Effective interventions to influence workplace sitting are needed, as office-based workers demonstrate high levels of continued sitting, and sitting too much is associated with adverse health ...effects. Therefore, we developed a theory-driven, Web-based, interactive, computer-tailored intervention aimed at reducing and interrupting sitting at work.
The objective of our study was to investigate the effects of this intervention on objectively measured sitting time, standing time, and breaks from sitting, as well as self-reported context-specific sitting among Flemish employees in a field-based approach.
Employees (n=213) participated in a 3-group randomized controlled trial that assessed outcomes at baseline, 1-month follow-up, and 3-month follow-up through self-reports. A subsample (n=122) were willing to wear an activity monitor (activPAL) from Monday to Friday. The tailored group received an automated Web-based, computer-tailored intervention including personalized feedback and tips on how to reduce or interrupt workplace sitting. The generic group received an automated Web-based generic advice with tips. The control group was a wait-list control condition, initially receiving no intervention. Intervention effects were tested with repeated-measures multivariate analysis of variance.
The tailored intervention was successful in decreasing self-reported total workday sitting (time × group: P<.001), sitting at work (time × group: P<.001), and leisure time sitting (time × group: P=.03), and in increasing objectively measured breaks at work (time × group: P=.07); this was not the case in the other conditions. The changes in self-reported total nonworkday sitting, sitting during transport, television viewing, and personal computer use, objectively measured total sitting time, and sitting and standing time at work did not differ between conditions.
Our results point out the significance of computer tailoring for sedentary behavior and its potential use in public health promotion, as the effects of the tailored condition were superior to the generic and control conditions.
Clinicaltrials.gov NCT02672215; http://clinicaltrials.gov/ct2/show/NCT02672215 (Archived by WebCite at http://www.webcitation.org/6glPFBLWv).
BACKGROUND: The purpose of this systematic review was to determine the relationship between a wide range of physical environmental characteristics and different contexts of active transportation in ...6- to 12-year-old children across different continents. METHODS: A systematic search was conducted in six databases (Pubmed, Web of Science, Cinahl, SportDiscus, TRIS and Cochrane) resulting in 65 papers, eligible for inclusion. The investigated physical environmental variables were grouped into six categories: walkability, accessibility, walk/cycle facilities, aesthetics, safety, recreation facilities. RESULTS: The majority of the studies were conducted in North America (n = 35), Europe (n = 17) and Australia (n = 11). Active transportation to school (walking or cycling) was positively associated with walkability. Walking to school was positively associated with walkability, density and accessibility. Evidence for a possible association was found for traffic safety and all forms of active transportation to school. No convincing evidence was found for associations between the physical environment and active transportation during leisure. General safety and traffic safety were associated with active transportation to school in North America and Australia but not associated with active transportation to school in Europe. CONCLUSIONS: The physical environment was mainly associated with active transportation to school. Continent specific associations were found, indicating that safety measures were most important in relation to active commuting to school in North America and Australia. There is a need for longitudinal studies and studies conducted in Asia, Africa and South-America and studies focusing specifically on active transportation during leisure.
Society has to cope with a large burden of health issues. There is need to find solutions to prevent diseases and help individuals live healthier lifestyles. Individual needs and circumstances vary ...greatly and one size fit all solutions do not tend to work well. More tailored solutions centred on individuals' needs and circumstances can be developed in collaboration with these individuals. This process, known as co-creation, has shown promise but it requires guiding principles to improve its effectiveness. The aim of this study was to identify a key set of principles and recommendations for co-creating public health interventions.
These principles were collaboratively developed through analysing a set of case studies targeting different health behaviours (such as reducing sitting and improving strength and balance) in different groups of people (such as adolescent schoolgirls and older adults living in the community).
The key principles of co-creation are presented in four stages: Planning (what is the purpose of the co-creation; and who should be involved?); Conducting (what activities can be used during co-creation; and how to ensure buy-in and commitment?); Evaluating (how do we know the process and the outcome are valid and effective?) and Reporting (how to report the findings?). Three models are proposed to show how co-created solutions can be scaled up to a population level.
These recommendations aim to help the co-creation of public health interventions by providing a framework and governance to guide the process.
Due to the chronic disease burden on society, there is a need for preventive public health interventions to stimulate society towards a healthier lifestyle. To deal with the complex variability between individual lifestyles and settings, collaborating with end-users to develop interventions tailored to their unique circumstances has been suggested as a potential way to improve effectiveness and adherence. Co-creation of public health interventions using participatory methodologies has shown promise but lacks a framework to make this process systematic. The aim of this paper was to identify and set key principles and recommendations for systematically applying participatory methodologies to co-create and evaluate public health interventions.
These principles and recommendations were derived using an iterative reflection process, combining key learning from published literature in addition to critical reflection on three case studies conducted by research groups in three European institutions, all of whom have expertise in co-creating public health interventions using different participatory methodologies.
Key principles and recommendations for using participatory methodologies in public health intervention co-creation are presented for the stages of: Planning (framing the aim of the study and identifying the appropriate sampling strategy); Conducting (defining the procedure, in addition to manifesting ownership); Evaluating (the process and the effectiveness) and Reporting (providing guidelines to report the findings). Three scaling models are proposed to demonstrate how to scale locally developed interventions to a population level.
These recommendations aim to facilitate public health intervention co-creation and evaluation utilising participatory methodologies by ensuring the process is systematic and reproducible.
To determine the role of physical activity intensity and bout-duration in modulating associations between physical activity and cardiometabolic risk markers.
A cross-sectional study using the ...International Children's Accelerometry Database (ICAD) including 38,306 observations (in 29,734 individuals aged 4-18 years). Accelerometry data was summarized as time accumulated in 16 combinations of intensity thresholds (≥500 to ≥3000 counts/min) and bout-durations (≥1 to ≥10 min). Outcomes were body mass index (BMI, kg/m
), waist circumference, biochemical markers, blood pressure, and a composite score of these metabolic markers. A second composite score excluded the adiposity component. Linear mixed models were applied to elucidate the associations and expressed per 10 min difference in daily activity above the intensity/bout-duration combination. Estimates (and variance) from each of the 16 combinations of intensity and bout-duration examined in the linear mixed models were analyzed in meta-regression to investigate trends in the association.
Each 10 min positive difference in physical activity was significantly and inversely associated with the risk factors irrespective of the combination of intensity and bout-duration. In meta-regression, each 1000 counts/min increase in intensity threshold was associated with a -0.027 (95% CI: -0.039 to -0.014) standard deviations lower composite risk score, and a -0.064 (95% CI: -0.09 to -0.038) kg/m
lower BMI. Conversely, meta-regression suggested bout-duration was not significantly associated with effect-sizes (per 1 min increase in bout-duration: -0.002 (95% CI: -0.005 to 0.0005) standard deviations for the composite risk score, and -0.005 (95% CI: -0.012 to 0.002) kg/m
for BMI).
Time spent at higher intensity physical activity was the main determinant of variation in cardiometabolic risk factors, not bout-duration. Greater magnitude of associations was consistently observed with higher intensities. These results suggest that, in children and adolescents, physical activity, preferably at higher intensities, of any bout-duration should be promoted.
The Health through Sport conceptual model links sport participation with physical, social and psychological outcomes and stresses the need for more understanding between these outcomes. The present ...study aims to uncover how sport participation, physical activity, social capital and mental health are interrelated by examining these outcomes in one model.
A cross-sectional survey was conducted in nine disadvantaged communities in Antwerp (Belgium). Two hundred adults (aged 18-56) per community were randomly selected and visited at home to fill out a questionnaire on socio-demographics, sport participation, physical activity, social capital and mental health. A sample of 414 adults participated in the study.
Structural Equation Modeling analysis showed that sport participation (β = .095) and not total physical activity (β = .027) was associated with better mental health. No association was found between sport participation and community social capital (β = .009) or individual social capital (β = .045). Furthermore, only community social capital was linked with physical activity (β = .114), individual social capital was not (β = -.013). In contrast, only individual social capital was directly associated with mental health (β = .152), community social capital was not (β = .070).
This study emphasizes the importance of sport participation and individual social capital to improve mental health in disadvantaged communities. It further gives a unique insight into the functionalities of how sport participation, physical activity, social capital and mental health are interrelated. Implications for policy are that cross-sector initiatives between the sport, social and health sector need to be supported as their outcomes are directly linked to one another.
A healthy lifestyle may improve mental health. It is yet not known whether and how a mobile intervention can be of help in achieving this in adolescents. This study investigated the effectiveness and ...perceived underlying mechanisms of the mobile health (mHealth) intervention #LIFEGOALS to promote healthy lifestyles and mental health. #LIFEGOALS is an evidence-based app with activity tracker, including self-regulation techniques, gamification elements, a support chatbot, and health narrative videos.
A quasi-randomized controlled trial (N = 279) with 12-week intervention period and process evaluation interviews (n = 13) took place during the COVID-19 pandemic. Adolescents (12-15y) from the general population were allocated at school-level to the intervention (n = 184) or to a no-intervention group (n = 95). Health-related quality of life (HRQoL), psychological well-being, mood, self-perception, peer support, resilience, depressed feelings, sleep quality and breakfast frequency were assessed via a web-based survey; physical activity, sedentary time, and sleep routine via Axivity accelerometers. Multilevel generalized linear models were fitted to investigate intervention effects and moderation by pandemic-related measures. Interviews were coded using thematic analysis.
Non-usage attrition was high: 18% of the participants in the intervention group never used the app. An additional 30% stopped usage by the second week. Beneficial intervention effects were found for physical activity (χ
= 4.36, P = .04), sedentary behavior (χ
= 6.44, P = .01), sleep quality (χ
= 6.11, P = .01), and mood (χ
= 2.30, P = .02). However, effects on activity-related behavior were only present for adolescents having normal sports access, and effects on mood only for adolescents with full in-school education. HRQoL (χ
= 14.72, P < .001), mood (χ
= 6.03, P = .01), and peer support (χ
= 13.69, P < .001) worsened in adolescents with pandemic-induced remote-education. Interviewees reported that the reward system, self-regulation guidance, and increased health awareness had contributed to their behavior change. They also pointed to the importance of social factors, quality of technology and autonomy for mHealth effectiveness.
#LIFEGOALS showed mixed results on health behaviors and mental health. The findings highlight the role of contextual factors for mHealth promotion in adolescence, and provide suggestions to optimize support by a chatbot and narrative episodes.
ClinicalTrials.gov NCT04719858, registered on 22/01/2021.
Active commuting to school can contribute to daily physical activity levels in children. Insight into the determinants of active commuting is needed, to promote such behavior in children living ...within a feasible commuting distance from school. This study determined feasible distances for walking and cycling to school (criterion distances) in 11- to 12-year-old Belgian children. For children living within these criterion distances from school, the correlation between parental perceptions of the environment, the number of motorized vehicles per family and the commuting mode (active/passive) to school was investigated.
Parents (n = 696) were contacted through 44 randomly selected classes of the final year (sixth grade) in elementary schools in East- and West-Flanders. Parental environmental perceptions were obtained using the parent version of Neighborhood Environment Walkability Scale for Youth (NEWS-Y). Information about active commuting to school was obtained using a self-reported questionnaire for parents. Distances from the children's home to school were objectively measured with Routenet online route planner. Criterion distances were set at the distance in which at least 85% of the active commuters lived. After the determination of these criterion distances, multilevel analyses were conducted to determine correlates of active commuting to school within these distances.
Almost sixty percent (59.3%) of the total sample commuted actively to school. Criterion distances were set at 1.5 kilometers for walking and 3.0 kilometers for cycling. In the range of 2.01 - 2.50 kilometers household distance from school, the number of passive commuters exceeded the number of active commuters. For children who were living less than 3.0 kilometers away from school, only perceived accessibility by the parents was positively associated with active commuting to school. Within the group of active commuters, a longer distance to school was associated with more cycling to school compared to walking to school.
Household distance from school is an important correlate of transport mode to school in children. Interventions to promote active commuting in 11-12 year olds should be focusing on children who are living within the criterion distance of 3.0 kilometers from school by improving the accessibility en route from children's home to school.
To describe the design of the Feel4Diabetes-intervention and the baseline characteristics of the study sample.
School- and community-based intervention with cluster-randomized design, aiming to ...promote healthy lifestyle and tackle obesity and obesity-related metabolic risk factors for the prevention of type 2 diabetes among families from vulnerable population groups. The intervention was implemented in 2016-2018 and included: (i) the 'all-families' component, provided to all children and their families via a school- and community-based intervention; and (ii) an additional component, the 'high-risk families' component, provided to high-risk families for diabetes as identified with a discrete manner by the FINDRISC questionnaire, which comprised seven counselling sessions (2016-2017) and a text-messaging intervention (2017-2018) delivered by trained health professionals in out-of-school settings. Although the intervention was adjusted to local needs and contextual circumstances, standardized protocols and procedures were used across all countries for the process, impact, outcome and cost-effectiveness evaluation of the intervention.
Primary schools and municipalities in six European countries.
Families (primary-school children, their parents and grandparents) were recruited from the overall population in low/middle-income countries (Bulgaria, Hungary), from low socio-economic areas in high-income countries (Belgium, Finland) and from countries under austerity measures (Greece, Spain).
The Feel4Diabetes-intervention reached 30 309 families from 236 primary schools. In total, 20 442 families were screened and 12 193 'all families' and 2230 'high-risk families' were measured at baseline.
The Feel4Diabetes-intervention is expected to provide evidence-based results and key learnings that could guide the design and scaling-up of affordable and potentially cost-effective population-based interventions for the prevention of type 2 diabetes.
Some types of sedentary behaviors tend to cluster in individuals or groups of older adults. Insight into how these different types of sedentary behavior cluster is needed, as recent research suggests ...that not all types of sedentary behavior may have the same negative effects on physical and mental health. Therefore, the aim of this study was to identify sex-specific typologies of older adults' sedentary behavior, and to examine their associations with health-related and socio-demographic factors.
Cross-sectional data were collected as part of the BEPAS Seniors, and the Busschaert study among 696 Flemish older adults (60+). Typologies of self-reported sedentary behavior were identified using latent profile analysis, and associations with health-related and sociodemographic factors were examined using analyses of variances.
Five distinct typologies were identified from seven sedentary behaviors (television time, computer time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in men, and three typologies were identified from six sedentary behaviors (television time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in women. Typologies that are characterized by high television time seem to be related to more negative health outcomes, like a higher BMI, less grip strength, and a lower physical and mental health-related quality-of-life. Typologies that are represented by high computer time and motorized transport seem to be related to more positive health outcomes, such as a lower body mass index, more grip strength and a higher physical and mental health-related quality-of-life.
Although causal direction between identified typologies and health outcomes remains uncertain, our results suggests that future interventions should better focus on specific types of sedentary behavior (e.g. television time), or patterns of sedentary behavior, rather than on total sedentary behavior.
From a health perspective it is suggested to promote a positive balance between time spent in light intensity physical activity (LIPA) and sedentary behaviour (SB) (i.e. spending more time in LIPA ...than time spent in SB). However, no studies have reported prevalence rates of the LIPA-SB balance yet. The aim of this study was to objectively investigate the time spent in SB, in LIPA and moderate-to-vigorous intensity physical activity (MVPA) in four Belgian age groups and to explore which proportion of the population had a favorable balance between LIPA and SB and combined this with recommended amount of MVPA.
Accelerometer data from 7 cross-sectional studies (N=2083) in four age groups (preschoolers, primary schoolchildren, secondary schoolchildren and adults) were aggregated. Differences in SB and PA between age groups and between men and women were determined by two-way MANCOVA. LIPA-SB balance was calculated and participants were categorized into one of four groups: (1) positive LIPA-SB balance (LIPA> SB) & sufficient MVPA (2) negative LIPA-SB balance & sufficient MVPA (3) positive LIPA-SB balance & insufficient MVPA (4) negative LIPA-SB balance & insufficient MVPA.
For the total sample, 55% of the waking time was spent in SB, 39% in LIPA and 6% in MVPA. Differences in SB between age groups was dependent from gender (p<0.001). Further, a positive LIPA-SB balance was assessed in 18% of the total sample and only 10% combined this positive balance with recommended amount of MVPA. Secondary schoolgirls were most at risk, with only 1% of the sample combining a positive LIPA-SB balance with sufficient MVPA. Another risk group was the large proportion (43%) of adult men who combined sufficient MVPA with a negative LIPA-SB balance.
A high proportion of the Belgian population is at risk if taking into account both SB and PA levels. Secondary schoolgirls have the unhealthiest SB and PA profile and are therefore an important target group for interventions both increasing MVPA and decreasing SB. In men more attention should be given in promoting a positive LIPA-SB balance independently from their compliance with the MVPA guidelines.
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