How future orientations predict healthy outcomes: Information avoidance as a potential mechanism

Abstract

This study elucidates whether information avoidance may help explain demonstrated links between future orientation and health. In an online study, college students reported their self-reported prevention and detection health behaviors and responded to a prevention and detection health message. Path analyses indicated that information avoidance mediated the relationship with the future orientations (optimism and consideration of future consequences), such that, greater and more positive future orientations were associated with less information avoidance, and less information avoidance was associated with greater self-reported health behaviors and positive responses to health information. Correlational analyses revealed that information avoidance was particularly related to health outcomes, and to a greater extent than future orientations. Our findings join a growing literature showing the importance of information avoidance for a variety of health behaviors and suggest a potential intervention target for individuals whose characteristic ways of (not) thinking about their future might keep them unaware and unhealthy.

Keywords

consideration of future consequences future orientation health behavior health psychology information avoidance optimism outcome expectations

When deciding whether to engage in a health behavior, what goes through an individuals’ mind? If they consider the future benefits of a behavior, their decision may be different than if they only consider the immediate costs, such as the discomfort of exercising or the anxiety of a diagnostic test. Furthermore, some people may be eager to learn information about their health, whereas others may prefer to remain blissfully unaware. Indeed, people differ in the amount of consideration they give to the future outcomes of their behavior (Strathman et al., 1994), how positively they expect things to be in the future (Scheier et al., 1994), as well as their tendency to avoid learning new information relevant to their health (Howell and Shepperd, 2016). In this study, we examine the relationships between two aspects of future orientation—consideration of future consequences and dispositional optimism—and information avoidance in the prediction of health behaviors and responses to health messages. Each of these constructs predict outcomes such as achievement, wellbeing, and health (see Carver et al., 2010; Kooij et al., 2018; Sweeny et al., 2010 for reviews). We elucidate the extent to which information avoidance may help explain links between future orientation and health.

Consideration of future consequences and dispositional optimism both predict healthy behavior. Consideration of future consequences (CFC; Strathman et al., 1994) is the degree to which individuals consider the distant versus immediate consequences of their behavior. People high in CFC prioritize distant over immediate consequences, whereas people low in CFC prioritize immediate over distant consequences. A meta-analysis by Murphy and Dockray (2018) examined the role of CFC in predicting a variety of health behaviors: health promotive behaviors, health risk behaviors, and illness-prevention and detection behaviors. Although effect sizes were small (effect size r’s ranging from 0.08 to 0.19), higher CFC was significantly associated with healthier behavior. Similar results were found when Sweeney and Culcea (2017) meta-analyzed the literature examining the influence of future orientation (broadly defined) on diet, physical activity, and body mass index.

Dispositional optimism reflects the extent to which people hold favorable expectations for the future (Carver et al., 2010). Rasmussen et al. (2009) assessed the relationship between optimism and a variety of physical health outcomes, finding that those with more optimistic expectations for their future live longer, have better physical health, and fewer symptoms than more pessimistic people (meta-analytic effect size r = 0.17). Of course, one pathway through which optimism may influence physical health is through healthy behavior. Boehm et al.’s (2018) meta-analysis showed that, indeed, people high in optimism eat better, exercise more, and smoke less (effect size r’s ranging from 0.07 to 0.12).

While some work has explored possible mechanisms of this relationship between future orientation and health behavior—including regulatory focus (Joireman et al., 2012; Kees et al., 2007, 2010; Pennington and Roese, 2003), coping strategies (Chua et al., 2015), and goal-related self-regulation (Baird et al., 2021)—much remains unknown about why future orientation leads to positive outcomes. Both CFC and dispositional optimism are considered stable and trait-like, rather than malleable. Identifying the mechanisms by which these aspects of future orientation influence healthy behavior can reveal potentially malleable factors, as well as highlight additional processes by which future orientation influences health.

Information avoidance and future orientation

Inasmuch as people differ in their future orientations, people also differ in the degree to which they avoid health information, such as the status of their current health, learning the benefits of changing their behavior, or asking for diagnostic testing (Howell and Shepperd, 2016). Engaging with health information is an important step in deciding whether to enact a health behavior. This is particularly important as some health behaviors, whether explicitly or implicitly, involve learning information. For example, to engage in a detection behavior (e.g. cancer screening) an individual must be willing to learn about their health risk or status; the behavior itself is acquiring information. It is not surprising then that health information avoidance predicts less interest in receiving genetic testing, for example, Taber et al. (2015). Prevention behaviors (e.g. physical exercise, diet, quitting smoking), although not explicitly behaviors of information acquisition, may also require acquiring information but to a lesser degree than detection behaviors (e.g. monitoring goal progress, learning that one is out of shape or has a stronger dependence on nicotine than anticipated). Indeed, people high in information avoidance self-monitor behaviors less often and are less engaged in weight loss intervention (Schumacher et al., 2021). Thus, information avoidance should predict less engagement in detection behaviors, and may also predict less engagement in prevention behaviors as well.

Theory and research suggest that future orientation may be associated with information avoidance, potentially explaining some of the established relationship between future orientation and positive health outcomes. According to Socioemotional Selectivity Theory (SST; Carstensen et al., 1999), when individuals perceive time as unlimited, they prioritize learning information. When time is perceived as limited, individuals seek out more immediate and emotionally rewarding goals. Research in SST often uses age as a proxy for future orientation, as it is assumed that with age, individual’s futures are perceived as more limited. However, the critical construct in SST is future orientation rather than age (Demeyer and De Raedt, 2014). The theory, then, suggests that future-oriented individuals should prioritize acquiring information more so than present-oriented individuals. Another theory that proposes a role of information avoidance in future orientation is Aspinwall and Taylor’s (1997) model of proactive coping, which outlines the stages individuals take to preemptively cope with anticipated future events. The second stage of this five-stage model consists of attention and recognition of potential stressors and threatening information. During this stage, future orientation is thought to be a particularly important determinant of whether an individual will choose to avoid information; those more future-oriented should be less likely to avoid information in an effort to prepare themselves for a potential future event. Taken together, the expectation that higher CFC should lead to less information avoidance has theoretical support, but no prior empirical evidence. Thus, we treated this as an exploratory hypothesis.¹

Research suggests that dispositional optimism should be associated with less information avoidance (Howell and Shepperd, 2016). This may be because optimism is considered a psychological resource, and people with greater psychological resources may be less inclined to avoid information (Howell and Shepperd, 2016). Optimism is also associated with less use of avoidant coping strategies in response to stress (Nes and Segerstrom, 2006). Although optimism has been found to be negatively associated with information avoidance, no empirical tests of mediation have been conducted to our knowledge. Therefore, we expected to find a mediation effect of information avoidance on the relationship between optimism and health outcomes and treated this as a directional hypothesis.

Present work

We examined whether information avoidance mediated the relationship between future orientation; assessed as both CFC and dispositional optimism. This was tested on two sets of health outcomes. The first set was self-reported health behaviors, including both detection and prevention behaviors. We expected information avoidance to predict performance of detection behaviors as they are inherently acts of information acquisition, and also explored whether information avoidance would predict prevention behaviors as well. We recognized, though, that individuals may vary in their perception of the behavioral function for any given health behavior (e.g. one might perceive cancer screenings as a detection behavior, while another perceives it as a prevention behavior). Accordingly, we also utilized a second set of outcomes that manipulated individuals’ perception of a health behavior’s function in a persuasive message. Participants read about a mouth rinse in two scenarios: once as a prevention behavior (anti-cavity), and once as a detection behavior (plaque-disclosing). We then assessed perceptions of the message, attitudes toward the mouth rinse, and intentions to use such a mouth rinse, noting that message perceptions and attitudes toward an advocated behavior are valid measures of defensive responses including avoidance (outperforming even behavioral measures such as attention and time spent reading; Good and Abraham, 2007). We hypothesized that greater information avoidance would manifest in less favorable perceptions, attitudes, and intentions in response to the detection message, and explored effects on outcomes regarding the prevention message.

Method

Participants

Participants were recruited through a university subject pool where they were granted course credit for participation. To be eligible, individuals needed to be 18 years or older and English-speaking. Surveys were administered online via Qualtrics. A total of 639 individuals participated in the study. Based on pre-registered exclusion criteria (via OSF) participants were excluded if they failed an attention check (n = 55), elected to not include their data (n = 46), did not try to or were not able to follow instructions (n = 10), did not finish the survey (n = 4), spent too much time on survey (over 4000 seconds; n = 41), and spent too little time on the survey (under 1000 seconds; n = 10). A final total of 473 participants were retained for further analyses.

Procedure

The Institutional Review Board at Kent State University approved our online survey (Approval #20-308). Participants provided electronic informed consent and completed an initial battery of questionnaires including current dental hygiene practices, self-reported health behavior, and measures of individual differences in CFC and general information avoidance. Participants viewed a series of three health messages: a general plaque message, a prevention mouth rinse message (i.e. anti-cavity), and a detection mouth rinse message (i.e. plaque-disclosing). The order of the two mouth rinse messages was counterbalanced. The prevention and detection messages were presented on the screen for 90 seconds each. After each message, participants reported their perceptions of each message, attitudes toward each mouth rinse, and intentions regarding each mouth rinse. Lastly, participants completed a final battery of questionnaires, including measures of individual differences in dental health specific information avoidance, dispositional optimism, and demographic information.

Materials

The general plaque message, which participants saw first, detailed (a) how plaque is formed and how cavities develop, (b) development of gum disease, and (c) the role of plaque in tooth decay and gum disease. The two mouth rinse messages, which participants saw second and third, detailed (a) how the mouth rinse works, (b) how to properly use the mouth rinse, and (c) recommendation to use the mouth rinse. There was an even number of positively and negatively framed statements in the messages, and each message encouraged regular use of the mouth rinse. The general plaque message, prevention message, and detection message contained approximately 330 words, 220 words, and 300 words, respectively. The discrepancy of total words between the mouth rinse messages is due to more steps required for the detection mouth rinse.

Measures

Consideration of future consequences

A 12-item scale assessed individual differences in CFC (Strathman et al., 1994). The scale contained seven items assessing a focus on immediate outcomes (e.g. “I only act to satisfy immediate concerns, figuring the future will take care of itself”) and five items assessing a focus on future outcomes (e.g. “I consider how things might be in the future and try to influence those things with my day to day behavior”). Response options were on a 5-point scale (1 = very untrue of me to 5 = very true of me). There is yet a consensus as to whether CFC is unidimensional or a two-factor structure (Joireman and King, 2016). CFC yielded a one-factor structure in the present data, so the original scoring method by creating a composite score through averaging the seven reverse-scored immediate items and five future items (α = 0.77) was used. Higher scores indicated a greater tendency to consider future consequences.

Dispositional optimism

The 6-item Life Orientation Test-revised assessed individual differences in dispositional optimism (LOT; Scheier et al., 1994). The scale contained three items assessing optimism (e.g. “In uncertain times, I usually expect the best”) and three items assessing pessimism (e.g. “I hardly ever expect things to go my way”). Response options were on a 5-point scale (1 = I disagree a little and 5 = I disagree a lot). Pessimism items were reversed scores and combined with the optimism items to create one composite score (α = 0.80). Higher scores indicated greater optimism.

Information avoidance

An 8-item scale assessed individual differences in information avoidance (Howell and Shepperd, 2016). The scale was presented first with general topics to assess information avoidance in a broad sense (e.g. “I would avoid learning information about myself”). The scale was presented a second time with topics specific to dental health (e.g. “I would avoid learning that I have gum disease”), which were used only in analyses predicting responses to the mouth rinse messages. Response options were on a 7-point scale (1 = strongly disagree, 7 = strongly agree). By averaging the items, four of which were reverse scored, a composite score was created for general information avoidance (α = 0.80) and dental health information avoidance (α = 0.85). Higher scores indicated a greater tendency to avoid information.

Participant information

Participants reported their demographic information, including age, gender, and ethnicity. Modeled after Updegraff et al. (2007), we assessed participants’ brushing and flossing behavior in the last month (1 = once per week or less to 5 = two or more times per day) and frequency of dentist appointments in the last 2 years (0–6).

Manipulation check

After each message, participants were asked “In the flyer you just viewed, how did the mouth rinse work?” Response options were 0 = The mouth rinse detects plaque buildup visibly after each use. It is used to determine how effective one’s current hygiene practices are and 1 = The mouth rinse prevents plaque buildup with each use. It is used to enhance one’s hygiene practices. So, for example, if a participant saw the detection message first and the prevention message second, the correct responses would be 1 for the first prompt and 0 for the second.

Outcomes

Message responses

Three outcomes were assessed: perceptions, attitudes, and intentions. These three outcomes were measured using items from prior work on health messaging in a dental hygiene context (i.e. flossing; Updegraff et al., 2007) and adapted for the context of mouth rinse. Message perceptions were assessed with eight items concerning how much they thought the message was informative, persuasive, involving, interesting, clear, useful, important, and helpful. Response options were on a 5-point scale (1 = not at all to 5 = extremely), and averaged to create a composite score for message perceptions (detection message perceptions α = 0.83; prevention message perceptions α = 0.85). Attitudes regarding the mouth rinse in the messages were assessed with four items. Specifically, participants reported their opinion concerning whether using mouthwash is harmful–beneficial, ineffective–effective, unimportant–important, and bad–good. Each adjective was on opposite sides of a 7-point scale (e.g. 1 = harmful to 7 = beneficial). Res-ponses were averaged to create a composite score for mouth rinse attitudes (detection mouth rinse attitudes α = 0.86; prevention mouth rinse attitudes α = 0.88). Behavioral intentions were assessed with two items, “In the next week, how likely are you to buy a mouth rinse like the one described in the message?” and “In the next week, how likely are you to use a mouth rinse like the one described in the message?” Response options were on a 5-point scale (1 = extremely unlikely to 5 = extremely likely). Responses were averaged to create a composite score for behavioral intentions (detection intentions α = 0.93; prevention intentions α = 0.84).

Self-reported health behavior

Participants reported whether they regularly engage in 16 different health behaviors. Half were detection behaviors: getting X-rays at the dentist, scheduling a doctor’s appointment for testing when presenting with sick symptoms, cancer screening (including breast cancer, colon cancer, cervical cancer, or any others), STD testing, taking one’s temperature when feeling feverish, going to the eye doctor for an eye examination, taking an online risk screening, and monitoring one’s weight. The other half were prevention behaviors: flu shots, taking vitamins, washing hands/using hand sanitizer, wearing sunscreen, engaging in exercise, eating a healthy diet, avoiding excess use of alcohol and drugs, and yearly checkup with one’s doctor. Behaviors were made relevant to the expected health behaviors of college-age participants. Response options were yes/no, so any composite variables summed the amount of yes responses.

Other measures, including motivation orientation, monitoring and blunting strategies, mindfulness, temporal orientation, and self-compassion were also assessed (see Supplementary File Appendix A for the order of the questionnaires in the full study). These variables were not expected to have influence on those of interest to the present study and will not be discussed further. All data analyzed in the present work is available in Figshare and contains no identifying participant information.

Results

Participants were mainly women (n = 364; 77%), White (n = 360; 76.1%), and young adults (M = 19.7; SD = 3.56; range 18–57). See Supplementary File (Appendix B) for full demographic information on the sample. They were mostly adherent to dental health behaviors; 56% reported brushing their teeth two or more times a day (n = 279), 26% reported flossing their teeth every other day or more (n = 121), and 42% of participants reported visiting the dentist four or more times in the last 2 years (n = 197). As mentioned previously, a manipulation check was conducted twice—once for each message—since participants were exposed to both messages. As such, manipulation checks were assessed with a repeated-measures t-test. In each case, participants indicated whether they believed the function of the mouth rinse was to 0 = detect plaque buildup visibly after each use (i.e. the correct response for the detection message) or 1 = prevent plaque buildup with each use (i.e. the correct response for the prevention condition). Most participants correctly reported the function of the prevention mouth rinse was to prevent plaque buildup with each use (M = 0.88, SE = 0.01) and the function of the detection mouth rinse was to detect plaque buildup visibly after each use (M = 0.06, SE = 0.01). These means were significantly different from one another, repeated-measures t = 39.67, p < 0.001, and thus the manipulations are considered successful.

Models were run separately for prevention message outcomes (i.e. message perceptions; mouth rinse attitudes; and behavioral intentions), detection message outcomes (i.e. message perceptions; mouth rinse attitudes; and behavioral intentions), and self-reported health behavior (prevention behaviors; detection behaviors). Data were analyzed using Stata version 17.0 BE-Basic Edition (StataCorp, 2021). The pre-registered analysis plan² included structural equation model estimation to test mediational relationships. Because this work was guided by theory and prior research, models were tested regardless of initial correlation results between the X (e.g. CFC and optimism) and Y variables (e.g. prevention health behaviors), since direct effects need not be significant for indirect effects to exist (Rucker et al., 2011; Zhao et al., 2010). In discussing results, we use the term mediation when both a correlation between future orientation and an outcome and an indirect path through information avoidance are significant; we use the term indirect effect when only an indirect path through information avoidance is significant (Mathieu and Taylor, 2006). The proportion mediated was used as the effect size, which is the ratio of the indirect effect to the total effect. To provide an additional unit for effect size estimation, the standardized coefficients of the indirect paths were also reported.

Table 1 reports the means, standard deviations, and correlation matrix of all dependent and independent variables. Participants’ means scores were above the scale midpoints on CFC (M = 3.47, SD = 0.49; range 1–5) and optimism (M = 3.02, SD = 0.79; range 1–5) and below the midpoints on both information avoidance measures (General M = 2.48, SD = 0.92; Dental Specific M = 1.99, SD = 0.89; range 1–7). Of interest, CFC correlated significantly with general information avoidance (r = −0.32, p < 0.001) and dental health specific information avoidance (r = −0.24, p < 0.001). Optimism was also significantly correlated with information avoidance measures (r = −0.29, p < 0.001; r = −0.13, p < 0.01, respectively). Thus, the future orientation constructs were negatively related to information avoidance.

Table 1.

Correlation matrix for all dependent and predictor variables.

Variable	M (SD)	1	2	3	4	5	6	7	8	9	10	11
1. P Message perceptions	3.80 (0.61)
2. P Mouth rinse attitudes	6.36 (0.86)	0.46***
3. P Behavioral intentions	3.62 (1.08)	0.46***	0.36***
4. D Message perceptions	3.87 (0.59)	0.63***	0.29***	0.31***
5. D Mouth rinse attitudes	6.27 (0.85)	0.36***	0.57***	0.19***	0.49***
6. D Behavioral intentions	3.11 (1.21)	0.27***	0.14**	0.49***	0.51***	0.39***
7. P Health behaviors	7.50 (1.68)	0.00	0.06	0.02	−0.00	0.04	0.01
8. D Health behaviors	3.06 (1.21)	0.07	0.09*	0.10*	0.12**	0.14**	0.13**	0.28***
9. IA–G	2.48 (0.92)	−0.12**	−0.11*	0.01	−0.14**	−0.13**	0.03	−0.20***	−0.18***
10. IA–DH	1.99 (0.89)	−0.20***	−0.25***	−0.12**	−0.26***	−0.30***	−0.06	−0.14**	−0.10*	0.49***
11. CFC	3.47 (0.49)	0.05	0.10*	−0.00	0.06	0.07	−0.08	0.18***	0.07	−0.32***	−0.24***
12. LOT	3.02 (0.79)	0.12**	0.10*	0.07	0.12*	0.09*	0.10*	0.14**	0.04	−0.28***	−0.13**	0.12**

P and D refer to prevention and detection, respectively. IA–G and IA–DH refer to general information avoidance and dental health information avoidance, respectively. CFC refers to consideration of future consequences. LOT refers to life orientation test which measures optimism. M and SD refer to mean and standard deviation, respectively.

p < 0.05. **p < 0.01. ***p < 0.001.

Exploratory hypothesis 1, which tested whether information avoidance would mediate the relationship between CFC and health outcomes, was supported (see Table 2 for results and Supplementary File for figures of all significant results). General information avoidance had mediated or indirect effects between CFC and two of the four prevention outcomes: prevention message perceptions (β = 0.05, p = 0.018; indirect) and prevention health behaviors (β = 0.14, p = 0.004; mediated). General information avoidance had indirect effects between CFC and three of the four detection outcomes: detection message perceptions (β = 0.05, p = 0.011), detection mouth rinse attitudes (β = 0.07, p = 0.018), and detection health behaviors (β = 0.17, p = 0.001). Dental health information avoidance had mediated or indirect effects between CFC and all the prevention message outcomes: prevention message perceptions (β = 0.06, p = 0.001; indirect), prevention mouth rinse attitudes (β = 0.10, p < 0.001; mediated) and prevention mouth rinse intentions (β = 0.07, p = 0.013; indirect). Dental health information avoidance had indirect effects between CFC and two of the three detection message outcomes: detection message perceptions (β = 0.08, p < 0.001) and detection mouth rinse attitudes (β = 0.13, p < 0.001). Directions of the effect showed that high CFC was associated with less avoidance, and less avoidance was associated with more positive outcomes. Effect sizes for the mediated effects are reported in Table 2. The proportion mediated by general information avoidance ranged from 28% to 84% and dental health information avoidance from 16% to 59%. Standardized coefficients of the indirect effect were also calculated to provide an additional estimate of the effect sizes. The standardized effects for general information avoidance ranged from 0.03 to 0.06 and dental health information avoidance from 0.02 to 0.07. Across some of the analyses, effect sizes were unable to be calculated due to inconsistent mediation (Preacher and Kelley, 2011) in which the result was negative or above 1. Nonetheless, the two outcomes where neither proportion mediated effect sizes nor standardized mediation paths were able to be computed (i.e. prevention and detection mouth rinse intentions) had nonsignificant indirect effects.

Table 2.

Information avoidance mediation of relationship between CFC and optimism and outcomes.

	Pathways			Indirect effects
Variable	A	b	c’	Estimate	% Mediated	Stand coef
CFC
Outcome	General information avoidance
Prevention message perceptions	−0.60***	−0.08*	0.01	0.05*	83.53	0.04
Prevention mouth rinse attitudes	−0.60***	−0.08	0.13	0.05	28.14	0.03
Prevention mouth rinse intentions	−0.60***	0.02	0.00	−0.01	100.00^†	0.00^†
Detection message perceptions	−0.60***	−0.08**	0.02	0.05*	70.00	0.04
Detection mouth rinse attitudes	−0.60***	−0.11*	0.05	0.07*	56.84	0.04
Detection mouth rinse intentions	−0.60***	0.00	−0.19	−0.00	0.42	0.00^†
Prevention health behaviors	−0.60***	−0.23**	0.30*	0.14**	31.57	0.05
Detection health behaviors	−0.60***	−0.29***	0.16	0.18**	52.17	0.06
	Dental health information avoidance
Prevention message perceptions	−0.44***	−0.14***	−0.00	0.06**	100.00^†	0.05
Prevention mouth rinse attitudes	−0.44***	−0.23***	0.07	0.10***	58.97	0.06
Prevention mouth rinse intentions	−0.44***	−0.16**	−0.08	0.07*	0.00^†	0.03
Detection message perceptions	−0.44***	−0.18***	−0.01	0.08***	100.00^†	0.06
Detection mouth rinse attitudes	−0.44***	−0.29***	−0.01	0.13***	100.00^†	0.07
Detection mouth rinse intentions	−0.44***	−0.12	−0.25*	0.05	0.00^†	0.02
Optimism
Outcome	General information avoidance
Prevention message perceptions	−0.33***	−0.06*	0.07*	0.02	22.03	0.03
Prevention mouth rinse attitudes	−0.33***	−0.08	0.09	0.03	23.70	0.02
Prevention mouth rinse intentions	−0.33***	0.04	0.11	−0.01	0.00^†	0.00^†
Detection message perceptions	−0.33***	−0.07*	0.06	0.02*	27.62	0.03
Detection mouth rinse attitudes	−0.33***	−0.10*	0.07	0.03*	34.87	0.03
Detection mouth rinse intentions	−0.33***	0.08	0.19*	−0.03	0.00^†	0.00^†
Prevention health behaviors	−0.33***	−0.23**	0.22**	0.08**	25.84	0.05
Detection health behaviors	−0.33***	−0.34***	−0.03	0.11***	100.00^†	0.05
	Dental health information avoidance
Prevention message perceptions	−0.14**	−0.13***	0.08*	0.02*	18.78	0.02
Prevention mouth rinse attitudes	−0.14**	−0.23***	0.08	0.03*	29.00	0.03
Prevention mouth rinse intentions	−0.14**	−0.14*	0.07	0.02	21.22	0.01
Detection message perceptions	−0.14**	−0.17***	0.06	0.02*	27.73	0.03
Detection mouth rinse attitudes	−0.14**	−0.28***	−0.01	0.04*	100.00^†	0.04
Detection mouth rinse intentions	−0.14**	−0.07	0.15*	0.01	5.99	0.01

Paths a, b, and c’ refer to the effect of CFC or optimism on the corresponding information avoidance construct, the effect of the corresponding information avoidance construct on the outcome, and the direct effect of CFC on the outcome, respectively. Estimate refers to the pathway representing the indirect effect, which is the product of paths a × b. % Mediated refers to the ratio of the indirect effect to the total effect ((a × b)/(c’ + a × b)), resulting in the proportion mediated by the corresponding information avoidance construct. Stand Coef refers to the standardized coefficient of the indirect effect.

†

Values are clipped between 0 and 100, due to inconsistent mediation (Preacher and Kelley, 2011).

p < 0.05. **p < 0.01. ***p < 0.001.

Hypothesis 2, which predicted that optimism would relate negatively to information avoidance, and in turn information avoidance would negatively predict outcomes, was also supported (see Table 2 for results and Supplementary File for figures of all significant results). General information avoidance mediated the relationship between optimism and one of the four prevention outcomes: prevention health behaviors (β = 0.08, p = 0.004). General information avoidance mediated the relationship between optimism and three of the four detection outcomes: detection message perceptions (β = 0.02, p = 0.027), detection mouth rinse attitudes (β = 0.03, p = 0.026), and detection health behaviors (β = 0.11, p < 0.001). Dental health information avoidance mediated the relationship between optimism and two of the three prevention message outcomes: prevention message perceptions (β = 0.02, p = 0.022) and prevention mouth rinse attitudes (β = 0.03, p = 0.014). Dental health information avoidance mediated the relationship between optimism and two of the three detection message outcomes: detection message perceptions (β = 0.02, p = 0.013) and detection mouth rinse attitudes β = 0.04, p = 0.011). Effect sizes for the mediated effects are reported in Table 2. The proportion mediated by general information avoidance ranged from 22% to 35% and dental health information avoidance from 9% to 29%. The standardized effect sizes for general information avoidance ranged from 0.02 to 0.05 and dental health information avoidance from 0.01 to 0.04. Akin to the CFC model, the two outcomes where neither proportion mediated effect size nor standardized mediation paths were able to be computed (i.e. prevention and detection mouth rinse intentions) also yielded nonsignificant indirect effects.

Discussion

Future orientation is known to predict health and healthy behavior, but little empirical work has focused on why future orientation may lead to these positive outcomes (Baird et al., 2021). Information avoidance is a possible determinant of positive health outcomes, which theory ties to future orientation (Aspinwall and Taylor, 1997; Carstensen et al., 1999). We examined whether information avoidance (or, rather, a lack thereof) explains the relationship between future orientation—conceptualized as consideration of future consequences (CFC) and dispositional optimism—and health-related outcomes. In a sample of young adults, we tested this on outcomes concerning responses to dental hygiene health messages and self-reported health behaviors. Significant mediated or indirect effects were observed for both self-reported engagement in a variety of health behaviors (prevention and detection), as well as responses to persuasive messages advocating a behavior with either a prevention or detection function. Furthermore, associations between measures of information avoidance and message responses were, as a whole, stronger when a domain-specific measure (vs general measure) of information avoidance was used.

Information avoidance, thus, joins a limited array of potential mechanisms explaining the relationship between future orientation and health. In our sample, indirect effects were present even in cases when there was no overall relationship between future orientation and outcomes, which suggests other unmeasured indirect effects may exist. Joireman et al. (2012) tested whether regulatory focus acted as a mediating variable. Regulatory focus is an individual’s tendency to prioritize seeking their ideal goals (promotion orientation) versus avoiding negative outcomes (prevention orientation). Results showed that future orientation predicted attitudes and intentions for healthy eating and exercise indirectly through promotion orientation. They also found that immediate orientation was associated with a prevention orientation, and other work (Ferrer et al., 2017) shows health-related prevention orientation associated with information avoidance relating to cancer. Maladaptive coping strategies, which include aspects of avoidance, also mediated the relationship between future orientation and outcomes (Chua et al., 2015). Finally, in a recent meta-analysis, Baird et al. (2021) examined whether self-regulatory processes like goal monitoring, goal operating, goal setting, and self-regulatory ability mediate the relationship between future time perspective and positive outcomes. They found that goal operating, self-regulatory ability, and, most interestingly to the present work, goal monitoring (which involves the learning of information), were all significant mediators (see also Schumacher et al., 2021). One explanation regarding how the present results fit within this prior work is that information avoidance may be a common denominator among those mediating variables (i.e. promotion-orientation, coping strategies, goal monitoring), given that acquiring rather than avoiding information is a common theme throughout. Another possibility—which would confirm the above observation that there may unmeasured indirect effects present—is that information avoidance is just one part of a larger self-regulatory framework through which future orientation contributes to promotion-focused orientations, better coping skills, goal-oriented behaviors, and less information avoidance.

We also found that future orientation predicted more favorable responses to persuasive health messages (i.e. message perceptions and behavioral attitudes), indirectly via information avoidance. Thus, future orientation may promote health via greater receptivity to health information. These findings are consistent with prior work showing that people high (vs low) in CFC tend to respond more positively to health messages, particularly those that emphasize long-term health benefits (e.g. Orbell and Kyriakaki, 2008). Our work shows that this may be due, in part, to a tendency for people low in CFC to avoid processing such messages. Providing further support, information avoidance measures—particularly the dental-specific information avoidance measure—were more strongly associated, on whole, with responses to the messages than CFC.

We did not, however, find consistent support for the indirect effects of information avoidance on intentions to use or buy a prevention mouth rinse, nor on intentions to use or buy the detection mouth rinse. While the lack of support for indirect effects on the mouth rinse intention outcomes might suggest a limited effect of information avoidance on behavior, we did find evidence of the mediation effects on other prevention behaviors and indirect effects on other detection behaviors.

The strength of the relationship between information avoidance and health outcomes is notable. The correlations between our measures of information avoidance with health outcomes were stronger, in most cases, than the correlation between CFC or optimism with health outcomes. Furthermore, the correlation between information avoidance and self-reported behaviors in this sample is comparable to the correlation between theory of planned behavior components (subjective norms, attitudes) and behavior as reported in meta-analyses of smoking behavior (Topa and Moriano, 2010), chronic illness-related behaviors (Rich et al., 2015), and a broad range of behaviors (Notani, 1998). Thus, our results support further examination of information avoidance as a determinant of healthy behavior.

What do our findings suggest for intervention? Given that future orientation—whether conceptualized as CFC or optimism—is considered a stable, trait-like construct, we argue that interventions that address information avoidance may be promising. Howell and Shepperd (2012) found self-affirmation to reduce tendencies to avoid information. Self-affirmation involves acts that focus on aspects of a person’s identity and ideals, which helps one to overcome threats such as learning unpleasant information about one’s health (Cohen and Sherman, 2014). Another method researchers have found to successfully reduce health information avoidance is contemplation, which involves weighing the benefits of seeking information and detriments of avoiding information (Howell et al., 2016). Both interventions are easy-to-implement solutions that require little resources like time or money.

Limitations and future directions

We used a cross-sectional, nonexperimental method to examine mediational effects, which suggests rather than demonstrates a causal relationship. Despite this limitation, we believe that it is less likely that information avoidance tendencies contribute to future orientation than vice versa (cf. Carstensen et al., 1999). Nonetheless, future work should examine this phenomenon using methods better equipped to demonstrate causation. Although the magnitudes of the mediated paths were also small, they should be interpreted in relation to the magnitudes of meta-associations between future orientation and health outcomes (meta-analytic r’s = 0.07–0.19; Boehm et al., 2018; Murphy and Dockray, 2018; Rasmussen et al., 2009). We also note instances of inconsistent mediation that influenced the interpretability of the “proportion mediated” measure of mediation effect size. These were a minority of cases where the direction of the unmediated effect of future orientation was opposite to that of the mediated effect, suggesting multiple ways in which future orientation may predict outcomes. However, these were cases where the indirect effects of information avoidance were not statistically significant.

Our sample was predominantly young, White, and female, which could limit the generalizability of our findings. In their meta-analysis, Murphy and Dockray (2018) found that sex, mean age, and whether samples were college students did not moderate the effect of CFC on health outcomes. Furthermore, Shanahan et al. (2021) found that sex and age did not moderate the effect of optimism on health outcomes. Finally, as the health information avoidance literature is developing, it remains unclear whether and how groups differ in information avoidance (Howell et al., 2020). A second limitation of a convenience sample is that young adults may be less interested in learning about dental hygiene than others. Although manipulation checks indicated participants presumably attended to the messages, their engagement with and responses to the messages may have been reduced. Additionally, young adults may have prior negative attitudes toward using mouth rinse which could reduce the effect of any other predictor variables or influence of health information. To offset this limitation, we also assessed self-reported engagement in a host of other health behaviors expected to be more relevant to young adults (e.g. flu shots, sunscreen use, and avoiding excess alcohol intake). Effects were robust to a range of outcomes with possible varying levels of interest among the sample.

A final limitation of the present work was the one-dimensional operationalization of information avoidance. While we were able to conceptualize future orientation in multiple ways (i.e. CFC and dispositional optimism) as well as assessing multiple types of outcomes (i.e. responses to health messages and self-reported health behaviors), including multiple avoidance measures would be ideal. Information avoidance can manifest in many ways (Sweeny et al., 2010); it can be passive or active, a one-time behavior or a general tendency, some view it as a continuum between information avoidance and information seeking while others view the two as independent. Although we assessed information avoidance in both a general and domain-specific manner, both scales measured individual differences in information avoidance tendencies. Finally, the low levels of information avoidance in our sample may limit the extent to which we can expect similar findings in samples who are high in avoidance. Future work would need to assess whether this mediation effect extends to other types of avoidance, such as one-time behaviors like avoiding a doctor’s appointment or taking an educational pamphlet about a relevant health disorder, as well as information seeking behaviors versus active information avoidant behaviors.

Conclusion

We show information avoidance to be both a novel potential mechanism explaining the relationship between future orientation and health behavior, as well as a meaningful predictor of self-reported health behavior and responses to health messages. We also show that information avoidance is associated not only with detection behaviors, but with prevention behaviors as well. Our findings join a growing literature showing the importance of information avoidance for a variety of health behaviors and suggest a potential intervention target for individuals whose characteristic ways of (not) thinking about their future might keep them unaware and unhealthy.

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Footnotes

Acknowledgements

We thank Toni Santoro for their contributions to this study.

Data sharing statement

The current article is accompanied by the relevant raw data generated during and/or analysed during the study, including files detailing the analyses and either the complete database or other relevant raw data. These files are available in the Figshare repository and accessible as Supplemental Material via the Sage Journals platform. Ethics approval, participant permissions, and all other relevant approvals were granted for this data sharing.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethics approval

This study was approved by the Institutional Review Board at Kent State University (Approval #20-308). Ethics approval, participant permissions, and all other relevant approvals were granted for this data sharing.

Informed consent

All participants provided written electronic informed consent.

Preregistration

This study was preregistered via OSF.

ORCID iD

Karigan P Capps

Notes

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