Theory-based Approach Research Articles

Effects of a 6-month physical activity behavioral intervention in patients following metabolic bariatric surgery: a randomized controlled trial

BackgroundPromoting habitual physical activity (PA) among bariatric patients is crucial for long-term surgery success, yet it poses a significant challenge for healthcare practitioners. ObjectivesThis randomized controlled trial (RCT) aimed to examine the effectiveness of a theory-based behavioral intervention on PA level in post-metabolic bariatric surgery (MBS) patients. SettingUniversity Hospital, Israel MethodsForty-four patients undergoing MBS were randomized to the intervention (n = 22) or control group (n = 22). The intervention group received a 6-month PA counseling program based on self-determination theory (SDT) and the 5 A’s framework, while the control group received usual care. PA level and self-efficacy for exercise (SEE) were assessed pre- (baseline, preintervention) and postoperatively (14 and 28 weeks follow-ups). Anthropometrics, physical function, cardiovascular, and biochemical outcomes were measured at all time points. ResultsThe intervention group showed greater increases in PA levels across time compared to the control group. A significant increase in step counts from baseline to 14 weeks was observed (P = .003). Significant differences favoring the intervention group were observed in self-reported PA changes from baseline to 14- and 28-week follow-ups (P = .020 and P = .024, respectively). Additionally, Physical function, as assessed by the 6-minute walk test (6MWT) and the 5 sit-to-stand (5STS) test, significantly improved in the intervention group throughout the follow-up period (P < .05 for all), However, no between-group differences were observed in SEE, anthropometrics, cardiovascular, or biochemical parameters. ConclusionsA 6-month behavioral intervention post-MBS significantly enhanced patients' PA levels and physical function. Given its theory-based approach and structured protocol, this intervention could be disseminated to support MBS clinicians and centers. Further research with longer follow-up period is warranted to confirm these findings and assess the long-term effects.

Entrepreneurial Hype Among Early Employees: A Theory-Based Approach to Hype

Entrepreneurial Hype Among Early Employees: A Theory-Based Approach to Hype

Pitch-Tracking Metrics as a Predictor of Future Shoulder and Elbow Injuries in Major League Baseball Pitchers: A Machine-Learning and Game-Theory Based Analysis.

Understanding interactions between multiple risk factors for shoulder and elbow injuries in Major League Baseball (MLB) pitchers is important to identify potential avenues by which risk can be reduced while minimizing impact on player performance. To apply a novel game theory-based approach to develop a machine-learning model predictive of next-season shoulder and elbow injuries in MLB pitchers and use this model to understand interdependencies and interaction effects between the most important risk factors. Case-control study; Level of evidence, 3. Pitcher demographics, workload measures, pitch-tracking metrics, and injury data between 2017 and 2022 were used to construct a database of MLB pitcher-years, where each item in the database corresponded to a pitcher's information and metrics for that year. An extreme gradient boosting machine-learning model was trained to predict next-season shoulder and elbow injuries utilizing Shapley additive explanation values to quantify feature importance as well as interdependencies and interaction effects between predictive variables. A total of 3808 pitcher-years were included in this analysis; 606 (15.9%) of these involved a shoulder or elbow injury resulting in placement on the MLB injured list. Of the >65 candidate features (including workload, demographic, and pitch-tracking metrics), the most important contributors to predicting shoulder/elbow injury were increased: pitch velocity (all pitch types), utilization of sliders (SLs), fastball (FB) spin rate, FB horizontal movement, and player age. The strongest game theory interaction effects were that higher FB velocity did not alter a younger pitcher's predicted risk of shoulder/elbow injury versus older pitchers, risk of shoulder/elbow injury increased with the number of high-velocity pitches thrown (regardless of pitch type and in an additive fashion), and FB velocity <95 mph (<152.9 kph) demonstrated strong negative interaction effects with higher SL percentage, suggesting that the overall predicted risk of injury for pitchers throwing a high number of SLs could be attenuated by lower FB velocity. Pitch-tracking metrics were substantially more predictive of future injury than player demographics and workload metrics. There were many significant game theory interdependencies of injury risk. Notably, the increased risk of injury that was conferred by throwing with a high velocity was even further magnified if the pitchers were also older, threw a high percentage of SLs, and/or threw a greater number of pitches.

Silencing the Rainbow: The Territorial Effects of Pride Cancellation

The pause in Pride parades, particularly highlighted during the pandemic, reminds us of the importance of actively including and celebrating nonheteronormative communities. It emphasizes the need to cultivate understanding and acceptance of diverse sexual orientations. This study navigates through the landscape of Madrid Pride, focusing on understanding how its unforeseen cancellation in 2020 due to the pandemic has influenced the LGBTQIA+ communities’ sense of space, openness, safety, visibility, acceptance, and equality. The research is centered on examining the connection between Pride parades and the appropriation of spaces that are typically seen as conforming to heterosexual norms. It takes into account the viewpoints of the LGBTQIA+ community (including activists, organizations, and potential attendees) as well as the perspectives of event stakeholders (such as organizations, local community members, and tourism professionals). A social representation theory‐based inductive approach was utilized, drawing from queer and feminist perspectives. The cancellation of Pride events in recent times has caused significant disruption to the de‐ and reterritorialization of space. This has had far-reaching implications in both the political and social realms for the LGBTQIA+ community and its public profile.

Mixture Theory-Based Finite Element Approach for Analyzing the Edematous Condition of Biological Soft Tissues.

In hydrated soft biological tissues experiencing edema, which is typically associated with various disorders, excessive fluid accumulates and is encapsulated by impermeable membranes. In certain cases of edema, an indentation induced by pressure persists even after the load is removed. The depth and duration of this indentation are used to assess the treatment response. This study presents a mixture theory-based approach to analyzing the edematous condition. The finite element analysis formulation was grounded in mixture theory, with the solid displacement, pore water pressure, and fluid relative velocity as the unknown variables. To ensure tangential fluid flow at the surface of tissues with complex shapes, we transformed the coordinates of the fluid velocity vector at each time step and node, allowing for the incorporation of the transmembrane component of fluid flow as a Dirichlet boundary condition. Using this proposed method, we successfully replicated the distinct behavior of pitting edema, which is characterized by a prolonged recovery time from indentation. Consequently, the proposed method offers valuable insights into the finite element analysis of the edematous condition in biological tissues.

Developing Catchment Area Data Dashboards for Cancer Centers: A Stakeholder- engaged Approach.

Data dashboards that can communicate complex and diverse catchment area data effectively can transform cancer prevention and care delivery and strengthen community engagement efforts. Engaging stakeholders in data dashboard development, by seeking their inputs and collecting feedback, has the potential to maximize user-centeredness. To describe a systematic, stakeholder-driven, and theory-based approach for developing catchment area data visualization tools for cancer centers. Cancer-relevant catchment area data were identified from national- and state-level data sources (including cancer registries, national surveys, and administrative claims databases). A prototype tool for data visualization was designed, developed, and tested based on the OPT-In [ O rganize, P lan, T est, In tegrate] framework. A working group of multi-disciplinary experts collected stakeholder feedback through formative assessment to understand data and design preferences. Thematic areas, data elements, and the composition and placement of data visuals in the prototype were identified and refined by working group members. Visualizations were rendered in Tableau © and embedded in a public-facing website. A mixed-method approach was used to assess the understandability and actionability of the tool and to collect open-ended feedback that informed action items for improvisation. We developed a visualization dashboard that illustrates cancer incidence and mortality, risk factor prevalence, healthcare access, and social determinants of health for the Hollings Cancer Center catchment area. Color-coded maps, time-series plots, and graphs illustrate these catchment area data. A total of 21 participants representing key stakeholders [general audience (n=4), community advisory board members and other representatives (n=7), and researchers (n=10)] were identified. The understandability and actionability scores exceeded the minimum (80%) threshold. Stakeholders' feedback confirmed that the tool is effective in communicating cancer data and is useful for education and advocacy. Themes that emerged from qualitative data suggest that additional changes to the tool such as a warm color palette, data source transparency, and the addition of analytical features (data overlaying and area-resolution selection) would further enhance the tool. Integration of communication efforts and messages within a broader context is in progress. A catchment area data resource developed through a systematic, stakeholder- driven, and theory-based approach can meet (and surpass) benchmarks for understandability and actionability, and lead to an overall positive response from stakeholders. Creating channels for advocacy and forming community partnerships will be the next step necessary to promote policies and programs for improving cancer outcomes in the catchment areas.

Food safety concerns and purchase choices among poor households in the urbanising global south: the case of Dhaka, Bangladesh

This article looks into the daily food shopping practices of poor urban households in Dhaka, Bangladesh. A practice theory-based approach is used to explore their coping strategies in response to food safety concerns. By applying an exploratory sequential mixed-method approach, two groups of shoppers are compared: recently arrived migrants and established residents, as recently arrived migrants are expected to face an unfamiliar food shopping environment and therefore need to reconsider their daily food shopping practices. The results describe the elements of food shopping practices: food items, retail outlets, affordability, social networks, and relations to work. We identified the absence of freshness, undesirable substances, and the need for more information about the production process as the primary food safety concerns. Among both shopper groups, the harmful consequence of applying the chemical 'formalin' to food is considered the most prominent food safety concern. Major coping strategies include identifying (un)safe food, proactive selection, and establishing and maintaining social relationships. The two groups have similarities and differences in using skills and competencies to mitigate their food safety concerns in everyday shopping practices. Recently arrived migrants have to adapt to urban lifestyles and reconfigure their food safety concerns into their daily food shopping practices.

Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System

Remote condition monitoring (RCM) aims to ensure the availability of railway assets. Previous work has indicated the importance of a user-centred RCM design approach based on cognitive principles, but there has been no known demonstration of the application of these principles. The following paper takes this theory-based approach and applies it to the design of an RCM system for the rail pantograph/Overhead Line (OHL) system. The paper first presents a high-level conceptual architecture, based on four stages of cognitive decision-making (notification, acceptance, analysis and clearance), linked to the wider monitoring architecture. Second, the paper uses cognitive principles to propose demonstration Human–Machine Interface designs for the OHL system. These HMIs were presented in an evaluation with subject matter experts. The outcomes of the process generated user-centred design recommendations for RCM. Furthermore, the evaluation suggested the importance of multiple paths through the HMI dependent on the type and urgency of fault. Finally, the outcomes of the evaluation also highlighted the importance of considering context when deploying user-centred RCM.

Beyond Hair Loss: Exploring the Evolution of Androgenetic Alopecia Research based on text mining and Bibliometrics

In this study, word dynamics, co-occurring phrases, and keyword frequency in the area of androgenetic alopecia were thoroughly analysed during a ten-year period. The study tracks changes in word usage, frequency, and context and identifies variations in the distribution and usage patterns of keywords, subjects, and co-occurring phrases using natural language processing techniques and graph theory-based approaches. The research reveals hidden linkages and patterns between diverse ideas, shedding light on the co-occurrence patterns of numerous phrases in the literature on androgenetic alopecia. The study emphasises the value of clearly visualising and disseminating findings to a large audience. In order to communicate the findings of the 10-year trend analysis to patrons, legislatures, and other pertinent audiences, data visualisation tools, infographics, and reports are used. This makes sure that the results are useful, effective, and easily accessible so that they can guide the creation of policies and decisions pertaining to androgenetic alopecia. The results of this study may have substantial ramifications for academics, medical professionals, policymakers, and other industry participants. The research can lead future research paths, prioritise research areas, and suggest areas that require additional examination by highlighting key research challenges and pointing out gaps in the study of androgenetic alopecia. Identifying emergent study issues, analysing the changing patterns in the area, and establishing research strategies can all benefit from an analysis of word dynamics and correlations among terms. The results reveal hidden relationships and patterns, advance knowledge of the research environment in this area, and influence androgenetic alopecia research objectives and policies.

Surge arrester optimal placement in distribution networks: A decision theory-based approach

The study introduces a novel method for optimizing surge arrester placement in the distribution line to mitigate lightning-induced overvoltages, employing a single-objective optimization algorithm. The study assesses the effectiveness of two types of surge arresters concerning discharge energy in reducing lightning overvoltage. Moreover, the comparative analysis takes into account the transfer of overvoltage performance from protected to unprotected towers, highlighting its significance in estimating flashover rates under different protection alternatives. Decision theory analysis is employed in the present study to identify optimal arrester locations. The study aims to minimize the expected flashover rate and identify the most effective protection strategy by considering futures/scenarios associated with different values of peak current and lightning locations. The results of numerical applications also showed the substantial impact of lightning surge transfer within the network, emphasizing the imperative to incorporate this phenomenon into lightning protection modelling.

Impacts of multiple environmental factors on soil bacterial community assembly in heavy metal polluted paddy fields

Soil microorganisms play pivotal roles in driving essential biogeochemical processes in terrestrial ecosystems, and they are sensitive to heavy metal pollution. However, our understanding of multiple environmental factors interaction in heavy metal polluted paddy fields to shape microbial community assembly remain limited. In the current study, we used 16S rRNA amplicon sequencing to characterize the microbial community composition in paddy soils collected from a typical industry town in Taihu region, eastern China. The results revealed that Cd and Pb were the major pollutant, and Proteobacteria, Acidobacteria and Chloroflexi were the dominate indigenous bacterial phyla. Linear regression and random forest analysis demonstrated that soil pH was the most important predictor of bacterial diversity. Mantel analysis showed that bacterial community structure was mainly driven by pH, CEC, silt, sand, AK, total Cd and DTPA-Cd. The constructed bacterial co-occurrence network, utilizing a random matrix theory-based approach, exhibited non-random with scale-free and modularity features. The major modules within the networks also showed significant correlations with soil pH. Overall, our study indicated that soil physiochemical properties made predominant contribution to bacterial community diversity, structure and their association in Cd/Pb polluted paddy fields. These findings expand our knowledge of the key environmental drivers and co-occurrence patterns of bacterial community in polluted paddy fields.

Coping and end-of-life decision-making in ALS: A qualitative interview study.

How do people with amyotrophic lateral sclerosis (PALS) deal with their diagnosis and engage in end-of-life decision-making? What informational or supportive needs do they have for counselling about life-sustaining treatment and end-of-life care? Which correlating conditions and influences relate to these needs and how do they connect to the wish to die or wish to live? We conducted a qualitative interview study with 13 people with ALS in Germany from March 2019 to April 2021. Data collection and analysis followed a grounded theory-based approach and revealed close relationships between coping, informational needs and the preparedness for decision-making. We identified the coping strategies 'avoid thinking about end-of-life' and its counterpart, 'planning ahead to be well-prepared,' and differentiated the latter into the patterns 'withdrawing from life and taking precautions against life-prolongation' and 'searching for a new meaning in life and preparing for life-sustaining treatment'. The approaches are based on individual perceptions, attitudes and motives and can be positively/negatively reinforced by healthcare professionals (HCP), family and other interpersonal networks, but also by disease progression and in reaction to health care services. Type and degree of needs concerning information and counselling differed according to coping strategies. These strategies may vary over time, resulting in different support needs. Our findings signify that deep insight is needed into PALS' coping processes to understand their decision-making about life-sustaining treatment. Healthcare professionals should be sensitive to illness experiences beyond medical aspects and foster coping as a biographical process to better support people with ALS.

Enriching Laser Powder Bed Fusion Part Data Using Category Theory

Laser powder bed fusion (LPBF) is a promising metal additive manufacturing technology for producing functional components. However, there are still a lot of obstacles to overcome before this technology is considered mature and trustworthy for wider industrial applications. One of the biggest obstacles is the difficulty in ensuring the repeatability of process and the reproducibility of products. To tackle this challenge, a prerequisite is to represent and communicate the data from the part realisation process in an unambiguous and rigorous manner. In this paper, a semantically enriched LPBF part data model is developed using a category theory-based modelling approach. Firstly, a set of objects and morphisms are created to construct categories for design, process planning, part build, post-processing, and qualification. Twenty functors are then established to communicate these categories. Finally, an application of the developed model is illustrated via the realisation of an LPBF truncheon.

Spatial downscaling of rainfall from general circulation models: A chaos theory-based framework

The present study proposes a new chaos theory-based local approximation technique for the spatial downscaling of rainfall from GCM outputs. The approach involves: (1) reconstruction of relevant climate data in the state space using an embedding technique; and (2) use of a neighbor search procedure in the reconstructed space and local approximation for downscaling. The approach is implemented for downscaling of monthly rainfall at a spatial resolution of 0.25° × 0.25° across India. The effectiveness of the approach is first demonstrated using the NCEP/NCAR reanalysis dataset (1979–2014) and then tested using the outputs from five CMIP6 GCMs (CMCC-ESM2, CESM2-WACCM, CMCC-CM2-HR4, E3SM-1-1-ECA, and BCC-ESM1) (1961–2014). Eight climate variables (eastward wind, northward wind, relative humidity, specific humidity, surface temperature, air temperature, sea level pressure, and geopotential height) are considered as predictors, with the observed rainfall data from the India Meteorological Department (IMD) as the predictand. Three statistical measures, namely correlation coefficient (CC), Nash-Sutcliffe efficiency (NSE), and normalized root mean square error (NRMSE), are used for evaluation. The results indicate that the local approximation downscaling approach yields promising results for both reanalysis dataset and GCM outputs. Among the five GCMs, the CMCC-ESM2 model yields the best results. Comparison of the results from the chaos theory-based approach with those of the linear regression-based approach indicates better performance of the former. The proposed framework is general and can be applied for any climate variable, GCM, and region.

Incentive Mechanism Against Bounded Rationality for Federated Learning-Enabled Internet of UAVs: A Prospect Theory-Based Approach

Incentive Mechanism Against Bounded Rationality for Federated Learning-Enabled Internet of UAVs: A Prospect Theory-Based Approach

Residual profit splitting: a theory-based approach to tax multinationals

Residual profit splitting: a theory-based approach to tax multinationals

Comparison of patch-based and synoptic connectivity algorithms with graph theory metrics. A case study in Reggio Calabria metropolitan area (South Italy)

ContextPredicting and mapping connectivity between habitats and populations is critical to addressing habitat loss and biodiversity issues. Several strategies in the literature exist to understand, restore, and preserve ecological connectivity. The main issue of the current research is to identify which connectivity modeling strategies are the most reliable for planning purposes. ObjectivesOur goals in this paper were to compare connectivity predictions using a wide variety of commonly used approaches to improve the understanding of the similarities and differences in the predictions of these methods. Specifically, we investigated the differences in connectivity predictions related to the connectivity algorithm, the number and distribution of source points, and the threshold distance at which connectivity is allowed between locations. First, we separately applied different strategies and methods commonly used in the literature to model connectivity in the same study area. Then, going through a series of hypotheses, we compared the different models to confirm or disprove the initial hypotheses. MethodsWe proposed 4 main hypotheses and 14 combinations of them, hypothesizing that what most influences the results of connectivity models are different dispersal distance thresholds; differences in connectivity algorithms, especially kernel, path, and graph theory-based approaches; differences in predictions produced by two different software tools, UNICOR and Graphab; use of source points derived from a synoptic or patch-based perspective. ResultsWe found that the dominant pattern of differences in the predictions of different connectivity analyses was related to the method of analysis, with clear differences between kernel, path, and graph-theory approaches and relatively little effect due to the density and distribution of source points or the distance threshold used to define dispersal capability. ConclusionsThis work provides one of the first comparisons of spatial predictions of different methods, frameworks, and parameterizations of connectivity models. Our results support environmental planning by clarifying what most influences predictions of movement patterns and how the predicted connectivity networks differ between different analytical frameworks.

Comparative study of nonlinear wave-induced global loads on a container ship in regular head waves predicted with numerical methods based on weakly nonlinear potential theory and Reynolds-averaged Navier-Stokes equations

ABSTRACT Wave-induced loads on the ship hull are of significant interest for many reasons. These loads, however, are highly nonlinear in realistic wave conditions and consequently difficult to predict. In this paper, we analysed and compared wave-induced loads acting on a container vessel in regular head waves computed with a newly developed weakly nonlinear potential theory-based code and a flow solver based on the Reynolds-averaged Navier-Stokes Equations. We investigated both long and short waves with moderate steepness. Initially, the vessel was completely fixed and the ship motions were suppressed, then the vessel was free to heave and pitch motion. We compared time-histories of the computed wave-induced longitudinal and vertical global forces as well as the pitch moment and we deeply analysed nonlinear effects by applying a Fourier transformation and comparing the corresponding harmonic amplitude up to the fourth order. Our results demonstrated satisfactory agreement between the two numerical methods, especially when the ship was free to heave and pitch. Additionally, the results provided detailed information about the capabilities and limitations of the weakly nonlinear potential theory-based approach.

Development of a Behavior-Change Intervention toward Benzodiazepine Deprescribing in Older Adults Living in Nursing Homes

ObjectiveWe aimed to develop a context-specific intervention toward benzodiazepine deprescribing in nursing homes (NHs), with insights from behavior-change theories and involvement of stakeholders. DesignSelection of behavior change techniques (BCTs), through online survey and group discussion, followed by operationalization of these BCTs into intervention components. Setting and ParticipantsThe intervention was developed for Belgian NHs, involving various stakeholders: health care professionals (HCPs), NH administrators, and policy makers. MethodsUsing the Theory and Techniques Tool, we preselected the BCTs linked to one of the 9 Theoretical Domain Framework domains identified as being the main barriers for benzodiazepine deprescribing in Belgian NHs. These were then presented to stakeholders. Based on the APEASE (Acceptability, Practicability, Effectiveness, Affordability, Side-effects, and Ethics) criteria, participants ranked BCTs through an online survey, and then performed final selection during a group discussion. Selected BCTs were operationalized into intervention components, with specific contents and methods of delivery validated by stakeholders. ResultsThirty-seven potential BCTs were identified. Eighteen stakeholders participated in the survey, and 7 in the group discussion. This led to the final inclusion of 9 BCTs: instruction on how to perform the behavior, information about health consequences, pros and cons, problem solving, goal setting (behavior), social comparison, restructuring physical environment, restructuring social environment, and graded tasks. These BCTs were operationalized into a 6-component intervention: process and goal setting, HCP education, physical environment adaptations, audit and feedback, NH residents’ and relatives’ increased awareness, and multidisciplinary work. Conclusion and ImplicationsUse of a theory-based approach toward intervention development has the potential to improve the probability of its feasibility and effectiveness in tackling barriers to benzodiazepine deprescribing. By doing so, we have developed a multifaceted approach with actions taken at the patient, HCP, and NH levels. Our novel 6-component intervention will be evaluated in a pilot cluster-randomized controlled trial to assess its feasibility.

A strategic framework for optimal decisions in football 1-vs-1 shot-taking situations: an integrated approach of machine learning, theory-based modeling, and game theory

Complex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategies involved can provide an objective basis for decision-making. One such critical scenario is shot-taking in football, where decisions, such as whether the attacker should shoot or pass the ball and whether the defender should attempt to block the shot, play a crucial role in the outcome of the game. However, there are currently no effective data-driven and/or theory-based approaches to analyzing such situations. To address this issue, we proposed a novel Shooting Payoff Computation (SPC) framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model. Conventionally, successes or failures (1 or 0) are used as payoffs, while a success shot (goal) is extremely rare in football. Therefore, we proposed the Expected Probability of Shot On Target (xSOT) metric to evaluate players’ actions even if the shot results in no goal; this allows for effective differentiation and comparison between different shots and even enables counterfactual shot situation analysis. In our experiments, we have validated the SPC framework by comparing it with baseline and ablated models. Furthermore, we have observed a high correlation between the xSOT and existing metrics. This alignment of information suggests that xSOT provides valuable insights. Lastly, as an illustration, we studied optimal strategies in the World Cup 2022 and analyzed a shot situation in EURO 2020.