Background: Past research suggests that knowledge supports- but strong illness expectations thwart- adoption of protective behaviors (e.g., handwashing). Strong illness expectations may place COVID-19 essential workers at risk. It is unclear, however, whether knowledge can moderate the negative effects of pessimistic illness expectations on protective behaviors. We test COVID-19 knowledge as a moderator of the effects of (1) pessimistic illness expectations and (2) essential worker status on adherence to protective behaviors. Methods: Participants (n = 350) completed measures of knowledge, illness expectations, and protective behaviors. We used chi-square tests to examine associations between variables and logistic regressions to test the moderation models predicting adherence (low, high) while controlling for demographics. Results: Knowledge, illness expectations, and adherence were significantly associated with each other (p < 0.05). Essential workers had stronger illness expectations and lower knowledge than did non-essential workers (p < 0.001). Logistic regressions showed a non-significant Worker Status × Knowledge interaction (p = 0.59) but a significant Knowledge × Illness Expectations interaction (p < 0.05) indicating that those with strong illness expectations and low knowledge were disproportionately at risk of failing to adhere to recommended behaviors. Conclusions: Knowledge promotes protective behaviors by buffering the negative effects of pessimistic illness expectations. Essential workers are more likely to have low levels of knowledge with strong illness expectations, suggesting that educational policies may be warranted.

Background

As individuals age, chronic health difficulties may disrupt physical and social well-being. Individuals can turn to online communities to interact with similar peers, which may help buffer negative effects resulting from health difficulties.

Objective

This study investigated the reasons that older adults join a diabetes online community to better understand the specific resources that are being sought.

Methods

We used semantic network analyses to categorize the reasons participants provided for joining a community during the sign-up process.

Results

The most frequent reasons for joining were to seek information about their health condition, to help with self-management of health difficulties, for feelings of informational and social support, and for having a community with whom to share. Women were more likely to go online for sharing and companionship as well as for information and social support reasons, whereas men were more likely to go online for general information and self-management reasons.

Conclusions

This study shows the reasons older adults seek to join a diabetes online community: for increased information and support regarding chronic health difficulties. Practitioners may want to consider ways to promote access to online communities among their older patients as a source of health information and a resource to provide a sense of community.

Background

This case study describes the formation of the Intercultural Diabetes Online Community Research council (iDOCr) and community advisory board (CAB) to engage the diabetes community and researchers in the codesign of community-based participatory research (CBPR) to examine online peer support in type 2 diabetes (T2D).

Method

Social media engagement was the foundation for CBPR knowledge generation. During the project, the iDOCr council and CAB (n = 27) met quarterly via video conferencing and three times in person during national diabetes meetings. Data from four Twitter chats were used to explore the usefulness and utility of Twitter data to learn about concerns and priorities of the diabetes online community (DOC) and supported the evolution of iDOCr, the development of a research question and the design of a CBPR study.

Results

The iDOCr project (1) created a diverse CAB; (2) raised awareness of iDOCr and online peer support, which resulted in support and trust from key opinion leaders within the DOC to enable future partnerships for research and funding; (3) engaged with English- and Spanish-speaking DOC users through social media; and (4) designed a CBPR study supported by Twitter chat data analyses.

Conclusions

Integrating the voice of people with diabetes (PWD) and the DOC in designing CBPR, through use of a CAB, ensures the most important and relevant research questions are asked. Additional research focused on online peer support may increase health care provider confidence in referring PWD to this low-cost and relatively accessible resource with the potential power to advance health.

We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg 2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320-1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg 2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r ∼27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.