Physical Activity Dependence on Relative Temperature and Humidity Characteristics in a Young, Insufficiently Active Population: A Weather Typing Analysis.

BACKGROUND: Physical activity (PA) is an important contributor to one's physical and mental health both acutely and across the lifespan. Much research has done on the ambient environment's impact on PA; however, these studies have used absolute values of atmospheric measures such as temperature and humidity, which vary spatiotemporally and make comparisons between studies which differ in location or time of year difficult to square with one another. METHODS: Here, we employ the Global Weather Type Classification, Version 2, to determine the combined impact of temperature and humidity on PA in a sample of insufficiently active young adults. We conducted secondary analyses of data from a single-group behavioral intervention trial that varied the number of digital messages sent daily. Young adults (n = 81) wore Fitbit Versa smartwatches for a 6-month period sometime between April 2019 and July 2020, and location was tracked using a custom smartphone application. RESULTS: Mixed linear models indicated that, across 8179 person-days, PA was significantly lower on days with humid conditions and significantly higher on warm dry days, though the latter relationship was no longer significant when controlling for timing in relation to the COVID-19 pandemic declaration. Demographic factors did not affect the relationship between weather and PA. CONCLUSIONS: Results are a first step in providing additional guidance for encouraging PA in insufficiently active individuals given forecasted daily weather conditions. Future work should examine seasonal variability in the weather type-PA relationship without the influence of a world-altering event influencing results.

Wearable device adherence among insufficiently-active young adults is independent of identity and motivation for physical activity.

Wearable devices are increasingly being integrated to improve prevention, chronic disease management and rehabilitation. Inferences about individual differences in device-measured physical activity depends on devices being worn long enough to obtain representative samples of behavior. Little is known about how psychological factors are associated with device wear time adherence. This study evaluated associations between identity, behavioral regulations, and device wear adherence during an ambulatory monitoring period. Young adults who reported insufficient physical activity (N = 271) were recruited for two studies before and after the SARS-COVID-19 pandemic declaration. Participants completed a baseline assessment and wore an Actigraph GT3X + accelerometer on their waist for seven consecutive days. Multiple linear regression indicated that wear time was positively associated with age, negatively associated with integrated regulation for physical activity, and greater after (versus before) the pandemic declaration. Overall, the model accounted for limited variance in device wear time. Exercise identity and exercise motivation were not associated with young adults' adherence to wearing the physical activity monitors. Researchers and clinicians can use wearable devices with young adults with minimal concern about systematic motivational biases impacting adherence to device wear.

Lp Quasi-norm Minimization: Algorithm and Applications.

Sparsity finds applications in diverse areas such as statistics, machine learning, and signal processing. Computations over sparse structures are less complex compared to their dense counterparts and need less storage. This paper proposes a heuristic method for retrieving sparse approximate solutions of optimization problems via minimizing the ℓp quasi-norm, where 0

Multi-Input Multi-Output Dynamic Modelling of Type 2 Diabetes Progression.

Type 2 Diabetes Mellitus (T2D) is a chronic health condition that affects millions of people globally. Early identification of risk can support preventive intervention and therefore slow down disease progression. Risk characterization is also necessary to monitor the mechanisms behind the pathology through the analysis of the interrelationships between the predictors and their time course. In this work, a multi-input multi-output Gaussian Process model is proposed to describe the evolution of different biomarkers in patients who will/will not develop T2D considering the interdependencies between outputs. The preliminary results obtained suggest that the trends in biomarkers captured by the model are coherent with the literature and with real-world data, demonstrating the value of multi-input multi-output approaches. In future developments, the proposed method could be applied to assess how the biomarkers evolve and interact with each other in groups of patients having in common one or more risk factors.

Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis.

BACKGROUND: Digital smartphone messaging can be used to promote physical activity to large populations with limited cost. It is not clear which psychological constructs should be targeted by digital messages to promote physical activity. This gap presents a challenge for developing optimal content for digital messaging interventions. OBJECTIVE: The aim of this study is to compare affectively framed and social cognitively framed messages on subsequent changes in physical activity using dynamical modeling techniques. METHODS: We conducted a secondary analysis of data collected from a digital messaging intervention in insufficiently active young adults (18-29 years) recruited between April 2019 and July 2020 who wore a Fitbit smartwatch for 6 months. Participants received 0 to 6 messages at random per day across the intervention period. Messages were drawn from 3 content libraries: affectively framed, social cognitively framed, or inspirational quotes. Person-specific dynamical models were identified, and model features of impulse response and cumulative step response were extracted for comparison. Two-way repeated-measures ANOVAs evaluated the main effects and interaction of message type and day type on model features. This early-phase work with novel dynamic features may have been underpowered to detect differences between message types so results were interpreted descriptively. RESULTS: Messages (n=20,689) were paired with valid physical activity monitoring data from 45 participants for analysis. Received messages were distributed as 40% affective (8299/20,689 messages), 39% social-cognitive (8187/20,689 messages), and 20% inspirational quotes (4219/20,689 messages). There were no statistically significant main effects for message type when evaluating the steady state of step responses. Participants demonstrated heterogeneity in intervention response: some had their strongest responses to affectively framed messages, some had their strongest responses to social cognitively framed messages, and some had their strongest responses to the inspirational quote messages. CONCLUSIONS: No single type of digital message content universally promotes physical activity. Future work should evaluate the effects of multiple message types so that content can be continuously tuned based on person-specific responses to each message type.

Dose-response relations between the frequency of two types of momentary feedback prompts and daily physical activity.

OBJECTIVE: Self-monitoring and behavioral feedback are widely used to help people monitor progress toward daily physical activity goals. Little information exists about the optimal dosing parameters for these techniques or if they are interchangeable in digital physical activity interventions. This study used a within-person experimental design to evaluate associations between the frequency of two different prompt types (one for each technique) and daily physical activity. METHOD: Insufficiently active young adults were assigned monthly physical activity goals and wore smartwatches with activity trackers for 3 months. They received zero to six randomly selected and timed watch-based prompts each day, with individual prompts either providing behavioral feedback or prompting the participant to self-monitor. RESULTS: Physical activity increased significantly over the 3-month period (step counts d = 1.03; moderate-to-vigorous physical activity duration d = 0.99). Mixed linear models revealed that daily step counts were positively associated with the frequency of daily self-monitoring prompts up to approximately three prompts/day (d = 0.22) after which additional prompts provided minimal or reduced benefit. Daily step counts were not associated with the frequency of behavioral feedback prompts. Daily moderate-to-vigorous physical activity was not associated with the frequency of either prompt. CONCLUSIONS: Self-monitoring and behavioral feedback are not interchangeable behavior change techniques in digital physical activity interventions, and only self-monitoring prompts show signs of a dose-response association with physical activity volume. Activity trackers, such as smartwatches and mobile apps, should provide an option to replace behavioral feedback prompts with self-monitoring prompts to promote physical activity among insufficiently active young adults. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Broad versus narrow bandwidth measures of experienced automaticity for physical activity.

Objective: Physical activity is important for health, yet most young adults are insufficiently active. Physical activity is regulated in part, by habit, typically operationalised as automaticity. Little is known about the characteristics of automaticity, or whether broad bandwidth unidimensional measures of automaticity for physical activity are superior to narrower bandwidth multi- dimensional measures. Design: This secondary analysis (N = 238) investigated the nature of automaticity, and relations between the dimensions of automaticity, global automaticity, and physical activity.Main Outcome Measures: The structure of the Generic Multifaceted Automaticity Scale (GMAS) was examined by confirmatory factor analyses. Structural equation models were estimated to evaluate relations between automaticity (measured on the GMAS and the Self- Report Behavioral Automaticity Index, SRBAI) and device- measured activity.Results: The hypothesised 3- factor structure of the GMAS was rejected, in favour of a 2- factor solution. Lack of intention/control and efficiency were associated with global automaticity, but not physical activity. Global automaticity was associated with moderate to vigorous physical activity and daily steps, but not light physical activity.Conclusion: Multi- dimensional measures of automaticity may not provide a more nuanced understanding of automaticity when predicting overall physical activity.

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration.

BACKGROUND: The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity. PURPOSE: This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions. METHODS: Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020. RESULTS: Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration. CONCLUSIONS: Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.

Physical inactivity is recognized as one of the major risk factors for cardiovascular disease, diabetes, and many cancers. Most American adults fail to achieve recommended levels of physical activity. Interventions to promote physical activity in young adults are needed to reduce long-term chronic disease risk. The COVID-19 pandemic declaration abruptly changed many individuals' environments and lifestyles. These contextual changes adversely impacted physical activity levels but little is known about how these changes specifically impacted the dynamics of people's physical activity or responses to micro-interventions for promoting physical activity. Using data collected from Fitbit smartwatches before and after the pandemic declaration, we applied tools from control systems engineering to develop person-specific dynamic models of physical activity responses to messaging interventions, and investigated how physical activity dynamics changed from before to after the pandemic declaration. Step counts decreased significantly on weekdays. The average speed of participants' responses to intervention messages did not change significantly, but intervention response dynamics had limited consistency from before to after the pandemic declaration. In short, participants changed how they responded to interventions after the pandemic declaration but the magnitude and patterns of change varied across participants. Person-specific, adaptive interventions can be useful for promoting physical activity when behavioral systems are stimulated to reorganize by external factors.

Affective judgments, environmental determinants, and physical activity in emerging and young adults.

OBJECTIVE: Affective judgements represent a promising target for promoting physical activity among adults. This study examined whether relations between affective judgments and physical activity are robust after adjusting for social, built, and natural environmental determinants. DESIGN: Prospective cross-sectional study with 173 adults (70.1% female) aged 18-29 years who self-reported less than 90 minutes of moderate-to-vigorous physical activity. MAIN OUTCOME MEASURE: Physical activity volume (total daily step count, total activity counts) and durations of intensity-specific physical activity (light-intensity activity and moderate-to-vigorous intensity) were assessed for a seven-day period via waist-worn ActiGraph wGT3X-BT accelerometer. RESULTS: Affective judgements were not statistically associated with measures of physical activity volume or intensity-specific physical activity after adjusting for environmental influences. Support for exercise from friends was positively associated with measures of physical activity volume and moderate-to-vigorous physical activity duration. More favorable perceptions of the built environment were positively associated with moderate-to-vigorous intensity physical activity and negatively associated with duration of light-intensity physical activity. Longer photoperiods were associated with more light-intensity physical activity. CONCLUSION: Physical activity interventions for young and emerging adults reporting inactivity should target environmental determinants first and possibly wait until participants have a motivational stake in physical activity before targeting affective judgments.

Wayset-based guidance of multirotor aerial vehicles using robust tube-based model predictive control.

The present paper is concerned with the wayset-based guidance of underactuated multirotor aerial vehicles (MAVs). A hierarchical guidance and control structure is first established, in which the guidance is realized as a supervisory loop. The lower-level stabilizing attitude and position control laws are assumed to be available. On the other hand, the outer-loop guidance is designed based on a fixed-horizon tube-based robust model predictive control (MPC), which conducts the MAV to visit a given sequence of waysets, without violating their state and control bounds, and allowing the vehicle to rest in each wayset for a specified period. The MPC is designed using a reduced-order closed-loop dynamic model describing the vehicle's translation, which is derived considering the stabilizing position and attitude control laws and the assumption of a time-scale separation between the closed-loop translational and rotational dynamics. This model is put into a discrete-time linear state-space representation subject to additive bounded random disturbance and measurement noise. The properties of the proposed method, which includes the MPC recursive feasibility and robust stability as well as the overall guidance feasibility, are analytically studied. The method is also numerically evaluated using a realistic quadrotor dynamic model, showing its effectiveness and confirming its properties.

Dynamic models of stress-smoking responses based on high-frequency sensor data.

Self-reports indicate that stress increases the risk for smoking; however, intensive data from sensors can provide a more nuanced understanding of stress in the moments leading up to and following smoking events. Identifying personalized dynamical models of stress-smoking responses can improve characterizations of smoking responses following stress, but techniques used to identify these models require intensive longitudinal data. This study leveraged advances in wearable sensing technology and digital markers of stress and smoking to identify person-specific models of stress and smoking system dynamics by considering stress immediately before, during, and after smoking events. Adult smokers (n = 45) wore the AutoSense chestband (respiration-inductive plethysmograph, electrocardiogram, accelerometer) with MotionSense (accelerometers, gyroscopes) on each wrist for three days prior to a quit attempt. The odds of minute-level smoking events were regressed on minute-level stress probabilities to identify person-specific dynamic models of smoking responses to stress. Simulated pulse responses to a continuous stress episode revealed a consistent pattern of increased odds of smoking either shortly after the beginning of the simulated stress episode or with a delay, for all participants. This pattern is followed by a dramatic reduction in the probability of smoking thereafter, for about half of the participants (49%). Sensor-detected stress probabilities indicate a vulnerability for smoking that may be used as a tailoring variable for just-in-time interventions to support quit attempts.

Person-specific dose-finding for a digital messaging intervention to promote physical activity.

OBJECTIVE: Digital messaging is an established method for promoting physical activity. Systematic approaches for dose-finding have not been widely used in behavioral intervention development. We apply system identification tools from control systems engineering to estimate dynamical models and inform decision rules for digital messaging intervention to promote physical activity. METHOD: Insufficiently active emerging and young adults (n = 45) wore an activity monitor that recorded minute-level step counts and heart rate and received 0-6 digital messages daily on their smartphone for 6 months. Messages were drawn from 3 content libraries (move more, sit less, inspirational quotes). Location recordings via location services in the user's smartphone were used to lookup weather indices at the time and place of message delivery. Following system identification, responses to each message type were simulated under different conditions. Response features were extracted to summarize dynamic processes. RESULTS: A generic model based on composite data was conservative and did not capture the heterogeneous responses evident in person-specific models. No messages were uniformly ineffective but responses to specific message content in different contexts varied between people. Exterior temperature at the time of message receipt moderated the size of some message effects. CONCLUSIONS: A generic model of message effects on physical activity can provide the initial evidence for context-sensitive decision rules in a just-in-time adaptive intervention, but it is likely to be error-prone and inefficient. As individual data accumulates, person-specific models should be estimated to optimize treatment and evolve as people are exposed to new environments and accumulate new experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Ergodic opinion dynamics over networks: learning influences from partial observations.

In this paper we address the problem of inferring direct influences in social networks from partial samples of a class of opinion dynamics. The interest is motivated by the study of several complex systems arising in social sciences, where a population of agents interacts according to a communication graph. These dynamics over networks often exhibit an oscillatory behavior, given the stochastic effects or the random nature of the local interactions process. Inspired by recent results on estimation of vector autoregressive processes, we propose a method to estimate the social network topology and the strength of the interconnections starting from partial observations of the interactions, when the whole sample path cannot be observed due to limitations of the observation process. Besides the design of the method, our main contributions include a rigorous proof of the convergence of the proposed estimators and the evaluation of the performance in terms of complexity and number of sample. Extensive simulations on randomly generated networks show the effectiveness of the proposed technique.

Seasons, weather, and device-measured movement behaviors: a scoping review from 2006 to 2020.

BACKGROUND: This scoping review summarized research on (a) seasonal differences in physical activity and sedentary behavior, and (b) specific weather indices associated with those behaviors. METHODS: PubMed, CINAHL, and SPORTDiscus were searched to identify relevant studies. After identifying and screening 1459 articles, data were extracted from 110 articles with 118,189 participants from 30 countries (almost exclusively high-income countries) on five continents. RESULTS: Both physical activity volume and moderate-to-vigorous physical activity (MVPA) were greater in summer than winter. Sedentary behavior was greater in winter than either spring or summer, and insufficient evidence existed to draw conclusions about seasonal differences in light physical activity. Physical activity volume and MVPA duration were positively associated with both the photoperiod and temperature, and negatively associated with precipitation. Sedentary behavior was negatively associated with photoperiod and positively associated with precipitation. Insufficient evidence existed to draw conclusions about light physical activity and specific weather indices. Many weather indices have been neglected in this literature (e.g., air quality, barometric pressure, cloud coverage, humidity, snow, visibility, windchill). CONCLUSIONS: The natural environment can influence health by facilitating or inhibiting physical activity. Behavioral interventions should be sensitive to potential weather impacts. Extreme weather conditions brought about by climate change may compromise health-enhancing physical activity in the short term and, over longer periods of time, stimulate human migration in search of more suitable environmental niches.

Engineering Person-Specific Behavioral Interventions to Promote Physical Activity.

Physical activity is dynamic, complex, and often regulated idiosyncratically. In this article, we review how techniques used in control systems engineering are being applied to refine physical activity theory and interventions. We hypothesize that person-specific adaptive behavioral interventions grounded in system identification and model predictive control will lead to greater physical activity than more generic, conventional intervention approaches.

Temporal Dynamics of Treatment Receipt in a Text Message Intervention for Physical Activity: Single-Group, Within-Person Trial.

BACKGROUND: Mobile technology has increased the reach of health behavior interventions but raised new challenges in assessing the fidelity of treatment receipt. Fidelity can be compromised if participant fatigue or burden reduces engagement, leading to missed or delayed treatments for just-in-time interventions. OBJECTIVE: This study aimed to investigate the temporal dynamics of text message receipt confirmations. METHODS: Community-dwelling adults (N=10) were sent five text messages daily for 4 months (5598 messages sent in total), with a financial incentive to confirm receipt of 75% or more messages. RESULTS: Overall, the message receipt confirmation rate was very high (5504/5598, 98.32%) and timely (eg, two-thirds of confirmations within 2 min). Confirmation times were slightly slower on weekends (vs weekdays) and as a function of the cumulative time in the study. Neither time of message delivery nor message content was associated with message confirmation latencies. CONCLUSIONS: Participants receiving financial incentives to confirm text message receipt exhibit extremely high and fast confirmation rates, although receipt confirmations were somewhat less timely on weekends (vs weekdays) and later in the intervention. The social calendar and treatment fatigue should be considered when planning text message-based interventions, especially if treatments are intended for a just-in-time delivery that requires extended engagement and precise timing.

Identification of switched autoregressive exogenous systems from large noisy datasets.

The article introduces novel methodologies for the identification of coefficients of switching autoregressive moving average with exogenous input systems and switched autoregressive exogenous linear models. We consider cases where system's outputs are contaminated by possibly large values of noise for both cases of measurement noise and process noise. It is assumed that only partial information on the probability distribution of the noise is available. Given input-output data, we aim at identifying switched system coefficients and parameters of the distribution of the noise, which are compatible with the collected data. We demonstrate the efficiency of the proposed approach with several academic examples. The method is shown to be effective in the situations where a large number of measurements is available; cases in which previous approaches based on polynomial or mixed-integer optimization cannot he applied due to very large computational burden.

Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods.

OBJECTIVES: The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention. DESIGN & METHOD: To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message. RESULTS: Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends. CONCLUSIONS: This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions.

A fully distributed traffic allocation algorithm for nonconcave utility maximization in connectionless communication networks.

As IP video services have emerged to be the predominant Internet application, how to optimize the Internet resource allocation, while satisfying the quality of experience (QoE) for users of video services and other Internet applications becomes a challenge. This is because the QoE perceived by a user of video services can be characterized by a staircase function of the data rate, which is nonconcave and hence it is "hard" to find the optimal operating point. The work in this paper aims at tackling this challenge. It considers the packet routing problem among multiple end points in packet switching networks based on a connectionless, hop-by-hop forwarding paradigm. We model this traffic allocation problem using a fluid flow model and let the link bandwidth be the only resource to be shared. To maximize the utilization of resources and avoid congestion, we formulate the problem as a network utility maximization problem. More precisely, the objective of this paper is to design a Fully Distributed Traffic Allocation Algorithm (FDTAA) that is applicable to a large class of nonconcave utility functions. Moreover, FDTAA runs in a fully distributed way: it enables each router to independently address and route each data unit using immediate local information in parallel, without referring to any global information of the communication network. FDTAA requires minimum computation workload, since the routing decision made at each router is solely based on the destination information carried in each unit. In addition, the network utility values corresponding to the FDTAA iterate sequence converge to the optimal network utility value at the rate of (1/K), where K is the iteration counter. These theoretical results are exemplified by the simulation performed on an example communication network.