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两种水平的技术辅助个性化营养对饮食摄入的影响:一项随机试验。

Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial.

机构信息

Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, Australia.

School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia.

出版信息

Nutrients. 2020 Oct 29;12(11):3334. doi: 10.3390/nu12113334.


DOI:10.3390/nu12113334
PMID:33138210
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7693517/
Abstract

Advances in web and mobile technologies have created efficiencies relating to collection, analysis and interpretation of dietary intake data. This study compared the impact of two levels of nutrition support: (1) low personalization, comprising a web-based personalized nutrition feedback report generated using the Australian Eating Survey (AES) food frequency questionnaire data; and (2) high personalization, involving structured video calls with a dietitian using the AES report plus dietary self-monitoring with text message feedback. Intake was measured at baseline and 12 weeks using the AES and diet quality using the Australian Recommended Food Score (ARFS). Fifty participants (aged 39.2 ± 12.5 years; Body Mass Index 26.4 ± 6.0 kg/m; 86.0% female) completed baseline measures. Significant ( < 0.05) between-group differences in dietary changes favored the high personalization group for total ARFS (5.6 points (95% CI 1.3 to 10.0)) and ARFS sub-scales of meat (0.9 points (0.4 to 1.6)), vegetarian alternatives (0.8 points (0.1 to 1.4)), and dairy (1.3 points (0.3 to 2.3)). Additional significant changes in favor of the high personalization group occurred for proportion of energy intake derived from energy-dense, nutrient-poor foods (-7.2% (-13.8% to -0.5%)) and takeaway foods sub-group (-3.4% (-6.5% to 0.3%). Significant within-group changes were observed for 12 dietary variables in the high personalization group vs one variable for low personalization. A higher level of personalized support combining the AES report with one-on-one dietitian video calls and dietary self-monitoring resulted in greater dietary change compared to the AES report alone. These findings suggest nutrition-related web and mobile technologies in combination with personalized dietitian delivered advice have a greater impact compared to when used alone.

摘要

网络和移动技术的进步提高了膳食摄入数据的收集、分析和解释效率。本研究比较了两种营养支持水平的影响:(1)低个性化,包括使用澳大利亚饮食调查(AES)食物频率问卷数据生成的基于网络的个性化营养反馈报告;(2)高度个性化,涉及营养师使用 AES 报告进行结构化视频通话和通过短信反馈进行饮食自我监测。使用 AES 测量基线和 12 周时的摄入量,并使用澳大利亚推荐食物评分(ARFS)评估饮食质量。50 名参与者(年龄 39.2±12.5 岁;BMI 26.4±6.0 kg/m;86.0%为女性)完成了基线测量。膳食变化的组间差异有统计学意义(<0.05),高度个性化组的总 ARFS(5.6 分(95%CI 1.3 至 10.0))和 ARFS 亚量表(肉 0.9 分(0.4 至 1.6))、素食替代品 0.8 分(0.1 至 1.4))和乳制品 1.3 分(0.3 至 2.3))均有所改善。高度个性化组还发生了其他一些显著变化,即能量摄入中来自高能量、低营养食物的比例增加(-7.2%(-13.8%至-0.5%))和外卖食品亚组增加(-3.4%(-6.5%至 0.3%)。在高度个性化组中,有 12 项饮食变量发生了显著变化,而低个性化组只有 1 项发生了变化。与仅使用 AES 报告相比,结合 AES 报告、营养师一对一视频通话和饮食自我监测的更高水平个性化支持可导致更大的饮食变化。这些发现表明,与单独使用相比,与营养相关的网络和移动技术与个性化营养师提供的建议相结合,具有更大的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/fa8e62ef0cb3/nutrients-12-03334-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/7e9376c6965b/nutrients-12-03334-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/fbe157a216c6/nutrients-12-03334-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/fa8e62ef0cb3/nutrients-12-03334-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/7e9376c6965b/nutrients-12-03334-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/fbe157a216c6/nutrients-12-03334-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb00/7693517/fa8e62ef0cb3/nutrients-12-03334-g003.jpg

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本文引用的文献

[1]
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Compr Rev Food Sci Food Saf. 2019-11

[2]
Investigating the Efficacy and Cost-Effectiveness of Technology-Delivered Personalized Feedback on Dietary Patterns in Young Australian Adults in the Advice, Ideas, and Motivation for My Eating (Aim4Me) Study: Protocol for a Randomized Controlled Trial.

JMIR Res Protoc. 2020-5-22

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Lancet. 2019-4-4

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Nutrients. 2019-1-16

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J Hum Nutr Diet. 2019-1-2

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J Med Internet Res. 2018-10-15

[9]
Assessing Dietary Outcomes in Intervention Studies: Pitfalls, Strategies, and Research Needs.

Nutrients. 2018-7-31

[10]
Validation of a Smartphone Image-Based Dietary Assessment Method for Pregnant Women.

Nutrients. 2017-1-18

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