Breaking barriers: using the behavior change wheel to develop a tailored intervention to overcome workplace inhibitors to breaking up sitting time

© The Author(s). 2019. Background: The workplace is a prominent domain for excessive sitting. The consequences of increased sitting time include adverse health outcomes such as cardiovascular disease and poor mental wellbeing. There is evidence that breaking up sitting could improve health, however, any such intervention in the workplace would need to be informed by a theoretical evidence-based framework. The aim of this study was to use the Behaviour Change Wheel (BCW) to develop a tailored intervention to break up and reduce workplace sitting in desk-based workers. Methods: The BCW guide was followed for this qualitative, pre-intervention development study. Semi-structured interviews were conducted with 25 office workers (26–59years, mean age 40.9 [SD=10.8] years; 68% female) who were purposively recruited from local council offices and a university in the East of England region. The interview questions were developed using the Theoretical Domains Framework (TDF). Transcripts were deductively analysed using the COM-B (Capability, Opportunity, Motivation – Behaviour) model of behaviour. The Behaviour Change Technique Taxonomy Version 1 (BCTv1) was thereafter used to identify possible strategies that could be used to facilitate change in sitting behaviour of office workers in a future intervention. Results: Qualitative analysis using COM-B identified that participants felt that they had the physical Capability to break up their sitting time, however, some lacked the psychological Capability in relation to the knowledge of both guidelines for sitting time and the consequences of excess sitting. Social and physical Opportunity was identified as important, such as a supportive organisational culture (social) and the need for environmental resources (physical). Motivation was highlighted as a core target for intervention, both reflective Motivation, such as beliefs about capability and intention and automatic in terms of overcoming habit through reinforcement. Seven intervention functions and three policy categories from the BCW were identified as relevant. Finally, 39 behaviour change techniques (BCTs) were identified as potential active components for an intervention to break up sitting time in the workplace. Conclusions: The TDF, COM-B model and BCW can be successfully applied through a systematic process to understand the drivers of behaviour of office workers to develop a co-created intervention that can be used to break up and decrease sitting in the workplace. Intervention designers should consider the identified BCW factors and BCTs when developing interventions to reduce and break up workplace sitting

Metaheurestic algorithm based hybrid model for identification of building sale prices

The overall cost of a building depends on several variables such as economical, project physical and financial variables. The CCB (construction cost of building) also depends on deviations of several indices which are not control in an easy way. Therefore, the overall sales prices of a building may not be controlled due to these indices. In this chapter, a metaheuristic algorithm based hybrid model for identification of building's sales prices is presented, which is developed by using conventional Feedforward Neural Network (FNN).table The identification accuracy of FNN is varies with respect to the number of input variables and its modal parameters such as weight (w) and bias (b). In this chapter, the number of most relevant input variables are selected by using Relief F Attribute evaluator (RFAE) with the help of ranker search method. After selecting most appropriate variables, the FNN parameters are optimized by using particle swarm optimization (PSO) based metaheuristic algorithm (MA). The total 208 intelligent models have been designed and validated using 372 real side construction cost dataset of three to nine story buildings. The validated results by FNN and PSO-FNN show that selected variables gives better results as compared with other models.Scopu

Data-Driven Intelligent Model for Sale Price Prediction and Monitoring of a Building

The construction cost forecasting and monitoring plays an important role in a building condition assessment. The construction cost of a building (CCB) not only depends on the method of construction, equipment, labor, and material but also depends on type, scheduling, project locality, and project duration, etc. Moreover, abrupt variations in economic indices and attributes (i.e., WPI: wholesale price, liquidity, building services, etc.) are reasons for cost variation and deviate the CCB, which are not possible to monitor and/or identify in easy way during the current economic scenario. So, these indices may be snubbed in CCB. In this chapter, a data-driven intelligent model for sale price monitoring and detection of a building is presented which may be utilized in hospital planning. For the implementation of the proposed approach, the cost's data of construction for 372 buildings of three to nine stories have been utilized. The recorded dataset has physical, financial, and economic variables and indices of real sites. The proposed approach includes the comparative analysis of conventional statistical and advanced soft computing techniques. The obtained results show that monitor and/or identification of CCB is higher in case of soft computing technique than statistical method.Scopu