e.g. mhealth
Search Results (1 to 10 of 265 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 86 Journal of Medical Internet Research
- 37 JMIR Formative Research
- 26 JMIR mHealth and uHealth
- 25 JMIR Public Health and Surveillance
- 25 JMIR Research Protocols
- 14 JMIR Mental Health
- 9 JMIR Serious Games
- 7 JMIR Pediatrics and Parenting
- 6 JMIR Human Factors
- 6 JMIR Infodemiology
- 5 Interactive Journal of Medical Research
- 4 JMIRx Med
- 3 JMIR Medical Informatics
- 2 JMIR Cancer
- 2 JMIR Medical Education
- 1 Iproceedings
- 1 JMIR Aging
- 1 JMIR Biomedical Engineering
- 1 JMIR Cardio
- 1 JMIR Dermatology
- 1 JMIR Perioperative Medicine
- 1 JMIR Rehabilitation and Assistive Technologies
- 1 Online Journal of Public Health Informatics
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Data
- 0 Journal of Participatory Medicine
- 0 JMIR Nursing
- 0 JMIRx Bio
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 JMIR XR and Spatial Computing (JMXR)
Given the importance of incorporating behavior change mechanisms to promote positive health behaviors, CBT is regarded as a fundamental therapeutic component in the delivery of m Health apps for individuals experiencing mental health challenges. In this context, Tudor-Sfetea et al [24] found that compared with non-CBT-based interventions, CBT-based digital interventions are associated with greater user engagement and sustained app use, both of which contributed to facilitating behavioral change.
JMIR Mhealth Uhealth 2025;13:e60871
Download Citation: END BibTex RIS
A total of three major types of digital BA interventions were identified across studies: (1) i BA interventions that deliver web-based therapies through self-guided or minimally guided digital platforms that help patients with depression develop new behavioral activities; (2) electronic messaging-based BA interventions that use messaging technologies to deliver periodic prompts and support via SMS, email, or app notifications to encourage behavior change: and (3) telehealth-based BA interventions that offer real-time
J Med Internet Res 2025;27:e68054
Download Citation: END BibTex RIS
Second, all agitated and potentially aggressive behaviors were documented by independent observers (research interns) or staff (clinical staff and teachers), including the timeframe and specific description of the behavior. Aggressive behavior was coded according to the behavior program coding schema (“traffic light model”) as used in the daycare and clinic (see Table 1). “Green” behavior was classified as normal or very good behavior (eg, receiving a compliment for correct behavior).
JMIR Form Res 2025;9:e65559
Download Citation: END BibTex RIS
This is attributed to the varying functionalities that are accessible and the absence of support for these functionalities in influencing user behavior.
Research has indicated that the integration of behavior change theory offers the potential for both positive behavior modification and amelioration of negative emotions [14]. Behavior modification approaches are comprised of three essential components: goals and planning, feedback and monitoring, and repetition and replacement [15].
JMIR Mhealth Uhealth 2025;13:e63022
Download Citation: END BibTex RIS
Boosting Blood Donations Through Facebook Engagement: Randomized Controlled Field Trial
Despite the acknowledged importance of social media in the field of blood donation, only 1 recent study has tested its effects on donation behavior using a design that allows for causal conclusions. Harrell et al [9] examined the impact of the Facebook blood donation tool—which connects users to nearby donation locations—by comparing blood donation data across areas with different rollout dates of the tool.
J Med Internet Res 2025;27:e64740
Download Citation: END BibTex RIS
Studies were excluded if they reported on the development or design of a digital health intervention without reporting on its effectiveness on the behavioral outcomes of Indigenous women and children, reported on mixed Indigenous and non-Indigenous populations without reporting subgroup data, or applied the intervention directly to children with the intention to change maternal behavior.
J Med Internet Res 2025;27:e57019
Download Citation: END BibTex RIS
On March 6, 2013, the National Institute of Mental Health (NIMH) released a request for applications titled “Advancing Eating Disorders Research through Dimensional Studies of Biology and Behavior (R01)” to stimulate research using the Research Domain Criteria (RDo C) framework to identify mechanisms underlying eating disorders. In response, our team submitted an application that addressed key requirements.
JMIR Res Protoc 2025;14:e66554
Download Citation: END BibTex RIS
Logging weight in the app on a regular basis was encouraged to promote self-monitoring, an evidence-based behavior change strategy shown to correlate with improved weight management outcomes.
Simply logging into the platform to view educational content, track health metrics, or interact with coaches was considered an important signal of ongoing engagement.
J Med Internet Res 2025;27:e69466
Download Citation: END BibTex RIS
Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data
One of the major benefits of leveraging fitness trackers is the potential to gain a unique and objective insight into an individual’s natural behavior. However, if the analysis of wear time is not appropriately integrated into the data processing pipeline, that purported insight can become biased and imprecise, driving the results away from reality.
JMIR Mhealth Uhealth 2025;13:e46149
Download Citation: END BibTex RIS
According to the Theory of Planned Behavior [21], individuals’ intentions to engage in or discontinue a behavior are shaped by their attitudes toward the behavior, perceived control over it, and social norms. In the context of smart device use, it is likely that students’ perceptions of how device use affects their sleep and well-being, as well as their ingrained habits, play a key role in their decision to reduce or stop using smart devices at bedtime.
JMIR Form Res 2025;9:e67223
Download Citation: END BibTex RIS