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.
Background: A large number of people participate in individual or unorganized sports on a recreational level. Furthermore, many participants drop out because of injury or lowered motivation. Potentially, physical activity–related apps could motivate people during sport participation and help them to follow and maintain a healthy active lifestyle. It remains unclear what the quality of running, cycling, and walking apps is and how it can be assessed. Quality of these apps was defined as having a positive influence on participation in recreational sports. This information will show which features need to be assessed when rating physical activity–related app quality. Objective: The aim of this study was to identify expert perception on which features are important for the effectiveness of physical activity–related apps for participation in individual, recreational sports. Methods: Data were gathered via an expert panel approach using the nominal group technique. Two expert panels were organized to identify and rank app features relevant for sport participation. Experts were researchers or professionals in the field of industrial design and information technology (technology expert panel) and in the field of behavior change, health, and human movement sciences who had affinity with physical activity–related apps (health science expert panel). Of the 24 experts who were approached, 11 (46%) agreed to participate. Each panel session consisted of three consultation rounds. The 10 most important features per expert were collected. We calculated the frequency of the top 10 features and the mean importance score per feature (0-100). The sessions were taped and transcribed verbatim; a thematic analysis was conducted on the qualitative data. Results: In the technology expert panel, applied feedback and feedforward (91.3) and fun (91.3) were found most important (scale 0-100). Together with flexibility and look and feel, these features were mentioned most often (all n=4 [number of experts]; importance scores=41.3 and 43.8, respectively). The experts in the health science expert panels a and b found instructional feedback (95.0), motivating or challenging (95.0), peer rating and use (92.0), motivating feedback (91.3), and monitoring or statistics (91.0) most important. Most often ranked features were monitoring or statistics, motivating feedback, works good technically, tailoring starting point, fun, usability anticipating or context awareness, and privacy (all n=3-4 [number of experts]; importance scores=16.7-95.0). The qualitative analysis resulted in four overarching themes: (1) combination behavior change, technical, and design features needed; (2) extended feedback and tailoring is advised; (3) theoretical or evidence base as standard; and (4) entry requirements related to app use. Conclusions: The results show that a variety of features, including design, technical, and behavior change, are considered important for the effectiveness of physical activity–related apps by experts from different fields of expertise. These insights may assist in the development of an improved app rating scale.
LINK
Information and communications technologies (ICTs) in human services are on the rise and raise concerns about their place and impact on the daily activities of professionals and clients. This article describes a study in which a social mobile application was developed for job coaches and employees and implemented in a pilot phase. The aim of the mobile application was to provide a better communication between employees and their job coaches and to provide more up-to-date information about the organization. The application consisted of a personal web environment and app with vacancies, personal news, events, tips, and promotions. A qualitative methodology was used in the form of focus groups and in-depth interviews. The results of this study show that the participants are partly positive about the social mobile application. It can be concluded that the use of mobile technologies can be beneficial in a range of human services practice settings for both professionals and clients and, therefore, requires more attention from the academic field to focus on this relatively new but promising theme.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
Lack of physical activity in urban contexts is an increasing health risk in The Netherlands and Brazil. Exercise applications (apps) are seen as potential ways of increasing physical activity. However, physical activity apps in app stores commonly lack a scientific base. Consequently, it remains unknown what specific content messages should contain and how messages can be personalized to the individual. Moreover, it is unknown how their effects depend on the physical urban environment in which people live and on personal characteristics and attitudes. The current project aims to get insight in how mobile personalized technology can motivate urban residents to become physically active. More specifically, we aim to gain insight into the effectiveness of elements within an exercise app (motivational feedback, goal setting, individualized messages, gaming elements (gamification) for making people more physically active, and how the effectiveness depends on characteristics of the individual and the urban setting. This results in a flexible exercise app for inactive citizens based on theories in data mining, machine learning, exercise psychology, behavioral change and gamification. The sensors on the mobile phone, together with sensors (beacons) in public spaces, combined with sociodemographic and land use information will generate a massive amount of data. The project involves analysis in two ways. First, a unique feature of our project is that we apply machine learning/data mining techniques to optimize the app specification for each individual in a dynamic and iterative research design (Sequential Multiple Assignment Randomised Trial (SMART)), by testing the effectiveness of specific messages given personal and urban characteristics. Second, the implementation of the app in Sao Paolo and Amsterdam will provide us with (big) data on use of functionalities, physical activity, motivation etc. allowing us to investigate in detail the effects of personalized technology on lifestyle in different geographical and cultural contexts.
“De fabriek van de toekomst bevindt zich in een gepersonaliseerde, klantcentrische wereld”, aldus de Roadmap Smart Industry van de de Top Sector High Tech Systems & Materials1. Om als Nederlands bedrijfsleven ten volle de mogelijkheden te benutten die ICT biedt, is het noodzakelijk de verbinding te leggen naar wat klanten voelen, denken en willen. Op het moment dat het relevant is, niet alleen vooraf of achteraf. In 2016 heeft een aantal organisaties, bedrijven en onderzoeksinstellingen zich verenigd in een consortium dat als doel heeft om de interactie met klanten te verbeteren door tijdig en adequaat in te spelen op de behoefte van de klant. Realtime inzicht in hoe individuen hun interactie met organisaties beleven maakt het mogelijk betekenisvolle diensten te ontwerpen die in toon, inhoud en presentatie passen bij de actuele behoefte van de klant in kwestie. Dit inzicht kan steeds beter verkregen worden uit digitale data die ontstaan tijdens de interactie. Te denken valt hierbij aan de uitwisseling van emails, het bezoeken van websites of het gebruiken van mobile apps. De toename van data en nieuwe technologieën biedt mogelijkheden om zowel de kwaliteit van product en dienstverlening te verhogen als het belang van de klant te waarborgen. Hierbij is maatschappelijk verantwoorde omgang met data voor alle betrokkenen essentieel. Dit betekent niet alleen respect voor privacy maar ook transparantie en begrijpelijke presentatie van gegevens, bijvoorbeeld door visualisatie. Methoden en technieken om op een verantwoorde manier meer inzicht te krijgen in hoe personen de interactie met organisaties beleven en welke factoren daarbij bepalend zijn, bieden de industrie kansen om hun doelgroepen veel meer te betrekken en laten participeren, zowel voor, tijdens als na de levering van producten en diensten. Voor het verkrijgen van een betrouwbaar en volledig beeld vanuit het perspectief van de klant zijn verschillende analysetechnieken, zoals text mining, process mining en sentiment mining voorhanden. Elk van deze technieken geeft echter slechts een deel van het hele verhaal en het ontbreekt aan methoden en tools om ze te integreren. Daardoor blijft ook kennis gefragmenteerd en is snel en effectief interveniëren lastig. Alleen door analysetechnieken te combineren is het mogelijk een compleet beeld te krijgen. In het project Verantwoorde Belevingsgeoriënteerde Interactie op basis van Data-analyse, kortweg VERBIND, wordt een framework ontwikkeld dat de integratie van verschillende data-analysetechnieken, vanuit een maatschappelijk verantwoorde houding, mogelijk maakt. Uniek aan het project is het multidisciplinaire karakter met deelname van uiteenlopende markt- en onderzoekspartijen.
