e.g. mhealth
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We asked a subset of participants if they would be willing to report personal data in the intervention to facilitate identity-based matching and personalization; all of them said yes.
When you introduce that [the demographic questions] you should say: in order to really focus in on you, we’re going to ask you these questions […] I want to know you’re focusing in on me, because then it’s worth my while to continue.
JMIR Form Res 2025;9:e59691
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Trends and Gaps in Digital Precision Hypertension Management: Scoping Review
Personalization characteristics of the digital interventions (n=30).
J Med Internet Res 2025;27:e59841
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We conclude with a practical discussion on selecting an appropriate model or ensemble and feature set, contextualizing the work around more general concerns in model training and selection for dynamic personalization based on EMA.
Figure 1 shows the area under the receiver operating characteristic curve of various models in a 5-fold cross-validation on training data, and Table 1 presents their area under the curve (AUC) scores on training and test sets.
JMIR Mhealth Uhealth 2025;13:e57255
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This paper develops a data-driven algorithm that includes both passive indicators of user behavior and self-reported measures to identify individuals at a high risk of early attrition in 3 DMHIs; as such, it provides a framework that helps in the personalization of DMHIs to suit individual users based on each individual’s attrition risk.
To predict attrition in DMHIs, there are 2 main considerations [18].
JMIR Ment Health 2024;11:e51567
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This evaluation demonstrated the potential of the digital notification system to facilitate stratification and personalization of care. The results show that most young people who elicited a notification received evidence-based treatment, including brief interventions (ie, safety checks and safety plan [20-23]), and long-term psychological interventions [24-26].
J Med Internet Res 2024;26:e60879
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Features for m Health, such as personalization and cultural tailoring, are needed to create sustainable behavior changes necessary for successful GDM self-management.
Research that defines personalization and cultural tailoring as 2 distinct concepts and explores their impacts on GDM self-management will help refine and optimize intervention models [24]. Personalization caters to users on an individual level, not at the group, community, or population level [25].
JMIR Diabetes 2024;9:e58327
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This is referred to as tailoring and personalization in the context of e Health technologies. Personalization and tailoring seem to be logical ways to overcome suboptimal effectiveness, but there is no clear agreement on how to define personalization and tailoring and what the differences and similarities are. In addition, it remains unclear how personalization and tailoring are being applied and can be applied to e Health technologies.
J Med Internet Res 2024;26:e50497
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Further, personalization in mental health monitoring systems is increasingly important [4]. Innovations in digital phenotyping [41] exemplify this trend. This is further advanced by groundbreaking approaches like those proposed by Gerczuk et al [42], using zero-shot personalization strategies for large speech foundation models in mood recognition.
The application of artificial intelligence and deep learning techniques in mental health monitoring has seen significant growth.
JMIR Ment Health 2024;11:e59512
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