Determining the Impact of Emotive Intelligent Spaces

Overview

Many children (age 3-6) living in the Mountain West (MW) region face unique challenges that can affect their health and welfare, such as lower socioeconomic status, and limited access to healthcare and education. The proposed project aims to address those health and education gaps by improving children's self-regulation (i.e., the ability to control emotional and behavioral impulses), a critical cognitive skill that underpins future mental health and academic achievement. The project will test the effectiveness of an innovative intervention mechanism, the Emotive Intelligent Space (EIS). The EIS consists of two adjacent 3 x 5 sq. ft. wooden wall panels with colored LED lights, creating a 90-degree semi-private space. The adaptable colored lightings are controlled by a machine learning algorithm that is developed based on a co-investigator's prior study. The EIS harnesses the power of artificial intelligence to detect children's emotions from physiological data in real-time and to translate physiological signals into environmental changes (i.e., adaptable colored lighting) that adequately respond to children's emotions, resulting in improved self-regulation, physiological stress responses, and cognitive performance. The objective of this proposal is to determine the effect of EIS on children's (age 3-6) self-regulation, physiological, and cognitive outcomes by employing a repeated ABAB experimental design (A = no intervention, B = EIS intervention). The hypothesis is that EIS will positively impact children's self-regulation, physiological stress response, and cognitive performance. Based on a priori power analysis, 40 preschool and kindergarten children will be recruited from early childhood programs in the rural areas near Moscow, ID. During the experiment, children will be assessed under a combination of A and B conditions. A digital wristband will capture children's real-time physiological responses (i.e., Galvanic skin response, body temperature, and blood volume pulse). A machine learning algorithm will immediately translate the physiological data into three basic emotions (i.e., happy, angry/fearful, sad) represented by children's choice of colors on the EIS. A series of ANCOVA analyses will be used to determine the mean differences in self-regulation, physiological, and cognitive scores under baseline and treatment conditions.

Specific Aims The percentage of young children living in the Mountain West (MW) rural areas is higher than the national average, placing them at a greater risk of poorer health and education outcomes. The proposed project aims to improve those young children's self-regulation - a critical cognitive skill that underpins future mental health and academic outcomes. Self-regulation has been studied as a promising treatment of various mental disorders such as depression and anxiety. A longitudinal study indicates a strong association between kindergarten children's self-regulation skill and their inhibitory control two decades later (inhibitory control was indicated by substance abuse, crimes, and mental disorders). The lack of inhibitory control is a prominent issue among adults in the MW region, as suggested by a nation-wide longitudinal study, with several MW states' average scores ranked near the bottom in the U.S. 7(e.g., Idaho#37, Wyoming#46, Nevada#50). Research also linked the early self-regulation skill to long-term academic achievement, as shown in several early literacy and mathematics intervention studies, further emphasizing an urgent need for interventions targeting self-regulation during a developmentally sensitive age period (age 3-6). To reduce rural children's health and education disparities, we propose to test the effectiveness of an innovative intervention mechanism, Emotive Intelligent Space (EIS), on children's self-regulation, physiological, and cognitive outcomes. The EIS consists of two 3 x 5 sq. ft. wooden wall panels with LED lights, creating a 90-degree semi-private space. The adaptable colored lightings are controlled by a machine learning algorithm that is developed based on a co-investigator's published prior research11. The EIS harnesses the power of artificial intelligence to detect children's emotions from physiological data collected by a digital wristband in real-time and to translate these data into environmental changes (i.e., adaptable colored lighting) that adequately respond to children's emotions12,13. The colored lighting on the EIS is a developmentally appropriate intervention mechanism for young children (age 3-6) for their non-invasive, adaptive, responsive, and low-cost design. The purpose of this proposal is to determine the effect of the EIS on children's behavioral, physiological, and cognitive outcomes using a quasi-experimental design. Forty children in rural areas near Moscow, ID will be recruited via existing collaborative relationships such as U of I research extension programs and Rural Sociology and Economics programs. The EIS could potentially alleviate many challenges facing rural families with young children (e.g., limited access to affordable, quality health and education resources). In the specific aims listed below, children's age, gender, and socioeconomic status (SES) will be included as covariates. Specific Aim 1: To determine whether EIS influences children's self-regulation. We hypothesize that children will demonstrate better self-regulation under the EIS condition than under the baseline condition. Specific Aim 2: To determine whether EIS impacts children's physiological responses. We hypothesize that children under the EIS condition will exhibit reduced stress responses (e.g., Improved galvanic skin response, body temperature, and blood volume pulse), as compared to their baseline scores. Specific Aim 3: To determine whether EIS affects children's cognitive performance. We hypothesize that children under the EIS color treatment will have better cognitive performance scores than their scores under the baseline condition. Research Strategy Approach Participants. Based on the priori power analysis (described in a later section), we will recruit 40 typically developing children (3-6 years old) from local early childhood programs in the rural areas near Moscow, ID, where the University of Idaho (U of I) is located. Preliminary testing will be conducted with the children in the Child Development Lab that is affiliated with the PI's home department. All participants will undergo a color deficiency assessment (i.e., Ishihara Color Vision Test) to determine their eligibility. Setting. The study will take place in the Ramsay Research Suite in the Niccolls Building on the U of I main campus. The EIS will be securely installed in a corner of the research lab that is 20 feet 6 inches wide by 45 feet long with a ceiling height of 9 feet 9 inches. The room has three windows on the east side, and the window blinds are always closed to maintain the optimal temperature. There are four round table and four chairs in the lab's performance area. The lab has white walls, vinyl flooring, and dim fluorescent lightings. The EIS, our intervention mechanism, consists of two double-layer wooden panels. Each panel is 3 feet wide by 5 feet high and made from double layers of wood that allow colored LED lights to diffuse evenly through the panels. The EIS will be placed on the left and front of participants, creating a 90-degree semi-private space. Research Method. A reversal within-group design (i.e., ABAB) method will be used. During the first phase, A, a baseline and control will be established for the dependent variables - the level of child behavior under the natural lighting before the intervention is introduced. If the dependent variables (i.e., self-regulation, physiological responses, and cognitive performance) changed with the introduction of the intervention and then returned to the baseline scores with the removal of the intervention, we can conclude that there is strong evidence of a treatment effect. The ABAB design greatly increases the internal validity of this project. Procedures. Participating parents will contact the research team to schedule an initial meeting (Day 1) where they will complete five tasks: 1) Researchers will explain the research procedure. 2) The participating children's parents will sign informed consent forms and complete a demographic survey. 3) Each child will undergo a color deficiency test to determine their eligibility for the study. Eligible participants are typically developing children without color deficiencies. 4) Eligible participants will select colors to represent three basic emotions (sad, happy, and angry/fearful) among randomly arranged six colored cards (red 5R 7/8, yellow 5Y 9/8, green 5G 7/8, blue 5B 6/8, purple 5P 7/8, and white N 9.5) representing the major color families in the Munsell color system, which was used in a co-investigator's previous study36. Each child's preferred colors will be used in their treatment conditions. 5) Parents will schedule another appointment for the experiment. On the experiment day (Day 2), a trained research assistant will first obtain the participating child's verbal assent. The research assistant will then lead the child to the research lab, describe the protocol, and ask the child to wear a digital wristband and practice tasks while wearing the wristband. At the beginning of each experiment condition, the child will be asked to describe their current emotional status using a Q-sort (a chart with faces representing different emotions) to triangulate EIS emotion interpretation data. To establish the baseline, children complete tasks (Head-Toes-Knees-Shoulders \[HTKS\] form A and Woodcock-Johnson-4 \[WJ-4\] cognitive assessment form A), which will take 5-7 minutes. The HTKS task will be video recorded and used for self-regulation coding. The child will then go to a different room to rest for 5 minutes while the research assistant stages the treatment condition (i.e., program the child preferred colors in the computer). For the next treatment condition, children will return to the lab after a brief rest period and complete a parallel version of the two tasks completed under the baseline condition (i.e., HTKS and WJ-4 form Bs; to mitigate threats to internal validity) with the presence of EIS. Each child will repeat the same procedure with another set of baseline and intervention conditions. The entire data collection session will last 30 to 40 minutes. Light snacks will be available on-site for the children during rest periods. Participants will receive a $40-dollar gift-card and books/toys as incentives. Measures. The impact of EIS on children's self-regulation and cognitive performance will be assessed using multiple measures. Three outcomes of this study include children's self-regulation, physiological responses, and cognitive performance. Each outcome will be measured under the baseline and treatment conditions. These measurements are discussed under each specific aim as follows. Aim 1. To determine whether EIS influences children's self-regulation, controlling for gender, age, and socioeconomic status (SES). The outcome of Aim 1 (self-regulation) will be measured by the HTKS task, which assesses a child's self-regulation. This task requires cognitive skills such as inhibitory control, attention, and working memory. Children will be asked to play a game where they must do the opposite of what the experimenter says. It is a well-validated and reliable measure of self-regulation. Aim 2. To determine whether EIS impacts children's physiological responses, controlling for age, gender, and SES. The outcome of Aim 2 (physiological response) will be measured by E4 Wristbands. Physiological data (i.e., Galvanic skin response, body temperature, blood volume pulse) are gathered in real-time with a medical-grade wearable device (i.e., E4 wristbands). Aim 3. To determine whether EIS affects children's cognitive performance, controlling for age, gender, and SES. The outcome of Aim 3 (cognitive performance) will be measured by WJ-4 - Number Reversed subset. WJ-4 is a brief, age-appropriate, and well-validated cognitive ability assessment that evaluates young children's ability to retrieve and retain information required for ongoing cognitive processes. Covariates (i.e., children's age, gender, and SES) will be measured with a demographic questionnaire. SES will be calculated based on household annual income and parents' highest education level. The composite SES score will be calculated as the unweighted sum of the z scores, ranging from -3.02 to 18. Statistical Analysis. We will compare physiological, self-regulation, and cognitive mean scores under baseline and intervention conditions using repeated-measures ANCOVA. We will test the null hypothesis that there is no difference in the dependent variables between time points 1-2 (A-B), 2-3 (B-A) and 3-4 (A-B) after controlling for the covariates (i.e., children's gender, age, and SES).