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Section Editors' Highlights

New Content ItemWelcome to the BMC Public Health Section Editors' Highlights page. Here, the Section Editors have chosen recent papers published in their section of the journal that are of particular importance or interest to highlight to a wider audience.

Chronic disease epidemiology

Women’s perception, attitudes, and intended behavior towards predictive epigenetic risk testing for female cancers in 5 European countries: a cross-sectional online survey

Chosen by: Jianguang Ji

Epigenomic alterations, by incorporating with other well-known risk factors, become the promising biomarkers to predict an individual’s cancer risk recently. However, it is still largely unknown about the public’s attitudes to accept the adoption of epigenetic markers into clinical practice. Using data from five European countries, the authors found that around 75% women would like to take a predictive epigenetic test if it is freely available due to their willingness to guide healthcare strategies and to change their lifestyle to prevent cancer development. The rest of them who would not participate in the epigenetic test are afraid of unnecessary cancer worry and unintended lifestyle changes as well as unaccepted healthcare interventions. The data suggest that the introduction of predictive epigenetic tests for cancer risks in clinical practice will require a balanced and transparent communication to prevent unjustified concerns.

Energy balance-related behaviors

Tailoring lifestyle interventions to low socio-economic populations: a qualitative study

Chosen by: Richard Rosenkranz

Low-socioeconomic groups can be elusive with respect to public health efforts, and interventions to prevent or reduce obesity may need modification or special consideration when designed for hard-to-reach populations. In the present study, the authors qualitatively investigated viewpoints of the service providers and participants within a lifestyle behavior change intervention set in a low-socioeconomic area of the UK. Results showed that this population needed tailored interventions, along with additional focus on behavior change techniques, rather than merely disseminating information. Other important factors to consider in designing interventions were cost, cultural diversity, language and literacy barriers, and the potential for disengagement in this hard-to-reach population. 

Patterns and correlates of physical activity in adult Norwegians: a forecasted evolution up to 2025 based on machine learning approach

Chosen by: Carol Maher

Activity patterns have changed dramatically in modern societies due to changing socioeconomic and environmental factors leading to reduced physical activity and increased sedentary behaviour. Societal changes in physical activity are commonly examined in terms of percentage of the population meeting physical activity guidelines (typically, defined based on weekly duration). However, the health impacts of physical activity differ in terms of other characteristics of physical activity, such as intensity, frequency, bouts, and disruption of prolonged sedentary behaviour.

This study uses a highly novel approach, machine learning, to analyse and identify patterns in a large dataset of periodic national physical activity surveys conducted in Norway from 1985 to 2013. In addition, the machine learning is used to model predicted changes in physical activity patterns through to 2025.

Results showed that physical activity frequency increased from 1985 to 2013, while the duration and intensity of physical activity were relatively stable across this period. However, the machine learning predictive models suggest that overall duration can be expected to reduce to 2025, while intensity and frequency are likely to increase.

For Norway, the results of the predictive models are concerning, suggesting that physical inactivity is likely to rise, though the detrimental health effects of this may at least be partially offset by the predicted increases in physical activity intensity and frequency. More generally, the results highlight that population physical activity is continuing to evolve. The patterns in which society accumulates physical activity are changing, and this will have flow on effects to societal health outcomes.

Technological developments in recent decades have produced large volumes of data which present analytic challenges. Machine is a useful tool to deal with large data. This first application of machine learning to population physical activity data is commendable, insightful, and signals exciting analytic opportunities ahead for population health.

Occupational health

A systematic review of working conditions and occupational health among immigrants in Europe and Canada

Chosen by: Isabelle Niedhammer

Few studies are available on the working conditions and occupational health of immigrants in Europe and Canada. This study is one of the first literature reviews to provide advanced knowledge on this topic. The authors suggest that immigrant workers experience poorer working conditions and occupational health than native workers. They also underline the need for more studies on the immigrant populations, and especially on the causal pathways that may explain the associations between immigrant status, working conditions and health outcomes.