Background By 2030, the number of US adults age ≥65 will exceed 70 million. Their quality of life has been declared a national priority by the US government. Objective Assess effects of an eHealth intervention for older adults on quality of life, independence, and related outcomes. Design Multi-site, 2-arm (1:1), non-blinded randomized clinical trial. Recruitment November 2013 to May 2015; data collection through November 2016. Setting Three Wisconsin communities (urban, suburban, and rural). Participants Purposive community-based sample, 390 adults age ≥65 with health challenges. Exclusions: long-term care, inability to get out of bed/chair unassisted. Intervention Access (vs. no access) to interactive website (ElderTree) designed to improve quality of life, social connection, and independence. Measures Primary outcome: quality of life (PROMIS Global Health). Secondary: independence (Instrumental Activities of Daily Living); social support (MOS Social Support); depression (Patient Health Questionnaire-8); falls prevention (Falls Behavioral Scale). Moderation: healthcare use (Medical Services Utilization). Both groups completed all measures at baseline, 6, and 12 months. Results Three hundred ten participants (79%) completed the 12-month survey. There were no main effects of ElderTree over time. Moderation analyses indicated that among participants with high primary care use, ElderTree (vs. control) led to better trajectories for mental quality of life (OR=0.32, 95% CI 0.10–0.54, P=0.005), social support received (OR=0.17, 95% CI 0.05–0.29, P=0.007), social support provided (OR=0.29, 95% CI 0.13–0.45, P<0.001), and depression (OR= −0.20, 95% CI −0.39 to −0.01, P=0.034). Supplemental analyses suggested ElderTree may be more effective among people with multiple (vs. 0 or 1) chronic conditions. Limitations Once randomized, participants were not blind to the condition; self-reports may be subject to memory bias. Conclusion Interventions like ET may help improve quality of life and socio-emotional outcomes among older adults with more illness burden. Our next study focuses on this population. Trial Registration ClinicalTrials.gov; registration ID number: NCT02128789

The study examines whether physicians' framing of clinical interactions is related to patient shared decision-making (SDM) satisfaction when using a clinical decision support tool (CDST) concerning mammographic screening. To answer this question, we combined (a) system log data from a CDST, (b) content coding of the physicians' message framing while using the CDST, and (c) a post-visit patient survey to assess SDM satisfaction concerning screening mammography. Results suggest that two types of message frames -consequence frames and numerical framesmoderated the relationship of the CDST on SDM satisfaction. When the CDST displayed low risk of breast cancer for a patient, physicians were able to improve the cognitive aspects of SDM satisfaction by framing the consequences of mammography screening in positive terms. However, when the physician delivered the numerical information in relative, rather than absolute terms, the patient's SDM satisfaction was reduced. Our study advances previous message framing effect research in health communication from experimental settings to clinical encounters. It also discusses the importance of delivering risk-congruent frames in clinical settings.

The social mediation role of mobile technology is typified by mHealth apps designed to connect individuals to others and support substance use disorder (SUD) recovery. In this study, we examined the use and utility of one such app designed to support people living with HIV (PLWH) and SUD. Drawing on Ling’s emphasis on reciprocity and micro-coordination in mobile telephony as a social mediation technology, we gathered digital trace data from app logs to construct two metrics, initiation (i.e. whether a particular feature is engaged on a given day) and intensity (i.e. degree of involvement in the activity when engaged on that day), at three levels of communication—networked (one-to-many), dyadic (one-to-one), and intraindividual (self-to-self). We consider these system features alongside use of information resources, games and relaxation links, a meeting and events calendar, and support tools to address use urges. We found few differences in patterns of use by race, sex, and age, though African Americans were less likely to engage in intraindividual expression, whereas women and older users were more likely to make use of this feature. The initiation and intensity of network and dyadic reception, as well as the intensity of network expression, predicts recovery outcomes as measured on a weekly “check-in” survey, suggesting the utility of mobile log data for digital phenotyping in mHealth. By implementing this app during the COVID-19 pandemic, the study also found the disruption caused by national lockdown was negatively related to the app use.

In this paper, we design and implement a map dashboard that combines spatio-temporal visualization and interactive narrative to comprehensively illustrate the 2020 US presidential election. Specifically, our dashboard takes campaign rallies and major events as narrative clues and integrates multi-perspective factors (e.g., the spatial spread of COVID-19, social distancing adherence, poll results) for visualization and statistical analysis. Compared with traditional methods and products, our integrated multi-perspective solution better balances the narrative property and the geovisualization property of a dashboard, making it suitable for illustrating social or political events that happened on a large geographic scale. The result shows that our narrative-based geovisualization dashboard may be used for demonstrating and associating multiple factors with partisanship and has the potential to help users explore the interaction between policies controlling COVID-19, social distancing, and partisanship across the country during the 2020 US presidential election.

Background Social media platforms have been increasingly used to express suicidal thoughts, feelings, and acts, raising public concerns over time. A large body of literature has explored the suicide risks identified by people’s expressions on social media. However, there is not enough evidence to conclude that social media provides public surveillance for suicide without aligning suicide risks detected on social media with actual suicidal behaviors. Corroborating this alignment is a crucial foundation for suicide prevention and intervention through social media and for estimating and predicting suicide in countries with no reliable suicide statistics. Objective This study aimed to corroborate whether the suicide risks identified on social media align with actual suicidal behaviors. This aim was achieved by tracking suicide risks detected by 62 million tweets posted in Japan over a 10-year period and assessing the locational and temporal alignment of such suicide risks with actual suicide behaviors recorded in national suicide statistics. Methods This study used a human-in-the-loop approach to identify suicide-risk tweets posted in Japan from January 2013 to December 2022. This approach involved keyword-filtered data mining, data scanning by human efforts, and data refinement via an advanced natural language processing model termed Bidirectional Encoder Representations from Transformers. The tweet-identified suicide risks were then compared with actual suicide records in both temporal and spatial dimensions to validate if they were statistically correlated. Results Twitter-identified suicide risks and actual suicide records were temporally correlated by month in the 10 years from 2013 to 2022 (correlation coefficient=0.533; P<.001); this correlation coefficient is higher at 0.652 when we advanced the Twitter-identified suicide risks 1 month earlier to compare with the actual suicide records. These 2 indicators were also spatially correlated by city with a correlation coefficient of 0.699 (P<.001) for the 10-year period. Among the 267 cities with the top quintile of suicide risks identified from both tweets and actual suicide records, 73.5% (n=196) of cities overlapped. In addition, Twitter-identified suicide risks were at a relatively lower level after midnight compared to a higher level in the afternoon, as well as a higher level on Sundays and Saturdays compared to weekdays. Conclusions Social media platforms provide an anonymous space where people express their suicidal thoughts, ideation, and acts. Such expressions can serve as an alternative source to estimating and predicting suicide in countries without reliable suicide statistics. It can also provide real-time tracking of suicide risks, serving as an early warning for suicide. The identification of areas where suicide risks are highly concentrated is crucial for location-based mental health planning, enabling suicide prevention and intervention through social media in a spatially and temporally explicit manner.