Coronary artery disease is the leading cause of heart disease, and while it can be assessed through transthoracic Doppler echocardiography (TTDE) by observing changes in coronary flow, manual analysis of TTDE is time consuming and subject to bias. In a previous study, a program was created to automatically analyze coronary flow patterns by parsing Doppler videos into a single continuous image, binarizing and separating the image into cardiac cycles, and extracting data values from each of these cycles. The program significantly reduced variability and time to complete TTDE analysis, but some obstacles such as interfering noise and varying video sizes left room to increase the program’s accuracy. The goal of this current study was to refine the existing automation algorithm and heuristics by (1) moving the program to a Python environment, (2) increasing the program’s ability to handle challenging cases and video variations, and (3) removing unrepresentative cardiac cycles from the final data set. With this improved analysis, examiners can use the automatic program to easily and accurately identify the early signs of serious heart diseases. CMD is a nonobstructive coronary artery disease, meaning that although there is no physical blockage, oxygenated blood is unable to move through smaller blood vessels at an adequate rate to maintain physiological demand3. This condition has been shown to be strongly associated with diabetes, and when paired with myocardial ischemia and myocardial diseases it is referred to as nonobstructive coronary artery disease (INOCA). CMD is one of the earliest signs of heart disease which can lead to myocardial infarction, heart failure, and/or stroke2. Functional, structural, and biomechanical deficits in coronary resistance microvessels (CRMs) are associated with CMD and are indicators that can be observed before the appearance of symptoms such as atherosclerosis (blockages in the arteries)2. With early and accurate identification of CMD, more serious and life-threatening cardiac conditions can be treated and prevented before they become deleterious.Coronary microvascular disease (CMD) is a heart condition affecting the smaller blood vessels that branch off from the main coronary arteries. Impairments in the coronary microcirculation disrupt the healthy regulation of myocardial blood flow and nutrient exchange4. Transthoracic Doppler echocardiography (TTDE) is an affordable and non-invasive method used to assess cardiovascular function through direct measurements of coronary blood flow (CBF), with potential to assess CMD. CBF is measured from one of the main coronary arteries under both baseline and stress (hyperemic) conditions, and this yields uniquely characteristic flow patterns in which diastole predominates and which can be analyzed to indicate impaired CBF5. For example, coronary flow velocity reserve (CFVR) is indicative of the amount of additional blood flow that the microvasculature can carry under stress; CFVR is lower in cases of coronary artery disease, even in otherwise asymptomatic subjects or in patients with INOCA6. This change in CBF likely represents a combination of functional, structural, and biomechanical impairments1. For example, previous studies by our laboratory observed inward hypertrophic remodeling associated with reduced CBF. This structural remodeling occurred before occlusive macrovascular atherosclerosis, which emphasizes the importance of early examination of coronary microcirculation7. However, manual analysis of TTDE CBF can be time consuming and subject to both intra-rater and inter-rater bias8. To resolve these issues, our groups began developing a MATLAB program to automatically extract data values from coronary flow patterns of TTDE video files9.While indirect methods to diagnose CMD are available, they are fraught with subjectivity. Positron emission tomography (PET) and magnetic resonance imaging (MRI) offer value in identifying impairments in cardiac perfusions, but currently include no direct measures to diagnose CMD9. While the accuracy of the simple regression model is comparable to human evaluators, the parameter variability suggested that with continued testing and program adjustments, automatic analysis of TTDEs could become increasingly more accurate and capable of processing challenging videos.For each cardiac cycle in the TTDE flow pattern, the original MATLAB program automatically extracted several parameters including the peak velocity and velocity time integral, which are commonly used to quantify coronary health. CFVR was then calculated as the average peak hyperemic velocity divided by the average peak baseline velocity. When analyzing 98 baseline files and 117 hyperemic files both manually and with the automatic program, linear regression analysis showed significantly reduced variability when using automatic analysis, and the time to analyze videos was reduced from 1500 to 50 min. However, agreement between manual and automatic parameter output ranged from less than 1% difference to over 55% difference for certain variablesExtensive testing identified several potential areas in which improved analysis was possible, including the removal of interfering noise, the identification and analysis of fainter cardiac cycles, and the verification of peak selection in the ECG region. The original program was also limited to a single video height and width in pixels, which excluded the analysis of many Doppler videos. In this study, we present the results of an effort to improve the accuracy of the first-generation program through development of several key areas of analysis. The original program was developed in MATLAB but was recapitulated in Python in order to leverage OpenCV for computer vision and Google’s TensorFlow for downstream machine learning. Therefore, we have implemented an updated and improved program for extracting cardiac Doppler parameters from the Doppler videos using Python and a variety of best-in-class open-source Python libraries for image and signal processing. This approach allows easier distribution and community maintenance of the software, and it enabled us to address several of the data-processing limitations inherent in the MATLAB version as well. As Python is also an industry standard for machine learning development, changing to that environment allows us to leverage innovations in machine learning from both academia and industry much faster going forward.We hypothesized that the use of OpenCV and modified heuristics could better address the original program’s limitations, and that these refinements would produce a comprehensive and accurate method for examiners to classify coronary flow issues through interpretation of CFVR values and other patterns in parameter output. These improvements allow examiners to take advantage of the speed and consistency offered by automated analysis without sacrificing diagnostic accuracy in assessing coronary diseases. A diagram of the conceptual blocks and logic flow of the new Python version of the software is shown in Fig. 10. Doppler readings were measured at 1% isoflurane (baseline) and 3% isoflurane (hyperemia), and all measurements were taken from the left main coronary artery of the mice as previously described10. These videos were exported as .avi files from the VevoLab 3.1.1 software and analyzed offline using the improved program. Mice were housed under a 12-h light/dark cycle at 22 °C and 60% humidity. They were allowed ad libitum access to water and were fed standard laboratory mice chow. This study was conducted in accordance with National Institutes of Health Guidelines and was approved by the Institutional Animal Care and Use Committee at the Abigail Wexner Research Institute at Nationwide Children’s Hospital.TTDE video files with approximately 20 distinct heart beats each were acquired from 12-week, 16-week, and 36-week old normal Db/db and type 2 diabetic (T2DM) db/db mice (Jackson Laboratories) at both baseline and hyperemic (high flow) conditionsThe improved program was written in Python, and utilized the following libraries: sys, cv2 (OpenCV), PIL, scipy, skimage, matplotlib, tkinter (Tk), pandas, and numpy. Initial data processing began with prompts to select the folder containing the video files to be analyzed, input a name for the output excel file, select the type of analysis as ‘Doppler’ or ‘Combined’ , and finally to select each video file to be analyzed. The new interface expanded on the functionality of the original program by allowing more than one baseline and/or hyperemic video file to be selected for analysis in each run as well as by accepting videos with any pixel height and width. The user was then prompted to enter the peak velocity value in mm/s on the Doppler window’s scale for each video, as well as the probe angle and minimum/maximum penetration in mm from the B Mode window if combined analysis was selected.Once all parameters had been entered, the program parsed each video by inspecting the difference between subsequent video frames to identify frames where the scroll bar reset from the right to the left side of the Doppler window. These frames were concatenated into a single continuous image which was then cropped to the region of interest containing the coronary flow pattern and electrocardiogram (ECG) recording. A Gaussian filter was applied, the image was dilated with a linear structuring element, and then a global threshold value was calculated using OpenCV and Otsu’s method for image binarization. A representative binarized image is depicted in Fig. 9. All parameters were output to a Microsoft Excel file in .xlsx format.The calculated threshold value was adjusted by the user in a ‘Scroll Test’ window, where the threshold was incremented or decremented to visually inspect how the level of filtering would affect the amount of data captured in the binarized Doppler image. Increasing the threshold value removed additional noise, while decreasing the threshold expanded the included envelope. Once the threshold value was verified, the program removed any remaining noise and filled small holes in the image using OpenCV. The updated program applied more complete noise removal by identifying all contours in the image and removing any objects that were not within ten pixels of the horizontal baseline. This corrected for any small noise objects and was especially useful in removing any ‘top noise’ located at the top of the Doppler window. Next, the program split the Doppler region into cardiac cycles by identifying peaks in the corresponding ECG pattern. Peaks were initially identified using scipy, and then an added check compared the distance between each peak to remove extra peaks that were too close together and to fill larger empty gaps with estimated peak locations. Finally, the program extracted the following parameters from each cardiac cycle: peak velocity, diastolic velocity, decay velocity, systolic rise time, diastolic rise time, diastolic decay time, systolic slope, diastolic slope, decay slope, heart rate, and velocity time integral. These were the same parameters extracted in the original MATLAB programThe program generated an image of the coronary flow pattern with diastolic velocity (indicating the beginning of the diastolic phase), peak velocity (maximum velocity for each cycle), decay velocity (point at which acceleration switches signs closest to peak diastolic deceleration), peak diastolic deceleration (minimum acceleration), and end of cycle (correlating with the peaks found in the ECG region) indicated with green, yellow, pink, red, and blue points, respectively. An example of this image is depicted in Fig. 10. The algorithm began by masking the first frames of both the color mode and corresponding Doppler videos and identifying the borders of the B mode window and the center lines indicating where vessel measurements were taken from. For each video frame of the color mode file, these values were used to crop to a region around the center location before rotating the image based on the angle of the probe. The corresponding length of each pixel in mm was calibrated from the minimum and maximum probe depths entered by the user at the beginning of video analysis. The program then masked the image and identified any contours, and if the contour was large enough and in the correct location to exclude noise or ventricle filling, the diameter of the identified vessel was then calculated by finding the average distance between the left and right vessel walls of the object. The program output the minimum, maximum, mean, median, mode, and standard deviation of all diameters for each analyzed video. An example of a measured vessel is shown in Fig. The program also included an option for analysis of coronary diameters B-mode color videos, which is required to calculate CBFA collection of 18 Doppler video sets evenly distributed between 12-, 16-, and 36- week old healthy and diabetic mice were processed with both the original MATLAB program and the improved Python program. All tests were performed on the same computer by a single tester who entered in any prompted values and adjusted the threshold value for binarization as needed to fully capture the coronary flow pattern without including noise. Each video set included one baseline and one hyperemic video, and the Python program also analyzed the corresponding color mode videos acquired at baseline and hyperemic conditions. Videos were intentionally selected by the tester through visual inspection of video files in order to demonstrate a wide range of processing difficulty, from videos containing distinct Doppler regions with little noise to videos that the MATLAB program struggled to handle. Some challenging patterns included interfering noise or ‘top noise’ descending from the top of the Doppler image, poor contrast between background noise and the Doppler signal, and inconsistent ECG readings that led to the incorrect separation of cardiac cycles. Testing with the improved program could then demonstrate through specific examples that modified heuristics were better able to handle challenging videos, while videos with clearer signals that had already been fully captured by the original program continued to generate similar data.10:The table of parameters for each cardiac cycle generated by the two programs were saved to a Microsoft Excel file, and for each parameter the mean and standard deviation (SD) across all cycles were calculated. With color mode analysis included, CBF could be calculated using the equation as previously described by usp < 0.05 was then performed to compare the average peak velocity values and CBF. The calculated data was then categorized into groups based on the obstacles present in the video for comparison and evaluation of the improved program’s effectiveness.The percent difference between the MATLAB and Python average values and standard deviations were then calculated for each parameter. The percent difference was a useful statistic to uniformly evaluate the change in values between MATLAB and Python program analysis as opposed to the numerical change which varied based on the maximum velocity of each individual video’s scale. An f-test was performed to compare the peak velocity values of the two data sets and to determine if the variances of the sets were equal. Finally, a t-test  with a significance level of https://zenodo.org/record/6308961#.Yh0Uk99OlE4.The software described in this study may be downloaded anonymously for non-commercial use from the following repository: Overall, standard deviation decreased from the MATLAB program to the Python program Table . Standarp values indicated statistical significance when the Python program made significant improvements to the video’s analysis, such as through removal of top noise, extraction of cycles that were missed in the original analysis, or removal of unrepresentative peaks from the final data set. For cases where the original analysis was accurate, the p values suggested that the two data sets were equal. In Table p value of 0.20, which did not indicate significance between the peak velocity values of the two programs. On the other hand, baseline videos that had several cardiac cycles that were not fully captured by the MATLAB program but which were correctly analyzed by the Python program had an average p value of 0.004, which did indicate significant differences. Videos with top noise and ECG inaccuracies saw similarly lower p values.When examining the two tailed t-tests performed between the peak velocity values of the MATLAB and Python programs, Average peak velocity and VTI values tended to increase when using the new algorithm, with the exception of videos where noise at the top of the Doppler region had been captured by the MATLAB program. The change in each individual baseline video’s peak velocity from the MATLAB to the Python program is displayed in Fig. Several examples of the specific changes that contributed to overall performance improvement are investigated in the rest of this section. Removal of interfering top noise from the Doppler envelope made it possible for the improved program to capture the correct cardiac velocities. The program also added checks to verify ECG peak values so that cardiac cycles weren’t skipped or broken into multiple sections. Finally, the program fully captured fainter cardiac cycles that had been previously overlooked and removed unrepresentative cycles from consideration, both of which were changes that decreased standard deviation and increased average peak velocity and VTI values. When making comparisons, peak velocity and VTI values were selected as the parameters for analysis because they are most representative of the analyzed Doppler region and are the values most commonly utilized in clinical practice.Many of the videos analyzed in this data set demonstrated the Python program’s ability to identify and remove top noise from the binarized image. To accomplish this, the new algorithm added steps to eliminate any large areas of noise which weren’t close to the baseline of the image. For example, the representative baseline and hyperemic videos displayed in Fig. Removing top noise to more accurately capture the correct velocity values often produced a decrease in average values for the Python program’s values as velocities were no longer forced to the top of the video. On average, baseline videos with top noise had a 95.6% ± 27.5% decrease in average peak velocity values, and hyperemic videos had a 10.4% ± 9.0% decrease in average VTI values  they were removed from the final data set. Removing this information helped to produce more uniform results by not taking into consideration either incorrectly analyzed cardiac cycles or cycles that may have been correctly captured but were not representative of the Doppler region’s overall trends. For example, in Fig. Removing the unrepresentative cardiac cycles from this representative video resulted in a 102.21% decrease in standard deviation for the peak velocity and a 57.53% decrease in standard deviation for the VTI. The average peak velocity then increased by 16.8%, and VTI increased by 17.05%. For this analysis, videos in which unrepresentative cycles were removed were grouped with videos which improved identification of fainter peaks because both modifications accomplished the common purpose of removing inaccurate lower values from analysis. Because of this, both adaptations saw a similar increase in peak velocity and VTI values and decrease in standard deviation values.Additional color mode baseline and hyperemic files analyzed with each set of Doppler videos produced vessel diameters which could be used to calculate blood flow through the measured vessel. Baseline videos identified an average of 19 video frames containing vessels for analysis, and hyperemic videos averaged 51 frames analyzed for vessel diameters. Vessel diameters increased by an average of 34.37% from baseline to hyperemic conditions, which correlates with the increased stimulation of blood flow in the vessel. The new color mode analysis algorithm also generated an image for each video frame containing a measured vessel, and these images could then be inspected for accurate identification.11, the new program was able to resolve significant reductions in CBF in T2DM db/db mice at 12, 16, and 36 weeks of age. Importantly, coronary flow reserve (CFR) was also impaired in db/db mice at 16 and 36 weeks of age compared to normal.With the inclusion of vessel diameter calculations, coronary blood flow could also be calculated Table . In agre9. Here, we present improvements to the original program. This improved program took advantage of OpenCV and other Python libraries, and with improved heuristics was able to handle a larger scope of data inputs and accurately analyze more challenging Doppler videos.The early identification of CMD has the potential to allow for the early identification and potential prevention of more serious heart problems such as myocardial ischemia, atherosclerosis, and heart failure. TTDE is an effective and non-invasive method used to assess coronary flow by observing coronary flow patterns, and automatic analysis of coronary blood flow was demonstrated in a previous study by this laboratory to reduce the time required for analysis and the bias typical of manually-analyzed TTDE filesThis study aimed to refactor the original program, transition to a Python environment for use of the OpenCV and Tensorflow libraries, and to add additional checks and improvements devised from use of the original program in order to increase analysis accuracy and more effectively handle difficult cases. The new code improves handling of interference from top noise, validates identification of ECG peaks, correctly estimates parameters from fainter peaks, and rejects unrepresentative data. In addition, the program functionality was expanded by accepting videos of any pixel height and width and allowing multiple baseline and hyperemic videos to be analyzed in one run.One major advantage of moving the program to Python was the use of OpenCV for video processing and image analysis. The MATLAB program interpreted each video frame as a cell array of pixel values and analyzed the images to identify the horizontal baseline position, regions of interest to crop to, and threshold values for binarization. The Python program utilized functions of the OpenCV library to accomplish these steps, as well as for grayscale conversion and for applying a gaussian filter and dilation to the Doppler region before calculating the binarization threshold. The findContours function was especially useful in adding modified heuristics to identify top noise and other noise objects that needed to be removed.The Python program took advantage of several other Python libraries for specific analysis steps; numpy was used for array manipulation and mathematical calculations as parsed images were treated as arrays of pixel values, tkinter was used to create GUIs for user interaction, and matplotlib was used to plot critical values on the images of the coronary flow pattern that were saved from each processed video. As future developments are added, the Python environment will be able to utilize TensorFlow, scikit-learn, and other libraries for further data analysis and machine learning algorithms.The data and examples provided specifically demonstrate the program’s ability to remove top noise, to improve identification of peak ECG values, to better capture fainter cardiac cycles, and to remove unrepresentative cardiac cycles from analysis. Overall, this resulted in decreased standard deviations from the original to the improved program. This decrease in standard deviation indicates a more uniform analysis of each cardiac cycle and the proper removal of inaccurate cycles. Increased average peak velocities and VTI values in cases except those dealing with top noise interference also demonstrate the program’s improved analysis as unrepresentative cycles were removed and fainter peaks that had previously been only partially captured were fully analyzed.Performing open software development strengthens the research community. Any research group can either contribute to this project to improve the software or they are free to develop a different tool using our work as a foundation. Open software due to its transparency also increases reproducibility in research. The software can be directly examined without any delay should a specific need to know arise when assessing research that uses the software. As both our software and Python are free, we believe this also adds to the portability and potential impact of our work.12. The program analyzed the Doppler region in frames containing three heartbeats at a time, with each frame taking between 10 and 40 s to analyze, and calculated a subset of the parameters found in in this study; peak diastolic velocity, peak diastolic acceleration, beginning diastolic phase, peak systolic velocity, and peak diastolic deceleration. When analyzing 200 videos from 100 patients, linear regression indicated strong correlation to manual analysis in PSV  and PDV .A handful of similar programs have been developed to use automated analysis to reduce processing time and parameter variability. Many of these programs rely on partial-automation combined with expert analysis to enhance accuracy without removing manual intervention. For example, a program developed in MATLAB was used to crop video frames to the region of interest containing the Doppler envelope and apply a binarization threshold adjusted by the user, similar to the verification included in this current study’s algorithm13. The program tested Doppler strips of several heartbeats at a time and followed a similar procedure of cropping to the region of interest, binarizing the image to capture the Doppler envelope, and extracting critical values from each cardiac cycle. The program was advantageous in that it also didn’t rely on QRS complex peaks in the ECG region to divide the Doppler region into cardiac cycles, but instead relied only on the Doppler data to separate cycles. However, the program did not account for some of the added heuristics implemented in this current study, such as discarding unrepresentative peaks from consideration and removing top noise from the Doppler envelope. Due to these challenges, when analyzing heartbeats from 18 patients through 9 manual and 1 automatic analysis, the automated measurements were outside the range of manual values 9.5% of the time for VTI values and 3.9% of the time for peak velocity values. However, overall this program displayed strong correspondence in identified VTI and PV values to expert analysis, and saw a tenfold reduction in time for analysis, as opposed to 30-fold reduction seen by the programs in this current study.A similar study focused on removing all manual intervention from Doppler aortic flow analysis in order to minimize bias and analysis timeRemoving user interaction in favor of more computer automation would help to increase consistency, especially in identification of the correct threshold level for image binarization. However balancing user interactivity with complete automation is necessary for evaluators to adjust for errors and special cases, so allowing for a manual adjustment of the threshold value for image binarization is most effective. The option to adjust the binarization threshold is a critical element that needs to remain in order for a trained expert in coronary flow to assess the suitability of the pattern moving in and out of view of the Doppler during the cardiac cycle—a phenomenon that’s difficult to automate.Comparison of the data values and plots generated from the original MATLAB and improved Python programs serve to demonstrate the increased accuracy of the updated algorithm to automatically measure CBF, specifically its ability to process a wider range of video sizes, special cases, and inaccurate readings that the original program did not have checks to handle. The improved program is able to remove top noise and other large noise artifacts, to verify the correct identification of ECG peaks, to better capture fainter peaks in the Doppler region, and to remove unrepresentative values from the final set of parameters. The program accepts any video pixel height and width and allows for the analysis of more than one baseline and hyperemic video at a time. Videos that had already been accurately analyzed by the MATLAB program continued to output similar data values, while videos that were corrected showed decreased standard deviation and increased peak velocity and VTI values. Finally, the improved program was able to automatically resolve differences in CBF in a mouse model in which we’ve previously demonstrated impaired CBF. The program has achieved its goal of improving algorithm heuristics in order to better handle special cases, and can be used by examiners as an efficient, fast, and exact way to automatically analyze coronary Doppler echocardiograms.


The purpose of this study is multifold: First, to develop an educational program using artificial intelligence (AI) in middle school free semester system of South Korea. Second, to verify the program’s effectiveness, the study clarified the definition of AI and AI education and considered their meaning in technology education. This study used three steps: preparation, development, and improvement. In the preparation step, this study set the theme and purpose of the AI program and selected the free semester activity type “theme selection activity.” After analyzing the technology curriculum and extracting AI-related elements in the development step, this study laid out the program for 16 h of class time. In the improvement step, to augment the program’s validity, the researcher revised and supplemented it as a whole through expert consultation. This research differentiated the developed program from the AI education program of other subjects and specialized it, focusing on the specificity of technology education. The study emphasized the social impact of the latest technology, ethics of AI, physical computing using AI, and technological problem-solving activity using AI. The final developed program was applied to the students, and students participated in a pretest and posttest. The study used the PATT and AI competency test tools. The PATT results showed a significant increase in the mean of both constructs in “interest in technology” and “career aspirations in technology.” In AI competency, the mean of two constructs increased significantly in “social impact of AI” and “AI performance.” In particular, “AI performance” showed the largest increase. There was no statistically significant change in “interaction with AI.” The study results confirmed that the developed AI program was effective in technology education and career exploration, which is the primary purpose of the free semester. In addition, it was possible to confirm the technology educational value of the AI education program centered on technological problem-solving. These research results have implications for bringing AI into technology education. As the field of education emphasizes career education, each provincial education office tries to develop students’ dreams and talents through various programs and teaching–learning methods in Republic of Korea. Career experience is implemented from the elementary school level while high schools aim to deepen career education. Meanwhile, in middle schools, the Ministry of Education implemented a nationwide free semester and free year system (from now on “free semester”) in 2016  Artificial Intelligence Lab and made significant contributions to the development of artificial intelligence. Among the latest artificial intelligence technologies, “perceptron (perception + neuron),” the core of neural network algorithms, was first mentioned by Frank Rosenblatt in 1958  proposed “Computer Science For K-12 Standards” in the United States. Furthermore, the CSTA formed a joint council for AI education in 2018 with the Association for the Advancement of Artificial Intelligence (AAAI). After that, CSTA and AAAI created the Artificial Intelligence for K-12 Standards (AI4K12), proposing five big ideas for AI education, and are making efforts in AI education  education  were real-time interactive video classes. The study applied the AI education program to 23 middle school students who participated in the experiment, conducting “technology attitude” and “AI competency” tests. The study used the experimental method of a single group pretest and posttest.The study implemented the program as a free-semester theme selection activity, allowing students to select it in the same way as the general free-semester program selection method. Although each school's situation is different, students generally have four theme selection programs to choose from depending on their career path, and this program was chosen as one of them by the participants. Before the program started, the researcher informed the students and parents of the study’s research ethics. The Institutional Review Board (task management number 2021–73) reviewed and approved this study. There were a total of 23 volunteer participants. The students were all first graders (12 ~ 13 years old) attending the middle school free year system, and there were 19 male students and 4 female students, for a total of 23 students. The students have experience in SW education in elementary school practical subjects. Students had a learning experience (2-h class) about the impact of AI on society with the subject of future technology and social change in the technology class in the first year of middle school. Educators introduced information subjects in the second year, so students did not receive education on related content.This research conducted a pretest and posttest of a single group to investigate the study’s effect. The study performed quantitative tests through two instruments on “technology attitude” and “AI competency” before and after the program. After completing the program agreement, the researcher conducted a pretest before the program application and a posttest after the end of the program. The environment did not include the researcher ; thus, students completed the test remotely at any time from one to three days after the last class.The study piloted the test tool on 200 first-year middle school students to verify its reliability before application to study participants. As a result, the reliability of 19 items of The Pupils’ Attitude toward Technology (PATT) was 0.930. In the AI competency test that measured four constructs, the reliability of 12 items of three constructs was 0.812. Meanwhile, the reliability of one construct was lower than 0.7 (a characteristic of AI), so this study excluded it from test tool.The PATT tool, developed by Bame et al. , tests sThis study measured two constructs among the six constructs in the original instrument after the reviews of previous studies on PATT. This study measured “interest in technology” and “career aspirations in technology.” as shown in Table Previous studies related to attitudes towards technology showed that middle school students’ interest in technology subjects was not high. The degree of interest differed depending on whether there was an experiment or practice Kim, . In addiThis study used the AI competencies instrument by mixing two test tools related to AI. The first is an attitude test tool for AI for middle school students developed by Kim and Lee . The AI This study used an eight-item test tool with two constructs, “Social impact of AI” (four items) and “Interaction with AI” (four items) as shown in Table In addition to the two constructs of “AI attitude,” the researcher used the second test tool to measure “AI performance” in solving technology problems. This study used Choi’s  test devn < 30), the researcher performed a normalization test to examine normality according to the central limit theorem  curriculum. After that, the researcher subdivided the goal of the theme selection activity by each class, selecting and organizing the program contents according to student and teacher roles.Experts reviewed the program at the improvement stage, and the researcher revised and supplemented the program according to the expert review.As a technology, the trend of patent applications for the latest AI technology is increasing in all fields of industry  as shown in Table 4This study could extract elements related to AI directly or indirectly across a wide area of technology education. For example, in the technology system area, manufacturing technology includes content elements associated with AI such as “the fourth industrial revolution, system design and automation .” Physical computing, automation, etc., are not directly related to AI but can apply AI indirectly as a system to solve problems. Physical activities in the real world can motivate learners to explore and manufacture these systems.Using computers and AI can link information and communication technology in technology systems to “communication technology problem-solving activities.” In problem-solving activities, learners can experience machine learning. For example, a learner can recognize and learn an image or voice using sensors such as cameras or microphones. In addition, students can learn about data and information and ethical issues of AI with creative activities.In technology utilization, students can recognize the social impact of AI as a future technology along with occupations. Additionally, in connection with the technology system area, it is possible to find problems in that can be solved by the use of AI.After arranging the learning contents based on the program’s purpose, the researcher organized the learning content for the 16 h of instruction See Fig. , structuIn order to review the applicability and validity of the program developed in this study, the researcher asked for consulting from four teachers (educators) and one AI expert with expertise in technology education and convergence education. Table The researcher finally completed the program by modifying and supplementing the program based on the expert consultations. Figure In the first and second classes, the researcher added the activity of investigating occupations and social changes in the fourth industrial revolution. In addition, the program explained the origins of AI, emphasizing the technological aspect by introducing AI as the latest technology.Recently, the field of education mainly pursued the latest technology and did not try to deal with the impact of the latest technology on society. However, in the first and second classes, since this is a very familiar area in technology education, the emphasis was on preserving the uniqueness of technology education such as the social change affected by technology  like Scratch block coding.In the ninth to twelfth classes, the students studied remote control technology with Micro:bit (radio communication technology) in physical computing to learn communication technology elements. The students learned how to code the transmitter and receiver using Micro:bit and communicate with friends using radio waves, making it possible to design high-quality projects. In addition, the researcher reorganized the class to enable cooperative learning by adding “group composition and professional distributing roles.” The students were assigned the roles of “team leader,” “engineer,” and “recorder,” thus giving them a more professional experience and a sense of responsibility. Afterward, the students were given an unstructured package, and each team selected a project theme using AI. The program added interim presentations in the eleventh and twelfth classes to share ideas in the results presentation and the process. See Fig. Fig. 5MatIn the thirteenth to sixteenth classes, projects were carried out based on the theme chosen by each team. Various projects have been created, such as the AI that plays an OST when a character is censored, the AI that recognizes friends’ faces, and the AI that represent the famous K-drama. During the final presentation, ideas were shared based on the project portfolio. See Fig. Fig. 6Varp < 0.05) and − 2.907 (p < 0.01), respectively.The PATT test measured the attitude toward technology in two constructs, “interest in technology” and “career aspirations in technology,” finding a significant increase in both areas  and − 4.023 (p < 0.001), respectively, allowing meaningful observation. On the other hand, “interaction with AI” was − 1.196 (p ≧ 0.05), with a higher p-value than 0.05, which was not statistically significant as it had a high probability of significance.For AI competencies, the study measured three constructs, “social impact of AI,” “interaction with AI,” and “AI performance.” Cronbach’s α of the survey tool was 0.850 in the pretest and 0.866 in the posttest, demonstrating reliability.  Table The analysis made it possible to observe that the AI competencies improved after applying the program. In particular, AI performance showed the largest increase. It was possible to confirm that the program developed in this study improved the AI competencies after 16 h of instruction. Simultaneously, the experiment confirmed its value as an AI program.Because the current technology education lacks research on free semesters and AI education, this study developed an AI program for implementing free semester technology education in schools and analyzed its effectiveness. Technology in-service teachers have difficulty teaching free semester classes Kim, . In addiIn this study, starting from an awareness of the above problem, the research purpose was to develop an AI program for implementing in technology education. This study’s program development applied Mager and Beach’s  three stThe first class begins by dealing with the impact of the latest technology on society from various perspectives. Recently, the field of education mainly pursued the latest technology and did not try to deal with the impact of the latest technology on society. On the other hand, this is a very familiar area in technology education, and it can preserve the uniqueness of technology education. In addition to the learning topics of general machine learning programs, the program includes system design and automation  and communication technology. Although physical computing and communication technology are not directly related to AI, students could use them as tools to solve problems during technological problem-solving activities. On the other hand, the study attempted to expand the information and communication ethics in the previous information and communication technology by adding AI ethics. Although the emphasis on information and communication ethics was not high in domestic technology education, the researchers recognize and emphasize its importance as a new ethical issue in the AI era.The biggest difference between this AI program and other AI programs is the active sharing-based technology problem-solving activities. The students worked on technological problem-solving as a group activity, and the teacher emphasized cooperation inside and outside the group. Learners were able to share each other’s problem-solving ideas (including source code) from time to time through the web platform. In addition to the teacher’s assessment of the finished product, it was possible to give feedback on ideas sharing activities among students through interim and final presentations. This program is different because most of the previous research on AI in technology education focused on individual activities, conducting cooperative learning only in a few sessions, and previous studies had no presentation . In addition, as technology developers, students can professionally divide their roles and experience self-directed cooperative development projects. Students can experience various career paths within technology education and positively change attitudes toward technology-related jobs.Second, this AI program, developed with a focus on technology education, improves AI competencies. The research extracted AI elements dealing with technology education and developed an AI program through expert consultation based on previous research. In the AI program, students learn AI basics, the social impact of AI, data relating to AI, AI ethics, and AI-based problem-solving. In particular, learners can do overall AI elements and cultivate AI competency through technological problem-solving-based projects. Through this program, students can improve their AI competencies, such as their attitude toward AI and AI performance.Third, the free semester theme selection AI program has value as technology education. AI programs improve AI competencies and attitudes toward technology. In addition, it provides opportunities for career exploration to realize the purpose of the free semester. Thus, the developed free semester theme selection AI program is effective in technology education. Therefore, this program is a free semester AI program should be implemented in technology education.The following recommendations are based on the results of this study.First, further research should include more participants to improve the study’s generalizability. The current study used nonparametric statistical analysis because there were only 23 participants. Thus, future studies should supplement the limitations of statistical methods, including a longer-term study for a comprehensive understanding of the learning results.Second, there is a need for curriculum research that can improve the perception of technology. The increase in students’ interest in technology in this study suggests a need to change technology classes. Lee  pointed Third, it is necessary to study the latest technology education. Specifically, AI is a very influential field. Furthermore, when excluding research related to AI in elementary education, there were relatively few studies in secondary technology education compared to other subjects. Studies related to the free semester were also lacking. Therefore, educators should treat AI as a technology in the technology curriculum. However, there are only relevant elements, and there is no achievement standard directly related to AI in the current curriculum. Therefore, studying the curriculum aspect of the latest technologies such as AI is necessary. In addition, it is essential to conduct research on technology curriculum to carefully examine changes in the latest technologies like AI and reflect them in the curriculum. Thus, this work should not indiscriminately bring in AI technology. Instead, it is necessary to study AI education in technology education by distinguishing it from science and information subjects, focusing on the purpose of technology subjects.


The World Health Organization (WHO) 2017 Global Conference in Montevideo, Uruguay, was dedicated to promoting successful cases and best practices in fighting and preventing noncommunicable disease (NCDs). The global effort undertaken by WHO aims to reduce road traffic deaths in order to meet goal number 3.4 of the sustainable development goals. To describe two Brazilian road safety prevention programs, presented at the WHO 2017 Global Conference: São Paulo Traffic Safety Movement (Movimento Paulista de Segurança no Trânsito) and Safe Life Program of Brasília (Programa Brasília Vida Segura), along with their governance structures, models and results. This was a descriptive case study conducted in São Paulo and Brasilia from 2015 to 2018. These programs aimed to reduce the number of deaths caused by road accidents to 8.3 deaths per 100,000 inhabitants in São Paulo by 2020 and in Brasília by 2016; and to reduce harmful use of alcohol by 10% by 2020. These two initiatives were designed, managed and operated to bring together government and civil society, i.e. industry, academia, non-governmental organizations (NGOs), etc., around the common goal of saving lives. They were collaborative and guided by sharing of best practices, learning and information, thereby making it possible to attain more and better results. Their format enables reproduction in cities across all Brazilian regions. The results attest to the efficacy of the programs implemented in these two cities. In Brasília, the initiative helped reduce the number of traffic-related deaths by 35% (2017). In the same year in the state of São Paulo, 7,600 deaths were avoided. Both programs are innovative public policies that deal with health issues caused by the external agents that ultimately account for the rapid increase in days lost to disability. Prevention of external causes of deaths and injuries, such as traffic violence, strongly correlates with changes in habits and actions, especially excessive consumption of alcohol, and with NCDs in Brazil. Noncommunicable diseases (NCDs) are currently one of the greatest challenges faced within public health. Their impact is so severe that the World Health Organization (WHO) has come to classify them as an epidemic. According to the organization, around 36 million people die prematurely due to cardiovascular and respiratory diseases, cancer and diabetes worldwide every year.Just as drinking too much alcohol is a risk factor for NCDs, the association of drinking and driving is one of the main causes of traffic accidents.Traffic accidents can cause chronic or permanent physical or mental damage. These effects require lifelong care for the individuals involved and will often prevent them from having a normal routine or from working. The resultant morbidity can be understood as a noncommunicable disease (NCD). In speaking about NCDs, external causes such as accidents should be thought of as factors that are just as important as cardiovascular and respiratory factors. The social impact of these occurrences arises from the cost of disability-adjusted life years. According to the Global Burden of Disease Study (2015),To describe two innovative Brazilian programs relating to noncommunicable diseases that were presented at the World Health Organization (WHO) 2017 Global Conference.This was a descriptive case study conducted in São Paulo in 2015 about two programs aimed to reduce the number of deaths caused by road accidents to 8.3 deaths per 100,000 inhabitants in São Paulo by 2020 and in Brasília by 2016; and to reduce harmful use of alcohol by 10% by 2020. The two programs were named the São Paulo Traffic Safety Movement (Movimento Paulista de Segurança no Trânsito) and Safe Life Program of Brasília (Programa Brasília Vida Segura). They were presented at the Global Conference on NCDs and targeted the two challenges that WHO had proposed: prevention and combating of noncommunicable diseases and reduction of traffic-related deaths.These two initiatives were designed, managed and operated to bring together government and civil society, i.e. industry, academia, non-governmental organizations (NGOs), etc., around the common goal of saving lives. They were collaborative and guided by sharing of best practices, learning and information, thereby making it possible to attain more and better results. Their format enables reproduction in cities across all Brazilian regions.The data collection conducted in Brazil  is part of a research initiative carried out in partnership with the Pacific Institute for Research and Evaluation (PIRE). The study protocol was duly evaluated and approved by an Internal Review Board from the North American institution .The application of the model yielded surprising results that went beyond reduction of deaths and injuries in traffic accidents. Educational actions that were developed in schools and public spaces made the programs more sustainable. These interventions highlighted the positive effects of tackling harmful use of alcohol and the association of risks between drinking and driving, among other related topics.The São Paulo Traffic Safety Movement was launched in 2015 and is an example of a result-oriented public policy aimed towards preventing and combating NCDs. It has very strong educational leanings and can be understood as a pioneering initiative for combating chronic noncommunicable diseases. Its actions, developed in an integrated manner, are aimed at promotion of road safety as a whole. Its implementation includes information, awareness and oversight vectors.This project was conceived as a coalition between the public sector, private sector, academia and around 20 entities within civil society. It reflects the commitment of the state of São Paulo towards addressing NCDs. To this end, the state involved nine government departments, which shared information and acted in tandem. This movement made it possible for actions that were being developed separately or that were scattered among various state departments, to start to be analyzed as a group, in a way that was result-driven. This meant efforts were synchronized so that everyone could work towards the same goal: to reach the objective established by WHO.vida, dê preferencia) as its motto and its aim is to reduce the number of deaths caused by road accidents to 8.3 deaths per 100,000 inhabitants in São Paulo by 2020.The São Paulo Traffic Safety Movement has taken “make way for life”  was one of them. In total, 122 actions were identified, and these were divided into three types: engineering, education and communication plus control. Each of the municipalities thus selected received a customized action plan, so that the program could be implemented quickly and effectively.The implementation of the program evolved rapidly, and all 645 municipalities in São Paulo are now part of the movement. The first results from the movement have shown that the design and dynamics of the program are on track to achieve the stated goal. In 2016, 460 lives were saved, in the 15 municipalities that were initially chosen to receive the program. This number represented 6.5% fewer deaths than in 2015. In the following year (2017), an astounding 7,600 casualties were avoided. The aim of the movement now is to get to 2020 with a maximum of 3,880 accidents involving victims. If nothing were to be done, this number would double, reaching 7,760 accidents. The conception of the São Paulo Traffic Safety Movement considered that the challenges proposed by WHO to address the growth of NCDs required a collaborative model. Thus, the coalition and the combination of efforts of different public, private and civil society agents was created based on this very principle.The private sector contributed the resources needed for implementation and management of the movement, shared knowledge about process management and brought the project into a result-driven culture. It also invested the energy of its team in training professionals and mobilizing communities that would be benefited through the project. The state of São Paulo has made a political commitment to make public agents available and to link them together  for actions aimed towards preventing and combating excessive consumption of alcoholic beverages and its association with driving.Through communication and engagement actions, civil society agents contributed towards dissemination of information and guidance to the population, thereby multiplying the impact of the project. This whole process used a method developed by Falconi, the consulting firm that was in partnership with the project.The traffic accident management information system of the state of São Paulo (Infosiga SP) can be considered to be a legacy created by the movement for society. The system is a database that collects information from traffic accidents throughout the state. The information is updated monthly, which constitutes a major advance, and it provides fundamental support for the safety and agility of public policies that are aimed towards preventing and combating accidents, since other databases are only updated once a year.By cross-referencing the information available in Infosiga SP, it is possible to obtain data on the locations of accidents, their causes and category, the victims’ age group and sex, vehicle specifications, the day and time, the most critical places and the aftermath of these accidents. Infosiga SP can make calculations at state, municipal and urban road levels, which makes it possible to create comparisons and cross-check the results in places where preventive policies have been implemented and in places where there have not been any actions targeting these issues.The data facilitates better targeted actions, mitigation or elimination of risks and establishment of preventive policies and of protection for individuals with the most vulnerable profiles. The data also enable creation and improvement of processes and systems for urgent care of injured subjects, thereby avoiding harsher consequences and subsequent death or irreversible damage. They also provide support for decisions regarding strategies for traffic signaling and control.Infosiga SP integrates information from the 645 municipalities in the state of São Paulo. The system is fed with data from reports filed with the civil police, which are used to calculate traffic deaths. In addition, information from the military police and the federal highway police are used in formulating statistics on traffic accidents with victims. Inspired by the success of the São Paulo Traffic Safety Movement, the Safe Life Program of Brasília (Programa Brasília Vida Segura) went even further and set more ambitious goals. The goal of this project was to reduce harmful use of alcohol by 10% by 2020. This challenge meant bringing forward the deadline set by WHO itself for the same rate to be reached around the world. In order to meet its goals, the Safe Life Program of Brasília was based on three factors: road safety, health and education. The first two  have been running since 2016 and have shown results that indicate that the model adopted is appropriate. The education factor was designed as a school and community-based preventive program to be implemented in 2019.The goal of the Safe Life Program of Brasília was to halve the number of lives lost every year in traffic accidents in the Federal District. The target was in line with the goals set by the United Nations for the decade of action for road safety. In terms of numbers, the Program predicts that, by 2020, road traffic deaths will be at a maximum of 250, whereas this number reached 392 in 2016. In order to better understand the traffic scenario in the Federal District, assess its risks and define the actions needed to eliminate them, the team of experts in charge of the Safe Life Program of Brasília undertook a comprehensive diagnosis. Their survey identified 230 actions that would be needed within the fields of engineering, education and control, following the same model that had already been adopted in São Paulo. It was also found that 34% of the fatalities in 2016 were related to alcohol consumption. Regarding age groups, the highest percentage of deaths was registered in the group of people between 30 and 39 years of age (34%), followed by the age ranges of 20 to 29 years and 40 to 49 years, both of which had a fatality rate of 21%. Pedestrians were the cohort that was most affected (34%), followed by drivers (29%). Regarding sex, the vast majority of the victims were men (89%).All the effort to save lives was worthwhile. In 2017 alone, 133 lives were saved in traffic. This meant 34% fewer deaths (down to 255), compared with the total of 392 people who had lost their lives in traffic accidents in the Federal District in 2016. In other words, in just one year, the program almost reached the target set for 2020.In addition to setting the target of decreasing hospitalizations caused by harmful use of alcohol by 10% by 2020, the Safe Life Program of Brasília has implemented a tool that was recommended by WHO for treating and managing cases, called brief intervention. This procedure is preceded by an alcohol use disorders identification test (AUDIT screening), which efficiently identifies the individuals who are at greater risk. Following this, the care protocol is defined according to the needs and conditions of each patient.This intervention model was chosen based on studies and information that were obtained through official databases and then analyzed by specialists who were working in the program. Furthermore, the methodology recommended by WHO was completely concordant with the family health program (Programa Saúde da Família) and with the care provided at the primary healthcare units  that the government maintains. Integrated work facilitates prevention because it enables identification and treatment of problems that are derived from harmful use of alcohol, at their initial stages.In order to further elaborate the diagnoses upon which activities are based, the experts delved into databases that were made available by government bodies responsible for public health. This initiative allowed the professionals to identify and quantify the hospitalizations caused by harmful use of alcohol. Studying the information thus obtained allowed the researchers to have a better understanding of the scenario, both in relation to the causes for hospitalization and in relation to the patients’ profiles. Based on this assessment, they defined the group of men aged 40 to 49 years as the critical profile for the project, and pointed out the causes of the problem and its possible solutions.With the aim of disseminating this new concept for preventing and combating harmful use of alcohol to the population, the Safe Life Program of Brasília trained 41 healthcare agents (doctors and nurses). These professionals received 15 hours of training and became able to put the protocols thus devised into practice. In total, 1,645 people living in the satellite towns of Ceilândia and Taguatinga, which were the locations chosen for beginning the implementation of the project, underwent screening and 77 of them were referred for treatment.In only one year at work, the preliminary results from the Safe Life Program of Brasília indicated that the healthcare activities within the program were able to reduce the number of hospitalizations relating to harmful use of alcohol. To achieve the goals for reducing NCDs, great efforts towards implement the guidelines established by WHO have been made and, so far, within a brief period of time, there has been consistent progress.Prevention of underage alcohol consumption is the third goal of the project. A randomized survey was conducted in schools from April to May 2018 to understand the context of alcohol use among underage students from 13 to 17 years old in public and private schools.The data obtained will help define specific actions aimed at reducing alcohol use among school students. Interventions will take place in public schools, but before they are disseminated in all the places benefited by the project, they will be tested in pilot areas. The results from the survey will provide a baseline for monitoring and managing the project. With these data, it will be possible to create a database, evaluate the effectiveness of the measures adopted and identify places or groups of people that need to be prioritized.The governance of the Safe Life Program of Brasília was based on a public-private partnership model, which allows for agile management and speedy results. The program is coordinated by a nationally based non-governmental organization (NGO) that focuses on developing leadership, activities and knowledge regarding public policies.This NGO is at the heart of the organizational chart and its role is to integrate the other actors. Both the format and the governance of the Safe Life Program of Brasília make it possible to state that the project constitutes an innovative public-private partnership for preventing NCDs.The two projects described here (São Paulo Traffic Safety Movement and Safe Life Program of Brasília) have brought governments closer to the goals established by WHO for prevention and reduction of NCDs. Participation and mobilization of private sector efforts have ensured agility in the projects and have stimulated formation and strengthening of a results-driven culture. The transfers of knowledge and process management technology and the development of concrete action plans have helped governments meet the recommendations and objectives proposed by WHO.Based on the knowledge obtained and the results achieved, it is possible to state that these programs are examples of innovative policies that aim to prevent and combat the risk factors involved in NCDs. The project management shared between the private sector, civil society and government has given rise to a sustainable public policy model that can be reproduced in other Brazilian states. Involvement of the road safety agencies, which work to combat traffic accidents in their various areas of activity, and the synergy achieved through formation and implementation of management committees, have been fundamental for the success of the project.It is also important to highlight that one of the greatest defining features of the models presented here is that they both use and cross-reference information. Data that previously were scattered across various departments and sectors of government dealing with traffic, healthcare and surveillance have been analyzed in an integrated manner. The system enables anticipation of trends, for action in critical locations, for support of education and training campaigns, for structuring of emergency room care systems and for rehabilitation services and their care teams. Adoption of public policies based on the knowledge and results obtained from the two programs provides solid support for enabling Brazil to meet the sustainable development goals, i.e. to ensure healthy lives and promote wellbeing at all ages.Both programs are innovative public policies that deal with health issues caused by external agents that ultimately account for rapid increases in days lost due to disability. Prevention of external causes of deaths and injuries, such as traffic-related violence, strongly correlates with changes in habits and actions, especially excessive consumption of alcohol, and with NCDs in Brazil.


People with spinal cord injury (SCI) experience a plethora of health conditions that hinder their health and wellness. This qualitative retrospective evaluation describes the perceptions of 14 peoples with SCI, several months after they completed an eight-week telewellness community program . The program offered daily online classes that covered three core wellness domains  and one health coaching session to introduce participants to eight other wellness domains . Qualitative analysis resulted in 4 themes related to program benefits, likes, and improvement recommendations. First, participants valued the program for the social support provided by a sense of community and relationship building with peers. Second, self-regulation was facilitated by the comprehensiveness of the program components, easy online access, and shared lifestyle goals for self-improvement among peers. Third, participants reported improved psychological wellbeing and adopted healthy behaviors that were maintained long after the program. Last, future programs should include flexible class times, post-program support, specific exercise adaptations for people with limited arm function, and supplementary in-person meetings. These preliminary findings demonstrate that MENTOR may benefit the wellbeing of people with SCI and warrant further study. The connection between healthcare and wellness is typically not available in conventional healthcare systems in the United States and most other parts of the world . With rehealth and function and in the aggregate, can impose substantial limitations in rates of participation in general life activities including employment, social and community engagement and performing instrumental activities of daily living.Unhealthy behaviors such as a poor diet and lack of exercise can predispose people with SCI to even greater levels of adverse health consequences when they overlay such secondary conditions as pain  , 8, exceExpertise, support, and resources to address specific health issues or measures to prevent them are difficult to find, particularly in key areas of integrative wellness that include exercise, nutrition, mindfulness, sleep, and other less known, but important health-enhancing behaviors , 17. ThiPhysicians may not feel comfortable addressing certain physical or cognitive conditions associated with the disability , which aPeople with SCI are also at increased risk for developing chronic health conditions at a much higher rate than the general population, including heart disease, obesity, and type 2 diabetes . These cThere is a growing body of evidence that convincingly supports the effectiveness and superiority of non-invasive wellness interventions that are consistent with contemporary lifestyle medicine in the promotion of lifelong health and wellness , 24. UndThe MENTOR program  has been offered to people with SCI and other physical disabilities across the U.S. for the past year. MENTOR is a community project  funded by the Center for Disease Control and Prevention. The purpose of this retrospective evaluation was to examine the perceptions of a subset of people with SCI who completed MENTOR, with a specific focus on program benefits and likes and dislikes related to the implementation of the program, with the intent to use these findings to refine the next iteration of MENTOR.This was a retrospective, qualitative program evaluation to understand the end users' perspective from which to make future modifications to the program. The philosophical assumptions that underpinned all phases of the research were critical realism ontological perspective  and an interpretivism epistemological perspective . Put simply, the research time acknowledged that bias from participants and the team is unavoidable and should be considered throughout the study. Participants experienced different perspectives of the program, and recollections of the program could be heavily influenced by the interaction between the interviewer and analysts, as well as the presentation of the results. The team agreed upon the philosophical assumptions and aimed to incorporate only a minimal level of bias/interpretivism when conducting all aspects of the project.core domains because they are offered weekly as hands-on sessions taught by qualified specialists in these respective fields. The remaining wellness domains are offered in a 1-h session, once per week, across the 8-week program. Further details of the program can be found elsewhere  for 1 h each day, and each class was split into 30 min of exercise form and safety, followed by 30 min of performing the exercise. The fitness activities were adapted based on the participants' functional level. Participants with SCI performed the exercise routines from a seated position.The nutrition classes were taught once a week (Wednesday) by a registered dietitian and focused on preparing meals, learning new recipes that focused on increasing fruit and vegetable consumption, and budgeting groceries to include healthier options and reduce processed foods. All the recommended foods were approved by the Women and Infants/Children (WIC) and Supplemental Nutrition Assistance Program (SNAP) U.S. federal assistance programs.The mindfulness classes were taught once a week (Monday) by a trained instructor who emphasized various aspects of traditional mindfulness techniques that included comfortable positioning techniques for those in a wheelchair, proper breathing control, and strategies to combat negative feelings or psychological issues.The eight sub-domains included self-care skills, core values, outdoor time in nature, sleep, contribution to others, arts and leisure, relationships, and spiritual practice. Participants practiced these domains once a week (Friday) with their assigned groups and trained health coaches, who emphasized the “high level pointers” for improving each of these domains. For example, under sleep, various suggestions were provided based on the sleep literature to improve sleep hygiene. These included use of a white or pink noise machine, maintaining an ideal room temperature (64–66 degrees), using a weighted blanket, increasing room darkness, avoiding blue light from cell phones, going to bed and waking up at a regular time, along with several other suggestions. These sessions were meant to help participants understand how to self-manage their health within these domains, and independently seek and utilize resources to improve any wellness domain that they were having difficulty with or wanting to learn more about its potential benefit for improving their health.saturated  (This study aimed to recruit a convenience sample of 20 participants or recruit participants until surface-level themes were estions) . ParticiThe entire MENTOR program was conducted remotely and included no in-person interactions. Participants were arranged in groups of 6–10 participants. The program was offered in waves, and 2 weeks prior to the start of each wave, the participant coordinator recontacted participants to confirm their interest and to provide details on their schedules, equipment, etc. Weekly check-ins were conducted with participants as reminders for upcoming classes for the week and what materials they should read or complete  prior to the session. If a participant did not show up for class and did not notify their health coach in advance, he/she was called to make sure everything was ok and to document the reason for missing the class. Participants were asked to attend all of the classes through Zoom videoconference software. They were able to interact or contact their health coach or instructors prior to or after the class if they had any questions or a specific issue. They were sent a small set of inexpensive exercise equipment  that was shipped to their home prior to the start of the program. They were also sent a MENTOR journal and were asked to record various activities that they did during the week that related to one or more wellness domains.At the beginning of the program, participants were provided with program logistics, and were introduced to MENTOR's web-based commercial platform . Because the program was completely online, there was a telelogistics support protocol in place. MENTOR components, such as online enrollment, assessment, and implementation were managed by a telelogistics support team. This was made up of four program staff and six health coaches. The local institutional reviewer board of the University granted a research exemption for this study designating it as a quality improvement evaluation study of an existing program.via telephone to participate in a program evaluation interview. The interview lasted no longer than 30 min and included ~10 questions. The questions were semi-structured and asked participants about their overall thoughts of the program, likes/dislikes, potential benefits, and general questions pertaining to the 3 core components of the program . The questions also included a general ice-breaker question (asking about what the participant likes to do in their free time), and a question asking what recommendations they would have for improving the program. The interviews were conducted by three research staff who had several years of experience in wellness-related fields for people with disabilities. None of the interviewers were involved with the delivery of MENTOR. The interviewers were instructed to ask follow-up questions to clarify and expand on noteworthy topics that participants reported. Interviews were audio recorded and transcribed by a 3rd party transcription company. Participants were thanked with a $20 electronic debit card for completing the interview.Several months after completing MENTOR, participants were contacted Inductive thematic analysis  was usedInterviews were stopped upon interviewing a total of 14 people, because the analysts determined that the surface-level themes achieved informed power. The mean age of the sample was 50 ± 16 years and there were 6 males (42.9%) and 8 (57.1%) females. Regarding race, 6 self-identified as Caucasian; 5 African American, 1 American Indian/Alaskan Native, and 2 were Unknown. Participants were from several different cities across the U.S including Birmingham, AL, Boston, MA, Chicago IL, Denver, CO, and New York, New York.social support, self-regulation motivational factors, lifestyle changes, and factors that affected participation. These themes are described below and presented with their supporting sub-themes and codes in The resultant themes incorporated rich participant feedback related to Social support was an enjoyable component of MENTOR. Participants reported that the group-based classes provided a valuably intimate sense of community with other members. Value was created by either receiving or providing verbal support. Examples of these interactions included recommending adaptations for various activities or overcoming other physical or emotional life challenges. Small groups fostered in-depth communication and relationship building, which led to some participants creating peer friendships and bonds outside of the class after the program ended. Groups were balanced by participants who were vocal and others who were less conversive.Self-regulation motivational factors were attributed to the holistic nature of the program, the accessible online platform, and shared lifestyle goals. The program offered a variety of opportunities for participants to self-select classes or certain components that they felt best fit their personal needs. Some participants found themselves enjoying and attending all the classes across the 8-week program, which was a substantial time investment (5 hr/week for 8 weeks). Some participants reported that nutrition was the most helpful toward improving their lifestyle, while others reported that the mindfulness component was most beneficial. Since MENTOR was provided through an online platform, participants appreciated that the classes could be conveniently attended without requiring transportation to an onsite facility. Moreover, self-improvement goals were often shared by other members, which strengthened their motivation to attend the classes.Lifestyle changes were a positive benefit of participation in MENTOR. Participants reported that they adopted healthy behaviors and sustained these behaviors long after the program ended. Participants made valuable changes to their nutritional behaviors to eat healthier. Nutritional guidance was provided to participants at a depth they had not expected or previously received. Mindfulness strategies were novel to most participants and helped them cope with pain and other life stressors. Most importantly, nutrition and mindfulness strategies were sustained after the program ended.Participation was affected by several factors. Regarding facilitating factors, participants reported that the program content was perceived to be of high quality, which was attributed to the professional and knowledgeable coaches. Coaches were perceived as prompt, knowledgeable of disability, helpful, and confident. Additionally, participants reported that exercises were generally well tailored to their functional and cognitive abilities. Regarding barriers to participation, some participants with more severe functional impairment  reported that exercises were not suitable to their functional ability. Specifically, people with tetraplegia desired more in-depth adaptations for their arms that would increase the ease of difficulty with performing similar movements as others in the class, while also exhibiting sufficient effort or work to provide a physiologic health benefit. Additionally, some participants reported that they would like the option to attend in-person classes. These participants did not feel engaged through telecommunication. Some participants' ability to attend the classes was restricted by their availability . To remedy this, participants recommended more options for class times. Even though the daily class regime was provided to participants as a way of allowing them to choose various components that could fit into their daily routine, participants were disappointed by having to miss class components.This retrospective evaluation of a holistic, community-based program found that people with SCI value comprehensive online wellness for providing a sense of peer-support and promoting sustainable, health-enhancing behaviors. Participants also reported improved psychological well-being. This finding is similar with benefits reported in a scoping review of 20 qualitative clinical studies examining wellness interventions for people with SCI . Of noten = 20). One study published in 2016 found that a more medically focused wellness intervention improved function, mood, and quality of life , which could have affected their ability to recall specific aspects or benefits of the program. Second, the sample included a slightly larger number of females than males, which may not be representative of SCI, which generally has a higher incidence among males . Third, The need among people with SCI to improve health and prevent/manage secondary conditions can be greatly enhanced by increasing their access to the social determinants of wellness. As a government-sponsored program, MENTOR has the capacity over the next 5 years to reach more and more people with SCI across the U.S. in an effort to provide quality programs and services to this underserved population. The social determinants of wellness can have a profound effect on improving health and reducing the risk of age-associated chronic diseases in people with SCI.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of Alabama at Birmingham Institutional Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.RY, MC, and CL were responsible for conducting the interviews. BL and MC analyzed the data. TS, JR, and BL created the initial draft of the manuscript. H-JY and JW created the second manuscript draft. All authors contributed to the final manuscript draft.Funding for this study was provided by the Centers for Disease Control and Prevention (CDC), Division of Birth Defects and Developmental Disabilities (NCBDDD), Disability and Health Promotion Branch, Grant #NU27DD000022.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.