Utilizing Smartphone Technology To Monitor Improvements In The Healthiness Of The Food Supply
Notice bibliographique
Résumé
Purpose To date, there has been little success in efforts to stem the tide of diet‐related ill health attributable to increased availability of highly processed foods providing high levels of saturated fat, sugar and salt. Central to the challenge faced by policy makers in addressing this is the absence of current data to properly define, understand or respond to the problem. Methods The Food Monitoring Group was established in 2010 with the objective of collecting data describing the nutritional composition of the global packaged food supply. The Group harnessed smartphone technology to develop a database that contains brand‐specific information on the nutritional composition of >250,000 foods globally. The “Data Collector App” is made available to all countries around the world to enable simple, low‐cost collection of food composition data on the packaged food supply. Results Data have been used to monitor the global food supply. E.g. data from Australia were used to monitor the implementation of government sodium reduction targets. Comparisons of sodium levels over time exposed the limited progress in achieving the targets, with data presented at the individual company level. Data from India highlighted the incompleteness of nutritional labelling as another key issue in the field and was used to push government to better enforce existing labelling standards in the country. Data are also being used to fuel the FoodSwitch smartphone application, which allows consumers in Australia, NZ, the UK, China, India and South Africa to scan the barcodes of food items in‐store and be directed to healthier brands of similar products. If a product is missing from the database, app users are prompted to take 3 photos of the missing item. This alone has resulted in more than 700,000 photos being sent in by users to date, and a minimum of 300 new photos being submitted daily. These crowd‐sourced data are also used to help drive improvements in national food supplies. The app already has more than 760,000 downloads in Australia, NZ and the UK alone, with launches in the USA, Canada, Hong Kong and Switzerland planned for 2016. Conclusion Smartphone technology has helped collect large amounts of information about the healthiness of the global food supply. These data have been used for both monitoring of the nutritional content of foods, government initiatives to improve the food supply, and existing labelling initiatives. The data have also been used to educate consumers through the FoodSwitch smartphone application, which in turn helps crowd‐source additional data on the food supply, thus allowing for low‐cost, real‐time tracking of the healthiness of the global food supply. Support or Funding Information E Dunford is supported by a NHMRC Early Career Fellowship.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».