Evidence map›Paper›PMID 32886147›Full record

ArticleEuropean journal of nutrition2021

Using wearable cameras to monitor eating and drinking behaviours during transport journeys.

Alyse Davies, Virginia Chan, Adrian Bauman, Louise Signal, Cameron Hosking, Luke Gemming, Margaret Allman-Farinelli

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of nutrition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Automated Fluid Intake Detection Using RGB Videos.Sensors (Basel, Switzerland) · 2022
    Article
  9. Article
  10. Impacts of Image Obfuscation on Fine-grained Activity Recognition in Egocentric Video.Proceedings of the ... IEEE International Conference on Pervasive Computing and Communications Workshops : PerCom ... IEEE International Conference on Pervasive Computing and Communications. Workshops · 2022
    Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Observational
  17. Feasibility Study Comparing Physical Activity Classifications from Accelerometers with Wearable Camera Data.International journal of environmental research and public health · 2020
    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

7 authors.

Alyse DaviesNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia. alyse.davies@sydney.edu.au.ORCID http://orcid.org/0000-0002-9448-7539
Virginia ChanNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.ORCID http://orcid.org/0000-0002-8839-896X
Adrian BaumanPrevention Research Centre, School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia.ORCID http://orcid.org/0000-0002-0369-4621
Louise SignalDepartment of Public Health, Health Promotion & Policy Research Unit, University of Otago, Wellington South, PO Box 7343, Wellington, 6242, New Zealand.ORCID http://orcid.org/0000-0003-4715-6718
Cameron HoskingCharles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.ORCID http://orcid.org/0000-0002-5563-8832
Luke GemmingNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.ORCID http://orcid.org/0000-0003-2884-5753
Margaret Allman-FarinelliNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, 2006, Australia.ORCID http://orcid.org/0000-0002-6478-1374

Funding

Australian Research Council LP150100831
6 · The paper itself

Abstract

purposeYoung adults are vulnerable to weight gain and dietary behaviours such as 'eating on the run' are likely contributors. The objective of this study was to examine eating and drinking behaviours during transport journeys in a sample of young adults using wearable cameras that take continuous images every 30 s.

methodsSeventy-eight 18-30 year olds wore an Autographer wearable camera for three consecutive days. Image coding schedules were designed to assess physical activity (included transportation) and diet. For the general description of data, frequency analysis was calculated as image number (percentage) and mean (± SD) or median (IQR) when appropriate.

resultsA total of 281,041 images were coded and 32,529 (14%) of images involved transport. The median (IQR) camera wear time was 8 h per day (7-9 h). The camera images identified 52 participants (67%) either eating or drinking during transport (excluding water). A total of 143 eating and drinking occasions were identified, averaging 3 occasions per person over the three study days. Fifty five (38%) eating episodes were identified by the camera images of which 27 (49%) were discretionary and 88 (62%) drinking episodes were identified of which (45%) were discretionary.

conclusionThis study confirms that transport is a potential setting for intervention. Young adults are consuming discretionary food and beverages during transport which may contribute to energy-dense diets and compromise diet quality. Substituting unhealthy with healthy food advertising and potentially prohibiting eating and drinking whilst on public transport is suggested.

Indexed as

DietWearable Electronic DevicesDrinking BehaviorEatingExerciseFeeding BehaviorFoodHumansYoung AdultDietary behavioursPhysical activityTransportationWearable camerasYoung adults

Identifiers

What OpenQuestion holds

Textmetadata
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.