The number of older adult has increased significantly in most current societies. One problem that is accentuated in the stage of old age is loneliness which is a serious health risk. Therefore, new methods for early detection of this condition that make use of new non-intrusive technologies are required. Loneliness includes four main factors (family, spousal, social and existential crisis). In this paper, four predictive models to determine the level of loneliness of each factor are proposed, focusing on the activities that can be monitored using a Smartphone. Predictive models have been evaluated on basis of their accuracy, sensitivity, specificity, predictive values, type I and type II error rates. This paper also presents the results of the experimentation of the proposed approach in practice and with real users through a mobile application called “!‘Vive!” that implements the predictive models.

Early diagnosis of social isolation in older adults can prevent physical and cognitive impairment or further impoverishment of their social network. This diagnosis is usually performed by personal and periodic application of psychological assessment instruments. This situation encourages the development of novel approaches able to monitor risk situations in social interactions to obtain early diagnosis and implement appropriate measures. This paper presents the development of a prediction model of social isolation in older adults through Ambient Intelligence (AmI) and Social Networking Sites (SNSs). The predictive model has been evaluated in terms of its accuracy, sensitivity, specificity, predictive values. This paper also presents the results of an experimental test applying the proposed approach with real users, obtaining a prediction accuracy of 87% and a type II error rate of 15%. The proposed model will benefit institutions interested in developing technological solutions to detect early stages of social isolation, resulting in improving the quality of life of older adults.