Purpose: In a co-creation process, we identified four interventions to link citizens with type 2 diabetes (T2D), cardiovascular disease (CVD), and/or obesity with physical activities in civil society organizations when ending a municipal rehabilitation program. This process included citizens in the target group, civil society organizations, municipal stakeholders (including politicians), and researchers. The aim of this study is to investigate the effect of these interventions on physical activity engagement following a municipal rehabilitation program.

Methods: In this pilot study, four co-created interventions were implemented in T2D, CVD, and obesity rehabilitation programs in a Danish municipality: 1. A link worker; 2. A visiting program; 3. A digital platform; and 4. Co-created physical activities. Data from citizens participating in these programs were collected 0 and 6 months after the rehabilitation program. Outcomes included changes in physical activity engagement in civil society organizations; objectively measured physical activity (i.e., light, moderate, and vigorous intensities) and sitting time; 6-minute walk distance; body composition; health-related quality of life (the 12-item short-form health survey); and mental well-being (the WHO-5 well-being index). All outcomes were analyzed using repeated measures mixed linear models with random effects (participant ID).

Results: Among the 33 included citizens (58% women, median (25th; 75th percentile) age of 67.6 (63.9; 74.1) years), six were diagnosed with T2D; nine with CVD; and 18 were obese. Of the 21 citizens (64%) who were not engaged in physical activities in civil society organizations before entering the rehabilitation program, 67% started and remained active in an organization at 6-month follow-up. Physical activity increased by a mean (95% confidence interval) of 18.5 (2.8; 34.1) min/day from 0 to 6 months after the rehabilitation program. This was mainly explained by an increase in light intensity physical activity of 15.6 (2.4; 28.8) min/day. The other outcomes remained unchanged from 0 to 6 months after the rehabilitation program.

Conclusion: This study suggests that the co-created interventions led to increased physical activity engagement in civil society organizations and increased physical activity time. This supports co-creation of interventions as a method to link municipal rehabilitation programs with physical activities in civil society organizations.

Purpose: Physical activity (PA) can attenuate the adverse effects of aromatase inhibitor (AI) hormonal therapy in women diagnosed with hormone-receptor positive breast cancer, and subsequently improve cancer-related quality of life (QoL) and psychological well-being (PWB). But many survivors do not meet PA guidelines. The PAC-WOMAN trial was designed to promote sustained changes in PA and QoL in this population. This study aimed to test associations of 4-month changes in PA with improvements in cancer-related QoL and PBW in PAC-WOMAN’s participants.

Methods: A total of 110 women on AI therapy (age: 56.1±7.6 yr; BMI: 28.1±5.9 kg/m2; AI therapy length: 23.4±20.1 months) were randomly assigned to a brief PA counseling group, a structured exercise group, or a waitlist control. They completed measures of PA (IPAQ and Activity Choice Index), QoL (EORTC QLQ-C30 and BR23) and psychological well-being (HADS depression subscale and general indicators of PBW) at baseline and intervention’s end (4 months). Residualized change scores, regressed on baseline scores, were created. Spearman correlations tested associations between change scores for the overall sample and per group. Mann-Whitney tests explored differences between participants who met (vs not) PA guidelines at 4 months.

Results: Positive changes in PA showed small-medium associations with improvements in several domains of cancer-related QoL, namely global health status, role functioning, fatigue, pain, body image, sexual functioning, breast symptoms, and musculoskeletal symptoms (r=0.26-0.41; p’s<0.05). Increases in lifestyle PA (i.e., physically active choices) was the only PA measure correlated with improvements in psychological well-being (r=0.29; p=0.021) and depressive symptoms (r=-0.39; p=0.002). Correlations were repeated for each trial group and showed similar trends. Participants meeting PA guidelines at 4 months revealed greater improvement in global health status, physical functioning, role functioning, and body image than those who did not meet (p’s<0.05; Cohen’s d=0.46-0.78).

Conclusions: These findings provide further support for the beneficial effects of PA in breast cancer survivors, indicating that increasing either structured or lifestyle PA can result in improvements in several domains of QoL and PWB in women undergoing AI hormonal therapy. Future research should explore for whom these benefits might be more pronounced, by identifying pretreatment moderators (e.g., therapy length).

Purpose:
This qualitative investigation explored physical activity (PA) and dietary behaviour before, during and after cancer treatment from the perspectives of young people who have had cancer, their parents and the healthcare professionals (HCPs) who deliver their care.

Methods:
Young people who have had cancer (aged 12-25 years), their parents and HCPs were recruited. Interviews were guided by semi-structured interview guides, that focused on PA and dietary behaviour i) before, during and after cancer treatment, ii) barriers and facilitators to engaging in these behaviours, and iii) recommendations for a PA and dietary behaviour change intervention. Interviews were audio-recorded, transcribed verbatim and analysed using a thematic analysis approach.

Results/Findings:
Interviews (n=31) were completed with HCPs (n=9), young people (n=10) and parents (n=12). Four key themes were identified and are denoted by T in this abstract. Changes in ‘PA across the cancer journey’(T1) were reported, with a sharp decline in PA occurring upon treatment commencement. Following treatment completion, PA levels were closely related to the number, and severity, of the treatment-related side-effects being experienced. ‘The complexity of dietary behavior’(T2) was emphasised, with many young people developing negative food associations or aversions both during and after treatment. ‘The dearth of PA and dietary support after treatment’(T3) was reported by all participants. Families who had accessed community physiotherapy services reported a lack of expertise among such providers with regard to the management of treatment-related side-effects which negatively influenced physical function. All participants expressed ‘the urgent need for PA and dietary support after treatment’(T4). All participants agreed that a multi-component intervention which is practical, educational, fun, and age-appropriate, that adopts an individualised approach, would be suitable for, and appealing to, young people who have had cancer.

Conclusions:
This novel investigation is the first to i) simultaneously present and synthesise the views of young people who have had cancer, their parents and HCPs, and ii) qualitatively explore PA and dietary behavior among young people who have had cancer in Ireland. The results emphasise the urgent need for PA and dietary support for this community to increase their health, well-being and quality of life.

Background: There is considerable interest in evaluating 24-hour activity cycles; however, the ideal tools and measures for doing so have yet to be identified. Considerable emphasis has been placed on monitor-based methods, but these provide limited information about the context of behaviors. The present study uses temporally linked monitor-based and report-based data from the Free–Living Activity Study for Health (FLASH) to understand the context of physical activity, sedentary behavior, and sleep during a 24-hour period. The study also provides novel insights about a promising new device called the Move 4 (movisens GmbH, Karlsruhe, Germany), a multi-sensor device with features that combine research-grade and consumer-devices functionalities.

Methods: 19 participants concurrently worn Move 4 and activPAL for 36 hours and completed an online recall using the online ACT24. Analyses focused on the use of equivalence tests to examine agreement between the Move 4 and the activPAL. The linked Act24 data was used to understand reasons for variability in the profiles.

Findings: With flexible equivalence zones (± 25%), the two devices produced equivalent estimates in all the 24-hour behavior categories with an exception of the time spent doing light intensity physical activity with sedentary p-value: 0.0013, 95 %CI (-83.76, 52.09), MVPA p-value: 0.004, 95 %CI (-7.57, 4.17) sleep p-value: 0.022, 95 %CI (-3.23, -4.76), Steps p-value: 0.032, 95 %CI (-413.10, 970.05), and LIPA p-value: 0.5720, 95 %CI (-1.21, 2.65). Contextual comparisons corroborated the equivalence findings and altogether suggested that Move 4 is a suitable tool to incorporate into physical activity assessments protocols, especially in MVPA and sedentary research although further research with bigger and diverse samples are warranted.

Purpose: In the U.S., Black adolescent girls living in low-income communities experience disproportionately high rates of obesity, low diet quality, and short sleep duration. Although cross-sectional research has suggested associations between diet, sleep, and obesity, longitudinal research is sparse. In a sample of Black adolescent girls, we aimed to identify longitudinal mechanistic associations between sleep, diet, and obesity.
Methods: We used longitudinal data from a sample of Black adolescent girls. Nocturnal sleep (total sleep duration, sleep quality) was measured using accelerometers worn > 7 days at T1 (enrollment, n = 441), T2 (6-month follow-up, n = 326), and T3 (18-month follow-up, n = 259). Using an adolescent-validated Food Frequency Questionnaire (FFQ), we estimated diet quality with the Healthy Eating Index (HEI-2020). We measured height and weight at all 3-times (calculated z-scores for body mass index, zBMI). We conducted longitudinal mediation using structural equation models (SEM) to examine the mechanistic roles of sleep, diet quality, and zBMI.
Results: At enrollment, girls were mean age 12.2 years (+ 0.71), 48.3% had overweight or obesity, and mean HEI of 55.8 (+ 7.49) . Model 1 included sleep duration and Model 2 sleep quality; both had a good fit with RMSEA < 0.5 and CFI > 0.99. Sleep duration at T1 was not associated with diet quality at T2 (a1 path: b 0.14 [95% CI: -0.53;0.82]), nor was diet associated with zBMI at T3 (b1 path: b -0.01 [-0.01;0.01]). The bootstrapped indirect effect was not significant [95% CI: -0.01;0.01]. Likewise, sleep quality at T1 was not associated with diet quality at T2 (a2 path: b 0.01 [-0.08; 0.11]), nor was diet associated with zBMI at T3 (b2 path: b -0.001 [-0.01; 0.01]). The bootstrapped indirect effect was not significant [95% CI: -0.01;0.01].
Conclusions: Diet was not a mediator between sleep and obesity among this sample of Black adolescent girls from low-income communities. The longitudinal design and direct measures of sleep and zBMI in a vulnerable group are strengths. Although diet may be a plausible mediator, other environmental, behavioral, and biological factors were not accounted for and should be further explored.

Purpose: Obesity represents a critical public health concern, demanding tailored solutions across urban and rural landscapes. While disparities in obesity and sociodemographic factors between rural and urban settings are individually recognized, tailored prevention strategies require a nuanced understanding of how these factors interact across these settings. This study aimed to examine differences in the association of obesity with sociodemographic factors between rural, non-metropolitan, and metropolitan areas.
Methods: We conducted secondary analysis on data for the 3144 counties of the United States collected from the 2023 County Health Rankings. Type of county was classified as rural, non-metropolitan, and metropolitan using the United States Department of Agriculture Economic Research Service 2023 Rural-Urban Continuum Codes. The sociodemographic factors considered were type of county, age (operationalized as percentage of adults over 65 years of age), gender (percentage of population who were female), ethnic minority (percentage of population who were not non-Hispanic whites), and food insecurity (percentage of population who did not have access to a reliable source of food during the past year). The outcome was obesity (percentage of adults living with obesity). Median household income (in dollars) was included as a covariate. Multilevel regression was conducted with the focal predictor as the interaction between type of county and each sociodemographic factor, holding the remaining factors constant.
Results: In rural and non-metropolitan areas, a higher percentage of ethnic minority and food insecurity is associated with a higher prevalence of obesity, and this interaction is significantly stronger than in metropolitan areas [β(rural×ethnicminority)=0.03, p<0.001, β(nonmetro×ethnicminority)=0.03, p<0.001; β(rural×foodinsecurity)=0.06, p=0.03, β(nonmetro×foodinsecurity)=0.08, p=0.008]. In metropolitan areas, a higher percentage of adults over 65 years of age and a lower proportion of female population is associated with a higher prevalence of obesity, and this interaction is significantly stronger than in rural and non-metropolitan areas [β(rural×age)=-0.10, p<0.001, β(nonmetro×age)=-0.09, p<0.001; β(rural×gender)=0.26, p<0.001, β(nonmetro×gender)=0.24, p<0.001].
Conclusions: Ethnicity and food insecurity were strong predictors of obesity in rural and non-metropolitan areas whereas age and gender were strong predictors of obesity in metropolitan areas. These findings highlight the importance of tailoring obesity prevention interventions to unique sociodemographic characteristics for each area to optimize their effectiveness.

Introduction: While the relationship between physical activity (PA), health and well-being has been well-established, the link between PA and sustainable development is a more recent phenomenon. PA has been described as “choice-based” or “necessity-driven”, and environmental justice is needed for PA security to be achieved. We explored these relationships across high- and low-income country settings using publicly available data. Methods: Data from 143 countries (LMIC/UMIC, N=99,HIC,N=44) were gathered (WHO Global Health Observatory, OurWorldinData and Worldometer), including: GINI coefficient, World Bank classification, %urbanised, % living in slums, life expectancy, %food insecure, obesity, diabetes prevalences, deaths due to cardiovascular disease (CVD), road incidents, personal violence, pollution, access to public transport, public open space (POS), human rights index, national PA plans and physical inactivity prevalence (PiA).Regression and mediation analyses were conducted on factors related to environmental justice that may explain differences in PiA in HIC vs LMIC/UMIC settings. Results: PiA prevalence was higher in HIC (32.7%) vs LMIC/UMIC (25.4%, P<0.001), despite more national PA plans (89% vs 59%). Urbanisation, access to public transport and POS, obesity, diabetes,CVD deaths%,and life expectancy were also significantly higher in HIC. Slum dwelling and food insecurity were 10-20 fold higher in LMIC/UMIC vs HIC (P<0.001). Deaths from air pollution, violence and road incidents were also higher in LMIC/UMIC. In LMIC/UMIC, PiA was inversely correlated to food insecurity, %slum dwelling (P<0.001) and positively correlated to %urbanisation. PiA was inversely associated with access to POS in HIC (r=-0.46) and positively associated with POS in LMIC/UMIC (r=0.37, P<0.001). Mediation analyses demonstrated the link between PiA and HIC was positively mediated by %urbanisation, whereas, the path between HIC vs LMIC/UMIC and PiA was inversely mediated by %slum dwelling, and personal violence (P<0.001). Conclusion: Results suggest that urban,built environment attributes impact on PiA differently in HIC vs LMIC/UMIC settings. Slum dwelling persons from vulnerable communities in LMIC/UMIC settings are more physically active, which reflects “necessity-based” vs “choice-based” PA. Further, POS may not address environmental equity in LMIC/UMICs. PA security should be considered as a rights-based issue for urban planners and policy makers to level the urban playing fields.

Purpose: Food insecurity (FI) rates on US university campuses have surged alongside shifting student demographics. Despite perceptions of privilege, campuses today accommodate a diverse student body, including historically marginalized groups. This study aims to evaluate students' nutritional and food security, elucidating disparities among sociodemographic segments to guide the development and adaptation of interventions.
Methods: Using a mixed methods design, we conducted a cross-sectional online survey among undergraduate students at a large public university in Fall 2023 (n=662). Measures encompassed Food Security (USDA Adult FS Module) and Nutritional Security (NS) (using a novel set of metrics). Participants self-reported their GPAs and college food experiences. Demographic data were obtained from institutional records. Chi‐square tests and logistic regression models evaluated group variations. Semi-structured interviews (n=20) were conducted with students reporting FI, with qualitative data analysis (inductive and deductive) scheduled for completion in March 2024.
Results: 58% of students reported experiencing FI, while 55% reported low NS, indicating their lack of confidence in accessing healthy foods without constraints or worry. Racial minority groups, including Hispanic/Latino (FI OR=5.4, 95%CI:2.5–11.8; NS OR=3.2, 95%CI:1.7–6.3), Black/African American (FI OR=2.8, 95%CI:1.4–5.4; NS OR=2.6, 95%CI:1.3–4.9), and international students (FI OR=2.5, 95%CI:1.1–5.3; NS OR=3.7, 95%CI:1.6–8.4) had significantly higher odds of experiencing FI and low NS compared to their White American counterparts. Seeking employment vs. not needing one showed increased odds for FI (OR=5.1, 95%CI:3.1–8.5) and low NS (OR=3.0, 95%CI:1.9–4.9). Pell Grant eligibility and independent living arrangements (on or off-campus) were associated with higher odds of FI and low NS. Enrollment in campus meal plans showed no associations. Multivariate logistic regression, adjusting for sociodemographic variables, housing, meal plans, and employment, revealed that students facing FI had significantly higher odds of achieving GPAs below three (OR=1.6, 95%CI:1.04–2.48) than food-secure students.
Conclusion: FI is associated with lower educational attainment. Race/ethnicity/origin and job-seeking status emerged as crucial food and nutrition security predictors. Integrating qualitative insights will provide essential context that differentiates nutritional and food security. Addressing FI could mitigate racial disparities in academic achievement, underscoring the urgency of targeted interventions.

Title: Embracing Indigenous Knowledge and Traditional Practices in Community Health Promotion Programs

Brittany McBeath**, Dr. Martine Lévesque, Dr. Lucie Lévesque, Sonia Perillat-Amédée, Revathi Sahajpal, Dr. Marie-Claude Tremblay & Dr. Treena Delormier
**Presenting & Corresponding Author

Purpose
This study addresses the persistent reliance on conventional health promotion approaches within Indigenous communities, despite ongoing decolonization efforts. The primary aim is to explore the concept of culturally grounded health promotion - what it looks like and how it is implemented - from the perspectives of diverse First Nations communities participating in the Kahnawà:ke Schools Diabetes Prevention Program (KSDPP) Community Mobilization Training.

Methods
Employing a community-engaged research approach, this study utilized talking circles to better understand the concept of culturally grounded health promotion, as part of the KSDPP Community Mobilization Training. Talking circles are an Indigenous method of sharing stories; participants typically sit in a circle and take turns speaking while holding a sacred object such as a feather, stone or a talking stick. Thirty-six participants from five communities participated in the audio-recorded talking circles. Inductive thematic analysis was used to analyse the data.

Results
The findings reveal rich insights into culturally grounded health promotion in Indigenous communities. The research captured salient themes such as traditional foods, land-based activities, ceremony, Indigenous language, and traditional knowledge. Distinct processes for cultural knowledge translation were also identified including educational, social collaborative, adaptative, and individual-level processes. Communities emphasized the need for a paradigm shift, advocating for Indigenous-led initiatives grounded in culture and challenging the relevance of Western frameworks.

Conclusions
Health promotion programs grounded in Indigenous culture encompass a broad spectrum of activities, ranging from incorporating traditional foods into diets to learning ancestral languages and engaging in land-based activities. Unfortunately, access to such traditional knowledge has diminished within our communities due to historical colonial influences and legislation. Nevertheless, there is a growing resurgence in the adoption of Indigenous knowledge translation methods to facilitate the transmission of health promoting knowledge aligned with Indigenous knowledge and traditional practices. Advocating for initiatives grounded in Indigenous culture and leadership is crucial for community health promotion efforts. Revitalizing this knowledge base and establishing sustainable methods for intergenerational transmission is imperative to the health and wellbeing of Indigenous peoples.