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\"Internet addiction is an emergent disorder in the psychiatric and psychological fields. This book provides a theoretical framework to understand how to define and conceptualize compulsive use of the Internet from a clinical perspective. With various theoretical models from the psychiatric, psychological, communication, and sociological fields, it explores the prevalence of the disorder and the most addictive or problematic online applications, such as online pornography, Internet gambling, and online games. Evidenced-based treatment approaches are provided as well. A must-have for every clinician\"--Provided by publisher.

eHealth is developing rapidly and brings with it a promise to reduce social health inequalities (SHIs). Yet, it appears that it also has the potential to increase them. The general objective of this review was to set out how to ensure that eHealth contributes to reducing SHIs rather than exacerbating them. This review has three objectives: (1) identifying characteristics of people at risk of experiencing social inequality in health; (2) determining the possibilities of developing eHealth tools that avoid increasing SHI; and (3) modeling the process of using an eHealth tool by people vulnerable to SHI. Following the EPPI approach (Evidence for Policy and Practice of Information of the Institute of Education at the University of London), two databases were searched for the terms SHIs and eHealth and their derivatives in titles and abstracts. Qualitative, quantitative, and mixed articles were included and evaluated. The software NVivo (QSR International) was employed to extract the data and allow for a metasynthesis of the data. Of the 73 articles retained, 10 were theoretical, 7 were from reviews, and 56 were based on empirical studies. Of the latter, 40 used a quantitative approach, 8 used a qualitative approach, 4 used mixed methods approach, and only 4 were based on participatory research-action approach. The digital divide in eHealth is a serious barrier and contributes greatly to SHI. Ethnicity and low income are the most commonly used characteristics to identify people at risk of SHI. The most promising actions for reducing SHI via eHealth are to aim for universal access to the tool of eHealth, become aware of users' literacy level, create eHealth tools that respect the cultural attributes of future users, and encourage the participation of people at risk of SHI. eHealth has the potential to widen the gulf between those at risk of SHI and the rest of the population. The widespread expansion of eHealth technologies calls for rigorous consideration of interventions, which are not likely to exacerbate SHI.

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.

Mobile phone use and the adoption of healthy lifestyle software apps (\"health apps\") are rapidly proliferating. There is limited information on the users of health apps in terms of their social demographic and health characteristics, intentions to change, and actual health behaviors. The objectives of our study were to (1) to describe the sociodemographic characteristics associated with health app use in a recent US nationally representative sample; (2) to assess the attitudinal and behavioral predictors of the use of health apps for health promotion; and (3) to examine the association between the use of health-related apps and meeting the recommended guidelines for fruit and vegetable intake and physical activity. Data on users of mobile devices and health apps were analyzed from the National Cancer Institute's 2015 Health Information National Trends Survey (HINTS), which was designed to provide nationally representative estimates for health information in the United States and is publicly available on the Internet. We used multivariable logistic regression models to assess sociodemographic predictors of mobile device and health app use and examine the associations between app use, intentions to change behavior, and actual behavioral change for fruit and vegetable consumption, physical activity, and weight loss. From the 3677 total HINTS respondents, older individuals (45-64 years, odds ratio, OR 0.56, 95% CI 0.47-68; 65+ years, OR 0.19, 95% CI 0.14-0.24), males (OR 0.80, 95% CI 0.66-0.94), and having degree (OR 2.83, 95% CI 2.18-3.70) or less than high school education (OR 0.43, 95% CI 0.24-0.72) were all significantly associated with a reduced likelihood of having adopted health apps. Similarly, both age and education were significant variables for predicting whether a person had adopted a mobile device, especially if that person was a college graduate (OR 3.30). Individuals with apps were significantly more likely to report intentions to improve fruit (63.8% with apps vs 58.5% without apps, P=.01) and vegetable (74.9% vs 64.3%, P<.01) consumption, physical activity (83.0% vs 65.4%, P<.01), and weight loss (83.4% vs 71.8%, P<.01). Individuals with apps were also more likely to meet recommendations for physical activity compared with those without a device or health apps (56.2% with apps vs 47.8% without apps, P<.01). The main users of health apps were individuals who were younger, had more education, reported excellent health, and had a higher income. Although differences persist for gender, age, and educational attainment, many individual sociodemographic factors are becoming less potent in influencing engagement with mobile devices and health app use. App use was associated with intentions to change diet and physical activity and meeting physical activity recommendations.

Health care policy encourages better electronic connectivity between patient and the office practice. However, whether patients are able to partner with the practice in using communication technologies is not known. We sought to determine (1) the proportion of clinic patients who use internet and cell phone text messaging technologies, (2) the level of patient interest in using these technologies for the purpose of managing clinical appointments and patient education. Consecutive adult patients, clinicians and staff at an urban community health center were surveyed during a one-week period in order to estimate the frequency of technology use by patients. A total of 308 survey cards were collected during the designated week (response rate of 85% (308/362). One-third (34.0%, 105) of surveyed patients used the internet and text messaging daily or weekly, while nearly two-thirds (59.7%, 182) never used these technologies. There were no racial or gender differences in the proportion of patients who used the internet daily or weekly. In contrast, AfricanAmericans used text messaging more often than whites (28.2 vs. 21.4%, P < .05), and females more than males (30.8 vs. 18.5%, P < .05). Younger patients (>50) used the internet and text messaging more often than older patients (50.6 vs. 16.6%, 44.3 vs. 7.3%, respectively). Despite the low use of both technologies, patient's interest in managing clinic appointments was high (40.3% for the Internet and 56.8% for text messaging). Clinicians and staff estimated patient's daily/weekly use of internet and cellphone messaging at 40.3% (±22.0), and 56.8% (±25.7), respectively. Most patients at this urban community health center reported never using the internet or cell phone text messaging. Clinicians overestimated technology use by patients. Planning for clinic infrastructure, quality improvement, and patient education should include assessment of technology use patterns by patients.

In 2015, 90% of US young adults with Internet access used social media. Digital and social media are highly prevalent modalities through which young adults explore identity formation, and by extension, learn and transmit norms about health and risk behaviors during this developmental life stage. The purpose of this study was to provide updated estimates of social media use from 2014 to 2016 and correlates of social media use and access to digital technology in data collected from a national sample of US young adults in 2016. Young adult participants aged 18-24 years in Wave 7 (October 2014, N=1259) and Wave 9 (February 2016, N=989) of the Truth Initiative Young Adult Cohort Study were asked about use frequency for 11 social media sites and access to digital devices, in addition to sociodemographic characteristics. Regular use was defined as using a given social media site at least weekly. Weighted analyses estimated the prevalence of use of each social media site, overlap between regular use of specific sites, and correlates of using a greater number of social media sites regularly. Bivariate analyses identified sociodemographic correlates of access to specific digital devices. In 2014, 89.42% (weighted n, 1126/1298) of young adults reported regular use of at least one social media site. This increased to 97.5% (weighted n, 965/989) of young adults in 2016. Among regular users of social media sites in 2016, the top five sites were Tumblr (85.5%), Vine (84.7%), Snapchat (81.7%), Instagram (80.7%), and LinkedIn (78.9%). Respondents reported regularly using an average of 7.6 social media sites, with 85% using 6 or more sites regularly. Overall, 87% of young adults reported access or use of a smartphone with Internet access, 74% a desktop or laptop computer with Internet access, 41% a tablet with Internet access, 29% a smart TV or video game console with Internet access, 11% a cell phone without Internet access, and 3% none of these. Access to all digital devices with Internet was lower in those reporting a lower subjective financial situation; there were also significant differences in access to specific digital devices with Internet by race, ethnicity, and education. The high mean number of social media sites used regularly and the substantial overlap in use of multiple social media sites reflect the rapidly changing social media environment. Mobile devices are a primary channel for social media, and our study highlights disparities in access to digital technologies with Internet access among US young adults by race/ethnicity, education, and subjective financial status. Findings from this study may guide the development and implementation of future health interventions for young adults delivered via the Internet or social media sites.

Individuals are increasingly turning to online environments to find information about science and to follow scientific developments. It is therefore crucial for scientists and scientific institutions to consider empirical findings from research in online science communication when thinking about science in the public sphere. After providing a snapshot of the current media landscape, this paper reviews recent major research findings related to science communication in the online environment and their implications for science in the 21st century. Particular emphasis is given to the bias introduced by search engines, the nature of scientific content encountered online, and the potential impact of the Internet on audiences’ knowledge and attitudes toward science.

As for all individuals, the Internet is important in the everyday life of older adults. Research on older adults' use of the Internet has merely focused on users versus nonusers and consequences of Internet use and nonuse. Older adults are a heterogeneous group, which may implicate that their use of the Internet is diverse as well. Older adults can use the Internet for different activities, and this usage can be of influence on benefits the Internet can have for them. The aim of this paper was to describe the diversity or heterogeneity in the activities for which older adults use the Internet and determine whether diversity is related to social or health-related variables. We used data of a national representative Internet panel in the Netherlands. Panel members aged 65 years and older and who have access to and use the Internet were selected (N=1418). We conducted a latent class analysis based on the Internet activities that panel members reported to spend time on. Second, we described the identified clusters with descriptive statistics and compared the clusters using analysis of variance (ANOVA) and chi-square tests. Four clusters were distinguished. Cluster 1 was labeled as the \"practical users\" (36.88%, n=523). These respondents mainly used the Internet for practical and financial purposes such as searching for information, comparing products, and banking. Respondents in Cluster 2, the \"minimizers\" (32.23%, n=457), reported lowest frequency on most Internet activities, are older (mean age 73 years), and spent the smallest time on the Internet. Cluster 3 was labeled as the \"maximizers\" (17.77%, n=252); these respondents used the Internet for various activities, spent most time on the Internet, and were relatively younger (mean age below 70 years). Respondents in Cluster 4, the \"social users,\" mainly used the Internet for social and leisure-related activities such as gaming and social network sites. The identified clusters significantly differed in age (P<.001, ω =0.07), time spent on the Internet (P<.001, ω =0.12), and frequency of downloading apps (P<.001, ω =0.14), with medium to large effect sizes. Social and health-related variables were significantly different between the clusters, except social and emotional loneliness. However, effect sizes were small. The minimizers scored significantly lower on psychological well-being, instrumental activities of daily living (iADL), and experienced health compared with the practical users and maximizers. Older adults are a diverse group in terms of their activities on the Internet. This underlines the importance to look beyond use versus nonuse when studying older adults' Internet use. The clusters we have identified in this study can help tailor the development and deployment of eHealth intervention to specific segments of the older population.

Objectives: To measure the coverage, gender distribution, and participants’ prevalence of genital Chlamydia trachomatis infections using a new internet based self selective testing approach by means of home sampling in the general population in Sweden. To investigate factors associated with these measures. Methods: Observational survey of people actively ordering coded testing packages, and checking their test results, from a known website. No personal invitations were sent out. All inhabitants (256 885 men and women) in a Swedish county were eligible to participate. A special interest was taken in coverage, gender distribution, participants’ chlamydia prevalence, and determinants for infection. Results: Testing was highest in the age group 20–24 years, where 298/9495 (3.1%) of all women and 171/9574 (1.8%) of all men in the population participated. The overall population participation (all ages) was 906/256 885 (0.4%). 40% (364/906) of the tests were from men and 60% (542/906) from women. The prevalence of Chlamydia trachomatis infection was 6.0% (95% CI: 3.6% to 8.4%) among male participants and 4.6% (95% CI: 2.8% to 6.4%) among female participants. Prevalence increased with decreasing age. Believing to be infected and having symptoms were the strongest determinants of infection. Conclusion: Simplifying and increasing the accessibility of chlamydia testing by means of internet and home sampling proved feasible. Self risk assessment improves the chance of finding people infected by Chlamydia trachomatis, especially among men, if an accessible testing method is offered. This new method can serve as a supplement to regular preventive methods and might encourage young people to be tested.