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Lonneke M Boer,1 Maarten van der Heijden,2 Nathalie ME van Kuijk,1 Peter JF Lucas,2 Jan H Vercoulen,3,4 Willem JJ Assendelft,1 Erik W Bischoff,1 Tjard R Schermer1,5 1Department of Primary and Community Care, Radboud university medical center, Radboud Institute for Health Sciences, Nijmegen, the Netherlands; 2Department of Computing Sciences, Radboud University, Nijmegen, the Netherlands; 3Department of Medical Psychology, Radboud university medical center, Radboud Institute for Health Sciences, Nijmegen, the Netherlands; 4Department of Pulmonary Diseases, Radboud university medical center, Radboud Institute for Health Sciences, Nijmegen, the Netherlands; 5Netherlands Institute for Health Services Research (NIVEL), Utrecht, the Netherlands Background: To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symptom-related yes-or-no questions and the measurement of peripheral capillary oxygen saturation (SpO2), forced expiratory volume in one second (FEV1), and body temperature. Automated treatment advice is based on a decision model built by clinical expert panel opinion and Bayesian network modeling. The current paper describes the validity of ACCESS. Methods: We performed secondary analyses on data from a 3-month prospective observational study in which patients with COPD registered respiratory symptoms daily on diary cards and measured SpO2, FEV1, and body temperature. We examined the validity of the most important treatment advice of ACCESS, ie, to contact the health care professional, against symptom- and event-based exacerbations. Results: Fifty-four patients completed 2,928 diary cards. One or more of the different pieces of ACCESS advice were provided in 71.7% of all cases. We identified 115 symptom-based exacerbations. Cross-tabulation showed a sensitivity of 97.4% (95% CI 92.0–99.3), specificity of 65.6% (95% CI 63.5–67.6), and positive and negative predictive value of 13.4% (95% CI 11.2–15.9) and 99.8% (95% CI 99.3–99.9), respectively, for ACCESS’ advice to contact a health care professional in case of an exacerbation. Conclusion: In many cases (71.7%), ACCESS gave at least one self-management advice to lower symptom burden, showing that ACCES provides self-management support for both day-to-day symptom variations and exacerbations. High sensitivity shows that if there is an exacerbation, ACCESS will advise patients to contact a health care professional. The high negative predictive value leads us to conclude that when ACCES does not provide the advice to contact a health care professional, the risk of an exacerbation is very low. Thus, ACCESS can safely be used in patients with COPD to support self-management in case of an exacerbation. Keywords: COPD, exacerbations, telehealth, software application, treatment advice, self-management, health, mobile health, automated device, diagnostic accuracy