Search for: All records

Poor diets are a leading cause of morbidity and mortality. Exposure to low-quality food environments saturated with fast food outlets is hypothesized to negatively impact diet. However, food environment research has predominantly focused on static food environments around home neighborhoods and generated mixed findings. In this work, we leverage population-scale mobility data in the U.S. to examine 62M people’s visits to food outlets and evaluate how food choice is influenced by the food environments people are exposed to as they move through their daily routines. We find that a 10% increase in exposure to fast food outlets in mobile environments increases individuals’ odds of visitation by 20%. Using our results, we simulate multiple policy strategies for intervening on food environments to reduce fast-food outlet visits. This analysis suggests that optimal interventions are informed by spatial, temporal, and behavioral features and could have 2x to 4x larger effect than traditional interventions focused on home food environments.

 

Food insecurity spiked in some U.S. regions during the COVID-19 pandemic, as did food access challenges. Concerns were raised that these food issues were more prominent in food deserts, or neighborhoods lacking access to a grocery store or supermarket. Using data collected from a representative sample of Los Angeles County adults between April and October 2020, this study examined relationships between self-reported food insecurity, perceived food access barriers, and residing in a food desert, and examined differences across key geographic regions of the county. There was little relationship between residing in a food desert and experiencing food insecurity. However, perceived grocery store closures/limited hours and not having access to a vehicle were commonly reported barriers to food access, which were associated with more food insecurity. These findings suggest that geographic disparities in food access impact food insecurity. Efforts to address food insecurity should center on achieving food justice and addressing disparities across geographic regions.

 

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.

 

Objective: The COVID-19 pandemic increased food insufficiency: a severe form of food insecurity. Drawing on an ecological framework, we aimed to understand factors that contributed to changes in food insufficiency from April to December 2020, in a large urban population hard hit by the pandemic. Design: We conducted internet surveys every 2 weeks in April–December 2020, including a subset of items from the Food Insecurity Experience Scale. Longitudinal analysis identified predictors of food insufficiency, using fixed effects models. Setting: Los Angeles County, which has a diverse population of 10 million residents. Participants: A representative sample of 1535 adults in Los Angeles County who are participants in the Understanding Coronavirus in America tracking survey. Results: Rates of food insufficiency spiked in the first year of the pandemic, especially among participants living in poverty, in middle adulthood and with larger households. Government food assistance from the Supplemental Nutrition Assistance Program was significantly associated with reduced food insufficiency over time, while other forms of assistance such as help from family and friends or stimulus funds were not. Conclusions: The findings highlight that during a crisis, there is value in rapidly monitoring food insufficiency and investing in government food benefits. 

Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review includedN = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors’ ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.