ObjectivesFly-in fly-out (FIFO) work involves commuting long distances to the worksite and living in provided accommodation for 1–4 weeks while on shift. While the potentially detrimental impact of ...FIFO work on the health and well-being of workers has been documented, little attention has been paid to how workers, or their partners, cope with this impact. This study sought to investigate how workers and their partners negotiate the impact of FIFO on their mental health and well-being.DesignThe study design was qualitative. FIFO workers and partners responded to open-ended questions on concerns about the FIFO lifestyle and the support they use.SettingAustralian FIFO workers and partners responded to the questions via email.ParticipantsParticipants were 34 FIFO workers (25 men, M age=41 years) and 26 partners of FIFO workers (26 women, M age=40 years).ResultsParticipant-validated thematic analysis generated three main themes: managing multiple roles, impact on mental health and well-being, and social support needs. Results revealed difficulties in adjusting between the responsibilities of perceptually distinct on-shift and off-shift lives, and managing potential psychological distance that develops while workers are on site. Participants emphasised the importance of maintaining quality communication and support from family members. Workers and partners attempted to maintain mental health and well-being by regularly engaging with support networks, although many felt organisational support was tokenistic, stigmatised or lacking.ConclusionsRecommendations for enhancing support provided by FIFO organisations are offered. In particular, organisations should emphasise the importance of good mental health and well-being, maintain transparency regarding potential challenges of FIFO lifestyles, and offer professional support for managing multiple social roles and effective communication.
Bedtime procrastination, the volitional delay of going to bed without any external circumstances causing the delay, is linked to multiple indicators of inadequate sleep. Intervening to reduce bedtime ...procrastination may be an important avenue to improve sleep outcomes, yet the phenomenon remains poorly understood in populations at risk for bedtime procrastination. New career starters, those who have graduated from tertiary education and started a new full-time job within the past 12 months, may be susceptible to problematic bedtime procrastination and are at an opportune time for a 'fresh start' to change behaviour.
The objectives of this study were to understand how bedtime procrastination is experienced and perceived by new career starters, to identify the enablers and barriers to behaviour change in new career starters and to explore themes for future interventions.
Data were collected through in-depth semi-structured interviews with 28 participants.
Inductive thematic analysis was used to find seven themes: (1) negative feelings before and during bedtime procrastination; (2) wanting to versus knowing I shouldn't; (3) difficulty falling asleep; (4) influence of automatic processes; (5) consequences of bedtime procrastination; (6) lack of self-control and (7) technology captures late-night attention. Participants emphasised the need for me-time, self-negotiation to continue procrastinating and knowledge of the value of sleep.
Findings suggest that bedtime procrastination involves both reflective and automatic cognitive processes. Future interventions would benefit from a dual-process approach, using cognitive and behavioural techniques to reduce bedtime procrastination.
High screen time in children and its detrimental health effects is a major public health problem. How much screen time adults think is appropriate for children remains little explored, as well as ...whether adults' screen time behaviour would determine their views on screen time restrictions for children. This study aimed to investigate how adults' screen time behaviour influences their views on screen time restrictions for children, including differences by gender and parental status.
In 2013, 2034 Australian adults participated in an online survey conducted by the Population Research Laboratory at Central Queensland University, Rockhampton. Adult screen time behaviour was assessed using the Workforce Sitting Questionnaire. Adults reported the maximum time children aged between 5-12 years should be allowed to spend watching TV and using a computer. Ordinal logistic regression was used to compare adult screen time behaviour with views on screen time restrictions for children.
Most adults (68%) held the view that children should be allowed no more than 2 h of TV viewing and computer use on school days, whilst fewer adults (44%) thought this screen time limit is needed on weekend days. Women would impose higher screen time restrictions for children than men (p < 0.01). Most adults themselves spent > 2 h on watching TV and using the computer at home on work days (66%) and non-work days (88%). Adults spending ≤ 2 h/day in leisure-related screen time were less likely to permit children > 2 h/day of screen time. These associations did not differ by adult gender and parental status.
Most adults think it is appropriate to limit children's screen time to the recommended ≤ 2 h/day but few adults themselves adhere to this screen time limit. Adults with lower screen use may be more inclined to limit children's screen time. Strategies to reduce screen time in children may also need to target adult screen use.
This study aimed to investigate the validity of the Active Australia Survey across different subgroups and its responsiveness to change, as few previous studies have examined this.
The Active ...Australia Survey was validated against the ActiGraph as an objective measure of physical activity. Participants (n = 465) wore the ActiGraph for 7 days and subsequently completed the Active Australia Survey. Moderate activity, vigorous activity and total moderate and vigorous physical activity were compared using Spearman rank-order correlations. Changes in physical activity between baseline and 3-month assessments were correlated to examine responsiveness to change. The data were stratified to assess outcomes according to different subgroups (e.g., gender, age, weight, activity levels).
With regards to the validity, a significant correlation of ρ = 0.19 was found for moderate physical activity, ρ = 0.33 for vigorous physical activity and ρ = 0.23 for moderate and vigorous physical activity combined. For vigorous physical activity correlations were higher than 0.3 for most subgroups, whereas they were only higher than 0.3 in those with a healthy weight for the other activity outcomes. With regards to responsiveness to change, a correlation of ρ = 0.32 was found for moderate physical activity, ρ = 0.19 for vigorous physical activity and ρ = 0.35 for moderate and vigorous physical activity combined. For moderate and vigorous activity combined correlations were higher than 0.4 for several subgroups, but never for vigorous physical activity.
Little evidence for the validity of Active Australia Survey was found, although the responsiveness to change was acceptable for several subgroups. Findings from studies using the Active Australia Survey should be interpreted with caution.
World Health Organisation Universal Trial Number: U111-1119-1755. Australian New Zealand Clinical Trials Registry, ACTRN12611000157976 . Registration date: 8 March 2011.
This study aimed to examine older adults’ physical activity intentions and preferred implementation intentions, and how intentions and preferred implementation intentions differ between older, middle ...aged and younger adults. A cross-sectional Australian wide telephone survey of 1217 respondents was conducted in 2016. Multiple and ordinal regression analyses were conducted to compare intentions and preferred implementation intentions between older (65 +), middle aged (45–64) and younger adults (< 45). A higher percentage of older adults had no intentions to engage in regular physical activity within the next 6 months (60%) compared to younger adults (25%). Older adults’ most popular preferences included being active at least once a day and for 30 min or less and were more likely to prefer more frequent and shorter sessions compared to younger adults. Both older and middle aged adults were more likely to prefer slower paced physical activity compared to younger adults who preferred fast paced physical activity. Physical activity interventions for older adults should address the high percentage of older adults with no intentions and public health campaigns for older adults should promote 30 min daily sessions of slow paced activity.
Many adults are insufficiently physically active, have prolonged sedentary behaviour and report poor sleep. These behaviours can be improved by interventions that include education, goal setting, ...self-monitoring, and feedback strategies. Few interventions have explicitly targeted these behaviours simultaneously or examined the relative efficacy of different self-monitoring methods.
This study aims to compare the efficacy of two self-monitoring methods in an app-based multi-behaviour intervention to improve objectively measured physical activity, sedentary, and sleep behaviours, in a 9 week 2-arm randomised trial. Participants will be adults (n = 64) who report being physically inactive, sitting >8 h/day and frequent insufficient sleep (≥14 days out of last 30). The "Balanced" intervention is delivered via a smartphone 'app', and includes education materials (guidelines, strategies to promote change in behaviour), goal setting, self-monitoring and feedback support. Participants will be randomly allocated to either a device-entered or user-entered self-monitoring method. The device-entered group will be provided with a activity tracker to self-monitor behaviours. The user-entered group will recall and manually record behaviours. Assessments will be conducted at 0, 3, 6, and 9 weeks. Physical activity, sedentary behaviour and sleep-wake behaviours will be measured using the wrist worn Geneactiv accelerometer. Linear mixed models will be used to examine differences between groups and over time using an alpha of 0.01.
This study will evaluate an app-based multi-behavioural intervention to improve physical activity, sedentary behaviour and sleep; and the relative efficacy of two different approaches to self-monitoring these behaviours. Outcomes will provide information to inform future interventions and self-monitoring targeting these behaviours.
ACTRN12615000182594 (Australian New Zealand Clinical Trials Registry. Registry URL: www.anzctr.org.au ; registered prospectively on 25 February 2015).
Purpose: To compare the effect of 12-weeks of cycling training and competition versus recreational cycling on successful aging across physical, psychological, cognitive, and social functioning ...domains in mid-aged adults. Methods: Recreational cyclists were randomly assigned to an intervention (n = 13, M age = 47.18 years) and comparison (n = 13, M age = 46.91 years) group. Analysis of Covariance was used on self-reported pre-post data to determine changes across time and differences between groups on outcomes. Results: The intervention group scored higher on the role limitation due to physical problems measure of physical functioning (p = .045) and the social activity measure of social functioning (p = .008) with large effect sizes (η
p
2
> .14). The remaining physical, psychological, cognitive, and social functioning measures were not significantly different (p > .05) between groups with small to medium effect sizes (η
p
2
> .01 to ≤ .06). Conclusion: Cycling training and competition promotes better physical and social functioning than recreational cycling. This finding indicates that an intervention that incorporates the training and competition aspects of sport may promote positive outcomes that are above and beyond those that can be gained from participation in recreational physical activity. Objective measurements on larger samples across a broader range of sports are required to confirm and extend these findings.
Studying at university is stressful, which can lead to unhealthy lifestyle behaviors. This study aimed to explore perceived reasons and barriers preventing Australian nursing students from engaging ...in a healthy lifestyle and strategies to overcome barriers. Fifty‐four bachelor of nursing students participated in seven focus groups between July and November 2018. Participants defined healthy lifestyle behaviors as eating well; regular physical activity; regular water consumption; limiting alcohol, caffeine, and nicotine; good sleep quality; stress management and relaxation; and regular social interaction and support. They identified individual (lack of motivation, existing bad habits, lack of knowledge), environmental (time, finances, limited access to healthy food and physical activity resources), and psychosocial (competing priorities, increased learning cognitive load, lack of social interaction and support, compassion fatigue, and shift work) barriers preventing healthy lifestyle. Participants proposed several individual and system‐related strategies to overcome barriers. Despite portraying a comprehensive understanding of healthy lifestyle behaviors, students reported finding difficulty in attaining healthy lifestyles. Strategies proposed by students may inform targeted interventions aiming to increase overall health of students, reduce attrition rates, and promote workforce retention post‐graduation.
PDF
