Objective: Quality of life is influenced by psychological and social factors. Our main objective is to better understand and characterize the impact of social and psychological characteristic in quality of life in aging population with and without diabetes and other chronic disease.

Method: The data was collected at the national level. The sample is composed of 1,330 people 62.2% of which were female, with ages ranging between 55 and 75 years old. 48.2% of the sample mention having a chronic condition, 34.4% of which had diabetes.

Three regression models were created in order to understand the quality of life in aging population with and without chronic illness in a biopsychosocial perspective.

Results: Results showed that quality of life in aging population is influenced by psychological factors (purpose of life and stress management skills) and by social factors (social support satisfaction and relationships with supervisor at work). Having a chronic disease, such as diabetes, can also influence quality of life.

Conclusions: Our study allowed us to conclude that quality of life is influenced by physical health, psychological health and social health. The psychological factors presented a more systematic and strong influence in quality of life in population with and without chronic disease.

We are witnessing a rapidly growing type of Mobile Health. Mobile devices have allowed the control of activity levels in people, their habits and even their mobility through GPS. Vital signs, moods, sleep disorders, heart rates, or skin temperature can also be monitored. Literature specializing in psychosocial therapies addresses the usefulness of mobile phones in patients in general, with greater production from psychology and to a lesser extent from social work. We highlight the research developed in the USA and identify opportunities and limitations in terms of data privacy, access to and use of mobile phones; to reach isolated or poor populations or to facilitate access to social and health services. We conclude with possible orientations, hypotheses and goals for prospective investigation works.

Objective: To explore the relationship between only children’s sleep quality and social support and its influencing factors to provide solid evidence for sleep disorder improvement.

Methods: A total of 13,080 health participants in 18 provinces of China were chosen by random cluster sampling and were assessed by the Chinese People’s Social Support Scale (CPSSS) and the Chinese People’s Sleep Disorder Scale (CPSDS). A variety of statistical methods, such as descriptive statistical analysis, t-test for independent sample, correlation analysis and multiple linear regression analysis, were employed for data processing.

Results: (1) The scores of motile abnormal sleep and immotile abnormal sleep in the only-child group were lower than in the corresponding group (P<0.05). The scores of lethargy in the only-child group were higher than in the corresponding group (P<0.05). The scores of motile abnormal sleep, immotile abnormal sleep in the married group were higher than in the unmarried group (P<0.05). The scores of lethargy and daytime function in the married group were lower than in the unmarried group. All factor scores of sleep disorders in the urban group were higher than in the rural group (P<0.05). (2) The social support scores of Chinese samples in the only-child or not-only-child groups and married status had significant differences (P<0.05); however, the social support score differences of rural-urban groups were not statistically significant (P>0.05). (3) All factors of social support positively correlated with sleep disorders (P<0.05). Multiple regression analysis suggested that all factors of social support were selected into the regressive functions of lethargy and immotile abnormal sleep; they could predict the current status of the above three factors of sleep disorders (P=0.000). Subjective social support and objective social support were selected in the regressive function of daytime function and insomnia; they could predict the current status of daytime function and insomnia (P=0.000).

Conclusion: The current status of social support and sleep disorders in an only child’s demographic sample have significant differences; social support is closely related to an only child’s sleep disorder, and it can predict the only child’s sleep disorder.