Patterns of sun safety behaviors in parents: Associations with physical activity, sedentary behavior, and access to neighborhood physical activity resources

Patterns of sun safety behaviors in parents: Associations with physical activity, sedentary behavior, and access to neighborhood physical activity resources

Preventive Medicine 132 (2020) 105976 Contents lists available at ScienceDirect Preventive Medicine journal homepage: ...

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Preventive Medicine 132 (2020) 105976

Contents lists available at ScienceDirect

Preventive Medicine journal homepage:

Patterns of sun safety behaviors in parents: Associations with physical activity, sedentary behavior, and access to neighborhood physical activity resources☆


Jenna D. Gilchrista,b, , Kasey L. Morrisc, Laura A. Dwyerd, David E. Conroyb,e a

Department of Public Health and Health Systems, University of Waterloo, Canada Department of Kinesiology, The Pennsylvania State University, University Park, PA, United States of America c National Cancer Institute, Rockville, MD, United States of America d Cape Fox Facilities Services, Manassas, VA, United States of America e Department of Preventive Medicine, Northwestern University, Chicago, IL, United States of America b



Keywords: Sunburn Sun safety Sedentary behaviour Physical activity Skin cancer Melanoma Built environment Tanning

Exposure to ultraviolet rays is associated with increased risk of sunburn – a biomarker of skin cancer risk – and physical activity can increase exposure. Sun safety behaviors can mitigate the increased risk of skin cancer. The objective of this cross-sectional study was to determine associations between physical activity behaviors, access to neighborhood physical activity resources, and sunburn across different patterning of sun safety behaviors. Data collected in 2014 from parents in the United States were analyzed (N = 1680; 75% female, primarily between the ages of 35–44 and 45–59, and 67% White). Latent class analysis was conducted to identify classes of sun safety behaviors based on engagement in sun protective behaviors (wearing a hat, shirt with sleeves, and seeking shade) and sun exposure (tanning outdoors). The latent classes were then examined as moderators of the association between physical activity related variables and sunburn. Three classes were identified corresponding to Low, Moderate, and High Risk for sunburn. There was no evidence of moderation, so equality constraints were imposed across the classes. Moderate-to-vigorous physical activity (MVPA) (odds ratio [OR] = 1.09) and neighborhood environments favoring physical activity (OR = 1.39) were associated with an increased likelihood of sunburn. Greater engagement in physical activity and access to built environments that favour activity are associated with a higher likelihood of sunburn, regardless of sun safety behaviors. Physically active parents are a vulnerable population for melanoma, and cancer prevention efforts focused on physical activity should also address sun safety.

More people are diagnosed with skin cancer each year in the U.S. than all other cancers combined (American Cancer Society, 2019). The incidence of skin cancer continues to rise at a rate of 3% per year while the incidence of melanoma, the deadliest form of skin cancer, is increasing more rapidly than any other cancer (American Cancer Society 2019). The annual cost of treating skin cancers is estimated at $8.1 billion (Guy et al., 2015b). Exposure to ultraviolet (UV) rays is a primary cause of sunburn and most skin cancers (Narayanan et al., 2010). Sunburn is often used as a biomarker of skin cancer risk, because sunburn at any age is associated with increased skin cancer risk (Armstrong and Kricker, 2001; Dennis et al., 2008).

Sunburn risk can be mitigated by protecting skin from excessive UV exposure. A number of protective strategies are available (e.g., using sunscreen, wearing protective clothing, seeking shade) while risk behaviors such as tanning exacerbate sunburn risk. The effectiveness of such strategies for mitigating risk varies depending on the specific behavior and how such behaviors are deployed in tandem (Morris and Perna, 2018). Sunscreen use is one of the most commonly reported UV protective behaviors among US adults (Holman et al., 2018b). However, evidence that sunscreen protects against sunburn is equivocal. Recent findings indicate that sunscreen use is positively associated with sunburn and is negatively associated with sunburn only when used in

☆ The FLASHE Study was funded by the National Cancer Institute (NCI) under contract number HHSN261201200039I issued to Westat, Inc. We acknowledge assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). ⁎ Corresponding author at: Department of Public Health and Health Systems, University of Waterloo, Ontario N2L 3G1, Canada. E-mail address: [email protected] (J.D. Gilchrist). Received 23 July 2019; Received in revised form 26 November 2019; Accepted 28 December 2019 Available online 03 January 2020 0091-7435/ © 2019 Published by Elsevier Inc.

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Parents were eligible if they were aged 18 years or older and lived with at least one child aged 12–17 years for at least 50% of the time (Oh et al., 2017){Citation}. FLASHE was reviewed and approved by the US Office of Management and Budget, the National Cancer Institute's Special Studies IRB, and Westat's IRB.

combination with other protective behaviors (e.g., seeking shade, wearing protective clothing) (Morris and Perna, 2018). Such findings are consistent with recommendations that sunscreen be used in combination with other protective strategies (USDHHS, 2018) and suggest that in the presence of other sun protective behaviors, sunscreen may not provide additional protection. Behavioral and environmental factors may present heightened risk for sunburn (Holman et al., 2014). Physical activity, although associated with lower risk for many cancers, presents an increased risk of skin cancer (Holman et al., 2014; Moore et al., 2016). Thus, for physically active adults, protection against UV ray exposure is especially important for mitigating the increased risk of sunburn and skin cancer. One way in which engaging in physical activity presents an increased risk of sunburn is that being active outdoors presents increased exposure to UV rays. Further, the effectiveness of sunscreen is diminished when sweating or immersed in water. Reapplication is therefore needed regularly and an important aspect of effective sunscreen use, however reapplication rates among physically active adults are generally low (Buller et al., 2012). Thus, sunscreen use alone may be insufficient for mitigating the risk of sunburn and should be examined against the broader pattern of sun safety behaviors used to protect against sunburn. The objectives of this study were twofold. The first objective was to use a latent class framework to identify classes of adults defined by their profiles across four sun safety behaviors (i.e., shade seeking, wearing a hat, protective clothing, and spending time tanning outdoors). Latent class analysis (LCA) is a mixture model that identifies unobservable subgroups within a population who share a particular combination of co-occurring sun safety behaviors, and who may be at different risk for sunburn based on the particular combination of behaviors. The second objective was to examine whether these classes moderated the association between sunscreen use and physical activity variables including moderate-to-vigorous physical activity (MVPA), the interaction between sunscreen use and MVPA, access to neighborhood physical activity resources, walking, and sedentary behavior with sunburn. Walking was examined separately from MVPA because of noted differences in sunburn incidence (Tribby et al., 2019). Sedentary behavior was also included because sunbathing is a popular sedentary behavior associated with sunburn. Further, researchers have shown that there is greater UV exposure to the lower body while in a sitting position relative to a standing position which may lead to increased skin cancer risk among more sedentary individuals (Parisi et al., 2003). Understanding how patterns of sun safety behaviors may moderate the risk associated with physical activity-related variables may provide useful insight into the risk factors for sunburn and can help to provide an effective framework for identifying at-risk individuals and mitigate risk through targeted prevention efforts.

1.2. Measures 1.2.1. Demographic variables The following demographic questions were included: sex, age, race/ ethnicity, marital status, height, and weight. Body mass index (BMI) was calculated as weight (kg)/height (m2). 1.2.2. Sun safety behaviors and sunburn Participants completed six items from the Sun Habits Survey (Glanz et al., 2008) which includes questions on sun protection habits including shade seeking, wearing a hat, protective clothing, and spending time tanning outdoors, as well as sunscreen use and number of sunburns within the past year. All items were rated on a 5-point Likert scale that ranged from 1 (never) to 5 (always). Number of sunburns ranged from 0 (0 burns in the past year) to 5 (5 or more burns in the past year). Based on the skewed nature of the variables and to facilitate data analysis, all items except sunscreen were recoded into binary variables. Wearing a shirt with sleeves, hat, and seeking shade/umbrella were recoded into: never, rarely, or sometimes = 1; and often, always = 2. Time spent in the sun to get a tan was recoded into: never spend time in sun to get a tan = 1; rarely, sometimes, often, always spend time in sun to get a tan = 2. Sunburn was recoded into: no burns in the past 12 months = 1; at least 1 burn in the past 12 months = 2. Internal consistency for the four sun safety behaviors (i.e., shade seeking, wearing a hat, protective clothing, and spending time tanning outdoors) was 0.46. 1.2.3. Physical activity and sedentary behavior Physical activity and sedentary behavior were assessed using the International Physical Activity Questionnaire (IPAQ) Short Form (Craig et al., 2003), which asked participants to indicate the usual amount of time spent engaged in walking, moderate, and vigorous activity each day over the past week. Time spent in moderate and vigorous activity were combined to create a score that represented the average number of hours of MVPA engaged in each day over the past 7 days. Time spent sedentary was assessed by asking participants to indicate how many hours they spent sitting on average during a weekday over the past 7 days. 1.2.4. Access to physical activity resources in neighborhood One item assessed availability of physical activity resources in respondents' neighborhood based on the Neighborhood Environment Walkability Scale for Youth (NEWS-Y; Rosenberg et al., 2009). Participants indicated their agreement that their neighborhood has several free or low-cost facilities, such as parks, walking trails, bike paths, recreation centers, playgrounds, etc. using Likert scale response options ranging from 1 (strongly disagree) to 4 (strongly agree).

1. Methods 1.1. Participants and procedure Data from the Family Life, Activity, Sun, Health, and Eating (FLASHE) study (Nebeling et al., 2017) was used to address study objectives. FLASHE is a U.S. survey funded by the National Cancer Institute that examined sun safety, diet, physical activity, and other behavioral correlates among a national sample of dyads of parents and their adolescent children. The current study uses the data from parents only. FLASHE is a cross-sectional dataset administered by Westat, Inc. who were responsible for survey administration and data safety monitoring between April and October 2014. Parents were recruited through the Ipsos Consumer Opinion Panel. Ipsos selected a sample of panel members to be screened for FLASHE eligibility. Although not a nationally representative sample, this sample was selected using balancing techniques to be similar to U.S. population on parent gender, Census division, household income and size, and race/ethnicity.

1.3. Data analysis Latent class analysis (LCA) was conducted in MPlus 6.12 using a manual three-step approach (Vermunt, 2010) to test study hypotheses. First, we defined latent classes of adults with particular patterns across the four sun safety behaviors (i.e., shade seeking, wearing a hat, protective clothing, and spending time tanning outdoors). Model selection was based on a number of fit indices including the Bayesian information criteria (BIC), Akaike information criteria (AIC), and the sample size adjusted Bayesian information criteria (aBIC), where a lower number indicates better fitting models. The Lo Mendel Likelihood Ratio Test (LMR-LRT) and the bootstrap likelihood ratio test (BLRT) were included 2

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classification and use of sunscreen. Additionally, parents reporting greater access to free or low-cost facilities, such as parks, walking trails, bike paths, recreation centers, playgrounds, etc. had 39% greater odds of sunburn, regardless of risk classification and sunscreen use. Finally, younger parents were more likely than parents over 60 years of age to report a sunburn and non-Hispanic White, Hispanic, and other race parents were more likely to report a sunburn compared to Black participants (see Table 4).

where a model with k classes is compared to a model with k-1 classes. A significant value indicates a model with k classes is a better fit. Model interpretability was also considered. Entropy values were also estimated which provide information on the distinction between classes. Following class enumeration, the measurement parameters of the latent classes were fixed to account for classification error and the predictors of sunburn (i.e., sunscreen, MVPA, the interaction between sunscreen and MVPA, sedentary behavior, walking, access to neighborhood physical activity resources, and demographic variables) were estimated for each class (Nylund-Gibson et al., 2014). The paths for age, sex, and race/ethnicity were constrained to be equal across all three classes while all other paths were freely estimated in each class. Both sunscreen and MVPA were centered prior to analysis and an interaction term using the centered variables was created to examine the interaction between MVPA and sunscreen use. Evidence for moderation is reflected in a significant Wald χ2 test statistic.

3. Discussion Physical activity plays an important role in improving cancer prevention and control but recent findings raise concerns about physical activity increasing risk for malignant melanoma, the deadliest form of skin cancer (McTiernan et al., 2019; Moore et al., 2016). Physical activity is associated with elevated risk for sunburn – a clinical marker of skin cancer (Holman et al., 2018b; Morris and Perna, 2018). Sun safety behaviors can help mitigate risk of sunburn, but their effectiveness has not yet been examined in the context of physical activity behavior. The present study identified three latent classes that corresponded to low, medium, and high levels of sunburn risk based on participants' sun safety behaviors. Engagement in MVPA and access to physical activity resources in the neighborhood were predictors of sunburn regardless of risk classification and sunscreen use. Consideration of individuals' activity levels is an important aspect for managing skin cancer risk. Prior work has used a decision-tree model to identify interactions between multiple sun safety behaviors that differentiated sunburn risk (Morris and Perna, 2018). Both studies support the notion of behavioral clustering (Spring et al., 2012); however, they differed in the number of clusters required. The latent class model yielded a simpler solution than the decision tree model. The latent class model was consistent with notions of “composite risk” based on the aggregation of behaviors whereas the decision tree model allowed risk to vary as a function of more complex interactions between individual behaviors. Differences in study findings could also reflect differences in the samples. The majority of participants in the FLASHE sample were female whereas the previous sample had equal representation of both males and females. There were also differences noted for sun safety behaviors between the two samples. Morris and Perna (2018) reported that shade seeking was the most common behavior and wearing long sleeves was the least common behavior whereas participants in the FLASHE study reported seeking shade only 32% of the time and were much more likely to wear sleeves than any other behavior. Physical activity was associated with increased likelihood of reporting a sunburn, regardless of sunscreen use or class membership. This finding aligned with previous research among Australian adults indicating that for every hour increase in physical activity there was a 2% increase in the odds of reporting a sunburn in the past year and a 4% increase in the odds of reporting a sunburn in the past weekend (Jardine et al., 2012). These results may help to explain why physical activity increases risk for melanoma yet decreases risk for 13 other cancers (Moore et al., 2016). Findings support that aspects of the built environment are associated with sunburn. Neighborhood resources, such as access to outdoor recreation areas, increase the likelihood of reporting a sunburn by 39%. Modifying aspects of the built environment to encourage both participation and sun safety is therefore needed. Structuring environments to reduce UV exposure through the inclusion of sunscreen dispensers (e.g., at beaches, along running paths, and athletic facilities; Hamant and Adams, 2005) and increasing shaded areas may help to decrease risk of sunburn (Glanz et al., 2002). Although a number of cities have adopted sunscreen dispensers in public areas, evidence for the effectiveness of this approach is limited. Indeed, there is a noted gap in research examining environmental manipulations of skin cancer interventions, specifically addressing how interventions alter clinically meaningful outcomes (i.e., sunburn; Perna et al., 2017; Taber et al., 2018).

2. Results A total of 1839 participants provided demographic data, however only 1793 completed the physical activity and sun safety measures. Participants providing only demographic data did not differ on age, sex, or race/ethnicity from those completing all measures and were excluded from analyses. Improbable values (> 24 h of total activity and sedentary behavior per day) were noted and removed from the analysis leaving a total analytical sample of N = 1680. Participants were mostly non-Hispanic White, middle-aged, female, and reported engaging in both sun protective and sun exposure behaviors with 35.7% of the sample reporting at least one sunburn in the past year. Based on the fit indices and interpretability of the item-response probabilities, a threeclass model was selected that corresponded to classes representing low, moderate, and high risk for sunburn based on the likelihood of engaging in certain patterns of sun safety behaviors. Descriptive statistics across the three classes are presented in Table 1 and fit indices for the different models are presented in Table 2. Class prevalence and itemresponse probabilities for the three-class model are presented in Table 3. The final model used to classify individuals into latent classes was characterized by high average posterior probabilities for both the Moderate (0.82) and High Risk (0.84) latent classes suggesting low classification error, while average posterior probabilities for the Low Risk group was somewhat lower (0.65), although approached 0.70 which is generally considered to represent well-separated classes (Nagin, 2005). This suggests that it is easy to distinguish between the Moderate and High Risk classes, but less so for the Low Risk class. However, classification error in class assignment is accounted for in the regression analyses. Corresponding to their risk classification, 22.9% of those in the Low Risk class reported at least one sunburn in the past year relative to 32.5% of those in the Moderate Risk class and 45% of those in the High Risk class.1 The Wald χ2 test was not significant (χ2 = 4.97, p = .96) suggesting the associations between the predictor variables and sunburn did not differ between the latent classes. As a result, a subsequent model was estimated with paths constrained to be equal across all classes. Across all three classes, MVPA and access to neighborhood physical activity resources were associated with an increased likelihood of sunburn. That is, for every hour increase in physical activity per day, parents had 9% greater odds of sunburn, regardless of their risk 1 A multinomial logistic regression was conducted to examine month of survey completion (April through September) as a predictor of class membership. The High Risk group was set as the reference class. Month of survey completion did not significantly predict the odds of belonging to either the Low Risk class or the Moderate Risk class relative to the High Risk class (all 95% confidence intervals crossed 0).


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Table 1 Participant characteristics and descriptives overall and across classes. Variable Sex Female Male Age 18–34 35–44 45–59 > 60 Race/ethnicity Non-Hispanic White Black Hispanic Other Marital status Married Divorced, widowed, separated Never married Member of an unmarried couple BMI Underweight Normal weight Overweight Obese Sun safety behaviors Wear sleeves Wear hat Seek shade/use umbrella Spend time in sun to get a tan Sunburn Sunscreen MVPA (hours per day) Walk (hours per day) Sedentary behavior (hours per weekday) Physical activity resources

Overall % (frequency)

Low risk % (frequency)

Moderate risk % (frequency)

High risk % (frequency)

75.1 (1228) 24.9 (407)

83.1 (299) 16.9 (61)

65.8 (375) 34.2 (195)

78.7 (543) 21.3 (147)

10.4 (173) 42.3 (703) 41.8 (695) 3.0 (50)

11.4 (42) 48.2 (177) 36.2 (133) 1.9 (7)

9.3 (54) 41.5 (240) 43.9 (254) 4.0 (23)

10.7 (77) 39.9 (286) 43.0 (308) 2.8 (20)

67.3 (1131) 16.5 (277) 7.0 (118) 5.8 (97)

45.0 (165) 36.5 (134) 8.4 (31) 6.8 (31)

67.6 (391) 16.3 (94) 8.1 (47) 6.1 (35)

78.7 (564) 6.6 (47) 5.3 (38) 5.2 (37)

72.1 (1170) 12.9 (210) 9.6 (155) 5.4 (88)

66.6 (239) 13.1 (47) 14.2 (51) 6.1 (22)

73.9 (420) 10.7 (61) 9.9 (56) 5.5 (31)

73.1 (498) 14.8 (101) 7.0 (48) 5.0 (34)

1.3 (21) 34.5 (579) 29.0 (488) 30.3 (509)

0.8 (3) 28.6 (105) 27.2 (100) 38.7 (142)

2.1 (12) 31.1 (180) 31.1 (180) 32.4 (187)

0.8 (6) 40.2 (288) 28.5 (204) 24.4 (175)

69.2 (1123) 21.0 (341) 31.8 (514) 59.6 (1001) 35.7 (581) M (SD) 3.05 (1.20) 1.90 (2.85) 0.70 (0.99) 5.15 (3.80) 2.70 (1.11)

63.9 (232) 0 (0) 9.9 (36) 0 (0) 22.9 (84) M (SD) 2.60 (1.31) 1.23 (1.22) 0.58 (0.70) 4.96 (4.07) 2.62 (1.10)

94.3 (544) 55.1 (316) 76.2 (438) 47.8 (276) 32.5 (187) M (SD) 3.37 (1.22) 1.61 (1.63) 0.66 (0.70) 5.19 (3.90) 2.78 (1.14)

50.2 (334) 3.3 (22) 5.3 (35) 100 (717) 45 (301) M (SD) 3.03 (1.04) 2.45 (3.93) 0.68 (0.76) 5.21 (3.56) 2.66 (1.08)

Note. BMI = body mass index; MVPA = moderate-to-vigorous physical activity. Table 2 Fit indices for models with one, two, and three classes. 1 class

2 classes

3 classes

11 −3977.96 272.26 7963.92 7985.62 7972.91 – – –

6 −3866.13 23.25 7750.26 7799.09 7770.50 p < .001 p < .001 0.42

1 −3854.51 0.52 7737.02 7812.99 7768.51 p = .001 p < .001 0.56

Table 3 Class prevalence and item-response probabilities for three-class model: probability of endorsing item given latent class. Item

df Log-likelihood Pearson chi square AIC BIC aBIC LMR-LRT BLRT Entropy

Sleeves Hat Shade/umbrella Outdoor tanning

Latent class Low risk (18%)

Moderate risk (40%)

High risk (42%)

0.71 0.00 0.28 0.00

0.87 0.47 0.58 0.52

0.50 0.06 0.07 0.92

Note. Those in the High Risk class may or may not report wearing sleeves, were unlikely to report wearing a hat or seeking shade/using an umbrella and were extremely likely to report spending time in the sun to get a tan. Those in the Moderate Risk class were more likely to report wearing sleeves that covered their shoulders and seeking shade/using an umbrella and may or may not wear a hat and spend time in the sun to get a tan. Those in the Low Risk class were very likely to report wearing sleeves that covered the shoulders and were very unlikely to report spending time in the sun to get a tan.

Note. N = 1679. df = degrees of freedom; AIC = Akaike's Information Criterion; BIC = Bayesian Information Criterion; aBIC = sample size adjusted Bayesian information criteria; LMR-LRT = Lo-Mendell-Rubin Adjusted Likelihood Ratio Test; BLRT = Bootstrap Likelihood Ratio Test. Degrees of freedom were negative when more than three classes were estimated so only models with one, two, and three classes were assessed for model fit.

There exist opportunities for integrating sun safety behaviors into physical activity guidelines. The American Academy of Pediatrics noted that outdoor physical activity is encouraged but should be done in a sun-safe manner (Balk, 2011). Similar integration into future physical activity guidelines may be helpful for mitigating skin cancer risk. Physicians are well positioned to offer counselling on both physical activity and sun safety yet rarely offer such counselling in tandem. Counselling should not be limited to patients deemed ‘high risk’ for skin cancer but rather counselling should be provided to all adults, in particular active adults or those with access to more physical activity resources in their neighborhood. Targeting multiple health behaviors has

potential to maximize reach, health benefits, and cost effectiveness. To the best of our knowledge, this paper also provided the first evidence that sedentary behavior was not associated with sunburn. Although sedentary behavior is not associated with increased risk, nor was it associated with decreased risk of sunburn. This is an important and novel finding given that many outdoor leisure activities carried out in seated postures while being exposed to UV rays (e.g., dining outdoors, spectators at sporting events, and intentional outdoor tanning). Moreover, indoor sedentary time involves limited UV exposure but not enough to reduce risk.


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2014). Finally, the observational design limits causal inferences about the role of physical activity in risk management.

Table 4 Multiple group logistic regression predicting sunburn. Variable




95% CI

Sunscreen MVPA MVPA*sunscreen Walking Sedentary behavior Access to neighborhood physical activity resources Sex Age 18–34 Age 35–44 Age 45–59 Hispanic Non-Hispanic White Other race

0.08 0.09 −0.04 0.05 0.03 0.33

1.08 1.09 0.96 0.96 1.05 1.39

0.06 0.04 0.04 0.08 0.02 0.12

−0.03, 0.19 0.01, 0.18 −0.11, 0.03 −0.11, 0.20 0.00, 0.06 0.10, 0.57

−0.22 1.89 1.55 1.43 1.72 2.25 1.61

0.81 6.62 4.71 4.18 5.58 9.49 5.00

0.14 0.49 0.47 0.47 0.33 0.27 0.35

−0.49, 0.06 0.93, 2.86 0.64, 2.47 0.52, 2.34 1.07, 2.36 1.72, 2.79 0.93, 2.29

4. Conclusions

Note. Estimates are unstandardized. 95% CI = 95% confidence interval. Male = 1, Female = 2. Age and race were dummy coded. For age, ≥60 was set as the reference category and black was set as the reference category for race/ ethnicity.

Opportunity to mitigate against skin cancer risk occurs at multiple levels of influence and may also coincide with efforts to shape the built environment to promote physical activity. Physical activity is often positioned as a means to improve health, yet emerging findings linking it with increased skin cancer risk are concerning. Evidence that physical activity is associated with sunburn regardless of risk classification suggests that a targeted approach based on sun safety behaviors may not be effective. Other approaches such as informing physically active individuals about the risks associated with sunburn, modifying elements of the environment where people are likely to be active, or integrating sun safe messaging into existing technology tied to physical activity (e.g., Fitbits) may be more effective for reducing sunburn. Informed by the specific contexts where they co-occur, health promotion efforts targeting sun safety behaviors among physically active individuals can help mitigate skin cancer risk.

3.1. Limitations and future directions

CRediT author statement

The present findings should be interpreted in light of important limitations. The participants in this study were primarily non-Hispanic white, middle-aged females so caution is warranted when generalizing conclusions to other groups. However, this sample represents a vulnerable population (i.e. non-Hispanic whites) who report more sunburns and are at greater risk of melanoma than other racial/ethnic groups (Guy et al., 2015a; Holman et al., 2019). There are gender differences in skin cancer incidence among adults so these findings should be replicated with more gender-balanced samples. This paper also focused on patterns of sun protective behavior and their role in mitigating physical activity-related sunburn risk in parents. Parents and children may mutually influence each other's behavior and the FLASHE dataset can be used to test those hypotheses in future research. The FLASHE study design was cross-sectional which prevents examination of the directionality of associations or changes in sun protective behavior patterns over time. Some sun safety behaviors were not commonly reported so there was little variance across the classes (i.e., wearing a hat, seeking shade/using an umbrella). However, the estimates presented here are consistent with previous reports (Holman et al., 2018a). As well, the frequency of sunscreen reapplication was not assessed nor was the sun sensitivity of the participants which has shown to augment engagement in specific sun safety behaviors and sunburn risk (Holman et al., 2014, Holman et al., 2018a, 2018b; Narbutt et al., 2019). The context for physical activity was not assessed so we could not ascertain whether activity took place inside or outside. It is possible that more active people are more likely to report a sunburn because they exercise at gyms that also offer tanning beds (Pagoto et al., 2018). Indeed, a better understanding of the contexts in which physical activity (and sedentary behavior) occurs and the nature of the activities undertaken is needed to improve efforts at sunburn prevention. Adopting a more granular approach may help elucidate whether certain individuals are more at risk than others (e.g., beachgoers) as well as the optimal times for interventions (e.g., weekend/weekday; White et al., 2019). It was also unknown whether parents were actually using sun protection during physical activity as the items only referred to behaviors engaged in during a warm, sunny day, and not physical activity specifically. Sedentary behavior was not associated with sunburn; however, it was only assessed during weekdays and weekend sedentary time was not captured. Weekends typically involve more leisure time so sedentary behavior might displace time spent in physical activity and reduce risk. Both sedentary behavior and physical activity were assessed using selfreport methods which are often underestimated and overestimated, respectively, relative to accelerometer derived estimates (Dyrstad et al.,

Jenna Gilchrist: Conceptualization, Formal analysis, WritingOriginal draft preparation. Kasey Morris: Conceptualization, Writing – Review & Editing. Laura Dwyer: Conceptualization, Writing – Review & Editing David Conroy: Conceptualization, Writing – Review & Editing, Supervision. References Cancer Society, American, 2019. Skin cancer. Retrieved from. cancer/skin-cancer.html. Armstrong, B.K., Kricker, A., 2001. The epidemiology of UV induced skin cancer. J. Photochem. Photobiol. B Biol. 63, 8–18. Balk, S.J., 2011. Policy statement–ultraviolet radiation: a hazard to children and adolescents. Pediatrics 127, 588–597. Buller, D.B., Andersen, P.A., Walkosz, B.J., Scott, M.D., Maloy, J.A., Dignan, M.B., Cutter, G.R., 2012. Compliance with sunscreen advice in a survey of adults engaged in outdoor winter recreation at high-elevation ski areas. J. Am. Acad. Dermatol. 66, 63–70. Craig, C.L., Marshall, A.L., Sjöström, M., Bauman, A.E., Booth, M.L., Ainsworth, B.E., ... Oja, P., 2003. International physical activity questionnaire: 12-country reliability and validity. Med & Sci Sports Exerc 35, 1381–1395. Dennis, L.K., Vanbeek, M.J., Beane Freeman, L.E., Smith, B.J., Dawson, D.V., Coughlin, J.A., 2008. Sunburns and risk of cutaneous melanoma: does age matter? A comprehensive meta-analysis. Ann. Epidemiol. 18, 614–627. annepidem.2008.04.006. Dyrstad, S.M., Hansen, B.H., Holme, I.M., Anderssen, S.A., 2014. Comparison of self-reported versus accelerometer-measured physical activity. Med. Sci. Sports Exerc. 46, 99–106. Glanz, K., Geller, A.C., Shigaki, D., Maddock, J.E., Isnec, M.R., 2002. A randomized trial of skin cancer prevention in aquatics settings: the pool cool program. Health Psychol. 21, 579–587. Glanz, K., Yaroch, A.L., Dancel, M., Saraiya, M., Crane, L.A., Buller, D.B., ... Robinson, J.K., 2008. Measures of sun exposure and sun protection practices for behavioral and epidemiologic research. Arch Dermatol 144, 217–222. Guy, G.P., Machlin, S.R., Ekwueme, D.U., Yabroff, K.R., 2015a. Prevalence and costs of skin cancer treatment in the U.S., 2002−2006 and 2007−2011. Am. J. Prev. Med. 48, 183–187. Guy, G.P., Thomas, C.C., Thompson, T., Watson, M., Massetti, G.M., Richardson, L.C., 2015b. Vital signs: melanoma incidence and mortality trends and projections—United States, 1982–2030. MMWR Morb. Mortal. Wkly Rep. 64, 591. Hamant, E.S., Adams, B.B., 2005. Sunscreen use among collegiate athletes. J. Am. Acad. Dermatol. 53, 237–241. Holman, D.M., Berkowitz, Z., Guy, G.P., Hartman, A.M., Perna, F.M., 2014. The association between demographic and behavioral characteristics and sunburn among U.S. adults — National Health Interview Survey, 2010. Prev. Med. 63, 6–12. https://doi. org/10.1016/j.ypmed.2014.02.018. Holman, D.M., Ding, H., Guy, G.P., Watson, M., Hartman, A.M., Perna, F.M., 2018a. Prevalence of sun protection use and sunburn and Association of Demographic and Behaviorial Characteristics with sunburn among US adults. JAMA Dermatology 154, 561–568. Holman, D.M., Ding, H., Berkowitz, Z., Hartman, A.M., Perna, F.M., 2019. Sunburn prevalence among US adults, National Health Interview Survey 2005, 2010, and 2015. J. Am. Acad. Dermatol. 80, 817–820.


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028. Pagoto, S.L., Nahar, V.K., Frisard, C., Conroy, D.E., Lemon, S.C., Oleski, J., Hillhouse, J., 2018. A comparison of tanning habits among gym tanners and other tanners. JAMA Dermatology 154, 1090–1091. Parisi, A.V., Kimlin, M.G., Lester, R., Turnbull, D., 2003. Lower body anatomical distribution of solar ultraviolet radiation on the human form in standing and sitting postures. J. Photochem. Photobiol. B Biol. 69, 1–6. Perna, F.M., Dwyer, L.A., Tesauro, G., Taber, J.M., Norton, W.E., Hartman, A.M., Geller, A.C., 2017. Research on skin cancer–related behaviors and outcomes in the NIH grant portfolio, 2000-2014: Skin cancer intervention across the cancer control continuum (SCI-3C). JAMA Dermatology 153, 398–405. 2016.6216. Rosenberg, D., Ding, D., Sallis, J.F., Kerr, J., Norman, G.J., Durant, N., Saelens, B.E., 2009. Neighborhood Environment Walkability Scale for Youth (NEWS-Y): reliability and relationship with physical activity. Prev. Med. 49, 213–218. 1016/j.ypmed.2009.07.011. Spring, B., Moller, A.C., Coons, M.J., 2012. Multiple health behaviours: Overview and implications. J. Public Health 34, i3–i10. Taber, J.M., Dickerman, B.A., Okhovat, J.P., Geller, A.C., Dwyer, L.A., Hartman, A.M., Perna, F.M., 2018. Skin cancer interventions across the cancer control continuum: Review of technology, environment, and theory. Prev. Med. 111, 451–458. https:// Tribby, C.P., Berrigan, D., Perna, F.M., 2019. Cross-sectional association between walking and sunburn: A potential trade-off between cancer prevention and risk factors. Ann. Behav. Med. US Department of Health and Human Services, 2018. The surgeon General's call to action to prevent skin cancer. Vermunt, J.K, 2010. Latent class modeling with covariates: Two improved three-step approaches. Political Analysis 18, 450–469. White, K.M., Zhao, X., Starfelt Sutton, L.C., Young, R.M., Hamilton, K., Hawkes, A.L., Leske, S., 2019. Effectiveness of a theory-based sun-safe randomised behavioural change trial among Australian adolescents. Psycho-Oncology 28, 505–510.

Holman, D.M., Kapelos, G.T., Shoemaker, M., Watson, M., 2018b. Shade as an environmental design tool for skin cancer prevention. Am. J. Public Health 108, 1607–1612. Jardine, A., Bright, M., Knight, L., Perina, H., Vardon, P., Harper, C., 2012. Does physical activity increase the risk of unsafe sun exposure? Health Promotion Journal of Australia 23, 52–57. McTiernan, A., Friedenreich, C.M., Katzmarzyk, P.T., Powell, K.E., Macko, R., Buchner, D., 2019. Physical activity in cancer prevention and survival: A systematic review. Med Sci Sports Exerc 51, 1252–1261. Moore, S.C., Lee, I.M., Weiderpass, E., Campbell, P.T., Sampson, J.N., Kitahara, C.M., ... Adami, H.O., 2016. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. JAMA internal medicine 176, 816–825. Morris, K.L., Perna, F.M., 2018. Decision tree model vs traditional measures to identify patterns of sun-protective behaviors and sun sensitivity associated with sunburn. JAMA Dermatology 154, 897–902. 1646. Nagin, D.S., 2005. Group-based Modeling of Development. Harvard University Press, Cambridge, MA. Narayanan, D.L., Saladi, R.N., Fox, J.L., 2010. Review: Ultraviolet radiation and skin cancer. Int. J. Dermatol. 49, 978–986. 04474.x. Narbutt, J., Philipsen, P.A., Harrison, G.I., Morgan, K.A., Lawrence, K.P., Baczynska, K.A., ... Bell, M., 2019. Optimal sunscreen use prevents holiday erythema. British Journal of Dermatology 180, 604–614. Nebeling, L.C., Hennessy, E., Oh, A.Y., Dwyer, L.A., Patrick, H., Blanck, H.M., ... Yaroch, A.L., 2017. The FLASHE study: Survey development, dyadic perspectives, and participant characteristics. American Journal of Preventive Medicine 52, 839–848. Nylund-Gibson, K., Grimm, R., Quirk, M., Furlong, M., 2014. A latent transition mixture model using the three-step specification. Struct. Equ. Model. Multidiscip. J. 21, 439–454. Oh, A.Y., Davis, T., Dwyer, L.A., Hennessy, E., Li, T., Yaroch, A.L., Nebeling, L.C., 2017. Recruitment, enrollment, and response of parent–adolescent dyads in the FLASHE study. Am. J. Prev. Med. 52, 849–855.