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Long-Term Effectiveness of a Multi-Strategy Choice Architecture Intervention in Increasing Healthy Food Choices of High-School Students From Online Canteens (Click & Crunch High Schools): Cluster Randomized Controlled Trial

Long-Term Effectiveness of a Multi-Strategy Choice Architecture Intervention in Increasing Healthy Food Choices of High-School Students From Online Canteens (Click & Crunch High Schools): Cluster Randomized Controlled Trial

Interventions that incorporate choice architecture strategies (eg, provision of information, changing default options, and using incentives) [10] are effective in improving adolescent diet-related outcomes. A recent systematic review found that out of 137 included choice architecture interventions that aimed to modify child or adolescent diet-related outcomes, 74% were effective [10].

Tessa Delaney, Jacklyn Jackson, Christophe Lecathelinais, Tara Clinton-McHarg, Hannah Lamont, Sze Lin Yoong, Luke Wolfenden, Rachel Sutherland, Rebecca Wyse

J Med Internet Res 2024;26:e51108

Self-Reported Preferences for Help-Seeking and Barriers to Using Mental Health Supports Among Internal Medicine Residents: Exploratory Use of an Econometric Best-Worst Scaling Framework for Gathering Physician Wellness Preferences

Self-Reported Preferences for Help-Seeking and Barriers to Using Mental Health Supports Among Internal Medicine Residents: Exploratory Use of an Econometric Best-Worst Scaling Framework for Gathering Physician Wellness Preferences

Best-worst scaling (BWS) is a type of discrete choice experiment (DCE) based on economic choice modeling theory [13]. In this conceptual framework, individual and aggregate preferences to surveyed items can be developed by forcing respondents to choose from among two or more alternatives. BWS has been developed to resolve many of the biases associated with rating scales and ranking studies [9].

Andrew Wu, Varsha Radhakrishnan, Elizabeth Targan, Timothy M Scarella, John Torous, Kevin P Hill

JMIR Med Educ 2021;7(4):e28623

Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study

Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study

We also conducted a discrete choice experiment (DCE) to quantify and measure peoples’ preferences for different diagnosis methods and identify factors that disrupted and impacted peoples’ decision-making behaviors. We designed a web-based questionnaire to collect participants’ demographic information and investigate patients’ preferences for different diagnosis strategies (Multimedia Appendix 1). In brief, the questionnaire included 7 similar hypothetical scenarios.

Taoran Liu, Winghei Tsang, Yifei Xie, Kang Tian, Fengqiu Huang, Yanhui Chen, Oiying Lau, Guanrui Feng, Jianhao Du, Bojia Chu, Tingyu Shi, Junjie Zhao, Yiming Cai, Xueyan Hu, Babatunde Akinwunmi, Jian Huang, Casper J P Zhang, Wai-Kit Ming

J Med Internet Res 2021;23(3):e26997

The Influence of Physician Information on Patients’ Choice of Physician in mHealth Services Using China’s Chunyu Doctor App: Eye-Tracking and Questionnaire Study

The Influence of Physician Information on Patients’ Choice of Physician in mHealth Services Using China’s Chunyu Doctor App: Eye-Tracking and Questionnaire Study

However, in the field of m Health user behavior, there is still a lack of in-depth research on patients’ choice of physician and its influencing factors. In fact, patients’ choice of physicians who offer m Health is different from the choice they make within the traditional route. First, m Health provides information about physicians far beyond the traditional medical model.

Wei Shan, Ying Wang, Jing Luan, Pengfei Tang

JMIR Mhealth Uhealth 2019;7(10):e15544