{"id":12704,"date":"2021-07-09T19:04:41","date_gmt":"2021-07-09T10:04:41","guid":{"rendered":"https:\/\/adc-japan.com\/uk\/?p=12704"},"modified":"2026-10-18T07:59:54","modified_gmt":"2026-10-17T22:59:54","slug":"the-uchida-kraepelin-test-and-digital-transformation-dx","status":"publish","type":"post","link":"https:\/\/adc-japan.com\/uk\/en\/tips-en\/12704.html","title":{"rendered":"The Uchida-Kraepelin Test and Digital Trans\u00adformation (DX)"},"content":{"rendered":"<aside class=\"uk-ai-translation-notice\" lang=\"en\" style=\"box-sizing:border-box;clear:both;margin:0 0 1.5em;padding:1em;border:1px solid #ccd5de;border-left:4px solid #486581;background:#f4f7fa;color:#243746;font-size:14px;line-height:1.8;overflow-wrap:anywhere;\">\n<p style=\"margin:0;\">This page has been automatically translated from Japanese using generative AI. Translations may contain errors. Please contact us to confirm important information.<\/p>\n<\/aside>\n<h3>Using DX to work with human characteristics<\/h3>\n<p>The Uchida-Kraepelin Test is particularly well suited to working with people&#8217;s ability characteristics and personality\/behavioral characteristics in digital transformation (DX).<\/p>\n<h3>What is DX?<\/h3>\n<p>Digital transformation refers to the idea that <strong><span style=\"color: #ff6600;\">the spread of IT changes people&#8217;s lives for the better in every aspect<\/span><\/strong>. The concept is generally attributed to Professor Erik Stolterman of Ume\u00e5 University in Sweden in 2004, and is commonly abbreviated as DX.<\/p>\n<p>Japan&#8217;s Ministry of Economy, Trade and Industry describes DX in its DX Promotion Guidelines as follows (paraphrased):<\/p>\n<p><span style=\"color: #ff6600;\"><strong>Businesses respond to rapid changes in their environment by using data and digital technologies to transform products, services and business models around customer and societal needs, while also transforming operations, organizations, processes and corporate culture to establish competitive advantage.<\/strong><\/span><\/p>\n<h3>The Uchida-Kraepelin Test and DX<\/h3>\n<p>Because the Uchida-Kraepelin Test is taken with paper and pencil, it may seem unrelated to digital technology.<br \/>\nYet its distinctive method consists solely of an addition task, producing data such as work volume and errors for each minute. In this sense, it is digital by nature.<br \/>\nThe graphs and text in the Judgment report simply visualize digital data so that people can read it easily.<\/p>\n<figure class=\"uk-dx-image\" style=\"margin:0 0 24px;\"><a href=\"https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en.png\"><img decoding=\"async\" width=\"1680\" height=\"830\" class=\"wp-image-11963 alignnone\" src=\"https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en.png\" alt=\"Uchida-Kraepelin Test: digitize ability and behavioral characteristics; Big data\" style=\"max-width:100%;height:auto;width:600px;\" srcset=\"https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en.png 1680w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en-300x148.png 300w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en-1024x506.png 1024w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en-768x379.png 768w, https:\/\/adc-japan.com\/uk\/wp-content\/uploads\/2026\/10\/uk-applications-en-1536x759.png 1536w\" sizes=\"(max-width: 1680px) 100vw, 1680px\" \/><\/a><figcaption>\n<details>\n<summary>Uchida-Kraepelin Test: digitize ability and behavioral characteristics<\/summary>\n<div class=\"uk-dx-diagram\" style=\"max-width:600px;border:1px solid #ccc;padding:16px;box-sizing:border-box;line-height:1.7;overflow-wrap:anywhere;\">\n<p style=\"background:#20478f;color:#fff;padding:12px;text-align:center;\"><strong>Uchida-Kraepelin Test: digitize ability and behavioral characteristics<\/strong><br \/>\u2193<br \/>Raw test results<\/p>\n<p style=\"background:#e7f0f5;padding:12px;text-align:center;\"><strong>Big data<\/strong><br \/>&#x2194; Compare with clinical data<br \/>\u2193 Develop and update Judgment models<br \/>Judgment results from different models<\/p>\n<ul style=\"margin-bottom:0;\">\n<li>Job fit: hiring Judgment, placement and transfers<\/li>\n<li>Safety management: accident prevention and licensing systems<\/li>\n<li>Education and training: educational planning, further-study advice, social education and career support<\/li>\n<li>Mental health: emotional well-being, health assessment and return-to-work support<\/li>\n<\/ul>\n<\/div>\n<\/details>\n<\/figcaption><\/figure>\n<p>The test mainly yields two types of data:<\/p>\n<ol>\n<li>Raw test data, including work volume and errors for each minute, and age<\/li>\n<li>Judgment result data obtained by passing raw data through a Judgment model<\/li>\n<\/ol>\n<p>A Judgment model is a function derived by comparing raw test data with clinical data\u2014such as typical work behavior, learning style, compatibility with particular duties, accidents and health. Models are continually updated to remain suited to purposes that change over time.<\/p>\n<p>The CSV file supplied with the Uchida-Kraepelin cloud Judgment service contains the results generated by applying a particular model to raw data.<\/p>\n<p>If an appropriate model already exists, its Judgment results can be used. If not, AI can analyze relationships between clinical data and raw data or existing Judgment results to help develop a new model.<\/p>\n<p>Consider the Uchida-Kraepelin Test as a resource for your DX initiatives.<\/p>\n<h3>A DX proposal: using the test to help reduce road accidents<\/h3>\n<p>SDG 3, concerning health and well-being for all, includes target 3.6 on halving deaths and injuries from road traffic accidents. Its original deadline was 2020. Among the many possible road-safety approaches, let us consider an application of the Uchida-Kraepelin Test.<\/p>\n<p>To assess its usefulness, we must examine whether ability and personality\/behavioral characteristics correlate with accidents. The original article reports different distributions of these characteristics when comparing people who had caused a serious accident with people who had never caused one.<\/p>\n<p>Using actual data and a provisional Judgment criterion intended to distinguish lower- and higher-risk tendencies, each population was divided into Groups A and B as shown below.<\/p>\n<div class=\"uk-dx-data-table\" tabindex=\"0\" role=\"region\" aria-label=\"Road-safety illustration: comparison of groups\" style=\"max-width:100%;overflow-x:auto;\">\n<table style=\"min-width:420px;width:100%;\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\">\n<tbody>\n<tr style=\"height: 18.0pt;\">\n<td class=\"xl65\" style=\"height: 18pt; width: 52pt; text-align: center;\" width=\"69\" height=\"24\"><\/td>\n<td class=\"xl65\" style=\"border-left: none; width: 52pt; text-align: center;\" align=\"left\" width=\"69\">Accident group<\/td>\n<td class=\"xl65\" style=\"border-left: none; width: 52pt; text-align: center;\" align=\"left\" width=\"69\">Accident-free group<\/td>\n<\/tr>\n<tr style=\"height: 18.0pt;\">\n<td class=\"xl65\" style=\"height: 18pt; border-top: none; text-align: center;\" align=\"left\" height=\"24\">Group A<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">38%<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">76%<\/td>\n<\/tr>\n<tr style=\"height: 18.0pt;\">\n<td class=\"xl65\" style=\"height: 18pt; border-top: none; text-align: center;\" align=\"left\" height=\"24\">Group B<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">62%<\/td>\n<td class=\"xl66\" style=\"border-top: none; border-left: none; text-align: center;\" align=\"right\">24%<\/td>\n<\/tr>\n<tr>\n<td style=\"border-top: none; text-align: center;\">Total<\/td>\n<td style=\"border-top: none; border-left: none; text-align: center;\">100%<\/td>\n<td style=\"border-top: none; border-left: none; text-align: center;\">100%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The groups cannot separate all accident-involved people from accident-free people. In this comparison, however, Group A has the lower accident-related tendency.<\/p>\n<p>What would happen to the illustrative accident rate if Group B did not drive? The 2021 Japanese article uses an annual accident probability of about 1%. Applying that assumption to a population of 10,000 gives the following table.<\/p>\n<div class=\"uk-dx-data-table\" tabindex=\"0\" role=\"region\" aria-label=\"Road-safety illustration: comparison of groups\" style=\"max-width:100%;overflow-x:auto;\">\n<table style=\"min-width:420px;width:100%;\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"69\"><\/td>\n<td style=\"text-align: center;\" width=\"68\">Accident group<\/td>\n<td style=\"text-align: center;\" width=\"69\">Accident-free group<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Group A<\/td>\n<td style=\"text-align: center;\">38<\/td>\n<td style=\"text-align: center;\">7524<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Group B<\/td>\n<td style=\"text-align: center;\">62<\/td>\n<td style=\"text-align: center;\">2376<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: center;\">Total<\/td>\n<td style=\"text-align: center;\">100 (1%)<\/td>\n<td style=\"text-align: center;\">9900 (99%)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>The original article calculates:<br \/>\nGroup A: 38 \u00f7 7524 \u2248 0.5%<br \/>\nGroup B: 62 \u00f7 2376 \u2248 2.6%<br \/>\nIn this illustration, the Group B figure is more than five times the Group A figure. If Group B did not drive, the rate among the remaining drivers would be about 0.5% rather than the assumed 1%\u2014approximately half. See the calculation note below for the correct within-group denominators.<br \/>\nHowever, withholding driving licenses from Group B would also exclude 2,376 people without an accident, making that approach unrealistic.<br \/>\nThis is where information technology might help: the article proposes prioritizing Group B when encouraging a switch to Advanced Safety Vehicles (ASVs) or automated vehicles, with the aim of achieving a similar safety benefit.<\/p>\n<p>The original article describes Japan as having relatively few traffic accidents internationally and suggests further potential in countries with less-developed licensing systems or traffic rules. These are the article&#8217;s proposals, not verified effects in every country.<br \/>\nHealth and well-being for all.<\/p>\n<div class=\"uk-editorial-note\" style=\"border-left:4px solid #777;padding:12px;background:#f5f5f5;\"><strong>Notes on the original example and calculations<\/strong><\/p>\n<p>This translation retains the tables and example from the Japanese article published on 9 July 2021. The 1% annual rate is an assumption used in that example, not a current national statistic. SDG target 3.6 had a 2020 deadline. The original formulas divide accidents by accident-free people; proportions within each complete group are 38\/(38+7524) \u2248 0.50% and 62\/(62+2376) \u2248 2.54%. The comparison is illustrative, not proof of causation, validated individual risk prediction, or a guaranteed reduction from the test, ASVs or automated driving. Applying a model in another population requires validation. Do not make hiring, licensing or other eligibility decisions on this test alone.<\/p>\n<p><a href=\"https:\/\/sdgs.un.org\/goals\/goal3\">SDG 3.6 \u2014 United Nations<\/a><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>This page has been automatically translated from Japanese using generative AI. 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