Wearable Devices Research Articles

Refining smart healthcare care for mental health and substance use disorders: A patient-centred, evidence-based approach

In this article, we comment on the article by Zhang et al , which explores the familiarity, awareness, and usage of smart medical care and its correlation with mental health and personality traits. The use of intelligent healthcare technologies in treating mental disorders and substance use disorders shows significant promise, but involves certain challenges, such as limited access, low technological literacy, and privacy concerns. These barriers disproportionately affect deprived populations and individuals with severe mental health conditions. We highlight the positive impact of smart healthcare solutions, such as telemedicine and wearable technologies, on patient engagement, remote monitoring, and treatment adherence. To overcome these challenges, we propose strategies, such as improving user-friendliness, ensuring equitable access to digital interventions, enhancing cybersecurity, and integrating smart healthcare into clinical workflows. Training healthcare providers and developing policies to ensure the ethical use of patient data are essential. When implemented thoughtfully, smart healthcare technologies can revolutionize mental health and substance use disorder treatment, improve patient outcomes, and reduce healthcare inequities.

A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices.

A new lightweight deep learning model optimized with pruning and dynamic quantization to detect freezing gait on wearable devices.

A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.

A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.

Self-regulating heating and self-powered flexible fiber fabrics at low temperature

Self-regulating heating and self-powered flexible fiber fabrics at low temperature

From lab to real-life: A three-stage validation of wearable technology for stress monitoring.

From lab to real-life: A three-stage validation of wearable technology for stress monitoring.

Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning

Towards Understanding the Impact of Participant and its Wearable Devices in Federated Learning

Opportunities and challenges of noise interference suppression algorithms for dynamic ECG signals in wearable devices: A review

Opportunities and challenges of noise interference suppression algorithms for dynamic ECG signals in wearable devices: A review

Stress monitoring using low-cost electroencephalogram devices: A systematic literature review.

The use of low-cost, consumer-grade wearable health monitoring devices has become increasingly prevalent in mental health research, including stress studies. While cortisol response magnitude remains the gold standard for stress assessment, an expanding body of research employs low-cost EEG devices as primary tools for recording biomarker data, often combined with wrist and ring-based wearables. However, the technical variability among low-cost EEG devices, particularly in sensor count and placement according to the 10-20 Electrode Placement System, poses challenges for reproducibility in study outcomes. This review aims to provide an overview of the growing application of low-cost EEG devices and machine learning techniques for assessing brain function, with a focus on stress detection. It also highlights the strengths and weaknesses of various machine learning methods commonly used in stress research, and evaluates the reproducibility of reported findings along with sensor count and placement importance. A comprehensive review was conducted of published studies utilizing EEG devices for stress detection and their associated machine learning approaches. Searches were performed across databases including Scopus, Google Scholar, ScienceDirect, Nature, and PubMed, yielding 69 relevant articles for analysis. The selected studies were synthesized into four thematic categories: stress assessment using EEG, low-cost EEG devices, datasets for EEG-based stress measurement, and machine learning techniques for EEG-based stress analysis. For machine learning-focused studies, validation and reproducibility methods were critically assessed. Study quality was evaluated and scored using the IJMEDI checklist. The review identified several studies employing low-cost EEG devices to monitor brain activity during stress and relaxation phases, with many reporting high predictive accuracy using various machine learning validation techniques. However, only 54% of the studies included health screening prior to experimentation, and 58% were categorized as low-powered due to limited sample sizes. Additionally, few studies validated their results using an independent validation set or cortisol response as a correlating biomarker and there was a lack of consensus on data pre-processing and sensor placement as a key contributor to improving model generalization and accuracy. Low-cost consumer-grade wearable devices, including EEG and wrist-based monitors, are increasingly utilized in stress-related research, offering promising avenues for non-invasive biomarker monitoring. However, significant gaps remain in standardizing EEG signal processing and sensor placement, both of which are critical for enhancing model generalization and accuracy. Furthermore, the limited use of independent validation sets and cortisol response as correlating biomarkers highlights the need for more robust validation methodologies. Future research should focus on addressing these limitations and establishing consensus on data pre-processing techniques and sensor configurations to improve the reliability and reproducibility of findings in this growing field.

Measuring Physical Activity in Hospital Settings: A Scoping Review of Randomized Controlled Trials.

Measuring Physical Activity in Hospital Settings: A Scoping Review of Randomized Controlled Trials.

AI-based personalized real-time risk prediction for behavioral management in psychiatric wards using multimodal data.

AI-based personalized real-time risk prediction for behavioral management in psychiatric wards using multimodal data.

A wearable ring oximeter for detection of sleep disordered breathing.

Wearable devices have been developed that can continuously monitor physiologic variables. One device, Circul® (Bodimetrics Corp, Los Angeles, CA), with a form factor of a ring, measures several variables (SpO2, movement, heart rate). To evaluate the potential utility of this device in detecting sleep breathing disorders. OSA severity measured by the oxygen desaturation Index (ODI) using a wearable oximetric Circul® ring (ODI as defined by 3% desaturation - cODI3%) was compared with the gold standard, polysomnography (PSG) in patients suspected of having sleep-disordered breathing. The Circul data was autoscored by the device's software; a technician scored the PSG data. The sensitivity (S), and specificity (E) for the different thresholds for cODI3% compared to the PSG AHI and PSG ODI, were calculated. 164 patients (age=44.8 years+12.3 (SD)) were enrolled. Using the PSG-derived AHI as the reference for classification, the best cut-off points were: OSA=AHI≥5: cODI3%≥4.3 (S 87.8%, E 93.8%); OSA=AHI≥15: cODI3%≥13.1 (S 76%, E 100%); OSA=AHI≥30=: cODI3%≥16.2 (S 85.7%, E 92%); Using the ODI from the PSG as the reference for classification, the respective cut-off points were: OSA=ODI≥5: cODI3%≥4.3 (S 93.4%, E 88.9%); OSA=ODI≥15:cODI3%≥13.1 (S85.2%, E98.4%); OSA=ODI≥30: cODI3%≥18.7 (S 98.4%, E 92.2%). Circul® oximetry demonstrated good diagnostic accuracy compared to the gold standard in determining OSA severity. cODI3% greater than 13 suggests that significant OSA might be present.

Advances in Food-As-Medicine Interventions and Their Impact on Future Food Production, Processing, and Supply Chains.

Advances in Food-As-Medicine Interventions and Their Impact on Future Food Production, Processing, and Supply Chains.

Boosted synergistic catalytic performance of Pt single-atom catalyst NiS2/Pt on wearable hydrogel biosensor for sweat lactic acid analysis.

Boosted synergistic catalytic performance of Pt single-atom catalyst NiS2/Pt on wearable hydrogel biosensor for sweat lactic acid analysis.

Wearable SERS devices in health management: Challenges and prospects.

Wearable SERS devices in health management: Challenges and prospects.

Feasibility and acceptability of remote activity and patient-reported outcome monitoring in men with prostate cancer undergoing androgen deprivation therapy.

e17090 Background: ADT, a standard treatment for prostate cancer (PC), is associated with significant side effects such as fatigue, muscle loss, weight gain, and mood changes, which can lead to physical function decline and poor quality of life. Wearable technology for continuous activity monitoring combined with routine patient-reported outcome (PRO) collection may provide a more complete and accurate picture of a patient’s treatment experience, offering valuable information for guiding symptom management. This study aimed to evaluate the feasibility and acceptability of remote patient monitoring with wearable technology and PROs in men diagnosed with PC undergoing ADT. Methods: Men diagnosed with PC undergoing ADT, recruited from two healthcare facilities as part of the DigiPRO trial (NCT04575402), wore an activity monitor (Fitbit Charge HR) continuously to monitor daily activity and sleep and completed monthly electronic PROs (NIH PROMIS and PRO-CTCAE) over a 12-week study period. Feasibility was defined as >60% of patients adhering to wearing the device and completing PROs at the 12-week primary study endpoint. Acceptability was also assessed through qualitative interviews among a subset of patients, gathering feedback on the usability and impact of the wearable devices. Additionally, a survey was conducted among clinicians with experience treating PC patients to assess their interest in accessing and using the wearable and PRO data. The proportion of patients adhering to wearing the devices and completing PROs at 12 weeks was calculated. Descriptive statistics were computed to summarize clinician survey responses, and inductive thematic analysis was applied to patient interview data. Results: All 40 PC participants (100%) (Median age: 70 years, range 51-87, 60% White/37% Black) consistently used the monitoring devices and 38 participants (95%) completed monthly PROs over the 12-week period. From the interviews, patients reported that the devices were easy to use and enjoyed wearing them to monitor their PA and sleep. Clinicians expressed overall support for accessing the device data, where 7/10 clinicians responded that they would access patient wearable activity data in the medical chart and believe EMR-integrated wearable activity data would help them better take care of patients. Conclusions: Our findings suggest that remote activity and PRO monitoring are feasible and acceptable for PC patients, with high device adherence, PRO completion, and perceived benefits of use observed by clinicians and patients. Clinicians and researchers should consider incorporating remote activity and PRO monitoring into future research studies. Clinical trial information: NCT04575402 .

Fabrication of highly tough, self-healing sodium alginate/polyacrylamide and copper based nanocomposite hydrogel and its application as strain and pressure sensor for human health monitoring and signature recognition.

Fabrication of highly tough, self-healing sodium alginate/polyacrylamide and copper based nanocomposite hydrogel and its application as strain and pressure sensor for human health monitoring and signature recognition.

Fatigue life and performance evaluation of wearable flexible thermoelectric devices under thermomechanical loads

Fatigue life and performance evaluation of wearable flexible thermoelectric devices under thermomechanical loads

In vitro antimicrobial photodynamic therapy for methicillin-resistant Staphylococcus aureus using flexible organic light-emitting diode.

In vitro antimicrobial photodynamic therapy for methicillin-resistant Staphylococcus aureus using flexible organic light-emitting diode.

Flexible wearable microfiber device for wide-range sound source localization

Flexible wearable microfiber device for wide-range sound source localization

Design and baseline characteristics of an implementation study to increase activity with social incentives: The STEP together trial.

Design and baseline characteristics of an implementation study to increase activity with social incentives: The STEP together trial.