e.g. mhealth
Search Results (1 to 10 of 47 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 24 Journal of Medical Internet Research
- 8 JMIR Formative Research
- 6 JMIR mHealth and uHealth
- 3 JMIR Research Protocols
- 2 Interactive Journal of Medical Research
- 1 JMIR Aging
- 1 JMIR Dermatology
- 1 JMIR Public Health and Surveillance
- 1 JMIR Rehabilitation and Assistive Technologies
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Human Factors
- 0 JMIR Medical Informatics
- 0 JMIR Serious Games
- 0 JMIR Mental Health
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Cancer
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 Journal of Participatory Medicine
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
Reference 5: Attrition in web-based treatment for problem drinkers Reference 30: The law of attritionattritionEngagement with and Adherence to Digital Health Interventions, Law of Attrition
J Med Internet Res 2024;26:e54248
Download Citation: END BibTex RIS
In this program, feasibility was operationalized through the evaluation of the recruitment rate, attrition rate, incidence of reported adverse events (eg, discomfort, injuries), and incidence of reported technical difficulties (eg, synchronizing data from the WMD to the smartphone app). The recruitment rate was calculated by dividing the number of participants who were recruited and randomized by the number of eligible participants.
J Med Internet Res 2024;26:e52435
Download Citation: END BibTex RIS
The methodology, consent and recruitment process, and baseline attrition have been described in detail elsewhere [9,88]. The study’s methods and results have been reported in accordance with the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) guidelines (Multimedia Appendix 1) [89]. Data were deidentified to protect participants’ identities.
J Med Internet Res 2024;26:e57322
Download Citation: END BibTex RIS
Although the national Prevention of Mother to Child Transmission program, launched in earnest in 2004, has reduced the vertical transmission rate to approximately 3%, there remain around 280,000 children and adolescents nationwide aged
Poor pediatric linkage to care, high attrition rates, and high mortality rates have been described from a number of cohort studies across the country [4-6].
JMIR Public Health Surveill 2024;10:e40796
Download Citation: END BibTex RIS
attritionEngagement with and Adherence to Digital Health Interventions, Law of Attrition
JMIR Form Res 2024;8:e46420
Download Citation: END BibTex RIS
This pattern was first recognized in 2005 and coined The Law of Attrition [11].
As early as 2009, systematic reviews identified poor adherence and lack of compliance as engagement issues that required attention [12]. These issues persist, as demonstrated by a recent meta-analytic review on digital interventions for depression, which showed efficacy but highlighted compliance as a major challenge [13].
Adherence and compliance are complex and rooted in several systemic and individual factors [14-19].
JMIR Form Res 2024;8:e52558
Download Citation: END BibTex RIS
Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement
To fulfill the promise of m Health apps to improve health outcomes, apps need to be improved so they reduce participant attrition. Health care apps do not work for people who do not use them. To date, app feedback, notifications and reminders, in-app support, gamification, and participant compensation have not been consistently successful in eliminating participant dropout.
Interact J Med Res 2024;13:e51974
Download Citation: END BibTex RIS
In the same meta-analysis, observational real-world studies had even higher (49%) attrition compared to randomized controlled trials. Individual disengagement in RPM programs may arise from a wide range of factors, such as technological difficulties, lack of face-to-face encounters, or irrelevant content [10]. In addition, systemic disparities exist in terms of digital access and literacy, especially among vulnerable populations, leading to inequities in RPM adoption and use [10-13].
JMIR Mhealth Uhealth 2024;12:e51236
Download Citation: END BibTex RIS
The overall attrition rate quantifies the level of attrition for the whole sample in a clinical trial, and the differential attrition rate refers to the level of attrition in the intervention group compared with that in the comparison group [26]. Attrition in clinical trials may introduce bias by modifying the random composition of the trial groups, limiting the generalizability of the study, and reducing the study power owing to reduced sample size [13,27].
J Med Internet Res 2024;26:e48168
Download Citation: END BibTex RIS
Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study
Retention is a crucial metric in mobile health research that provides insights into the duration of participant engagement before attrition. High levels of attrition can pose significant challenges to the feasibility of a study and potentially compromise the validity of its outcomes. Retention can be assessed by calculating the average length of time that participants contribute to data collection before dropping out, which is an essential component of study design and management.
JMIR Form Res 2023;7:e51507
Download Citation: END BibTex RIS