Smartphone Apps for Measuring Human Health and Climate Change Co-Benefits: A Comparison and Quality Rating of Available Apps
Bibliographic record
Abstract
BACKGROUND: Climate change and the burden of noncommunicable diseases are major global challenges. Opportunities exist to investigate health and climate change co-benefits through a shift from motorized to active transport (walking and cycling) and a shift in dietary patterns away from a globalized diet to reduced consumption of meat and energy dense foods. Given the ubiquitous use and proliferation of smartphone apps, an opportunity exists to use this technology to capture individual travel and dietary behavior and the associated impact on the environment and health. OBJECTIVE: The objective of the study is to identify, describe the features, and rate the quality of existing smartphone apps which capture personal travel and dietary behavior and simultaneously estimate the carbon cost and potential health consequences of these actions. METHODS: The Google Play and Apple App Stores were searched between October 19 and November 6, 2015, and a secondary Google search using the apps filter was conducted between August 8 and September 18, 2016. Eligible apps were required to estimate the carbon cost of personal behaviors with the potential to include features to maximize health outcomes. The quality of included apps was assessed by 2 researchers using the Mobile Application Rating Scale (MARS). RESULTS: Out of 7213 results, 40 apps were identified and rated. Multiple travel-related apps were identified, however no apps solely focused on the carbon impact or health consequences of dietary behavior. None of the rated apps provided sufficient information on the health consequences of travel and dietary behavior. Some apps included features to maximize participant engagement and encourage behavior change towards reduced greenhouse gas emissions. Most apps were rated as acceptable quality as determined by the MARS; 1 was of poor quality and 10 apps were of good quality. Interrater reliability of the 2 evaluators was excellent (ICC=0.94, 95% CI 0.87-0.97). CONCLUSIONS: Existing apps capturing travel and dietary behavior and the associated health and environmental impact are of mixed quality. Most apps do not include all desirable features or provide sufficient health information. Further research is needed to determine the potential of smartphone apps to evoke behavior change resulting in climate change and health co-benefits.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".