Leveraging Bluetooth low-energy technology to improve contact tracing among healthcare personnel in hospital setting during the coronavirus disease 2019 (COVID-19) pandemic.

To improve contact tracing for healthcare workers, we built and configured a Bluetooth low-energy system. We predicted close contacts with great accuracy and provided an additional contact yield of 14.8%. This system would decrease the effective reproduction number by 56% and would unnecessarily quarantine 0.74% of employees weekly.

Associations between daily step count trajectories and clinical outcomes among adults with comorbid obesity and depression.

Purpose: To examine the relationship between features of daily measured step count trajectories and clinical outcomes among people with comorbid obesity and depression in the ENGAGE-2 Trial. Methods: This post hoc analysis used data from the ENGAGE-2 trial where adults (n=106) with comorbid obesity (BMI ≥30.0 or 27.0 if Asian) and depressive symptoms (Patient Health Questionnaire-9 score ≥10) were randomized (2:1) to receive the experimental intervention or usual care. Daily step count trajectories over the first 60 days (Fitbit Alta HR) were characterized using functional principal component analyses. 7-day and 30-day trajectories were also explored. Functional principal component scores that described features of step count trajectories were entered into linear mixed models to predict weight (kg), depression (Symptom Checklist-20), and anxiety (Generalized Anxiety Disorder Questionnaire-7) at 2-months (2M) and 6-months (6M). Results: Features of 60-day step count trajectories were interpreted as overall sustained high, continuous decline, and disrupted decline. Overall sustained high step count was associated with low anxiety (2M, ß=-0.78, p<.05; 6M, ß=-0.80, p<.05) and low depressive symptoms (6M, ß=-0.15, p<.05). Continuous decline in step count was associated with high weight (2M, ß=0.58, p<.05). Disrupted decline was not associated with clinical outcomes at 2M or 6M. Features of 30-day step count trajectories were also associated with weight (2M, 6M), depression (6M), and anxiety (2M, 6M); Features of 7-day step count trajectories were not associated with weight, depression, or anxiety at 2M or 6M. Conclusions: Features of step count trajectories identified using functional principal component analysis were associated with depression, anxiety, and weight outcomes among adults with comorbid obesity and depression. Functional principal component analysis may be a useful analytic method that leverages daily measured physical activity levels to allow for precise tailoring of future behavioral interventions.

Feasibility and Acceptability of a Preoperative Multimodal Mobile Health Assessment in Spine Surgery Candidates.

BACKGROUND: Rapid growth in smartphone use has expanded opportunities to use mobile health (mHealth) technology to collect real-time patient-reported and objective biometric data. These data may have important implication for personalized treatments of degenerative spine disease. However, no large-scale study has examined the feasibility and acceptability of these methods in spine surgery patients. OBJECTIVE: To evaluate the feasibility and acceptability of a multimodal preoperative mHealth assessment in patients with degenerative spine disease. METHODS: Adults undergoing elective spine surgery were provided with Fitbit trackers and sent preoperative ecological momentary assessments (EMAs) assessing pain, disability, mood, and catastrophizing 5 times daily for 3 weeks. Objective adherence rates and a subjective acceptability survey were used to evaluate feasibility of these methods. RESULTS: The 77 included participants completed an average of 82 EMAs each, with an average completion rate of 86%. Younger age and chronic pulmonary disease were significantly associated with lower EMA adherence. Seventy-two (93%) participants completed Fitbit monitoring and wore the Fitbits for an average of 247 hours each. On average, participants wore the Fitbits for at least 12 hours per day for 15 days. Only worse mood scores were independently associated with lower Fitbit adherence. Most participants endorsed positive experiences with the study protocol, including 91% who said they would be willing to complete EMAs to improve their preoperative surgical guidance. CONCLUSION: Spine fusion candidates successfully completed a preoperative multimodal mHealth assessment with high acceptability. The intensive longitudinal data collected may provide new insights that improve patient selection and treatment guidance.

4,300 steps per day prior to surgery are associated with improved outcomes after pancreatectomy.

BACKGROUND: Decreased preoperative physical fitness and low physical activity have been associated with preoperative functional reserve and surgical complications. We sought to evaluate daily step count as a measure of physical activity and its relationship with post-pancreatectomy outcomes. METHODS: Patients undergoing pancreatectomy were given a remote telemonitoring device to measure their preoperative levels of physical activity. Patient activity, demographics, and perioperative outcomes were collected and compared in univariate and multivariate logistic regression analysis. RESULTS: 73 patients were included. 45 (61.6%) patients developed complications, with 17 (23.3%) of those patients developing severe complications. These patients walked 3437.8 (SD 1976.7) average daily steps, compared to 5918.8 (SD 2851.1) in patients without severe complications (p < 0.001). In logistic regression analysis, patients who walked less than 4274.5 steps had significantly higher odds of severe complications (OR = 7.5 (CI 2.1, 26.8), p = 0.002). CONCLUSION: Average daily steps below 4274.5 before surgery are associated with severe complications after pancreatectomy. Preoperative physical activity levels may represent a modifiable target for prehabilitation protocols.

Cross-trial prediction of depression remission using problem-solving therapy: A machine learning approach.

BACKGROUND: Psychotherapy is a standard depression treatment; however, determining a patient's prognosis with therapy relies on clinical judgment that is subject to trial-and-error and provider variability. PURPOSE: To develop machine learning (ML) algorithms to predict depression remission for patients undergoing 6 months of problem-solving therapy (PST). METHOD: Using data from the treatment arm of 2 randomized trials, ML models were trained and validated on ENGAGE-2 (ClinicalTrials.gov, #NCT03841682) and tested on RAINBOW (ClinicalTrials.gov, #NCT02246413) for predictions at baseline and at 2-months. Primary outcome was depression remission using the Depression Symptom Checklist (SCL-20) score < 0.5 at 6 months. Predictor variables included baseline characteristics (sociodemographic, behavioral, clinical, psychosocial) and intervention engagement through 2-months. RESULTS: Of the 26 candidate variables, 8 for baseline and 11 for 2-months were predictive of depression remission, and used to train the models. The best-performing model predicted remission with an accuracy significantly greater than chance in internal validation using the ENGAGE-2 cohort, at baseline [72.6% (SD = 3.6%), p < 0.0001] and at 2-months [72.3% (5.1%), p < 0.0001], and in external validation with the RAINBOW cohort at baseline [58.3% (0%), p < 0.0001] and at 2-months [62.3% (0%), p < 0.0001]. Model-agnostic explanations highlighted key predictors of depression remission at the cohort and patient levels, including female sex, lower self-reported sleep disturbance, lower sleep-related impairment, and lower negative problem orientation. CONCLUSIONS: ML models using clinical and patient-reported data can predict depression remission for patients undergoing PST, affording opportunities for prospective identification of likely responders, and for developing personalized early treatment optimization along the patient care trajectory.

Comparing stress prediction models using smartwatch physiological signals and participant self-reports.

Recent advances in wearable technology have facilitated the non-obtrusive monitoring of physiological signals, creating opportunities to monitor and predict stress. Researchers have utilized machine learning methods using these physiological signals to develop stress prediction models. Many of these prediction models have utilized objective stressor tasks (e.g., a public speaking task or solving math problems). Alternatively, the subjective user responses with self-reports have also been used for measuring stress. In this paper, we describe a methodological approach (a) to compare the prediction performance of models developed using objective markers of stress using participant-reported subjective markers of stress from self-reports; and (b) to develop personalized stress models by accounting for inter-individual differences. Towards this end, we conducted a laboratory-based study with 32 healthy volunteers. Participants completed a series of stressor tasks-social, cognitive and physical-wearing an instrumented commercial smartwatch that collected physiological signals and participant responses using timed self-reports. After extensive data preprocessing using a combination of signal processing techniques, we developed two types of models: objective stress models using the stressor tasks as labels; and subjective stress models using participant responses to each task as the label for that stress task. We trained and tested several machine learning algorithms-support vector machine (SVM), random forest (RF), gradient boosted trees (GBT), AdaBoost, and Logistic Regression (LR)-and evaluated their performance. SVM had the best performance for the models using the objective stressor (i.e., stressor tasks) with an AUROC of 0.790 and an F-1 score of 0.623. SVM also had the highest performance for the models using the subjective stress (i.e., participant self-reports) with an AUROC of 0.719 and an F-1 score of 0.520. Model performance improved with a personalized threshold model to an AUROC of 0.751 and an F-1 score of 0.599. The performance of the stress models using an instrumented commercial smartwatch was comparable to similar models from other state-of-the-art laboratory-based studies. However, the subjective stress models had a lower performance, indicating the need for further research on the use of self-reports for stress-related studies. The improvement in performance with the personalized threshold-based models provide new directions for building stress prediction models.

Predicting Outcomes in Patients Undergoing Pancreatectomy Using Wearable Technology and Machine Learning: Prospective Cohort Study.

BACKGROUND: Pancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity. OBJECTIVE: The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning. METHODS: In this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves. RESULTS: Between February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875. CONCLUSIONS: Machine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics.

Efficient and Reversible Electron Doping of Semiconductor-Enriched Single-Walled Carbon Nanotubes by Using Decamethylcobaltocene.

Single-walled carbon nanotubes (SWCNTs) offer great potential for field-effect transistors and integrated circuit applications due to their extraordinary electrical properties. To date, as-made SWCNT transistors are usually p-type in air, and it still remains challenging for realizing n-type devices. Herein, we present efficient and reversible electron doping of semiconductor-enriched single-walled carbon nanotubes (s-SWCNTs) by firstly utilizing decamethylcobaltocene (DMC) deposited by a simple spin-coating process at room temperature as an electron donor. A n-type transistor behavior with high on current, large I on /I off ratio and excellent uniformity is obtained by surface charge transfer from the electron donor DMC to acceptor s-SWCNTs, which is further corroborated by the Raman spectra and the ab initio simulation results. The DMC dopant molecules could be reversibly removed by immersion in N, N-Dimethylformamide solvent, indicating its reversibility and providing another way to control the carrier concentration effectively as well as selective removal of surface dopants on demand. Furthermore, the n-type behaviors including threshold voltage, on current, field-effect mobility, contact resistances, etc. are well controllable by adjusting the surface doping concentration. This work paves the way to explore and obtain high-performance n-type nanotubes for future complementary CMOS circuit and system applications.

Temperature-dependent transport and hysteretic behaviors induced by interfacial states in MoS2 field-effect transistors with lead-zirconate-titanate ferroelectric gating.

We report the temperature and gate-voltage-dependent electrical properties of lead-zirconate-titanate-gated MoS2 field-effect transistors (MoS2-PZT FETs) within a temperature range of 300 to 380 K. The MoS2 transistors with PZT gating exhibit large reproducible clockwise hysteresis, which is induced by the dynamic charge-trapping/de-trapping process of interfacial states between PZT films and MoS2 channels under the modulation of ferroelectric polarization of PZT films. In this way, the modulation of the gate effect on the hysteresis behavior has been achieved by activating the dynamic charge-trapping/de-trapping process in the interfacial states under different V gs . Moreover, the temperature dependence of the current in the range of 300 to 380 K indicates thermally activated hysteretic behaviors. The hysteresis in the transfer characteristics of MoS2-PZT FETs shows a simultaneous enlargement with increasing temperature, which can be attributed to the thermally sensitive dynamic trapping/de-trapping process of interfacial states.

Adjustable hydrazine modulation of single-wall carbon nanotube network field effect transistors from p-type to n-type.

Single-wall carbon nanotube (SWCNT) network field effect transistors (FETs), which show decent p-type electronic properties, have been fabricated. The use of hydrazine as an aqueous solution and a strong n-type dopant for the SWCNTs is demonstrated in this paper. The electrical properties are obviously tuned by hydrazine treatment at different concentrations on the surface of the SWCNT network FETs. The transport behavior of SWCNTs can be modulated from p-type to n-type, demonstrating the controllable and adjustable doping effect of hydrazine. With a higher concentration of hydrazine, more electrons can be transferred from the hydrazine molecules to the SWCNT network films, thus resulting in a change of threshold voltage, carrier mobility and on-current. By cleaning the device, the hydrazine doping effects vanish, which indicates that the doping effects of hydrazine are reversible. Through x-ray photoelectron spectroscopy (XPS) characterization, the doping effects of hydrazine have also been studied.

Controllable Hysteresis and Threshold Voltage of Single-Walled Carbon Nano-tube Transistors with Ferroelectric Polymer Top-Gate Insulators.

Double-gated field effect transistors have been fabricated using the SWCNT networks as channel layer and the organic ferroelectric P(VDF-TrFE) film spin-coated as top gate insulators. Standard photolithography process has been adopted to achieve the patterning of organic P(VDF-TrFE) films and top-gate electrodes, which is compatible with conventional CMOS process technology. An effective way for modulating the threshold voltage in the channel of P(VDF-TrFE) top-gate transistors under polarization has been reported. The introduction of functional P(VDF-TrFE) gate dielectric also provides us an alternative method to suppress the initial hysteresis of SWCNT networks and obtain a controllable ferroelectric hysteresis behavior. Applied bottom gate voltage has been found to be another effective way to highly control the threshold voltage of the networked SWCNTs based FETs by electrostatic doping effect.