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87 Virtual Driving Relates to Real-World Risky Driving
- Kathryn N Devlin, Molly Split, Jocelyn Ang, Sophia Lopes, Aleksandar Gonevski, Oluwatoniloba Ogunkoya, Tasmia Hasan, Maria Schultheis
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 489-490
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Objective:
Driving is a cognitively demanding activity commonly affected by brain injury and illness. Accurate driving assessment is essential for reducing risk, optimizing independence, and informing driving-related interventions. Virtual reality driving simulation (VRDS) enables safe, sensitive, objective, and standardized measurement of driving abilities. VRDS has been validated in relation to self-reports and driver records. However, self-reports are subjective, and driver records include only major events (collisions, violations). Video telematics platforms can measure naturalistic driving in a more objective and sensitive manner. The present study used video telematics to examine relationships between VRDS performance and directly observed naturalistic driving.
Participants and Methods:20 healthy adult drivers (ages 23-61, mean age=36; 75% women) completed a VRDS assessment that included 1) driving on a straight road, 2) following a truck on a highway, and 3) reacting to a child running into a street to retrieve a ball. Primary VRDS measures were 1) speed and lane management on the straight road; 2) speed and following distance management in the truck-following task; and 3) reaction time, stopping, and distance from the child in the child-ball task. Participants also completed 28 days of naturalistic driving with a video telematics platform in their vehicle. Driving events were detected automatically using accelerometer, GPS, and video data, and driving behaviors were coded by driving risk analysts. The primary naturalistic measure was the number of unsafe driving behaviors per hour driven; specific driving behaviors served as exploratory variables. We examined correlations between VRDS and naturalistic driving variables. Given limited statistical power, we reported correlations that were small-to-medium or greater (r>.2) in primary analyses and medium-to-large or greater (r>.4) in exploratory analyses.
Results:On average, drivers exhibited approximately one unsafe driving behavior per hour (M=0.9, SD=0.9, range=0.1-2.7). Common behaviors were failing to stop, unsafe following distance, speeding, and cell phone use. No collisions occurred. Average lane position in VRDS (specifically, leftward deviation from the center of the lane) was correlated with more real-world unsafe driving behaviors per hour (r=.35, p=.13), as were higher average straight road speed (r=.26, p=.27), greater straight road speed variability (r=.28, p=.24), and failing to stop for the child in the child-ball task (r=.22, p=.36). In exploratory analyses, failing to stop for the child was associated with real-world distracted driving (r=.45, p=.047), greater lane position variability in VRDS was associated with real-world unsafe following distance (r=.57, p=.009), and greater speed variability in VRDS was associated with real-world seat belt non-use/misuse (r=.49, p=.03).
Conclusions:The present findings provide preliminary evidence that VRDS variables are related to directly observed naturalistic driving, supporting the potential utility of VRDS as a sensitive, ecologically valid driving evaluation tool. As the present study used a small sample of healthy drivers, further research will explore this topic in larger samples and in clinical populations, such as acquired brain injury. Future work will also investigate whether incorporating VRDS with conventional driving evaluation tools (e.g., neuropsychological tests, behind-the-wheel assessments) can enhance the ability of clinical driving evaluations to predict real-world risky driving.
The potential of artificial intelligence in enhancing adult weight loss: a scoping review
- Han Shi Jocelyn Chew, Wei How Darryl Ang, Ying Lau
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- Journal:
- Public Health Nutrition / Volume 24 / Issue 8 / June 2021
- Published online by Cambridge University Press:
- 17 February 2021, pp. 1993-2020
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Objective:
To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss.
Design:A scoping review of global literature published from inception to 15 December 2020 was conducted according to Arksey and O’Malley’s five-step framework. Eight databases (CINAHL, Cochrane–Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science) were searched. Included studies were independently screened for eligibility by two reviewers with good interrater reliability (k = 0·96).
Results:Sixty-six out of 5573 potential studies were included, representing more than 2031 participants. Three tenets of self-regulation were identified – self-monitoring (n 66, 100 %), optimisation of goal setting (n 10, 15·2 %) and self-control (n 10, 15·2 %). Articles were also categorised into three AI applications, namely machine perception (n 50), predictive analytics only (n 6) and real-time analytics with personalised micro-interventions (n 10). Machine perception focused on recognising food items, eating behaviours, physical activities and estimating energy balance. Predictive analytics focused on predicting weight loss, intervention adherence, dietary lapses and emotional eating. Studies on the last theme focused on evaluating AI-assisted weight management interventions that instantaneously collected behavioural data, optimised prediction models for behavioural lapse events and enhance behavioural self-control through adaptive and personalised nudges/prompts. Only six studies reported average weight losses (2·4–4·7 %) of which two were statistically significant.
Conclusion:The use of AI for weight loss is still undeveloped. Based on the current study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.