![]() (500 respondents were interviewed via a landline telephone, and 1,502 were interviewed via a cellphone, including 1,071 who had no landline telephone.) The survey was conducted by interviewers under the direction of ABT Associates, Inc. The national sample included 2002 adults who were 18 years of age or older and lived in one of the 50 U.S. The analysis in this report is based on telephone interviews that were conducted on 3–10 January 2018. ![]() In total, 88% of 18 to 29 year olds report using social media (compared to 78% to 37% of older age groups), and young adults spend more time (averaging over 3 hours daily) on social media than older adults. Young adults are heavy users of social media (i.e., social networking sites, including Facebook, Twitter, YouTube, Twitter, Instagram, Snapchat, and Tumblr). Social media is part of people’s routines and is an essential way to communicate, shop, find things to do, and check the news. It’s how people are aided in their daily life. “Social media is how people communicate, look for events, notice stores, and brands, and find the weather” (Josh Loewen, digital marketing director of The Status Bureau). On social media, participants are able to “obtain guidance and recommendations from others, share experiences, locate goods and services, and make purchases”. It is defined as “an emerging interdisciplinary research field that aims at combining, extending, and adapting methods for the analysis of social media data”. The term “Social Media Analytics” has gained a great deal of attention. For example, social media data can be analyzed in order to gain insights into issues, trends, influential actors, and other kinds of information. The growth of social media usage opens up new opportunities for analyzing several aspects of, and patterns in, communication. When using social networks correctly for marketing, companies can significantly improve their brand awareness, customer satisfaction, quality, reach, and profit. Overall, understanding where and at what frequency users are on social media can be a key competitive advantage. As age increases, social media use decreases, while bigger household income means that social media are used more. The results show that people with high household incomes and high education use social media the most. The analysis used a dataset that contains information related to 2002 respondents from the U.S. What about gender? What about education, income, age or social status? This paper answers some of these questions using statistical analyses and by dividing overall social media use into selected social media, i.e., Facebook, Instagram, Snapchat, YouTube, and Twitter. The increasing popularity of social media raises a number of questions regarding why we use it so much and what aspects influence this activity. Conclusions: CFS patients had more abrupt interruptions of voluntary physical activity during diurnal periods in normal daily life, probed by the decreased correlation in the negative modulus maxima of the wavelet-transformed activity data, possibly due to their exaggerated fatigue.Social media has evolved over the last decade to become an important driver for acquiring and spreading information in different domains such as business, entertainment, crisis management, and politics. Such a difference was identified neither by the DFA nor WTMM method. ![]() ![]() The WTNMM method revealed that, in diurnal activities, CFS patients had significantly (p<0.01) smaller fractal scaling exponent (0.87 &PLUSMN 0.03) compared to controls (1.01 &PLUSMN 0.03). No group difference was found in nocturnal activities. Results: Both for CFS and CON, we found the fractal time structures in their diurnal physical activity records for at least up to 35 minutes. We compared the fractal scaling exponents for CFS and CON by each method. Thus, we further developed a new method, the wavelet transform negative modulus maxima (WTNMM) method, which could evaluate the temporal correlation at the interruption of activities. We hypothesized that, due to their illness- and/or fatigue-induced resting episodes, altered physical activity patterns in CFS patients might be observed at the interruption of activity bursts. Methods: Fractal scaling exponent of diurnal and nocturnal physical activity time series in 10 CFS patients and 6 healthy control subjects (CON) were calculated by the detrended fluctuation analysis (DFA) and the wavelet transform modulus maxima (WTMM) method. Our objectives were to study the temporal correlation of physical activity time series in patients with chronic fatigue syndrome (CFS) during normal daily life and to examine if it could identify the altered physical activity in these patients. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |