Evidence map›Paper›PMID 30897833›Full record

ArticleNutrients2019

Predicting Real-Life Eating Behaviours Using Single School Lunches in Adolescents.

Billy Langlet, Petter Fagerberg, Anastasios Delopoulos, Vasileios Papapanagiotou, Christos Diou, Christos Maramis, Nikolaos Maglaveras, Anna Anvret, Ioannis Ioakimidis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

9 authors.

Billy LangletInnovative Use of Mobile Phones to Promote Physical Activity and Nutrition across the Lifespan (the IMPACT) Research Group, Department of Biosciences and Nutrition, Karolinska Institutet, 14152 Stockholm, Sweden. billy.langlet@ki.se.ORCID 0000-0003-2806-6186
Petter FagerbergInnovative Use of Mobile Phones to Promote Physical Activity and Nutrition across the Lifespan (the IMPACT) Research Group, Department of Biosciences and Nutrition, Karolinska Institutet, 14152 Stockholm, Sweden. petter.fagerberg@ki.se.ORCID 0000-0002-1443-3128
Anastasios DelopoulosMultimedia Understanding Group, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. adelo@eng.auth.gr.ORCID 0000-0001-8220-8486
Vasileios PapapanagiotouMultimedia Understanding Group, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. vassilis@mug.ee.auth.gr.
Christos DiouMultimedia Understanding Group, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece. diou@mug.ee.auth.gr.ORCID 0000-0002-2461-1928
Christos MaramisLaboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece. chmaramis@med.auth.gr.ORCID 0000-0002-6417-0784
Nikolaos MaglaverasLaboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece. nikolaos.maglaveras@northwestern.edu.
Anna AnvretMandometer Clinics, 14104 Stockholm, Sweden. Anna.Anvret@mando.se.
Ioannis IoakimidisInnovative Use of Mobile Phones to Promote Physical Activity and Nutrition across the Lifespan (the IMPACT) Research Group, Department of Biosciences and Nutrition, Karolinska Institutet, 14152 Stockholm, Sweden. Ioannis.Ioakimidis@ki.se.

Funding

FP7 Information and Communication Technologies 610746
6 · The paper itself

Abstract

Large portion sizes and a high eating rate are associated with high energy intake and obesity. Most individuals maintain their food intake weight (g) and eating rate (g/min) rank in relation to their peers, despite food and environmental manipulations. Single meal measures may enable identification of "large portion eaters" and "fast eaters," finding individuals at risk of developing obesity. The aim of this study was to predict real-life food intake weight and eating rate based on one school lunch. Twenty-four high-school students with a mean (±SD) age of 16.8 yr (±0.7) and body mass index of 21.9 (±4.1) were recruited, using no exclusion criteria. Food intake weight and eating rate was first self-rated ("Less," "Average" or "More than peers"), then objectively recorded during one school lunch (absolute weight of consumed food in grams). Afterwards, subjects recorded as many main meals (breakfasts, lunches and dinners) as possible in real-life for a period of at least two weeks, using a Bluetooth connected weight scale and a smartphone application. On average participants recorded 18.9 (7.3) meals during the study. Real-life food intake weight was 327.4 g (±110.6), which was significantly lower (

Indexed as

Feeding BehaviorFood ServicesLunchPortion SizeSchoolsAdolescentEatingEnergy IntakeFemaleHumansMaleCohen’s kappaconfidence intervaleating behaviourmeal durationnovel technologynutritionoverweightrecording frequencyreliabilitysensory science

Identifiers

PMID30897833
PMCPMC6471169

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.