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The Effects of Social Presence on Adherence-Focused Guidance in Problematic Cannabis Users: Protocol for the CANreduce 2.0 Randomized Controlled Trial

The Effects of Social Presence on Adherence-Focused Guidance in Problematic Cannabis Users: Protocol for the CANreduce 2.0 Randomized Controlled Trial

With CANreduce 2.0, we try to address this problem by implementing adherence-focused guidance, which has already been documented to be effective at increasing adherence to Web-based self-help relative to Web-based self-help alone [23,24]. The concept of adherence-focused guidance is primarily based on the supportive-accountability model of guidance in Web-based interventions [25] that asserts that unguided self-help programs are often less effective than those that are guided [26].

Manuel Patrick Amann, Severin Haug, Andreas Wenger, Christian Baumgartner, David D Ebert, Thomas Berger, Lars Stark, Marc Walter, Michael P Schaub

JMIR Res Protoc 2018;7(1):e30

Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment

Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment

One of the primary problems with daily practice for m Health interventions is that they are associated with poor adherence and attrition. Nearly 50% of people who started using an m Health app at one point reported that they no longer used them [1]. Other interventions delivered remotely through the internet often have extremely high attrition rates [5].We must tackle these adherence and attrition problems so as to have a positive impact on users of m Health apps.

Aaron Springer, Anusha Venkatakrishnan, Shiwali Mohan, Lester Nelson, Michael Silva, Peter Pirolli

JMIR Mhealth Uhealth 2018;6(7):e157

Importance of Active Participation in Obesity Management Through Mobile Health Care Programs: Substudy of a Randomized Controlled Trial

Importance of Active Participation in Obesity Management Through Mobile Health Care Programs: Substudy of a Randomized Controlled Trial

However, the effect of adherence to the Smart Care service has not been analyzed among participants. Recent studies related to mobile health care services have focused primarily on comparing information and communication technology intervention efficacy between users and nonusers, so there is little research on whether adherence and weight loss outcomes are positively correlated.

Bumjo Oh, Ga-Hye Yi, Min Kyu Han, Jong Seung Kim, Chang Hee Lee, Belong Cho, Hee Cheol Kang

JMIR Mhealth Uhealth 2018;6(1):e2

Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them

Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them

In these interventions, adherence, defined as the percentage of users who complete all lessons, falls to a level as low as 1.0% [7] or even 0.5% [8]. The higher rates of adherence in human-supported interventions can be explained in favor of therapists who do an effective job in motivating clients during their change process [5]. However, positive effects of electronic interventions have also been found by using features such as reminders and tailored advice [9].

Mark R Scholten, Saskia M Kelders, Julia EWC Van Gemert-Pijnen

J Med Internet Res 2017;19(11):e383

Citizen Worry and Adherence in Response to Government Restrictions in Switzerland During the COVID-19 Pandemic: Repeated Cross-Sectional Online Surveys

Citizen Worry and Adherence in Response to Government Restrictions in Switzerland During the COVID-19 Pandemic: Repeated Cross-Sectional Online Surveys

Respondents rated (1) worry about the pandemic situation, (2) self-reported adherence to government restrictions, and (3) perceived adherence of others to government restrictions, on visual analog scales from 0 to 100 (0=not at all; 100=in all situations). Items were not randomized. Participants could go back to earlier questions at any time. Adaptive questioning was used for several items. The first 2 surveys took up 6 screens, and the 2 latter, 7.

Vanessa Kraege, Céline Dumans-Louis, Céline Maglieri, Séverine Bochatay, Marie-Anne Durand, Antoine Garnier, Kevin Selby, Christian von Plessen

Interact J Med Res 2025;14:e55636

Quality Assessment of Smartphone Medication Management Apps in France: Systematic Search

Quality Assessment of Smartphone Medication Management Apps in France: Systematic Search

Therapeutic adherence is defined by the World Health Organization as “the extent to which the behaviors of a person required to take medication, follow a diet and/or change lifestyle correspond to the recommendations agreed with a healthcare professional” [1]. It is estimated to be around 50% for people with chronic diseases in high-income countries [1].

Mickael Toïgo, Julie Marc, Maurice Hayot, Lionel Moulis, Francois Carbonnel

JMIR Mhealth Uhealth 2024;12:e54866

An mHealth Intervention to Address Depression and Improve Antiretroviral Therapy Adherence Among Youths Living With HIV in Uganda: Protocol for a Pilot Randomized Controlled Trial

An mHealth Intervention to Address Depression and Improve Antiretroviral Therapy Adherence Among Youths Living With HIV in Uganda: Protocol for a Pilot Randomized Controlled Trial

While high levels of ART adherence are necessary for young people to benefit individually from ART, as well as for reducing the risk of HIV transmission, depression has been found to significantly impede ART adherence [10,11]. Previous studies have found that patients with depression are nearly 3 times more likely to be nonadherent to medication regimens than patients without depression [12,13].

Proscovia Nabunya, Patricia Cavazos-Rehg, James Mugisha, Erin Kasson, Olive Imelda Namuyaba, Claire Najjuuko, Edward Nsubuga, Lindsey M Filiatreau, Abel Mwebembezi, Fred M Ssewamala

JMIR Res Protoc 2024;13:e54635

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data

Confusion matrix for both random forest predictions (models 1 and 2) in the test data set (30% of data) depicting the predicted outcome (prediction) versus actual adherence status (reference). Model 1: prediction of adherence based on sociodemographic and clinical variables. Model 2: prediction of adherence adding the usage behavior within the first week.

Franziska Wenger, Caroline Allenhof, Simon Schreynemackers, Ulrich Hegerl, Hanna Reich

JMIR Form Res 2024;8:e53768

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