Abstract Background and Objective older adults have increased risk of social isolation, loneliness and cognitive functioning impairment, but the relationships among these factors are not conclusive. We investigated the potential mediation mechanism of loneliness on the association between social isolation and cognitive functioning among Chinese older adults within their cultural context. Design secondary analysis of the baseline wave (2011–12) of the harmonised China Health and Retirement Longitudinal Study. Setting and Subjects community-dwelling older adults in China (N = 7,410 participants aged 60–101 years). Methods we applied a multiple indicator multiple cause approach to determine whether the construct of social isolation is well defined by four indicators (social activity engagement, weekly adult children contact, caregiving for grandchildren and living alone) and used structural equation modelling to examine the direct and indirect effects among variables of interest. Results the results demonstrated that social activity engagement, weekly adult children contact and caregiving for grandchildren were significantly related to social isolation (β = −0.26 to −0.28) (Living alone was fixed to 1 for model identification.) The indirect effect of social isolation on cognitive functioning through loneliness was significant (β = −0.15), indicating loneliness was an important mediator. However, the direct effect of social isolation on cognitive functioning also remained significant (β = −0.83), suggesting a partial mediation effect. Conclusions our study highlights the mediation role of loneliness in the relationship between social isolation and cognitive functioning among Chinese older adults. The findings support the beneficial effects of maintaining social relations and coping with feelings of loneliness on older adults’ cognitive functioning.

Objectives Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs). Methods In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed. Results A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review. Discussion PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.

Air quality is important, varies across time and space, and is largely invisible. Pioneering past work deploying air quality monitors in residential environments found that study participants improved their awareness of and engagement with air quality. However, these systems fielded a single monitor and did not support user-specified annotations, inhibiting their utility. We developed MAAV-a system to Measure Air quality, Annotate data streams, and Visualize real-time PM 2.5 levelsto explore how participants engage with an air quality system addressing these challenges. MAAV supports collecting data from multiple air quality monitors, annotating that data through multiple modalities, and sending text message prompts when it detects a PM 2.5 spike. MAAV also features an interactive tablet interface for displaying measurement data and annotations. Through six long-term field deployments (20-47 weeks, mean 37.7 weeks), participants found these system features important for understanding the air quality in and around their homes. Participants gained new insights from between-monitor comparisons, reflected on past PM 2.5 spikes with the help of their annotations, and adapted their system usage as they familiarized themselves with their air quality data and MAAV. These results yield important insights for designing residential sensing systems that integrate into users' everyday lives.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention—the starting point for delivery of “All the right care, but only the right care,” an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

BACKGROUND: Women with chronic health conditions benefit from reproductive planning and access to highly effective contraception. OBJECTIVE: To determine the prevalence of and relationship between chronic health conditions and use of highly effective contraception among reproductive-age women. DESIGN: Retrospective cohort study using electronic health records. PARTICIPANTS: We identified all women 16-49 years who accessed care in the two largest health systems in the US Intermountain West between January 2010 and December 2014. MAIN MEASURES: We employed administrative codes to identify highly effective contraception and flag chronic health conditions listed in the US Medical Eligibility Criteria for Contraceptive Use (US MEC) and known to increase risk of adverse pregnancy outcomes. We described use of highly effective contraception by demographics and chronic conditions. We used multinomial logistic regression to relate demographic and disease status to contraceptive use. KEY RESULTS: Of 741,612 women assessed, 32.4% had at least one chronic health condition and 7.3% had two or more chronic conditions. Overall, 7.6% of women with a chronic health condition used highly effective contraception vs. 5.1% of women without a chronic condition. Women with chronic conditions were more likely to rely on public health insurance. The proportion of women using long-acting reversible contraception did not increase with chronic condition number (5.8% with 1 condition vs. 3.2% with 5 or more). In regression models adjusted for age, race, ethnicity, and payer, women with chronic conditions were more likely than those without chronic conditions to use highly effective contraception (aRR 1.4; 95% CI 1.4-1.5). Public insurance coverage was associated with both use of long-acting reversible contraception (aRR 2.2; 95% CI 2.1-2.3) and permanent contraception (aRR 2.9; 95% CI 2.7-3.1). CONCLUSIONS: Nearly a third of reproductive-age women in a regional health system have one or more chronic health condition. Public insurance increases the likelihood that women with a chronic health condition use highly effective contraception.