In the United States, adolescents comprise 12.8% of the population (Office of Disease Prevention and Health Promotion, 2021). As rapid physical and psychological changes occur during adolescence, this population is vulnerable to peer, school, mass media, family, neighborhood, and societal influences that could affect their lifestyle behaviors and well-being (Lachytova et al., 2017; Office of Disease Prevention and Health Promotion, 2021). Behavior patterns that are developed during this period are key to establishing long-term health and risk for developing diseases in later stages of life (Office of Disease Prevention and Health Promotion, 2021). Over the past decade, there has been an increase in the prevalence of overweight, obesity, and non-communicable diseases among adolescents in the United States (Fryar et al., 2021). Certain modifiable lifestyle behaviors like healthier dietary practices, high levels of physical activity, not smoking, low alcohol consumption, less time in sedentary behavior, and duration and quality of sleep, protect against the risk of developing non-communicable diseases (Haapasalo et al., 2018; Rosenkranz et al., 2013; Stenlund et al., 2021). Furthermore, detrimental lifestyle behaviors like smoking, alcohol consumption, and sedentary behavior, have also been shown to be related to depression, (McDowell et al., 2017; Wiles et al., 2011) psychological distress (Hamer et al., 2008), anxiety (McDowell et al., 2017), and increased risk of developing chronic diseases in adolescents (Kelder et al., 1994).

Compared to negative health outcomes, research focusing on the effects that lifestyle has on positive health outcomes like well-being is less common (Haapasalo et al., 2018; Rosenkranz et al., 2013). One study conducted in college students linked the benefits of sufficient physical activity levels, having lunch, and daily intake of fruits and vegetables, with higher happiness levels (Haapasalo et al., 2018). In addition, higher levels of physical activity, sleep duration, healthier diet habits, not smoking, and lower sitting time are also related to aspirational health outcomes, such as excellent self-rated health (Haapasalo et al., 2018). Furthermore, previous research findings have highlighted a significant association between frequent family meals and better nutritional health in children across various sociodemographic groups (Dallacker et al., 2018). One systematic review that evaluated the associations between family meal frequency and psychosocial outcomes showed that frequent family meals were associated with lower odds of developing eating disorders, alcohol and substance abuse, violent behavior, and depression (Harrison et al., 2015). Moreover, researchers found that frequent family meals were positively associated with higher ratings of increased self-esteem and more success in school success (Dallacker et al., 2018; Harrison et al., 2015). These positive psychological or “salutogenic” effects should be the focus to enhance health promotion instead of more traditional disease prevention and treatment approaches (Rosenkranz et al., 2013). In a salutogenic approach, it is important to emphasize the factors that lead to general well-being and the factors that create and maintain good health instead of looking for the cause of the diseases (Bhattacharya et al., 2020). Using a salutogenic approach can be helpful for detecting influences on aspirational levels of health (Rosenkranz et al., 2013). The salutogenic approach has been shown to actively promote health—as compared with a pathogenic approach that focuses on risk factors (Antonovsky, 1996). According to Antonovsky (1987), there are six main aspects in the salutogenic approach (Antonovsky, 1987). The first aspect is understanding health as a continuum, focusing on the changing interaction between health-promoting factors and stressors in people’s lives. The second aspect is to pay attention to a person's story and not only consider them a diagnosis or a disease. The third aspect focuses on the salutary factors that contribute directly to health. The fourth aspect focuses on how stress can be viewed as salutogenic, neutral, or pathogenic. Because stress is everywhere, a salutogenic approach could improve the stressors in daily life. The fifth aspect is related to therapy and people’s ability to actively adapt to different situations. The sixth and last aspect is focused on research to generate hypotheses to explain salutogenesis (Antonovsky, 1996; Vinje et al., 2022). In summary, a salutogenic approach considers all persons, at any moment and any time, focusing on salutary factors of daily living, and understanding health as being in constant change, not only as a separation of healthy and sick people (Antonovsky, 1996; Vinje et al., 2022). Additionally, prior evidence suggests that positively framed messages (such as gaining health benefits) can be more powerful than negatively framed messages for promoting enhancement of healthful behaviors in adult populations, (Rosenkranz et al., 2013) but there is a need for more research in adolescent populations.

Self-rated health is a simple, easy-to-administer health indicator that is frequently used in general population surveys and psychological and clinical research due to good reliability and predictive validity, as well as relevance for public health research (Bombak, 2013; Rosenkranz et al., 2013; Schnittker & Bacak, 2014). In many cases, self-rated health indicators provide a more accurate assessment of respondent views of their overall individual health status as compared with more specific health questions (Bombak, 2013; Breidablik et al., 2009). Inquiring about self-rated health allows for efficient assessment of multiple characteristics of health in combination. Therefore, the ability of self-rated health to efficiently capture various aspects of respondents’ health is optimized (Bombak, 2013; Breidablik et al., 2009). Previous research suggests that excellent self-rated health is associated with positive health outcomes and decreased mortality in adult populations (Keyes, 2007; Prendergast et al., 2016; Seligman, 2008). In addition, researchers have found that self-rated health was correlated with many indicators of well-being in older populations (Badri et al., 2021). However, more evidence is needed that focuses on well-being or aspirational health markers in younger people (Huppert & So, 2013), as there is a lack of evidence concerning adolescents specifically (Kelder et al., 1994; te Velde et al., 2007; Telama et al., 2005). Because many health behaviors are established in childhood and track into adulthood (Kelder et al., 1994; te Velde et al., 2007; Telama et al., 2005), prevention strategies (also known as upstream interventions that aim to prevent problems in a whole community) are needed to enhance the benefits of engaging in positive health-related behaviors that have been associated with both short- and longer-term beneficial health outcomes in youth populations (Gallagher & Updegraff, 2011; Rosenkranz et al., 2013). To contribute to filling this current gap in the literature, the present study aimed to examine whether lifestyle behavioral exposures in a United States adolescent population were associated with excellent self-rated health.

Methods

Design and Sample

The present study is a secondary analysis using data from The Family, Life, Activity, Sun, Health, and Eating (FLASHE) study. The FLASHE study is a cross-sectional study conducted by the National Cancer Institute from April to October 2014. A detailed description of the FLASHE study has been published previously (Westat, 2015). The study examined behavioral measures like diet, physical activity and sedentary behavior, sun safety, sleep, and tobacco use in adolescents (Westat, 2015). A total of 5,088 adolescents met the inclusion criteria for FLASHE (National Cancer Institute, 2020). From those, only 1,250 completed all of the procedures and measures that were evaluated in the present study. This study aimed to assess lifestyle behaviors (i.e., beneficial and detrimental food intake; perception of the importance of family meals; free-time physical activity; sedentary behavior time; having no trouble sleeping and having a regular bedtime) in the United States adolescent population and their associations with the outcome of excellent self-rated health.

Measures

American adolescents (n = 1,250) aged 12–17 years, who matched the U.S. population as closely as possible, according to gender, census division, household size and income, and race/ethnicity (Westat, 2015), completed several survey instruments, covering dietary practices, physical activity, sleep, and other health-related behaviors. Adolescents completed a 49-question survey addressing dietary habits in the FLASHE study. The three variables analyzed from this survey, as included in the FLASHE data set, were total daily beneficial food intake, total daily detrimental food intake, and perception of the importance of family meals (National Cancer Institute, 2017). Food frequencies were reported from zero to three times per day and then converted to a daily frequency value (National Cancer Institute, 2017). The beneficial food intake variable included 100% fruit juice, water, fruit, green salads, other non-fried vegetables, cooked beans, whole grain bread, cooked whole grains, non-sugary cereal, and other potatoes. The food items included in the beneficial category are consistent with the recommendations of the Dietary Guidelines for Americans, the World Cancer Research Fund International, and the American Cancer Society. In contrast, the detrimental food variable included pizza, heat and ready-to-serve food, tacos, fried chicken, burgers, processed meat, fried potatoes, candy/chocolate, cookies/cake, potato chips, frozen desserts, sugary cereals, sweetened fruit drinks, soda, energy drinks, and sports drinks (National Cancer Institute, 2017). The detrimental food items are not consistent with the recommendations from Dietary Guidelines for Americans, the World Cancer Research Fund International, or the American Cancer Society. The FLASHE study committee assigned the relative healthfulness of foods according to a literature review of obesity and cancer-related outcomes (National Cancer Institute, 2017). The importance of family meals (agreement that: “In my family, it is important that we eat at least one meal a day together.”) was also included in the analysis. Adolescents detailed their responses using a five-point Likert scale (National Cancer Institute, 2017).

Adolescent physical activity was measured in FLASHE using a 78-question survey from The Youth Activity Profile (Welk et al., 2021). The two variables used in the present analysis were typical weekly physical activity in free time, and typical weekly sedentary behavior. Total physical activity was measured with the participant reports of the frequency they engaged in physical activity in their free time during the last week: I did not do physical activity in my free time, I sometimes (1–2 times last week) did physical activity in my free time, I often (3–4 times last week) did physical activity in my free time, I quite often (5–6 times last week) did physical activity in my free time, and I very often (7 or more times last week) did physical activity in my free time) (National Cancer Institute, 2017b). Adolescent sedentary behavior (SB) was assessed through a question regarding typical sedentary habits at home (I spent almost none of my free time sitting, I spent little time sitting during my free time, I spent a moderate amount of time sitting during my free time, I spent a lot of time sitting during my free time, and I spent almost all of my free time sitting) (National Cancer Institute, 2017b). Sleep was also included in the physical activity survey. Having trouble staying asleep (yes/no) and having a regular bedtime (yes/no) were included in the present study (National Cancer Institute, 2017b). In addition, adolescents were asked how many hours of sleep they usually get to evaluate whether they meet the sleep guidelines for adolescents (8–10 hours per 24 hours regularly to promote optimal health) (Paruthi et al., 2016).

Health status was part of the demographic questionnaire. Adolescents classified their health as excellent, very good, good, fair, or poor by answering the question “In general, would you say your health is…” (National Cancer Institute, 2017a). Race and ethnicity were also self-reported using the following categories: American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, Hispanic, Non-Hispanic Black or African American Only, Non-Hispanic White only, and Non-Hispanic Other (National Cancer Institute, 2017a). Additionally, proxy variables for economic status included adolescent responses to crime as a barrier to performing physical activity in their neighborhood when walking at night, and type of school (attending private or public school, or homeschooling) (National Cancer Institute, 2017a, 2017b) due to the limited information of other socioeconomic status in the survey. Furthermore, tobacco use was evaluated using adolescents’ responses to smoking at least 100 cigarettes in their lifetimes (Haapasalo et al., 2018; Rosenkranz et al., 2013; National Cancer Institute, 2017b). “Lastly, participants self-reported their weights and heights, which were used to calculate their body mass index (BMI) percentiles and to determine appropriate weight status classifications. Adolescents who had a BMI percentile between 5^th and <85^th were classified as having a healthy weight status. Adolescents who had a BMI between 85^th and <95^th were classified as having overweight, and adolescents who had a BMI percentile at or greater than 95^th were classified as having obesity.”

Statistical Analyses

Data from the FLASHE study were analyzed using IBM SPSS Statistics V25 (IBM, Armonk, NY, USA). First, crude odds ratios and adjusted odds ratios were calculated to assess relationships between exposures to the health behaviors of interest, and the outcome variable of excellent self-rated health. Then, logistic regression analyses (alpha=0.05; 95% confidence intervals) were used to examine the associations between lifestyle behavioral exposures and the outcome variable of excellent self-rated health. Univariate, unadjusted relationships between each behavior and excellent self-rated health were evaluated. The initial model included all three nutrition-related variables, adjusting for weight status. The second model was adjusted for nutrition, weight status, and other lifestyle factors. The final fully adjusted model was adjusted for nutrition, weight status, and other lifestyle factors and also was adjusted for the demographic variables. A total of 1,250 adolescents who had complete data for all variables were included in the analyses.

Results

Participant Characteristics

Table 1 summarizes the adolescents’ characteristics by sex. The mean age for males was 14.5y (SD=1.61y) and 14.4y (SD=1.57y) for females. Approximately 62.9% of males and 65.5% of females were non-Hispanic White. In addition, a high percentage of both sexes (77.8% of males and 75.3% of females) reported that crime was not a barrier to performing physical activity. The majority of adolescents (92.7% of males, 96.4% of females) attended public schools. Only 1.4% of males and 2.1% of females reported having smoked at least 100 cigarettes in their lifetime. Furthermore, 89.6% of males and 83.2% of females reported having no trouble sleeping, and 62.6% of males and 59% of females reported having a regular bedtime. Additionally, 74.1% of males and 73.6% of females met the daily sleeping duration guidelines.

The majority of males (71.2%) and females (74%) were classified as having normal weight. In addition, 13.9% of males had obesity compared to 11.9% of females. Nearly half of males (44.6%) and females (49.8%) strongly agreed that family meals are important. Finally, 47.1% of males and 34.8% of females reported excellent health status, while only 0.5% of males and 0.3% of females reported having poor health status. Additionally, in the partially adjusted and adjusted models, having overweight or obesity was significantly inversely associated with excellent self-rated health (Overweight OR=0.55, 95%CI=0.38–080 p<0.002; Obesity OR=0.29, 95%CI=0.19–0.45 p<0.001).

Table 1. Adolescents Characteristics According to Sex (N=1,250)

Variable	Males Mean (SD)	Females Mean (SD)
Age (years)	14.5 (1.61)	14.4 (1.57)
Height (cm)	169.6 (11.40)	161.5 (8.68)
Weight (kg)	64.5 (17.40)	57.9 (14.80)
BMI Percentile	61.8 (30.35)	60.2 (28.84)
Beneficial food intake (daily frequency)	5.5 (2.96)	5.7 (3.24)
Detrimental food intake (daily frequency)	5.1 (2.80)	4.6 (2.81)
Agree that family meals are important (5-point scale)^a	4.1 (1.08)	4.1 (1.13)
Typical PA in free time (days)	3.1 (1.16)	2.8 (1.14)
Typical SB in free time (hours per day)	2.9 (0.95)	3.1 (1.00)
Sleep (hours)	8.7 (1.40)	8.8 (1.28)

	Male %(n)	Female %(n)
Family meals important
Strongly agree	44.6 (279)	49.8 (311)
Agree	31.5 (197)	26.3 (164)
Not Agree	24 (150)	23.9 (149)
Have no trouble sleeping	89.6 (561)	83.2 (519)
Have regular bedtime	62.6 (392)	59 (368)
Meeting sleep guidelines	74.1 (464)	73.6 (459)
Weight Status
Healthy weight	71.2 (446)	74 (462)
Overweight	14.9 (93)	14.1 (88)
Obese	13.9 (87)	11.9 (74)
Smoked 100 cigarettes	1.4 (9)	2.1 (13)
Not white	37.1 (232)	34.5 (215)
Non-Hispanic white	62.9 (394)	65.5 (409)
Public school	92.7 (580)	93.4 (583)
Crime barrier to PA
Agree	22.2 (139)	24.7 (154)
Not Agree	77.8 (487)	75.3 (470)
Health Status^b
Excellent	47.1 (295)	34.8 (217)
Very good	35.8 (224)	42.8 (267)
Good	12.6 (79)	15.7 (98)
Fair	3.7 (23)	6.1 (38)
Poor	0.5 (3)	0.3 (2)

Note: BMI: body mass index, PA: physical activity, SB: sedentary behavior, NH white: non-Hispanic white

^a on a five-point Likert Scale 1-Strongly disagree that is important to eat at least one family meal together and 5-Strongly agree that is important to eat at least one family meal together.

^bon a five-point Likert Scale1-Poor health status and 5-Excellent health status.

Table 2. Associations between Lifestyle Variables and Excellent Self-Rated Health (N=1,250)

Variable	Unadjusted	Partially Adjusted	Fully Adjusted
Variable	OR (95% CI)	OR (95% CI)^a	OR (95% CI)^b
Beneficial food intake (daily)*	1.12 (1.08–1.16)	1.05 (1.01–1.10)	1.06 (1.02–1.11)
Detrimental food intake (daily)*	0.99 (0.96–1.04)	1.00 (0.96–1.05)	0.99 (0.95–1.05)
Family meals are important
Do not agree ✝	1.00	1.00	1.00
Somewhat agree	1.40 (1.01–1.95)	1.09 (0.77–1.55)	1.05 (0.73–1.50)
Strongly agree	2.40 (1.79–3.24)	1.87 (1.34–2.591)	1.88 (1.35–2.63)
Typical weekly PA in free time
None✝	1.00	1.00	1.00
Sometimes (1‒2 times)	1.19 (0.76–1.19)	1.14 (0.71–1.85)	1.04 (0.64–1.68)
Often (3‒4 times)	1.83 (1.17–2.85)	1.40 (0.86–2.29)	1.29 (0.79–2.13)
Quite often (5‒6 times)	3.30 (2.11–5.17)	2.31 (1.41–3.80)	2.02 (1.22–3.36)
Very often (7+ times)	3.89 (2.30–6.58)	2.63 (1.47–4.69)	2.17 (1.20–3.92)
Typical weekly SB in free time
Almost none ✝	1.00	1.00	1.00
Little time	1.15 (0.67–1.99)	1.11 (0.60–2.03)	1.09 (0.59–2.02)
Moderate time	0.62 (0.37–1.06)	0.76 (0.42–1.37)	0.79 (0.43–1.43)
Lot of time	0.36 (0.21–0.64)	0.65 (0.34–1.22)	0.69 (0.36–1.30)
Trouble Sleeping	0.33 (0.22–0.48)	0.39 (0.25–0.59)	0.42 (0.27–0.64)
Regular bedtime	1.41 (1.23–1.79)	1.09 (0.85–1.42)	0.98 (0.75–1.29)
Meet sleep guidelines	1.21 (0.93–1.57)	1.00 (0.75–1.33)	1.00 (0.75–1.34)
Weight Status
Not overweight or obese✝	1.00	1.00	1.00
Overweight	0.54 (0.38–0.75)	0.58 (0.41–0.84)	0.55 (0.38–0.80)
Obese	0.26 (0.17–0.40)	0.31 (0.20–0.48)	0.29 (0.19–0.45)
Smoked at least 100 cigarettes	0.53 (0.21–1.38)	0.77 (0.26–2.31)	0.92 (0.30–2.85)
Age*	0.87 (0.81–0.94)		0.89 (0.82–0.97)
Sex of adolescent	0.60 (0.48–0.75)		0.58 (0.45–0.75)
NH white	1.16 (0.91–1.47)		1.05 (0.81–1.37)
In private school	1.25 (0.80–1.93)		0.94 (0.58–1.52)
Crime barrier to PA
Not Agree✝	1.00		1.00
Agree	0.73 (0.55–0.95)		0.82 (0.60–1.12)

Note: ^*Refers to non-categorical variables and the odds associated with a one-unit change in the variable level. CI: Confidence interval, PA: physical activity, SB: sedentary behavior, NH white: non-Hispanic white

^aAdjusted for nutrition, weight status, and other lifestyle factors

^bIncludes additional adjustments for demographic variables

✝Reference category.

Discussion

Following a salutogenic approach, the current study examined associations between health behaviors (diet, physical activity, sleep behaviors, and other health behaviors) and excellent self-rated health in American adolescents. The main finding of the study was that after adjusting for covariates, daily beneficial food intake, perception that family meals are important, frequent levels of physical activity in free time, and not having trouble sleeping, were significantly positively associated with excellent self-rated health. Although these positive associations were attenuated after controlling for demographic variables, they remained statistically significant, and are likely to be of public health significance within the adolescent population. The results showed that a daily beneficial food intake in adolescents was associated with a 6% increase in the odds of reporting excellent self-rated health. In addition, the perception that family meals are important is associated with an 88% increase in reporting excellent self-rated health in adolescent populations. Performing physical activity seven or more times a week was associated with reporting 2.17 times higher odds of excellent self-rated health. Furthermore, having no trouble sleeping was associated with a 58% increase in reporting excellent self-rated health. Even though the FLASHE study was conducted several years ago, there is little evidence to date that shows how healthier lifestyles are associated to excellent self-rated health in adolescents.

The current study outcomes align with the previous research that is available, which also indicated that physical activity was positively associated with excellent self-rated health in youth populations (Choudhury & M., 2011; Zullig & White, 2010). Previous research findings from a cross-sectional study with Australian adults showed that diet, higher levels of physical activity, sleep quality, and duration were also positively associated with excellent self-rated health with a similar magnitude to the results of the present study (Haapasalo et al., 2018). However, to our knowledge there are no current studies that evaluate all of these variables in adolescent populations.

The current results are also similar to previous research indicating positive relationships between beneficial lifestyle behaviors and positive health outcomes and life-satisfaction among adolescents (Marques et al., 2017). A cross-sectional study conducted in adults supported these findings and suggested that higher engagement with risky health behaviors such as risky alcohol consumption, physical inactivity, and poor diet, is more likely to be associated with poorer health outcomes (Duncan et al., 2014). A cohort study conducted in Norway with 8,828 adolescents (10–19 years) explored the predictive value of adolescents' self-rated health with "multi-illness" in early adulthood. After adjusting for baseline variables including chronic diseases, frequency of health care attendance, sex, and age, the results showed that adolescents who had reported "very good" or "good" self-rated health were classified as "healthy" in their early adulthood. The findings showed a clear association between adolescents' poorer self-rated health and the risk of having "multi-illness" in early adulthood, suggesting the poor self-rated health may serve as a proxy for later multimorbidity (Hetlevik et al., 2020). This evidence supports the importance of implementing interventions in the early stages of life, given the uniquely vulnerable period of adolescence (Gootman et al., 2009).

In addition, the current results were similar to a systematic review that evaluated the salutogenic approach and its relationship with eating behavior (Veiga et al., 2022). Among the 15 articles reviewed, 11 showed that implementing a salutogenic approach had a positive relationship with several healthy eating behaviors, including a higher intake of beneficial foods like fresh fruits and vegetables (Veiga et al., 2022). Another systematic review that included 21 articles highlighted the positive relationship between a salutogenic orientation and eight health-related behaviors, including eating habits. This evidence indicates that the salutogenic approach may be associated with fewer risk behaviors, more healthful eating habits, and other healthy behaviors like higher levels of physical activity, less smoking and alcohol consumption, and more frequent tooth brushing (da-Silva-Domingues et al., 2022).

Stronger agreement with family meal importance consistently resulted in a higher likelihood of reporting excellent self-rated health as compared to a lower agreement. These results are consistent with several previous studies. For example, a cross-sectional study by Videon and Manning (2003) showed that parental presence at the evening meal was positively related to a higher intake of fruits and vegetables (Videon & Manning, 2003). In another study of 2,516 adolescents conducted by Neumark-Sztainer and colleagues, family meals were associated with benefits for dietary intake, disordered eating behaviors, substance use, and psychosocial health (Neumark-Sztainer et al., 2010). Adolescents who reported performing physical activity in their free time seven or more times per week, had higher odds of reporting excellent health compared to adolescents who did not do physical activity in their free time. Additionally, a dose-response relationship between physical activity and self-rated health has been observed in several studies, with higher levels of physical activity positively associated with self-rated health among adolescents. For example, results of a systematic review showed that out of 62 publications evaluating relationships between physical activity and self-rated health in adolescents, 58 studies reported statistically significant positive associations (Zhang et al., 2020). In the current study, an imperfect dose-response association was observed in the unadjusted analyses among adolescents reporting spending a little, moderate, or a lot of time in sedentary behaviors, but statistical adjustment for confounders attenuated the observed associations to the point of non-significance. Health promotion through decreased sedentary behavior time has been previously reported, both alone and in combination with other health behavior changes in other studies (Rezende et al., 2016; Vandelanotte et al., 2013).

One strength of the present study is that evaluates several health behaviors simultaneously, compared to previous studies that are only focused on evaluating physical activity or dietary patterns separately. Several potential confounding variables relevant to health and key demographics were included in the adjusted analysis and results were robust in showing that healthier lifestyle behaviors were associated with the aspirational outcome of excellent self-rated health. In addition, promoting and implementing salutogenic approaches in adolescent populations, that encourage adolescents to actively participate in their own well-being, enhances the process of developing personal skills to strengthen societies and improve overall quality of life. These salutary factors should be researched in longitudinal studies to investigate strategies to help adolescents optimize their health and quality of life. The FLASHE study is inherently limited due to its cross-sectional nature. Consequently, conclusions cannot imply causation. Additionally, while the FLASHE participants were representative of the United States adolescent population in 2014, the availability of specific measures of socioeconomic status was limited. Socioeconomic status is an important determinant of health, so the absence of a valid assessment to apply in adjusted models may potentially confound results. Because of the dichotomization of some lifestyle behaviors, the ability to evaluate dose-response associations between lifestyle variables and self-rated health was limited. Future research should include stronger study designs to address temporality and to better determine causal and dose-response relationships. In addition, a valid measure of socioeconomic status is needed to properly determine its influence and ensure a representative population sample. Lastly, the self-reported nature of the measures may allow various types of bias like self-report bias, recall bias, or social desirability bias. Also, the measure of excellent self-rated health was self-reported and therefore subjective. However, evidence has shown that patient-reported health status is an independent predictor of mortality, hospitalization, and cost of care (Rosenkranz et al., 2013). For that reason, these measures are highly relevant in public health. Furthermore, we acknowledge that circumstances could be different and that there is a likelihood that following the 2020 pandemic, adolescents may have additional barriers to healthful lifestyle behaviors and optimal wellbeing. Though limited, the current study is valuable because it includes a large, generally representative sample, uses validated and appropriate measures of multiple health behaviors, and is novel due to the salutogenic orientation. To our knowledge, this study is the first to report on adolescent health behaviors and excellent self-rated health. The present study indicated that adolescents engaging in healthy lifestyle behaviors were more likely to report excellent self-rated health. Specifically, engaging in positive lifestyle behaviors like beneficial food consumption, having at least one family meal a day, higher levels of physical activity and limited sedentary behavior time, and not having trouble staying asleep were positively associated with excellent self-rated health in this adolescent population. These results support the potential for future efforts to implement salutogenic interventions that address health behaviors to improve health outcomes in adolescents.

Correspondence should be addressed to

Richard R Rosenkranz

Bigelow Health Sciences Building, Rm 329

4505 S. Maryland Pkwy

University of Nevada Las Vegas, Mail Code 3019

Las Vegas, NV 89154

richard.rosenkranz@unlv.edu

702-895-5198

Richard Rosenkranz 0000-0002-8242-3546

Emily Mailey 0000-0001-7672-445X

Sara K Rosenkranz0000-0002-3297-1640

Ana Gonzalez-Alvarez 0000-0002-7724-4287

Author Contributions

Conceptualization, R.R.R. and K.A.K.; Methodology, R.R. and K.A.K.; Investigation, K.A.K; Writing – Original Draft, A.G.A; Writing – Review & Editing, A.G.A., S.K.R., E.M., and R.R.R.; Supervision, R.R.R.

Declaration of conflicting interests

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

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0).

References

Antonovsky, A. (1987). Unraveling the mystery of health : how people manage stress and stay well (1st edition. ed.). Jossey-Bass.

Antonovsky, A. (1996). The salutogenic model as a theory to guide health promotion. Health promotion international, 11(1), 11-18. https://doi.org/10.1093/heapro/11.1.11

Badri, M. A., Yang, G., Al Khaili, M., Al Bahar, M., Al Rashdi, A., & Al Hyas, L. (2021). Hierarchical regression of wellbeing and self-rated health among older adults in abu dhabi. International journal of environmental research and public health, 18(15), 8006. https://doi.org/10.3390/ijerph18158006

Bhattacharya, S., Pradhan, K., Bashar, M., Tripathi, S., Thiyagarajan, A., Srivastava, A., & Singh, A. (2020). Salutogenesis: A bona fide guide towards health preservation. Journal of family medicine and primary care, 9(1), 16-19. https://doi.org/10.4103/jfmpc.jfmpc_260_19

Bombak, A. E. (2013). Self-rated health and public health: A critical perspective. Frontiers in public health, 1(MAY), 15-15. https://doi.org/10.3389/fpubh.2013.00015

Breidablik, H.-J., Meland, E., & Lydersen, S. (2009). Self-rated health during adolescence: stability and predictors of change (Young-HUNT study, Norway). European journal of public health, 19(1), 73-78. https://doi.org/10.1093/eurpub/ckn111

Choudhury, M. (2011). The relationship between in-school physical activity and life satisfaction, self-rated health, academic performance, and out-of-school physical activity: A Canadian study Queen’s University]. Kingston, Ontario, Canada. https://qspace.library.queensu.ca/handle/1974/6281

da-Silva-Domingues, H., del-Pino-Casado, R., Palomino-Moral, P. Á., López Martínez, C., Moreno-Cámara, S., & Frías-Osuna, A. (2022). Relationship between sense of coherence and health-related behaviours in adolescents and young adults: a systematic review. BMC public health, 22(1), 477-477. https://doi.org/10.1186/s12889-022-12816-7

Dallacker, M., Hertwig, R., & Mata, J. (2018). The frequency of family meals and nutritional health in children: a meta‐analysis. Obesity reviews, 19(5), 638-653. https://doi.org/10.1111/obr.12659

Duncan, M. J., Kline, C. E., Vandelanotte, C., Sargent, C., Rogers, N. L., & Di Milia, L. (2014). Cross-sectional associations between multiple lifestyle behaviors and health-related quality of life in the 10,000 steps cohort. PloS one, 9(4), e94184-e94184. https://doi.org/10.1371/journal.pone.0094184

Fryar CD, Carroll MD, & Afful J. (2021). Prevalence of Overweight, Obesity, and Severe Obesity Among Children and Adolescents Aged 2–19 Years: United States, 1963–1965 Through 2017–2018. Division of Health and Nutrition Examination Surveys. National Center for Health Statistics. Centers for Disease Control and Prevention -CDC-. Retrieved January 30th from https://www.cdc.gov/nchs/data/hestat/obesity-child-17-18/obesity-child.htm#:~:text=Results%20from%20the%202017%E2%80%932018,and%20another%2016.1%25%20are%20overweight.

Gallagher, K. M., & Updegraff, J. A. (2011). Health Message Framing Effects on Attitudes, Intentions, and Behavior: A Meta-analytic Review. Annals of behavioral medicine, 43(1), 101-116. https://doi.org/10.1007/s12160-011-9308-7

Gootman, J. A., Lawrence, R. S., & Sim, L. J. (2009). Adolescent health services missing opportunities. National Academies Press.

Haapasalo, V., de Vries, H., Vandelanotte, C., Rosenkranz, R. R., & Duncan, M. J. (2018). Cross-sectional associations between multiple lifestyle behaviours and excellent well-being in Australian adults. Preventive medicine, 116, 119-125. https://doi.org/10.1016/j.ypmed.2018.09.003

Hamer, M., Stamatakis, E., & Steptoe, A. (2008). Dose response relationship between physical activity and mental health: the Scottish health survey. Naturopathy digest, 3(8).

Harrison, M. E., Norris, M. L., Obeid, N., Fu, M., Weinstangel, H., & Sampson, M. (2015). Systematic review of the effects of family meal frequency on psychosocial outcomes in youth. Canadian family physician, 61(2), E96-E106.

Hetlevik, Ø., Meland, E., Hufthammer, K. O., Breidablik, H. J., Jahanlu, D., & Vie, T. L. (2020). Self-rated health in adolescence as a predictor of ‘multi-illness’ in early adulthood: A prospective registry-based Norwegian HUNT study. SSM - population health, 11, 100604-100604. https://doi.org/10.1016/j.ssmph.2020.100604

Huppert, F. A., & So, T. T. C. (2013). Flourishing Across Europe: Application of a New Conceptual Framework for Defining Well-Being. Social indicators research, 110(3), 837-861. https://doi.org/10.1007/s11205-011-9966-7

Kelder, S. H., Perry, C. L., Klepp, K. I., & Lytle, L. L. (1994). Longitudinal tracking of adolescent smoking, physical activity, and food choice behaviors. American journal of public health (1971), 84(7), 1121-1126. https://doi.org/10.2105/AJPH.84.7.1121

Keyes, C. L. M. (2007). Promoting and Protecting Mental Health as Flourishing: A Complementary Strategy for Improving National Mental Health. The American psychologist, 62(2), 95-108. https://doi.org/10.1037/0003-066X.62.2.95

Lachytova, M., Katreniakova, Z., Mikula, P., Jendrichovsky, M., & Nagyova, I. (2017). Associations between self-rated health, mental health problems and physical inactivity among urban adolescents. European journal of public health, 27(6), 984-989. https://doi.org/10.1093/eurpub/ckx051

Marques, A., Mota, J., Gaspar, T., & de Matos, M. G. (2017). Associations between self-reported fitness and self-rated health, life-satisfaction and health-related quality of life among adolescents. Journal of exercise science and fitness, 15(1), 8-11. https://doi.org/10.1016/j.jesf.2017.03.001

McDowell, C. P., MacDonncha, C., & Herring, M. P. (2017). Brief report: Associations of physical activity with anxiety and depression symptoms and status among adolescents. Journal of adolescence (London, England.), 55(1), 1-4. https://doi.org/10.1016/j.adolescence.2016.12.004

National Cancer Institute. (2017). FLASHE - Teen Diet Survey. Division of Cancer Control and Population Sciences. U.S. Department of Health and Human Services. National Institutes of Health. https://cancercontrol.cancer.gov/sites/default/files/2020-06/teen_diet_puf_instrument.pdf

National Cancer Institute. (2017a). FLASHE Annotated Teen Demographic Survey. Division of Cancer Control and Population Sciences. U.S. Department of Health and Human Services. National Institutes of Health. Retrieved July from https://cancercontrol.cancer.gov/sites/default/files/2020-06/teen_demo_puf_instrument.pdf

National Cancer Institute. (2017b). FLASHE- Annotated Teen Physical Activity Survey. Division of Cancer Control and Population Sciences. U.S. Department of Health and Human Services. National Institutes of Health. https://cancercontrol.cancer.gov/sites/default/files/2020-06/teen_pa_puf_instrument.pdf

National Cancer Institute. (2020). Family Life, Activity, Sun, Health, and Eating (FLASHE). About the FLASHE study. Division of Cancer Control and Population Sciences. Retrieved February 8 from https://cancercontrol.cancer.gov/brp/hbrb/flashe-study

Neumark-Sztainer, D., Larson, N. I., Fulkerson, J. A., Eisenberg, M. E., & Story, M. (2010). Family meals and adolescents: what have we learned from Project EAT (Eating Among Teens)? Public health nutrition, 13(7), 1113-1121. https://doi.org/10.1017/S1368980010000169

Office of Disease Prevention and Health Promotion. (2021). Adolescent Health. https://www.healthypeople.gov/2020/topics-objectives/topic/Adolescent-Health

Paruthi, S., Brooks, L. J., D'Ambrosio, C., Hall, W. A., Kotagal, S., Lloyd, R. M., Malow, B. A., Maski, K., Nicholas, C., Quan, S. F., Rosen, C. L., Troester, M. M., & Wise, M. S. (2016). Consensus Statement of the American Academy of Sleep Medicine on the Recommended Amount of Sleep for Healthy Children: Methodology and Discussion. Journal of clinical sleep medicine, 12(11), 1549-1561. https://doi.org/10.5664/jcsm.6288

Prendergast, K. B., Mackay, L. M., & Schofield, G. M. (2016). The Clustering of Lifestyle Behaviours in New Zealand and their Relationship with Optimal Wellbeing. International journal of behavioral medicine, 23(5), 571-579. https://doi.org/10.1007/s12529-016-9552-0

Rezende, L. F. M., Sá, T. H., Mielke, G. I., Viscondi, J. Y. K., Rey-López, J. P., & Garcia, L. M. T. (2016). All-Cause Mortality Attributable to Sitting Time: Analysis of 54 Countries Worldwide. American journal of preventive medicine, 51(2), 253-263. https://doi.org/10.1016/j.amepre.2016.01.022

Rosenkranz, R. R., Duncan, M. J., Rosenkranz, S. K., & Kolt, G. S. (2013). Active lifestyles related to excellent self-rated health and quality of life: Cross sectional findings from 194,545 participants in the 45 and Up Study. BMC public health, 13(1), 1071-1071. https://doi.org/10.1186/1471-2458-13-1071

Schnittker, J., & Bacak, V. (2014). The increasing predictive validity of self-rated health. PloS one, 9(1), e84933-e84933. https://doi.org/10.1371/journal.pone.0084933

Seligman, M. E. P. (2008). Positive Health. Applied psychology, 57(s1), 3-18. https://doi.org/10.1111/j.1464-0597.2008.00351.x

Stenlund, S., Koivumaa-Honkanen, H., Sillanmäki, L., Lagström, H., Rautava, P., & Suominen, S. (2021). Health behavior of working-aged Finns predicts self-reported life satisfaction in a population-based 9-years follow-up. BMC Public Health, 21(1), 1-1815. https://doi.org/10.1186/s12889-021-11796-4

te Velde, S. J., Twisk, J. W. R., & Brug, J. (2007). Tracking of fruit and vegetable consumption from adolescence into adulthood and its longitudinal association with overweight (vol 98, pg 434, 2007). British journal of nutrition, 98(4), 871-871. https://doi.org/10.1017/S0007114507815856

Telama, R., Yang, X., Viikari, J., Välimäki, I., Wanne, O., & Raitakari, O. (2005). Physical activity from childhood to adulthood: A 21-year tracking study. American journal of preventive medicine, 28(3), 267-273. https://doi.org/10.1016/j.amepre.2004.12.003

Vandelanotte, C., Duncan, M. J., Short, C., Rockloff, M., Ronan, K., Happell, B., & Di Milia, L. (2013). Associations between occupational indicators and total, work-based and leisure-time sitting: A cross-sectional study. BMC public health, 13(1), 1110-1110. https://doi.org/10.1186/1471-2458-13-1110

Veiga, G. R. S., Padilha, B. M., Bueno, N. B., Santos, J. R. L., Nunes, L. F., Florencio, T. M. M. T., & Lima, M. d. C. (2022). Salutogenesis, nutritional status and eating behaviour: a systematic review. Public health nutrition, 25(9), 2517-2529. https://doi.org/10.1017/S1368980021004444

Videon, T. M., & Manning, C. K. (2003). Influences on adolescent eating patterns: the importance of family meals. Journal of adolescent health, 32(5), 365-373. https://doi.org/10.1016/S1054-139X(02)00711-5

Vinje, H. F., Langeland, E., & Bull, T. (2022). Aaron Antonovsky’s Development of Salutogenesis, 1979–1994. Springer.

Welk, G. J., Saint-Maurice, P. F., Dixon, P. M., Hibbing, P. R., Bai, Y., McLoughlin, G. M., & Pereira da Silva, M. (2021). Calibration of the Online Youth Activity Profile Assessment for School-Based Applications. Journal for the measurement of physical behaviour, 4(3), 236-246. https://doi.org/10.1123/jmpb.2020-0048

Westat. (2015). Family Life, Activity, Sun, Health, and Eating (FLASHE) Study Methodology Report https://cancercontrol.cancer.gov/sites/default/files/2020-06/flashe_methods_report.pdf

Wiles, N. J., Haase, A. M., Lawlor, D. A., Ness, A., & Lewis, G. (2011). Physical activity and depression in adolescents: cross-sectional findings from the ALSPAC cohort. Social Psychiatry and Psychiatric Epidemiology, 47(7), 1023-1033. https://doi.org/10.1007/s00127-011-0422-4

Zhang, T., Lu, G., & Wu, X. Y. (2020). Associations between physical activity, sedentary behaviour and self-rated health among the general population of children and adolescents: A systematic review and meta-analysis. BMC public health, 20(1), 1343-1343. https://doi.org/10.1186/s12889-020-09447-1

Zullig, K. J., & White, R. J. (2010). Physical Activity, Life Satisfaction, and Self-Rated Health of Middle School Students. Applied Research in Quality of Life, 6(3), 277-289. https://doi.org/10.1007/s11482-010-9129-z