Quantifying vehicle control from physiology in type 1 diabetes

Driving among individuals with chronic conditions: A systematic review of applied research using kinematic driving sensors

BACKGROUND

Kinematic driving data studies are a novel methodology relevant to health care, but prior studies have considerable variance in their methods, populations, and findings suggesting a need for critical analysis and appraisal for feasibility and methodological guidelines.

METHODS

We assessed kinematic driving studies of adults with chronic conditions for study feasibility, characteristics, and key findings, to generate recommendations for future study designs, and to identify promising directions for applications of kinematic driving data. PRISMA was used to guide the review and searches included PubMed, CINAHL, and Compendex. Of 379 abstract/titles screened, 49 full-text articles were reviewed, and 29 articles met inclusion criteria of analyzing trip-level kinematic driving data from adult drivers with chronic conditions.

RESULTS

The predominant chronic conditions studied were Alzheimer's disease and related Dementias, obstructive sleep apnea, and diabetes mellitus. Study objectives included feasibility testing of kinematic driving data collection in the context of chronic conditions, comparisons of simulation with real-world kinematic driving behavior, assessments of driving behavior effects associated with chronic conditions, and prognostication or disease classification drawn from kinematic driving data. Across the studies, there was no consensus on devices, measures, or sampling parameters; however, studies showed evidence that driving behavior could reliably differentiate between adults with chronic conditions and healthy controls.

CONCLUSIONS

Vehicle sensors can provide driver-specific measures relevant to clinical assessment and interventions. Using kinematic driving data to assess and address driving measures of individuals with multiple chronic conditions is positioned to amplify a functional outcome measure that matters to patients.

Vehicle Control as a Measure of Real-World Driving Performance in Patients With Rheumatoid Arthritis

OBJECTIVE

To quantify vehicle control as a metric of automobile driving performance in patients with rheumatoid arthritis (RA).

METHODS

Naturalistic driving assessments were completed in patients with active RA and controls without disease. Data were collected using in-car, sensor-based instrumentation installed in the participants' own vehicles to observe typical driving habits. RA disease status, disease activity, and functional status were associated with vehicle control (lateral [steering] and longitudinal [braking/accelerating] acceleration variability) using mixed-effect linear regression models stratified by road type (defined by roadway speed limit).

RESULTS

Across 1,292 driving hours, RA drivers (n = 33) demonstrated differences in vehicle control compared to controls (n = 23), with evidence of significant statistical interaction between disease status and road type (P < 0.001). On residential roads, participants with RA demonstrated overall lower braking/accelerating variability than controls (P ≤ 0.004) and, when disease activity was low, lower steering variability (P = 0.03). On interstates/highways, RA was associated with increased steering variability among those with moderate/high Clinical Disease Activity Index scores (P = 0.04). In models limited to RA, increases in disease activity and physical disability over 12 weeks of observation were associated with a significant increase in braking/accelerating variability on interstate/highways (both P < 0.05).

CONCLUSION

Using novel naturalistic assessments, we linked RA and worsening RA disease severity with aberrant vehicle control. These findings support the need for further research to map these observed patterns in vehicle control to metrics of driver risk and, in turn, to link patterns of real-world driving behavior to diagnosis and disease activity.

Risk of motorcycle collisions among patients with type 2 diabetes: a population-based cohort study with age and sex stratifications in Taiwan

AIMS

To investigate the overall and sex-age-specific absolute and relative risks of motorcycle collisions at road traffic accidents among patients with type 2 diabetes.

METHODS

A cohort study in Taiwan was conducted by following 989,495 patients with type 2 diabetes and the same number of matched controls recruited between 2010 and 2012 to the end of 2016. Collision events by motorcycle driver victims were identified from the Police-reported Traffic Accident Registry. Overall and sex-age-specific incidence rates of collision involving motorcycle driver victims were estimated under Poisson assumption. The Cox proportional hazard regression models were performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of collision in association with type 2 diabetes.

RESULTS

Over an up to 7 years of follow-up, patients with type 2 diabetes had a higher incidence rate of motorcycle collision than controls at 1.16 and 0.89 per 100 person-years, respectively, which represented a significantly elevated HR of 1.28 (95% CI 1.27-1.30) after adjusting for potential confounders including various diabetic complications. The elevated HR was similarly seen in both men and women patients, and was significantly decreasing with increasing age regardless of sex. Little evidence supported the dose-response relationship between duration of type 2 diabetes and motorcycle collision risk.

CONCLUSIONS

After adjustment for common diabetic complications and comorbidities that could impair driving performance, patients with type 2 diabetes still suffered from increased risk of motorcycle collisions, regardless of sex, but was more evident in younger than in older patients.