免费看黄色大片-久久精品毛片-欧美日韩亚洲视频-日韩电影二区-天天射夜夜-色屁屁ts人妖系列二区-欧美色图12p-美女被c出水-日韩的一区二区-美女高潮流白浆视频-日韩精品一区二区久久-全部免费毛片在线播放网站-99精品国产在热久久婷婷-午夜精品理论片-亚洲人成网在线播放

Social media buffers depression among older adults with pain: study

Source: Xinhua| 2018-10-19 01:31:08|Editor: ZD
Video PlayerClose

CHICAGO, Oct. 18 (Xinhua) -- A study of the University of Michigan (UM) reports that using social media can reduce the negative health effects of curtailed social contact among seniors with pain.

UM researchers used data from a nationally representative survey involving more than 3,400 Medicare beneficiaries aged 65 and older in 2011. The respondents were asked about depression, pain and their social participation.

The data, however, does not distinguish between the types of social media that older adults use. To capture if purported benefits were from social media and not just from general internet use, the analysis was adjusted for various online uses such as paying bills or shopping for groceries.

The findings showed older adults who experienced pain were less likely to participate in social activities that require face-to-face interactions, which offers mental benefits.

Still, social media may preserve cognitive function and psychological well-being in this population, the researchers said.

"This is critical because the onset of pain can often lead to a downward spiral of social isolation and depression, resulting in adverse outcomes for the health of older adults," said Shannon Ang, the study's lead author and doctoral candidate at the UM Department of Sociology and Institute for Social Research.

The results may be possibly extended to other forms of conditions, say chronic illnesses, functional limitations, that, like pain, also restrict physical activity outside of the home.

The findings are significant among an aging society where social isolation and loneliness are key determinants of well-being, said Ang.

The findings have been published in Journals of Gerontology, Series B.

TOP STORIES
EDITOR’S CHOICE
MOST VIEWED
EXPLORE XINHUANET
010020070750000000000000011100001375425371