Perceptions of Homelessness and Mental Illness

Homelessness is a growing issue in California, with residents consistently expressing concern and voting to dedicate funds to address the problem. Despite the abundance of public support for addressing this issue, California has more than half of all unsheltered people in the country, indicating a potential disconnect between the public’s desire to help and their knowledge of how to do so. To address this possibility, we employed a survey designed to quantify the public’s understanding of the homeless population, the stigma of homelessness and mental illness, and how misperceptions may lead to suboptimal homeless policy. This approach will determine if certain misperceptions are associated with resistance to effective policy solutions. This will serve as a roadmap for the public outreach required in order to generate support for more effective solutions to the homelessness problem. With a sample of undergraduate students (n=77), this presentation will compare the results of the public’s perceptions of the homeless to known ground-truth values. Regression analysis was used to examine the relationship between the accuracy of perceptions about homeless demographics and the levels of support for various treatment solutions. We compared the means of our undergraduate participants to the means of a national sample from a recent study to investigate differences in views of homeless and support for certain policies. Our findings indicate a more liberal attitude towards homeless from undergraduate students as opposed to the general population, but a misunderstanding of the causes of homelessness as well as a stigma associated with both homeless and mental illness. Our results also reflect a need for interventions to correct misperceptions and encourage support for beneficial policies

Smart Homes for Smart Health: Developing an Interactive System to Reduce In-Home Secondhand Smoke

Smoke from any source is potentially harmful because it contains fine particulate matter that is associated with acute and chronic conditions. Second-hand smoke (SHS) is particularly unsafe for children due to biological characteristics (higher breathing rates, immature lungs and underdeveloped immune systems) that make it difficult to filter toxins.To address this concern, we recently completed Project Fresh Air (PFA), an NIH-funded R01 intervention that installed air particle sensors in the households of tobacco smokers who lived with children. The purpose of our research is to investigate and develop efficient smart home devices that monitor SHS in various living spaces to specifically protect the children residents, specifically Amazon Alexa and Awair Air Quality devices. The development of these efficient smart home devices will be used in a study to decrease second-hand smoke exposure to children living in homes with smoking guardians. We developed the infrastructure of the components that was coded into the Alexa skill program. As well as investigated the capabilities of the air particle detection system (Awair device) to best integrate it into the Alexa program. Furthermore, a skill map was designed to outline the progress of our development which also allowed the development of the Alexa skill to be understood by a wider audience. After the design and development of the Amazon Alexa and Awair Air Quality devices, a participant survey was created to assess participant feasibility. We expect the participants to be able to easily engage with the devices and successfully be aware of their smoking behavior. Overall, the success of our devices will allow participants to create a cleaner environment for both their own health and the health of their children

Intervention to Modify Perceptions of Homelessness

As public opinion is known to impact policy formation, assessing how the public misperceives the homeless population is important to prevent non-informed policies from being adopted. This study focused on correcting misperceptions about the homeless as a means to garner support for public policies that are known to improve the lives of homeless individuals and those with mental illness. The research study implemented two forms of virtual interventions (refutation texts and personal anecdotes from homeless individuals) plus a control and assessed support for various policies before and after the trial. The goal of the study was to determine which intervention modality most effectively corrects misperceptions, reduces stigmatized attitudes, and influences support for effective policy solutions. Participants recruited via Amazon Mechanical Turk in California (N = 319) were randomly assigned to either the control group or one of the two intervention groups. A regression model was used to compare the means between the intervention and control groups and determine if the intervention had a significant effect on participant opinions and demographic ratings. I hypothesized that misperceptions regarding homelessness causes would be positively associated with support for ineffective policies. I also hypothesized that participant demographics would play a role in opinions, with conservative participants and older participants believing in more stigmatized causes. In terms of the interventions, I hypothesized that the anecdotes will be more effective than the refutation texts in promoting sympathy and humanizing the homeless in the eyes of the participants due to the effectiveness of interventions that utilized the contact hypothesis. Conversely, I hypothesized that the refutation text intervention, rather than the anecdotes, would result in more accurate perceptions of causes, more support for known effective policies, and more accurate estimates of the demographic breakdown

Pitcher Effectiveness: A Step Forward for In Game Analytics and Pitcher Evaluation

With the introduction of Statcast in 2015, baseball analytics have become more precise. Statcast allows every play to be accurately tracked and the data it generates is easily accessible through Baseball Savant, which opens the opportunity for improved performance statistics to be developed. In this paper we propose a new tool, Pitcher Effectiveness, that uses Statcast data to evaluate starting pitchers dynamically, based on the results of in-game outcomes after each pitch. Pitcher Effectiveness successfully predicts instances where starting pitchers give up several runs, which we believe make it a new and important tool for the in-game and post-game evaluation of starting pitchers

Application of Real Field Connected Vehicle Data for Aggressive Driving Identification on Horizontal Curves

The emerging technology of connected vehicles generates a vast amount of data that could be used to enhance roadway safety. In this paper, we focused on safety applications of a real field connected vehicle data on a horizontal curve. The database contains connected vehicle data that were collected on public roads in Ann Arbor, Michigan with instrumented vehicles. Horizontal curve negotiations are associated with a great number of accidents, which are mainly attributed to driving errors. Aggressive/risky driving is a contributing factor to the high rate of crashes on horizontal curves. Using basic safety message data in connected vehicle data set, this paper modeled aggressive/risky driving while negotiating a horizontal curve. The model was developed using the machine learning method of random forest to classify the value of time to lane crossing (TLC), a proxy for aggressive/risky driving, based on a set of motion-related metrics as features. Three scenarios were investigated considering different TLCs value for tagging aggressive driving moments. The model contributed to high detection accuracy in all three scenarios. This suggests that the motion-related variables used in the random forest model can accurately reflect drivers\u27 instantaneous decisions and identify their aggressive driving behavior. The results of this paper inform the design of warning/feedback systems and control assistance from unsafe events which are transmittable through vehicles-to-vehicles and vehicles-to-infrastructure applications

The Moderating Effect of Socioeconomic Status and Walkability on the Efficacy of Physical Activity Interventions

To enable physical activity (PA) interventions to better tailor procedures to participant characteristics, we investigated the role of neighborhood socioeconomic status (SES) and walkability on the differential effectiveness of adaptive versus static activity goals (AG vs. SG) and immediate versus delayed (IR vs. DR) reinforcement in a PA trial. Data was collected as a part of the WalkIT Arizona study, where healthy, inactive adults (n = 512) were instructed to wear an accelerometer daily for one year and were provided with daily goals for moderate-to-vigorous PA (MVPA). The intersection of goal types (adaptive and static) as well as reinforcement types (immediate and delayed) created four groups. Participants were block-randomized into one of four groups according to high/low neighborhood walkability and high/low neighborhood income. A linear regression model was fit to the data to predict mean daily MVPA based on the interaction of intervention condition and neighborhood walkability/income quadrant. Each neighborhood walkability/SES quadrant level and intervention group interaction was statistically significant. In high walkability/high SES and low walkability/high SES groups, daily MVPA was highest for the AG/IR intervention and lowest for the SG/DR intervention (β = 12.18, p \u3c .001; β = 9.11, p \u3c .001, respectively). In the low walkability/low SES group, MVPA was also lowest for the SG/DR intervention but was highest for the SG/IR intervention. (β= 9.12, p \u3c .001). Results were qualitatively different in the high walkability/low SES group, where the most MVPA was seen for the SG/DR intervention, while the least was observed for AG/DR (β = 5.66 , p \u3c .001). The results show that in a low-income/high-walkability environment, static goals and delayed reinforcement were most effective, which is the opposite of what was seen in other neighborhoods. These findings can be used to customize future physical activity interventions so that intervention strategies are most appropriate for participants’ demographic/environmental settings

Time of Day Preferences and Daily Temporal Consistency for Predicting the Sustained Use of a Commercial Meditation App: Longitudinal Observational Study

Background: The intensive data typically collected by mobile health (mHealth) apps allows factors associated with persistent use to be investigated, which is an important objective given users’ well-known struggles with sustaining healthy behavior. Objective: Data from a commercial meditation app (n=14,879; 899,071 total app uses) were analyzed to assess the validity of commonly given habit formation advice to meditate at the same time every day, preferably in the morning. Methods: First, the change in probability of meditating in 4 nonoverlapping time windows (morning, midday, evening, and late night) on a given day over the first 180 days after creating a meditation app account was calculated via generalized additive mixed models. Second, users’ time of day preferences were calculated as the percentage of all meditation sessions that occurred within each of the 4 time windows. Additionally, the temporal consistency of daily meditation behavior was calculated as the entropy of the timing of app usage sessions. Linear regression was used to examine the effect of time of day preference and temporal consistency on two outcomes: (1) short-term engagement, defined as the number of meditation sessions completed within the sixth and seventh month of a user’s account, and (2) long-term use, defined as the days until a user’s last observed meditation session. Results: Large reductions in the probability of meditation at any time of day were seen over the first 180 days after creating an account, but this effect was smallest for morning meditation sessions (63.4% reduction vs reductions ranging from 67.8% to 74.5% for other times). A greater proportion of meditation in the morning was also significantly associated with better short-term engagement (regression coefficient B=2.76, P\u3c.001) and long-term use (B=50.6, P\u3c.001). The opposite was true for late-night meditation sessions (short-term: B=–2.06, P\u3c.001; long-term: B=–51.7, P=.001). Significant relationships were not found for midday sessions (any outcome) or for evening sessions when examining long-term use. Additionally, temporal consistency in the performance of morning meditation sessions was associated with better short-term engagement (B=–1.64, P\u3c.001) but worse long-term use (B=55.8, P\u3c.001). Similar-sized temporal consistency effects were found for all other time windows. Conclusions: Meditating in the morning was associated with higher rates of maintaining a meditation practice with the app. This is consistent with findings from other studies that have hypothesized that the strength of existing morning routines and circadian rhythms may make the morning an ideal time to build new habits. In the long term, less temporal consistency in meditation sessions was associated with more persistent app use, suggesting there are benefits from maintaining flexibility in behavior performance. These findings improve our understanding of how to promote enduring healthy lifestyles and can inform the design of mHealth strategies for maintaining behavior changes

Spatial Frequency Implications for Global and Local Processing in Autistic Children

Visual processing in humans is done by integrating and updating multiple streams of global and local sensory input. Interaction between these two systems can be disrupted in individuals with ASD and other learning disabilities. When this integration is not done smoothly, it becomes difficult to see the “big picture”, which has been found to have implications on emotion recognition, social skills, and conversation skills. An example of this phenomenon is local interference, which is when local details are prioritized over the global features. Previous research in this field has aimed to decrease local interference by developing and evaluating a filter to help direct ASD patients towards normative processing of the global features in images. Within this process, this research focuses on whether an image’s spatial frequency was affected by the filter and how spatial frequency impacted the filter’s functionality. Spatial frequency can be defined as a measure of the periodic distribution of light versus dark in image. In this work, we isolated “hot spots”, which are areas in the image where the eye gaze of normative individuals fixated. Using the OpenCV package in Python, I implemented an algorithm to detect hotspots and draw a contour around each one. I then drew rectangles around the contours in each image and calculated the spatial frequency within each rectangle. Statistical analysis will reveal whether the spatial frequency of hot spots had an impact on the differences in normative and ASD fixations. We plan to use these findings to improve the image filter and conduct further research in this field

The Effects of Health-Promoting Signs Encouraging Stair Use in Parking Structures

This research study aims to promote physical activity by encouraging stair use rather than elevators using persuasive point-of-choice prompts. The current investigation is comprised of two sub-studies: pilot testing, which we have completed; and the main study, which will be conducted in the fall. While most studies in this area use observation to count pedestrian traffic, a novel component of the current research is that we will use a pressure mat to measure stair and elevator use. As such, before completing the main study, we completed two pilot studies to test the feasibility of the mat technology and the messaging of the persuasive prompts. We researched the type of mat that would be ideal for recording pedestrian traffic and considered features such as wired/wireless, battery-powered, open-switch, minimal threshold activation, and high-frequency recording. We completed various trials to determine the validity and accuracy of the mats in different settings and situations and the results of this analysis will be discussed. Additionally, the results from the survey conducted to evaluate potential messages on the point-of-choice prompts will also be detailed. Within this survey, message categories were narrowed to motivational/encouraging and nudging/humor messages. The survey was administered in Chapman University classes and students’ responses to several sample messages in each of these categories were compared via a series of six, semantic differential adjectives, rated on seven-point scales. Qualitative feedback on an open-ended question after each message was also solicited. The highest rated messages will be used in the main study. There are three key components to the main study, which we will complete in the fall: 1) objectively measure stair and elevator use for two weeks; 2) introduce point-of-choice prompts and measure stair and elevator use for two weeks; 3) remove signs and continue recording for an additional two weeks to observe potential effects

Burstiness and Stochasticity in the Malleability of Physical Activity

This study examined whether patterns of self-organization in physical activity (PA) predicted long-term success in a yearlong PA intervention. Increased moderate to vigorous PA (MVPA) was targeted in insufficiently active adults (N = 512) via goal setting and financial reinforcement. The degree to which inverse power law distributions, which are reflective of self-organization, summarized (a) daily MVPA and (b) time elapsed between meeting daily goals (goal attainment interresponse times) was calculated. Goal attainment interresponse times were also used to calculate burstiness, the degree to which meeting daily goals clustered in time. Inverse power laws accurately summarized interresponse times, but not daily MVPA. For participants with higher levels of MVPA early in the study, burstiness in reaching goals was associated with long-term resistance to intervention, while stochasticity in meeting goals predicted receptiveness to intervention. These results suggest that burstiness may measure self-organizing resistance to change, while PA stochasticity could be a precondition for behavioral malleability