Precision prevention in occupational health: a conceptual analysis and development of a unified understanding and an integrative framework.

Introduction: Precision prevention implements highly precise, tailored health interventions for individuals by directly addressing personal and environmental determinants of health. However, precision prevention does not yet appear to be fully established in occupational health. There are numerous understandings and conceptual approaches, but these have not yet been systematically presented or synthesized. Therefore, this conceptual analysis aims to propose a unified understanding and develop an integrative conceptual framework for precision prevention in occupational health. Methods: Firstly, to systematically present definitions and frameworks of precision prevention in occupational health, six international databases were searched for studies published between January 2010 and January 2024 that used the term precision prevention or its synonyms in the context of occupational health. Secondly, a qualitative content analysis was conducted to analyze the existing definitions and propose a unified understanding. Thirdly, based on the identified frameworks, a multi-stage exploratory development process was applied to develop and propose an integrative conceptual framework for precision prevention in occupational health. Results: After screening 3,681 articles, 154 publications were reviewed, wherein 29 definitions of precision prevention and 64 different frameworks were found, which can be summarized in eight higher-order categories. The qualitative content analysis revealed seven themes and illustrated many different wordings. The proposed unified understanding of precision prevention in occupational health takes up the identified themes. It includes, among other things, a contrast to a "one-size-fits-all approach" with a risk- and resource-oriented data collection and innovative data analytics with profiling to provide and improve tailored interventions. The developed and proposed integrative conceptual framework comprises three overarching stages: (1) data generation, (2) data management lifecycle and (3) interventions (development, implementation and adaptation). Discussion: Although there are already numerous studies on precision prevention in occupational health, this conceptual analysis offers, for the first time, a proposal for a unified understanding and an integrative conceptual framework. However, the proposed unified understanding and the developed integrative conceptual framework should only be seen as an initial proposal that should be critically discussed and further developed to expand and strengthen both research on precision prevention in occupational health and its practical application in the workplace.

Connecting precision nutrition with the Food is Medicine approach.

Two initiatives are reshaping how we can approach and address the persistent and widely prevalent challenge of malnutrition, the leading global risk factor for morbidity and mortality. First is the focus on precision nutrition to identify inter- and intra-individual variation in our responses to diet, and its determinants. Second is the Food is Medicine (FIM) approach, an umbrella term for programs and services that link nutrition and health through the provision of food (e.g., tailored meals, produce prescriptions) and access to healthcare services. This article outlines how interventions and programs using FIM can synergize with precision nutrition approaches to make individual- or population-level tailored nutrition accessible and affordable, help to reduce the risk of metabolic diseases, and improve quality of life.

Tabula Rasa: A Case Study in Chronic Pain Management Using Individual-Centric AI.

There are numerous behavioural, social and environmental factors that influence the symptomatology of a chronic health condition. These factors and how they manifest are often very specific to the individual, which creates challenges for applying macro population health approaches and insights to guide treatment. An artificial intelligence system, referred to as a non-axiomatic reasoning system (NARS), is presented. Learning in NARS is incremental and ongoing. A practical application of NARS in chronic pain management is demonstrated, as NARS can establish associations with behavioural activities that might exacerbate pain levels and revise the strengths of these associations over time. The system has potential application in any condition requiring patient-centric adaption.

Using Neuropsychological Profiling to Tailor Mental Health Care for Children and Youth: a Quality Improvement Project to Measure Feasibility.

OBJECTIVE: Precision child and youth mental healthcare has great potential to improve treatment success by tailoring interventions to individual needs. An innovative care pathway in a pediatric mental health outpatient clinic was designed to allow for neuropsychology data to be integrated in psychotherapeutic care. This paper describes the feasibility of this new pathway, including implementation outcomes, acceptability, and potential for future integration. METHOD: The target population was outpatients 6-17 years old referred for individual treatment to a tertiary outpatient mental health (OPMH) clinic. The new care pathway was co-developed by neuropsychologists and mental health practitioners. A logic model was created to guide the evaluation, which was informed by the Reach Effectiveness Adoption Implementation Maintenance framework. As part of the logic model, a stepped assessment protocol was implemented, and reports on neuropsychological function were shared with patients, caregivers, and care providers. Evaluation data were collected from phone surveys, questionnaires, a focus group, and administrative records. RESULTS: Forty-two patients scheduled to receive therapy over a 6-month period were offered the opportunity to participate in the new care pathway and 39 (93%) agreed. Self-reported outcome data showed that 83% of patients and 94% of caregivers valued neuropsychology-informed care, with some describing it as transformative. Almost all practitioners (91%) reported that the project added value to their clinical care. There were no adverse effects on participants nor the flow of patients through the system. CONCLUSIONS: Neuropsychology-informed pediatric OPMH care was feasible and well-received. Clinical effectiveness should be studied in an experimental trial.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease.

BACKGROUND: Precision Health aims to revolutionize disease prevention by leveraging information across multiple omic datasets (multi-omics). However, existing methods generally do not consider personalized environmental risk factors (e.g., environmental pollutants). OBJECTIVE: To develop and apply a precision health framework which combines multiomic integration (including early, intermediate, and late integration, representing sequential stages at which omics layers are combined for modeling) with mediation approaches (including high-dimensional mediation to identify biomarkers, mediation with latent factors to identify pathways, and integrated/quasi-mediation to identify high-risk subpopulations) to identify novel biomarkers of prenatal mercury induced metabolic dysfunction-associated fatty liver disease (MAFLD), elucidate molecular pathways linking prenatal mercury with MAFLD in children, and identify high-risk children based on integrated exposure and multiomics data. METHODS: This prospective cohort study used data from 420 mother-child pairs from the Human Early Life Exposome (HELIX) project. Mercury concentrations were determined in maternal or cord blood from pregnancy. Cytokeratin 18 (CK-18; a MAFLD biomarker) and five omics layers (DNA Methylation, gene transcription, microRNA, proteins, and metabolites) were measured in blood in childhood (age 6-10 years). RESULTS: Each standard deviation increase in prenatal mercury was associated with a 0.11 [95% confidence interval: 0.02-0.21] standard deviation increase in CK-18. High dimensional mediation analysis identified 10 biomarkers linking prenatal mercury and CK-18, including six CpG sites and four transcripts. Mediation with latent factors identified molecular pathways linking mercury and MAFLD, including altered cytokine signaling and hepatic stellate cell activation. Integrated/quasi-mediation identified high risk subgroups of children based on unique combinations of exposure levels, omics profiles (driven by epigenetic markers), and MAFLD. CONCLUSIONS: Prenatal mercury exposure is associated with elevated liver enzymes in childhood, likely through alterations in DNA methylation and gene expression. Our analytic framework can be applied across many different fields and serve as a resource to help guide future precision health investigations.

Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning.

Innovative intraoral ultrasound devices with smart artificial intelligence-based identification for dento-anatomy could provide crucial information for oral health diagnosis and treatment and shed light on real-time detection of developmental dentistry. However, the grand challenge is that the current ultrasound technologies are meant for external use due to their bulkiness and low frequency. We report a compact versatile ultrasound intraoral device that consists of a rotational probe head robustly pivoted around a hand-held and portable handle for real-time imaging of intraoral anatomy using high-frequency ultrasonography (up to 25 MHz). The intraoral ultrasound device that could be adjusted for various orientations of the imaging planes by rotating the head provides real-time, high-resolution ultrasonograms of intraoral structures, including dento-periodontium of most tooth types and maxillary palate. Machine learning-based algorithms are integrated to automate the identification of important structures, including alveolar bone and cementum-enamel junction. The intraoral ultrasound device smartened with artificial intelligence could innovate oral health diagnosis and treatment plans toward precision health and patient care.

Precision Health Symptom Science in Oncology Nursing.

Precision health is an emerging approach to predicting, preventing, treating, and managing disease. A goal of precision health symptom science research is the reliable prediction of patients' symptom burden to optimize robu.

Precision Diabetes Education and Support Considering Patients' Behavioral and Psychological Phenotype: A Q-Methodology Study.

BACKGROUND: The prevalence of type 2 diabetes is growing, and diabetes burden is increasing. Precision health in diabetes education and support employs different intervention strategies, depending on an individual's viewpoint on diabetes and self-management behaviors, to improve patients' treatment adherence, clinical outcomes, and quality of life. OBJECTIVE: To classify the behavioral and psychological phenotypes of self-management behaviors in adults taking oral glucose-lowering medications to develop a theory-driven, person-centered group intervention applicable to busy clinical settings. METHODS: Q-methodology was used. From January to August 2020, 73 participants (48 male, 25 female) were invited to do Q-sorting with 33 statements. The principal component technique, followed by varimax rotation, was used for factor analysis. The Summary of Diabetes Self-Care Activity questionnaire and HbA1c in the past 6 months were included to obtain comprehensive understanding. RESULTS: Fifty-one sorts (35 male, 16 female) loaded on 1 of 4 factors: factor A (n = 18): Needing emotional support with enhancing problem-solving skills group; factor B (n = 15): Self-help group; factor C (n = 6): Needing personalized coaching group; and factor D (n = 12): Needing basic diabetes education group. CONCLUSIONS: Each factor demonstrated a different need for diabetes education and support. Younger participants (factor D) had the poorest diabetes self-management behaviors and required basic diabetes education. Further research is warranted to develop a screening tool to classify the typologies and adopt the findings in a busy clinical setting.

Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance.

Objective: This study investigated the impact of wearable technologies, particularly advanced biomechanical analytics and machine learning, on sports performance monitoring and intervention strategies within the realm of physiotherapy. The primary aims were to evaluate key performance metrics, individual athlete variations and the efficacy of machine learning-driven adaptive interventions. Methods: The research employed an observational cross-sectional design, focusing on the collection and analysis of real-world biomechanical data from athletes engaged in sports physiotherapy. A representative sample of athletes from Bahawalpur participated, utilizing Dring Stadium as the primary data collection venue. Wearable devices, including inertial sensors (MPU6050, MPU9250), electromyography (EMG) sensors (MyoWare Muscle Sensor), pressure sensors (FlexiForce sensor) and haptic feedback sensors, were strategically chosen for their ability to capture diverse biomechanical parameters. Results: Key performance metrics, such as heart rate (mean: 76.5 bpm, SD: 3.2, min: 72, max: 80), joint angles (mean: 112.3 degrees, SD: 6.8, min: 105, max: 120), muscle activation (mean: 43.2%, SD: 4.5, min: 38, max: 48) and stress and strain features (mean: [112.3 ], SD: [6.5 ]), were analyzed and presented in summary tables. Individual athlete analyses highlighted variations in performance metrics, emphasizing the need for personalized monitoring and intervention strategies. The impact of wearable technologies on athletic performance was quantified through a comparison of metrics recorded with and without sensors. Results consistently demonstrated improvements in monitored parameters, affirming the significance of wearable technologies. Conclusions: The study suggests that wearable technologies, when combined with advanced biomechanical analytics and machine learning, can enhance athletic performance in sports physiotherapy. Real-time monitoring allows for precise intervention adjustments, demonstrating the potential of machine learning-driven adaptive interventions.

The Gut Microbiome and Symptom Burden After Kidney Transplantation: An Overview and Research Opportunities.

Many kidney transplant recipients continue to experience high symptom burden despite restoration of kidney function. High symptom burden is a significant driver of quality of life. In the post-transplant setting, high symptom burden has been linked to negative outcomes including medication non-adherence, allograft rejection, graft loss, and even mortality. Symbiotic bacteria (microbiota) in the human gastrointestinal tract critically interact with the immune, endocrine, and neurological systems to maintain homeostasis of the host. The gut microbiome has been proposed as an underlying mechanism mediating symptoms in several chronic medical conditions including irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia, and psychoneurological disorders via the gut-brain-microbiota axis, a bidirectional signaling pathway between the enteric and central nervous system. Post-transplant exposure to antibiotics, antivirals, and immunosuppressant medications results in significant alterations in gut microbiota community composition and function, which in turn alter these commensal microorganisms' protective effects. This overview will discuss the current state of the science on the effects of the gut microbiome on symptom burden in kidney transplantation and future directions to guide this field of study.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care.

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

Space research to explore novel biochemical insights on Earth.

Travel to space has overcome unprecedent technological challenges and this has resulted in transfer of these technological results on Earth to better our lives. Health technology, medical devices, and research advancements in human biology are the first beneficiaries of this transfer. The real breakthrough came with the International Space Station, which endorsed multidisciplinary international scientific collaborations and boosted the research on pathophysiological adaptation of astronauts to life on space. These studies evidenced that life in space appeared to have exposed the astronauts to an accelerated aging-related pathophysiological dysregulation across multiple systems. In this review we emphasize the interaction between several biomarkers and their alteration in concentrations/expression/function by space stress factors. These altered interactions, suggest that different biochemical and hormonal factors, and cell signals, contribute to a complex network of pathophysiological mechanisms, orchestrating the homeostatic dysregulation of various organs/metabolic pathways. The main effects of space travel on altering cell organelles biology, ultrastructure, and cross-talk, have been observed in cell aging as well as in the disruption of metabolic pathways, which are also the causal factor of rare inherited metabolic disorders, one of the major pediatric health issue. The pathophysiologic breakthrough from space research could allow the development of precision health both on Earth and Space by promoting the validation of improved biomarker-based risk scores and the exploration of new pathophysiologic hypotheses and therapeutic targets. Nonstandard abbreviations: International Space Station (ISS), Artificial Intelligence (AI), European Space Agency (ESA), National Aeronautics and Space Agency (NASA), Low Earth Orbit (LEO), high sensitive troponin (hs-cTn), high sensitive troponin I (hs-cTn I), high sensitive troponin T, Brain Natriuretic Peptide (BNP), N terminal Brain Natriuretic Peptide (NT-BNP), cardiovascular disease (CVD), parathyroid hormone (PTH), urinary hydroxyproline (uHP), urinary C- and N-terminal telopeptides (uCTX and uNTX), pyridinoline (PYD), deoxypyridinoline (DPD), half-time (HF), serum Bone Alkaline Phosphatase (sBSAP), serum Alkaline Phosphatase (sAP), Carboxy-terminal Propeptide of Type 1 Procollagen (P1CP), serum Osteocalcin (sOC)), advanced glycation end products (AGEs), glycated hemoglobin A1c (HbA1c), Insulin-like growth factor 1 (IGF1), Growth Hormone (GH), amino acid (AA), ß-hydroxy-ß methyl butyrate (HMB), maple syrup urine disease (MSUD), non-communicable diseases (NCDs).

Ethical and Equitable Digital Health Research: Ensuring Self-Determination in Data Governance for Racialized Communities.

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

Achieving Comprehensive, Patient-Centered Cancer Services: Optimizing the Role of Advanced Practice Nurses at the Core of Precision Health.

OBJECTIVES: The field of oncology has been revolutionized by precision medicine, driven by advancements in molecular and genomic profiling. High-throughput genomic sequencing and non-invasive diagnostic methods have deepened our understanding of cancer biology, leading to personalized treatment approaches. Precision health expands on precision medicine, emphasizing holistic healthcare, integrating molecular profiling and genomics, physiology, behavioral, and social and environmental factors. Precision health encompasses traditional and emerging data, including electronic health records, patient-generated health data, and artificial intelligence-based health technologies. This article aims to explore the opportunities and challenges faced by advanced practice nurses (APNs) within the precision health paradigm. METHODS: We searched for peer-reviewed and professional relevant studies and articles on advanced practice nursing, oncology, precision medicine and precision health, and symptom science. RESULTS: APNs' roles and competencies align with the core principles of precision health, allowing for personalized interventions based on comprehensive patient characteristics. We identified educational needs and policy gaps as limitations faced by APNs in fully embracing precision health. CONCLUSION: APNs, including nurse practitioners and clinical nurse specialists, are ideally positioned to advance precision health. Nevertheless, it is imperative to overcome a series of barriers to fully leverage APNs' potential in this context. IMPLICATIONS FOR NURSING PRACTICE: APNs can significantly contribute to precision health through their competencies in predictive, preventive, and health promotion strategies, personalized and collaborative care plans, ethical considerations, and interdisciplinary collaboration. However, there is a need to foster education in genetics and genomics, encourage continuous professional development, and enhance understanding of artificial intelligence-related technologies and digital health. Furthermore, APNs' scope of practice needs to be reflected in policy making and legislation to enable effective contribution of APNs to precision health.

The Bitemporal Lens Model-toward a holistic approach to chronic disease prevention with digital biomarkers.

Objectives: We introduce the Bitemporal Lens Model, a comprehensive methodology for chronic disease prevention using digital biomarkers. Materials and Methods: The Bitemporal Lens Model integrates the change-point model, focusing on critical disease-specific parameters, and the recurrent-pattern model, emphasizing lifestyle and behavioral patterns, for early risk identification. Results: By incorporating both the change-point and recurrent-pattern models, the Bitemporal Lens Model offers a comprehensive approach to preventive healthcare, enabling a more nuanced understanding of individual health trajectories, demonstrated through its application in cardiovascular disease prevention. Discussion: We explore the benefits of the Bitemporal Lens Model, highlighting its capacity for personalized risk assessment through the integration of two distinct lenses. We also acknowledge challenges associated with handling intricate data across dual temporal dimensions, maintaining data integrity, and addressing ethical concerns pertaining to privacy and data protection. Conclusion: The Bitemporal Lens Model presents a novel approach to enhancing preventive healthcare effectiveness.