Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges

Sensors, Journal Year: 2023, Volume and Issue: 23(12), P. 5406 - 5406

Published: June 7, 2023

Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine environment, enabling sensitive selective detection quantification diverse analytes. In biomedicine, these facilitated disease diagnosis, drug discovery, point-of-care devices. environmental monitoring, they played a crucial role assessing air, water, soil quality, as well ensured food safety. Despite notable progress, numerous challenges persist. This review article addresses recent developments micro- for biomedical challenges, focusing on enhancing basic sensing techniques through micro/nanotechnology. Additionally, it explores applications addressing current both domains. The concludes by emphasizing need further research to expand capabilities sensors/devices, enhance sensitivity selectivity, integrate wireless communication energy-harvesting technologies, optimize sample preparation, material selection, automated components sensor design, fabrication, characterization.

Language: Английский

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Artificial intelligence for medicine 2025: Navigating the endless frontier

The Innovation Medicine, Journal Year: 2025, Volume and Issue: unknown, P. 100120 - 100120

Published: Jan. 1, 2025

<p>Artificial intelligence (AI) is driving transformative changes in the field of medicine, with its successful application relying on accurate data and rigorous quality standards. By integrating clinical information, pathology, medical imaging, physiological signals, omics data, AI significantly enhances precision research into disease mechanisms patient prognoses. technologies also demonstrate exceptional potential drug development, surgical automation, brain-computer interface (BCI) research. Through simulation biological systems prediction intervention outcomes, enables researchers to rapidly translate innovations practical applications. While challenges such as computational demands, software ethical considerations persist, future remains highly promising. plays a pivotal role addressing societal issues like low birth rates aging populations. can contribute mitigating rate through enhanced ovarian reserve evaluation, menopause forecasting, optimization Assisted Reproductive Technologies (ART), sperm analysis selection, endometrial receptivity fertility remote consultations. In posed by an population, facilitate development dementia models, cognitive health monitoring strategies, early screening systems, AI-driven telemedicine platforms, intelligent smart companion robots, environments for aging-in-place. profoundly shapes medicine.</p>

Language: Английский

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Wearable Sensing Systems for Monitoring Mental Health

Sensors, Journal Year: 2022, Volume and Issue: 22(3), P. 994 - 994

Published: Jan. 27, 2022

Wearable systems for monitoring biological signals have opened the door to personalized healthcare and advanced a great deal over past decade with development of flexible electronics, efficient energy storage, wireless data transmission, information processing technologies. As there are cumulative understanding mechanisms underlying mental processes increasing desire lifetime wellbeing, various wearable sensors been devised monitor status from physiological activities, physical movements, biochemical profiles in body fluids. This review summarizes recent progress that can be utilized healthcare, especially focusing on (i.e., biomarkers associated status, sensing modalities, device materials) discussing their promises challenges.

Language: Английский

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Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care

Issues in Mental Health Nursing, Journal Year: 2023, Volume and Issue: 44(10), P. 1020 - 1034

Published: Oct. 3, 2023

This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence health. They also facilitate remote monitoring, bridge geographical gaps, support clinical decision-making. The evolution virtual assistants AI-enhanced therapeutic interventions are discussed. These technological advancements reshape nurse-patient interactions while ensuring personalized, efficient, high-quality addresses AI's ethical responsible use emphasizing privacy, data security, balance between human interaction tools. As applications care continue to evolve, this encourages continued innovation advocating implementation, thereby optimally leveraging potential nursing.

Language: Английский

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Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e44548 - e44548

Published: March 31, 2023

Rapid proliferation of mental health interventions delivered through conversational agents (CAs) calls for high-quality evidence to support their implementation and adoption. Selecting appropriate outcomes, instruments measuring assessment methods are crucial ensuring that evaluated effectively with a high level quality.We aimed identify the types outcome measurement instruments, used assess clinical, user experience, technical outcomes in studies effectiveness CA health.We undertook scoping review relevant literature health. We performed comprehensive search electronic databases, including PubMed, Cochrane Central Register Controlled Trials, Embase (Ovid), PsychINFO, Web Science, as well Google Scholar Google. included experimental evaluating interventions. The screening data extraction were independently by 2 authors parallel. Descriptive thematic analyses findings performed.We 32 targeted promotion well-being (17/32, 53%) treatment monitoring symptoms (21/32, 66%). reported 203 measure clinical (123/203, 60.6%), experience (75/203, 36.9%), (2/203, 1.0%), other (3/203, 1.5%). Most only 1 study (150/203, 73.9%) self-reported questionnaires (170/203, 83.7%), most electronically via survey platforms (61/203, 30.0%). No validity was cited more than half (107/203, 52.7%), which largely created or adapted they (95/107, 88.8%).The diversity choice employed on CAs point need an established minimum core set greater use validated instruments. Future should also capitalize affordances made available smartphones streamline evaluation reduce participants' input burden inherent self-reporting.

Language: Английский

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Wearable technologies for health research: Opportunities, limitations, and practical and conceptual considerations

Brain Behavior and Immunity, Journal Year: 2023, Volume and Issue: 113, P. 444 - 452

Published: Aug. 8, 2023

One of the most notable limitations laboratory-based health research is its inability to continuously monitor health-relevant physiological processes as individuals go about their daily lives. As a result, we have generated large amounts data with unknown generalizability real-world situations and also created schism between where are collected (i.e., in lab) need intervene prevent disease field). Devices using noninvasive wearable technology changing all this, however, ability provide high-frequency assessments peoples' ever-changing states life manner that relatively noninvasive, affordable, scalable. Here, discuss critical points every researcher should keep mind when these wearables research, spanning device metric decisions, hardware software selection, quality sampling rate issues, on stress an example throughout. We address usability participant acceptability how "digital biomarker" behavioral can be integrated enhance basic science intervention studies. Finally, summarize 10 key questions addressed make study strong possible. Collectively, keeping improve our psychobiology human health, intervene, precisely it matters most:

Language: Английский

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Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life

Pain, Journal Year: 2024, Volume and Issue: 165(6), P. 1348 - 1360

Published: Jan. 23, 2024

Technology offers possibilities for quantification of behaviors and physiological changes relevance to chronic pain, using wearable sensors devices suitable data collection in daily life contexts. We conducted a scoping review passive sensor technologies that sample psychological interest including social situations. Sixty articles met our criteria from the 2783 citations retrieved searching. Three-quarters recruited people were with mostly musculoskeletal, remainder acute or episodic pain; those pain had mean age 43 (few studies sampled adolescents children) 60% women. Thirty-seven performed laboratory clinical settings settings. Most used only 1 type technology, 76 types overall. The commonest was accelerometry (mainly contexts), followed by motion capture settings), smaller number collecting autonomic activity, vocal signals, brain activity. Subjective self-report provided "ground truth" mood, other variables, but often at different timescale automatically collected data, many reported weak relationships between technological relevant constructs, instance, fear movement muscle There relatively little discussion practical issues: frequency sampling, missing human reasons, users' experience, particularly when users did not receive any form. conclude some suggestions content process future this field.

Language: Английский

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Adherence to a digital therapeutic mediates the relationship between momentary self-regulation and health risk behaviors

Frontiers in Digital Health, Journal Year: 2025, Volume and Issue: 7

Published: Feb. 4, 2025

Smoking, obesity, and insufficient physical activity are modifiable health risk behaviors. Self-regulation is one fundamental behavior change mechanism often incorporated within digital therapeutics as it varies momentarily across time contexts may play a causal role in improving these However, the of momentary self-regulation achieving has been infrequently examined. Using novel scale, this study examined how targeting through therapeutic impacts adherence to two different behavioral outcomes. This prospective interventional included data for 28 days from 50 participants with obesity binge eating disorder who smoked regularly. An evidence-based therapeutic, called Laddr™, provided tools. Participants reported on their via ecological assessments behaviors were measured steps taken tracker breathalyzed carbon monoxide. Medical regimen was assessed daily Laddr usage. Bayesian dynamic mediation models used examine moment-to-moment effects between subscales, medical adherence, In sample, perseverance [β 1 = 0.17, 95% CI (0.06, 0.45)] emotion regulation 0.12, (0.03, 0.27)] targets positively predicted following day, higher subsequently positive predictor same day both 2 0.335, (0.030, 0.717)] 0.389, (0.080, 0.738)]. smoking target 0.91, (0.60, 1.24)]. not CO values -0.09, (-0.24, 0.09)]. provides evidence that can modify relationships self-regulation, Together, work demonstrated ability digitally assess transdiagnostic mediating effect pro-health ClinicalTrials.gov, identifier (NCT03774433).

Language: Английский

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From screens to scenes: A survey of embodied AI in healthcare

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103033 - 103033

Published: Feb. 1, 2025

Language: Английский

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Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study

JMIR mhealth and uhealth, Journal Year: 2023, Volume and Issue: 11, P. e45405 - e45405

Published: March 20, 2023

Depressive and manic episodes within bipolar disorder (BD) major depressive (MDD) involve altered mood, sleep, activity, alongside physiological alterations wearables can capture. Firstly, we explored whether wearable data could predict (aim 1) the severity of an acute affective episode at intra-individual level 2) polarity euthymia among different individuals. Secondarily, which were related to prior predictions, generalization across patients, associations between symptoms data. We conducted a prospective exploratory observational study including patients with BD MDD on (manic, depressed, mixed) whose recorded using research-grade (Empatica E4) 3 consecutive time points (acute, response, remission episode). Euthymic healthy controls during single session (approximately 48 h). Manic assessed standardized psychometric scales. Physiological included following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), electrodermal activity (EDA). Invalid removed rule-based filter, channels aligned 1-second units segmented window lengths 32 seconds, as best-performing parameters. developed deep learning predictive models, channels' individual contribution permutation feature importance analysis, computed scales' items normalized mutual information (NMI). present novel, fully automated method for preprocessing analysis from device, viable supervised pipeline time-series analyses. Overall, 35 sessions (1512 hours) 12 mixed, euthymic) 7 (mean age 39.7, SD 12.6 years; 6/19, 32% female) analyzed. The mood was predicted moderate (62%-85%) accuracies 1), their (70%) accuracy 2). most relevant features former tasks ACC, EDA, HR. There fair agreement in classification (Kendall W=0.383). Generalization models unseen overall low accuracy, except models. ACC associated "increased motor activity" (NMI>0.55), "insomnia" (NMI=0.6), "motor inhibition" (NMI=0.75). EDA "aggressive behavior" (NMI=1.0) "psychic anxiety" (NMI=0.52). show potential identify specific mania depression quantitatively, both MDD. Motor stress-related (EDA HR) stand out digital biomarkers predicting depression, respectively. These findings represent promising pathway toward personalized psychiatry, allow early identification intervention episodes.

Language: Английский

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