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Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study

Digital Gaming and Exercise Among Youth With Type 1 Diabetes: Cross-Sectional Analysis of Data From the Type 1 Diabetes Exercise Initiative Pediatric Study

Type 1 diabetes (T1 D) is a common chronic medical condition among children [1]. It is characterized by a loss of endogenous insulin production in the pancreas and an inability to self-regulate blood glucose levels. The daily treatment for T1 D includes vigilant glucose monitoring, carbohydrate counting, and intensive insulin delivery, with the goal of trying to achieve near-normal glucose levels [2].

Susana R Patton, Robin L Gal, Simon Bergford, Peter Calhoun, Mark A Clements, Jennifer L Sherr, Michael C Riddell

JMIR Pediatr Parent 2024;7:e57198


Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study

Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study

Activities included sitting on a chair or lying on a bed, washing of dishes and simulated loading and unloading of a dishwasher, sweeping or vacuuming of a small room, organizing a room or adjusting furniture in the room, scrubbing of walls and carpet/floor, and self-paced ascending and descending of a flight of stairs. These activities were preceded and followed by two 5-min segments of seated rest.

Ravi Kondama G Reddy, Rubin Pooni, Dessi P Zaharieva, Brian Senf, Joseph El Youssef, Eyal Dassau, Francis J Doyle III, Mark A Clements, Michael R Rickels, Susana R Patton, Jessica R Castle, Michael C Riddell, Peter G Jacobs

JMIR Mhealth Uhealth 2018;6(12):e10338

An “All-Data-on-Hand” Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study

An “All-Data-on-Hand” Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study

We constructed a predictive model using a recurrent neural network–based approach suited to processing time series and other sequential data [13]. We specifically developed and evaluated the performance characteristics of a long short-term memory (LSTM) model to predict the 180-day risk of DKA-related hospitalization among youth with T1 D [14]. We developed a model to predict DKA-related hospitalizations within the T1 D population of diabetes centers.

David D Williams, Diana Ferro, Colin Mullaney, Lydia Skrabonja, Mitchell S Barnes, Susana R Patton, Brent Lockee, Erin M Tallon, Craig A Vandervelden, Cintya Schweisberger, Sanjeev Mehta, Ryan McDonough, Marcus Lind, Leonard D'Avolio, Mark A Clements

JMIR Diabetes 2023;8:e47592

A Digital Parenting Intervention With Intimate Partner Violence Prevention Content: Quantitative Pre-Post Pilot Study

A Digital Parenting Intervention With Intimate Partner Violence Prevention Content: Quantitative Pre-Post Pilot Study

A randomized controlled trial (RCT) of the program found promising short- and long-term effects, with reductions in IPV and physical punishment of children at both 21 months posttest and at a 6-year follow-up [18]. Despite the promising potential of parenting programs to tackle multiple forms of family violence, scaling up parenting interventions remains a challenge [24].

Moa Schafer, Jamie Lachman, Paula Zinser, Francisco Antonio Calderón Alfaro, Qing Han, Chiara Facciola, Lily Clements, Frances Gardner, Genevieve Haupt Ronnie, Ross Sheil

JMIR Form Res 2025;9:e58611


Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP): Protocol for a Randomized Clinical Trial to Test a Video-Based Telehealth Intervention

Reducing Emotional Distress for Childhood Hypoglycemia in Parents (REDCHiP): Protocol for a Randomized Clinical Trial to Test a Video-Based Telehealth Intervention

Building on past research, we developed a theoretical model for caregiver fear of hypoglycemia (Figure 1) which identifies child and caregiver variables that may underlie caregiver fear of hypoglycemia. For example, we have data suggesting that a child’s T1 D history, including past experience with hypoglycemia, and a child’s sleeping behavior may relate to caregiver fear of hypoglycemia [15,17,18].

Susana R Patton, Andrew McConville, Arwen M Marker, Alexandra D Monzon, Kimberly A Driscoll, Mark A Clements

JMIR Res Protoc 2020;9(8):e17877

Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study

Predicting Participant Compliance With Fitness Tracker Wearing and Ecological Momentary Assessment Protocols in Information Workers: Observational Study

A 4-day study involving 3601 participants [13] found that higher compliance, defined as wearing time, was associated with being older, not smoking, and having a full-time job, tertiary education, and high self-reported health, whereas no associations were found with income level or sex. As noted earlier, the study by Evenson et al [34] defined compliance as wearing an accelerometer for 10 hours a day, 3 days out of 6 days, in a study of 15,153 participants in a Hispanic community.

Gonzalo J Martinez, Stephen M Mattingly, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Jessica Young, Nitesh Chawla, Munmun De Choudhury, Sidney D'Mello, Gloria Mark, Aaron Striegel

JMIR Mhealth Uhealth 2021;9(11):e22218

Knowledge and Behaviors Toward COVID-19 Among US Residents During the Early Days of the Pandemic: Cross-Sectional Online Questionnaire

Knowledge and Behaviors Toward COVID-19 Among US Residents During the Early Days of the Pandemic: Cross-Sectional Online Questionnaire

The deficit model of PUS posits that a lack of support for science (and a subsequent rejection of recommendations) is due to a lack of understanding about science, and if scientists can find a way to fill this knowledge deficit, then support for science will increase. A more contemporary view of PUS is that the public’s knowledge is not deficient, but rather there is a deficit in trust of science and in scientific experts specifically.

John M Clements

JMIR Public Health Surveill 2020;6(2):e19161

Serious Games Based on Cognitive Bias Modification and Learned Helplessness Paradigms for the Treatment of Depression: Design and Acceptability Study

Serious Games Based on Cognitive Bias Modification and Learned Helplessness Paradigms for the Treatment of Depression: Design and Acceptability Study

(A) The avatar faces an obstacle, a jumping frog. (B) The avatar faces an obstacle, a pit. (C) The avatar faces a longer pit with floating platforms. (D) The instructions for the cognitive bias modification paradigm. (E) An incongruent flanker task as a part of the cognitive bias modification paradigm. The user must press the button corresponding to the middle arrow in the flanker task. (F) A green circle as a part of the cognitive bias modification paradigm.

Arka Ghosh, Jagriti Agnihotri, Sradha Bhalotia, Bharat Kumar Sati, Latika Agarwal, Akash A, Swastika Tandon, Komal Meena, Shreyash Raj, Yatin Azad, Silky Gupta, Nitin Gupta

JMIR Serious Games 2023;11:e37105

Understanding Mental Health App Use Among Community College Students: Web-Based Survey Study

Understanding Mental Health App Use Among Community College Students: Web-Based Survey Study

Mental health concerns are a significant issue among college students [1,2], and the last decade has seen a rise in mental health concerns among students [3]. Community college students, in particular, face a growing crisis of mental health concerns. A survey conducted by the Wisconsin HOPE Lab found that almost 49.4% of respondents across 10 community colleges in seven states reported mental health issues [4].

Judith Borghouts, Elizabeth V Eikey, Gloria Mark, Cinthia De Leon, Stephen M Schueller, Margaret Schneider, Nicole Stadnick, Kai Zheng, Dana B Mukamel, Dara H Sorkin

J Med Internet Res 2021;23(9):e27745

Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review

Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review

For example, engagement can be referred to as the time a user spends on an intervention. However, the time spent on an intervention varies between different types of interventions, and little time spent using a DMHI does not have to be a negative feature per se. To get a comprehensive understanding of people’s use of DMHIs, we use a broader definition of user engagement.

Judith Borghouts, Elizabeth Eikey, Gloria Mark, Cinthia De Leon, Stephen M Schueller, Margaret Schneider, Nicole Stadnick, Kai Zheng, Dana Mukamel, Dara H Sorkin

J Med Internet Res 2021;23(3):e24387