Toward achieving precision health

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.

Construction of Fully Integrated and Energy Self-Sufficient NO2 Gas Sensors Utilizing Zinc-Air Batteries

Exploration of novel self-powered gas sensors free of external energy supply restrictions, such as light illumination and mechanical vibration, for flexible and wearable applications is in urgent need. Herein, this work constructs a flexible and self-powered NO2 gas sensor based on zinc-air batteries (ZABs) with the cathode of the ZABs also acting as the gas-sensitive layer. Furthermore, the SiO2 coating film, serving as a hydrophobic layer, increases the three-phase interfaces for the NO2 reduction reaction. The constructed sensors exhibit a high sensing response (0.3 V @ 5 ppm), an ultralow detection limit (61 ppb), a fast sensing process (129 and 103 s), and excellent selectivity. Moreover, the sensors also possess a wide working temperature range and a low working temperature tolerance (0.34 V at -15 °C). Simulations indicate that the hydrophobic surface at the cathode-hydrogel interface will accommodate more NO2 gas molecules at the reaction sites and prevent the influence of inner water evaporation and direct dissolution of NO2 in the electrolyte, which is beneficial to the enhanced gas sensing abilities. Finally, the self-powered sensing device is incorporated into a smart sensing system for practical applications. This work will pave a new insight into the construction of integrated and energy self-sufficient smart gas sensing systems.

Just-in-time adaptive interventions for adolescent and young adult health and well-being: protocol for a systematic review

INTRODUCTION

Health behaviours such as exercise and diet strongly influence well-being and disease risk, providing the opportunity for interventions tailored to diverse individual contexts. Precise behaviour interventions are critical during adolescence and young adulthood (ages 10-25), a formative period shaping lifelong well-being. We will conduct a systematic review of just-in-time adaptive interventions (JITAIs) for health behaviour and well-being in adolescents and young adults (AYAs). A JITAI is an emerging digital health design that provides precise health support by monitoring and adjusting to individual, specific and evolving contexts in real time. Despite demonstrated potential, no published reviews have explored how JITAIs can dynamically adapt to intersectional health factors of diverse AYAs. We will identify the JITAIs' distal and proximal outcomes and their tailoring mechanisms, and report their effectiveness. We will also explore studies' considerations of health equity. This will form a comprehensive assessment of JITAIs and their role in promoting health behaviours of AYAs. We will integrate evidence to guide the development and implementation of precise, effective and equitable digital health interventions for AYAs.

METHODS AND ANALYSIS

In adherence to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines, we will conduct a systematic search across multiple databases, including CENTRAL, MEDLINE and WHO Global Index Medicus. We will include peer-reviewed studies on JITAIs targeting health of AYAs in multiple languages. Two independent reviewers will conduct screening and data extraction of study and participant characteristics, JITAI designs, health outcome measures and equity considerations. We will provide a narrative synthesis of findings and, if data allows, conduct a meta-analysis.

ETHICS AND DISSEMINATION

As we will not collect primary data, we do not require ethical approval. We will disseminate the review findings through peer-reviewed journal publication, conferences and stakeholder meetings to inform participatory research.

PROSPERO REGISTRATION NUMBER

CRD42023473117.

Precision prevention in worksite health-A scoping review on research trends and gaps

OBJECTIVES

To map the current state of precision prevention research in the workplace setting, specifically to study contexts and characteristics, and to analyze the precision prevention approach in the stages of risk assessment/data monitoring, data analytics, and the health promotion interventions implemented.

METHODS

Six international databases were searched for studies published between January 2010 and May 2023, using the term "precision prevention" or its synonyms in the context of worksite health promotion.

RESULTS

After screening 3,249 articles, 129 studies were reviewed. Around three-quarters of the studies addressed an intervention (95/129, 74%). Only 14% (18/129) of the articles primarily focused on risk assessment and data monitoring, and 12% of the articles (16/129) mainly included data analytics studies. Most of the studies focused on behavioral outcomes (61/160, 38%), followed by psychological (37/160, 23%) and physiological (31/160, 19%) outcomes of health (multiple answers were possible). In terms of study designs, randomized controlled trials were used in more than a third of all studies (39%), followed by cross-sectional studies (18%), while newer designs (e.g., just-in-time-adaptive-interventions) are currently rarely used. The main data analyses of all studies were regression analyses (44% with analyses of variance or linear mixed models), whereas machine learning methods (e.g., Algorithms, Markov Models) were conducted only in 8% of the articles.

DISCUSSION

Although there is a growing number of precision prevention studies in the workplace, there are still research gaps in applying new data analysis methods (e.g., machine learning) and implementing innovative study designs. In the future, it is desirable to take a holistic approach to precision prevention in the workplace that encompasses all the stages of precision prevention (risk assessment/data monitoring, data analytics and interventions) and links them together as a cycle.

Exploring biorepository donation patterns, experiences, and recommendations: a mixed-methods study among Appalachian adults enrolled in a sugary drink reduction program

Background

Under-represented subgroups in biomarker research linked to behavioral health trials may impact the promise of precision health. This mixed methods study examines biorepository donations across an Appalachian sample enrolled in a sugary drink reduction intervention trial.

Methods

Participants enrolled in the behavioral trial were asked to join an optional biomarker study and were tracked for enrollment and biospecimen returns (stool and/or buccal sample). At 6 months, participants completed a summative interview on decision-making process, experiences collecting samples, and recommendations to encourage biospecimen donation. Return rates were analyzed across demographics (i.e., age, gender, race, education, income, health literacy status, and rurality status) using chi-squares. Qualitative data were content coded with differences compared by biomarker study enrollment and donation choices.

Results

Of the 249 invited participants, 171 (61%) enrolled, and 63% (n = 157) returned buccal samples and 49% (n = 122) returned stool samples. Metro residing participants were significantly more likely (56%) to return stool samples compared to non-metro (39%) counterparts [x2(1) = 6.61; p = 0.01]. Buccal sample return had a similar trend, 67 and 57%, respectively for metro vs. non-metro [x2(1) = 2.84; p = 0.09]. An additional trend indicated that older (≥40 years) participants were more likely (55%) to donate stool samples than younger (43%) participants [x2(1) = 3.39; p = 0.07]. No other demographics were significantly associated with biospecimen return. Qualitative data indicated that societal (66-81%) and personal (41-51%) benefits were the most reported reasons for deciding to donate one or both samples, whereas mistrust (3-11%) and negative perceptions of the collection process (44-71%) were cited the most by those who declined one or both samples. Clear instructions (60%) and simple collection kits (73%) were donation facilitators while challenges included difficult stool collection kits (16%) and inconveniently located FedEx centers (16%). Recommendations to encourage future biorepository donation were to clarify benefits to science and others (58%), provide commensurate incentives (25%), explain purpose (19%) and privacy protections (20%), and assure ease in sample collection (19%).

Conclusion

Study findings suggest the need for biomarker research awareness campaigns. Researchers planning for future biomarker studies in medically underserved regions, like Appalachia, may be able to apply findings to optimize enrollment.

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces "NIMEQ-SACNet," a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.

An Evolutionary Concept Analysis of Precision Medicine, and Its Contribution to a Precision Health Model for Nursing Practice

Precision medicine is a new concept that has been routinely encountered in the literature for little more than a decade. With increasing use, it becomes crucial to understand the meaning of this concept as it is applied in various settings. An evolutionary concept analysis was conducted to develop an understanding of the essential features of precision medicine and its use. The analysis led to a comprehensive list of the antecedents, attributes, and consequences of precision medicine in multiple settings. With this understanding, precision medicine becomes part of the broader practice of precision health, an important process proposed by nursing scholars to provide complete, holistic care to our patients. A model for precision health is presented as a framework for care.

Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review

Background

Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder patients never achieve remission. Transcranial Direct Current Stimulation is a non-invasive intervention used to treat individuals diagnosed with major depressive disorder and bipolar disorder. Since the last transcranial direct current stimulation review specifically focusing on cognitive symptoms in major depressive disorder, twice as many papers have been published.

Methods

A systematic review was conducted with 5 electronic databases from database inception until March 21, 2022. Randomized controlled trials with at least 1 arm evaluating transcranial direct current stimulation in adults (diagnosed with major depressive disorder or bipolar disorder using the Diagnostic and Statistical Manual of Mental Disorders or International Classification of Diseases criteria) aged 18 or older were included. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were adopted.

Results

: A total of 972 participants were included across 14 studies (60.5% female; mean age of 47.0 years [SD = 16.8]). Nine studies focused on participants with major depressive disorder and all studies used the Diagnostic and Statistical Manual of Mental Disorders to diagnose the participants. Seven out of the 14 studies showed significant improvements in at least 1 cognitive outcome measure in the active transcranial direct current stimulation group compared to the sham group. Several cognitive measures were used across studies, and 12 of the 14 studies reported mild-to-moderate side effects from treatment.

Conclusion

: Current transcranial direct current stimulation literature has shown limited evidence for the treatment of cognitive impairments in major depressive disorder and bipolar disorder. Future research that applies machine learning algorithms may enable us to distinguish responders from non-responders, increasing clinical benefits of transcranial direct current stimulation.

Go Across Immersive Technology: A Preliminary Study of the Design and Development of a System for Gait Training Using Virtual Reality

Virtual reality (VR) allows visuotactile interaction in a virtual environment. VR has several potential applications such as surgical training, phobia treatments, and gait rehabilitation. However, further interface development is required. Therefore, the objective of this study was to develop a noninvasive wearable device control to a VR gait training program. It consists of custom-made insoles with vibratory actuators, and plantar pressure sensor-based wireless interface with a VR game. System usability testing involved a habituation period and three gaming sessions. Significant gait improvement was associated with game scores (P < 0.05). This VR gait training system allowed real-time virtual immersive interaction with anticipatory stimulus and feedback during gait.

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future

The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.

Machine Learning-Enabled Intelligent Gesture Recognition and Communication System Using Printed Strain Sensors

Gesture contains abundant and complicated information in daily life; as a consequence, gesture recognition attracts a wide range of application prospects and academic values as an important way of achieving human-machine interactions (HMIs). Here, we report an intelligent system consisting of a smart glove made by printed CNT-graphene/PDMS strain sensors. The smart glove shows excellent fitness, comfort, and lightness for human hands. Inspired by machine learning strategies, several objects and gestures can be well classified and implemented by a customized artificial neural network. Several data sets of different sign language gestures and object-grabbing gestures were established, and the result shows that the intelligent system can achieve an average accuracy of 97% and up to 99.4% for a number of gesture groups. Moreover, a robot hand is connected to this system, which is able to react to the motion of human hands with certain gestures where simple sign communication is achieved. These features provide a feasible practical application scheme for gesture recognition in HMIs.

Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care

Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at maintaining good health throughout the lifespan. However, how can precision health impact care for people with a terminal or life-limiting condition? We examine here the ethical, equity, and societal/relational implications of two precision health modalities, (1) integrated systems biology/multi-omics analysis for disease prognostication and (2) digital health technologies for health status monitoring and communication. We focus on three main ethical and societal considerations: benefits and risks associated with integration of these modalities into the palliative care system; inclusion of underrepresented and marginalized groups in technology development and deployment; and the impact of high-tech modalities on palliative care's highly personalized and "high-touch" practice. We conclude with 10 recommendations for ensuring that precision health technologies, such as multi-omics prognostication and digital health monitoring, for palliative care are developed, tested, and implemented ethically, inclusively, and equitably.

An antibody-based molecular switch for continuous small-molecule biosensing

We present a generalizable approach for designing biosensors that can continuously detect small-molecule biomarkers in real time and without sample preparation. This is achieved by converting existing antibodies into target-responsive "antibody-switches" that enable continuous optical biosensing. To engineer these switches, antibodies are linked to a molecular competitor through a DNA scaffold, such that competitive target binding induces scaffold switching and fluorescent signaling of changing target concentrations. As a demonstration, we designed antibody-switches that achieve rapid, sample preparation-free sensing of digoxigenin and cortisol in undiluted plasma. We showed that, by substituting the molecular competitor, we can further modulate the sensitivity of our cortisol switch to achieve detection at concentrations spanning 3.3 nanomolar to 3.3 millimolar. Last, we integrated this switch with a fiber optic sensor to achieve continuous sensing of cortisol in a buffer and blood with <5-min time resolution. We believe that this modular sensor design can enable continuous biosensor development for many biomarkers.

Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors.

One-Week Suicide Risk Prediction Using Real-Time Smartphone Monitoring: Prospective Cohort Study

BACKGROUND

Suicide is a major global public health issue that is becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological advances provide invaluable tools with which we may evolve toward a personalized, predictive approach.

OBJECTIVE

We aim to predict the short-term (1-week) risk of suicide by identifying changes in behavioral patterns characterized through real-time smartphone monitoring in a cohort of patients with suicidal ideation.

METHODS

We recruited 225 patients between February 2018 and March 2020 with a history of suicidal thoughts and behavior as part of the multicenter SmartCrisis study. Throughout 6 months of follow-up, we collected information on the risk of suicide or mental health crises. All participants underwent voluntary passive monitoring using data generated by their own smartphones, including distance walked and steps taken, time spent at home, and app usage. The algorithm constructs daily activity profiles for each patient according to these data and detects changes in the distribution of these profiles over time. Such changes are considered critical periods, and their relationship with suicide-risk events was tested.

RESULTS

During follow-up, 18 (8%) participants attempted suicide, and 14 (6.2%) presented to the emergency department for psychiatric care. The behavioral changes identified by the algorithm predicted suicide risk in a time frame of 1 week with an area under the curve of 0.78, indicating good accuracy.

CONCLUSIONS

We describe an innovative method to identify mental health crises based on passively collected information from patients' smartphones. This technology could be applied to homogeneous groups of patients to identify different types of crises.

A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group.

Global Alliance for the Promotion of Physical Activity: the Hamburg Declaration

Non-communicable diseases (NCDs), including coronary heart disease, stroke, hypertension, type 2 diabetes, dementia, depression and cancers, are on the rise worldwide and are often associated with a lack of physical activity (PA). Globally, the levels of PA among individuals are below WHO recommendations. A lack of PA can increase morbidity and mortality, worsen the quality of life and increase the economic burden on individuals and society. In response to this trend, numerous organisations came together under one umbrella in Hamburg, Germany, in April 2021 and signed the 'Hamburg Declaration'. This represented an international commitment to take all necessary actions to increase PA and improve the health of individuals to entire communities. Individuals and organisations are working together as the 'Global Alliance for the Promotion of Physical Activity' to drive long-term individual and population-wide behaviour change by collaborating with all stakeholders in the community: active hospitals, physical activity specialists, community services and healthcare providers, all achieving sustainable health goals for their patients/clients. The 'Hamburg Declaration' calls on national and international policymakers to take concrete action to promote daily PA and exercise at a population level and in healthcare settings.

Occupational resource profiles for an addressee orientation in occupational health management: a segmentation analysis

Introduction

In order to make sustainable decisions in precision prevention and health promotion, it is important to adequately assess people's demands and resources at work. To reach them in an addressee-oriented way, a segmentation of employers and employees based on occupational resources is a promising option. We identified profiles based on personal and perceived organizational resources. Furthermore, we used job demands for profile descriptions to obtain a deeper understanding of the profiles, characterizing people with similar occupational resources.

Methods

Personal occupational resources (occupational health literacy and self-efficacy) and perceived organizational resources (job decision latitude and participation in health at work) were assessed among employers and employees (n = 828) in small- and medium-sized enterprises in Germany. Job demands, socioeconomic status, and hierarchy levels in the company were used for further profile descriptions.

Results

A six-profile solution fitted best to the data based on cluster and profile analyses. One profile was characterized by above-average occupational resources, and another profile was characterized by below-average resources. The other four profiles showed that the individual and perceived organizational resources contrasted. Either organizational resources such as job decision latitude existed and personal resources were not highly developed or people had high individual motivation but few possibilities to participate in health at work. People with medium or high job demands as well as people with low socioeconomic status were most frequently in below-average resource profiles. Employers with high hierarchy levels were overrepresented in the above-average profiles with high organizational resources.

Discussion

Following the segmentation of the addressees, organizations might be supported in identifying needs and areas for prevention and health promotion. Interventions can be optimally developed, tailored, and coordinated through a deeper understanding of job demands and resources. Especially employees with low socioeconomic status and high job demands might profit from an addressee-orientated approach based on resource profiles. For example, employees obtain an overview of their occupational resource profile to recognize the development potential for safe and healthy behavior at work. Follow-up research should be used to examine how this feedback to employers and employees is implemented and how it affects the sustainability of tailored interventions.

Coordination Rigidity in the Gait, Posture, and Speech of Persons with Parkinson's Disease

Parkinson's disease (PD) is associated with reduced coordination abilities. These can result either in random or rigid patterns of movement. The latter, described here as coordination rigidity (CR), have been studied less often. We explored whether CR was present in gait, quiet stance, and speech-tasks involving coordination among multiple joints and muscles. Kinematic and voice recordings were used to compute measures describing the dynamics of systems with multiple degrees of freedom and nonlinear interactions. After clinical evaluation, patients with moderate stage PD were compared against matched healthy participants. In the PD group, gait dynamics was associated with decreased dynamic divergence-lower instability-in the vertical axis. Postural fluctuations were associated with increased regularity in the anterior-posterior axis, and voice dynamics with increased predictability, all consistent with CR. The clinical relevance of CR was confirmed by showing that some of those features contribute to disease classification with supervised machine learning (82/81/85% accuracy/sensitivity/specificity).

The Role of Salivary C-Reactive Protein in Diagnosis of Neonatal Sepsis

Background

Neonatal sepsis remains one of the leading causes of morbidity and mortality among neonates.

Objective

to evaluate the role of salivary C-reactive protein (CRP) as a diagnostic marker in neonatal sepsis.

Methods

This case-control study was carried out on 90 neonates including 45 neonates with symptoms and signs suggestive of neonatal sepsis and 45 healthy neonates as controls. All neonates were subjected to full history taking, thorough clinical examination, laboratory investigations including complete blood count (CBC), serum CRP, blood culture, and salivary CRP.

Results

Septic neonates showed significantly higher salivary CRP compared to controls; it was significantly associated with positive serum CRP, blood culture, and hematological scoring system (HSS). It was significantly higher in neonates who died compared to those who survived. Twenty one cases with positive salivary CRP showed significant positive correlations with the length of the neonatal intensive care unit stay, total white blood cell (WBC) count, mean platelet volume (MPV), neutrophils to lymphocytes ratio (NLR), serum CRP, HSS, significant negative correlations with gestational age, birth weight, APGAR at 1, 5 min, and platelet count. Receiver operating characteristic (ROC) curve for salivary CRP showed that at cut-off value of 0.135 mg/L, the sensitivity was (81.8%) and the specificity was (75.6%) for detecting neonatal sepsis.

Conclusion

Salivary CRP was significantly higher in the septic group, and was associated with positive serum CRP. Hence, salivary CRP could be considered a novel non-invasive biomarker for diagnosing neonatal sepsis.

Passive monitoring by smart toilets for precision health

Smart toilets are a key tool for enabling precision health monitoring in the home, but such passive monitoring has ethical considerations.

A time differentiated dietary intervention effect on the biomarkers of exposure to pyrethroids and neonicotinoids pesticides

Tailoring medical models to the right person or risk subgroups delivered at the right time is important in personalized medicine/prevention initiatives. The CIRCA-CHEM randomized 2x2 crossover pilot trial investigated whether the consumption of fruits/vegetables within a time-restricted daily window would affect urinary biomarkers of exposure to neonicotinoids (6-chloronicotinic acid, 6-CN) and pyrethroids (3-phenoxybenzoic acid, 3-PBA) pesticides, a biomarker of oxidative damage (4-hydroxynonenal, 4-HNE) and the associated urinary NMR metabolome. A statistically significant difference (p < 0.001) in both creatinine-adjusted 6-CN and 3-PBA levels was observed between the two-time dietary intervention windows (morning vs. evening). In the evening intervention period, pesticides biomarker levels were higher compared to the baseline, whereas in the morning period, pesticide levels remained unchanged. Positive associations were observed between pesticides and 4-HNE suggesting a diurnal chrono-window of pesticide toxicity. The discovery of a chronotoxicity window associated with chrono-disrupted metabolism of food contaminants may find use in personalized medicine initiatives.

Impact of health literacy and its interventions on health outcomes in those with atrial fibrillation: a systematic review protocol

INTRODUCTION

Atrial fibrillation (AF) is associated with increased risk of stroke, heart failure and death. Health literacy, an aspect that falls within precision health, has been recognised as an important factor. We will be focusing on the impact of these interventions specifically to AF and its health outcomes.

METHODS AND ANALYSIS

This protocol is informed by the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. The results will be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to determine the impacts of health literacy interventions on AF outcomes. Searches will be carried out on databases including MEDLINE, EMBASE, Web of Science, CINAHL, Emcare, Cochrane Library and Google Scholar. Citations will be collected via Endnote 20, then into Covidence for duplicate removal, and article screening. Extraction will occur using a standardised extraction tool and studies will be synthesised using best evidence synthesis. Downs and Black's checklist will be used for risk of bias and assessment of overall quality of evidence will use the Grading of Recommendations, Assessment, Development and Evaluation approach.

ETHICS AND DISSEMINATION

Approval from human research ethics committee is not required. Dissemination will occur in peer-reviewed journals and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42022304835.

Multimodal machine learning in precision health: A scoping review

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.

Precision health in behaviour change interventions: A scoping review

Precision health seeks to optimise behavioural interventions by delivering personalised support to those in need, when and where they need it. Conceptualised a decade ago, progress toward this vision of personally relevant and effective population-wide interventions continues to evolve. This scoping review aimed to map the state of precision health behaviour change intervention research. This review included studies from a broader precision health review. Six databases were searched for studies published between January 2010 and June 2020, using the terms 'precision health' or its synonyms, and including an intervention targeting modifiable health behaviour(s) that was evaluated experimentally. Thirty-one studies were included, 12 being RCTs (39%), and 17 with weak study design (55%). Most interventions targeted physical activity (27/31, 87%) and/or diet (24/31, 77%), with 74% (23/31) targeting two to four health behaviours. Interventions were personalised via human interaction in 55% (17/31) and digitally in 35% (11/31). Data used for personalising interventions was largely self-reported, by survey or diary (14/31, 45%), or digitally (14/31, 45%). Data was mostly behavioural or lifestyle (20/31, 65%), and physiologic, biochemical or clinical (15/31, 48%), with no studies utilising genetic/genomic data. This review demonstrated that precision health behaviour change interventions remain dependent on human-led, low-tech personalisation, and have not fully considered the interaction between behaviour and the social and environmental contexts of individuals. Further research is needed to understand the relationship between personalisation and intervention effectiveness, working toward the development of sophisticated and scalable behaviour change interventions that have tangible public health impact.

Parallel Field-Effect Nanosensors Detect Trace Biomarkers Rapidly at Physiological High-Ionic-Strength Conditions

Sensitivity and speed of detection are contradicting demands that profoundly impact the electrical sensing of molecular biomarkers. Although single-molecule sensitivity can now be achieved with single-nanotube field-effect transistors, these tiny sensors, with a diameter less than 1 nm, may take hours to days to capture the molecular target at trace concentrations. Here, we show that this sensitivity-speed challenge can be addressed using covalently functionalized double-wall CNTs that form many individualized, parallel pathways between two electrodes. Each carrier that travels across the electrodes is forced to take one of these pathways that are fully gated chemically by the target-probe binding events. This sensor design allows us to electrically detect Lyme disease oligonucleotide biomarkers directly at the physiological high-salt concentrations, simultaneously achieving both ultrahigh sensitivity (as low as 1 fM) and detection speed (<15 s). This unexpectedly simple strategy may open opportunities for sensor designs to broadly achieve instant detection of trace biomarkers and real-time probing of biomolecular functions directly at their physiological states.

Community pharmacists’ perceived value on precision medicine, desired training components, and exposure during pharmacy education: Malaysia’s experience

Background: Precision medicine beckons new horizons for therapy geared to one’s genetics, lifestyle, and environmental determinants. Molecular, pathology, and clinical diagnostics can be integrated to provide pharmaceutical care.

Aims: The value and appeal of precision medicine to community pharmacists, knowledge attained, and training programmes perceived as necessary were evaluated.

Methods: Over 10 months, a published questionnaire, which was also digitally accessible during the COVID-19 outbreak, was distributed by hand, via email and social media. 300 community pharmacists across 9 districts in an urban state in Malaysia, self-administered and returned completed versions (response rate 75%). Three- or five-point Likert scale and multiple-choice responses were analysed using SPSS to assess whether or not exposure through the pharmacy curricula impacted current knowledge, perception and willingness to pursue precision medicine.

Results: Respondents were largely: females (N = 196, 65.3%) and practicing for up to 10 years (N = 190, 66.3%). Although knowledge levels were moderate (76%), positive perceptions were showcased (94%), and 80% were willing to integrate precision medicine into their daily practice. Although 61% did not or do not recall having had prior exposure to pharmacogenomics as part of their pharmacy school curricula, many (93%) were willing to attain knowledge by undergoing additional training. Desired training included current pharmacogenetic testing available (17%), interpretation of the test results (15%), and ethical considerations (13%). Community pharmacists who had 0.5–10 years’ work experience possessed greater knowledge (μ = 1.48, CI 1.35–1.61, p = 0.017), than the pharmacists who had 21–40 years of work experience (μ = 1.28, CI 1.05–1.51, p = 0.021). Exposure to the subject during pharmacy education positively impacted the willingness to integrate precision medicine in daily practice (p = 0.035).

Conclusion: Community pharmacists were receptive to and valued precision medicine. A relatively high number had prior exposure to concepts of precision medicine through the pharmacy curriculum, and were therefore willing to adopt the practice in their day-to-day provision of healthcare. With adequate training centred on bioethics, utilising pharmacogenetic testing, and interpretation of the results, community pharmacists will be equipped for the provision of precision medicine services in the foreseeable future.

Smartphone camera oximetry in an induced hypoxemia study

Hypoxemia, a medical condition that occurs when the blood is not carrying enough oxygen to adequately supply the tissues, is a leading indicator for dangerous complications of respiratory diseases like asthma, COPD, and COVID-19. While purpose-built pulse oximeters can provide accurate blood-oxygen saturation (SpO₂) readings that allow for diagnosis of hypoxemia, enabling this capability in unmodified smartphone cameras via a software update could give more people access to important information about their health. Towards this goal, we performed the first clinical development validation on a smartphone camera-based SpO₂ sensing system using a varied fraction of inspired oxygen (FiO₂) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG methods on a wider range of SpO₂ values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE = 5.00% SpO₂ while identifying positive cases of low SpO₂ < 90% with 81% sensitivity and 79% specificity. We also provide the data in open-source format, so that others may build on this work.

Predicting Hospice Transitions in Dementia Caregiving Dyads: An Exploratory Machine Learning Approach

Background and Objectives

Hospice programs assist people with serious illness and their caregivers with aging in place, avoiding unnecessary hospitalizations, and remaining at home through the end-of-life. While evidence is emerging of the myriad of factors influencing end-of-life care transitions among persons living with dementia, current research is primarily cross- sectional and does not account for the effect that changes over time have on hospice care uptake, access, and equity within dyads.

Research Design and Methods

Secondary data analysis linking the National Health and Aging Trends Study to the National Study of Caregiving investigating important social determinants of health and quality-of-life factors of persons living with dementia and their primary caregivers (n = 117) on hospice utilization over 3 years (2015-2018). We employ cutting-edge machine learning approaches (correlation matrix analysis, principal component analysis, random forest [RF], and information gain ratio [IGR]).

Results

IGR indicators of hospice use include persons living with dementia having diabetes, a regular physician, a good memory rating, not relying on food stamps, not having chewing or swallowing problems, and whether health prevents them from enjoying life (accuracy = 0.685; sensitivity = 0.824; specificity = 0.537; area under the curve (AUC) = 0.743). RF indicates primary caregivers' age, and the person living with dementia's income, census division, number of days help provided by caregiver per month, and whether health prevents them from enjoying life predicts hospice use (accuracy = 0.624; sensitivity = 0.713; specificity = 0.557; AUC = 0.703).

Discussion and Implications

Our exploratory models create a starting point for the future development of precision health approaches that may be integrated into learning health systems that prompt providers with actionable information about who may benefit from discussions around serious illness goals-for-care. Future work is necessary to investigate those not considered in this study-that is, persons living with dementia who do not use hospice care so additional insights can be gathered around barriers to care.

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals’ multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.

A hands-free stool sampling system for monitoring intestinal health and disease

Analysis of stool offers simple, non-invasive monitoring for many gastrointestinal (GI) diseases and access to the gut microbiome, however adherence to stool sampling protocols remains a major challenge because of the prevalent dislike of handling one's feces. We present a technology that enables individual stool specimen collection from toilet wastewater for fecal protein and molecular assay. Human stool specimens and a benchtop test platform integrated with a commercial toilet were used to demonstrate reliable specimen collection over a wide range of stool consistencies by solid/liquid separation followed by spray-erosion. The obtained fecal suspensions were used to perform occult blood tests for GI cancer screening and for microbiome 16S rRNA analysis. Using occult blood home test kits, we found overall 90% agreement with standard sampling, 96% sensitivity and 86% specificity. Microbiome analysis revealed no significant difference in within-sample species diversity compared to standard sampling and specimen cross-contamination was below the detection limit of the assay. Furthermore, we report on the use of an analogue turbidity sensor to assess in real time loose stools for tracking of diarrhea. Implementation of this technology in residential settings will improve the quality of GI healthcare by facilitating increased adherence to routine stool monitoring.

Effectiveness of Web-Based Personalized Nutrition Advice for Adults Using the eNutri Web App: Evidence From the EatWellUK Randomized Controlled Trial

Background

Evidence suggests that eating behaviors and adherence to dietary guidelines can be improved using nutrition-related apps. Apps delivering personalized nutrition (PN) advice to users can provide individual support at scale with relatively low cost.

Objective

This study aims to investigate the effectiveness of a mobile web app (eNutri) that delivers automated PN advice for improving diet quality, relative to general population food-based dietary guidelines.

Methods

Nondiseased UK adults (aged >18 years) were randomized to PN advice or control advice (population-based healthy eating guidelines) in a 12-week controlled, parallel, single-blinded dietary intervention, which was delivered on the web. Dietary intake was assessed using the eNutri Food Frequency Questionnaire (FFQ). An 11-item US modified Alternative Healthy Eating Index (m-AHEI), which aligned with UK dietary and nutritional recommendations, was used to derive the automated PN advice. The primary outcome was a change in diet quality (m-AHEI) at 12 weeks. Participant surveys evaluated the PN report (week 12) and longer-term impact of the PN advice (mean 5.9, SD 0.65 months, after completion of the study).

Results

Following the baseline FFQ, 210 participants completed at least 1 additional FFQ, and 23 outliers were excluded for unfeasible dietary intakes. The mean interval between FFQs was 10.8 weeks. A total of 96 participants were included in the PN group (mean age 43.5, SD 15.9 years; mean BMI 24.8, SD 4.4 kg/m²) and 91 in the control group (mean age 42.8, SD 14.0 years; mean BMI 24.2, SD 4.4 kg/m²). Compared with that in the control group, the overall m-AHEI score increased by 3.5 out of 100 (95% CI 1.19-5.78) in the PN group, which was equivalent to an increase of 6.1% (P=.003). Specifically, the m-AHEI components nuts and legumes and red and processed meat showed significant improvements in the PN group (P=.04). At follow-up, 64% (27/42) of PN participants agreed that, compared with baseline, they were still following some (any) of the advice received and 31% (13/42) were still motivated to improve their diet.

Conclusions

These findings suggest that the eNutri app is an effective web-based tool for the automated delivery of PN advice. Furthermore, eNutri was demonstrated to improve short-term diet quality and increase engagement in healthy eating behaviors in UK adults, as compared with population-based healthy eating guidelines. This work represents an important landmark in the field of automatically delivered web-based personalized dietary interventions.

Trial Registration

ClinicalTrials.gov NCT03250858; https://clinicaltrials.gov/ct2/show/NCT03250858

Early detection of cancer

Survival improves when cancer is detected early. However, ~50% of cancers are at an advanced stage when diagnosed. Early detection of cancer or precancerous change allows early intervention to try to slow or prevent cancer development and lethality. To achieve early detection of all cancers, numerous challenges must be overcome. It is vital to better understand who is at greatest risk of developing cancer. We also need to elucidate the biology and trajectory of precancer and early cancer to identify consequential disease that requires intervention. Insights must be translated into sensitive and specific early detection technologies and be appropriately evaluated to support practical clinical implementation. Interdisciplinary collaboration is key; advances in technology and biological understanding highlight that it is time to accelerate early detection research and transform cancer survival.

The Need to Pair Molecular Monitoring Devices with Molecular Imaging to Personalize Health

By enabling the non-invasive monitoring and quantification of biomolecular processes, molecular imaging has dramatically improved our understanding of disease. In recent years, non-invasive access to the molecular drivers of health versus disease has emboldened the goal of precision health, which draws on concepts borrowed from process monitoring in engineering, wherein hundreds of sensors can be employed to develop a model which can be used to preventatively detect and diagnose problems. In translating this monitoring regime from inanimate machines to human beings, precision health posits that continual and on-the-spot monitoring are the next frontiers in molecular medicine. Early biomarker detection and clinical intervention improves individual outcomes and reduces the societal cost of treating chronic and late-stage diseases. However, in current clinical settings, methods of disease diagnoses and monitoring are typically intermittent, based on imprecise risk factors, or self-administered, making optimization of individual patient outcomes an ongoing challenge. Low-cost molecular monitoring devices capable of on-the-spot biomarker analysis at high frequencies, and even continuously, could alter this paradigm of therapy and disease prevention. When these devices are coupled with molecular imaging, they could work together to enable a complete picture of pathogenesis. To meet this need, an active area of research is the development of sensors capable of point-of-care diagnostic monitoring with an emphasis on clinical utility. However, a myriad of challenges must be met, foremost, an integration of the highly specialized molecular tools developed to understand and monitor the molecular causes of disease with clinically accessible techniques. Functioning on the principle of probe-analyte interactions yielding a transducible signal, probes enabling sensing and imaging significantly overlap in design considerations and targeting moieties, however differing in signal interpretation and readout. Integrating molecular sensors with molecular imaging can provide improved data on the personal biomarkers governing disease progression, furthering our understanding of pathogenesis, and providing a positive feedback loop toward identifying additional biomarkers and therapeutics. Coupling molecular imaging with molecular monitoring devices into the clinical paradigm is a key step toward achieving precision health.

A Naked Eye-Invisible Ratiometric Fluorescent Microneedle Tattoo for Real-Time Monitoring of Inflammatory Skin Conditions

The field of portable healthcare monitoring devices has an urgent need for the development of real-time, noninvasive sensing and detection methods for various physiological analytes. Currently, transdermal sensing techniques are severely limited in scope (i.e., measurement of heart rate or sweat composition), or else tend to be invasive, often needing to be performed in a clinical setting. This study proposes a minimally invasive alternative strategy, consisting of using dissolving polymeric microneedles to deliver naked eye-invisible functional fluorescent ratiometric microneedle tattoos directly to the skin for real-time monitoring and quantification of physiological and pathological parameters. Reactive oxygen species are overexpressed in the skin in association with various pathological conditions. Here, one demonstrates for the first time the microneedle-based delivery to the skin of active fluorescent sensors in the form of an invisible, ratiometric microneedle tattoo capable of sensing reactive oxygen species in a reconstructed human-based skin disease model, as well as an in vivo model of UV-induced dermal inflammation. One also elaborates a universal ratiometric quantification concept coupled with a custom-built, multiwavelength portable fluorescence detection system. Fully realized, this approach presents an opportunity for the minimally invasive monitoring of a broad range of physiological parameters through the skin.

Toilet-based continuous health monitoring using urine

Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

The Supportive Role of International Government Funds on the Progress of Sepsis Research During the Past Decade (2010-2019): A Narrative Review

This narrative review aimed to clarify the characteristics of international government support for sepsis research, trends in published literature on sepsis, and potential contributions of government-source grants to progress in sepsis research between fiscal years 2010 and 2019. The data in this study were collected from the National Institutes of Health (NIH, https://projectreporter.nih.gov/reporter.cfm/) of the United States of America (USA), National Natural Science Foundation of China (NSFC, https://isisn.nsfc.gov.cn/egrantweb/), and Japan Society for the Promotion of Science (JSPS, https://kaken.nii.ac.jp/). All sepsis-related projects approved by the NIH, NSFC, and JSPS were retrieved by searching the project titles, abstracts, and key words for “sepsis,” “septic shock,” or “sepsis inflammatory response syndrome” between 2010 and 2019. Representative sepsis-related studies published between Jan 2010 and Aug 2020 by the first/corresponding authors from these countries were obtained by searching the PubMed database using Medical Subject Heading terms for “sepsis” in representative journals, including Nature, Cell, Science, The Lancet, New England Journal of medicine (New Engl J Med), The Journal of American Medical Association (JAMA), Critical Care Medicine (CCM), Intensive Care Medicine (ICM), Chest, Annals of Emergency Medicine (Ann Emerg Med), and American Thoracic Society journals (ATS). The total/annual institutional budgets, major funding mechanisms and schemes, superior institutions and individual principal investigators, and published original research articles in the field of sepsis in the USA, China, and Japan during the past decade were investigated. The national supporting schemes of the NIH, NSFC, and JSPS were similar. Support from these institutions is quite important for the development of the field of “sepsis” which was acknowledged in 57–64% of original research articles published in CCM. For the future development of precision medicine in sepsis, more government funding support is necessary.

Users' Perceptions Toward mHealth Technologies for Health and Well-being Monitoring in Pregnancy Care: Qualitative Interview Study

Background

Mobile health (mHealth) technologies, such as wearable sensors, smart health devices, and mobile apps, that are capable of supporting pregnancy care are emerging. Although mHealth could be used to facilitate the tracking of health changes during pregnancy, challenges remain in data collection compliance and technology engagement among pregnant women. Understanding the interests, preferences, and requirements of pregnant women and those of clinicians is needed when designing and introducing mHealth solutions for supporting pregnant women’s monitoring of health and risk factors throughout their pregnancy journey.

Objective

This study aims to understand clinicians’ and pregnant women’s perceptions on the potential use of mHealth, including factors that may influence their engagement with mHealth technologies and the implications for technology design and implementation.

Methods

A qualitative study using semistructured interviews was conducted with 4 pregnant women, 4 postnatal women, and 13 clinicians working in perinatal care.

Results

Clinicians perceived the potential benefit of mHealth in supporting different levels of health and well-being monitoring, risk assessment, and care provision in pregnancy care. Most pregnant and postnatal female participants were open to the use of wearables and health monitoring devices and were more likely to use these technologies if they knew that clinicians were monitoring their data. Although it was acknowledged that some pregnancy-related medical conditions are suitable for an mHealth model of remote monitoring, the clinical and technical challenges in the introduction of mHealth for pregnancy care were also identified. Incorporating appropriate health and well-being measures, intelligently detecting any abnormalities, and providing tailored information for pregnant women were the critical aspects, whereas usability and data privacy were among the main concerns of the participants. Moreover, this study highlighted the challenges of engaging pregnant women in longitudinal mHealth monitoring, the additional work required for clinicians to monitor the data, and the need for an evidence-based technical solution.

Conclusions

Clinical, technical, and practical factors associated with the use of mHealth to monitor health and well-being in pregnant women need to be considered during the design and feasibility evaluation stages. Technical solutions and appropriate strategies for motivating pregnant women are critical to supporting their long-term data collection compliance and engagement with mHealth technology during pregnancy.

Trends and gaps in precision health research: a scoping review

OBJECTIVE

To determine progress and gaps in global precision health research, examining whether precision health studies integrate multiple types of information for health promotion or restoration.

DESIGN

Scoping review.

DATA SOURCES

Searches in Medline (OVID), PsycINFO (OVID), Embase, Scopus, Web of Science and grey literature (Google Scholar) were carried out in June 2020.

ELIGIBILITY CRITERIA

Studies should describe original precision health research; involve human participants, datasets or samples; and collect health-related information. Reviews, editorial articles, conference abstracts or posters, dissertations and articles not published in English were excluded.

DATA EXTRACTION AND SYNTHESIS

The following data were extracted in independent duplicate: author details, study objectives, technology developed, study design, health conditions addressed, precision health focus, data collected for personalisation, participant characteristics and sentence defining 'precision health'. Quantitative and qualitative data were summarised narratively in text and presented in tables and graphs.

RESULTS

After screening 8053 articles, 225 studies were reviewed. Almost half (105/225, 46.7%) of the studies focused on developing an intervention, primarily digital health promotion tools (80/225, 35.6%). Only 28.9% (65/225) of the studies used at least four types of participant data for tailoring, with personalisation usually based on behavioural (108/225, 48%), sociodemographic (100/225, 44.4%) and/or clinical (98/225, 43.6%) information. Participant median age was 48 years old (IQR 28-61), and the top three health conditions addressed were metabolic disorders (35/225, 15.6%), cardiovascular disease (29/225, 12.9%) and cancer (26/225, 11.6%). Only 68% of the studies (153/225) reported participants' gender, 38.7% (87/225) provided participants' race/ethnicity, and 20.4% (46/225) included people from socioeconomically disadvantaged backgrounds. More than 57% of the articles (130/225) have authors from only one discipline.

CONCLUSIONS

Although there is a growing number of precision health studies that test or develop interventions, there is a significant gap in the integration of multiple data types, systematic intervention assessment using randomised controlled trials and reporting of participant gender and ethnicity. Greater interdisciplinary collaboration is needed to gather multiple data types; collectively analyse big and complex data; and provide interventions that restore, maintain and/or promote good health for all, from birth to old age.

Individualized stress detection using an unmodified car steering wheel

In-car passive stress sensing could enable the monitoring of stress biomarkers while driving and reach millions of commuters daily (i.e., 123 million daily commuters in the US alone). Here, we present a nonintrusive method to detect stress solely from steering angle data of a regular car. The method uses inverse filtering to convert angular movement data into a biomechanical Mass Spring Damper model of the arm and extracts its damped natural frequency as an approximation of muscle stiffness, which in turn reflects stress. We ran a within-subject study (N = 22), in which commuters drove a vehicle around a closed circuit in both stress and calm conditions. As hypothesized, cohort analysis revealed a significantly higher damped natural frequency for the stress condition (P = .023, d = 0.723). Subsequent automation of the method achieved rapid (i.e., within 8 turns) stress detection in the individual with a detection accuracy of 77%.

Comparison of SERS pH probe responses after microencapsulation within hydrogel matrices

SIGNIFICANCE

Personalized medicine requires the tracking of an individual's metabolite levels over time to detect anomalies and evaluate the body's response to medications. Implanted sensors offer effective means to continuously monitor specific metabolite levels, provided they are accurate, stable over long time periods, and do no harm.

AIM

Four types of hydrogel embedded with pH-sensitive sensors were evaluated for their accuracy, sensitivity, reversibility, longevity, dynamic response, and consistency in static versus dynamic conditions and long-term storage.

APPROACH

Raman spectroscopy was first used to calibrate the intensity of pH-sensitive peaks of the Raman-active hydrogel sensors in a static pH environment. The dynamic response was then assessed for hydrogels exposed to changing pH conditions within a flow cell. Finally, the static pH response after 5 months of storage was determined.

RESULTS

All four types of hydrogels allowed the surface-enhanced Raman spectroscopy (SERS) sensors to respond to the pH level of the local environment without introducing interfering signals, resulting in consistent calibration curves. When the pH level changed, the probes in the gels were slow to reach steady-state, requiring several hours, and response times were found to vary among hydrogels. Only one type, poly(2-hydroxyethyl methacrylate) (pHEMA), lasted five months without significant degradation of dynamic range.

CONCLUSIONS

While all hydrogels appear to be viable candidates as biocompatible hosts for the SERS sensing chemistry, pHEMA was found to be most functionally stable over the long interval tested. Poly(ethylene glycol) hydrogels exhibit the most rapid response to changing pH. Since these two gel types are covalently cross-linked and do not generally degrade, they both offer advantages over sodium alginate for use as implants.

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions.

The application of artificial intelligence and data integration in COVID-19 studies: a scoping review

OBJECTIVE

To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling.

MATERIALS AND METHODS

We searched 2 major COVID-19 literature databases, the National Institutes of Health's LitCovid and the World Health Organization's COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening.

RESULTS

In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications.

DISCUSSION

Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias.

CONCLUSION

There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.

Continuous health monitoring: An opportunity for precision health

Continuous health monitoring and integrated diagnostic devices, worn on the body and used in the home, will help to identify and prevent early manifestations of disease. However, challenges lie ahead in validating new health monitoring technologies and in optimizing data analytics to extract actionable conclusions from continuously obtained health data.

When physical activity meets the physical environment: precision health insights from the intersection

BACKGROUND

The physical environment can facilitate or hinder physical activity. A challenge in promoting physical activity is ensuring that the physical environment is supportive and that these supports are appropriately tailored to the individual or group in question. Ideally, aspects of the environment that impact physical activity would be enhanced, but environmental changes take time, and identifying ways to provide more precision to physical activity recommendations might be helpful for specific individuals or groups. Therefore, moving beyond a "one size fits all" to a precision-based approach is critical.

MAIN BODY

To this end, we considered 4 critical aspects of the physical environment that influence physical activity (walkability, green space, traffic-related air pollution, and heat) and how these aspects could enhance our ability to precisely guide physical activity. Strategies to increase physical activity could include optimizing design of the built environment or mitigating of some of the environmental impediments to activity through personalized or population-wide interventions.

CONCLUSIONS

Although at present non-personalized approaches may be more widespread than those tailored to one person's physical environment, targeting intrinsic personal elements (e.g., medical conditions, sex, age, socioeconomic status) has interesting potential to enhance the likelihood and ability of individuals to participate in physical activity.

Precision health through prediction modelling: factors to consider before implementing a prediction model in clinical practice

INTRODUCTION Precision medical practice emphasises early detection, improved surveillance and prevention through targeted intervention. Prediction models can help identify high-risk individuals to be targeted for healthy behavioural changes or medical treatment to prevent disease development and assist both health professionals and patients to make informed decisions. Concerns exist regarding the adequacy, accuracy, validity and reliability of prediction models. AIM The purpose of this study is to introduce readers to the basic concept of prediction modelling in precision health and recommend factors to consider before implementing a prediction model in clinical practice. METHODS Prediction models developed maintaining proper process and with quality prediction and validation can be used in clinical practice to improve patient care. RESULTS Aspects of prediction models that should be considered before implementation include: appropriateness of the model for the intended purpose; adequacy of the model; validation, face validity and clinical impact studies of the model; a parsimonious model with data easily measured in clinical settings; and easily accessible models with decision support for successful implementation. DISCUSSION Choosing clinical prediction models requires cautious consideration and several practical factors before implementing a model in clinical practice.

Ultra-high-frequency radio-frequency acoustic molecular imaging with saline nanodroplets in living subjects

Molecular imaging is a crucial technique in clinical diagnostics but it relies on radioactive tracers or strong magnetic fields that are unsuitable for many patients, particularly infants and pregnant women. Ultra-high-frequency radio-frequency acoustic (UHF-RF-acoustic) imaging using non-ionizing RF pulses allows deep-tissue imaging with sub-millimetre spatial resolution. However, lack of biocompatible and targetable contrast agents has prevented the successful in vivo application of UHF-RF-acoustic imaging. Here we report our development of targetable nanodroplets for UHF-RF-acoustic molecular imaging of cancers. We synthesize all-liquid nanodroplets containing hypertonic saline that are stable for at least 2 weeks and can produce high-intensity UHF-RF-acoustic signals. Compared with concentration-matched iron oxide nanoparticles, our nanodroplets produce at least 1,600 times higher UHF-RF-acoustic signals at the same imaging depth. We demonstrate in vivo imaging using the targeted nanodroplets in a prostate cancer xenograft mouse model expressing gastrin release protein receptor (GRPR), and show that targeting specificity is increased by more than 2-fold compared with untargeted nanodroplets or prostate cancer cells not expressing this receptor.