Effect of Brief Produce Exposure and Unconstrained Grocery Gift Cards on Caregiver Influence on Diet of Elementary Age Children: A Randomized Clinical Trial.

Importance: Children's diets affect health trajectories but are difficult to influence, especially for resource-constrained families. Objective: To assess the effectiveness of providing 4 weeks of grocery gift cards and small produce boxes to caregivers on their ability to support healthy shifts in children's diets. Design, Setting, and Participants: This 2-group randomized clinical trial was conducted from May to July 2021, with 4 weeks of intervention and follow-up at 8 weeks. Resources were provided curbside at 3 schools, 3 housing sites, and 1 after-school site for use at home. Participants consisted of 1 index child ages 5 to 11 years with 1 index caregiver from 68 low-income families. Data were analyzed from July 2021 through March 2022. Interventions: During each week for 4 weeks, caregivers were offered 10-lb (4.5 kg) boxes of fruits and vegetables, $10.00 grocery gift cards, an additional $10.00 gift card over the last 3 weeks triggered by a task completion, and a 1-time choice of a $25.00 food preparation tool. Main Outcomes and Measures: Index child and caregiver diets were measured together over the phone at baseline, 4 weeks, and 8 weeks using the 2019 to 2020 Texas School Physical Activity and Nutrition (SPAN) tool, which measures the number of times food items were eaten over the prior day to report a SPAN Healthy Eating Index (SHEI) score and subscores for specific categories of foods (range, 0-57, with higher scores indicating a more healthful diet). Results: Among 68 children (mean [SD] age, 8.2 [1.7] years; 35 [51.5%] girls) and caregivers (mean [SD] age, 37.9 [7.9] years; 63 mothers [92.6%]) from primarily low-income families, 26 caregivers were Hispanic or Latino (38.2%), while 18 caregivers were Black (26.4%), 25 caregivers were White (36.7%), and 24 caregivers had more than 1 race (35.3%). Most families were below the federal poverty level (41 of 60 families that reported income [68.3%]). Per participating caregiver, a mean (SD) 2.7 [1.4] fruit and vegetable boxes and $42.35 ($25.46) worth of gift cards were picked up over 4 weeks. Mean (SE) child SPAN SHEI increased from 32.03 (0.62) times/d to 33.75 (0.69) times/d at 4 weeks (ie, postintervention) and 34.03 (0.69) times/d 4 weeks later (ie, at 8 weeks of follow-up). Mean (SE) child fruit and vegetable intake increased from 5.31 (0.47) times/d to 5.78 (0.51) times/d postintervention and 6.03 (0.51) times/d at follow-up. Children in the control group did not have improved diet (overall mean [SE] SHEI: 31.48 [0.58] times/d at baseline, 31.68 [0.54] times/d postintervention, and 31.81 [0.52] times/d at follow-up; mean [SE] fruit and vegetable intake: 5.21 [0.45] times/d at baseline, 4.77 [0.45] times/d postintervention, and 4.68 [0.41] times/d at follow-up). Compared with children in the control group, mean SHEI was increased for children in the intervention group by 2.07 times/d postintervention and 2.23 times/d at follow-up. Improvements as a function of program dose were statistically significant for child SHEI (P = .01) and fruit and vegetable intake (P = .03). No significant changes in caregiver diets were found. Conclusions and Relevance: This study found that easily accessed fruits and vegetables and unconstrained grocery store cards provided directly to caregivers over 4 weeks resulted in improvements in child diet, which were sustained over 4 additional weeks. Future work may investigate whether diet improvement from a brief intervention optimized for caregiver flexibility reflects a natural maximum or potential for greater improvements on extension. Trial Registration: ClinicalTrials.gov Identifier: NCT04827654.

Using kinematic driving data to detect sleep apnea treatment adherence.

People spend a significant amount of time behind the wheel of a car. Recent advances in data collection facilitate continuously monitoring this behavior. Previous work demonstrates the importance of this data in driving safety but does not extended beyond the driving domain. One potential extension of this data is to identify driver states related to health conditions such as obstructive sleep apnea (OSA). We collected driving data and medication adherence from a sample of 75 OSA patients over 3.5 months. We converted speed and acceleration behaviors to symbols using symbolic aggregate approximation and converted these symbols to pattern frequencies using a sliding window. The resulting frequency data was matched with treatment adherence information. A random forest model was trained on the data and evaluated using a held-aside test dataset. The random forest model detects lapses in treatment adherence. An assessment of variable importance suggests that the important patterns of driving in classification correspond to route decisions and patterns that may be associated with drowsy driving. The success of this approach suggests driving data may be valuable for evaluating new treatments, analyzing side effects of medications, and that the approach may benefit other drowsiness detection algorithms.

LINKING GPS DATA TO GIS DATABASES IN NATURALISTIC STUDIES: EXAMPLES FROM DRIVERS WITH OBSTRUCTIVE SLEEP APNEA.

In naturalistic studies, it is vital to give appropriate context when analyzing driving behaviors. Such contextualization can help address the hypotheses that explore a) how drivers perform within specific types of environment (e.g., road types, speed limits, etc.), and b) how often drivers are exposed to such specific environments. In order to perform this contextualization in an automated fashion, we are using Global Positioning System (GPS) data obtained at 1 Hz and merging this with Geographic Information Systems (GIS) databases maintained by the Iowa Department of Transportation (DOT). In this paper, we demonstrate our methods of doing this based on data from 43 drivers with obstructive sleep apnea (OSA). We also use maps from GIS software to illustrate how information can be displayed at the individual drive or day level, and we provide examples of some of the challenges that still need to be addressed.

FEEDBACK FROM NATURALISTIC DRIVING IMPROVES TREATMENT COMPLIANCE IN DRIVERS WITH OBSTRUCTIVE SLEEP APNEA.

As part of a study in drivers with obstructive sleep apnea (OSA), we conducted a randomized clinical trial to assess whether individualized feedback can increase compliance with continuous positive airway pressure (CPAP) therapy. After completing 3.5 months of naturalistic driving monitoring, OSA drivers were randomized either to receive an intervention, which was feedback regarding their own naturalistic driving record and CPAP compliance, or to receive no such intervention. In the week immediately after the intervention date, drivers receiving feedback (n=30) improved their CPAP usage by an average of 35.8 minutes per night (p=0.008; 95% CI=9.6, 62.0) to a mean level of 296 minutes. By contrast, CPAP usage in the non-feedback group (n=36) decreased an average of 27.5 minutes per night (p=0.022; 95% CI=4.0, 51.0) to a mean level of 236 minutes. The mean group-specific changes were higher (better) in the feedback group than in the non-feedback group during the first, second, and third weeks of follow-up (p<0.001, p=0.001, and p=0.027, respectively). By weeks 4 through 10, the effect of the feedback had lost its significance (p>0.25 in all cases). Our study suggests that CPAP compliance can be increased using individualized feedback, but that follow-up feedback sessions or reminders may be necessary for sustained improvement.

USING FEEDBACK FROM NATURALISTIC DRIVING TO IMPROVE TREATMENT ADHERENCE IN DRIVERS WITH OBSTRUCTIVE SLEEP APNEA.

We are studying the effects of individualized feedback upon adherence with therapy (CPAP) in ongoing research aimed at improving driving safety in at-risk individuals with obstructive sleep apnea (OSA). The feedback includes specific samples of the individual's own naturalistic driving record, both alert and drowsy, and record of CPAP adherence. We report on this methodology, provide data examples of CPAP usage, and show preliminary data on the results in the first eleven drivers who received this intervention.

Highway Healthcare: How Naturalistic Driving Data Index Adherence to CPAP Therapy in Obstructive Sleep Apnea.

Drowsy driving is a major factor in many vehicle crashes around the world. Sleep disorders, such as obstructive sleep apnea (OSA), underpin many of these crashes. Continuous positive airway pressure (CPAP) therapy is an effective treatment for sleep apnea but it requires consistent use and is often rejected by OSA patients. Rejection of CPAP treatment creates a dangerous on-road environment for both OSA sufferers and the general public. Algorithms capable of detecting CPAP use and its effects on driving are integral to identifying and mitigating this danger. This work uses naturalistic kinematic driving data to develop an algorithm which can detect nightly CPAP abstinence and adequate CPAP use. Speed and lateral acceleration data were collected using a data recorder in participant's primary vehicle and CPAP data were collected by downloading adherence data from participant CPAP machines. The speed and acceleration data were reduced to a set of symbols using Symbolic Aggregate approximation (SAX) time-series analysis. The symbols were converted into a sequence frequency dataset using sliding windows of size 1 to 10 s with a 1 Hz sampling rate. A Random Forest classifier was trained on the data to create a classification algorithm. On a held aside testing set, the Random Forest algorithm correctly identified 71% of the instances and had an area under the receiver operating characteristic curve of 0.76. The variable importance of the algorithm suggested that kinematic patterns associated with common drowsy driver crash types were key features in the algorithm's prediction performance.

NEUROPSYCHOLOGICAL PREDICTORS OF SAFETY IN URBAN LEFT-TURN SCENARIOS.

Left turns at urban intersections can be dangerous, especially when views are obstructed or pedestrians are present. Impairments in driver vision, motor, and cognition functions may further increase left-turn risk. We examined this problem in a simulated environment that included left-turn scenarios to study the driving behaviors of 28 drivers, ages 37 to 88 years, six of whom had "Useful Field of View" (UFOV) impairments. Subjects also completed a battery of neuropsychological tests. The simulated drive included an urban section with six left turns in three types of scenarios: 1) a semi truck blocking the view of oncoming traffic, 2) a lead vehicle obstruction, and 3) a pedestrian crossing ahead of the turning driver. Results showed a mean (SD) of 1.46 (1.60) collisions per driver (range 0 to 7), 83% of which occurred at intersections with semi trucks. Far visual acuity, contrast sensitivity, UFOV, Mini Mental State Examination, Trail-Making Test Part B, the Wisconsin Card Sort task, and age were all associated with the total number of collisions (Pearson correlation magnitudes between 0.37 to 0.77; p-values<0.05). Spearman correlations were less significant. Findings indicate that visual obstruction by on oncoming semi-truck is a particularly dangerous left-turn situation.

"CHOKING UNDER PRESSURE" IN OLDER DRIVERS.

Aging can impair executive control and emotion regulation, affecting driver decision-making and behavior, especially under stress. We used an interactive driving simulator to investigate ability to make safe left-turns across oncoming traffic under pressure in 13 older (> 65 years old) and 16 middle-aged (35-56 years old) drivers. Drivers made left-turns at an uncontrolled intersection with moderately heavy oncoming traffic. Gaps between oncoming vehicles varied and increased gradually from 2 s to 10 s. Drivers made two left-turns with a vehicle honking aggressively behind (pressure condition), and two left-turns without the honking vehicle (control condition). Results showed that middle-aged drivers made more cautious turning decisions under pressure (by waiting for larger and safer gaps, p < .001), but older drivers did not. Further, older driver turning paths deviated under pressure compared to the control condition (p < .05), but the middle-aged group did not. Moreover, across all subjects, better executive function was significantly correlated with larger increases of accepted gap size from control to honking (p < .01). The findings suggest that older drivers are more sensitive to traffic challenges from environmental pressure and that neural models of older driver performance and safety must factor in age-related changes in executive control and emotion processing.

The Language of Driving: Advantages and Applications of Symbolic Data Reduction for Analysis of Naturalistic Driving Data.

Recent advances in onboard vehicle data recording devices have created an abundance of naturalistic driving data. The amount of data exceeds the resources available for analysis; this situation forces researchers to focus on analyses of critical events and to use simple heuristics to identify those events. Critical event analysis eliminates the context that can be critical in understanding driver behavior and can reduce the generalizability of the analysis. This work introduced a method of naturalistic driving data analysis that would allow researchers to examine entire data sets by reducing the sets by more than 90%. The method utilized a symbolic data reduction algorithm, symbolic aggregate approximation (SAX), which reduced time series data to a string of letters. SAX can be applied to any continuous measurement, and SAX output can be reintegrated into a data set to preserve categorical information. This work explored the application of SAX to speed and acceleration data from a naturalistic driving data set and demonstrated SAX's integration with other methods that could begin to tame the complexity of naturalistic data.

EFFECTS OF ENVIRONMENTAL FACTORS ON NATURALISTIC DRIVING IN OBSTRUCTIVE SLEEP APNEA.

Reduced visibility and other environmental factors can impair driver ability to respond to roadway hazards. We examined the effects of reduced visibility on naturalistic driving in 66 drivers, including 45 at-risk drivers with obstructive sleep apnea (OSA) and 21 controls. We analyzed three months of electronic data using "black box" recorder technology and assessed the extent to which driver speed, longitudinal acceleration, and lateral acceleration metrics depend on ambient visibility from web-based environmental data archives. We calculated summary driving metrics within 10-second intervals, and reduced these to within-subject means and tested for associations of interest. OSA drivers did not differ from controls with respect to electronic measures or visibility conditions in which they drove. On average, drivers drove slower when visibility was reduced. After controlling for speed, variations in lateral and longitudinal acceleration were positively associated with high-visibility conditions. These findings suggest that drivers exert greater vehicular control when visibility is limited, and that this association is not just due to slower speeds. Weaker relationships between visibility and driving measures in OSA suggest reduced adaptive strategies. Our methods provide a framework for analyzing the effects of other environmental factors on driving, and we provide an additional example using wind speed.