Computer-implemented method for indicating a long-term stress level of a person by means of a data processing unit, comprising the steps of: - acquiring (S1) stressor data having a set of stressor data items, wherein the stressor data is acquired by a stressor data input unit which is implemented by one or more devices which include a personal desktop computer, a mobile device, a wearable and sensors, wherein the stressor data comprises physiological data, wherein the physiological data includes heart rate, blood pressure, heart rate variability, or concentration of stress-inducing hormones in body liquids; - analyzing (S4) stressor data values of the stressor data items by means of an artificial neural network to generate a stress level indicator representing a stress level for the long-termed stress level of the person, wherein the stress level is an asserted niveau of a physical and/or psychic reaction of a person caused by exposure to stressors, wherein the artificial neural network, ANN, is trained to provide output representing the stress level based on the stressor data values and an indication of previous stress state, wherein the artificial neural network is trained by available data of various persons who have developed a burnout syndrome, so that a normalization of the assessment of the stress level is made with a reference being the accumulated stressor exposure which have led to a burnout syndrome; wherein various data about stressors to which a person is exposed and about persons having developed a burnout syndrome is accumulated and considered in the artificial neural network as a common stress level evaluating model; and signaling (S5) the stress level, wherein the stress level is signaled to a local or remote output device which signalizes the stress level of a person by indicating it as a value or on a scale. - acquiring (S1) stressor data having a set of stressor data items, wherein the stressor data is acquired by a stressor data input unit which is implemented by one or more devices which include a personal desktop computer, a mobile device, a wearable and sensors, wherein the stressor data comprises physiological data, wherein the physiological data includes heart rate, blood pressure, heart rate variability, or concentration of stress-inducing hormones in body liquids; - analyzing (S4) stressor data values of the stressor data items by means of an artificial neural network to generate a stress level indicator representing a stress level for the long-termed stress level of the person, wherein the stress level is an asserted niveau of a physical and/or psychic reaction of a person caused by exposure to stressors, wherein the artificial neural network, ANN, is trained to provide output representing the stress level based on the stressor data values and an indication of previous stress state, wherein the artificial neural network is trained by available data of various persons who have developed a burnout syndrome, so that a normalization of the assessment of the stress level is made with a reference being the accumulated stressor exposure which have led to a burnout syndrome; wherein various data about stressors to which a person is exposed and about persons having developed a burnout syndrome is accumulated and considered in the artificial neural network as a common stress level evaluating model; and signaling (S5) the stress level, wherein the stress level is signaled to a local or remote output device which signalizes the stress level of a person by indicating it as a value or on a scale. <EOS>