Lyra Egan, Lauren A. Gardner, Nicola C. Newton, Siobhan O’Dean, Katrina E. Champion
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Primary outcomes included alcohol and tobacco use, and a composite indicator of poor diet. Secondary outcomes were knowledge, behavioural intentions, and psychological distress. Latent growth models assessed moderating effects of SES and geographical location on between-group change over 24-months.</p></div><div><h3>Results</h3><p>Geographical location moderated the intervention’s effect on odds of reporting a poor diet (OR = 1.79, 95% CI = 1.32–2.43, p < 0.001) and diet-related behavioural intentions (OR = 0.71, 95% CI = 0.56–0.89, p = 0.024) over time. Subset analyses indicated that intervention participants in regional areas had higher odds of reporting a poor diet (OR = 1.61, 95% CI = 1.13–2.29, p = 0.008), while those in metropolitan areas had higher odds of improving diet-related behavioural intentions (OR = 1.13, 95% CI = 1.01–1.27, p = 0.041), compared to the control group. No other significant moderation effects were observed.</p></div><div><h3>Conclusions</h3><p>While significant disparities were generally not observed, the geographical differences in intervention effects on diet and diet-related intentions suggest that co-designed and tailored approaches may benefit disadvantaged adolescents to address the disproportionately high rates of lifestyle risk behaviours among these priority populations.</p></div>","PeriodicalId":38066,"journal":{"name":"Preventive Medicine Reports","volume":"46 ","pages":"Article 102855"},"PeriodicalIF":2.4000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2211335524002705/pdfft?md5=44717df484285c5a6a24c063bb73b294&pid=1-s2.0-S2211335524002705-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Moderating effects of socioeconomic status and geographical location on the Health4Life school-based intervention\",\"authors\":\"Lyra Egan, Lauren A. 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引用次数: 0
摘要
本研究评估了社会经济地位（SES）和地理位置对基于电子健康的学校多种健康行为改变干预--Health4Life--在24个月内针对烟酒使用、饮食摄入、知识、行为意向和心理困扰的效果的调节作用。学校来自大都市（89%）和地区（11%），相对于研究人群，参与者的社会经济地位分为低（15%）、中（37%）和高（48%）。主要结果包括烟酒使用情况和不良饮食的综合指标。次要结果包括知识、行为意向和心理困扰。结果随着时间的推移，地理位置调节了干预对报告饮食不良几率（OR = 1.79，95% CI = 1.32-2.43，p <0.001）和饮食相关行为意向（OR = 0.71，95% CI = 0.56-0.89，p = 0.024）的影响。子集分析表明，与对照组相比，地区干预参与者报告饮食不良的几率更高（OR = 1.61，95% CI = 1.13-2.29，p = 0.008），而大都市干预参与者改善饮食相关行为意向的几率更高（OR = 1.13，95% CI = 1.01-1.27，p = 0.041）。结论虽然总体上没有观察到明显的差异，但对饮食和饮食相关意向的干预效果的地域差异表明，共同设计和量身定制的方法可能会使弱势青少年受益，以解决这些重点人群中生活方式风险行为发生率过高的问题。
Moderating effects of socioeconomic status and geographical location on the Health4Life school-based intervention
Objective
This study evaluated the moderating effects of socioeconomic status (SES) and geographical location on the efficacy of an eHealth school-based multiple health behaviour change intervention – Health4Life – in targeting alcohol and tobacco use, dietary intake, knowledge, behavioural intentions, and psychological distress over 24-months.
Methods
Data from the Health4Life cluster-randomised controlled trial conducted from 2019 to 2021 in 71 Australian secondary schools were analysed (N=6639; baseline age 11-14yrs). Schools were from metropolitan (89%) and regional (11%) areas, and participants’ SES was classified as low (15%), mid (37%), and high (48%) relative to the study population. Primary outcomes included alcohol and tobacco use, and a composite indicator of poor diet. Secondary outcomes were knowledge, behavioural intentions, and psychological distress. Latent growth models assessed moderating effects of SES and geographical location on between-group change over 24-months.
Results
Geographical location moderated the intervention’s effect on odds of reporting a poor diet (OR = 1.79, 95% CI = 1.32–2.43, p < 0.001) and diet-related behavioural intentions (OR = 0.71, 95% CI = 0.56–0.89, p = 0.024) over time. Subset analyses indicated that intervention participants in regional areas had higher odds of reporting a poor diet (OR = 1.61, 95% CI = 1.13–2.29, p = 0.008), while those in metropolitan areas had higher odds of improving diet-related behavioural intentions (OR = 1.13, 95% CI = 1.01–1.27, p = 0.041), compared to the control group. No other significant moderation effects were observed.
Conclusions
While significant disparities were generally not observed, the geographical differences in intervention effects on diet and diet-related intentions suggest that co-designed and tailored approaches may benefit disadvantaged adolescents to address the disproportionately high rates of lifestyle risk behaviours among these priority populations.
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