Utilizing Smartphone Technology To Monitor Improvements In The Healthiness Of The Food Supply
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".