A review of strategies and levels of community engagement in strengths-based and needs-based health communication interventions.

Background: Community engagement is key in health communication interventions that seek to incorporate community voices in their planning and implementation. Understanding what approaches and strategies are currently being used can help tailor programs in different social and cultural contexts. This review explores needs-based and strengths-based approaches and consensus and conflict strategies in community-based global health communications programs. Our objective is to examine the current state of the field, outline lessons learned, and identify gaps in existing programming to help guide future interventions. Methods: PubMed and Web of Science were searched for articles published between 2010 and 2023. Studies were included if they described a community-based health communication intervention and an ongoing or completed implementation. Interventions were coded then categorized according to their level of community engagement and as single, hybrid, or complex, depending upon the number of approaches and strategies used. Results: The search yielded 678 results and 42 were included in the final review and analysis. A vast majority 34 (81.0%) interventions utilized a needs-based approach and 24 (57.1%) utilized a strengths-based approach. Consensus as a strategy was utilized in 38 (90.5%) of the manuscripts and 9 (21.4%) implemented a conflict strategy. Interventions that combined approaches and strategies were more likely to leverage a higher level of community engagement. Conclusion: These results showcase the complicated nature of global health communication program planning and implementation. There is a lack of interventions that use conflict as a strategy to empower communities to act on their own behalf, even when at odds with existing power structures. Complex interventions that include all approaches and strategies demonstrate the potential for global health communication interventions to be at the cutting edge of public health practice.

Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.

BACKGROUND: Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. METHODS: The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive, dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after out-of-hospital cardiac arrest, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and digital imaging and communications in medicine files (DICOMs). We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an autoencoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features that are historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features. RESULTS: PRECICECAP is currently enrolling and will be completed in late 2025. CONCLUSIONS: Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.

Fusion Learning on Multiple-Tag RFID Measurements for Respiratory Rate Monitoring.

Future advances in the medical Internet of Things (IoT) will require sensors that are unobtrusive and passively powered. With the use of wireless, wearable, and passive knitted smart garment sensors, we monitor infant respiratory activity. We improve the utility of multi-tag Radio Frequency Identification (RFID) measurements via fusion learning across various features from multiple tags to determine the magnitude and temporal information of the artifacts. In this paper, we develop an algorithm that classifies and separates respiratory activity via a Regime Hidden Markov Model compounded with higher-order features of Minkowski and Mahalanobis distances. Our algorithm improves respiratory rate detection by increasing the Signal to Noise Ratio (SNR) on average from 17.12 dB to 34.74 dB. The effectiveness of our algorithm in increasing SNR shows that higher-order features can improve signal strength detection in RFID systems. Our algorithm can be extended to include more feature sources and can be used in a variety of machine learning algorithms for respiratory data classification, and other applications. Further work on the algorithm will include accurate parameterization of the algorithm's window size.