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Applying the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Adapt the CHAMP App for Pediatric Feeding Tube Weaning: Application and Case Report

Applying the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Adapt the CHAMP App for Pediatric Feeding Tube Weaning: Application and Case Report

This study highlights the use of the NASSS framework in identifying a novel opportunity for expansion of a RPM program and is an important first step towards expansion of the CHAMP App. However, several limitations should be taken into consideration when interpreting the findings.

Dana M Bakula, Alexandra Zax, Sarah Edwards, Kristina Nash, April Escobar, Rachel Graham, Amy Ricketts, Ryan Thompson, Sarah Bullard, Julianne Brogren, Leah Shimmens, Lori A Erickson

JMIR Form Res 2025;9:e67398

College Community–Based Physical Activity Support at a Public University During the COVID-19 Pandemic: Retrospective Longitudinal Analysis of Intra- Versus Interpersonal Components for Uptake and Outcome Association

College Community–Based Physical Activity Support at a Public University During the COVID-19 Pandemic: Retrospective Longitudinal Analysis of Intra- Versus Interpersonal Components for Uptake and Outcome Association

Wearables can also support outcome expectations because the metrics they collect and display such as step counts are associated with health benefits [1,2]. Finally, wearables can support sociocultural influences through methods such as group step challenges and social media postings of achievements [21,22]. Overall, this robust applicability to SCT shows that wearables should be explored to deliver both inter and intrapersonal intervention components among college students.

Garrett I Ash, Selene S Mak, Adrian D Haughton, Madilyn Augustine, Phillip O Bodurtha, Robert S Axtell, Beatrice Borsari, Jason J Liu, Shaoke Lou, Xin Xin, Lisa M Fucito, Sangchoon Jeon, Matthew Stults-Kolehmainen, Mark B Gerstein

JMIR Mhealth Uhealth 2025;13:e51707

Characterizing Behaviors That Influence the Implementation of Digital-Based Interventions in Health Care: Systematic Review

Characterizing Behaviors That Influence the Implementation of Digital-Based Interventions in Health Care: Systematic Review

Use of an implementation model, for example, the exploration, preparation, implementation, sustainment framework [41], which describes 4 well-defined phases that align with and guide the implementation process, may offer a useful next step for digital innovators in the implementation stage. Another important consideration beyond implementation is considering service design to help teams execute more successful digital health solution adoption projects [40].

Sajan B Patel, Fahad M Iqbal, Kyle Lam, Amish Acharya, Hutan Ashrafian, Ara Darzi

J Med Internet Res 2025;27:e56711

Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study

Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study

The use of these data allowed for the unique opportunity to evaluate the impact of specific choices made during the different data extraction and processing methods (ie, database zone steps) by comparing the 2 datasets in a three-step approach: (1) patient demographics, (2) epidemiology of concordant patients, and (3) health service use in 3 diagnosis groups. Ethical approval for this study was waived by the medical ethics committee of the University Medical Centre Groningen (reference number: 2020/309).

Melissa H J van Essen, Robin Twickler, Yvette M Weesie, Ilgin G Arslan, Feikje Groenhof, Lilian L Peters, Isabelle Bos, Robert A Verheij

J Med Internet Res 2025;27:e64628

Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions

Research Electronic Data Capture (REDCap) for Population-Based Data Collection in Low- and Middle-Income Countries: Opportunities, Challenges, and Solutions

Rigorous training for everyone involved in the study before participant enrollment begins is an important step to minimize errors and should include everything from enrolling participants to detailed instructions and practice for data collection and data entry processes [3]. Electronic data capture can also reduce errors at the point of data collection and entry.

Ha Thanh Le, Dung Viet Tien Vu, Thi Ngoc Anh Nguyen, Hang Tran Thi, Tan Viet Nguyen, Thao Phuong Tran, Aria Kekalih, Samita Rijal, Dewi Friska, Raph L. Hamers, Abhilasha Karkey, Mary Chambers, Jennifer Ilo Van Nuil, SPEAR and CoAct Team, Sonia Lewycka

J Med Internet Res 2025;27:e65377