Perceived Onset Time of Medical Conditions: The Interplay Between Subjective Fear and Risk in Four Lifestyle Domains

Abstract

Engaging in unhealthy behaviors (e.g., smoking, drinking) and not engaging in healthy ones (e.g., exercising, consuming fruit and vegetables) are both relatively prevalent among individuals despite the available information about their risks for health. People’s perception of an event’s time course can be used to gauge their risk perception for that event thus casting light on any possible misperception and suggesting directions for health-promoting interventions. This study investigates people’s perception of the time of onset of 5 noncommunicable diseases (e.g., “having high blood pressure”) associated with 4 health-related behaviors: Smoking, drinking, exercising, and eating fruit and vegetable. Participants from Italy (N = 214) and the UK (N = 151) gave onset time estimates of how long they thought it would take for 5 noncommunicable diseases to occur in the life of an 18-year-old person who starts or stops adopting those health-related behaviors. Results showed that participants who rated the noncommunicable diseases as more likely to themselves perceived the onset time of these diseases as more temporally proximal. Participants who were more afraid of developing the noncommunicable diseases estimated their onset time as delayed.

Keywords

Onset time delaying effect health behaviors risk perception affect heuristic omission bias

Introduction

According to the World Health Organization (WHO), noncommunicable diseases (NCDs), such as cardiovascular diseases, cancer, diabetes, are responsible for 71% of all deaths globally (World Health Organization, 2018). Although NCDs can be caused by several factors, lifestyle is one of the major predictors of their development. Specifically, four behaviors seem to play a key role in increasing the risk of NCDs: Tobacco smoking, alcohol consumption, physical inactivity, and unhealthy dietary habits. Moreover, recent empirical evidence has suggested that these four behaviors tend to co-occur and cluster together in both healthy populations (Cuenca-García et al., 2013; de Vries et al., 2008) and samples of patients with acute and chronic conditions (Ha et al., 2017; Monzani et al., 2018; Steca et al., 2017). Although these unhealthy behaviors are modifiable, they are still relatively prevalent, albeit with differences across countries, as shown by the percentages of cigarette smokers and individuals consuming alcohol (e.g., in the UK, in 2019, 14.1% of individuals aged 18 and above smoked (Office for National Statistics, 2020), while in Italy, in 2019, 18.4% of individuals aged 14 and above smoked (Istat, n.d.)).

Health-promoting behaviors encompass not only the avoidance of risky behaviors (for example, by not drinking, not smoking) but also the adoption of health-protective behaviors, such as making healthy dietary choices or doing physical exercise. However, these behaviors are made difficult by barriers, such as the resistance to new health information due to its perceived uncertainty (Rothman & Salovey, 2007), and, once adopted, the difficulty to maintain consistently (Hall & Fong, 2007).

Fruit and vegetable consumption is one of the behaviors that can reduce the risks of NCDs, such as cardiovascular diseases and some forms of cancer (Harris et al., 2014; He et al., 2006; Kothe et al., 2012). Accordingly, the WHO recommends a daily intake of more than 400 g of fruits and vegetables to reduce the risks of some chronic diseases and deficiencies (World Health Organization, 2019). This has been operationalized as a minimum of 5 intakes of 80 g of fruits and vegetables per day in several countries, including the United Kingdom (Harris et al., 2014).

Not only dietary habits but also physical activity can reduce the risk of some NCDs such as coronary heart disease, stroke, diabetes, hypertension, some forms of cancer, and depression (World Health Organization, 2010). Thus, the WHO recommends at least 150 minutes of medium-intensity or 75 minutes of high-intensity aerobic physical activity in a week for adults aged between 18 and 64 (World Health Organization, 2010).

Our study focused on both healthy (i.e., exercising, consuming fruit and vegetables) and unhealthy behaviors (i.e., smoking, drinking) to investigate individuals’ risk perceptions. Notwithstanding the information and awareness about the health risks associated with unhealthy behaviors such as smoking and alcohol consumption and the health benefits associated with healthy behaviors such as physical exercise and proper diet, the number of people who fail to follow the recommendations is considerable. This raises the question of whether people adequately perceive the risks from engaging in unhealthy behaviors and from not engaging in healthy ones, which our study aimed to address.

Risk perception and the temporal dynamics underlying health outcomes

Research has investigated several factors underlying unhealthy outcomes and individuals’ risk perceptions. For example, people’s optimistic bias regarding personal risk may interfere with their efforts to adopt health-promoting behaviors because they tend to believe that their own risk of developing NCDs is lower than the risk of others (e.g., Weinstein & Lyon, 1999). Indeed, empirical evidence has suggested that people tend to underestimate their personal risk from cigarette smoking (Arnett, 2000; Masiero et al., 2015, 2018; Weinstein et al., 2005) as well as alcohol consumption (Masiero et al., 2018; Wild et al., 2001) and unhealthy diet (Miles & Scaife, 2003; Scaife et al., 2006). Moreover, personality is a predictor of health-related behaviors (Booth‐Kewley & Vickers, 1994; Ozer & Benet-Martínez, 2006). Trait procrastination, which reflects a tendency to delay task initiation and/or completion (Milgram & Tenne, 2000), is related to treatment delay for health problems as well as reduced healthy behaviors (e.g., proper diet and physical activity) and intentions to engage in them (Sirois, 2004; Sirois et al., 2003). Similarly, based on the fact that the benefits deriving from the adoption of healthy behaviors usually occur in the future (Adams & White, 2009; Taylor et al., 2002), individual’s future orientation, that is one’s value for the future outcomes of present behaviors, is associated with health-related behaviors, such as substance non-use and vegetable consumption (Adams, 2012; Adams & White, 2009; Keough et al., 1999; Wardle & Steptoe, 2003).

In keeping with these studies on temporal-based individual differences in health habits and behaviors (such as valuing future outcomes, future planning and delaying), research has also focused on temporal perception to investigate individuals’ risk perception. Some scholars have investigated both unhealthy (drinking, smoking) and healthy (exercise, healthy diet) behaviors (Hall, 2007; Hall & Fong, 2007), focusing on time perceptions in healthy adults. The authors claimed that this perspective could give us insights into the rationality underlying health-protective and health-risk behaviors by considering the perceived temporal dynamics of costs and benefits of (un)healthy behaviors (Hall & Fong, 2007). Hall and colleagues found that while costs are perceived as temporally closer and benefits as occurring later for undertaking healthy behaviors, such as making a healthy dietary choice or doing exercise, the reverse is observed for health-risk behaviors such as drinking and smoking (Hall, 2007; Hall & Fong, 2007).

Sharing this focus on the temporal dynamics of the consequences of health behaviors, a recent study has shown that a cognitive process called “onset time delaying” can help differentiate smokers’ and non-smokers’ perception of the temporal frame of smoking-related conditions. Smokers tend to postpone the estimated onset time of smoking-related medical conditions (e.g., “lung cancer") in others by approximately five years on average compared to non-smokers (Pancani & Rusconi, 2018). The authors also investigated the impact of the individual’s risk perception and level of fear for the smoking-related conditions for which they were asked to judge the onset time. They found that participants’ self-perceptions of risk and fear interacted for mild smoking-related conditions only (e.g., “gingivitis”). When participants’ perceived fear was high, the onset time was less delayed at increasing levels of perceived risk. When perceived fear was low, no association between risk perception and onset time was found.

Although the onset time delaying effect seems a promising cognitive factor involved in behavioral habits, it has only been tested for smoking behavior. This leaves the issue of whether and to what extent this effect and its antecedents (i.e., risk perception, level of fear, and current behavior) can be generalized to other health-risk behaviors, such as alcohol consumption, as well as health-protective behaviors, such as fruit and vegetable consumption and physical exercise, unaddressed. The present study aimed to fill this gap and test the hypothesis of a ‘trans-disease process’ (Barlow et al., 2017; Bickel et al., 2012; Pancani & Rusconi, 2018), whereby time distortions could represent a “cognitive risk factor” that applies to a range of health-related behaviors.

The present study

The main aim of this study was to test the hypothesis of time distortion in the evaluation of health-related consequences as a ‘trans-disease process’ (Barlow et al., 2017; Bickel et al., 2012; Pancani & Rusconi, 2018). In particular, the study evaluated people’s onset time estimations for a set of NCDs associated with habits in four lifestyle domains: Smoking, alcohol consumption, physical activity, and fruit and vegetable intake. Specifically, for each lifestyle domain, we tested whether people’s perception of the onset time of NCDs estimated on a third person was influenced by the complex interaction among their actual behavior, their own perceived likelihood of developing these NCDs, and the affective impact that the NCDs have on themselves.

Based on the study by Pancani and Rusconi (2018), we hypothesized that people who self-report adopting unhealthier behaviors would be more likely than people who self-report adopting healthy behaviors to postpone the onset of NCDs for all the lifestyle domains we investigated. Moreover, we will explore whether people’s perceived onset time of NCDs is associated with self-perceptions of fear and risk.

Method

Participants

Three hundred and sixty-five participants aged 18 or older were recruited in Italy and the UK with a snowball sampling method (the Italian sample) and Prolific Academic (Prolific, 2018), an online platform used for data collection (the UK sample). They were mainly women (72.9% female) with a mean age of 29.39 (SD_age = 10.92, range = 18–68). Considering education, many participants (48.8%) possessed a Bachelor’s degree, 13.2% a Master’s degree, and 28.2% a high‐school diploma. A total of 7.4% of participants had a lower educational level, and 2.5% had a Ph.D. or a postgraduate educational degree. Finally, 32.9% of the participants were single, 60.2% were married or lived with their partner, 3.0% were divorced or separated, and 3.8% were widowed. The Italian sample encompassed 213 participants (75.1% female; M_age = 25.15, SD_age = 7.09, range: 18–68); considering education, many Italian participants (59.2%) possessed a Bachelor’s degree, 12.2% a Master’s degree, and 26.3% a high‐school diploma. A total of 2.3% had a Ph.D. or a postgraduate educational degree. The 35.2% of the participants were single, 63.4% were married or lived with their partner, and 1.4% were widowed. The UK sample included 152 participants (69.7% female; M_age = 35.33, SD_age = 12.48, range: 18–65); considering education, many participants (34.2%) possessed a Bachelor’s degree, 14.5% a Master’s degree, and 30.9% a high‐school diploma. A total of 17.8% of participants had a lower educational level, and 2.6% had a Ph.D. or a postgraduate educational degree. The 29.6% of the participants were single, 55.9% were married or lived with their partner, 7.2% were divorced or separated, and 7.8% were widowed.

The two samples did not differ in gender distribution [χ²(1, N = 365) = 1.30, p = .254], but the Italian sample was younger than the UK one [t(363) = −9.88, p < .001]. Moreover, the two samples differed in marital status [χ²(3, N = 365) = 24.94, p < .001] and educational level [χ²(4, N = 365) = 50.20, p < .001]; namely, in the Italian sample, there were fewer people with low educational level, fewer separeted/divorced and widowed people, but a higher number of people with a Bachelor’s degree. All participants were volunteers.¹

Materials and procedure

The research was presented as “a study of lifestyles”. We applied filters to Prolific Academic to select participants within the 18–70 age range, currently UK resident, and of UK nationality. In Italy, the study was advertised on social networks (e.g., Facebook). Individuals interested in participating were contacted via email and they were given a link to a survey on Qualtrics® (Quatrics, 2018), an Internet-based survey tool. At that link, participants found a brief introduction about the questions included in the survey, some instructions to complete it (e.g., read the questions carefully, be sincere, etc.), and the consent form approved by the Ethics Committee of a large UK university. The study was conducted in compliance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Data were collected during October and November 2018.

The following five NCDs were selected from a list of common negative outcomes of unhealthy behaviors by the authors of the present paper: “Premature skin aging,” “High blood pressure,” “Coronary artery disease,” “Stroke,” and “Cancer.” These NCDs were then used as items in three instruments adapted from Pancani and Rusconi (2018). The first two instruments aimed at measuring perceived likelihood and affective impact for the NCDs on a slider scale. The perceived likelihood was assessed by asking participants to indicate their likelihood to develop each NCD by moving the slider on a 0–100 scale, with 0 meaning “It is very unlikely that it will occur to me” and 100 meaning “It is very likely that it will occur to me”. The affective impact was measured by asking participants how much they were afraid of or hopeful of developing each NCD, from “Extremely hopeful” (−50) to “Extremely afraid” (+ 50).² Thus, while the perceived likelihood measure was meant to tap into participants’ risk perceptions in terms of perceived likelihood of developing the NCDs, the affective impact measure aimed at gauging the magnitude of hope vs. fear participants felt about developing them. The third instrument aimed at measuring the perceived onset time of the NCDs. The onset time was measured separately for each of the four lifestyle domains, that is, smoking behavior, alcohol consumption, physical activity, and fruit and vegetable intake, by asking participants to think about a person who:

Starts smoking at 18 years old and smokes every day from that moment on.

Starts drinking spirits at 18 years old and drinks spirits every day from that moment on.

Stops practicing physical activity regularly at 18 years old and does not start again from that moment on.

Stops eating fruits and vegetables regularly at 18 years old and does not start again from that moment on.

In relation to each target person, participants were asked to estimate the temporal distance between the initiation (for targets 1) and 2)) or the interruption (for targets 3) and 4)) of each health behavior and the onset of each of the five NCDs (i.e., premature skin aging, high blood pressure, coronary artery disease, stroke, and cancer) on an 11-point scale with the following options: “immediately” (1), “one day” (2), “one week” (3), “one month” (4), “three months” (5), “one year” (6), “five years” (7), “ten years” (8), “twenty years” (9), “thirty years or more” (10), and “never” (11) (for a similar method, see Pancani & Rusconi, 2018).

Then, participants’ adoption or avoidance of the health behaviors used in the onset time instrument (i.e., cigarette smoking, alcohol consumption, physical activity, and fruit and vegetable intake) were assessed through self-report measures. Specifically, participants were asked to provide their current smoking status (SS) choosing one of the following options: Current smoker, former smoker, and never smoker.

Alcohol consumption was measured with the self-report version of the Alcohol Use Disorders Identification Test ( AUDIT; Saunders, Aasland, Amundsen, et al., 1993; Saunders, Aasland, Babor, et al., 1993). The AUDIT is a 10-item screening tool developed by the WHO to assess alcohol consumption and drinking behaviors. Participants were asked to indicate their drinking behaviors on a 5-point (items 1–8) or a 3-point (items 9 and 10) Likert scale. A sample item is “How often do you have a drink containing alcohol?” with responses ranging from “Never” (0) to “4 or more times a week” (4). Sum scores range from 0 to 40 with higher values denoting higher alcohol consumption and more frequent drinking behaviors. Sum scores were then recoded so that higher levels reflected healthier drinking habits.

Physical activity was measured by the Global Physical Activity Questionnaire (GPAQ; Armstrong & Bull, 2006). The GPAQ is a 16-item screening tool developed by the WHO to assess metabolic equivalents (METs) that are commonly used to express the intensity of physical activity. Specifically, participants were asked to report the level and frequency of their physical activities in three different settings: Activity at work, travel to and from places, and recreational activities. A sample item is: “Does your work involve vigorous-intensity activity that causes large increases in breathing or heart rate like [carrying or lifting heavy loads, digging or construction work] for at least 10 minutes continuously?” with responses “no” (0) and “yes” (1). Scoring was computed as METs per week, that is the energy expenditure in terms of caloric consumption that can be ascribed to physical activity over a typical week. Higher scores of MET denote more frequent and intense physical activity.

Finally, fruit and vegetable intake was measured with the Behavioral Risk Factor Surveillance System (BRFSS) fruit and vegetable dietary intake module (Centers for Disease Control and Prevention, 2017). The BRFSS fruit and vegetable dietary intake module is a 6-item tool developed by the Centers for Disease and Prevention of the U.S. Department of Health and Human Services to assess the frequency of consumption of 100% fruit juice, fruit, beans (legumes), dark green vegetables, orange vegetables, and other vegetables over the past month. For each of the six items, participants were asked to indicate their intake on a 9-point Likert scale with the following options: “never” (0), “1–3 per month” (1), “once a week” (2), “2–4 per week” (3), “5–6 per week” (4), “once a day” (5), “2–3 per day” (6), “4–5 per day” (7), “6+ per day” (8). A sample item is: “During the past month, how many times did you drink 100% pure fruit juices? (do not include fruit-flavored drinks or drinks with added sugar).” Scores were computed as the sum of items’ responses and ranged from 0 to 48, where higher scores denoted more frequent intake of fruit and vegetable.

Data analysis

We performed multilevel regression models with the MIXED MODEL command of Jamovi 0.9 (Gallucci, 2019; The Jamovi Project, 2019). Specifically, we performed four multilevel regression models, one for each of the four lifestyles. In each regression, the onset time for the NCDs was considered as a within-subject dependent variable, perceived likelihood and affective impact as within-subject predictors, and participants’ healthy lifestyles (alcohol consumption, physical activity, and fruit and vegetable intake continuous scales were coded so that higher values indicated health-promoting behaviors; smoking status was treated as a three-level categorical variable) as a between-subject predictor. Besides the main effects, we included second- and third-order interaction terms and the random intercept for the dependent variable. We adopted a stepwise backward regression analysis approach. Thus, starting from the full model, non-significant, higher-order terms were eliminated one at a time, to obtain a final, more parsimonious model. If none of the interaction terms were significant, the final model included only the main effects of all the predictors. For significant interactions, simple slope analysis was performed to test the effect of a specific predictor at different levels (i.e., one standard deviation above and below the mean) of another predictor. Participants' age was entered in each model to control for any age-related differences in the onset time. All continuous independent variables (i.e., perceived likelihood, affective impact, and behavior measures except smoking status) were grand-mean centered. Smoking status was entered as two dummy coded variables in which “Smoker” represented the reference value. Thus, the first dummy variable assessed differences in the dependent variable between smokers and former smokers, while the second one evaluated differences between smokers and non-smokers.

Results

Descriptive statistics

Concerning the assessment of participants’ lifestyles, the sample included 87 smokers (23.8%), 47 former smokers (12.9%), and 231 non-smokers (63.3%). Reverse AUDIT score ranged from 6 to 40 (M = 32.62, SD = 5.08), GPAQ from 0 to 34,320 (M = 3,366.30, SD = 4,841.41), and BRFSS from 1 to 42 (M = 19.39, SD = 7.02).

Descriptive statistics about perceived likelihood, affective impact, and the four onset time measures were computed for each of the five NCDs and reported in Table 1. Overall, the reported onset time for NCDs ranged from 6.95 to 9.28, meaning that participants thought that NCDs would appear about one to twenty years after the target person started smoking or drinking or stopped exercising or eating healthy foods. By considering the overall mean of perceived likelihood of each NCD that ranged from 36.25 to 45.86, participants tended to perceive the five diseases as moderately unlikely to occur to themselves. People were also moderately fearful of each NCD, with an average rating of the affective impact of the five diseases ranging from 19.34 to 35.75. As hypothesized, very few participants reported negative values of affective impact (i.e., declared to be at least to a certain degree hopeful of developing the NCDs): 22 (6.0%) for premature skin aging, 21 (5.8%) for high blood pressure, 17 (4.7%) for coronary artery disease, 15 (4.1%) for stroke, and 15 (4.1%) for cancer. Thus, we used the labels “low fear” and “high fear” to describe the results of affective impact.

Table 1.

Descriptive statistics of perceived likelihood (range 0 “Very unlikely” – 100 “Very likely”), affective impact (range −50 “Extremely hopeful” – + 50 “Extremely afraid”), and onset time (range 1 “Immediately” – 11 “Never”) for each lifestyle.

	Premature skin aging	High blood pressure	Coronary artery disease	Stroke	Cancer
PL	39.04 (23.44)	45.86 (26.42)	36.25 (23.31)	38.79 (24.15)	44.58 (24.24)
AI	21.70 (19.28)	19.34 (17.90)	28.25 (19.15)	35.75 (19.79)	40.88 (18.72)
OT smoking	7.28 (1.92)	7.30 (2.20)	7.88 (1.89)	8.42 (1.85)	8.37 (1.82)
OT alcohol	7.87 (2.01)	6.95 (2.44)	7.69 (1.97)	8.21 (1.87)	8.58 (1.80)
OT physical activity	8.74 (2.04)	7.68 (2.12)	8.32 (1.85)	8.81 (1.77)	9.28 (1.74)
OT fruit and vegetables	8.21 (2.03)	7.84 (2.25)	8.25 (1.92)	8.82 (1.81)	8.98 (1.73)

Note: Means and standard deviations (in brackets) are reported for each noncommunicable disease. PL = Perceived likelihood; AI = Affective impact; OT = Onset time.

Onset time for NCDs related to cigarette smoking

The stepwise backward regression analysis led to the final model reported in Table 2. As can be seen in Figure 1,³ the perceived onset of NCDs was delayed for more fearful NCDs and those perceived as less likely. Moreover, we found a significant interaction between perceived likelihood and affective impact. The simple slope analysis showed that when NCDs were perceived as more fearful (i.e., one standard deviation above the affective impact’s grand-mean), the perceived likelihood did not affect the onset estimates of diseases (b = −8.55 × 10⁻⁴, SE = 0.002, t = −0.346, p < .729); on the other hand, when NCDs were perceived as less fearful (i.e., one standard deviation below the affective impact’s grand-mean), the higher the perceived likelihood, the closer the estimated onset time (b = −0.007, SE = 0.002, t = −2.878, p = .004). As for the between-subject variables, age significantly and positively predicted onset time (i.e., older participants delayed the onset time of NCDs more than younger participants did), whereas smoking status did not show any effect (i.e., onset time estimations did not differ between smokers and non-smokers and between smokers and former smokers). The variance of the random intercept was significant (σ² = 1.97; p < .001).

Table 2.

Results of the final multilevel regression model for the onset time of NCDs related to cigarette smoking.

Parameter	b (SE)	t (p-value)	95% C.I.
Intercept	7.874 (0.099)	79.523(<.001)	7.680, 8.068
Age	0.015 (0.008)	2.057 (.040)	0.001, 0.030
PL	−0.004 (0.002)	−2.077 (.038)	−0.008, −0.000
AI	0.020 (0.002)	9.070 (<.001)	0.015, 0.024
Former smokers^a	−0.342 (0.282)	−1.214 (.226)	−0.893, 0.210
Non-smokers^a	0.322 (0.194)	−1.662 (.097)	−0.701, 0.058
PL × AI	0.0001 (0.0000)	1.972 (.049)	0.0000, 0.0003

Note: a = “Smokers” is reference value; PL = Perceived likelihood; AI = Affective impact.

Figure 1.

Graphical representation of the final regression model for the onset time of NCDs related to cigarette smoking. Note: Healthy Behavior = Non-smokers; Unhealthy Behavior = Smokers.

Onset time for NCDs related to alcohol consumption

Table 3 shows the results of the final model for alcohol intake. As can be seen in Figure 2, similarly to cigarette smoking, the perceived onset of NCDs was delayed for more fearful NCDs and those perceived as less likely. Moreover, we found a significant interaction between perceived likelihood and affective impact. The results of the simple slope analysis were comparable to those obtained for cigarette smoking. When NCDs were perceived as more fearful (i.e., one standard deviation above the affective impact’s grand-mean), the perceived likelihood did not affect the onset estimates of diseases (b = −0.004, SE = 0.003, t = −1.662, p = .097); on the other hand, when NCDs were perceived as less fearful (i.e., one standard deviation below the affective impacts' grand-mean), the higher the perceived likelihood, the closer the estimated onset time (b = −0.012, SE = 0.003, t = −4.714, p < .001). Finally, the main effects of the between-subject variables were comparable to those found on cigarette smoking: Older participants tended to delay the onset time of NCDs to a greater extent than younger participants, but no significant effect of participants’ alcohol intake was found. The variance of the random intercept was significant (σ² = 2.40; p < .001).

Table 3.

Results of the final multilevel regression model for the onset time of NCDs related to alcohol consumption.

Parameter	b (SE)	t (p-value)	95% C.I.
Intercept	7.853 (0.088)	89.754 (<.001)	7.681, 8.024
Age	0.021 (0.008)	2.605 (.010)	0.005, 0.038
PL	−0.008 (0.002)	−4.108 (<.001)	−0.012, −0.004
AI	0.019 (0.002)	8.448 (<.001)	0.014, 0.023
Behavior	−0.013 (0.018)	−0.744 (.457)	−0.048, 0.021
PL x AI	0.0002 (0.0001)	2.359 (.018)	0.000, 0.0003

Note: The term “Behavior” represents the alcohol consumption of participants; higher values mean lower consumption; PL = Perceived likelihood; AI = Affective impact.

Figure 2.

Graphical representation of the final regression model for the onset time of NCDs related to alcohol consumption.

Onset time for NCDs related to physical activity

The results of the final model for physical activity are reported in Table 4. As shown in Figure 3, the pattern of influence of perceived likelihood, affective impact, and participants’ behavior (i.e., METs per week) on the onset time estimates for NCDs is more complex than what was found for the two previous lifestyles. Specifically, the perceived onset of NCDs was delayed for more sedentary people, more fearful NCDs, and less likely NCDs. Moreover, we found significant interactions between perceived likelihood and affective impact and between the latter and participants’ physical activity. A simple slope analysis on the former interaction showed that perceived likelihood did not affect the disease onset estimates (b = −1.32 × 10⁻⁴, SE = 0.002, t = −0.055, p = 0.956) for more fearful NCDs (i.e., one standard deviation above affective impact’s grand-mean), but it did for less fearful ones (i.e., one standard deviation below the affective impact’s grand-mean), moving their onset closer when perceived more likely (b = −0.012, SE = 0.002, t = −5.057, p < .001). Consistently with the main effect of affective impact, the simple slope analysis for the latter interaction showed a significant and positive effect of affective impact on onset time for both low (i.e., one standard deviation below physical activity’s grand-mean; b = 0.021, SE = 0.003, t = 7.497, p < .001) and high (i.e., one standard deviation above physical activity’s grand-mean; b = 0.014, SE = 0.003, t = 5.601, p < .001) level of physical activity per week. However, this effect was stronger for the less healthy lifestyle, meaning that more, as opposed to less, sedentary people tended to delay the onset time of NCDs with increasing levels of associated fear. Participants’ age did not predict the onset time. The variance of the random intercept was significant (σ² = 1.91; p < .001).

Table 4.

Results of the final multilevel regression model for onset time of NCDs related to physical activity.

Parameter	b (SE)	t (p-value)	95% C.I.
Intercept	8.570 (0.080)	107.653 (<.001)	8.414, 8.726
Age	0.001 (0.007)	0.189 (.850)	−0.013, 0.016
PL	−0.006 (0.002)	−3.286 (.001)	−0.010, −0.002
AI	0.018 (0.002)	−8.461 (<.001)	0.014, 0.022
Behavior	−4.50 × 10⁻⁵ (1.65 × 10⁻⁵)	−2.722 (.007)	−7.73 × 10⁻⁵, −1.26 × 10⁻⁵
PL × AI	0.0002 (0.0001)	−3.917 (<.001)	0.0001, 0.0004
AI × Behavior	−7.21 × 10⁻⁷ (3.47 × 10⁻⁷)	−2.076 (.038)	−1.40 × 10⁻⁶, −4.04 × 10⁻⁸

Note: The term “Behavior” represents the amount of physical activity of participants; higher values mean higher caloric consumption due to physical activity; PL = Perceived likelihood; AI = Affective impact.

Figure 3.

Graphical representation of the final regression model for the onset time of NCDs related to physical activity.

Onset time for NCDs related to low intake of fruit and vegetables

Table 5 reports the results of the final model for fruit and vegetable consumption. As can be seen in Figure 4, the perceived onset of NCDs was delayed for people eating less fruit and vegetables, fearful NCDs, and less likely NCDs. Moreover, we found a significant interaction between perceived likelihood and behavior, showing that the estimated likelihood did not affect the onset time for NCDs of people with lower intake of fruit and vegetables (i.e., one standard deviation below fruit and vegetable intake’s grand-mean; b = -0.001; SE = 0.003, t = −0.437, p = .662); on the other hand, for people eating more fruit and vegetables (i.e., one standard deviation above fruit and vegetable intake’s grand-mean), the higher the estimated likelihood of a NCD, the closer its onset time (b = −0.009, SE = 0.003, t = −3.353, p < .001). Participants’ age did not predict onset time. The variance of the random intercept was significant (σ² = 2.28; p < .001).

Table 5.

Results of the final multilevel regression model for the onset time of NCDs related to fruit and vegetable.

Parameter	b (SE)	t (p-value)	95% C.I.
Intercept	8.413 (0.085)	99.526 (<.001)	8.248, 8.579
Age	0.010 (0.008)	1.225 (.221)	−0.006, 0.025
PL	−0.005 (0.002)	−2.708 (.007)	−0.008, −0.001
AI	0.016 (0.002)	7.815 (<.001)	0.012, 0.020
Behavior	−0.029 (0.012)	−2.331 (.020)	−0.053, −0.005
PL x Behavior	−0.005 (0.003)	−2.053 (.040)	−0.001, −0.0002

Note: The term “Behavior” represents fruit and vegetable intake of participants; higher values mean greater consumption of fruits and vegetables; PL = Perceived likelihood; AI = Affective impact.

Figure 4.

Graphical representation of the final regression model for the onset time of NCDs related to fruit and vegetables intake.

Discussion

Noncommunicable diseases (NCDs), such as cancers, diabetes, and cardiovascular diseases, represent the major cause of death worldwide and lifestyle plays a key role in preventing and protecting from their development (World Health Organization, 2015). The current study empirically investigates a recently proposed cognitive factor that can be involved in the evaluation of healthy and unhealthy behaviors, namely the perceived onset time of NCDs related to unhealthy lifestyles (Pancani & Rusconi, 2018). Specifically, participants were asked to estimate how long it will take for five NCDs (high blood pressure, coronary artery disease, cancer, premature skin aging, and stroke) to occur in four target individuals’ life characterized by a specific unhealthy lifestyle: The regular adoption of unhealthy behavior (i.e., smoking, drinking alcohol) or the permanent cessation of a regular healthy behavior (i.e., doing physical activity, eating fruits and vegetables) at the age of 18. Our results suggest that time distortion in the evaluation of health-related consequences could represent a “trans-disease process” (Barlow et al., 2017; Bickel et al., 2012; Pancani & Rusconi, 2018) that applies to a range of health behaviors. Specifically, across these four different lifestyles, the results consistently showed that risk and affect had opposite effects on participants’ time estimates:

(1) Participants who perceived the NCDs as more likely to themselves (i.e., higher risk perception) perceived the onset time of the NCDs for the targeted individuals as more temporally proximal.

(2) Participants who were more afraid to develop the NCDs (i.e., higher affective impact) estimated the onset time of the NCDs for the targeted individuals farther in the future.

These results point to an independent effect of two constructs that have typically been associated ( Gerend & Maner, 2011; the ‘dread risk’ factor, Slovic, 1987). Although fear and risk perceptions can promote healthy behavior (Chipperfield et al., 2016; Dubayova et al., 2010), excessive levels of subjective fear might be counterproductive as shown by the greater onset time delaying effect in the current study.

The interplay between affect and risk in time estimates

Affect and risk not only had independent, opposite effects on participants’ time estimates, but they also interacted in three of the four lifestyle domains we examined. For smoking, alcohol consumption, and physical activity, we found a significant interaction between perceived likelihood and affective impact on the onset time of the NCDs estimates. Specifically, when the NCDs’ development was perceived as more personally fearful (high affective impact), risk perception did not influence their estimated onset time for the targeted individuals. Conversely, when the NCDs’ development was perceived as less fearful (low affective impact), the higher the perceived risk, the closer the onset time of the NCDs. In other words, one’s own increased perceived affect for a disease ‘knocked down’ the role of one’s own perceived risk in the disease’s onset time estimates, which was instead influential when affect levels were low (see the Limitations and future directions subsection for further discussion of this interaction effect).

The effect of participants’ fear emerged from these results highlights the importance of taking into account the role of emotions in the time perception of NCDs. This is not surprising as previous studies have shown the role played by the affect heuristic, namely a fast and experiential way to make judgments based on current feelings (e.g., Finucane et al., 2000; Slovic et al., 2007). Research on this mental shortcut has shown that people consciously or unconsciously rely on their affective reactions to the stimuli and events involved in their judgments and decisions (Blumenthal-Barby, 2016). In our task, the evaluation of NCDs might have elicited negative affect, self-reported as fear, in the participants. When negative affect was high, that could have tainted their NCDs’ onset time estimates about others overcoming the role of their own perceived risk. Although this process is consistent with our findings, future studies should further explore this aspect to assess whether the affect heuristic is actually involved in reasoning about NCDs’ onset time, for example by controlling for participants’ affective reactions through an emotion induction manipulation.

Surprisingly, we did not find the same perceived likelihood by affective impact interaction pattern for the fruit and vegetable intake. The lack of this effect may be ascribed to the specificity of this health behavior, compared to the other health-promoting behavior we considered (i.e., physical activity), as fruit and vegetable consumption accounts for only part of a healthy diet.

The influence of participants’ health behaviors

Concerning participants’ lifestyles, we found a pattern of results in line with the differentiation between health-promoting and health-risk behaviors found in the literature. Specifically, while health-risk behaviors, such as smoking and alcohol consumption, are correlated with each other and thus they cluster together, their correlation with health-promoting behaviors, such as physical activity and fruit and vegetable intake is low, and vice versa (Boudreaux et al., 2003; Clements-Thompson et al., 1998; Keller et al., 2008; Lippke et al., 2012). Similarly, in the current study, participants’ smoking behavior and alcohol consumption did not influence the onset time of targeted individuals for smoking and alcohol consumption, whereas delaying effects were found when considering both participants’ physical activity and fruit and vegetable intake. Specifically, the unhealthier the participants’ behaviors (i.e., the less physical exercise and less fruit and vegetable intake), the more delayed the NCDs’ onset time for the targeted individuals in relation to those behaviors was. Thus, the onset time estimates followed a different pattern depending on the cluster of health-risk vs. health-promoting behaviors.

The distinction between health-risk (smoking and alcohol consumption) and health-promoting (physical activity and fruit and vegetable intake) behaviors mimics the one between actions and inactions in our task. Indeed, the target individuals either started smoking/drinking or stopped doing physical exercise/consuming fruit and vegetables regularly. In other words, in our task, we asked participants to make time estimates about target individuals who either engaged in unhealthy behaviors, which entails specific actions (i.e., smoking cigarettes, drinking alcoholic beverages), or who stopped engaging in healthy behaviors, which entails stopping specific actions (i.e., stopping doing physical exercise and eating fruits and vegetables). The presence (vs. absence) of participants’ behavior main effect on NCDs’ onset time estimates might be thus interpreted in terms of omission bias, a phenomenon whereby a negative outcome following inaction is preferred to one that follows some action (Baron & Ritov, 2004; DiBonaventura & Chapman, 2008; Ritov & Baron, 1990; Spranca et al., 1991). In terms of healthy lifestyle, inactions, such as stopping health-protective behaviors (e.g., stopping doing physical exercise and consuming fruit and vegetables regularly), would be overlooked as “omissions”. Thus, time estimates related to their negative consequences (e.g., high blood pressure) would be perceived as delayed (i.e., ‘tagged’ as ‘more favorable’ and less risky) compared to those for actions, such as starting drinking and smoking, which, being actions as well as health-risk behaviors, could be perceived as more severe (i.e., more temporally proximal).

In the lifestyle domains of physical activity and fruit and vegetable intake, we also found two significant, although weaker, interactions. For physical activity, more afraid participants moved the NCDs’ onset time farther in the future, irrespective of their lifestyle, though this effect was stronger for those who declared to be less physically active. Furthermore, for the lifestyle of fruit and vegetable intake, the higher the risk perception, the closer the NCDs’ onset time was estimated for the target individual, but this occurred only for participants who declared a high level of fruit and vegetable intake. Although these interactions are less reliable, again the role of emotion and affect could at least partly account for the interactive role of fear with one’s own behavior in determining the onset time estimates for another person. In contrast, risk perception in the case of a specific inaction such as stopping consuming fruit and vegetables might have been preserved in participants who actually engaged in this healthy diet habit compared to those who did not and who were thus more susceptible to the onset time delaying effect.

Limitations and future directions

Despite meaningful results for the onset time delaying effect as s trans-disease process, the present study is not devoid of limitations. The main limitation concerns the correlational nature of the current study. Specifically, we have hypothesized and found an interaction effect between perceived likelihood and affective impact on people’s estimation of the onset time of NCDs: Only when the disease is perceived as less fearful, its estimated onset time gets closer when perceived likelihood increases. However, this pattern of interaction might be interpreted in the reverse direction. Thus, future research should further address the issue of the causal relationships among these variables by performing experimental and longitudinal studies.

Second, the procedure used to measure affective impact, perceived likelihood, and onset time of NCDs may have been difficult to understand by participants because it may involve some higher-level processes. However, this procedure has already been implemented and effectively used in a previous study assessing differences in the onset time of mild and severe medical conditions between smokers and non-smokers (Pancani & Rusconi, 2018). Moreover, our sample was relatively highly educated. Finally, in a piloting and preliminary phase of our investigations, we assessed the comprehensibility and ease of responding to the whole survey by conducting informal debriefing interviews with a few people. These people did not report any difficulties in understanding or responding to questions. However, future research might explore alternative and more intuitive methods to assess the onset time and other risk-related variables.

Third, there are relevant differences in the recruitment strategy between Italy and the UK. Specifically, while the Italian sample has been collected with a snowball sampling method, the British one has been recruited through Prolific Academic. This discrepancy might limit the generalizability of our results and the possibility of drawing further inferences concerning cross-cultural differences with respect to the relationships among actual lifestyles, onset time, perceived likelihood, and affective impact of NCDs.

Finally, we asked participants to estimate the onset time of a set of NCDs concerning another person rather than themselves. Following empirical evidence on the optimistic bias showing that people believe that they are less at risk of harmful consequences of unhealthy behaviors than the average person (e.g., Arnett, 2000; Weinstein & Lyon, 1999; Weinstein et al., 2005), asking people questions about the risk of others instead of their own risk may allow scholars to gauge their risk perception at the net of such bias. Our study was able to detect the role of participants’ personal perceptions of risk and affect in predicting the time estimates on others. However, a reference to another person rather than themselves could have impacted our participants’ onset time estimates. Accordingly, future studies should evaluate whether these results could be replicated when people are asked to refer to their own disease onset time.

Conclusions

The current study casts light on a cognitive determinant of people’s healthy lifestyle and judgment by investigating the onset-time delaying effect on a large and multi-country sample, which ensured the reliability of the effect found by Pancani and Rusconi (2018). The current study considered four different lifestyles (i.e., smoking, alcohol consumption, physical activity, and fruit and vegetable intake), mainly confirming that the estimation of the NCDs’ onset time on a targeted individual is a function of both participants’ own risk perception and affective impact related to the NCDs. In particular, we argue that, similarly to the affect heuristic, the fast and experiential feeling associated with the NCDs to be evaluated tainted participants’ onset time estimates related to another individual.

Examining four lifestyles allowed us to corroborate the distinction between health-promoting and health-risk behaviors (Lippke et al., 2012). This differentiation also fits with an interpretation of the differences of participants’ own behaviors on the onset time estimates in terms of omission bias (Baron & Ritov, 2004; DiBonaventura & Chapman, 2008; Ritov & Baron, 1990; Spranca et al., 1991), whereby the consequences of stopping healthy behaviors (i.e., stopping doing physical exercise, consuming fruit and vegetables) would be delayed in the future compared to those of starting health-risk behaviors (i.e., starting smoking, consuming alcohol) only for participants who themselves engaged in the unhealthy inactions of not doing physical exercise and consuming fruit and vegetables.

Future studies should further extend this strand of research by investigating the onset-time delaying effect on a more controlled sample (for example, by recruiting more smokers) and by testing optimism bias concerning onset-time estimates (Arnett, 2000; Weinstein, 1989, 1998; Weinstein et al., 2005).

Supplemental Material

sj-jpg-1-prx-10.1177_00332941211036028 - Supplemental material for Perceived Onset Time of Medical Conditions: The Interplay Between Subjective Fear and Risk in Four Lifestyle Domains

Supplemental material, sj-jpg-1-prx-10.1177_00332941211036028 for Perceived Onset Time of Medical Conditions: The Interplay Between Subjective Fear and Risk in Four Lifestyle Domains by Dario Monzani, Luca Pancani, Patrice Rusconi and Gabriella Pravettoni in Psychological Reports

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially supported by the Italian Ministry of Health with Ricerca Corrente and 5 × 1000 funds for IEO European Institute of Oncology IRCCS.

Data Availability Statement

All data are available at osf.io/wd6qb/

ORCID iD

Dario Monzani

Supplemental material

Supplementary material for this article is available online.

Notes

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