Skip to main content
Download PDF

Exploring the prioritisation of sleep, diet, and physical activity as pillars of health: correlates and associations with health behaviours in Australian adults

Journal of Activity, Sedentary and Sleep Behaviors volume 2, Article number: 26 (2023) Cite this article

Abstract

Background

Chronic disease is the leading cause of death globally. Sleep, diet, and physical activity are modifiable health behaviours that are key for reducing the burden of chronic disease. These health behaviours are collectively termed ‘The 3 Pillars of Health’ and are critical for populations who are at risk of poor health. Shiftworkers are one such at-risk population. To target behavioural change it is critical to first understand which of these health behaviours Australians currently prioritise. Therefore, this study aimed to investigate how Australians (including shift workers) prioritise sleep, physical activity, and diet, and examine the associations with correlates of health behaviours.

Methods

Two cohorts of Australian adults were sampled. A cohort of 1151 Australian adults (54% female, aged 18–65 years) including employed (in any work schedule), unemployed, studying, and retired completed a phone interview. A cohort of 533 Australian shiftwork-only adults (76% female, 18–72) completed an online survey. All participants were asked which health behaviour (sleep, physical activity, or diet) they prioritised in their own life. Behavioural correlates of sleep, diet, and physical activity (sleep duration, frequency of moderate to physical activity, healthy dietary behaviour), and years of shiftwork experience were also collected. Multinomial logistic regressions were used to investigate the association between the highest prioritised pillar of health and the behavioural correlates.

Results

Diet was prioritised by the Australian adults (49%), whereas sleep was prioritised by the shiftwork-only sample (68%). Australian adults who prioritised diet were significantly more likely to report diets with less fast-food consumption (p < 0.002) and more fruit consumption (p < 0.002) compared to those that prioritised sleep. For the shiftwork-only sample, those with 16–30 years of shiftwork experience were significantly more likely to prioritise sleep compared to diet (p < 0.05). However, prioritising sleep was not associated with meeting the sleep duration recommendations in the shiftwork-only sample.

Conclusions

Across two cohorts of Australians, prioritisation of health behaviour was only associated with actual behaviour for diet. This may reflect different motivations for prioritising different health behaviours, in addition to different capabilities to change different health behaviours. Future research should include longitudinal methodologies to understand how behaviour prioritisation changes over work- and life-span, and any associations with actual health behaviour.

Background

Chronic diseases, such as heart disease, diabetes, and obesity, are the leading cause of death globally [1, 2]. A strategy to reduce the burden of chronic disease is to target modifiable health behaviours such as smoking, nutrition, alcohol consumption, physical activity, sitting and sleep [3,4,5]. Current perspectives for health behaviours focus on ‘The Pillars of Health’ [3, 6, 7]. The behaviours included as pillars of health vary in the literature, but the most common are physical activity, diet, sleep, smoking, alcohol use, and relaxation [3, 6, 7]. These health behaviours are also promoted in public policy, for example, the American Heart Foundation’s ‘Life’s Essential 8’ [8], which champions many of the pillars including sleep, physical activity, and diet. Of the pillars of health represented in research and policy, sleep, physical activity, and diet are commonly referred to as ‘The Three Pillars of Health” as they are essential for life, impact each other, and are associated with a number of health outcomes [6, 9,10,11,12,13].

Inadequate sleep [14], insufficient physical activity [15], and an unhealthy diet [16] are each independently associated with an increased risk of chronic disease and therefore independently contribute to a large burden on the healthcare system. In 2016–2017 the healthcare costs associated with inadequate sleep in Australia were estimated to be almost $1.8 billion (AUD) [17], the economic impact attributable to inadequate consumption of fruit and vegetables results in total costs of over $2 billion (AUD) [18], and the annual healthcare and lost productivity costs associated with physical inactivity are in excess of $1.6 billion (AUD) [19]. Important components of adequate sleep, diet, and physical activity include obtaining 7–9 h sleep per night [20], meeting the Australian recommended serves of fruits (> 2 per day) and vegetables (> 5 per day) [21], and obtaining at least 150 min of physical activity per week at moderate-to-vigorous intensity [1].

The risk of chronic disease can also be heightened based on population-level differences, such as work schedule [22, 23]. For example the risk of chronic disease is greater in shiftworkers, that is, anyone working outside of conventional daytime hours (e.g., early morning, late evening, night-work, and on-call) [22, 24], compared to day workers. Shiftwork leads to circadian disruption, and is associated with altered sleep-wake patterns and disruptions to other health behaviours [22, 24,25,26], such as altered patterns of food intake [27], reduced physical activity [28, 29], and increased likelihood of smoking [30]. Consequently, shiftworkers are up to five times more likely to experience a coronary event than non-shift workers [31], as well as being at an increased risk of diabetes, high cholesterol and cardiovascular disease [32, 33]. Additionally, shiftworkers experience unique external barriers to sleep, diet, and physical activity that are important to consider. For example, external barriers such as hospital cafeterias being closed at night influences the food that shiftworkers can consume on shift [27], and a lack of a quiet sleeping environment at work can influence whether sleep is prioritised on shift [74]. As with the general population, facilitators to health behaviours can include workplace initiatives, clear focus on health benefits, and peer support [27, 64, 74].

Sleep, diet, and physical activity are independently associated with chronic disease risk, yet they are behaviours that do not occur independently and indeed engagement in one behaviour influences engagement in other behaviours [34]. For example, there is a bidirectional relationship between physical activity and sleep, such that increased physical activity is associated with better sleep and, conversely, poor sleep is associated with reduced physical activity [35,36,37,38,39]. Further, increasing physical activity is more effective for improving health outcomes when an individual also obtains adequate sleep [40]. Sleep quality and quantity can also be influenced by dietary behaviours [41, 42], and dietary behaviours can be influenced by sleep [43]. There is also evidence that engaging in all three behaviours is beneficial to health [44, 45]. For example, individuals who eat a healthy diet tend to engage in physical activity and get adequate sleep, and have lower risk of chronic disease [46]. Interventions that consider the influence of multiple behaviours (sleep, diet, and physical activity) may be important for reducing the burden of chronic disease risk.

In general, effective behaviour change at the individual level requires motivation, capability, and opportunity (as per the COM-B model of behaviour) [47]. Some evidence suggests that targeting multiple behavioural factors together is beneficial for the success of an intervention [48]. For example, it may be that if a person has sufficient motivation for changing one behaviour, this motivation can generalise to a motivation for becoming healthier overall. In this case, the regulation skills utilised to change one behaviour, such as planning and problem solving, could be applied to other behaviours [4]. However, while the skills needed to change health behaviours may be transferable, behaviour change, be it for one or multiple behaviours, is challenging especially as motivation wanes and priorities shift [49]. A lack of time and competing priorities (e.g. work-life conflict) are the most common reasons given for not prioritising health behaviours [50]. Therefore, if people are not engaging in any health behaviours, a change to three behaviours at once may not be feasible [46]. As a first step, consideration of how people prioritise behaviours should be explored. This is necessary for capturing a snapshot of current behaviours and attitudes to gauge the level of motivations and helps act as a starting point for interventions to change subsequent behaviours.

It is currently unknown how Australian adults prioritise the pillars of health (sleep, diet, and physical activity) and if prioritisation is related to how they engage in actual health behaviours. Further, we do not know if cohorts with different levels of chronic disease risk and work demands to the general public, e.g., shiftworkers, prioritise the pillars differently. Therefore, the current study investigated in a general population cohort, and shiftworking cohort: (a) what pillars of health Australians prioritise, and (b) what socio-demographic and health behaviour correlates are associated with this health behaviour prioritisation.

Method

Design

This paper presents data from two cross-sectional studies: Study 1 was a cohort of Australian adults, including employed (in any work schedule), unemployed, studying, and retired, and Study 2 was a cohort of current Australian shiftworkers. A STROBE (cross-sectional) checklist guides the reporting of both studies.

Study 1 uses data from the National Social Survey, a descriptive cross-sectional survey conducted by the Population Research Laboratory at CQUniversity. This survey comprised of core information assessing demographics, health behaviours, chronic diseases status, and quality of life. The current study analyses data from a subset of the questions from the 2017 (July-August) National Social Survey. Data from the questions referring to sleep duration have been previously published [51, 52]. All participants provided verbal informed consent prior to the start of the survey. Ethics was obtained from the CQUniversity Human Research Ethics Committee (H14/09-203).

Study 2 uses from a cross-sectional survey on the sleep hygiene knowledge of Australia shiftworkers conducted in June-August 2021. This survey comprised of questions on core demographics, sleep hygiene knowledge, sleep hygiene practices, sleep quantity, and sleep quality. Data from the questions referring to sleep hygiene have been published elsewhere [26]. The current study includes data from questions on the prioritisation of health behaviours, sleep quality, and sleep quantity. All participants provided informed consent and the study was granted ethics approval by the CQUniversity Human Research Ethics Committee (2021-03).

Participants

Study 1

All states and territories in Australia were sampled using randomly selected landlines from an Australian database and dual frame random digit dialling of phones to conduct computer-assisted-telephone-interviewing. For mobile telephone numbers, the respondent was the person who received the phone call and for landline telephone numbers, a respondent within each household was selected using a process to ensure an equal yet random selection of female and male participants. All participants were verbally told the purpose of the study and asked if they were aged 18 years or older and resided in Australia to ensure eligibility.

Study 2

Australian shiftworkers from all states and territories were eligible to participate in the online survey from June 2021 to August 2021. Online advertisements were posted on social media (Facebook and Twitter) and through convenience sampling, flyers were sent to companies that employed shiftworkers. Additional recruitment was conducted using convenience sampling and snowballing methods. Inclusion criteria included: self-identified as a current shiftworker, an Australian resident, and 18 years or older.

Procedure

Study 1

The research team at the Population Research Laboratory at CQUniversity conducted the interviews, after training in the computer-assisted telephone interviewing system. Interviews lasted approximately 40 min and were conducted at various times throughout the day and evening, seven days per week. Upon making contact, interviewers identified themselves and then asked screening questions to select the respondent. If the interviewers did not establish contact on their first call, a maximum of five call back attempts were made. No renumeration was provided.

Study 2

Participants were provided a link to the online survey, hosted on Qualtrics [53]. The survey took approximately 20 min to complete. Participants were informed that the survey was anonymous and voluntary, and no remuneration was provided. At the conclusion of the survey participants were provided with a link to a separate survey, also hosted on Qualtrics, where they had the option of providing their email address to receive a plain English summary of their results.

Measures

Both studies

Prioritising health behaviours

In Study 1 and 2, participants were asked to prioritise the three health behaviours of interest (‘The Pillars of Health’; sleep, diet, and physical activity). Participants were asked, ‘Which of the following most accurately reflects the order of importance for optimal health and wellbeing in your own life?’ with 6 options ordered from most to least important (1. sleep, diet, physical activity; 2. sleep, physical activity, diet; 3. diet, sleep, physical activity; 4. diet, physical activity, sleep; 5. physical activity, sleep, diet; 6. physical activity, diet, sleep).

Sleep

Sleep duration was assessed by asking “Thinking about a usual weekday, how much sleep do you regularly get?”. Response options were < 1 h, 1–2 h, 2–3 h, 3–4 h, 5–6 h, 6–7 h, 7–8 h, 8–9 h, 9–10 h, 10–11 h, 11–12 h, or > 12 h. Participants were categorised based on the National Sleep Foundation sleep recommendations for adults [54], which is 7–9 h of sleep per night. Therefore, sleep duration per night was coded into a categorical variable with two categories: meet recommendation (≥ 7 h - ≤9 h sleep) or does not meet the sleep recommendations (< 7 h - >9 h sleep). Participants were also asked about sleep disorders with the question ‘Do you currently suffer from any diagnosed, chronic (> 6 months) sleep-related disorder?’ with response options: sleep apnoea, snoring, insomnia, restless legs, another sleep-related disorder, a health condition that impacts sleep, or none. This was coded into a categorical variable: sleep disorder or no sleep disorder.

In Study 2, sleep duration was assessed by asking ‘how many hours of actual sleep do you get at bedtime?’. Participants were categorised by the National Sleep Foundation sleep recommendations for adults [54], which is 7–9 h of sleep per night. Therefore, sleep duration per night was coded as a categorical variable with two categories: meet recommendation (≥ 7 h - ≤9 h sleep) or does not meet the sleep recommendations (< 7 h or > 9 h sleep). Participants in Study 2 were also asked about sleep disorders, with the question, ‘Do you currently suffer from any diagnosed sleep-related disorders?’ with response options: sleep apnoea, snoring, insomnia, restless legs, another sleep-related disorder, a health condition that impacts sleep, or none. This question was also coded into a categorical variable: sleep disorder or no sleep disorder.

Study 1

Sociodemographic questions

Sociodemographic characteristics included gender (male, female, non-binary/prefer not to say), age (a continuous variable which was coded to a categorical variable with 4 levels: 18–34 years, 35–44 years, 45–54 years, > 55 years), and current work status (employed or not employed). Participants also self-reported height and weight, which were used to determine Body Mass Index (BMI; kg/m2). BMI categories were coded as underweight-normal (≤ 24.9 kg/m2), and overweight-obese (> 25.0 kg/m2). Detail on other sociodemographic characteristics measured in the National Social Survey but not included in the current analyses are published elsewhere [52, 55, 56].

Diet

To assess dietary behaviours, three aspects of a healthy diet were asked about: fruit, vegetable, and fast-food consumption. To assess fruit consumption, participants were asked “How many serves of fruit do you eat on a usual day? One serve of fruit is equivalent to one medium piece or two small pieces of fruit.” This was a continuous variable coded into a categorical variable: meeting the daily recommendations for fruit intake [21] (≥ 2 serves per day) or not meeting the daily recommendations for daily fruit intake (< 2 serves per day). To assess vegetable consumption, participants were asked ‘How many serves of vegetables do you eat on a usual day? One serve of vegetables is equivalent to half a cup of cooked vegetables or one cup of salad vegetables.’ This was a continuous variable, coded into a categorical variable: meeting the daily recommendations for vegetable intake [21] (≥ 5 serves per day) or not meeting the daily recommendations for daily fruit intake (< 5 serves per day). Fast-food consumption was assessed by asking ‘In the last week (the last 7 days), how many times did you eat something from a fast-food restaurant like McDonald’s, Hungry Jacks, KFC, etc? This also includes other fast-food and takeaway such as fish and chips, Chinese food, and pizza for example.’ Response options included: never, once, 2–3 times, 4–5 times, 6–7 times, more than 7 times. This was coded into a categorical variable: ≤ once a week or ≥ twice a week (based on previous research indicating that fast food consumption twice a week or more is associated with poorer health outcomes than once a week or less [57, 58]).

Physical activity

Physical activity was assessed via The Active Australia Survey which comprises eight items to assess the frequency and duration of walking, moderate and vigorous leisure physical activity, and vigorous gardening in the past 7 days. A combined total of moderate and vigorous physical activity (MVPA) was calculated as per previous research [59, 60]. Time spent doing vigorous gardening was not included in the calculations of vigorous physical activity, consistent with Active Australia Survey recommendations [61]. As the minimum recommendation for physical activity is 150 min per week [1], the MVPA variable was dichotomised into those that met this recommendation and those that did not. This aligns with Active Australia Survey data treatment recommendations [61].

Study 2

Sociodemographic questions

Demographic questions included gender (male, female, non-binary/prefer not to say); age (a continuous variable which was coded to a categorical variable with 4 levels: 18–34 years, 35–44 years, 45–54 years, > 55 years); and length of employment in shift work (a continuous variable that was coded into a categorical variable with 4 levels: 1–5 years, 6–15 years, 16–30 years, 30 + years). Participants were also asked what industry they were employed in, with response categories based on the Australian and New Zealand Standard Industrial Classification (ANZSIC; [62]) categories (mining, manufacturing, retail trade, accommodation and food services, transport, postal and warehousing, information media and telecommunications, public administration and safety, healthcare and social assistance, arts and recreation services, and government or defence). Questions on diet and physical activity were not included in study 2 as they were not part of the primary aims of the larger study which focussed on knowledge of sleep hygiene in shiftworkers [26].

Statistical analyses

Descriptive frequency analysis was used to report sociodemographic and health characteristics, and proportion of participants that prioritised each of the health behaviours first. All analyses were conducted using Jamovi 1.2.2 (The Jamovi Project, 2021).

For Study 1, multiple logistic regression models were used to examine the associations between prioritising each health behaviour first (i.e., sleep, diet, or physical activity), with correlates of sleep, diet, and physical activity (fruit consumption, vegetable consumption, fast food consumption, sleep duration, sleep disorders, and MVPA). The reference group was determined by which health behaviour was prioritised first by a majority of participants. For Study 1, this was diet. Due to the large number of comparisons, Bonferroni correction was applied where the critical p value was divided by the number of comparisons (0.05/21) to result in a new critical p value of p < 0.002. Significant differences (p < 0.05) as a function of demographic factors (age, gender) were tested for each of the outcome variables (fruit consumption, vegetable consumption, fast food consumption, sleep duration, sleep disorders, vigorous activity, and moderate activity) using Chi square analyses. Any demographic factors that were found to be associated with an outcome variable through the Chi square analyses were entered into the adjusted multinomial logistic regressions as a covariate. Age was significantly associated with fruit consumption, fast food consumption, sleep duration, and sleep disorders, and was included as a covariate for those analyses. Gender significantly associated with fruit consumption, vegetable consumption, fast food consumption, sleep disorders, and MVPA and was included as a covariate for those analyses.

For Study 2, multiple logistic regression models were used to examine the associations between prioritising each health behaviour first (i.e., sleep, diet, or physical activity), and sleep duration, shiftwork duration, and sleep disorders. The reference group was prioritising sleep first as this behaviour was prioritised first by a majority of the sample. The critical p value was kept at p < 0.05. Significant differences (p < 0.05) as a function of demographic factors (age, gender) were tested for each of the outcome variables (sleep duration, shiftwork duration, and sleep disorders) using Chi square analyses. Age and gender were significantly associated with sleep duration, shiftwork duration, and sleep disorders, and as such, were included as covariates for each multinomial regression.

Findings from Study 1 and Study 2 are presented separately below.

Results

Participants

A total of 1265 participants completed the 2017 National Social Survey and were included in Study 1. This was a response rate of 24%, which is comparable to previous National Social Surveys in 2015 and 2016 [55]. Of these 1265 participants, 114 either did not answer or responded “I don’t know” to the prioritisation question. Therefore, the final sample included in analyses was 1151 participants. As can be seen in Table 1, a majority of the sample in Study 1 was female (52.7%), over 55 years (54.8%), had a BMI > 25kgm2 indicating overweight-obese classification (62.3%), no diagnosed sleep disorder (69.8%), and employed (54.5%).

A total of 748 participants completed the online survey in Study 2. Of these 748 participants, 159 only answered the demographic questions, 55 did not complete the behaviour prioritisation question, and 1 participant was from a location outside of Australia and so was excluded from the sample. The final sample consisted of a total of 533 participants, with a majority female (76.2%), aged 18–34 years (52.5%), no diagnosed sleep disorder (52.5%), 6–15 years of shiftworking experience (42.6%), and a majority (64.4%) employed in the health care and social assistance industry.

Table 1 Demographic characteristics of participants in Study 1 and Study 2
Full size table

Australian adults (study 1)

In Study 1 diet was prioritised first by the majority of participants (49.3%, n = 567; Fig. 1), sleep was prioritised first by 29.2% of participants (n = 336), and physical activity was prioritised first by the least number of participants (21.5%, n = 248).

Fig. 1
figure 1

Percentage of participants in Study 1 (n = 1151) and Study 2 (n = 533) who prioritised each of the health behaviours (sleep, diet, physical activity)

Full size image

Diet was prioritised first by both employed (46%, n = 297) and unemployed (53%, n = 269) participants.

Fig. 2
figure 2

Percentage of participants in Study 1 (n = 1151) who prioritised each of the health behaviours (sleep, diet, physical activity) split by employment status (employed, unemployed)

Full size image

A majority of the Australian adults in Study 1 did not have sleep disorders (69.9%), did not meet the recommended sleep guidelines (70.3%), were categorised as reporting < 5 serves of vegetables per day (86.7%), reporting ≥ 2 serves of fruit per day (50.8%), and met the recommendation for more than 150 min of moderate- to vigorous-intensity physical activity per week (56.6%). Results of the multinomial logistic regressions demonstrated that, compared to those that prioritised diet first, those that prioritised sleep first were significantly less likely to be in the category of ≥ 2 serves of fruit per day, and significantly more likely to be in the category of ≥ 2 serves of fast food per week (Table 2; Fig. 3). There were no significant associations between those that prioritised diet first and those that prioritised physical activity first.

Table 2 Multinomial logistic regression analysis for Australian adults (Study 1)
Full size table
Fig. 3
figure 3

Stacked bar graphs showing the percentage of participants in Study 1 (n = 1151) who prioritised each of the health behaviours (diet, sleep, physical activity (PA)) first for each behavioural correlate (vegetable consumption, fruit consumption, fast food consumption, sleep disorder, sleep duration, moderate-vigorous physical activity)

Full size image

Australian shiftwork-only (study 2)

In Study 2, sleep was prioritised first by the majority of participants (67.7%, n = 361; Fig. 1), diet was the second highest (20.2%, n = 108), and physical activity was prioritised by the least number of participants (12.0%, n = 64). Sleep was prioritised first by participants in each of the industry groups (Fig. 4).

Fig. 4
figure 4

Percentage of participants in Study 2 (n = 533) who prioritised each of the health behaviours (sleep, diet, physical activity) in each shiftworking industry

Full size image

A majority of the participants did not report sleep disorders (68.4%), did not meet the recommended sleep duration guidelines (54.0%), and reported 6–15 years of shiftwork experience (42.6%). Only 5.6% of participants reported more than 30 years of shiftwork experience. Results of the multinomial logistic regressions revealed that, compared to those that prioritised diet first, those who prioritised sleep first were significantly more likely to report 16–30 years of shiftwork experience (Table 3; Fig. 5). There were no other significant associations.

Table 3 Multinomial regression analysis for Australian shiftwork-only participants (Study 2) for associations between behavioural correlates and prioritising the health behaviours first compared to sleep
Full size table
Fig. 5
figure 5

Stacked bar graphs showing the percentage of participants in Study 2 (n = 533) who prioritised each of the health behaviours (sleep, diet, physical activity) first for each behavioural correlate (sleep disorder, sleep duration, shiftwork experience)

Full size image

Discussion

This study explored how sleep, diet, and physical activity (as the three pillars of health) are prioritised by Australian adults across two cohorts and the association with health behaviour correlates. Overall, diet was prioritised first by a majority of the Australian adult sample, whereas sleep was prioritised first by a majority of the shiftworker-only sample.

For Australian adults, prioritising diet was associated with increased fruit consumption (greater likelihood of reporting ≥ 2 serves per day, meeting the Australian recommendations) and decreased fast food consumption (greater likelihood of reporting less than < 2 serves of fast food per week), compared to prioritising sleep. This finding suggests a link between behaviour prioritisation and actual behaviour for diet [63], which supports the notion that, for dietary behaviour, motivation (i.e. prioritising diet) can influence behaviour (i.e. decreased fast food consumption) [64]. Conversely, these findings may support the notion that, actual behaviour influenced participant prioritisation, with those that considered themselves as having a good diet choosing diet as their prioritised health behaviour when prompted. This follows a positive feedback loop as explained by the COM-B model of behaviour, with engaging in a behaviour increasing motivation to engage in the behaviour [47]. It may also be that changes were made to diet previously to improve diet quality and while diet is no longer prioritised, the healthy behaviours have remained. Although data were not collected on the underlying motivation for the prioritisation in this study, this should be a focus of future research. Additionally, some participants chose not to answer this question, and this may be because they did not prioritise any of the health behaviours. Future research is therefore warranted into how important each behaviour is, in addition to their ranking. While work demands and work-life conflict is often reported as influencing health behaviours [40], both employed and unemployed Australians reported the same pattern of behaviour prioritisation, with diet prioritised most frequently in both groups. It may be that while both groups prioritise diet, the reasons for prioritising diet, or the barriers to achieving a good diet, are different between employed and unemployed Australians [40]. This is an important focus of future research, perhaps with qualitative data that can explore the experiences of employed and unemployed Australians with health behaviour change.

The motivation-actual behaviour relationship was not found for physical activity in the sample of Australian adults, with no association between prioritising physical activity and meeting the weekly activity recommendation of ≥ 150 min/week. The finding that prioritising physical activity does not relate to actual behaviour is in contrast to the diet findings and suggests that motivation does not always influence behaviour. This may reflect different motivations for prioritising different health behaviours, in addition to opportunities and capabilities to enact change to different health behaviours, as discussed by the COM-B model For example, one study introduced a 3-month pilot intervention targeting all aspects of the COM-B model through promoting diet and physical activity in nurses, providing opportunities to measure physical activity (pedometers and a smartphone app), and using goal setting to promote capability. From this study it was reported that diet was easier to change than to increase physical activity [65]. If physical activity is perceived as harder to change, this may explain why it was the least prioritised health behaviour in the Australian adult cohort, consistent with population data showing that a majority of adults are not meeting the physical activity guidelines in Australia [1]. The physical activity threshold that was considered in this study was meeting the minimum guidelines for MPVA as this is associated with health benefits compared to not meeting this guideline [1]. As physical activity was examined using a dichotomous variable, it is unclear how different levels of physical activity may have influenced the results (e.g., individuals reporting some activity but not meeting the recommended amount, or individuals reporting more activity than the recommended amount). However, it may be that certain populations with greater physical activity, for example athletes, are more likely to prioritise their physical activity over diet and sleep. This also follows the COM-B model, such that those that engage in a behaviour at a high level are more motivated to engage in that behaviour again in the future. Understanding behaviour prioritisation in specific population groups is an important focus of future research, particularly to ensure that the other health behaviours are not neglected.

A majority of the sample of Australian adults were not meeting the Australian sleep recommendations (7–9 h per night) [51], including those individuals that prioritised sleep. This provides more evidence to suggest that prioritisation may not lead to actual behaviour for all health behaviours. While some individual sleep behaviours were analysed in the current paper, external factors can influence sleep duration including family, friends, work schedules, and outside demands [66,67,68]. It may be that Australian adults perceive low level of control over their sleep habits due to external factors and consequently do not prioritise sleep despite obtaining inadequate sleep. As with physical activity, this could reflect how perceived capability influences behaviour, with sleep potentially perceived as a challenging behaviour to improve. Future research could investigate the potential external barriers influencing sleep, for example, by using the Sleep Locus of Control Scale [69]. Additionally, given the relationship between sleep and diet [41, 42, 70, 71], improving sleep duration could be promoted as part of a strategy to improve diet given this is the health behaviour that is currently prioritised.

In the sample of shiftwork-only Australians, sleep was the most prioritised health behaviour, and this was seen in each of the shiftworking industries sampled. This may be because it is well known that shiftwork over a prolonged period has a pronounced, negative impact on sleep [72], with shiftworkers reporting inadequate sleep as a major problem across multiple studies [26, 73]. Prioritising this behaviour first may reflect an understanding of this problem in this cohort of shiftworkers. Further, in previously published findings from the current dataset of shiftwork-only Australian adults, 53.9% of the sample reported having heard of sleep hygiene, indicating some understanding of sleep hygiene [26] compared to a sample of shiftworking paramedics who reported little to no understanding of sleep hygiene [74]. However, despite understanding sleep hygiene principles, the majority of the sample did not meet the recommended guidelines for adequate sleep duration. This discrepancy in prioritisation and actual behaviour is consistent with the findings for sleep in the Australian adult sample, and is perhaps a reflection of the barriers to attaining adequate sleep that shiftworkers contend with due to work schedules (e.g. long hours, inconsistent wake and bedtimes, minimal breaks, and changing rosters [72]).

In the shiftwork-only sample, shiftwork experience was associated with prioritising sleep first. Those that prioritised sleep first were more likely to have 16–30 years of shiftwork experience compared to those who prioritised diet first. Participants with 16–30 years of shiftwork experience are likely to have been exposed to chronic sleep loss due to working shiftwork schedules over a prolonged period [75, 76], and this may be why they are more likely to prioritise sleep. Together, this suggests that motivation may not be the barrier to sleep health in shiftworkers, as a majority of the sample prioritised sleep despite obtaining an inadequate amount of sleep. Intervention efforts should consider improving the opportunity and capabilities that shiftworkers have to obtain adequate sleep, rather than motivation, particularly the external influences on sleep in this population. New guidelines for healthy sleep practices for shiftworkers have been published, with guidelines for behaviours such as napping, bedtime routines, and sleep environments [77]. Interventions utilising these guidelines should be a focus of future research. While studies have investigated sleep-related interventions in shiftworkers, the intervention type is heterogenous across the literature with mixed effectiveness for improving fatigue [78]. More studies are needed to develop a comprehensive sleep intervention that incorporates the capabilities, opportunities, and motivations of shiftworkers. One way to do this is to individualise sleep interventions for shiftworkers to take into account these specific capabilities, opportunities, and motivations. A recent study has demonstrated effectiveness of an individualised sleep and shiftwork education program in nurses on anxiety and insomnia scores [79] Further, increased focus on diet and physical activity as health behaviours that shiftworkers may have more control over may be beneficial.

The two cohorts included in this study prioritised health behaviours differently. The cohorts were different as the sample of Australian adults included some shiftworkers as well as other work types and people who weren’t employed. In addition to the make-up of the cohorts, the timing of the surveys was different. The National Social Survey 2017 survey was conducted prior to the COVID-19 pandemic, whereas the shiftwork study was conducted in 2021, during the pandemic. A recent review found that during the COVID-19 pandemic, the prevalence of sleep problems was high, with approximately 40% of the population impacted [78]. This is likely due to later bedtimes and waketimes or a reduction in night-time sleep [80]. Diet and physical activity were also impacted during COVID-19, with reductions to leisure-time physical activity and increases in sedentary behaviour [81], and changes to habitual dietary patterns [82]. While physical activity and diet were not assessed in Study 2, it may be that the changes to sleep, diet, and physical activity during COVID-19 contributed to different behaviour prioritisation for these participants.

To the best of our knowledge, this study is the first to explore the prioritisation of the three pillars of health in Australian adults and in shiftworkers. Therefore, findings from this study contribute to the literature on health behaviour change for populations at-risk for chronic diseases and inform the development of policies to promote behaviour change. However, there are several limitations of the current study to consider when interpreting the results. First, both studies included in this manuscript were cross-sectional and self-report and thus subject to self-report bias and inability to determine causal relationships. The limitations of self-report data are important to consider, for example self-report dietary measures are prone to the under-reporting of energy intake, and this varies based on body mass index and can be largely influenced by social desirability [83]. Second, given the cross-sectional nature of both studies, behaviour change over time was not assessed. Sleep, diet and physical activity behaviours are likely to change daily [49], as well as across the lifespan [13].There were key differences in the methodology of each study that limit direct comparison between the two studies. These differences include no questions on diet and physical activity in the shiftwork study, which means that whether these behaviours relate to prioritising diet or physical activity in this sample is unknown. Given the increasing understanding of the importance of diet [27, 84] and physical activity [85, 86] in shiftworkers, these behaviours are important to measure in future research. Further, the National Social Survey 2017 had questions on employment status and industry, but not whether participants were shiftworkers or not. Therefore, it is likely that the sample of Australian adults included participants working shiftwork and other work patterns, but we are unable to distinguish them specifically. The shiftworking population was majority female participants, which does not reflect Australian data that suggests there are more male shiftworkers than females. This likely reflects the high proportion of the sample working in the healthcare and social assistance industry, which are majority female. The results from this project may therefore be more generalisable to shiftworking females than males, and this is a consideration for recruitment for future research.

To build on the current findings there are multiple suggestions for future research, such as assessing longitudinal changes in health behaviours and the prioritisation of the three pillars of health. Further, future research should utilise qualitative methods to understand barriers, feasibility, and acceptability of behaviour change related to the pillars of health. Additionally, qualitative research could explore other factors, both internal and external, that influence health behaviours in both shiftworkers and non-shiftworkers. These factors will be important to consider in any behavioural intervention. What health behaviours people feel satisfied would also help to provide context on the underlying motivation for ranking those behaviours. Research on health behaviours should also continue to use samples of shiftworkers and non-shiftworkers, given shiftworkers are an at-risk population for chronic disease and experience unique organisational challenges that lead to circadian disruption.

This study explored how sleep, diet, and physical activity are prioritised by Australian adults across two cohorts and how the ranking was associated with health behaviour correlates. While prioritising diet was associated with healthier diet behaviour in Australian adults, overall, across both cohorts, behaviour prioritisation did not relate to actual behaviour. These findings suggest that behaviour prioritisation is only part of the story of behaviour change and future research should focus on comprehensive approaches to behaviour change promotion that target the barriers to specific behaviours.

Data Availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. AIHW E. Australia’s health 2014. Australia’s Health Series. Canberra, Australia: Australian Institute of Health and Welfare (AIHW); 2014.

    Google Scholar 

  2. Petersen PE, Ogawa H. The global burden of periodontal disease: towards integration with chronic disease prevention and control. Periodontol 2000. 2012;60(1):15–39.

    PubMed  Google Scholar 

  3. Kris-Etherton PM, Petersen KS, Després J-P, Anderson CA, Deedwania P, Furie KL, et al. Strategies for promotion of a healthy lifestyle in clinical settings: pillars of ideal cardiovascular health: a science advisory from the American Heart Association. Circulation. 2021;144(24):e495–e514.

    PubMed  Google Scholar 

  4. Thornton L, Gardner LA, Osman B, Green O, Champion KE, Bryant Z, et al. A multiple health behavior change, self-monitoring mobile app for adolescents: development and usability study of the Health4Life app. JMIR Formative Research. 2021;5(4):e25513.

    PubMed  PubMed Central  Google Scholar 

  5. Vincent GE, Jay SM, Sargent C, Vandelanotte C, Ridgers ND, Ferguson SA. Improving cardiometabolic health with diet, physical activity, and breaking up sitting: what about sleep? Front Physiol. 2017;8:865.

    PubMed  PubMed Central  Google Scholar 

  6. Badran M, Yassin BA, Fox N, Laher I, Ayas N. Epidemiology of sleep disturbances and cardiovascular consequences. Can J Cardiol. 2015;31(7):873–9.

    PubMed  Google Scholar 

  7. Cattaneo G, Bartrés-Faz D, Morris TP, Sánchez JS, Macià D, Tarrero C et al. The Barcelona Brain Health Initiative: a Cohort Study to define and promote determinants of Brain Health. Front Aging Neurosci. 2018;10.

  8. Foundation AH, Life’s. Essential 8 2022 [Available from: https://www.heart.org/en/healthy-living/healthy-lifestyle/lifes-essential-8.

  9. Singh TD, Sharma HB. How much time to spend in physical activity, sleep and be sedentary in 24 h to Achieve Good Health? Sleep and Vigilance. 2022:1–6.

  10. Wickham S-R. Lifestyle behaviours as predictors of Health: understanding the importance of Sleep, Diet and Physical Activity. on Mental Health and Well-Being: University of Otago; 2021.

    Google Scholar 

  11. Ghanemi A, Yoshioka M, St-Amand J, Exercise. Diet and sleeping as Regenerative Medicine Adjuvants: obesity and ageing as illustrations. Medicines. 2022;9(1):7.

    PubMed  PubMed Central  Google Scholar 

  12. Dorrian J, Coates A, Heath G, Banks S. Patterns of alcohol consumption and sleep in shiftworkers. Modulation of sleep by obesity. Diabetes, Age, and Diet: Elsevier; 2015. pp. 353–63.

    Google Scholar 

  13. Kankanhalli A, Saxena M, Wadhwa B. Combined interventions for physical activity, sleep, and diet using smartphone apps: a scoping literature review. Int J Med Informatics. 2019;123:54–67.

    Google Scholar 

  14. Manolis TA, Manolis AA, Apostolopoulos EJ, Melita H, Manolis AS. Cardiovascular complications of sleep disorders: a better night’s sleep for a healthier heart/from bench to bedside. Curr Vasc Pharmacol. 2021;19(2):210–32.

    CAS  PubMed  Google Scholar 

  15. Oja P, Kelly P, Murtagh EM, Murphy MH, Foster C, Titze S. Effects of frequency, intensity, duration and volume of walking interventions on CVD risk factors: a systematic review and meta-regression analysis of randomised controlled trials among inactive healthy adults. Br J Sports Med. 2018;52(12):769–75.

    PubMed  Google Scholar 

  16. Rees K, Takeda A, Martin N, Ellis L, Wijesekara D, Vepa A et al. Mediterranean-style diet for the primary and secondary prevention of cardiovascular disease: a cochrane review. Global Heart. 2020;15(1).

  17. Hillman DR, Murphy AS, Antic R, Pezzullo L. The economic cost of sleep disorders. Sleep. 2006;29(3):299–305.

    PubMed  Google Scholar 

  18. Turner GM, Larsen KA, Candy S, Ogilvy S, Ananthapavan J, Moodie M, et al. Squandering Australia’s food security—the environmental and economic costs of our unhealthy diet and the policy path we’re on. J Clean Prod. 2018;195:1581–99.

    Google Scholar 

  19. Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, Van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. The Lancet. 2016;388(10051):1311–24.

    Google Scholar 

  20. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation’s updated sleep duration recommendations. Sleep Health. 2015;1(4):233–43.

    PubMed  Google Scholar 

  21. Government A. Go for 2&5 2009 [Available from: http://www.gofor2and5.com.au.

  22. Ramin C, Devore EE, Wang W, Pierre-Paul J, Wegrzyn LR, Schernhammer ES. Night shift work at specific age ranges and chronic disease risk factors. Occup Environ Med. 2015;72(2):100–7.

    PubMed  Google Scholar 

  23. Crowther ME, Ferguson SA, Vincent GE, Reynolds AC. Non-pharmacological interventions to improve chronic disease risk factors and sleep in shift workers: a systematic review and meta-analysis. Clocks & Sleep. 2021;3(1):132–78.

    Google Scholar 

  24. Kecklund G, Axelsson J. Health consequences of shift work and insufficient sleep. BMJ. 2016;355.

  25. Vincent GE, Jay SM, Vandelanotte C, Ferguson SA. Breaking up sitting with light-intensity physical activity: implications for Shift-Workers. Int J Environ Res Public Health. 2017;14(10):1233.

    PubMed  PubMed Central  Google Scholar 

  26. Rampling CM, Gupta CC, Shriane AE, Ferguson SA, Rigney G, Vincent GE. Does knowledge of sleep hygiene recommendations match behaviour in australian shift workers? A cross-sectional study. BMJ Open. 2022;12(7):e059677.

    PubMed  PubMed Central  Google Scholar 

  27. Gupta CC, Coates AM, Dorrian J, Banks S. The factors influencing the eating behaviour of shiftworkers: what, when, where and why. Ind Health. 2019;57(4):419–53.

    PubMed  Google Scholar 

  28. Esquirol Y, Bongard V, Mabile L, Jonnier B, Soulat JM, Perret B. Shift work and metabolic syndrome: respective impacts of job strain, physical activity, and dietary rhythms. Chronobiol Int. 2009;26(3):544–59.

    PubMed  Google Scholar 

  29. Vincent G, Ferguson SA, Tran J, Larsen B, Wolkow A, Aisbett B. Sleep restriction during simulated wildfire suppression: effect on physical task performance. PLoS ONE. 2015;10(1):e0115329.

    PubMed  PubMed Central  Google Scholar 

  30. Bøggild H, Knutsson A. Shift work, risk factors and cardiovascular disease. Scand J Work Environ Health. 1999:85–99.

  31. Koller M. Health risks related to shift work. Int Arch Occup Environ Health. 1983;53(1):59–75.

    CAS  PubMed  Google Scholar 

  32. Knutsson A. In-depth review: shift work. Health disorders of shift workers. Occup Med (Chic Ill). 2003;53:103–8.

    Google Scholar 

  33. Amani R, Gill T. Shiftworking, nutrition and obesity: implications for workforce health-a systematic review. Asia Pac J Clin Nutr. 2013;22(4):698–708.

    Google Scholar 

  34. Oftedal S, Vandelanotte C, Duncan MJ. Patterns of diet, physical activity, sitting and sleep are associated with socio-demographic, behavioural, and health-risk indicators in adults. Int J Environ Res Public Health. 2019;16(13):2375.

    PubMed  PubMed Central  Google Scholar 

  35. Rayward AT, Burton NW, Brown WJ, Holliday EG, Plotnikoff RC, Duncan MJ. Associations between changes in activity and sleep quality and duration over 2 years. 2018.

  36. Miller D, Sargent C, Roach G, Scanlan A, Vincent G, Lastella M. Moderate-intensity exercise performed in the evening does not impair sleep in healthy males. Eur J Sport Sci. 2020;20(1):80–9.

    CAS  PubMed  Google Scholar 

  37. Memon AR, Vandelanotte C, Olds T, Duncan MJ, Vincent GE. Research combining physical activity and sleep: a bibliometric analysis. Percept Mot Skills. 2020;127(1):154–81.

    PubMed  Google Scholar 

  38. Vincent GE, Gupta CC, Sprajcer M, Vandelanotte C, Duncan MJ, Tucker P, et al. Are prolonged sitting and sleep restriction a dual curse for the modern workforce? A randomised controlled trial protocol. BMJ open. 2020;10(7):e040613.

    PubMed  PubMed Central  Google Scholar 

  39. Dominiak M, Kovac K, Reynolds AC, Ferguson SA, Vincent GE. The effect of a short burst of exercise during the night on subsequent sleep. J Sleep Res. 2021;30(2):e13077.

    PubMed  Google Scholar 

  40. Vincent GE, Jay SM, Sargent C, Kovac K, Vandelanotte C, Ridgers ND, et al. The impact of breaking up prolonged sitting on glucose metabolism and cognitive function when sleep is restricted. Neurobiol Sleep Circadian Rhythms. 2018;4:17–23.

    PubMed  Google Scholar 

  41. Binks H, Vincent E, Gupta G, Irwin C, Khalesi C. Effects of diet on sleep: a narrative review. Nutrients. 2020;12(4):936.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Gupta CC, Irwin C, Vincent GE, Khalesi S. The relationship between diet and sleep in older adults: a narrative review. Curr Nutr Rep. 2021;10(3):166–78.

    PubMed  Google Scholar 

  43. Fenton S, Burrows T, Skinner J, Duncan M. The influence of sleep health on dietary intake: a systematic review and meta-analysis of intervention studies. J Hum Nutr Dietetics. 2021;34(2):273–85.

    CAS  Google Scholar 

  44. Ding D, Rogers K, van der Ploeg H, Stamatakis E, Bauman AE. Traditional and emerging lifestyle risk behaviors and all-cause mortality in middle-aged and older adults: evidence from a large population-based australian cohort. PLoS Med. 2015;12(12):e1001917.

    PubMed  PubMed Central  Google Scholar 

  45. Haapasalo V, de Vries H, Vandelanotte C, Rosenkranz RR, Duncan MJ. Cross-sectional associations between multiple lifestyle behaviours and excellent well-being in australian adults. Prev Med. 2018;116:119–25.

    PubMed  Google Scholar 

  46. Spring B, Moller AC, Coons MJ. Multiple health behaviours: overview and implications. J Public Health. 2012;34(suppl1):i3–i10.

    Google Scholar 

  47. Michie S, Atkins L, West R. The behaviour change wheel. A guide to designing interventions. Volume 1003, 1st ed. Great Britain: Silverback Publishing; 2014. p. 1010.

    Google Scholar 

  48. James E, Freund M, Booth A, Duncan MJ, Johnson N, Short CE, et al. Comparative efficacy of simultaneous versus sequential multiple health behavior change interventions among adults: a systematic review of randomised trials. Prev Med. 2016;89:211–23.

    PubMed  Google Scholar 

  49. Rebar A, Rosenbaum S, Maher J. Responsiveness to change of the psychological determinants and outcomes of physical activity and sedentary behavior. 2020.

  50. Strazdins L, Welsh J, Korda R, Broom D, Paolucci F. Not all hours are equal: could time be a social determinant of health? Sociol Health Illn. 2016;38(1):21–42.

    PubMed  Google Scholar 

  51. Metse AP, Bowman JA. Prevalence of self-reported suboptimal sleep in Australia and receipt of sleep care: results from the 2017 National Social Survey. Sleep Health. 2020;6(1):100–9.

    PubMed  Google Scholar 

  52. Gupta CC, Duncan MJ, Ferguson SA, Rebar A, Sprajcer M, Khalesi S, et al. The discrepancy between knowledge of sleep recommendations and the actual sleep behaviour of australian adults. Behav Sleep Med. 2021;19(6):828–39.

    PubMed  Google Scholar 

  53. Qualtrics XM. Qualtrics 2005 [Available from: www.qualtrics.com.]

  54. Hirshkowitz M, Whiton K, Albert SM, Alessi C, Bruni O, DonCarlos L, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):40–3.

    PubMed  Google Scholar 

  55. Gordon S, Vandelanotte C, Rayward AT, Murawski B, Duncan MJ. Sociodemographic and behavioral correlates of insufficient sleep in australian adults. Sleep Health. 2019;5(1):12–7.

    PubMed  Google Scholar 

  56. Khalesi P, Saman, Vandelanotte P, Corneel, Thwaite B, Tanya, Russell P, Alex MT, Dawson P, Drew, Williams P, Susan L. Awareness and attitudes of gut health, probiotics and prebiotics in australian adults. J Diet Supplements. 2021;18(4):418–32.

    Google Scholar 

  57. Smith KJ, McNaughton SA, Gall SL, Blizzard L, Dwyer T, Venn AJ. Takeaway food consumption and its associations with diet quality and abdominal obesity: a cross-sectional study of young adults. Int J Behav Nutr Phys Activity. 2009;6(1):1–13.

    Google Scholar 

  58. Schröder H, Fïto M, Covas M. REGICOR investigators Association of fast food consumption with energy intake, diet quality, body mass index and the risk of obesity in a representative Mediterranean population. Br J Nutr. 2007;98:1274–80.

    PubMed  Google Scholar 

  59. de Moura BP, Marins JCB, Amorim PRS. Self selected walking speed in overweight adults: is this intensity enough to promote health benefits? Apunts Med de l’Esport. 2011;46(169):11–5.

    Google Scholar 

  60. Vandelanotte C, Duncan MJ, Stanton R, Rosenkranz RR, Caperchione CM, Rebar AL, et al. Validity and responsiveness to change of the active Australia Survey according to gender, age, BMI, education, and physical activity level and awareness. BMC Public Health. 2019;19(1):1–11.

    Google Scholar 

  61. AIoHa W. The active Australia Survey: a guide and manual for implementation, analysis and reporting. Volume 49. Australia: Australia Institute of Health and Welfare; 2003.

    Google Scholar 

  62. Statistics AB. Australian and New Zealand Standard Industrial Classification (ANZSIC) Codes and Titles 2006. 2006.

  63. Van Cappellen P, Rice EL, Catalino LI, Fredrickson BL. Positive affective processes underlie positive health behaviour change. Psychol Health. 2018;33(1):77–97.

    PubMed  Google Scholar 

  64. Rhodes RE, McEwan D, Rebar AL. Theories of physical activity behaviour change: a history and synthesis of approaches. Psychol Sport Exerc. 2019;42:100–9.

    Google Scholar 

  65. Torquati L, Kolbe-Alexander T, Pavey T, Leveritt M. Changing diet and physical activity in nurses: a pilot study and process evaluation highlighting challenges in workplace health promotion. J Nutr Educ Behav. 2018;50(10):1015–25.

    PubMed  Google Scholar 

  66. Åkerstedt T. Work schedules and sleep. J Experientia. 1984;40(5):417–22.

    Google Scholar 

  67. Artazcoz La, Borrell C, Benach J, Cortès I, Rohlfs I. Medicine. Women, family demands and health: the importance of employment status and socio-economic position. J Social Science. 2004;59(2):263–74.

    Google Scholar 

  68. Seifert CM, Chapman LS, Hart JK, Perez P. Enhancing intrinsic motivation in health promotion and wellness. Am J Health Promotion. 2012;26(3):1–12.

    Google Scholar 

  69. Vincent N, Sande G, Read C, Giannuzzi T. Sleep locus of control: report on a new scale. Behav Sleep Med. 2004;2(2):79–93.

    PubMed  Google Scholar 

  70. Gupta CC, Vincent GE, Coates AM, Khalesi S, Irwin C, Dorrian J, et al. A time to Rest, a time to Dine: Sleep, Time-Restricted eating, and Cardiometabolic Health. Nutrients. 2022;14(3):420.

    PubMed  PubMed Central  Google Scholar 

  71. Binks H, Vincent GE, Irwin C, Heidke P, Vandelanotte C, Williams SL, et al. Associations between sleep and lifestyle behaviours among australian nursing students: a cross-sectional study. Collegian. 2021;28(1):97–105.

    Google Scholar 

  72. Åkerstedt T, Wright KP. Sleep loss and fatigue in shift work and shift work disorder. Sleep Med Clin. 2009;4(2):257–71.

    PubMed  PubMed Central  Google Scholar 

  73. Epstein M, Söderström M, Jirwe M, Tucker P, Dahlgren A. Sleep and fatigue in newly graduated nurses—experiences and strategies for handling shiftwork. J Clin Nurs. 2020;29(1–2):184–94.

    PubMed  Google Scholar 

  74. Shriane AE, Russell AM, Ferguson SA, Rigney G, Vincent GE. Sleep hygiene in paramedics: What do they know, and what do they do? Sleep Health. 2023.

  75. Costa G. Sleep deprivation due to shift work. Handbook of clinical neurology. 2015;131:437 – 46.

  76. Åkerstedt T. Sleepiness as a consequence of shift work. Sleep. 1988;11(1):17–34.

    PubMed  Google Scholar 

  77. Shriane AE, Rigney G, Ferguson SA, Bin YS, Vincent GE. Healthy Sleep Practices for Shift Workers: Consensus Sleep Hygiene Guidelines using a Delphi Methodology. Sleep. 2023:zsad182.

  78. Querstret D, O’Brien K, Skene DJ, Maben J. Improving fatigue risk management in healthcare: a systematic scoping review of sleep-related/fatigue-management interventions for nurses and midwives. Int J Nurs Stud. 2020;106:103513.

    PubMed  Google Scholar 

  79. Jahrami H, BaHammam AS, Bragazzi NL, Saif Z, Faris M, Vitiello MV. Sleep problems during the COVID-19 pandemic by population: a systematic review and meta-analysis. J Clin Sleep Med. 2021;17(2):299–313.

    PubMed  PubMed Central  Google Scholar 

  80. Booker LA, Sletten TL, Barnes M, Alvaro P, Collins A, Chai-Coetzer CL, et al. The effectiveness of an individualized sleep and shift work education and coaching program to manage shift work disorder in nurses: a randomized controlled trial. J Clin Sleep Med. 2022;18(4):1035–45.

    PubMed  PubMed Central  Google Scholar 

  81. Gupta R, Grover S, Basu A, Krishnan V, Tripathi A, Subramanyam A, et al. Changes in sleep pattern and sleep quality during COVID-19 lockdown. Indian J Psychiatry. 2020;62(4):370–8.

    PubMed  PubMed Central  Google Scholar 

  82. Christensen A, Bond S, McKenna J. The COVID-19 conundrum: keeping safe while becoming inactive. A rapid review of physical activity, sedentary behaviour, and exercise in adults by gender and age. PLoS ONE. 2022;17(1):e0263053.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Bennett G, Young E, Butler I, Coe S. The impact of lockdown during the COVID-19 outbreak on dietary habits in various population groups: a scoping review. Front Nutr. 2021;8:626432.

    PubMed  PubMed Central  Google Scholar 

  84. Ravelli MN, Schoeller DA. Traditional self-reported dietary instruments are prone to inaccuracies and new approaches are needed. Front Nutr. 2020;7:90.

    PubMed  PubMed Central  Google Scholar 

  85. Gupta CC, Ferguson SA, Aisbett B, Dominiak M, Chappel SE, Sprajcer M, et al. Hot, tired and hungry: the snacking behaviour and food cravings of firefighters during multi-day simulated wildfire suppression. Nutrients. 2020;12(4):1160.

    PubMed  PubMed Central  Google Scholar 

  86. Crowther ME, Ferguson SA, Reynolds AC. Longitudinal studies of sleep, physical activity and nutritional intake in shift workers: a scoping review. Sleep Med Rev. 2022:101612.

  87. Flahr H, Brown WJ, Kolbe-Alexander TL. A systematic review of physical activity-based interventions in shift workers. Prev Med Rep. 2018;10:323–31.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The authors have no funding to declare.

Author information

Authors and Affiliations

  1. Appleton Institute, School of Health, Medical and Applied Sciences, Central Queensland University, Adelaide, South Australia, Australia

    Charlotte C Gupta, Sally A Ferguson, Amanda Rebar, Corneel Vandelanotte, Madeline Sprajcer, Saman Khalesi, Caroline Rampling, Gabrielle Rigney & Grace E Vincent

  2. School of Medicine & Public Health, Faculty of Health and Medicine, The University of Newcastle, University Drive, Callaghan, NSW, Australia

    Mitch J Duncan

  3. Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia

    Mitch J Duncan

  4. Institute for Breathing and Sleep, Austin Health, Heidelberg, VIC, Australia

    Lauren A. Booker

  5. La Trobe Rural Health School, La Trobe University, Bendigo, VIC, Australia

    Lauren A. Booker

Authors
  1. Charlotte C Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mitch J Duncan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sally A Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amanda Rebar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Corneel Vandelanotte
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Madeline Sprajcer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Saman Khalesi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lauren A. Booker
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Caroline Rampling
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gabrielle Rigney
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Grace E Vincent
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Data collection (Study 2): CCG, CR, GV. Data analysis (Study 1): CCG, LB, MJD. Data analysis (Study 2): CCG, CR. Manuscript preparation: CCG. Manuscript revisions and finalisation: CCG, MJD, SAF, AR, CV, MS, SK, LAB, CR, GR, GEV.

Corresponding author

Correspondence to Charlotte C Gupta.

Ethics declarations

Ethics approval and consent to participate

Participants in both studies provided informed consent. Study 1 was granted ethics approval from the CQUniversity Human Research Ethics Committee (H14/09-203). Study 2 was granted ethics approval by the CQUniversity Human Research Ethics Committee (2021-03).

Consent for publication

Consent for publication of images not required.

Competing interests

Authors Mitch J Duncan, Corneel Vandelanotte and Grace E Vincent are members of the Editorial Board for Journal of Activity, Sedentary and Sleep Behaviors. The paper was handled by a separate editor and has undergone a rigorous peer review process. Authors Mitch J Duncan, Corneel Vandelanotte and Grace E Vincent were not involved in the journal’s peer review of, or decisions related to, this manuscript.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, C.C., Duncan, M.J., Ferguson, S.A. et al. Exploring the prioritisation of sleep, diet, and physical activity as pillars of health: correlates and associations with health behaviours in Australian adults. JASSB 2, 26 (2023). https://doi.org/10.1186/s44167-023-00035-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s44167-023-00035-3

Keywords

Journal of Activity, Sedentary and Sleep Behaviors

ISSN: 2731-4391

Contact us