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Background: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. Objective: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. Methods: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. Results: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. Conclusions: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness. [ABSTRACT FROM AUTHOR]

Background: Holistic housing renovations combine physical housing improvements with social and socioeconomic interventions (e.g. referral to social services, debt counselling, involvement in decision-making, promoting social cohesion). In a deprived neighbourhood in Utrecht, the Netherlands, this paper examined residents' and professionals' experiences, ideas, and perceptions regarding holistic housing renovation, its health effects, and underlying mechanisms explaining those effects. Methods: Semi-structured in-depth interviews were conducted with 21 social housing residents exposed to holistic housing renovation, and 12 professionals involved in either the physical renovation or social interventions implemented. Residents were interviewed in various renovation stages (before, during, after renovation). Transcripts were deductively and inductively coded using qualitative software. Results: Residents experienced and professionals acknowledged renovation stress caused by nuisance from construction work (noise, dust), having to move stuff around, and temporary moving; lack of information and control; and perceived violation of privacy. Involvement in design choices was appreciated, and mental health improvement was expected on the long term due to improved housing quality and visual amenity benefits. Social contact between residents increased as the renovation became topic for small talk. Few comments were made regarding physical health effects. The interviews revealed a certain amount of distrust in and dissatisfaction with the housing corporation, construction company, and other authorities. Conclusions: Renovation stress, aggravated by lack of information and poor accessibility of housing corporation and construction company, negatively affects mental health and sense of control. Potential stress relievers are practical help with packing and moving furniture, and increased predictability by good and targeted communication. Social interventions can best be offered after renovation, when residents live in their renovated apartment and the nuisance and stress from the renovation is behind them. Social partners can use the period leading up to the renovation to show their faces, offer practical help to reduce renovation stress, and increase residents' trust in their organization and authorities in general. This might also contribute to residents' willingness to accept help with problems in the social domain after renovation. [ABSTRACT FROM AUTHOR]

Background: Each year, many help seekers in need contact health helplines for mental support. It is crucial that they receive support immediately, and that waiting times are minimal. In order to minimize delay, helplines must have adequate staffing levels, especially during peak hours. This has raised the need for means to predict the call and chat volumes ahead of time accurately. Motivated by this, in this paper, we analyze real-life data to develop models for accurately forecasting call volumes, for both phone and chat conversations for online mental health support. Methods: This research was conducted on real call and chat data (adequately anonymized) provided by 113 Suicide Prevention (Over ons | 113 Zelfmoordpreventie) (throughout referred to as '113'), the online helpline for suicide prevention in the Netherlands. Chat and phone call data were analyzed to better understand the important factors that influence the call arrival process. These factors were then used as input to several Machine Learning (ML) models to forecast the number of call and chat arrivals. Next to that, senior counselors of the helpline completed a web-based questionnaire after each shift to assess their perception of the workload. Results: This study has led to several remarkable and key insights. First, the most important factors that determine the call volumes for the helpline are the trend, and weekly and daily cyclic patterns (cycles), while monthly and yearly cycles were found to be non-significant predictors for the number of phone and chat conversations. Second, media events that were included in this study only have limited—and only short-term—impact on the call volumes. Third, so-called (S)ARIMA models are shown to lead to the most accurate prediction in the case of short-term forecasting, while simple linear models work best for long-term forecasting. Fourth, questionnaires filled in by senior counselors show that the experienced workload is mainly correlated to the number of chat conversations compared to phone calls. Conclusion: (S)ARIMA models can best be used to forecast the number of daily chats and phone calls with a MAPE of less than 10 in short-term forecasting. These models perform better than other models showing that the number of arrivals depends on historical data. These forecasts can be used as support for planning the number of counselors needed. Furthermore, the questionnaire data show that the workload experienced by senior counselors is more dependent on the number of chat arrivals and less on the number of available agents, showing the value of insight into the arrival process of conversations. [ABSTRACT FROM AUTHOR]

Background: Tests to predict the development of chronic diseases in those with a family history of the disease are becoming increasingly available and can identify those who may benefit most from preventive interventions. It is important to understand the acceptability of these predictive approaches to inform the development of tools to support decision making. Whilst data are lacking for many diseases, data are available for ischemic heart disease (IHD). Therefore, this study investigates the willingness of those with a family history of IHD to take a predictive test, and the effect of the test results on risk-related behaviours.Method: Medline, EMBASE, PsycINFO, LILACS and grey literature were searched. Primary research, including adult participants with a family history of IHD, and assessing a predictive test were included. Qualitative and quantitative outcomes measuring willingness to take a predictive test and the effect of test results on risk-related behaviours were also included. Data concerning study aims, participants, design, predictive test, intervention and findings were extracted. Study quality was assessed using the Standard Quality Assessment Criteria for Evaluating Research Papers from a Variety of Fields and a narrative synthesis undertaken.Results: Five quantitative and two qualitative studies were included. These were conducted in the Netherlands (n = 1), Australia (n = 1), USA (n = 1) and the UK (n = 4). Methodological quality ranged from moderate to good. Three studies found that most relatives were willing to take a predictive test, reporting family history (n = 2) and general practitioner (GP) recommendation (n = 1) as determinants of interest. Studies assessing the effect of test results on behavioural intentions (n = 2) found increased intentions to engage in physical activity and smoking cessation, but not healthy eating in those at increased risk of developing IHD. In studies examining actual behaviour change (n = 2) most participants reported engaging in at least one preventive behaviour, particularly medication adherence.Conclusion: The results suggests that predictive approaches are acceptable to those with a family history of IHD and have a positive impact on health behaviours. Further studies are needed to provide a comprehensive understanding of predictive approaches in IHD and other chronic conditions. [ABSTRACT FROM AUTHOR]

Background: Burnout is related to huge costs, for both individuals and organizations and is recognized as an occupational disease or work-related disorder in many European countries. Given that burnout is a major problem it is important to measure the levels of burnout in a valid and reliable way. The Burnout Assessment Tool (BAT) is a newly developed self-report questionnaire to measure burnout. So far, studies concerning the psychometric properties of the original version of the Burnout Assessment Tool (BAT) including 23 items show promising results and suggest that the instrument can be used in many different settings. For various reasons there is a need of a shorter instrument. For example, burnout questionnaires are typically included in employee surveys to evaluate psychosocial risk-factors, which according to the European Occupational Safety and Health Framework Directive, should be carried out in organizations on a regular basis. The aims of this paper are to develop a shorter version of the BAT, including only 12 items (BAT12) and to evaluate its construct validity and differential item functioning regarding age, gender and country.Methods: Using data from representative samples of working populations in the Netherlands and Belgium (Flanders) a shorter version of the BAT was developed by combining quantitative (Rasch analysis) and qualitative approaches (item content analysis and expert judgements). Construct validity of the new BAT12 was evaluated by means of Rasch analysis.Results: In an iterative procedure, deleting one item from each subscale at each step, a short version of the BAT - BAT12 was developed. The BAT12 fulfils the measurement criteria according to the Rasch model after accounting for local dependency between items within each subscale. The four subscales can be combined into a single burnout score.Conclusion: The new BAT12 developed in the present study maintains the breath of item content of the original version of the BAT. The new BAT12 has sound psychometric properties. The scale works invariantly for older and younger, women and men and across two countries. A shorter version of the BAT is timesaving compared to the BAT23 and can be used in e.g. employee surveys. [ABSTRACT FROM AUTHOR]