Robert J. Noonan

In this article, Noonan contends that boosting public support is vital for increasing pressure on the Government to enact change for the common good. He argues that the public health community can build public support for the required structural changes by challenging misinformation and sharing a new story.

This secondary data analysis aimed to demonstrate the utility of physical activity (PA) wrist accelerometer outcome reference values by identifying the PA volume (average acceleration) and intensity distribution (intensity gradient) centiles and values associated with body mass index (BMI) status (normal weight, overweight, and obese) and cardiorespiratory fitness (CRF, multistage shuttle runs test) status (low, moderate, and high) in children and adolescents. We assessed the dose–response associations between average acceleration and intensity gradient with BMI and CRF outcomes using restricted cubic spline linear mixed models. To aid translation of the findings, we calculated the increases in average acceleration needed to shift exemplar participants to “healthy” weight and CRF status. For boys and girls, there was a nonlinear inverse association between average acceleration and BMI. In both sexes, a positive dose–response was observed between average acceleration and intensity gradient with CRF. The values and centiles of average acceleration and intensity gradient that aligned with BMI and CRF statuses were identified. To move from an average acceleration associated with overweight to healthy weight, 10‐year‐old boys and girls would need to increase daily average acceleration by 23 m g (~30‐min running) and 16 m g (~18‐min running), respectively. These findings further demonstrate the importance of PA in relation to BMI and CRF and the utility of PA reference values for the translation of accelerometer outcomes into meaningful information. Additional studies demonstrating how PA reference values can be used to track behaviors and provide insights into health associations could inform practice further.

The dramatic rise in non-communicable diseases around the world but notably in high-income countries like the UK is a manifestation of a global economic system—capitalism—that prioritizes wealth over health. A decade ago, the former WHO Director-General, Margaret Chan highlighted how ‘efforts to prevent non-communicable diseases go against the business interests of powerful economic operators’ [United Nations. (2013) Take Action for the Sustainable Development Goals. https://www.un.org/sustainabledevelopment/sustainable-development-goals/ (last accessed 16 February 2024)]. While there is a growing literature on how politics and economics influence population health—for better or worse—less attention has been given to exploring how economic systems like capitalism influence people’s psychological well-being. To fill this gap, the following article examines how the continued pursuit of economic growth under capitalism (neoliberal free-market forms especially) impacts well-being through challenging basic psychological needs for security, autonomy, competence and relatedness. In doing so, I hope to shed important light on the sources and possible solutions to our growing health and social problems, and stimulate a conversation on how to achieve a healthier future for us all.

Health inequalities are differences in health between groups in society. Despite them being preventable they persist on a grand scale. At the beginning of 2024, the Institute of Health Equity revealed in their report titled: Health Inequalities, Lives Cut Short , that health inequalities caused 1 million early deaths in England over the past decade. While the number of studies on the prevalence of health inequalities in the UK has burgeoned, limited emphasis has been given to exploring the factors contributing to these (widening) health inequalities. In this commentary article I will describe how the Government’s relentless pursuit of economic growth and their failure to implement the necessary regulatory policies to mitigate against the insecurity and health effects neoliberal free market capitalism (referred to as capitalism herein) causes in pursuit of innovation, productivity and growth (economic dynamism) is one key driver underpinning this social injustice. I contend that if the priority really is to tackle health inequalities and ensure health for all then there is an imperative need to move beyond regulation alone to mitigate the worst effects of capitalist production; the goal of the economy has to change to fully restore the balance between economic growth and public health.

This opinion piece focuses on how in order to improve the nation’s poor health, the government needs to place more value on social justice and wellbeing as well as the use of regulation to positively change culture and health behaviour.

Physical activity improves physical and mental well-being and reduces mortality risk. However, only a quarter of adults globally meet recommended physical activity levels for health. Two common initiatives in the UK are Couch-to-5k (an app-assisted 9-week walk/run programme) and parkrun (a free, weekly, timed 5-km walk/run). It is not known how these initiatives are linked, how Couch-to-5k parkrunners compare to parkrunners, and the extent to which this influences their parkrun performance. The aims were to compare the characteristics and motives and to compare physical activity levels, parkrun performance and the impact of parkrun between Couch-to-5k parkrunners and parkrunners. Three thousand two hundred and ninety six Couch-to-5k parkrunners were compared to 55,923 parkrunners to explore age, sex, ethnicity, employment status, neighbourhood deprivation, motives, physical activity levels, parkrun performance and the impact of parkrun. Couch-to-5k parkrunners were slightly older, more likely to be female and work part-time, but similar in ethnicity, and neighbourhood deprivation compared with other parkrunners. Couch-to-5k parkrunners had different motives for participation and reported high levels of physical activity at registration, which remained to the point of survey completion. This group had slower parkrun times but, when registered for a year, completed a similar number of runs (11) per year. Larger proportions of Couch-to-5k parkrunners perceived positive impacts compared with other parkrunners and 65% of Couch-to-5k parkrunners reported improvements to their lifestyle. parkrun appears to be an effective pathway for those on the Couch-to-5k programme, and the promising positive association between the two initiatives may be effective in assisting previously inactive participants to take part in weekly physical activity.

Background: Over the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex. Methods: Wrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d⁻¹ and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences. Results: The analytical sample included 1250 participants. Physical activity peaked between ages 6.5–10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3^rd-50^th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls (p < .001) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group (p < .001). Conclusions: Percentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion.

This study aimed to (1) investigate gender-specific characteristics associated with low sport participation among UK adolescents, and (2) assess gender-specific BMI tracking, and gender-specific associations between BMI and self-esteem based on different levels of adolescent sport participation. Participants were 9046 (4523 female) UK adolescents. At 11- and 14 years self-esteem was self-reported and BMI was calculated from objectively measured height and weight. At 11- years sport participation was parent-reported. Gender-specific sport participation quartile cut-off values categorised boys and girls separately into four graded groups. Gender-specific χ² and independent samples t tests assessed differences in measured variables between the lowest (Q1) and highest (Q4) sport participation quartiles. Adjusted linear regression analyses examined BMI tracking and associations between BMI and self-esteem scores. Gender-specific analyses were conducted separately for sport participation quartiles. Compared to Q4 boys and girls, Q1 boys and girls were more likely to be non-White, low family income, have overweight/obesity at 11 years and report lower self-esteem at 11 years and 14 years. BMI at 11 years was positively associated with BMI at 14 years for boys and girls across sport participation quartiles. BMI at 11 years was inversely associated with self-esteem scores at 11 years for Q1 and Q2 boys, and Q1 and Q4 girls. BMI at 11 years was inversely associated with self-esteem scores at 14 years for Q1, Q3 and Q4 boys, and Q1, Q2, Q3 and Q4 girls. Gender and sport participation influence BMI tracking and the BMI and self-esteem association among adolescents.