58,011 research outputs found
Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions
Increasing evidence has shown that theory-based health behavior change
interventions are more effective than non-theory-based ones. However, only a
few segments of relevant studies were theory-based, especially the studies
conducted by non-psychology researchers. On the other hand, many mobile health
interventions, even those based on the behavioral theories, may still fail in
the absence of a user-centered design process. The gap between behavioral
theories and user-centered design increases the difficulty of designing and
implementing mobile health interventions. To bridge this gap, we propose a
holistic approach to designing theory-based mobile health interventions built
on the existing theories and frameworks of three categories: (1) behavioral
theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior,
and the Health Action Process Approach), (2) the technological models and
frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design
and Behavior Change Support System, and the Just-in-Time Adaptive
Interventions), and (3) the user-centered systematic approaches (e.g., the
CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic
approach provides researchers a lens to see the whole picture for developing
mobile health interventions
Integrating Taxonomies into Theory-Based Digital Health Interventions for Behavior Change: A Holistic Framework
Digital health interventions have been emerging in the last decade. Due to
their interdisciplinary nature, digital health interventions are guided and
influenced by theories (e.g., behavioral theories, behavior change
technologies, persuasive technology) from different research communities.
However, digital health interventions are always coded using various taxonomies
and reported in insufficient perspectives. The inconsistency and
incomprehensiveness will bring difficulty for conducting systematic reviews and
sharing contributions among communities. Based on existing related work,
therefore, we propose a holistic framework that embeds behavioral theories,
behavior change technique (BCT) taxonomy, and persuasive system design (PSD)
principles. Including four development steps, two toolboxes, and one workflow,
our framework aims to guide digital health intervention developers to design,
evaluate, and report their work in a formative and comprehensive way
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
In the last few years, Model Driven Development (MDD), Component-based
Software Development (CBSD), and context-oriented software have become
interesting alternatives for the design and construction of self-adaptive
software systems. In general, the ultimate goal of these technologies is to be
able to reduce development costs and effort, while improving the modularity,
flexibility, adaptability, and reliability of software systems. An analysis of
these technologies shows them all to include the principle of the separation of
concerns, and their further integration is a key factor to obtaining
high-quality and self-adaptable software systems. Each technology identifies
different concerns and deals with them separately in order to specify the
design of the self-adaptive applications, and, at the same time, support
software with adaptability and context-awareness. This research studies the
development methodologies that employ the principles of model-driven
development in building self-adaptive software systems. To this aim, this
article proposes an evaluation framework for analysing and evaluating the
features of model-driven approaches and their ability to support software with
self-adaptability and dependability in highly dynamic contextual environment.
Such evaluation framework can facilitate the software developers on selecting a
development methodology that suits their software requirements and reduces the
development effort of building self-adaptive software systems. This study
highlights the major drawbacks of the propped model-driven approaches in the
related works, and emphasise on considering the volatile aspects of
self-adaptive software in the analysis, design and implementation phases of the
development methodologies. In addition, we argue that the development
methodologies should leave the selection of modelling languages and modelling
tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition,
self-adaptive application, context oriented software developmen
A Framework proposal for monitoring and evaluating training in ERP implementation project
During the last years some researchers have studied the topic of critical success factors in ERP implementations, out of which 'training' is cited as one of the most ones. Up to this moment, there is not enough research on the management and operationalization of critical success factors within ERP implementation projects.Postprint (published version
Experience in Social Affective Applications: Methodologies and Case Study
New forms of social affective applications are emerging, bringing with them challenges in design and evaluation. We report on one such application, conveying well-being for both personal and group benefit, and consider why existing methodologies may not be suitable, before explaining and analyzing our proposed approach. We discuss our experience of using and writing about the methodology, in order to invite discussion about its suitability in particular, as well as the more general need for methodologies to examine experience and affect in social, connected situations. As these fields continue to interact, we hope that these discussions serve to aid in studying and learning from these types of application
Verification of interlocking systems using statistical model checking
In the railway domain, an interlocking is the system ensuring safe train
traffic inside a station by controlling its active elements such as the signals
or points. Modern interlockings are configured using particular data, called
application data, reflecting the track layout and defining the actions that the
interlocking can take. The safety of the train traffic relies thereby on
application data correctness, errors inside them can cause safety issues such
as derailments or collisions. Given the high level of safety required by such a
system, its verification is a critical concern. In addition to the safety, an
interlocking must also ensure that availability properties, stating that no
train would be stopped forever in a station, are satisfied. Most of the
research dealing with this verification relies on model checking. However, due
to the state space explosion problem, this approach does not scale for large
stations. More recently, a discrete event simulation approach limiting the
verification to a set of likely scenarios, was proposed. The simulation enables
the verification of larger stations, but with no proof that all the interesting
scenarios are covered by the simulation. In this paper, we apply an
intermediate statistical model checking approach, offering both the advantages
of model checking and simulation. Even if exhaustiveness is not obtained,
statistical model checking evaluates with a parametrizable confidence the
reliability and the availability of the entire system.Comment: 12 pages, 3 figures, 2 table
Intra-individual movement variability during skill transitions: A useful marker?
Applied research suggests athletes and coaches need to be challenged in knowing when and how much a movement should be consciously attended to. This is exacerbated when the skill is in transition between two more stable states, such as when an already well learnt skill is being refined. Using existing theory and research, this paper highlights the potential application of movement variability as a tool to inform a coachâs decision-making process when implementing a systematic approach to technical refinement. Of particular interest is the structure of co-variability between mechanical degrees-of-freedom (e.g., joints) within the movement systemâs entirety when undergoing a skill transition. Exemplar data from golf are presented, demonstrating the link between movement variability and mental effort as an important feature of automaticity, and thus intervention design throughout the different stages of refinement. Movement variability was shown to reduce when mental effort directed towards an individual aspect of the skill was high (target variable). The opposite pattern was apparent for variables unrelated to the technical refinement. Therefore, two related indicators, movement variability and mental effort, are offered as a basis through which the evaluation of automaticity during technical refinements may be made
Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines
Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods
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