The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized Trial.

BACKGROUND: Home blood pressure (BP) monitoring with lifestyle coaching is effective in managing hypertension and reducing cardiovascular risk. However, traditional manual lifestyle coaching models significantly limit availability due to high operating costs and personnel requirements. Furthermore, the lack of patient lifestyle monitoring and clinician time constraints can prevent personalized coaching on lifestyle modifications. OBJECTIVE: This study assesses the effectiveness of a fully digital, autonomous, and artificial intelligence (AI)-based lifestyle coaching program on achieving BP control among adults with hypertension. METHODS: Participants were enrolled in a single-arm nonrandomized trial in which they received a BP monitor and wearable activity tracker. Data were collected from these devices and a questionnaire mobile app, which were used to train personalized machine learning models that enabled precision lifestyle coaching delivered to participants via SMS text messaging and a mobile app. The primary outcomes included (1) the changes in systolic and diastolic BP from baseline to 12 and 24 weeks and (2) the percentage change of participants in the controlled, stage-1, and stage-2 hypertension categories from baseline to 12 and 24 weeks. Secondary outcomes included (1) the participant engagement rate as measured by data collection consistency and (2) the number of manual clinician outreaches. RESULTS: In total, 141 participants were monitored over 24 weeks. At 12 weeks, systolic and diastolic BP decreased by 5.6 mm Hg (95% CI -7.1 to -4.2; P<.001) and 3.8 mm Hg (95% CI -4.7 to -2.8; P<.001), respectively. Particularly, for participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 9.6 mm Hg (95% CI -12.2 to -6.9; P<.001) and 5.7 mm Hg (95% CI -7.6 to -3.9; P<.001), respectively. At 24 weeks, systolic and diastolic BP decreased by 8.1 mm Hg (95% CI -10.1 to -6.1; P<.001) and 5.1 mm Hg (95% CI -6.2 to -3.9; P<.001), respectively. For participants starting with stage-2 hypertension, systolic and diastolic BP decreased by 14.2 mm Hg (95% CI -17.7 to -10.7; P<.001) and 8.1 mm Hg (95% CI -10.4 to -5.7; P<.001), respectively, at 24 weeks. The percentage of participants with controlled BP increased by 17.2% (22/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The percentage of participants with stage-2 hypertension decreased by 25% (32/128; P<.001) and 26.5% (27/102; P<.001) from baseline to 12 and 24 weeks, respectively. The average weekly participant engagement rate was 92% (SD 3.9%), and only 5.9% (6/102) of the participants required manual outreach over 24 weeks. CONCLUSIONS: The study demonstrates the potential of fully digital, autonomous, and AI-based lifestyle coaching to achieve meaningful BP improvements and high engagement for patients with hypertension while substantially reducing clinician workloads. TRIAL REGISTRATION: ClinicalTrials.gov NCT06337734; https://clinicaltrials.gov/study/NCT06337734.

Behavioral Intervention for Adults With Autism on Distribution of Attention in Triadic Conversations: A/B-Tested Pre-Post Study.

BACKGROUND: Cross-neurotype differences in social communication patterns contribute to high unemployment rates among adults with autism. Adults with autism can be unsuccessful in job searches or terminated from employment due to mismatches between their social attention behaviors and society's expectations on workplace communication. OBJECTIVE: We propose a behavioral intervention concerning distribution of attention in triadic (three-way) conversations. Specifically, the objective is to determine whether providing personalized feedback to each individual with autism based on an analysis of their attention distribution behavior during an initial conversation session would cause them to modify their orientation behavior in a subsequent conversation session. METHODS: Our system uses an unobtrusive head orientation estimation model to track the focus of attention of each individual. Head orientation sequences from a conversation session are analyzed based on five statistical domains (eg, maximum exclusion duration and average contact duration) representing different types of attention distribution behavior. An intervention is provided to a participant if they exceeded the nonautistic average for that behavior by at least 2 SDs. The intervention uses data analysis and video modeling along with a constructive discussion about the targeted behaviors. Twenty-four individuals with autism with no intellectual disabilities participated in the study. The participants were divided into test and control groups of 12 participants each. RESULTS: Based on their attention distribution behavior in the initial conversation session, 11 of the 12 participants in the test group received an intervention in at least one domain. Of the 11 participants who received the intervention, 10 showed improvement in at least one domain on which they received feedback. Independent t tests for larger test groups (df>15) confirmed that the group improvements are statistically significant compared with the corresponding controls (P<.05). Crawford-Howell t tests confirmed that 78% of the interventions resulted in significant improvements when compared individually against corresponding controls (P<.05). Additional t tests comparing the first conversation sessions of the test and control groups and comparing the first and second conversation sessions of the control group resulted in nonsignificant differences, pointing to the intervention being the main effect behind the behavioral changes displayed by the test group, as opposed to confounding effects or group differences. CONCLUSIONS: Our proposed behavioral intervention offers a useful framework for practicing social attention behavior in multiparty conversations that are common in social and professional settings.

Classification of Patient Recovery from COVID-19 Symptoms using Consumer Wearables and Machine Learning.

Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics. Our system utilizes a Garmin wearable and symptom tracker mobile app for data collection. The data consists of vitals, lifestyle, and symptom information which is fused into an online report for clinicians to review. Symptom data collected via our mobile app is used to label the recovery status of each patient daily. We propose a ML-based binary patient recovery classifier which uses wearable data to estimate whether a patient has recovered from COVID-19 symptoms. We evaluate our method using leave-one-subject-out (LOSO) cross-validation, and find that Random Forest (RF) is the top performing model. Our method achieves an F1-score of 0.88 when applying our RF-based model personalization technique using weighted bootstrap aggregation. Our results demonstrate that ML-assisted remote monitoring using automatically collected wearable data can supplement or be used in place of manual daily symptom tracking which relies on patient compliance.

Processing of electronic waste in a counter current teeter-bed separator.

Advanced gravity separation of ground electronic waste (e-waste) in a teeter-bed separator was investigated. It was established that the Floatex Density Seprator (FDS) is a promising device for wet processing of e-waste to recover metal values physically. It was possible to enrich the metal content from 23% in the feed to 37% in the product in a single stage operation using the FDS with over 95% recovery of the metals. A two-stage processing scheme was developed that enriched the metal content further to 48.2%. The influence of the operating variables, namely, teeter water flow rate, bed pressure and feed rate were quantified. Low bed pressures and low teeter water rates produced higher mass yields with poorer product grades. On the contrary, a high bed pressure and high teeter water rate combination led to a lower mass yield but better product quality. A high feed rate introduced en-masse settling leading to higher yield but at a poorer product grade. For an FDS with 230 mm × 230 mm cross section and a height of 530 mm, the process condition with 6.6l pm teeter water rate, 5.27 kPa bed pressure and 82 kg/hr feed rate maximized the yield for a target product grade of 37% metal in a single pass.

Personalized Blood Pressure Estimation Using Photoplethysmography: A Transfer Learning Approach.

In this paper, we present a personalized deep learning approach to estimate blood pressure (BP) using the photoplethysmogram (PPG) signal. We propose a hybrid neural network architecture consisting of convolutional, recurrent, and fully connected layers that operates directly on the raw PPG time series and provides BP estimation every 5 seconds. To address the problem of limited personal PPG and BP data for individuals, we propose a transfer learning technique that personalizes specific layers of a network pre-trained with abundant data from other patients. We use the MIMIC III database which contains PPG and continuous BP data measured invasively via an arterial catheter to develop and analyze our approach. Our transfer learning technique, namely BP-CRNN-Transfer, achieves a mean absolute error (MAE) of 3.52 and 2.20 mmHg for SBP and DBP estimation, respectively, outperforming existing methods. Our approach satisfies both the BHS and AAMI blood pressure measurement standards for SBP and DBP. Moreover, our results demonstrate that as little as 50 data samples per person are required to train accurate personalized models. We carry out Bland-Altman and correlation analysis to compare our method to the invasive arterial catheter, which is the gold-standard BP measurement method.

Linotrins: Omega-3 oxylipins featuring an E,Z,E conjugated triene motif are present in the plant kingdom and alleviate inflammation in LPS-challenged microglial cells.

Alpha-linolenic acid (ALA), an essential omega-3 polyunsaturated fatty acid found in plants, exerts neuroprotection and anti-inflammatory effects in chronic and acute CNS disease models. However, the underlying mechanisms are not yet understood. Since ALA is not incorporated into the brain, the observed health benefits may result from some of its metabolites. The putative formation of dihydroxylated ALA derivatives (called linotrins) was recently shown in vitro in the presence of lipoxygenases. However, the in vitro biosynthesis of linotrins was neither stereoselective nor quantitatively efficient for studying their physiological roles as enantiomeric pure forms. Herein, we report the first stereo-controlled synthesis that features regio- and stereoselective hydrometalations of alkynes for assembling the sensitive E,Z,E-conjugated trienes, as well as LC-MS investigations that provide evidence of linotrins occurrence in plants. Moreover, strong anti-inflammatory effects on microglia highlight the potential physiological importance of linotrins and open new perspectives in search of CNS therapeutics.

Perspective: Opportunities and Challenges of Technology Tools in Dietary and Activity Assessment: Bridging Stakeholder Viewpoints.

The science and tools of measuring energy intake and output in humans have rapidly advanced in the last decade. Engineered devices such as wearables and sensors, software applications, and Web-based tools are now ubiquitous in both research and consumer environments. The assessment of energy expenditure in particular has progressed from reliance on self-report instruments to advanced technologies requiring collaboration across multiple disciplines, from optics to accelerometry. In contrast, assessing energy intake still heavily relies on self-report mechanisms. Although these tools have improved, moving from paper-based to online reporting, considerable room for refinement remains in existing tools, and great opportunities exist for novel, transformational tools, including those using spectroscopy and chemo-sensing. This report reviews the state of the science, and the opportunities and challenges in existing and emerging technologies, from the perspectives of 3 key stakeholders: researchers, users, and developers. Each stakeholder approaches these tools with unique requirements: researchers are concerned with validity, accuracy, data detail and abundance, and ethical use; users with ease of use and privacy; and developers with high adherence and utilization, intellectual property, licensing rights, and monetization. Cross-cutting concerns include frequent updating and integration of the food and nutrient databases on which assessments rely, improving accessibility and reducing disparities in use, and maintaining reliable technical assistance. These contextual challenges are discussed in terms of opportunities and further steps in the direction of personalized health.

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.

Background: Blood pressure (BP) is an essential indicator for human health and is known to be greatly influenced by lifestyle factors, like activity and sleep factors. However, the degree of impact of each lifestyle factor on BP is unknown and may vary between individuals. Our goal is to investigate the relationships between BP and lifestyle factors and provide personalized and precise recommendations to improve BP, as opposed to the current practice of general lifestyle recommendations. Method: Our proposed system consists of automated data collection using home BP monitors and wearable activity trackers and feature engineering techniques to address time-series data and enhance interpretability. We propose Random Forest with Shapley-Value-based Feature Selection to offer personalized BP modeling and top lifestyle factor identification, and subsequent generation of precise recommendations based on the top factors. Result: In collaboration with UC San Diego Health and Altman Clinical and Translational Research Institute, we performed a clinical study, applying our system to 25 patients with elevated BP or stage I hypertension for three consecutive months. Our study results validate our system's ability to provide accurate personalized BP models and identify the top features which can vary greatly between individuals. We also validate the effectiveness of personalized recommendations in a randomized controlled experiment. After receiving recommendations, the subjects in the experimental group decreased their BPs by 3.8 and 2.3 for systolic and diastolic BP, compared to the decrease of 0.3 and 0.9 for the subjects without recommendations. Conclusion: The study demonstrates the potential of using wearables and machine learning to develop personalized models and precise lifestyle recommendations to improve BP.

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual's best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

Towards On-Demand Virtual Physical Therapist: Machine Learning-Based Patient Action Understanding, Assessment and Task Recommendation.

In this paper, we propose a machine learning-based virtual physical therapist (PT) system to enable personalized remote training for patients with Parkinson's disease (PD). Three physical therapy tasks with multiple difficulty levels are selected to help patients with PD improve balance and mobility. Patients' movements are captured by a Kinect sensor. Criteria for each task are carefully designed by our PT co-author such that the patient's performance can be evaluated in an automated manner. Given the patient's motion data, we propose a two-phase human action understanding algorithm TPHAU to understand the patient's movements, and an error identification model to identify the patient's movement errors. To enable automated task recommendation, a machine learning-based model is trained from real patient and PT data to provide accurate, personalized, and timely task update recommendation for patients with PD, thereby emulating a real PT's behavior. Real patient data have been collected in the clinic to train the models. Experiments show that the proposed methods achieve high accuracy in patient action understanding, error identification and task recommendation. The proposed virtual PT system has the potential of enabling on-demand virtual care and significantly reducing cost for both patients and care providers.

Association and prognostic value of serum Cystatin C, IL-18 and Uric acid in urological patients with acute kidney injury.

PURPOSE: To assess the role of serum Cystatin C, IL-18 and Uric acid in acute kidney injury (AKI) in urological patients, along with their prognostic significance. MATERIALS AND METHODS: Prospective observational study included 61 cases, admitted in urology ward with baseline serum creatinine ≤1.5 mg/dL. All patients had at least one or more predisposing factors for AKI. Daily urine output and creatinine level were checked. Serum levels of biomarkers were measured at baseline and postoperatively after 24 h. Development of AKI and its outcome were analysed. RESULTS: Thirty nine patients (63.9%) developed AKI in the study. Patients with AKI were found to have a greater percentage rise of Cystatin C (118.7% v/s 81.8%, p = 0.005), IL-18 (59.0% v/s 25.5%, p = 0.004) and Uric acid (34.3% v/s 19.2%, p = 0.008) after 24 h. Absolute Uric acid level at day 1 was also significantly associated with AKI (5.18 ±â€¯0.91 v/s 4.45 ±â€¯0.86, p = 0.003). Risk stratification of AKI was poor for all biomarkers. Area under curve for Cystatin C, IL-18 and Uric acid was 0.715, 0.696 and 0.734 respectively. Renal function after 3 months, had a positive correlation with baseline creatinine and baseline Cystatin C levels (r = 0.56 & 0.39). CONCLUSIONS: Postoperative serum Cystatin C, IL-18 and Uric acid after 24 h were significantly associated with AKI. Baseline Cystatin C had moderate capability to predict short term renal function.

The silent burden of anemia in school age children: a community based study in West Bengal.

BACKGROUND: Under nutrition and anemia are common co-morbidities in school age children. Due to transition in dietary habits in developing countries, a paradoxical finding of coexistence of anemia and normal/over nutrition is also a cause of concern. OBJECTIVE: > T o assess the nutritional status and prevalence of anemia among school age children (6 - 16 years) residing in rural and urban areas of a district of West Bengal and also to find out the association between weight status, measured as Body Mass Index(BMI) and anemia. MATERIALS AND METHODS: Age, height & weight were measured in 86 rural and 86 urban school age (6 -16 years) children in rural and urban field practice areas of Midnapore Medical College. Their blood was estimated for haemoglobin concentration. RESULTS: Overall prevalence of anemia was 80.2%, and not significantly different between the rural (83.7%) and urban (76.7%) participants and across the genders both in rural (86.4% versus 80.9%) and urban (85.7% versus 72.4%) areas. Thinness was observed to be higher in urban area (48.8% versus 41.9%). However, severe thinness was higher in rural area (18.5% versus 13.9%). Significantly, higher proportion of boys revealed severely low BMI compared to girls in both rural (33.3% versus 4.5%) and urban (17.2% versus 7.1%) areas with no significant differences between the prevalence of anemia across the grades of underweight and normal nutritional status. CONCLUSIONS: Poor nutritional status and anemia are still, taking heavy toll and new program strategies are needed, particularly those that improve the overall nutrition status of children.

Prismatomerin, a new iridoid from Prismatomeris tetrandra. Structure elucidation, determination of absolute configuration, and cytotoxicity.

A new complex iridoid, prismatomerin (1), has been isolated from the leaves of Prismatomeris tetrandra, together with the known glucoside gaertneroside (4). The structures of 1 and 4 were determined by spectroscopic analysis, notably 2D NMR techniques. The (1R,5S,8S,9S,10S)-(-) absolute configuration of prismatomerin (1) was determined by comparison of the vibrational circular dichroism (VCD) spectrum calculated using density functional theory and the experimental VCD spectrum of the O-acetyl derivative 3. Prismatomerin (1) showed remarkable antitumor activity and also interfered with mitotic spindle formation.