BACKGROUND: The number of mobile apps that support smoking cessation is growing, indicating the potential of the mobile phone as a means to support cessation. Knowledge about the potential end users for cessation apps results in suggestions to target potential user groups in a dissemination strategy, leading to a possible increase in the satisfaction and adherence of cessation apps.OBJECTIVE: This study aimed to characterize potential end users for a specific mobile health (mHealth) smoking cessation app.METHODS: A quantitative study was conducted among 955 Dutch smokers and ex-smokers. The respondents were primarily recruited from addiction care facilities and hospitals through Web-based media via websites and forums. The respondents were surveyed on their demographics, smoking behavior, and personal innovativeness. The intention to use and the attitude toward a cessation app were determined on a 5-point Likert scale. To study the association between the characteristics and intention to use and attitude, univariate and multivariate ordinal logistic regression analyses were performed.RESULTS: The multivariate ordinal logistic regression showed that the number of previous quit attempts (odds ratio [OR] 4.1, 95% CI 2.4-7.0, and OR 3.5, 95% CI 2.0-5.9) and the score on the Fagerstrom Test of Nicotine Dependence (OR 0.8, 95% CI 0.8-0.9, and OR 0.8, 95% CI 0.8-0.9) positively correlates with the intention to use a cessation app and the attitude toward cessation apps, respectively. Personal innovativeness also positively correlates with the intention to use (OR 0.3, 95% CI 0.2-0.4) and the attitude towards (OR 0.2, 95% CI 0.1-0.4) a cessation app. No associations between demographics and the intention to use or the attitude toward using a cessation app were observed.CONCLUSIONS: This study is among the first to show that demographic characteristics such as age and level of education are not associated with the intention to use and the attitude toward using a cessation app when characteristics related specifically to the app, such as nicotine dependency and the number of quit attempts, are present in a multivariate regression model. This study shows that the use of mHealth apps depends on characteristics related to the content of the app rather than general user characteristics.
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
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.
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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.
