Evidence map›Paper›PMID 37660022›Full record

ArticleBMC public health2023

Protocol of a parallel, randomized controlled trial on the effects of a novel personalized nutrition approach by artificial intelligence in real world scenario.

Jingyuan Feng, Hongwei Liu, Shupeng Mai, Jin Su, Jing Sun, Jianjie Zhou, Yingyao Zhang, Yinyi Wang, Fan Wu, Guangyong Zheng and 1 more

Abstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Observational
  5. Review
  6. Review
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jingyuan Feng *School of Public Health, Fudan University, Shanghai, China.
Hongwei Liu *School of Public Health, Fudan University, Shanghai, China.
Shupeng Mai *Division of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Jin SuDivision of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.
Jing SunChinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China.
Jianjie ZhouBasebit (Shanghai) Information Technology Co., Ltd, Shanghai, China.
Yingyao ZhangBasebit (Shanghai) Information Technology Co., Ltd, Shanghai, China.
Yinyi WangDepartment of Nutrition and Food Science, Education, and Human Development, Steinhardt School of Culture, New York University, New York, USA.
Fan WuSchool of Public Health, Fudan University, Shanghai, China. wufan@shmu.edu.cn.
Guangyong ZhengInstitute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. gyzheng@picb.ac.cn.
Zhenni ZhuDivision of Health Risk Factors Monitoring and Control, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China. zhuzhenni@scdc.sh.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNutrition service needs are huge in China. Previous studies indicated that personalized nutrition (PN) interventions were effective. The aim of the present study is to identify the effectiveness and feasibility of a novel PN approach supported by artificial intelligence (AI).

methodsThis study is a two-arm parallel, randomized, controlled trial in real world scenario. The participants will be enrolled among who consume lunch at a staff canteen. In Phase I, a total of 170 eligible participants will be assigned to either intervention or control group on 1:1 ratio. The intervention group will be instructed to use the smartphone applet to record their lunches and reach the real-time AI-based information of dish nutrition evaluation and PN evaluation after meal consumption for 3 months. The control group will receive no nutrition information but be asked to record their lunches though the applet. Dietary pattern, body weight or blood pressure optimizing is expected after the intervention. In phase II, the applet will be free to all the diners (about 800) at the study canteen for another one year. Who use the applet at least 2 days per week will be regarded as the intervention group while the others will be the control group. Body metabolism normalization is expected after this period. Generalized linear mixed models will be used to identify the dietary, anthropometric and metabolic changes. DISCUSSION: This novel approach will provide real-time AI-based dish nutrition evaluation and PN evaluation after meal consumption in order to assist users with nutrition information to make wise food choice. This study is designed under a real-life scenario which facilitates translating the trial intervention into real-world practice.

trial registrationThis trial has been registered with the Chinese Clinical Trial Registry (ChiCTR2100051771; date registered: 03/10/2021).

Indexed as

Artificial IntelligenceNutritional StatusBody WeightHumansNutrition AssessmentRandomized Controlled Trials as TopicSoftwareArtificial intelligenceNutrition interventionPersonalized nutritionReal worldSmartphone applet

Identifiers

PMID37660022
PMCPMC10474697

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.