Background Decision-making about when to stop driving for older adults involves assessment of driving risk, availability of support or resources, and strong emotions about loss of independence. Although the risk of being involved in a fatal crash increases with age, driving cessation can negatively impact an older adult’s health and well-being. Decision aids can enhance the decision-making process by increasing knowledge of the risks and benefits of driving cessation and improve decision quality. The impact of decision aids regarding driving cessation for older adults is unknown. Methods The Advancing Understanding of Transportation Options (AUTO) study is a multi-site, two-armed randomized controlled trial that will test the impact of a decision aid on older adults’ decisions about changes in driving behaviors and cessation. AUTO will enroll 300 drivers age ≥ 70 years with a study partner (identified by each driver); the dyads will be randomized into two groups (n = 150/group). The decision aid group will view the web-based decision aid created by Healthwise at baseline and the control group will review information about driving that does not include evidence-based elements on risks and benefits and values clarification about driving decisions. The AUTO trial will compare the effect of the decision aid, versus control, on a) immediate decision quality (measured by the Decisional Conflict Scale; primary outcome); b) longitudinal psychosocial outcomes at 12 and 24 months (secondary outcomes); and c) longitudinal driving behaviors (including reduction or cessation) at 12 and 24 months (secondary outcomes). Planned stratified analyses will examine the effects in subgroups defined by cognitive function, decisional capacity, and readiness to stop driving. Discussion The AUTO study is the first large-scale randomized trial of a driving decision aid for older adults. Results from this study will directly inform clinical practice about how best to support older adults in decision-making about driving. Trial registration ClinicalTrials.gov: NCT04141891. Registered on October 28, 2019. Located at https://clinicaltrials.gov/ct2/show/NCT04141891

Objective We measured how long distraction by a smartphone affects simulated driving behaviors after the tasks are completed (i.e., the distraction hangover). Background Most drivers know that smartphones distract. Trying to limit distraction, drivers can use hands-free devices, where they only briefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating and back to driving adds an underappreciated, potentially long period to the total distraction time. Method Ninety-seven 21- to 78-year-old individuals who self-identified as active drivers and smartphone users engaged in a simulated driving scenario that included smartphone distractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eye tracking. Results The participants’ lateral speed was larger than baseline for 15 s after the end of a voice distraction and for up to 25 s after a text distraction. Correct identification of peripheral cues dropped about 5% per decade of age, and participants from the 71+ age group missed seeing about 50% of peripheral cues within 4 s of the distraction. During distraction, coherence with the lead car in a following task dropped from 0.54 to 0.045, and seven participants rear-ended the lead car. Breadth of scanning contracted by 50% after distraction. Conclusion Simulated driving performance drops dramatically after smartphone distraction for all ages and for both voice and texting. Application Public education should include the dangers of any smartphone use during driving, including hands-free.

No abstract

The mortality-to-incidence rate ratio (MIR) provides a population-based measure of survival which accounts for incidence. The use of MIR as a surveillance tool has shown that South Carolina (SC) exhibits more extreme racial differences in cancer incidence, mortality and MIR than other states or the nation. We assessed the effectiveness of MIR as a proxy for 5-year survival time (5YST) among breast cancer (BrCa) patients in South Carolina. Methods: The 5YST was computed from data on BrCA cases which were obtained retrospectively from the SC Central Cancer Registry from 2002 to 2010. The MIR was computed from Cancer incidence and mortality data which were obtained from the SC Community Access Network (SCAN). The underlying data for SCAN were generated from the SC Central Cancer Registry and SC DHEC Vital Records and used to construct MIRs. ArcGIS 10.2 was utilized to map BrCA MIRs by race for 46 counties within SC. Seven categories of MIR were derived using the national MIR for BrCA as reference. 5YST was computed for all BrCA cases in each county utilizing SAS software and this was mapped with MIR per county. Exploratory and geographically weighted regression analyses were conducted in ArcGIS to determine the relationship between MIR and MST. Results: A total of 2155 breast cancer patients (nWhites = 1557/72%; nBlacks = 598/28%) were reported in the study period. A visual inspection of the MIR maps by race showed that Blacks were in the highest MIR category while the MIR by 5YST map showed that higher MIR was likely associated with lower 5YST. By contrast, the MIRs for Whites were more evenly represented over the seven categories. Overall, the 5YST was 92.8% among blacks and 95.6% among whites. Assessment of MIR with MST in ArcGIS utilizing exploratory ArcGIS regression showed that there was statistically significant Global Moran's I p value indicative of clustering. Conclusions: The MIR proved useful for identifying disparities in BrCA incidence and mortality among Black and White women in SC. Cancer surveillance programs may use the MIR to monitor disparities across racial/ethnic groups and geographic regions going forward. MIRs have the potential to serve as an indicator of the long-term success of cancer surveillance programs.

No abstract

Background: Older adults are faced with many unique and highly consequential decisions such as those related to finances, healthcare, and everyday functioning (e.g., driving cessation). Given the significant impact of these decisions on independence, wellbeing, and safety, an understanding of how cognitive impairment may impact decision making in older age is important. Objective: To examine the impact of mild cognitive impairment (MCI) on responses to a modified version of the Short Portable Assessment of Capacity for Everyday Decision making (SPACED). Methods: Participants were community-dwelling, actively driving older adults (N = 301; M age = 77.1 years, SD = 5.1; 69.4% with a college degree or higher, 51.2% female, 95.3% White) enrolled in the Advancing Understanding of Transportation Options (AUTO) study. A generalized linear model adjusted for age, education, sex, randomization group, cognitive assessment method, and study site was used to examine the relationship between MCI status and decision making. Results: MCI status was associated with poorer decision making; participants with MCI missed an average of 2.17 times more points on the SPACED than those without MCI (adjusted mean ratio: 2.17, 95% CI: 1.02, 4.61, p = 0.044). Conclusion: This finding supports the idea that older adults with MCI exhibit poorer decision-making abilities than cognitively normal older adults. It also suggests that older adults with MCI may exhibit poorer decision making across a wide range of decision contexts.

No abstract

Objective: Studies exploring wireless-based systems to monitor patients in underinsured communities are lacking. We evaluated blood pressure (BP) control with wireless vs. conventional home-based BP machines in a pilot study nested within a larger trial in San Diego County focusing on optimizing cardiovascular risk reduction. Methods: Patients with a new diagnosis of hypertension (HTN), or those with BP above 140/90 on a current regimen were enrolled from three federally qualified healthcare center systems in low-income communities. Participants were randomized to conventional automated BP cuff (CBP) or a wireless cloud-based system (WBP), and followed with a clinical visit at 3, and 6 months. Exit surveys were conducted to evaluate patterns of use. Results: At the time of this analysis complete clinical data are available for 151 participants (71 WBP; 80 CBP). Baseline BPs did not differ between treatment arms (systolic: 149.9 mmHg WBP; 151 mmHg CBP; P=0.72; diastolic: 83.5mmHg WBP; 81.3 CBP; P=0.23). BP decreased significantly in both arms by the end of the pilot (Figure 1). No differences were seen between groups for both net change of BP (P=0.79) or for end mean systolic BP (P=0.96). Of 60 participants in WBP arm who completed the exit survey, those with BP still not at goal (N=20) were more likely to report “Strongly Agree” or “Agree” that the wireless system helped them feel more engaged in their clinical care (P=0.02). Discussion: Both wireless and conventional BP monitoring had a clinically significant impact on BP control among underinsured individuals in this study, and the wireless system appears to improve engagement in care.