Bidirectional associations of accelerometer-derived physical activity and stationary behavior with self-reported mental and physical health during midlife

Background Moderate-to-vigorous intensity physical activity (MVPA) is associated with favorable self-rated mental and physical health. Conversely, poor self-rated health in these domains could precede unfavorable shifts in activity. We evaluated bidirectional associations of accelerometer-estimated time spent in stationary behavior (SB), light intensity physical activity (LPA), and MVPA with self-rated health over 10 years in in the CARDIA longitudinal cohort study. Methods Participants (n = 894, age: 45.1 ± 3.5; 63% female; 38% black) with valid accelerometry wear and self-rated health at baseline (2005–6) and 10-year follow-up (2015–6) were included. Accelerometry data were harmonized between exams and measured mean total activity and duration (min/day) in SB, LPA, and MVPA; duration (min/day) in long-bout and short-bout SB (≥30 min vs. < 30 min) and MVPA (≥10 min vs. < 10 min) were also quantified. The Short-Form 12 Questionnaire measured both a mental component score (MCS) and physical component score (PCS) of self-rated health (points). Multivariable linear regression associated baseline accelerometry variables with 10-year changes in MCS and PCS. Similar models associated baseline MCS and PCS with 10-year changes in accelerometry measures. Results Over 10-years, average (SD) MCS increased 1.05 (9.07) points, PCS decreased by 1.54 (7.30) points, and activity shifted toward greater SB and less mean total activity, LPA, and MVPA (all p < 0.001). Only baseline short-bout MVPA was associated with greater 10-year increases in MCS (+ 0.92 points, p = 0.021), while baseline mean total activity, MVPA, and long-bout MVPA were associated with greater 10-year changes in PCS (+ 0.53 to + 1.47 points, all p < 0.005). In the reverse direction, higher baseline MCS and PCS were associated with favorable 10-year changes in mean total activity (+ 9.75 cpm, p = 0.040, and + 15.66 cpm, p < 0.001, respectively) and other accelerometry measures; for example, higher baseline MCS was associated with − 13.57 min/day of long-bout SB (p < 0.001) and higher baseline PCS was associated with + 2.83 min/day of MVPA (p < 0.001) in fully adjusted models. Conclusions The presence of bidirectional associations between SB and activity with self-rated health suggests that individuals with low overall activity levels and poor self-rated health are at high risk for further declines and supports intervention programming that aims to dually increase activity levels and improve self-rated health.

A class of solvable multidimensional stopping problems in the presence of Knightian uncertainty

We investigate the impact of Knightian uncertainty on the optimal timing policy of an ambiguity-averse decision-maker in the case where the underlying factor dynamics follow a multidimensional Brownian motion and the exercise payoff depends on either a linear combination of the factors or the radial part of the driving factor dynamics. We present a general characterization of the value of the optimal timing policy and the worst-case measure in terms of a family of explicitly identified excessive functions generating an appropriate class of supermartingales. In line with previous findings based on linear diffusions, we find that ambiguity accelerates timing in comparison with the unambiguous setting. Somewhat surprisingly, we find that ambiguity may lead to stationarity in models which typically do not possess stationary behavior. In this way, our results indicate that ambiguity may act as a stabilizing mechanism.

Is It Possible to Forecast the Price of Bitcoin?

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems

Engineering systems are often composed of many subsystems that interact with each other. These subsystems, referred to as disciplines, contain many types of uncertainty and in many cases are feedback-coupled with each other. In designing these complex systems, one needs to assess the stationary behavior of these systems for the sake of stability and reliability. This requires the system level uncertainty analysis of the multidisciplinary systems, which is often computationally intractable. To overcome this issue, techniques have been developed for capturing the stationary behavior of the coupled multidisciplinary systems through available data of individual disciplines. The accuracy and convergence of the existing techniques depend on a large amount of data from all disciplines, which are not available in many practical problems. Toward this, we have developed an adaptive methodology that adds the minimum possible number of samples from individual disciplines to achieve an accurate and reliable uncertainty propagation in coupled multidisciplinary systems. The proposed method models each discipline function via Gaussian process (GP) regression to derive a closed-form policy. This policy sequentially selects a new sample point that results in the highest uncertainty reduction over the distribution of the coupling design variables. The effectiveness of the proposed method is demonstrated in the uncertainty analysis of an aerostructural system and a coupled numerical example.

First Graders’ Stationary Behavior in Norwegian After-School Programs: A Mixed Methods Investigation

After-school programs (ASPs) might influence the activities and behaviors of children. The aim of the reported study was to investigate how stationary behavior unfolds during ASP time in a sample of Norwegian first graders. A total of 42 first graders from 14 ASPs were observed during one entire ASP day. ActiGraph accelerometers were used to measure the intensity of their physical activity (PA). Children were found to be involved in stationary behavior for 54.9% of the studied ASP time—a median of 79.5 min (IQR = 62.0). However, there was considerable variation among the children in the sample. Most stationary behavior—63.5% of all stationary behavior during ASP time—was accumulated when the children were sitting indoors. The proportion of stationary behavior was significantly higher indoors than outdoors, during adult-managed time than child-managed time, and during time spent together with other children than time spent alone (p < 0.05). In child-managed physical activity play outdoors, stationary behavior commonly occurred during short periods of standing still. Stationary behavior was usually rapidly broken up by longer periods of PA. Stationary periods involved activities in close relationship with other children and appeared to be important for social interaction and friendship building. The researchers suggest that ASP staff members should actively promote physical activity play that breaks up sedentary time and replaces some stationary behaviors with PA, especially among the least active children.

Physical Activity Among People With Lower Limb Amputation in Brazil

Background: Limb loss affects quality of life, well-being, and autonomy. The World Health Organization has launched a global action plan to reduce physical inactivity and presented recommendations of physical activity for people living with disability. Knowledge of the characteristics of people with lower limb amputation regarding physical activity is important. Thus, the aim of this study was to identify the quantity and type of physical activity done by people with lower limb amputation. Methods: The sample (N = 149) included adults aged 53.08 (17.24) years old with lower limb loss. Data collection was performed through the application of a sociodemographic, behavioral, and clinical data sheet and the Brazilian version of the Physical Activity Scale for Individuals with Physical Disabilities. Results: The total Physical Activity Scale for Individuals with Physical Disabilities score was between 0 and 65.79 metabolic equivalents of task per hour per day, which suggests low practice of physical activities. The most frequently practiced activities (79.9%) were those that involved stationary behavior. Women carried out more domestic activities, and subjects who used lower limb prosthesis and those with traumatic amputation reported higher practice of physical activity. Conclusions: People living with lower limb amputation, from different regions of Brazil, have low levels of physical activity and mainly carry out activities of stationary behavior.

Linear Kalman-Bucy filter with vector autoregressive signal and noise

The linear Kalman-Bucy filter problem for a system, at that a signal and a noise are vector independent stationary autoregressive processes with orders larger than 1, is investigated. The recurrent equations for filter and its error are delivered. The optimal way of the initial data definition is proposed. Some numerical examples are given. In one of them the algorithm leads to a stationary behavior at infinity. In the other example the Kalman- Bucy filter is impossible because the filter error goes to infinity. A behavior of a signal and its error is illustrated by a simulation of a signal and a noise as vector Gaussian stationary autoregressive processes. The simulation supports theoretical conclusions.

Agreement Between Different Days of activPAL and Actigraph GT3X Measurement of Sedentary Behavior and Physical Activity During the School Hours in Elementary Children

Introduction: The authors’ objective was to identify the minimum number of days required to measure sedentary behavior and physical activity in children during school hours. Methods: Fifty-three children from four classes of the second year of elementary school in a public school in Brazil were selected. Sedentary behavior and physical activity were evaluated using activPAL in the thigh and ActiGraph GT3X on the hip. The devices were used for 4 days during the 4 hr of school. Intraclass correlation coefficient (ICC) and Bland–Altman plots were used for statistical analysis (p < .05). Results: For sedentary/stationary behavior indicators, 1 day showed good agreement with 4 days (sitting time, ICC = .89; bias [limits of agreement 95%, LA95%] = 1.6 [45.1 to −41.9], standing time, ICC = .93; bias [LA95%] 1.1 [30.2 to −28.0], and stationary behavior, ICC = .56; bias [LA95%] = 0.2 [37.2 to −36.7]). However, 2 days were necessary for good agreement, with 4 days for physical activity indicators (walking time, ICC = .91; bias [LA95%] = 1.1 [12.0 to −9.7], light physical activity, ICC = .97; bias [LA95%] = 0.3 [7.6 to −7.0], moderate physical activity, ICC = .93; bias [LA95%] = 0.3 [2.3 to −1.6], and vigorous physical activity, ICC = .93; bias [LA95%] = 0.3 [3.1 to −2.5]). Conclusion: Therefore, 1 evaluation day seems enough to obtain representative data of school sedentary/stationary behavior, while 2 days are necessary for the evaluation of physical activity indicators during school hours.

Optimal strategies and pricing analysis in M/M/1 queues with a single working vacation and multiple vacations

This paper considers the customers’ equilibrium and socially optimal joining-balking behavior in single-server Markovian queues with a single working vacation and multiple vacations. Arriving customers decide whether to join the system or balk based on the system states and a linear reward-cost structure, which incorporates the desire of customers for service and their dislike to wait. We consider that the system states are almost unobservable and fully unobservable, respectively. For these two cases, we first analyze the stationary behavior of the system, and get the equilibrium strategies of the customers and compare them to socially optimal balking strategies using numerical examples. We also study the pricing problem that maximizes the server’s profit and derive the optimal pricing strategy. Finally, the social benefits of the almost and fully unobservable queues are compared by numerical examples.