ≤ .05, d = 1.41), Moderately-Negative (M = 23.96, p ≤ .05, d = 1.23), or Ambivalent (M = 19.50, p ≤ .05, d = 1.00). For movers, transitioning to Positive was associated with higher SLE compared to transitioning to Negative (M = 5.17, p ≤ .05, d = 0.27) or Moderately-Negative (M = 5.88, p ≤ .001, d = 0.30). Also, moving to Moderately-Negative was associated with a lower SLE than moving to Ambivalent (M = −3.37, p ≤ .05, d = −0.17). Staying Positive was associated with a lower likelihood of ever having smoked compared to staying Negative (M = −2.83, p ≤ .001, d = −2.22), Moderately-Negative (M = −3.10, p < .001, d = −2.44), or Ambivalent (M = −3.04, p ≤ .001, d = −2.39). Additionally, moving to Positive (compared to Negative) was associated with a significantly lower likelihood of lifetime smoking (M = −0.62, p ≤ .01, d = −0.49), whereas moving to Negative was associated with an increased likelihood of lifetime smoking compared to Moderately-Negative (M = 0.49, p ≤ .01, d = 0.38) and Ambivalent (M = 0.63, p ≤ .001, d = 0.49). There were multiple significant results for lifetime cannabis use. Staying Positive was associated with a lower likelihood of ever having used cannabis compared to staying Negative (M = −3.52, p ≤ .001, d = −4.00), Moderately-Negative (M = −2.71, p ≤ .001, d = −3.08), or Ambivalent (M = −3.43, p < .001, d = −3.90). Further, staying Negative was associated with an increased likelihood of lifetime cannabis use compared to staying Moderately-Negative (M = 0.81, p ≤ .05, d = 0.92), and staying Moderately-Negative was associated with less lifetime cannabis use compared to Ambivalent (M = −0.72, p ≤ .001, d = −0.82). Moving to Positive was associated with an increased likelihood of lifetime cannabis use than moving to Moderately-Negative (M = 0.16, p < .01, d = 0.18) or Ambivalent (M = 0.15, p < .05, d = 0.17). Also, moving to Negative was associated with an increased likelihood of lifetime cannabis use than moving to Moderately-Negative (M = 0.20, p < .05, d = 0.22) or Ambivalent (M = 0.19, p < .05, d = 0.22). Finally, staying Negative was associated with a higher frequency of dental visits compared to staying Ambivalent (M = 1.48, p ≤ .05, d = 1.20). 4.2. Post Hoc analyses Given that the results are somewhat compromised by the low number of stayers in each category across the three waves of data (see Table S1), we categorized participants into two groups, those who were only ever in the Negative or Moderately-Negative profile (always negative; n = 206) across the three waves, and those who were only ever in the Positive or Ambivalent profile (never negative; n = 347). For lifetime smoking (M = −0.62, p < .001, d = −0.49), lifetime cannabis use (M = −0.23, p < .05, d = −0.26), and dental visits (M = −0.40, p < .05, d = −0.32), the never negative group reported significantly lower scores than the always negative group. These two groups did not differ significantly on frequency of physical exercise, self-rated health, or SLE. 5. Discussion and conclusion We explored the degree to which remaining in, or transitioning to, time attitude profiles based on AATI-TA scores were associated with distal health outcomes. As previously reported, time attitude profiles were unstable across three years of adolescence (Authors, blinded). Indeed, the proportion of stayers in the present study (8.0%) was similar to that previously reported by Authors (blinded) for three waves of data. However, given that remaining in or transitioning to a positive profile was associated with better health outcomes, instability can be viewed as a positive thing. This point is particularly important given that evidence has been presented for the utility of temporal constructs in treatment settings (Davies & Filippopoulos, 2015; Hall, Fong, & Meng, 2014; Oyanadel, BuelaCasal, Araya, Olivares, & Vega, 2014). In other words, the combined learning from a number of cross-sectional studies (Authors, blinded) and the results herein suggest that a positive time attitudes profile is optimal in terms of physical and mental health and well-being, and further, that membership in time attitudes profiles is open to change across time. These combined results open the possibility for future research to examine the possibility of nudging individuals towards a positive profile (as has been demonstrated using the ZTPI and other temporal constructs in treatment settings), with the real possibility that such a nudge may have wideranging health benefits. In keeping with hypotheses, maintaining a positive time attitude profile over time was significantly related to higher SLE and less frequent lifetime use of cannabis and cigarettes; however, staying positive was not related to self-rated health, dental visits, or to 49
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frequency of physical exercise in the past week. Moving to a positive profile was associated with healthier outcomes for all variables except dental visits, whereas moving to a negative profile was associated with less positive outcomes. As illustrated in the literature review, studies in temporal psychology have yielded discrepant results for some health outcomes and given the exploratory nature of this study of time attitudes, it will be important for other researchers in future studies to replicate these findings. Nonetheless, the fact that the study is longitudinal and the findings were consistent with theory adds weight to the results. There were a few unexpected findings, however. First, it is not clear why remaining in the Negative profile was significantly related to a greater frequency of physical exercise than staying in the Moderately Negative or Ambivalent profiles. Does being Negative lead to more exercise to try and change one's mood or do adolescents who have to exercise regularly (e.g., for health reasons) have more negative affect? Similarly, staying in the Negative profile was related to a greater frequency of dental visits than staying Ambivalent. Are adolescents with dental problems more likely to be negative than ambivalent? These questions will be important ones to explore in future research, and it is worth noting that the findings did not include those who stayed in the Positive profile. Another unexpected finding was that moving to the Positive profile increased the probability of cannabis use during the lifetime, compared to moving to the Moderately Negative or Ambivalent profiles. The small effect sizes associated with the cannabis use comparison, as well as the fact that it did not apply to moving to the Negative profile, suggested that observed differences were not meaningful. Further studies will be required to tease out exactly why this might be the case, and qualitative research may be particularly helpful in understanding and contextualizing these behaviors. 5.1. Limitations and conclusion There are a number of important limitations that temper the interpretations from this study. Firstly, the impact of the study is diminished as a result of the fact that health indicators were not assessed in the same wave of data collection as the time attitudes. As previously discussed, the wave of data collection wherein the health variables were measured was opportunistic, an addition to a previously, ring-fenced longitudinal study. This limitation is compounded by the fact that, as discussed, time attitudes profile membership was unstable across time. However longitudinal studies are, by their nature, difficult to execute, and we have stressed the exploratory nature of the study. Linked to these issues is the limitation that there were relatively small numbers of stayers for comparisons. However, the instability of profile membership can also be viewed positively insofar as, if it proves possible to nudge individuals towards a positive time attitude, this may have positive effects on physical as well as mental health (Authors, blinded). In summary, the present study suggested that although individual time attitudes were not substantially correlated with health outcomes, time attitude profiles were meaningful predictors of health behaviors during adolescence: A conclusion that further substantiated the utility of person-centered, over variable-centered, analyses in the study of time attitudes. Future studies in which health outcomes and time attitudes are examined concurrently may reveal additional insights into this association. In conclusion, although the study design herein was not optimal (a linkage of opportunistically collected data to an existing longitudinal study), results suggested that, as has been demonstrated cross-sectionally, time attitudes development in early adolescence relates meaningfully to distal health outcomes. In particular, that maintaining, or transitioning to a positive profile is beneficial. Such preliminary findings pave the way for future studies to (a) examine if (at all) interventions can move adolescents towards a positive profile, and (b) the extent to which this movement has concurrent health benefits in a time of significant biological, psychological, and emotional turbulence. Acknowledgments This research was conducted as part of the STAMPP trial which is funded by the National Institute for Health Research Public Health Research (NIHR PHR) Programme (project grant number 10/3002/09). This paper presents independent research funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.adolescence.2018.09.002. References Adams, J., & Nettle, D. (2009). Time perspective, personality and smoking, body mass and physical activity: An empirical study. British Journal of Health Psychology, 14, 83–105. https://doi.org/10.1348/135910708X299664. Andretta, J. R., Worrell, F. C., Mello, Z. R., Dixson, D. D., & Baik, S. H. (2013). Demographic differences in adolescents' time attitudes. Journal of Adolescence, 36, 289–301. https://doi.org/10.1016/j.adolescence.2012.11.005. Apostolidis, T., Fieulaine, N., Simonin, L., & Rolland, G. (2006). Cannabis use, time perspective and risk perception: Evidence of a moderating effect. Psychology and Health, 21, 571–592. https://doi.org/10.1080/14768320500422683. Bluedorn, A. C. (2002). The human organization of time: Temporal realities and experience. Stanford, CA: Stanford University Press. Davies, S., & Filippopoulos, P. (2015). Changes in psychological time perspective during residential addiction treatment: A mixed-methods study. Journal of Groups in Addiction Recovery, 10, 249–270. https://doi.org/10.1080/1556035X.2015.1066728. Fieulaine, N. (2017). Time perspective and cannabis use: Why and how it is more complex than we think. In A. Kostic, & D. Chadee (Eds.). Time perspective: Theory and practice (pp. 195–215). London, UK: Palgrave Macmillan. https://doi.org/10.1057/978-1-137-60191-9_9. Griva, F., Tseferidi, S.-I., & Anagnostopoulos, F. (2015). Time to get healthy: Associations of time perspective with perceived health status and health behaviors.
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Psychology, Health & Medicine, 20, 25–33. https://doi.org/10.1080/13548506.2014.913798. Guthrie, L. C., Butler, S. C., Lessl, K., Ochi, O., & Ward, M. (2014). Time perspective and exercise, obesity, and smoking: Moderation of associations by age. American Journal of Health Promotion, 29, 9–16. https://doi.org/10.4278/ajhp.130122-QUAN-39. Hall, P. A., Fong, G. T., & Meng, G. (2014). Time perspective as a determinant of smoking cessation in four countries: Direct and mediated effects from the international tobacco control (ITC) 4-country surveys. Addictive Behaviors, 39, 1183–1190. https://doi.org/10.1016/j.addbeh.2014.03.019. Hobbs, G., & Vignoles, A. (2007). Is free school meal status a valid proxy for socio-economic status (in schools research)? London, UK: Centre for the Economics of Education. Knepple Carney, A., & Patrick, J.-H. (2017). Time for a change: Temporal perspectives and health goals. Personality and Individual Differences, 109, 220–224. https:// doi.org/10.1016/j.paid.2017.01.015. McGivern, R. F., Andersen, J., Byrd, D., Mutter, K. L., & Reilly, J. (2002). Cognitive efficiency on a match to sample task decreases at the onset of puberty in children. Brain and Cognition, 50, 73–89. https://doi.org/10.1016/S0278-2626(02)00012-X. Mello, Z. R., & Worrell, F. C. (2007). The adolescent and adult time inventory – English. Berkeley: The University of California. Retrieved from https://faculty.sfsu.edu/ ∼zmello/content/adolescent-time-inventory. Moksnes, U. K., Moljord, I. E. O., Espnes, G. A., & Byrne, D. G. (2010). The association between stress and emotional states in adolescents: The role of gender and selfesteem. Personality and Individual Differences, 49, 430–435. https://doi.org/10.1016/j.paid.2010.04.012. Morgan, G. B., Wells, K. E., Andretta, J. R., & Mckay, M. T. (2017). Temporal attitudes profile transition among adolescents: A longitudinal examination using moverstayer latent transition analysis. Psychological Assessment, 29, 890–901. https://doi.org/10.1037/pas0000406. National Records of Scotland. (2016). Life expectancy for areas within Scotland, 2013-2015. Retrieved from https://www.nrscotland.gov.uk/files//statistics/lifeexpectancy-areas-in-scotland/2013-2015/1315le.pdf. Northern Ireland Statistics and Research Agency (2015). Life expectancy. Retrieved from https://www.nisra.gov.uk/statistics/deaths/life-expectancy. Oyanadel, C., Buela-Casal, G., Araya, T., Olivares, C., & Vega, H. (2014). Time perception: Results of a brief group intervention to change time perspective profiles. Suma Psicológica, 21, 1–7. https://doi.org/10.1016/S0121-4381(14)70001-3. Pozolotina, T., & Olsen, S. O. (2018). Individual differences in time perspective, age, and smoking behaviour: A test of two present versus future conceptualizations. Journal of Substance Use, 23, 187–192. https://doi.org/10.1080/14659891.2017.1378741. Stephenson, M. T., Hoyle, R. H., Palmgreen, P., & Slater, M. D. (2003). Brief measures of sensation seeking for screening and large-scale surveys. Drug and Alcohol Dependence, 72, 279–286. https://doi.org/10.1016/j.drugalcdep.2003.08.003. Strathman, A., Gleicher, F., Boninger, D. S., & Edwards, C. S. (1994). The consideration of future consequences: Weighing immediate and distant outcomes of behavior. Journal of Personality and Social Psychology, 66, 742–752. https://doi.org/10.1037/0022-3514.66.4.742. Worrell, F. C., McKay, M. T., & Andretta, J. R. (2018). Psychometric properties of Adolescent Time Inventory-Time Attitude (ATI-TA) scores in three waves of longitudinal data. Psychological Assessment, 30, 106–115. https://doi.org/10.1037/pas0000457. Worrell, F. C., Mello, Z. R., & Buhl, M. (2013). Introducing English and German versions of the adolescent time attitude scale (ATAS). Assessment, 4, 496–510. https:// doi.org/10.1177/1073191110396202. Zimbardo, P. G., & Boyd, J. N. (1999). Time perspective: A valid, reliable individual- differences metric. Journal of Personality and Social Psychology, 77, 1271–1288. https://doi.org/10.1037/0022-3514.77.6.1271.
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Contents lists available at ScienceDirect
Journal of Adolescence journal homepage: www.elsevier.com/locate/adolescence
Time attitudes profile stability and transitions: An exploratory study on adolescent health behaviours among high school students
T
Michael T. McKaya,∗, James R. Andrettab, Jon C. Colea, Svenja Konowalczykc, Kevin E. Wellsd, Frank C. Worrelle a
Department of Psychological Sciences, University of Liverpool, UK Child Guidance Clinic, Superior Court of the District of Columbia, USA c University of Dortmund, Germany d Baylor University, USA e University of California, Berkeley, USA b
A R T IC LE I N F O
ABS TRA CT
Keywords: Time attitudes Mover-Stayer model Physical exercise Smoking Cannabis
Purpose: Time attitudes refer to individuals' feelings about the past, present, and future, and an increasing number of cross-sectional studies have demonstrated that positive time attitudes are significantly related to better health and well-being. We investigated time attitude profile membership and associated transitions longitudinally in United Kingdom-based adolescents, and assessed the relationship between time attitude profile development on health behaviours at + 21 months after the data collection involving time attitudes. Methods: Participants were high school students (N = 1306; 41.8% female, Mage 12.5–14.5 years). The Adolescent and Adult Time Inventory – Time Attitudes Scale was employed to identify profiles, and a mover-stayer latent transition analysis was employed to examine developmental changes. Data were also gathered on sensation seeking, and a range of health indicators were assessed: Past week frequency of physical exercise, self-rated health, subjective life expectancy, lifetime cannabis and smoking, and dental attendance. Results: Staying in a positive time attitude profile was related to higher subjective life expectancy, and less frequent use of cannabis and cigarettes (1.00 ≤ d ≤ 4.00). Further, moving to a positive profile predicted healthier outcomes for most health measures used. Conclusions: Notwithstanding the limitation that health outcomes in the present study were distal, the present study bolstered a developing cross-sectional literature supporting the association between positive time attitudes and better health and well-being outcomes. Future longitudinal studies which assess measures concurrently are required.
Research into the ways in which an individual's time perspective influences health behaviours has grown considerably in recent years. Across studies, time perspective has been accounted for using both broad and narrowed methods. Zimbardo and Boyd (1999) assessed time perspective with the Zimbardo Time Perspective Inventory (ZTPI) using cognitive, affective, and behavioural items on five dimensions: (a) past negative, (b) past positive, (c) present hedonistic, (d) present fatalistic, and (e) future. Consideration of Future Consequences (CFC; Strathman, Gleicher, Boninger, & Edwards, 1994) is another widely-used construct which assesses both the degree to which individuals consider the future implications of current behaviour, and the degree to which that consideration influences present behaviour. The CFC scale (Strathman et al., 1994) also includes cognitive and behavioural items. Third, temporal ∗
Corresponding author. Department of Psychological Sciences, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, UK. E-mail address: [email protected] (M.T. McKay).
https://doi.org/10.1016/j.adolescence.2018.09.002 Received 28 March 2018; Received in revised form 31 July 2018; Accepted 8 September 2018 0140-1971/ © 2018 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.
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focus concerns cognitions and describes the extent to which people characteristically devote their attention to perceptions of the past, present, and future (Bluedorn, 2002). Last, and the focus of the present study, time attitudes are the affective dimension of time perspective: The emotional and evaluative feelings toward the three time periods (Andretta, Worrell, Mello, Dixson, & Baik, 2013). With the exception of the CFC scale, all of these assess various aspects of temporal psychology (cognitive, affective, behavioural, and indeed, in some cases [ZTPI] an amalgam of all three) with reference to the past, present, and future. The CFC scale assesses consideration of only the present and future. However, while it might seem intuitive to believe that the aforementioned time perspective constructs measure temporal psychology similarly, a recent study (Authors, blinded) in University students reported only one moderate-sized correlation between scores on the combined 16 factors of the four scales. In terms of the broad time perspective literature, studies have demonstrated that a variety of temporal constructs are significantly related to health outcomes. Time perspective as conceptualised in the ZTPI (Zimbardo & Boyd, 1999) has been shown to be significantly related to cannabis use (Apostolidis, Fieulaine, Simonin, & Rolland, 2006; Fieulaine, 2017) and the frequency of physical exercise (Griva, Tseferidi, & Anagnostopoulos, 2015; Guthrie, Butler, Lessl, Ochi, & Ward, 2014). The extant literature in terms of smoking is more complex. Whereas some have reported no association between time perspective (assessed by the ZTPI) and smoking (e.g., Griva et al., 2015; Guthrie et al., 2014), Pozolotina and Olsen (2018) reported one significant but small correlation between ZTPI present fatalistic scores and smoking behaviour (r = 0.20); in this study, correlations between smoking and other ZTPI subscales were close to zero. Pozolotina and Olsenalos also reported statistically significant but small correlations between smoking and CFCimmediate (r = 0.17) and CFC-future (r = −0.10) scores. In a study on both smoking behaviour and frequency of physical exercise, Adams and Nettle (2009) reported that ZTPI future and CFC scores were significantly associated with current smoking status, and these results remained significant when socio-demographic factors were taken into account. Regarding frequency of physical exercise, only CFC was significantly associated with frequency of moderate intensity physical activity; however, this relationship did not remain significant after controlling for socio-demographic factors and five-factor personality constructs (Adams & Nettle, 2009). 1. Changes in time attitude profiles during early adolescence The present study was solely focused on time attitudes during early adolescence for several reasons. Beginning with age, early adolescence is marked by a surge in developmental changes. In fact, the development of synapses in the brain between the ages of 11 and 12 is so rapid that early adolescents have been shown to suffer affective setbacks (e.g., ability to recognize emotions in faces and words; McGirven, Andersen, Byrd, Mutter, & Reilly, 2002). Time attitudes have also been shown to change during early adolescence, where transitions to unfavourable profiles led to poor health behaviour outcomes (Morgan, Wells, Andretta, & Mckay, 2017). To provide an example, Morgan et al. (2017) reported that 12 and 13-year olds who transitioned from being relatively positive about all three time periods to being characterized by negative attitudes toward the future reported a concomitant increase in sensation seeking behaviour (d = 0.56). Turning to the narrowed scope on time attitudes, the construct was ideal because it is not encumbered by other constructs: Whereas the ZTPI uses an amalgam of cognitive, affective, and behavioural items, time attitudes are exclusively affective items. Further still, several studies using the Adolescent and Adult Time Inventory – Time Attitudes Scale (AATI-TA; Mello & Worrell, 2007) have yielded significant and meaningful associations between AATI-TA scores and alcohol-related behaviours and attitudes, subjective life expectancy (SLE), and psychiatric symptomatology (Authors, blinded I, II, III). Indeed, Knepple Carney and Patrick (2017), using the AATI-TA in an adult sample, reported that present positive, future positive, and future negative time attitudes were all significantly related to health intentions, albeit with small effect sizes. Although time attitudes might comprise just one dimension of time perspective, the construct is multivariate. In point of fact, individuals' have been shown hold attitudes towards the past, present, and future both simultaneously and to varying matters of degree (e.g., Andretta et al., 2013). For that reason, researchers have begun to account for heterogeneity in time attitudes using latent profile analysis (LPA), and the longitudinal extension of latent profile analysis, latent transition analysis (LTA; Morgan et al., 2017). LPA is one of many person-centered methods, where individual differences in time attitudes are appraised by grouping participants into categories based on both similarities and differences in attitudes towards the three time periods. Only after the identification of categories, or in this case time attitude profiles, are associations with covariates or distal outcomes assessed (Andretta et al., 2013). By contrast, in variable-centered analyses, such as correlation and regression, only the average associations between or among time attitudes and other constructs are captured in the sample. Across the extant person-centered studies, individuals with positive time attitude profiles have been found to have the best mental health and alcohol-related outcomes, with the reverse largely true for those with negative profiles (e.g., Andretta et al., 2013). However, and in keeping with the reality that adolescence is a period of intense biological, physical, and psychological change (e.g., Moksnes, Moljord, Espnes, & Byrne, 2010), previous longitudinal studies have reported relative instability in profile membership across time. For example, in a two-wave (+12 months) study, Authors (blinded) reported that 55.9% of the sample (n = 1100) were identified as movers, and 44.1% (n = 868) participants were identified as stayers. In an additional study using three waves of data (+24 months) based on a similar sample, Authors (blinded) reported that 91.2% (n = 1521) of participants were identified as movers, and only 8.8% (n = 146) of participants were identified as stayers (stayed in the same profile across all three waves). 2. The present study Because it remains unclear if this prospective relationship extends to a broader range of health behaviours, we examined the effect 45
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of time attitudes across the first three years of high school on self-rated health, frequency of physical exercise in the past week, subjective life expectancy (SLE), lifetime use of cannabis, lifetime smoking, and frequency of dental visits at a later point in time. These health behaviours have previously been examined in relation to other temporal constructs (e.g., time perspective using the ZTPI). We hypothesised that maintaining or transitioning to a positive time attitude profile would predict better health outcomes at +21 months after the collection of time attitude data. 3. Method 3.1. Participants Participants were 1306 school children (41.8% female) attending high schools in Northern Ireland (n = 992, 76.0%) and Scotland (n = 314, 24.0%). Data were collected in three waves (Wave 1: 12.5 years old, Wave 2: +12 months, and Wave 3: +24 months). Data on the health-related outcomes examined herein were gathered 21 months after Wave 3. Therefore, this study covered five years of adolescence. 3.2. Measures The AATI-TA (Mello & Worrell, 2007) is a 30-item instrument with six 5-item subscales assessing Past Positive, Past Negative, Present Positive, Present Negative, Future Positive, and Future Negative attitudes. A 5-point Likert-type scale with verbal and numerical anchors from 1(totally disagree) to 5 (totally agree) was used. Scores for each of the six subscales were computed by summing the responses to the five items and dividing by five. Evidence for the psychometric properties of AATI-TA scores has been shown in studies of adolescent participants in several countries, including the United Kingdom (e.g., Worrell, McKay, & Andretta, 2018; Worrell, Mello, & Buhl, 2013). Time attitudes data were gathered in Waves 1–3. Data on sensation seeking were also gathered as part of a broader study and used here as a covariate. Sensation seeking was measured using the four-item Brief Sensation Seeking Scale (BSSS-4; Stephenson, Hoyle, Palmgreen, & Slater, 2003). Responses to the four items were given on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Sensation seeking scores were computed by summing the responses to the four items and dividing by five. Scores in the present study were found to be internally consistent (α = 0.79). Health-related information was gathered at Wave 4 (+45 months from Wave 1, +21 months from Wave 3). SLE was assessed using a single question concerning participants' subjective probability of expecting to live to age 75. Participants were asked, “On a scale of 0 to 100, where 0 equals no chance, and 100 equals definitely, how likely do you think that it is that you will live to be 75 years old?” Integer options of “5s” (0, 5, 10, 15, 20, etc.) were available between 0 and 100. Age 75 was chosen both because it has been used elsewhere (Adams & Nettle, 2009; Authors, blinded), and because it is slightly below the actual average life expectancy in both Scotland (77.1 and 81.1 years for males and females, respectively; National Records of Scotland, 2016) and Northern Ireland (78.1 and 82.4 years for males and females, respectively; Northern Ireland Statistics and Research Agency, 2015). Participants were asked to indicate if they had smoked a cigarette or used cannabis, with the following response options: Never (coded 0); Yes, over a year ago (1); Yes, in the past year (2); Yes, in the past month (3); and Yes, in the past week (4). Physical activity in the past week was assessed using the following question: “Over the past seven days, on how many days were you physically active for a total of at least 60 min?” Response options ranged from 0 (no days) through 7 (indicating physical activity on each day). Participants were further asked to rate their own health using a Likert-type response where Excellent = 5 and Poor = 1. Finally, participants were asked to indicate how frequently they had attended the dentist in the past year. Data were also gathered on gender, country (Northern Ireland versus Scotland), and free school meals entitlement (FSM; 19.0% yes), an imperfect proxy for socio-economic status (Hobbs & Vignoles, 2007). Descriptive data for the health outcomes are displayed in Table 1. 3.3. Procedure LPA is a procedure used to examine the number of underlying subgroups, or latent profiles, of respondents who share similar patterns of latent factor scores. The goal of LPA is to identify the optimal number of profiles such that respondents within the same latent profile are as similar as possible while respondents in different latent profiles are as different as possible. For each time point, a Table 1 Descriptive Data for Health Outcomes.
Past week frequency of Physical Exercise Self-Rated Health Subjective Probability of living to 75 years old Lifetime Smoking Lifetime Cannabis use Frequency of past year Dental attendance
Range
M
SD
0–7 1–5 0–100 0–4
3.28 3.43 71.36 0.66 0.28 2.22
2.07 0.95 19.45 1.27 0.88 1.23
0–5
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Table 2 Log Odds Coefficients with Demographics and Sensation Seeking as Covariates.
Profile Time 1a Positives
Negatives
Moderately-Negatives
Profile Time 2a Positives
Negatives
Moderately-Negatives
Profile Time 3a Positives
Negatives
Moderately-Negatives
Effect
Logit
S.E.
t
Odds Ratio
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1
0.354 −0.135 0.320 0.462 0.460 0.427 0.275 0.404 0.173 0.409 0.163 0.184
0.210 0.181 0.230 0.239 0.232 0.200 0.247 0.242 0.195 0.174 0.222 0.180
1.689 −0.744 1.389 1.933 1.982 2.118 1.112 1.666 0.887 2.348 0.735 1.020
1.425 0.874 1.377 0.630 1.584 1.533 1.317 1.498 1.189 1.505 1.177 1.202
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T2 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T2 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T2
−0.010 −0.476 0.247 0.027 0.731* 0.802 0.329 0.663 0.178 0.002 0.392 0.198
0.219 0.193 0.230 0.172 0.226 0.211 0.264 0.238 0.201 0.175 0.217 0.153
−0.044 −2.467 1.072 0.155 3.239 0.391 1.245 2.793 0.889 0.010 1.810 1.291
0.990 0.622 1.280 1.027 2.077 1.086 1.389 1.941 1.195 1.002 1.480 1.219
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T3 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T3 Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T3
−0.196 −0.221 0.070 0.601 −0.090 0.306 0.381 0.661 −0.448 −0.096 0.403 0.110
0.242 0.186 0.251 0.212 0.265 0.228 0.273 0.230 0.207 0.168 0.205 0.163
−0.813 −1.185 0.281 2.833 −0.341 1.343 1.398 2.868 −2.163 −0.574 1.969 0.676
0.822 0.802 1.073 1.824 0.914 1.358 1.464 1.936 0.639 0.908 1.497 1.117
Gender (0 = male, 1 = female) Country (0 = Scotland, 1 = Ireland) FSM (0 = no, 1 = yes) Sensation Seeking T1 Sensation Seeking T2 Sensation Seeking T3
−0.058 −0.024 2.119 1.262 1.004 0.551
0.490 0.368 2.312 0.726 0.461 0.509
−0.119 −0.065 0.916 1.739 2.176 1.083
0.943 0.976 8.320 3.532 2.729 1.736
Mover-Stayerb
Note. FSM = Free School Meals Entitlement; T1 = Time 1; T2 = Time 2; T3 = Time 3. *p ≤ .001. a Ambivalents are the Comparison Group for Profile Comparisons. b Stayers are the Comparison Group for the Mover-Stayer Comparisons.
series of LPA models was conducted using six latent factor scores from the CFA. Altogether, we examined LPA models with between two and seven profiles, using nine different specifications of the covariance matrix, which allowed for various degrees of model complexity (compare, Authors, 2016). Considering various fit indices, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted BIC (aBIC), Integrated Likelihood Criterion with Bayesian-type Approximation (ICL-BIC), LoMendell-Rubin Likelihood Ratio Test (LMR), and the Bootstrapped Likelihood Ratio Test (BLRT), as well as previous literature, the solution with four profiles fitted best. Based on the parametrization, the covariances between the past positive and negative, present positive and negative, and future positive and negative time attitudes were allowed to differ across latent profiles. In contrast to Authors (2016), variances for each of the six time attitude scores were not allowed to differ across latent profiles. Initially, 1667 participants responded to the AATI-TA at the first three data collection points, and confirmatory factor analyses (CFAs) supported the six-factor structure of AATI-TA scores. Latent profile analyses (LPA) as well as latent transition analyses (LTA) were conducted using the six factor scores from the CFAs to explore time attitude profiles at Waves 1–3 (see Authors, blinded). At Wave 4, 1306 responded to the distal health-related outcome variables, which were the focus of the present study. Thus, in a first step, a mover-stayer LTA was conducted to explore latent transition patterns of these participants across profiles and time. In a second
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Table 3 Correlations Among AATI-TA and Distal Outcome Variables. Subscale Time 1 Past Positive Past Negative Present Positive Present Negative Future Positive Future Negative Time 2 Past Positive Past Negative Present Positive Present Negative Future Positive Future Negative Time 3 Past Positive Past Negative Present Positive Present Negative Future Positive Future Negative
Exercise
Health
Live 75
Smoke
Cannabis
Dentist
0.071 −0.065 0.069 −0.034 0.066 −0.063
0.140 −0.108 0.138 −0.106 0.089 −0.100
0.149 −0.088 0.179 −0.134 0.106 −0.097
−0.122 0.133 −0.165 0.176 −0.073 0.086
−0.070 0.042 −0.074 0.066 −0.048 0.020
0.015 0.011 0.007 −0.005 0.025 0.020
0.105 −0.053 0.087 −0.063 0.100 −0.112
0.194 −0.171 0.190 −0.166 0.157 −0.164
0.171 −0.130 0.194 −0.160 0.183 −0.167
−0.191 0.195 −0.197 0.204 −0.136 0.157
−0.090 0.096 −0.094 0.084 −0.059 0.041
0.045 0.026 0.086 −0.051 0.093 −0.031
0.093 −0.073 0.126 −0.127 0.149 −0.132
0.215 −0.180 0.266 −0.249 0.233 −0.231
0.177 −0.147 0.217 −0.211 0.219 −0.214
−0.175 0.180 −0.171 0.181 −0.117 0.166
−0.097 0.072 −0.093 0.080 −0.090 0.089
0.021 0.023 −0.008 −0.019 0.034 −0.002
Note. AATI-TA = Adolescent and Adult Time Attitudes Scale; Exercise = physical exercise in the past week; Health = self-rated health; Live = subjective life expectancy; Smoke = lifetime use of cigarettes; Cannabis = lifetime use of cannabis, Dentist = frequency of dentist's visits.
step, time-invariant (i.e., gender, country, FSM) and time-variant (i.e., sensation seeking) covariates were controlled for and added to the model (Table 2). Lastly, health-related distal outcome variables were correlated to AATI-TA (Table 3) and included in the final model. As can be seen in Tables 2 and 3, the effects of the covariates were generally small, as were the bivariate correlations between time attitudes and health outcomes.
4. Results Fig. 1 shows the profile characteristics at Time 1, 2, and 3. Overall, 104 (8.0%) stayed in the Positives profile, 23 (1.8%) in the Negatives profile, 70 (5.4%) in the Moderately-Negatives profile, and 70 (5.4%) in the Ambivalents profile. Means and standard deviations for distal outcomes are shown in Table 1, and the detailed profile transition pattern across waves one to three is available in Supplementary Table S1. Further, Supplementary Table S2 illustrates that gender, FSM, and country had a negligible effect on
Fig. 1. Latent profile means and frequencies at Time 1, 2, and 3 (LTA). PaP = Past Positive; PaN = Past Negative; PrP = Present Positive; PrN = Present Negative; FuP = Future Positive; FuN = Future Negative. 48
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profile transition. 4.1. Distal outcomes Means of distal outcome variables were calculated for each profile at Time 4. To explore differences between each of the profiles on the distal outcome variables, several Wald chi-square tests of parameter equalities were conducted. The p value, the associated mean difference, and Cohen's d are provided below. Cohen's d was calculated by dividing the reported mean difference by the standard deviation of the respective distal outcome. Results were adjusted for sensation seeking, FSM, gender, and country. Staying in the Negative profile was associated with a higher frequency of physical exercise in the past week than staying in the Moderately-Negative (M = 3.12, p ≤ .001, d = 1.51) or Ambivalent (M = 2.36, p ≤ .01, d = 1.14) profiles. Further, moving to Positive profile across time was associated with more frequent past-week physical exercise than moving to the Ambivalent profile, but this effect was modest (M = 0.57, p ≤ .05, d = 0.28). Among stayers, there was no significant association across time with self-rated health. However, moving to Positive was associated with a significant increase in self-rated health compared to moving to Negative (M = 0.37, p < .01, d = 0.39), Moderately-Negative (M = 0.29, p < .001, d = 0.31), or Ambivalent (M = 0.30, p < .001, d = 0.32). Results indicated that for SLE, there were significant results for staying Positive across time compared to staying Negative (M = 27.40, p ≤ .05, d = 1.41), Moderately-Negative (M = 23.96, p ≤ .05, d = 1.23), or Ambivalent (M = 19.50, p ≤ .05, d = 1.00). For movers, transitioning to Positive was associated with higher SLE compared to transitioning to Negative (M = 5.17, p ≤ .05, d = 0.27) or Moderately-Negative (M = 5.88, p ≤ .001, d = 0.30). Also, moving to Moderately-Negative was associated with a lower SLE than moving to Ambivalent (M = −3.37, p ≤ .05, d = −0.17). Staying Positive was associated with a lower likelihood of ever having smoked compared to staying Negative (M = −2.83, p ≤ .001, d = −2.22), Moderately-Negative (M = −3.10, p < .001, d = −2.44), or Ambivalent (M = −3.04, p ≤ .001, d = −2.39). Additionally, moving to Positive (compared to Negative) was associated with a significantly lower likelihood of lifetime smoking (M = −0.62, p ≤ .01, d = −0.49), whereas moving to Negative was associated with an increased likelihood of lifetime smoking compared to Moderately-Negative (M = 0.49, p ≤ .01, d = 0.38) and Ambivalent (M = 0.63, p ≤ .001, d = 0.49). There were multiple significant results for lifetime cannabis use. Staying Positive was associated with a lower likelihood of ever having used cannabis compared to staying Negative (M = −3.52, p ≤ .001, d = −4.00), Moderately-Negative (M = −2.71, p ≤ .001, d = −3.08), or Ambivalent (M = −3.43, p < .001, d = −3.90). Further, staying Negative was associated with an increased likelihood of lifetime cannabis use compared to staying Moderately-Negative (M = 0.81, p ≤ .05, d = 0.92), and staying Moderately-Negative was associated with less lifetime cannabis use compared to Ambivalent (M = −0.72, p ≤ .001, d = −0.82). Moving to Positive was associated with an increased likelihood of lifetime cannabis use than moving to Moderately-Negative (M = 0.16, p < .01, d = 0.18) or Ambivalent (M = 0.15, p < .05, d = 0.17). Also, moving to Negative was associated with an increased likelihood of lifetime cannabis use than moving to Moderately-Negative (M = 0.20, p < .05, d = 0.22) or Ambivalent (M = 0.19, p < .05, d = 0.22). Finally, staying Negative was associated with a higher frequency of dental visits compared to staying Ambivalent (M = 1.48, p ≤ .05, d = 1.20). 4.2. Post Hoc analyses Given that the results are somewhat compromised by the low number of stayers in each category across the three waves of data (see Table S1), we categorized participants into two groups, those who were only ever in the Negative or Moderately-Negative profile (always negative; n = 206) across the three waves, and those who were only ever in the Positive or Ambivalent profile (never negative; n = 347). For lifetime smoking (M = −0.62, p < .001, d = −0.49), lifetime cannabis use (M = −0.23, p < .05, d = −0.26), and dental visits (M = −0.40, p < .05, d = −0.32), the never negative group reported significantly lower scores than the always negative group. These two groups did not differ significantly on frequency of physical exercise, self-rated health, or SLE. 5. Discussion and conclusion We explored the degree to which remaining in, or transitioning to, time attitude profiles based on AATI-TA scores were associated with distal health outcomes. As previously reported, time attitude profiles were unstable across three years of adolescence (Authors, blinded). Indeed, the proportion of stayers in the present study (8.0%) was similar to that previously reported by Authors (blinded) for three waves of data. However, given that remaining in or transitioning to a positive profile was associated with better health outcomes, instability can be viewed as a positive thing. This point is particularly important given that evidence has been presented for the utility of temporal constructs in treatment settings (Davies & Filippopoulos, 2015; Hall, Fong, & Meng, 2014; Oyanadel, BuelaCasal, Araya, Olivares, & Vega, 2014). In other words, the combined learning from a number of cross-sectional studies (Authors, blinded) and the results herein suggest that a positive time attitudes profile is optimal in terms of physical and mental health and well-being, and further, that membership in time attitudes profiles is open to change across time. These combined results open the possibility for future research to examine the possibility of nudging individuals towards a positive profile (as has been demonstrated using the ZTPI and other temporal constructs in treatment settings), with the real possibility that such a nudge may have wideranging health benefits. In keeping with hypotheses, maintaining a positive time attitude profile over time was significantly related to higher SLE and less frequent lifetime use of cannabis and cigarettes; however, staying positive was not related to self-rated health, dental visits, or to 49
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frequency of physical exercise in the past week. Moving to a positive profile was associated with healthier outcomes for all variables except dental visits, whereas moving to a negative profile was associated with less positive outcomes. As illustrated in the literature review, studies in temporal psychology have yielded discrepant results for some health outcomes and given the exploratory nature of this study of time attitudes, it will be important for other researchers in future studies to replicate these findings. Nonetheless, the fact that the study is longitudinal and the findings were consistent with theory adds weight to the results. There were a few unexpected findings, however. First, it is not clear why remaining in the Negative profile was significantly related to a greater frequency of physical exercise than staying in the Moderately Negative or Ambivalent profiles. Does being Negative lead to more exercise to try and change one's mood or do adolescents who have to exercise regularly (e.g., for health reasons) have more negative affect? Similarly, staying in the Negative profile was related to a greater frequency of dental visits than staying Ambivalent. Are adolescents with dental problems more likely to be negative than ambivalent? These questions will be important ones to explore in future research, and it is worth noting that the findings did not include those who stayed in the Positive profile. Another unexpected finding was that moving to the Positive profile increased the probability of cannabis use during the lifetime, compared to moving to the Moderately Negative or Ambivalent profiles. The small effect sizes associated with the cannabis use comparison, as well as the fact that it did not apply to moving to the Negative profile, suggested that observed differences were not meaningful. Further studies will be required to tease out exactly why this might be the case, and qualitative research may be particularly helpful in understanding and contextualizing these behaviors. 5.1. Limitations and conclusion There are a number of important limitations that temper the interpretations from this study. Firstly, the impact of the study is diminished as a result of the fact that health indicators were not assessed in the same wave of data collection as the time attitudes. As previously discussed, the wave of data collection wherein the health variables were measured was opportunistic, an addition to a previously, ring-fenced longitudinal study. This limitation is compounded by the fact that, as discussed, time attitudes profile membership was unstable across time. However longitudinal studies are, by their nature, difficult to execute, and we have stressed the exploratory nature of the study. Linked to these issues is the limitation that there were relatively small numbers of stayers for comparisons. However, the instability of profile membership can also be viewed positively insofar as, if it proves possible to nudge individuals towards a positive time attitude, this may have positive effects on physical as well as mental health (Authors, blinded). In summary, the present study suggested that although individual time attitudes were not substantially correlated with health outcomes, time attitude profiles were meaningful predictors of health behaviors during adolescence: A conclusion that further substantiated the utility of person-centered, over variable-centered, analyses in the study of time attitudes. Future studies in which health outcomes and time attitudes are examined concurrently may reveal additional insights into this association. In conclusion, although the study design herein was not optimal (a linkage of opportunistically collected data to an existing longitudinal study), results suggested that, as has been demonstrated cross-sectionally, time attitudes development in early adolescence relates meaningfully to distal health outcomes. In particular, that maintaining, or transitioning to a positive profile is beneficial. Such preliminary findings pave the way for future studies to (a) examine if (at all) interventions can move adolescents towards a positive profile, and (b) the extent to which this movement has concurrent health benefits in a time of significant biological, psychological, and emotional turbulence. Acknowledgments This research was conducted as part of the STAMPP trial which is funded by the National Institute for Health Research Public Health Research (NIHR PHR) Programme (project grant number 10/3002/09). 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