Innovative methods for observing and changing complex health behaviors: Four propositions

Precision health initiatives aim to progressively move from traditional, group-level approaches to health diagnostics and treatments toward ones that are individualized, contextualized, and timely. This article aims to provide an overview of key methods and approaches that can help facilitate this transition in the health behavior change domain. This article is a narrative review of the methods used to observe and change complex health behaviors. On the basis of the available literature, we argue that health behavior change researchers should progressively transition from (i) low- to high-resolution behavioral assessments, (ii) group-only to group- and individual-level statistical inference, (iii) narrative theoretical models to dynamic computational models, and (iv) static to adaptive and continuous tuning interventions. Rather than providing an exhaustive and technical presentation of each method and approach, this article articulates why and how researchers interested in health behavior change can apply these innovative methods. Practical examples contributing to these efforts are presented. If successfully adopted and implemented, the four propositions in this article have the potential to greatly improve our public health and behavior change practices in the near future

Nonlinear Bayesian Estimation with Compactly Supported Wavelets

Bayesian estimation for nonlinear systems is still a challenging problem, as in general the type of the true probability density changes and the complexity increases over time. Hence, approximations of the occurring equations and/or of the underlying probability density functions are inevitable. In this paper, we propose an approximation of the conditional densities by wavelet expansions. This kind of representation allows a sparse set of characterizing coefficients, especially for smooth or piecewise smooth density functions. Besides its good approximation properties, fast algorithms operating on sparse vectors are applicable and thus, a good trade-off between approximation quality and run-time can be achieved. Moreover, due to its highly generic nature, it can be applied to a large class of nonlinear systems with a high modeling accuracy. In particular, the noise acting upon the system can be modeled by an arbitrary probability distribution and can influence the system in any way

Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets

In model predictive control, a high quality of control can only be achieved, if the model of the system reflects the real-world process as precisely as possible. Therefore, the controller should be capable of both handling a nonlinear system description and systematically incorporating uncertainties affecting the system. Since stochastic nonlinear model predictive control (SNMPC) problems in general cannot be solved in closed form, either the system model or the occurring densities have to be approximated. In this paper, we present an SNMPC framework, which approximates the densities and the reward function by their wavelet expansions. Due to the few requirements on the shape and family of the densities or reward function, the presented technique can be applied to a large class of SNMPC problems. For accelerating the optimization, we additionally present a novel thresholding technique, the so-called dynamic thresholding, which neglects coefficients that are insignificant, while at the same time guaranteeing that the optimal control input is still chosen. The capabilities of the proposed approach are demonstrated by simulations with a path planning scenario

Die Modifikation nicht-symptomspezifischer Parameter nach einer Reizkonfrontationstherapie

Die Studie untersucht das Befinden von 69 Angstpatienten in Bezug auf nicht symptomspezifische Parameter sowohl 6 Wochen als auch 1 Jahr nach Vollendung einer Reizkonfrontationstherapie. Untersucht wird dabei die Verbesserung der interpersonalen Probleme, der Selbstsicherheit, der Partnerschaft und der sozialen Unterstützung der Patienten und ihr Zusammenhang mit der Verbesserung der angstspezifischen Variablen. Zusätzlich wird die zu erwartende hoch signifikante Verbesserung in Bezug auf Angst und Depression nach der Therapie und beim Follow-up repliziert und anhand einer Kontrollgruppe von 171 Therapieablehnern eine positive Selektion der Experimentalgruppe ausgeschlossen

The impact of offices on Rye, New York

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of City Planning, 1964.Includes bibliographical references (leaves 69-71).by Herbert Martin Hekler.M.C.P