Background: Alcohol use is associated with an automatic tendency to approach alcohol, and the retraining of this tendency (cognitive bias modification [CBM]) shows therapeutic promise in clinical settings. To improve access to training and to enhance participant engagement, a mobile version of alcohol avoidance training was developed.Objective: The aims of this pilot study were to assess (1) adherence to a mobile health (mHealth) app; (2) changes in weekly alcohol use from before to after training; and (3) user experience with regard to the mHealth app.Methods: A self-selected nonclinical sample of 1082 participants, who were experiencing problems associated with alcohol, signed up to use the alcohol avoidance training app Breindebaas for 3 weeks with at least two training sessions per week. In each training session, 100 pictures (50 of alcoholic beverages and 50 of nonalcoholic beverages) were presented consecutively in a random order at the center of a touchscreen. Alcoholic beverages were swiped upward (away from the body), whereas nonalcoholic beverages were swiped downward (toward the body). During approach responses, the picture size increased to mimic an approach movement, and conversely, during avoidance responses, the picture size decreased to mimic avoidance. At baseline, we assessed sociodemographic characteristics, alcohol consumption, alcohol-related problems, use of other substances, self-efficacy, and craving. After 3 weeks, 37.89% (410/1082) of the participants (posttest responders) completed an online questionnaire evaluating adherence, alcohol consumption, and user satisfaction. Three months later, 19.03% (206/1082) of the participants (follow-up responders) filled in a follow-up questionnaire examining adherence and alcohol consumption.Results: The 410 posttest responders were older, were more commonly female, and had a higher education as compared with posttest dropouts. Among those who completed the study, 79.0% (324/410) were considered adherent as they completed four or more sessions, whereas 58.0% (238/410) performed the advised six or more training sessions. The study identified a significant reduction in alcohol consumption of 7.8 units per week after 3 weeks (95% CI 6.2-9.4, P<.001; n=410) and another reduction of 6.2 units at 3 months for follow-up responders (95% CI 3.7-8.7, P<.001; n=206). Posttest responders provided positive feedback regarding the fast-working, simple, and user-friendly design of the app. Almost half of the posttest responders reported gaining more control over their alcohol use. The repetitious and nonpersonalized nature of the intervention was suggested as a point for improvement.Conclusions: This is one of the first studies to employ alcohol avoidance training in a mobile app for problem drinkers. Preliminary findings suggest that a mobile CBM app fulfils a need for problem drinkers and may contribute to a reduction in alcohol use. Replicating these findings in a controlled study is warranted.
Moses - Mobile Sensing for Safety voor veliger een efficientere opereren brandweerlieden
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The paper investigates the use of mobile tools by museums in order to provide mobile access to their permanent collections and special exhibitions. In fact, it deals with the wider topic of how museums tackle the complex issue of communicating with their present and potential audience using modern (i.e., mobile in this case) technologies. The paper presents and discusses the results of a survey that was proposed to Dutch and Flemish museums mainly dealing with modern and contemporary art or with science and technology. We tried to derive some trends and best practices in order to identify a good way to provide an engaging (mobile) experience to museum visitors. These results, although not always stirring in terms of answer percentages and of what most museums seem to be doing with new media, do show a clear interest towards mobile technologies and openness to innovation in the Dutch cultural sector.
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The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated? Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in-principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. Signals from the CAN bus could be manually reverse engineered, but this process is extremely labour-intensive and time-consuming. In this project we investigate if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently.
Een goede voorbereiding is het halve werk, ook voor patiënten op de wachtlijst voor een chirurgische ingreep. We onderzoeken hoe de e-health-applicatie 'Beter Voorbereid' mensen helpt om sterker aan de start van een operatie te staan en zo sneller te herstellen.
Veel patiënten binnen de GGZ kampen met chronische pijn en depressie. Het bevorderen van een gezond beweegpatroon speelt een belangrijke rol in hun behandeling. Deze patiënten kunnen echter door emoties en veranderde prikkelverwerking signalen van het lichaam niet goed inschatten. Daarbij zijn hun klachten belemmerend in hun activiteiten waardoor motivatie vaak afwezig is. GGZ-professionals gebruiken zorgstandaarden waarbij uitgegaan wordt van 'one-size-fits-all' behandelprogramma's. Deze sluiten onvoldoende aan bij de behoefte aan gepersonaliseerde interventies uitgaande van zelfmanagement van de individuele patiënt. Dit pleit voor een instrument dat professionals helpt objectief inzicht te krijgen in het beweegpatroon van hun patiënten, dat gepersonaliseerde feedback geeft en ondersteunt bij de verdere individueel passende begeleiding van de patiënt. Zelfmeettechnologie ('activity trackers') lijkt hier goed te passen. De mogelijkheden om zelfmeettechnologie als basis voor de behandeling van deze patiënten te gebruiken zijn echter bij GGZ-professionals veelal onbekend. Daarnaast is het inzetten van alleen zelfmeettechnologie waarschijnlijk onvoldoende en is niet goed bekend hoe deze patiënten gemotiveerd kunnen worden om deze technologie te (blijven) gebruiken. In dit project willen de Hanzehogeschool Groningen, Inter-Psy, Transcare en MobileCare samen met professionals en patiënten en andere nog te betrekken partners (o.a. het Rob Giel Onderzoekscentrum als trekker van het eHealth netwerk Noord-Nederland heeft aangegeven een bijdrage te willen leveren) ontdekken hoe op een goede manier aan de bovenbeschreven behoefte van GGZ-professionals kan worden bijgedragen. Beoogd wordt om met deze subsidie een proof of concept te leveren van een digitaal instrument dat op basis van zelfmeettechnologie meerwaarde biedt in de behandeling van patiënten met chronische pijn en depressie. Deze proof of concept vormt de basis voor een te schrijven subsidievoorstel om dit verder te ontwikkelen.
