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This is an infographic of the algorithm publshed in the peer-reviewed article. It represents the opinions of the authors. For a full list of declarations, including funding and author disclosure statements, please read the full open-access article online.

Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies., Postprints der Universität Potsdam : Humanwissenschaftliche Reihe; 711

Background Whole-body electromyostimulation (WB-EMS) training is used in popular and health sports to improve muscular performance. Little is known about the possible psychological effects of WB-EMS training. The aim of the study is therefore to investigate the possible psychological effects of WB-EMS training on subjective well-being, relaxation, mood, and perceived stress. Materials and Methods Twenty-five healthy subjects underwent conventional WB-EMS training and Sham training (without the application of electrical stimulation) as part of a randomized, controlled pilot study in a crossover design. Subjective well-being and subjective relaxation were assessed using visual analog scales, the current state of mood was assessed with Multidimensional Mood State Questionnaires (MDBF), and the current level of stress was assessed with Recovery–Stress Questionnaires/Erholungs-Belastungs-Fragebögen (RESTQEBF) before and after training. Results WB-EMS training has a statistically significant positive effect on subjective well-being and subjective relaxation, as well as on the awake subscale of the MDBF. No significant main effect of sequence and no interaction effects were found. Also, compared to a Sham training session, a single WB-EMS training session had no significant effect on mood, nervousness, or the current level of stress. Conclusion Besides physiological effects, WB-EMS might also have a strong psychological impact. WB-EMS could be beneficial for people who, due to their limitations, have problems training on a regular basis and with adequate training intensity. Trial Registration German Clinical Trials Register, DRKS00012583, 22 June 2017.

Additional file 5: Supplement 5. Generated by the App ADA for case 5 (“Mild concussion”).

Additional file 2: Supplement 2. Generated by the App ADA for case 2 (“Ankle sprain”)

Additional file 1: Supplement 1. Generated by the App ADA for case 1 (“Tennis elbow”)

Additional file 3: Supplement 3. Generated by the App ADA for case 3 (“Delayed onset of muscle soreness”).

Additional file 4: Supplement 4. Generated by the App ADA for case 4 (“ACL rupture with chronic instability”).

5.410 JCR (2013) Q1, 22/164 Genetics & heredity UEM