Some people have difficulties with reading, writing or math, have a low IQ or have difficulties understanding health information. These people are on average less healthy. Yet, little collaboration exists with this group in health research. We want to change this and collaborate on a structural basis. We do this with a permanent advisory group with people from this group. The advisory group advises health scientists on research. We want to encourage fellow scientists to ask the advisory group for advice more often. In addition, we want advisory group members to be able to participate as researchers in some studies.

Power4FitFoot focuses on an ecosystem to support data-driven personalized and persuasive monitoring & coaching for cardiovascular patients with diabetic foot. The result will be an early warning system (EWS) on risk detection of deterioration (healthcare providers), with feedback and coaching (patients and informal caregivers). This project shows that the combination of big data technologies and real-time sensor data analytics is the key ingredient to tailor and personalize self-management programs. Power4FitFoot follows a participatory multidisciplinary development approach: researchers (medicine, computer and behavioral sciences) work together with patients, caregivers and industrial companies to develop personalized self-management products and services to prevent Diabetic Foot Ulcers (DFU) or amputations. We outline 4 complementary studies to accomplish our goals. Study 1 focuses on the identification of the major risk factors using existing retrospective data sets. Outcomes will be transformed into dependent variables we aim to predict. Study 2 entails the co-creation of a smart monitoring ecosystem (platform & body sensors) to enable personalized feedback via data analyses. Data sets from various sources (medical, lifestyle, geo-spatial) and with high variety will be collected real-time to further develop the prognostic models. Study 3 focuses on real-time coaching dealing with high risk contexts using the insights of the big data sets from both previous studies. Prognostic models will facilitate a dynamic and context aware heuristics for a self-management support system. Study 4 is an overarching study, to guarantee the infrastructure for data-driven monitoring & coaching and to develop a sustainable data management plan.

Background: Tobacco smoking causes 19,000 deaths per year in the Netherlands. Since 2007, a reduced-risk product is on the Dutch market: the electronic cigarette (e-cigarette). An e-cigarette delivers nicotine without many of the cancer causing and other toxic substances that are present in combustible tobacco smoke. Although some mainly see the harm reduction potential of e-cigarettes, others have concerns that e-cigarettes may appeal to youth and form a gateway to tobacco smoking. In the Netherlands, 39% of the 14-year olds report to have used e-cigarettes. Previous research: Researchers from the USA have examined smoking initiation among adolescents who experimented with e-cigarettes. They found that those who experimented with e-cigarettes were more likely to start smoking combustible tobacco products over the next year. It is important to ascertain whether this finding can be replicated in other countries. Approach: We will replicate the study from the USA by surveying a school-based cohort of adolescents in the Netherlands at baseline, after 6 months, and after 12 months. Main outcome measure is use of tobacco products within the prior 6 months at follow-up. Analyses are adjusted for sociodemographic, environmental, and intrapersonal risk factors for tobacco smoking. Implications: The findings from this study will have important implications for national and school policy and education. If the findings from the USA are replicated in the Netherlands, this is an indication that use of e-cigarettes among adolescents may be a gateway to tobacco smoking. Then, it will be important to prevent e-cigarette experimentation among adolescents. There are already some national policies in place in the Netherlands, but these could be expanded (e.g. licensing e-cigarettes as medicines instead of having them available as consumer products). Also, educational efforts to decrease e-cigarette experimentation may then need to be implemented, which is already being done in the USA.

Malaria in Rwanda is in its pre-elimination phase and there is a wish to move towards elimination. That requires adaptations in health systems and policies but it is not clear how. We hypothesize that participation and investment of the population beyond the vertically organised national malaria control program (PNLP) is the way to go forward. We will apply organizational change management systems and facilitating techniques proven within the field of organizational change and community building that (1) connect knowledge and ownership in the local community to the national and district malaria control program and that (2) will lead to design of health systems interventions towards malaria elimination. This process of expeditionary action and learning will be implemented with repeated participatory works. Annual household surveys into malaria, entomology, sociology, economy and indicators of ownership and willingness to invest will be conducted as well as a continuous monitoring of health service coverage and preventive measures. The process of intervention design and the interventions and their impact itself are topics of study for 4 PhD students. The community will be able to organise itself, start identifying weaknesses in local malaria control and start connecting to primary district and national health services to develop / adapt in interventions with available technologies that are appropriate and are financially sustainable. The required technology for facilitating groups processes will be transferred by experts from the 100th Village. One of the techniques is the ?open space? which was already successfully tested during the grant writing workshop. PhD project 1 facilitates the entire program, looks at group dynamics, self organisation, ownership and commitment and follows intervention design and implementation. PhD project 2 studies public health, health service coverage and the health management information system (HMIS), generates the malariometric data that steer intervention design. PhD project 3 focuses on Integrated vector management (IVM) and the integration in the context of the health systems. The farmer field school is an example of an intervention that has proved to fruitful. A driving force of social mobilization for disease control is a clear return on investment for the population. This return can be materially, but also spiritually. PhD project 4 will focus on the economic aspects, investments and return on investments.

Adolescent health risk behaviors emerge in a broad peer context in which youngsters select and influence each other based on risk behaviors and other attributes. Advanced longitudinal social network analysis can disentangle such influence and selection processes in adolescent networks. Two important new issues will be examined studying theoretically postulated processes in relationship and behavior development in depth. First, previous research neglected effects of being embedded in complex multilevel social structures. Deindividuation theory for example states that in larger groups anonymity increases and personal responsibility diffuses, leading to reduced concern for social evaluation and loss of identity. After adapting existing software we will study network and behavior development using a multilevel oriented methodology going beyond strict nesting structures of traditional multilevel analysis. I will examine different levels such as the individual level, dyads, triads, members of personal networks, relationships between those members, and larger networks in which those members could be embedded. I hypothesize that adolescents in complex larger social structures are more susceptible to influence and that selection into complex larger social structures is more strongly based on behavioral similarities. Second, besides friendships, different relationship types may cause risk behaviors to spread. Solely focusing on friendship networks does not provide enough information to explain why adolescents take up risk behaviors and how these behaviors spread. It is important to focus on other linkages between adolescents like identification with similar youth cultures and being linked in online networks. Influence and selection processes based on risk behaviors are stronger when focusing on different types of network ties in addition to friendship ties. By studying existing and newly collected data, this project will increase our understanding of fundamental principles that drive social network formation among adolescents and increase our knowledge about the spread of risk behaviors in a multilevel context.