Economic, Psychological, and Emotional Well-Being of Pregnant Women During the COVID-19 Pandemic.

INTRODUCTION: The intersection between perinatal mental health and the coronavirus disease 2019 (COVID-19) pandemic remains of significant public health importance. The current study examined the emotional and financial well-being and predictors of elevated depressive symptoms among pregnant women during the COVID-19 pandemic. METHODS: This online survey was conducted with 2118 women ≥18 years old who were pregnant at the time of the survey and living in the United States or Puerto Rico. Depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale, with scores ≥10 indicative of elevated depressive symptoms. The final logistic regression model included housing insecurity, financial distress, COVID-19 diagnosis, exposure to COVID-19, and demographic covariates. RESULTS: More than half the sample (53.8%) had elevated depressive symptoms. In logistic regression analyses, the odds of having elevated depressive symptoms were significantly higher for participants reporting housing insecurity (adjusted odds ratio [aOR], 1.56; 95% CI, 1.22-2.01), financial distress (aOR, 1.57; 95% CI, 1.17-2.12), COVID-19 diagnosis (aOR, 2.53; 95% CI, 1.53-4.17), and COVID-19 exposure (aOR, 1.41; 95% CI, 1.07-1.86), after adjusting for covariates. The association of elevated depressive symptoms with housing insecurity was especially strong among those who experienced COVID-19 (aOR, 6.04; 95% CI, 2.15-17.0). DISCUSSION: Our findings are consistent with previous literature revealing that diagnosis, exposure, concerns about family, and effects on financial stability were related to depressive symptoms during the pandemic. The relationships between financial and housing concerns with elevated depressive symptoms, independent of concerns about infection in family members, suggest that there may be direct and indirect effects of the pandemic on mental health.

FREE: A randomized controlled feasibility trial of a cognitive behavioral therapy and technology-assisted intervention to reduce fear of hypoglycemia in young adults with type 1 diabetes.

OBJECTIVE: The purpose of this study was to test the preliminary effectiveness of a cognitive behavioral therapy intervention (Fear Reduction Efficacy Evaluation [FREE]) designed to reduce fear of hypoglycemia in young adults with type 1 diabetes. The primary outcome was fear of hypoglycemia, secondary outcomes were A1C, and glycemic variability. METHODS: A randomized clinical trial was used to test an 8-week intervention (FREE) compared to an attention control (diabetes education) in 50 young adults with type 1 diabetes who experienced fear of hypoglycemia at baseline. All participants wore a continuous glucose monitor for the 8-week study period. Self-reported fear of hypoglycemia point-of-care A1C testing, continuous glucose monitor-derived glucose variability were measured at baseline, Week 8, and Week 12 (post-program). RESULTS: Compared to controls, those participating in the FREE intervention experienced a reduction in fear of hypoglycemia (SMD B = -8.52, p = 0.021), change in A1C (SMD B = 0.04, p = 0.841) and glycemic variability (glucose standard deviation SMD B = -2.5, p = 0.545) by the end of the intervention. This represented an 8.52% greater reduction in fear of hypoglycemia. CONCLUSION: A cognitive behavioral therapy intervention (FREE) resulted in improvements in fear of hypoglycemia. CLINICALTRIALS: govNCT03549104.

Multivariable Automated Insulin Delivery System for Handling Planned and Spontaneous Physical Activities.

BACKGROUND: Hybrid closed-loop control of glucose levels in people with type 1 diabetes mellitus (T1D) is limited by the requirements on users to manually announce physical activity (PA) and meals to the artificial pancreas system. Multivariable automated insulin delivery (mvAID) systems that can handle unannounced PAs and meals without any manual announcements by the user can improve glycemic control by modulating insulin dosing in response to the occurrence and intensity of spontaneous physical activities. METHODS: An mvAID system is developed to supplement the glucose measurements with additional physiological signals from a wristband device, with the signals analyzed using artificial intelligence algorithms to automatically detect the occurrence of PA and estimate its intensity. This additional information gained from the physiological signals enables more proactive insulin dosing adjustments in response to both planned exercise and spontaneous unanticipated physical activities. RESULTS: In silico studies of the mvAID illustrate the safety and efficacy of the system. The mvAID is translated to pilot clinical studies to assess its performance, and the clinical experiments demonstrate an increased time in range and reduced risk of hypoglycemia following unannounced PA and meals. CONCLUSIONS: The mvAID systems can increase the safety and efficacy of insulin delivery in the presence of unannounced physical activities and meals, leading to improved lives and less burden on people with T1D.

Association between sleep variability and time in range of glucose levels in patients with type 1 diabetes: Cross-sectional study.

OBJECTIVE: Sleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations among sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D. METHODS: Seventy-six non-shift-working adult T1D patients participated. Blinded 7-day continuous glucose monitoring (CGM) and hemoglobin A1C (A1C) were collected. Percentages of time-in-range (glucose levels 70-180 mg/dL) and glycemic variability (measured by the coefficient of variation [%CV]) were calculated from CGM. Sleep (duration and efficiency) was recorded using 7-day actigraphy. Variability (standard deviation) of midsleep time was used to represent sleep variability. Nonparametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by diabetes self-management questionnaire-revised. Multiple regression analyses were performed to identify independent predictors of glycemic parameters. RESULTS: Median (interquartile range) age was 34.0 (27.2, 43.1) years, 48 (63.2%) were female, and median (interquartile range) A1C was 6.8% (6.2, 7.4). Sleep duration, efficiency, and nonparametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in sleep variability was associated with 9.64% less time-in-range (B = -9.64, 95% confidence interval [-16.29, -2.99], p ≤ .001). A higher diabetes self-management questionnaire score was an independent predictor of lower A1C (B = -0.18, 95% confidence interval [-0.32, -0.04]). CONCLUSION: Greater sleep timing variability is independently associated with less time spent in the desirable glucose range in this T1D cohort. Reducing sleep timing variability could potentially lead to improved metabolic control and should be explored in future research. DATA AVAILABILITY STATEMENT: Data are available upon a reasonable request to the corresponding author.

A Mentoring Workgroup for Academic Role Transition Among Clinical-Track Nursing Faculty.

BACKGROUND: With the current ongoing nurse faculty shortage, mentorship can aid in career advancement, promotion, and retention for clinical assistant professors (CAPs) when hiring clinical-track faculty. METHOD: The organization, experiences, and outcomes of a CAP mentorship workgroup within a multi-campus research-intensive college of nursing are described. RESULTS: The CAP mentorship workgroup was guided by senior faculty and met monthly to provide CAPs with a better understanding of the promotion process, motivation to pursue scholarship, and peer support. Through this workgroup, seven CAPs have completed their probationary review process, two CAPs are in the process of being promoted to clinical associate professors, and more than 90% of CAPs have been retained. CONCLUSION: Mentorship for clinical-track faculty can positively influence faculty productivity and aid in CAP retention, which contributes to the success of nursing programs. [J Nurs Educ. 2023;62(3):183-186.].

Sleep-Opt-In: A Randomized Controlled Pilot Study to Improve Sleep and Glycemic Variability in Adults With Type 1 Diabetes.

PURPOSE: The purpose of this study was to evaluate the feasibility and acceptability of a technology-assisted behavioral sleep intervention (Sleep-Opt-In) and to examine the effects of Sleep-Opt-In on sleep duration and regularity, glucose indices, and patient-reported outcomes. Short sleep duration and irregular sleep schedules are associated with reduced glycemic control and greater glycemic variability. METHODS: A randomized controlled parallel-arm pilot study was employed. Adults with type 1 diabetes (n = 14) were recruited from the Midwest and randomized 3:2 to the sleep-optimization (Sleep-Opt-In) or Healthy Living attention control group. Sleep-Opt-In was an 8-week, remotely delivered intervention consisting of digital lessons, sleep tracker, and weekly coaching phone calls by a trained sleep coach. Assessments of sleep (actigraphy), glucose (A1C, continuous glucose monitoring), and patient-reported outcomes (questionnaires for daytime sleepiness, fatigue, diabetes distress, and depressive mood) were completed at baseline and at completion of the intervention. RESULTS: Sleep-Opt-In was feasible and acceptable. Those in Sleep-Opt-In with objectively confirmed short or irregular sleep demonstrated an improvement in sleep regularity (25 minutes), reduced glycemic variability (3.2%), and improved time in range (6.9%) compared to the Healthy Living attention control group. Patient-reported outcomes improved only for the Sleep-Opt-In group. Fatigue and depressive mood improved compared to the control. CONCLUSIONS: Sleep-Opt-In is feasible, acceptable, and promising for further evaluation as a means to improve sleep duration or regularity in the population of people with type 1 diabetes.

Cognitive Function and Self-Management Behaviors in Older Adults With Type 2 Diabetes.

BACKGROUND: Type 2 diabetes (T2D) is strongly associated with cognitive impairment. Decreased cognitive function could affect daily self-management behaviors critical for people with T2D. Executive function is significant for daily self-management, and decreased subjective cognitive function could be an early indicator of poor daily self-management. However, little is known about whether executive or subjective cognitive function affects daily self-management behaviors in older adults. OBJECTIVES: We investigated the effect of executive function or subjective cognitive function on daily self-management behaviors (diet, glucose management, physical activity, and physician contact) in older adults with T2D. METHODS: We used a cross-sectional, observational design with convenience sampling of 84 adults aged ≥60 years with T2D. Telephone-administered cognitive function tests measured participants' overall cognitive and executive function levels. Subjective cognitive function, diabetes self-management, and covariates, including demographic information (age, gender, race/ethnicity, and level of education), body mass index, depressive symptoms, and diabetes duration, were assessed using online surveys. Data were analyzed using bivariate correlation and backward stepwise regression. RESULTS: The mean age of the sample was 68.46 ± 5.41 years. Participants were predominantly female and White, and the majority had normal cognitive function. Controlling for demographics, body mass index, depressive symptoms, and diabetes duration, a decrease in executive function indicated by a greater number of errors made during the telephone-administered Oral Trail Making Test Part B relative to the sample was associated with poorer adherence to physician contact behaviors. Subjective cognitive function was not associated with any self-management behaviors. DISCUSSION: A reduction in executive function was associated with poorer adherence to physician contact behaviors in older adults with T2D and normal cognitive function; lack of adherence to physician contact behaviors could be an early indicator of declining cognitive function. Difficulties or changes in routine diabetes self-management behaviors should be closely monitored in older adults. Cognitive assessment should be followed when needed.

A physical activity-intensity driven glycemic model for type 1 diabetes.

BACKGROUND AND OBJECTIVE: The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered. METHODS: Several physiological models describing the glycemic response to physical activity are proposed by incorporating model terms proportional to the physical activity intensity and duration describing endogenous glucose production (EGP), glucose utilization, and glucose transfer from the plasma to tissues. Leveraging clinical data of T1D during physical activity, each model fit is assessed. RESULTS: The proposed model with terms accommodating EGP, glucose transfer, and insulin-independent glucose utilization allow for an improved simulation of physical activity glycemic responses with the greatest reduction in model error (mean absolute percentage error: 16.11 ± 4.82 vs. 19.49 ± 5.87, p = 0.002). CONCLUSIONS: The development of a physiologically plausible model with model terms representing each major contributor to glucose metabolism during physical activity can outperform traditional models with physical activity described through glucose utilization alone. This model accurately describes the relation of plasma insulin and physical activity intensity on glucose production and glucose utilization to generate the appropriately increasing, decreasing or stable glucose response for each physical activity condition. The proposed model will enable the in silico evaluation of automated insulin dosing algorithms designed to mitigate the effects of physical activity with the appropriate relationship between the reduction in basal insulin and the corresponding glycemic excursion.

Sleep optimization to improve glycemic control in adults with type 1 diabetes: study protocol for a randomized controlled parallel intervention trial.

BACKGROUND: Despite improvements in treatment regimens and technology, less than 20% of adults with type 1 diabetes (T1D) achieve glycemic targets. Sleep is increasingly recognized as a potentially modifiable target for improving glycemic control. Diabetes distress, poor self-management behaviors, and reduced quality of life have also been linked to sleep variability and insufficient sleep duration. A significant gap of knowledge exists regarding interventions to improve sleep and the effects of sleep optimization on glycemic control in T1D. The purpose of this study is to determine the efficacy of a T1D-specific sleep optimization intervention (Sleep-Opt) on the primary outcomes of sleep variability, sleep duration, and glycemic control (A1C); other glycemic parameters (glycemic variability, time-in-range [TIR]); diabetes distress; self-management behaviors; quality of life; and other patient-reported outcomes in adults with T1D and habitual increased sleep variability or short sleep duration. METHODS: A randomized controlled parallel-arm study will be employed in 120 adults (aged 18 to 65 years) with T1D. Participants will be screened for habitual sleep variability (> 1 h/week) or insufficient sleep duration (< 6.5 h per night). Eligible subjects will be randomized to the Sleep-Opt intervention group or healthy living attention control group for 12 weeks. A 1-week run-in period is planned, with baseline measures of sleep by actigraphy (sleep variability and duration), glycemia (A1C and related glycemic measures: glycemic variability and TIR using continuous glucose monitoring), and other secondary outcomes: diabetes distress, self-management behaviors, quality of life, and additional patient-reported outcomes. Sleep-Opt is a technology-assisted behavioral sleep intervention that we recently developed that leverages the rapidly increasing public interest in sleep tracking. Our behavioral intervention employs four elements: a wearable sleep tracker, didactic content, an interactive smartphone application, and brief telephone counseling. The attention control group will participate in a healthy living information program. Baseline measures will be repeated at midpoint, program completion, and post-program (weeks 6, 12, and 24, respectively) to determine differences between the two groups and sustainability of the intervention. DISCUSSION: A better understanding of strategies to improve sleep in persons with T1D has the potential to be an important component of diabetes. TRIAL REGISTRATION: Clinical Trial Registration: NCT04506151 .

Observational Study of Glycemic Impact of Anticipatory and Early-Race Athletic Competition Stress in Type 1 Diabetes.

Athletic competitions and the associated psychological stress are a challenge for people with type 1 diabetes (T1D). This study aims to understand the influence of anticipatory and early race competition stress on blood glucose concentrations and to identify personality, demographic, or behavioral traits indicative in the scope of the impact. Ten recreational athletes with T1D competed in an athletic competition and an exercise-intensity matched non-competition "training" session for comparison. The two hours prior to exercise and the first 30 minutes of exercise were compared between the paired exercise sessions to assess the influence of anticipatory and early race stress. The effectiveness index, average CGM glucose, and the ingested carbohydrate to injected insulin ratio were compared between the paired sessions through regression. In 9 of 12 races studied, an elevated CGM for the race over the individual training session was observed. The rate of change of the CGM during the first 30 minutes of exercise notably differed between the race and training (p = 0.02) with a less rapid decline in CGM occurring during the race for 11 of 12 paired sessions and an increasing CGM trend during the race for 7 of the 12 sessions with the rate of change (mean ± standard deviation) as 1.36 ± 6.07 and -2.59 ± 2.68 mg/dL per 5 minutes for the race and training, respectively. Individuals with longer durations of diabetes often decreased their carbohydrate-to-insulin ratio on race day, taking more insulin, than on the training day while the reverse was noted for those newly diagnosed (r = -0.52, p = 0.05). The presence of athletic competition stress can impact glycemia. With an increasing duration of diabetes, the athletes may be expecting elevated competition glucose concentrations and take preventive measures.

Impact of worries associated with COVID-19 on diabetes-related psychological symptoms in older adults with Type 2 diabetes.

This study examined the associations between worries associated with COVID-19, diabetes-specific distress, and depressive symptoms in older adults with type 2 diabetes (T2D), who are particularly vulnerable to COVID-19 and its psychological impacts. A cross-sectional online survey was conducted with 84 older adults with T2D from June to December 2020. Participants had little to moderate worries associated with COVID-19, with the greatest worries about the economy recession, followed by a family member catching COVID-19, lifestyle disruptions, and overwhelmed local hospitals. Bivariate correlation and tobit regression revealed that increases in worries associated with COVID-19 were associated with increased diabetes distress and depressive symptoms. Specifically, worries associated with COVID-19 increased diabetes-specific emotional burden and physician-related and regimen-related distress. Increased diabetes distress and depressive symptoms worsened by COVID-19 may ultimately lead to poor glucose control. Additional assessment by mental health experts should be considered for older adults with T2D during and after infectious disease pandemic.

Fluctuations in Self-Reported Symptoms Predict Objective Physical Activity in Adults With Type 2 Diabetes.

PURPOSE: The purpose of this study was to examine the association between self-reported symptoms including fatigue and sleep disturbance with moderate-intensity physical activity among adults with type 2 diabetes. METHODS: This report was a secondary analysis of a cross-sectional study. Data from 53 participants with at least 6 days of repeated measures were used. Daytime physical activity and energy expenditure were assessed using a wrist-worn accelerometer at the free-living setting. Fatigue upon awakening was measured using a 0 to 10 scale. Sleep (eg, restorative sleep, sleep duration, and sleep efficiency) was measured using the Consensus Sleep Diary for Morning. Data were analyzed using linear mixed models by including within- and between-person effects. RESULTS: Participants were predominantly females (54.7%) with a mean age of 60.3 years. Controlling for the covariates, at the individual level (within-person), fluctuations in restorative sleep and fatigue upon awakening predicted moderate-intensity PA. Similarly, at the individual level, fluctuations in restorative sleep and fatigue upon awakening predicted average hourly energy expenditure. However, at the group level (between-person), no significant associations were found between fatigue and restorative sleep with moderate-intensity physical activity. CONCLUSIONS: The study findings suggest that within-person fluctuations in fatigue and restorative sleep upon awakening predict daytime moderate-intensity physical activity. At the individual level, reducing fluctuations in fatigue and restorative sleep might encourage participation in physical activity. More research is warranted to uncover the underlying causes of fluctuations in fatigue and restorative sleep. Meanwhile, diabetes care and education specialists should pay attention to the within-person fluctuations of fatigue and sleep.

Diagnostic reasoning: relationships among expertise, accuracy, and ways that nurse practitioner students self-explain.

OBJECTIVES: To improve diagnostic ability, educators should employ multifocal strategies. One promising strategy is self-explanation, the purposeful technique of generating self-directed explanations during problem-solving. Students self-explain information in ways that range from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. In a previous phase of research, unique ways that family nurse practitioner (NP) students self-explain during diagnostic reasoning were identified and described. This study aims to (a) explore relationships between ways of self-explaining and diagnostic accuracy levels and (b) compare differences between students of varying expertise in terms of ways of self-explaining and diagnostic accuracy levels. Identifying high-quality diagnostic reasoning self-explanation types may facilitate development of more refined self-explanation educational strategies. METHODS: Thirty-seven family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern university diagnosed three written case studies while self-explaining. During the quantitative phase of a content analysis, associational and comparative data analysis techniques were applied. RESULTS: Expert students voiced significantly more clinical and biological inference self-explanations than did novice students. Diagnostic accuracy scores were significantly associated with biological inference scores. Clinical and biological inference scores accounted for 27% of the variance in diagnostic accuracy scores, with biological inference scores significantly influencing diagnostic accuracy scores. CONCLUSIONS: Not only were biologically focused self-explanations associated with diagnostic accuracy, but also their spoken frequency influenced levels of diagnostic accuracy. Educational curricula should support students to view patient presentations in terms of underlying biology from the onset of their education.

Ways that nurse practitioner students self-explain during diagnostic reasoning.

OBJECTIVES: An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners' memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? METHODS: Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. RESULTS: Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students' understanding of clinical data and reflect shallow information processing. CONCLUSIONS: Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management.

OBJECTIVE: Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions. METHODS: A wristband conducive of use by free-living ambulatory people is used. The measured physiological variables are analyzed to generate new quantifiable input features for PA and APS. Machine learning techniques estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments illustrate the improvement in GC prediction accuracy. RESULTS: The average mean absolute error (MAE) of one-hour-ahead GC predictions with testing data decreases from 35.1 to 31.9 mg/dL (p-value = 0.01) with the inclusion of PA information, and it decreases from 16.9 to 14.2 mg/dL (p-value = 0.006) with the inclusion of PA and APS information. CONCLUSION: The first-ever glucose prediction model is developed that incorporates measures of physical activity and acute psychological stress to improve GC prediction accuracy. SIGNIFICANCE: Modeling the effects of physical activity and acute psychological stress on glucose concentration values will improve diabetes management and enable informed meal, activity and insulin dosing decisions.

Discrimination of simultaneous psychological and physical stressors using wristband biosignals.

BACKGROUND AND OBJECTIVE: In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of chronic conditions such as diabetes by automated personalized insulin delivery. Monitoring APS noninvasively throughout free-living conditions remains challenging because the responses to APS and PA of many physiological variables measured by wearable devices are similar. METHODS: Various classification algorithms are compared to simultaneously detect and discriminate the PA (sedentary state, treadmill running, and stationary bike) and the type of APS (non-stress state, mental stress, and emotional anxiety). The impact of APS inducements is verified with commonly used self-reported questionnaires (The State-Trait Anxiety Inventory (STAI)). To aid the classification algorithms, novel features are generated from the physiological variables reported by a wristband device during 117 hours of experiments involving simultaneous APS inducement and PA. We also translate the APS assessment into a quantitative metric for use in predicting the adverse outcomes. RESULTS: An accurate classification of the concurrent PA and APS states is achieved with an overall classification accuracy of 99% for PA and 92% for APS. The average accuracy of APS detection during sedentary state, treadmill running, and stationary bike is 97.3, 94.1, and 84.5%, respectively. CONCLUSIONS: The simultaneous assessment of APS and PA throughout free-living conditions from a convenient wristband device is useful for monitoring the factors contributing to an elevated risk of acute events in people with chronic diseases like cardiovascular complications and diabetes.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

Online Glucose Prediction Using Computationally Efficient Sparse Kernel Filtering Algorithms in Type-1 Diabetes.

Streaming data from continuous glucose monitoring (CGM) systems enable the recursive identification of models to improve estimation accuracy for effective predictive glycemic control in patients with type-1 diabetes. A drawback of conventional recursive identification techniques is the increase in computational requirements, which is a concern for online and real-time applications such as the artificial pancreas systems implemented on handheld devices and smartphones where computational resources and memory are limited. To improve predictions in such computationally constrained hardware settings, efficient adaptive kernel filtering algorithms are developed in this paper to characterize the nonlinear glycemic variability by employing a sparsification criterion based on the information theory to reduce the computation time and complexity of the kernel filters without adversely deteriorating the predictive performance. Furthermore, the adaptive kernel filtering algorithms are designed to be insensitive to abnormal CGM measurements, thus compensating for measurement noise and disturbances. As such, the sparsification-based real-time model update framework can adapt the prediction models to accurately characterize the time-varying and nonlinear dynamics of glycemic measurements. The proposed recursive kernel filtering algorithms leveraging sparsity for improved computational efficiency are applied to both in-silico and clinical subjects, and the results demonstrate the effectiveness of the proposed methods.

Sleep disturbance and next-day physical activity in COPD patients.

Physical inactivity and sleep disturbance are more problematic in patients with chronic obstructive pulmonary disease (COPD) than in healthy individuals. The purpose of the study was to identify impacts of nighttime sleep on next-day physical activity in COPD patients. The study included 52 COPD patients reporting disturbed sleep. Sleep and physical activity were measured using an accelerometer for 5 days. Increased sleep latency was associated with less next-day physical activity during the afternoon (4-6 p.m.). Greater waking duration/times were associated with less next-morning (6-8 a.m.) physical activity. Greater total sleep time was associated with less next-morning (12-9 a.m.) physical activity, and greater sleep efficiency was associated with less next-morning (1-3 a.m.) and more next-evening (6-7 p.m.) physical activity. Results suggest that sleep disturbance had varying influences on next-day hourly physical activity. These results support the potential value of sleep management in promoting physical activity in COPD patients.