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The HIV epidemic disproportionately impacts transgender women in the United States. Cohort studies identify unique risks for affected populations, but use of facility-based methods may bias findings towards individuals living in research catchment areas, more engaged in health services, or, in the case of transgender populations, those who are open about their transgender identity. Digital clinical trials and other online research methods are increasingly common, providing opportunity to reach those not commonly engaged in research. Simultaneously, there is a need to understand potential biases associated with digital research, how these methods perform, and whether they are accepted across populations.

This study aims to assess the feasibility of developing and implementing an online cohort of transgender women to assess risks for HIV acquisition and other health experiences. this website Further, this study aims to evaluate how an online cohort compares to a site-based, technology-enhanced cohort for epidemiologic rto the online cohort launched in January 2019. Active recruitment stopped in May 2019, and enrollment officially closed in August 2020. A total of 580 participants enrolled into and are followed in the cohort. A recruitment-enrollment cascade was observed across screening, consent, and completion of study activities. Implementation experiences with HIV test kits highlight the need for heavy staff engagement to support participant engagement, visit completion, and retention, even with automated digital procedures.

This study is responsive to increasing research interest in digital observational and intervention research, particularly for populations who are most affected by the HIV epidemic and for those who may otherwise not participate in person. The progression across stages of the recruitment-enrollment cascade provides useful insight for implementation of cohort studies in the online environment.

DERR1-10.2196/29152.

DERR1-10.2196/29152.

A high percentage of patients with cancer experience cognitive impairment after cancer treatment, resulting in a decreased health-related quality of life and difficulty returning to work. Consequently, there is a need for effective treatment options to improve cognitive functioning in these patients. In a healthy aging population, multidomain web-based lifestyle interventions have been found to be effective in preventing cognitive decline and improving cognitive functioning.

This study aims to investigate the feasibility and effectiveness of the web-based lifestyle intervention Mijn Fitte Brein (My Fit Brain [MFB]) on cognitive functioning in patients with cancer returning to work.

The study consists of a feasibility study (N=10), followed by a randomized controlled trial (RCT; N=220). Patients will be recruited by their occupational physicians after their return to work following cancer treatment. Mijn Fitte Brein is organized into 4-week cycles in which patients set a lifestyle goal using the Goal Attbility study will be used to optimize the Mijn Fitte Brein intervention. Enrollment for the RCT will continue when possible. The feasibility study will take 6 months (including making adjustments to the intervention), and the RCT will take 2 years. The final results are expected in 2024. The results of the feasibility study and the RCT will be published in peer-reviewed journals.

This is the first time the feasibility and efficacy of a multidomain web-based lifestyle intervention will be studied in patients with cancer. If Mijn Fitte Brein is found to be effective in decreasing cognitive complaints in these patients returning to work, it will be a promising treatment option because of being both affordable and accessible.

Netherlands Trial Register NL8407; https//www.trialregister.nl/trial/8407.

DERR1-10.2196/22670.

DERR1-10.2196/22670.

Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets.

This study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men.

The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection ng a social media-based just-in-time adaptive intervention to target substance use and HIV risk behavior.Understanding the connectivity observed in the brain and how it emerges from local plasticity rules is a grand challenge in modern neuroscience. In the primary visual cortex (V1) of mice, synapses between excitatory pyramidal neurons and inhibitory parvalbumin-expressing (PV) interneurons tend to be stronger for neurons that respond to similar stimulus features, although these neurons are not topographically arranged according to their stimulus preference. The presence of such excitatory-inhibitory (E/I) neuronal assemblies indicates a stimulus-specific form of feedback inhibition. Here, we show that activity-dependent synaptic plasticity on input and output synapses of PV interneurons generates a circuit structure that is consistent with mouse V1. Computational modeling reveals that both forms of plasticity must act in synergy to form the observed E/I assemblies. Once established, these assemblies produce a stimulus-specific competition between pyramidal neurons. Our model suggests that activity-dependent plasticity can refine inhibitory circuits to actively shape cortical computations.Why do we sometimes opt for actions or items that we do not value the most? Under current neurocomputational theories, such preference reversals are typically interpreted in terms of errors that arise from the unreliable signaling of value to brain decision systems. But, an alternative explanation is that people may change their mind because they are reassessing the value of alternative options while pondering the decision. So, why do we carefully ponder some decisions, but not others? In this work, we derive a computational model of the metacognitive control of decisions or MCD. In brief, we assume that fast and automatic processes first provide initial (and largely uncertain) representations of options’ values, yielding prior estimates of decision difficulty. These uncertain value representations are then refined by deploying cognitive (e.g., attentional, mnesic) resources, the allocation of which is controlled by an effort-confidence tradeoff. Importantly, the anticipated benefit of allocating resources varies in a decision-by-decision manner according to the prior estimate of decision difficulty.