Our aim was to characterize health beliefs about the human papillomavirus (HPV) vaccine in a large set of Twitter posts (tweets). We collected a Twitter data set related to the HPV vaccine from 1 January 2014, to 31 December 2017. We proposed a deep-learning-based framework to mine health beliefs on the HPV vaccine from Twitter. Deep learning achieved high performance in terms of sensitivity, specificity, and accuracy. A retrospective analysis of health beliefs found that HPV vaccine beliefs may be evolving on Twitter.

The CONSORT-EHEALTH checklist is intended for authors of randomized trials evaluating webbased and Internet-based applications/interventions, including mobile interventions, electronic games (incl multiplayer games), social media, certain telehealth applications, and other interactive and/or networked electronic applications. Some of the items (e.g. all subitems under item 5description of the intervention) may also be applicable for other study designs.The goal of the CONSORT EHEALTH checklist and guideline is to be a) a guide for reporting for authors of RCTs, b) to form a basis for appraisal of an ehealth trial (in terms of validity)CONSORT-EHEALTH items/subitems are MANDATORY reporting items for studies published in the Journal of Medical Internet Research and other journals / scienti c societies endorsing the checklist.Items numbered 1., 2., 3., 4a., 4b etc are original CONSORT or CONSORT-NPT (non-pharmacologic treatment) items. Items with Roman numerals (i., ii, iii, iv etc.) are CONSORT-EHEALTH extensions/clari cations.As the CONSORT-EHEALTH checklist is still considered in a formative stage, we would ask that you also RATE ON A SCALE OF 1-5 how important/useful you feel each item is FOR THE PURPOSE OF THE CHECKLIST and reporting guideline (optional).

BackgroundResearch studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the “social support” perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual’s efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era.ObjectiveThe objective of this work is two-fold: (1) to harness digital communication data to capture essential meaning of communication and factors affecting a desired behavior change, and (2) to understand the applicability of existing behavior change theories to characterize peer-to-peer communication in online platforms.MethodsIn this paper, we describe grounded theory–based qualitative analysis of digital communication in QuitNet, an online community promoting smoking cessation. A database of 16,492 de-identified public messages from 1456 users from March 1-April 30, 2007, was used in our study. We analyzed 795 messages using grounded theory techniques to ensure thematic saturation. This analysis enabled identification of key concepts contained in the messages exchanged by QuitNet members, allowing us to understand the sociobehavioral intricacies underlying an individual’s efforts to cease smoking in a group setting. We further ascertained the relevance of the identified themes to theoretical constructs in existing behavior change theories (eg, Health Belief Model) and theoretically linked techniques of behavior change taxonomy.ResultsWe identified 43 different concepts, which were then grouped under 12 themes based on analysis of 795 messages. Examples of concepts include “sleepiness,” “pledge,” “patch,” “spouse,” and “slip.” Examples of themes include “traditions,” “social support,” “obstacles,” “relapse,” and “cravings.” Results indicate that themes consisting of member-generated strategies such as “virtual bonfires” and “pledges” were related to the highest number of theoretical constructs from the existing behavior change theories. In addition, results indicate that the member-generated communication content supports sociocognitive constructs from more than one behavior change model, unlike the majority of the existing theory-driven interventions.ConclusionsWith the onset of mobile phones and ubiquitous Internet connectivity, online social network data reflect the intricacies of human health behavior as experienced by health consumers in real time. This study offers methodological insights for qualitative investigations that examine the various kinds of behavioral constructs prevalent in the messages exchanged among users of online communities. Theoretically, this study establishes the manifestation of existing behavior change theories in QuitNet-like online health communities. Pragmatically, it sets...

Background An increasing number of mobile and wearable devices are available in the market. However, the extent to which these devices can be used to assist older adults to age in place remains unclear. Objective This study aimed to assess older adults’ perceptions of using mobile and connected health technologies. Methods Using a cross-sectional design, a total of 51 participants were recruited from a senior community center. Demographics and usage of mobile or wearable devices and online health communities were collected using a survey questionnaire. Descriptive statistics assessed usage of devices and online health communities. The Fisher exact test was used to examine the relationship between technology usage and having access to a smartphone. Results The sample was primarily comprised non-Hispanic white (35/51, 69%), educated (39/51, 76% any college), and female (36/51, 71%) participants, with an average age of 70 (SD 8) years. All participants were insured and nearly all lived at home (49/51, 94%). A total of 86% (44/51) of the participants had heard of wearable health devices, but only 18 out of 51 (35%) had ever used them. Over 80% (42/51) expressed interest in using such devices and were interested in tracking exercise and physical activity (46/51, 90%), sleep (38/51, 75%), blood pressure (34/51, 67%), diet (31/51, 61%), blood sugar (28/51, 55%), weight (26/51, 51%), and fall risk (23/51, 45%). The greatest concerns about using wearable devices were cost (31/51, 61%), safety (14/51, 28%), and privacy (13/51, 26%); one-fourth (12/51) reported having no concerns. They were mostly interested in sharing data from mobile and connected devices with their health care providers followed by family, online communities, friends, and no one. About 41% (21/51) of the older adults surveyed reported having ever heard of an online health community, and roughly 40% (20/51) of the participants reported being interested in joining such a community. Most participants reported having access to a smartphone (38/51, 74%), and those with such access were significantly more likely to show interest in using a wearable health device ( P <.001) and joining an online health community ( P =.05). Conclusions Our findings suggest that, although few older adults are currently using mobile and wearable devices and connected health technologies for managing health, they are open to this idea and are mostly interested in sharing such data with their health care providers. Further studies are warranted to explore strategies to balance the data sharing preference of older adults and how to best integrate mobile and wearable device data with clinical workflow for health care providers to promote healthy aging in place.

Modeling health-related affiliation networks through content-driven methods can enable the identification of specific content related to higher abstinence rates, which facilitates targeted health promotion.