Prevalence of breakfast skipping among children and adolescents: a cross-sectional population level study.

BACKGROUND: Interventions to promote breakfast consumption are a popular strategy to address early life inequalities. It is important to understand the epidemiology of children and adolescents who skip breakfast so that interventions and policy can be appropriately considered. This study investigated the prevalence of breakfast skipping among a contemporary, population-wide sample of children and adolescents in Australia. METHODS: Participants were grade 4-12 students (n = 71,390, 8-18 years) in South Australian government (public) schools who took part in the 2019 Wellbeing and Engagement Collection. The prevalence of breakfast skipping (never, sometimes, often, or always) was calculated for the overall sample and stratified by gender, school grade, socioeconomic status and geographical remoteness. Multinomial logistic regression analyses were conducted to determine the relative risk ratio of sometimes, often, and always skippers compared with never skippers, according to demographic characteristics. RESULTS: Overall, 55.0% of students reported never skipping breakfast, 17.4% reported sometimes skipping, 18.0% reported often skipping, and 9.5% reported always skipping breakfast. Skipping breakfast was more prevalent among females, students in senior grades, and those living in socioeconomically disadvantaged and regional and remote areas. Analyses disaggregated by gender revealed that grade level gradients in breakfast skipping were more marked among females compared to males. CONCLUSIONS: Breakfast skipping among children and adolescents appears considerably more prevalent than previous research suggests. Drivers of breakfast skipping across population sub-groups need to be explored to better inform strategies to promote breakfast consumption.

The education word gap emerges by 18 months: findings from an Australian prospective study.

BACKGROUND: The idea of the '30 million word gap' suggests families from more socioeconomically advantaged backgrounds engage in more verbal interactions with their child than disadvantaged families. Initial findings from the Language in Little Ones (LiLO) study up to 12 months showed no word gap between maternal education groups. METHODS: Families with either high or low maternal education were purposively recruited into a five-year prospective study. We report results from the first three waves of LiLO when children were 6, 12 and 18 months old. Day-long audio recordings, obtained using the Language Environment Analysis software, provided counts of adult words spoken to the child, child vocalizations and conversational turns. RESULTS: By the time children were 18 months old all three measures of talk were 0.5 to 0.7 SD higher among families with more education, but with large variation within education groups. Changes in talk from 6 to 18 months highlighted that families from low educated backgrounds were decreasing the amount they spoke to their children (- 4219.54, 95% CI -6054.13, - 2384.95), compared to families from high educated backgrounds who remained relatively stable across this age period (- 369.13, 95% CI - 2344.57, 1606.30). CONCLUSIONS: The socioeconomic word gap emerges between 12 and 18 months of age. Interventions to enhance maternal communication, child vocalisations and vocabulary development should begin prior to 18 months.

How many words are Australian children hearing in the first year of life?

BACKGROUND: There is evidence that parents from more socioeconomically disadvantaged backgrounds engage in fewer verbal interactions with their child than more advantaged parents. This leads to the so-called, '30 million-word gap'. This study aims to investigate the number of words children hear and the number of vocalizations children produce in their first year of life and examines whether these aspects of the early language home environment differ by maternal education. METHODS: Mothers were recruited into a five-year prospective cohort study and categorized into either high or low maternal education groups. Data was derived from the first two waves of the study, when the children were six and twelve months old. At both waves, children were involved in day-long audio recordings using the Language Environment Analysis software that provided automatic counts of adult words spoken to the child, child vocalizations and conversational turns. Descriptive results are presented by maternal education groups. RESULTS: There was large variation within each maternal education group, with the number of adult words spoken to the child ranging from 2958 to 39,583 at six months and 4389 to 45,849 at twelve months. There were no meaningful differences between adult words, child vocalizations or conversational turns across maternal education groups at either wave of data collection. CONCLUSIONS: These results show that a word gap related to maternal education is not apparent up to twelve months of age. The large variability among both maternal education groups suggests that universal interventions that encourage all parents to talk more to their child may be more appropriate than interventions targeted towards disadvantaged families during the first year of life.

Screen Time and Parent-Child Talk When Children Are Aged 12 to 36 Months.

Importance: Growing up in a language-rich home environment is important for children's language development in the early years. The concept of "technoference" (technology-based interference) suggests that screen time may be interfering with opportunities for talk and interactions between parent and child; however, limited longitudinal evidence exists exploring this association. Objective: To investigate the longitudinal association between screen time and 3 measures of parent-child talk (adult words, child vocalizations, and conversational turns) when children are 12 to 36 months of age. Design, Setting, and Participants: This Australian prospective cohort study used advanced speech recognition technology to capture young children's screen time and home language environment, on an average 16-hour day. Data were collected from 220 families once every 6 months in the family home when children were 12, 18, 24, 30, and 36 months of age, from January 1, 2018, to December 31, 2021. Statistical analysis took place from November 1, 2022, to July 31, 2023. Exposure: Language Environment Analysis (LENA) technology provided automated counts of children's language environment and exposure to electronic noise. The exposure of interest was screen time, which was calculated based on manual coding of LENA electronic noise audio segments. Main Outcomes and Measures: Three measures of parent-child talk were outcomes of focus: adult words, child vocalizations, and conversational turns. Separate models were run for each of the 3 outcomes and included adjustment for child sex, child age, maternal educational level, number of children at home, number of home activities, and primary caregiver's psychological distress. Results: The study included 220 families (120 girls [54.6%]; mean [SD] gestational age of children, 39.3 [1.5] weeks; mean [SD] age of mother at childbirth, 31.3 [4.8] years). Adjusted linear mixed-effect models demonstrated that increases in screen time were associated with decreases in measures of parent-child talk. The largest decreases were seen at 36 months, when an additional minute of screen time was associated with a reduction of 6.6 (95% CI, -11.7 to -1.5) adult words, 4.9 (95% CI, -6.1 to -3.7) child vocalizations, and 1.1 (95% CI, -1.4 to -0.8) conversational turns. Conclusion and Relevance: Findings of this study support the notion of technoference for Australian families, whereby young children's exposure to screen time is interfering with opportunities to talk and interact in their home environment. This finding has implications for interventions and supports aimed at promoting a language-rich home environment, with families needing support in understanding the potential association of screen time with opportunities for children and adults to talk and interact in their home environment.

Mental health profiles and academic achievement in Australian school students.

This study explored mental health profiles in Australian school students using indicators of well-being (i.e., optimism, life satisfaction, and happiness) and psychological distress (i.e., sadness and worries). The sample included 75,757 students (ages 8-18 years) who completed the 2019 South Australian Wellbeing and Engagement Collection. Latent profile analysis identified five mental health profiles consisting of (a) complete mental health (23%), (b) good mental health (33%), (c) moderate mental health (27%), (d) symptomatic but content (9%), and (e) troubled (8%). Findings provide partial support for the dual-factor model of mental health. Distal outcomes analysis on a sub-set of students (n = 24,466) found students with a symptomatic but content, moderate mental health, or troubled profile had poorer academic achievement than students with complete mental health. Implications for schools and education systems are discussed, including the need to pair clinical supports for students with psychological distress with population-level preventative health approaches to build psychological well-being.

Objectively measured infant and toddler screen time: Findings from a prospective study.

Objective: Screen time guidelines recommend no screens under two years due to the potential negative impacts on development. While current reports suggest many children exceed this, research relies on parent reports of their children's screen exposure. We objectively assess screen exposure during the first two years and how it differs by maternal education and gender. Methods: This Australian prospective cohort study used speech recognition technology to understand young children's screen exposure over an average day. Data collection occurred every six months when children were 6, 12, 18 and, 24 months old (n = 207). The technology provided automated counts of children's exposure to electronic noise. Audio segments were then coded as screen exposure. Prevalence of screen exposure was quantified, and differences between demographics examined. Results: At six months, children were exposed to an average of 1hr, 16 min (SD = 1hr, 36 min) of screens per day, increasing to an average of 2 h, 28 min (SD = 2 h, 4 min) by 24-months. Some children at six months were exposed to more than 3 h of screen time per day. Inequalities in exposure were evident as early as six months. Children from higher educated families were exposed to 1hr,43 min fewer screens per day, 95%CI (-2hr, 13 min, -1hr, 11 min) compared to lower educated households, with this difference remaining consistent as children age. Girls were exposed to an additional 12 min of screens 95%CI (-20 min, 44 min) per day compared to boys at six months, but this difference reduced to only 5 min by 24-months. Conclusion: Using an objective measure of screen exposure, many families exceed screen time guidelines, the extent increasing with child's age. Furthermore, substantial differences between maternal education groups emerge as young as six months old. This highlights the need for education and supports for parents around screen use in the early years, balanced within the realities of modern life.

Implementing a combined individual placement and support and vocational peer work program in integrated youth mental health settings.

AIM: To describe the implementation and outcomes of a combined individual placement and support (IPS) and vocational peer work program for young people with mental ill-health. METHODS: This uncontrolled pilot study co-located IPS workers and vocational peer workers within two integrated youth mental health services (provided to clients aged 15-25 years old). Employment outcomes included job placements (working 15 hours or more per week in a paid competitive job in the open employment market) and sustained employment (employed for at least 26 weeks). Participants who required additional assistance also received peer work. RESULTS: Of the 326 young people enrolled, 195 (59.8%) achieved competitive employment, including 157 (48.2%) in funder-approved placements. Among those in approved placements, 87 (55.4%) achieved sustained employment. For the 116 participants additionally receiving vocational peer work, 54 (46.6%) worked in funder-approved placements, of whom 27 (50.0%) achieved sustained employment. Among 210 participants who did not receive peer work, 103 (49.0%) worked in funder-approved placements, of whom 60 (58.3%) achieved sustained employment. CONCLUSIONS: The program achieved positive vocational outcomes and good fidelity to the IPS model. Approximately half of young people had employment placements, with a relatively high proportion maintained over time. The similar proportion of placements for those who did and did not receive peer work was encouraging given the IPS team determined that the former group needed additional support. Recommendations include upskilling the workforce, a data linkage system to obtain placement evidence, and using implementation science methodologies to understand how IPS programs are successfully embedded. This demands a coordinated effort between governments and funders, policymakers, services, and professional bodies.

Prevalence of electronic device use before bed among Australian children and adolescents: a cross-sectional population level study.

OBJECTIVE: To understand the prevalence of children and adolescents' electronic device use (EDU) in the hour before bed and identify sociodemographic groups that are at increased risk of problematic use. METHOD: A contemporary population wide sample of South Australian school students aged 8-18 years (n = 70,936) was utilised to present descriptive statistics of EDU before bed across sociodemographic groups. Data was collected from the 2019 Wellbeing and Engagement Collection, an annual self-report census of students' health, wellbeing and school engagement. RESULTS: 90% of children used an electronic device in the hour before bed at least one night a week, with 51% using a device every night. Older adolescent females in grades 10-12 were most frequent users and children from the most socioeconomically disadvantaged communities were also more likely to use electronic devices in the hour before bed. CONCLUSION: EDU before bed is highly prevalent among Australian children and adolescents and given the negative health and educational impacts, it requires immediate and widespread action from policy makers to ensure the health of Australia's next generation. IMPLICATIONS FOR PUBLIC HEALTH: The scale of the problem has now been identified; next steps rely on a public health approach to address this issue. This might include awareness raising campaigns and targeted interventions towards at-risk groups.

Shared Decision Making With Young People at Ultra High Risk of Psychotic Disorder.

Introduction: While the majority of young people who meet the criteria for being considered at increased risk of psychosis do not go on to develop a psychotic disorder, young people are currently being identified and treated in early intervention services. Ethical concerns have been raised concerning the decision about whether or not to provide treatment, and if so, what type of treatment. This study sought to support young people themselves to make these decisions with support from their clinician through a shared decision-making approach, facilitated by an online decision aid. Methods: This project used the International Patient Decision Aid Standards (IPDAS) to guide the development and piloting of an online decision aid across two phases: (1) qualitative, semi-structured focus groups with young people who were past clients and clinicians from an early psychosis service; and (2) pilot testing of the decision aid with clinicians and young people who were current clients to finalize the development. Results: Issues discussed by clinicians in the focus group were grouped into three main areas: (1) engagement phase; (2) assessment and priorities for treatment; and (3) initial and ongoing decision making. Clients focused on the context in which the decisions were made, including as they experienced initial feelings of resistance, and then acceptance of efforts made to describe and treat their mental health challenges. Clients highlighted the need for collaboration between themselves and their clinician, and the need to be equipped with the knowledge and tools to take care of themselves. These focus group data were used to refine the online decision aid. Pilot testing revealed that while it was overall useful and relevant, important limitations were noted by both clients and clinicians. Discussion: The use of a decision aid to facilitate shared decision making (SDM) in this area is feasible and has utility for both clients and clinicians. Use of such a tool can help to address the need to uphold the rights of young people as decision makers about their own care. Future efforts should embed decision aids within complex SDM interventions, and research to understand issues relating to implementation of these interventions.

Inside the black box of youth participation and engagement: Development and implementation of an organization-wide strategy for Orygen, a national youth mental health organization in Australia.

AIM: The involvement of young people in the development, implementation and evaluation of youth mental health services, policy and research programs is essential to ensure they are appropriate and responsive to the needs of young people. Despite the increasingly central role that youth engagement and participation plays internationally, such activities are rarely described in detail. This article aims to provide a thorough description of the development and implementation of an organization-wide, 3-year Youth Engagement and Participation Strategy for Orygen, a national youth mental health organization in Australia. METHODS: A descriptive account of the development and implementation of the Strategy, with detailed examples of programs and initiatives. RESULTS: The Strategy was developed based on available evidence, focus groups with key stakeholders and best practice principles. The implementation of the Strategy resulted in a number of programs being delivered that involved a range of young people from across Australia. Despite being successful overall, a number of challenges were experienced. Ongoing considerations include ensuring diversity of partnerships, 'raising the bar' of youth participation and creating meaningful pathways. CONCLUSIONS: Youth participation and engagement within a youth mental health context is best seen as an evolving ambition that must remain flexible to the needs of all stakeholders. Despite some challenges and ongoing fine-tuning, it is possible to successfully implement youth participation and engagement across all areas of youth mental health, including service design and delivery, research and translation, and policy.

Basic epidemiology of wellbeing among children and adolescents: A cross-sectional population level study.

Wellbeing and mental health are fundamental rights of children and adolescents essential for sustainable development. Understanding the epidemiology of child and adolescent wellbeing is essential to informing population health approaches to improving wellbeing and preventing mental illness. The present study estimated the prevalence of wellbeing and how wellbeing indicators were distributed across social and economic groups. This study used data from the 2019 Wellbeing and Engagement Collection; an annual census conducted in South Australian schools that measures self-reported wellbeing in students aged 8-18 years (n = 75,966). We estimated the prevalence (n, %) of low, medium and high wellbeing across five outcomes: life satisfaction, optimism, sadness, worries and happiness, overall and stratified by gender, age, language background, socio-economic position and geographical remoteness. The prevalence of low wellbeing on each indicator was: happiness 13%, optimism 16%, life satisfaction 22%, sadness 16% and worries 25%. The prevalence of low wellbeing increased with age, particularly for females. For example, 22.5% of females aged 8-10 years had high levels of worries compared to 43.6% of 15 to 18-year old females. Socioeconomic inequality in wellbeing was evident on all indicators, with 19.5% of children in the most disadvantaged communities having high levels of sadness compared to 12.5% of children in the most advantaged communities. Many children and adolescents experience low wellbeing on one or more indicators (40.7%). The scale of this problem warrants a population-level preventative health response, in addition to a clinical, individual-level responses to acute mental health needs. Universal school-based programs that support social and emotional wellbeing have a role to play in this response but need to be supported by universal and targeted responses from outside of the education system.