This study addresses the burgeoning global shortage of healthcare workers and the consequential overburdening of medical professionals, a challenge that is anticipated to intensify by 2030 [1]. It explores the adoption and perceptions of AI-powered mobile medical applications (MMAs) by physicians in the Netherlands, investigating whether doctors discuss or recommend these applications to patients and the frequency of their use in clinical practice. The research reveals a cautious but growing acceptance of MMAs among healthcare providers. Medical mobile applications, with a substantial part of IA-driven applications, are being recognized for their potential to alleviate workload. The findings suggest an emergent trust in AI-driven health technologies, underscored by recommendations from peers, yet tempered by concerns over data security and patient mental health, indicating a need for ongoing assessment and validation of these applications
DOCUMENT
ABSTRACT Objective: To evaluate the effectiveness of the WhiteTeeth mobile app, a theory-based mobile health (mHealth) program for promoting oral hygiene in adolescent orthodontic patients. Methods: In this parallel randomized controlled trial, the data of 132 adolescents were collected during three orthodontic check-ups: at baseline (T0), at 6-week follow-up (T1), and at 12-week follow-up (T2). The intervention group was given access to the WhiteTeeth app in addition to usual care (n=67). The control group received usual care only (n=65). The oral hygiene outcomes were the presence and the amount of dental plaque (Al-Anezi and Harradine plaque Index); and the total number of sites with gingival bleeding (Bleeding on Marginal Probing Index). Oral health behavior and its psychosocial factors were measured through a digital questionnaire. We performed linear mixed model analyses to determine the intervention effects. Results: At 6-week follow-up, the intervention led to a significant decrease in gingival bleeding (B=-3.74; 95%CI -6.84 to -0.65), and an increase in the use of fluoride mouth rinse (B=1.93; 95%CI 0.36 to 3.50). At 12-week follow-up, dental plaque accumulation (B=-11.32; 95%CI -20.57 to -2.07) and the number of sites covered. Conclusions: The results show that adolescents with fixed orthodontic appliances can be helped to improve their oral hygiene when usual care is combined with a mobile app that provides oral health education and automatic coaching. Netherlands Trial Registry Identifier: NTR6206: 20 February 2017.
LINK
Individual and unorganized sports with a health-related focus, such as recreational running, have grown extensively in the last decade. Consistent with this development, there has been an exponential increase in the availability and use of electronic monitoring devices such as smartphone applications (apps) and sports watches. These electronic devices could provide support and monitoring for unorganized runners, who have no access to professional trainers and coaches. The purpose of this paper is to gain insight into the characteristics of event runners who use running-related apps and sports watches. This knowledge is useful from research, design, and marketing perspectives to adequately address unorganized runners’ needs, and to support them in healthy and sustainable running through personalized technology. Data used in this study are drawn from the standardized online Eindhoven Running Survey 2014 (ERS14). In total, 2,172 participants in the Half Marathon Eindhoven 2014 completed the questionnaire (a response rate of 40.0%). Binary logistic regressions were used to analyze the impact of socio-demographic variables, running-related variables, and psychographic characteristics on the use of running-related apps and sports watches. Next, consumer profiles were identified. The results indicate that the use of monitoring devices is affected by socio-demographics as well as sports-related and psychographic variables, and this relationship depends on the type of monitoring device. Therefore, distinctive consumer profiles have been developed to provide a tool for designers and manufacturers of electronic running-related devices to better target (unorganized) runners’ needs through personalized and differentiated approaches. Apps are more likely to be used by younger, less experienced and involved runners. Hence, apps have the potential to target this group of novice, less trained, and unorganized runners. In contrast, sports watches are more likely to be used by a different group of runners, older and more experienced runners with higher involvement. Although apps and sports watches may potentially promote and stimulate sports participation, these electronic devices do require a more differentiated approach to target specific needs of runners. Considerable efforts in terms of personalization and tailoring have to be made to develop the full potential of these electronic devices as drivers for healthy and sustainable sports participation.
DOCUMENT
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.
