Published on in Vol 4, No 1 (2016): Jan-Mar
“Smart” RCTs: Development of a Smartphone App for Fully Automated Nutrition-Labeling Intervention Trials
“Smart” RCTs: Development of a Smartphone App for Fully Automated Nutrition-Labeling Intervention Trials
Journals
- Miller L, Sutter C, Wilson M, Bergman J, Beckett L, Gibson T. An Evaluation of an eHealth Tool Designed to Improve College Students’ Label-Reading Skills and Feelings of Empowerment to Choose Healthful Foods. Frontiers in Public Health 2018;5 View
- Humphrey G, Chu J, Dowling N, Rodda S, Merkouris S, Parag V, Newcombe D, Ho E, Nosa V, Ruwhui-Collins R, Whittaker R, Bullen C. Manaaki – a cognitive behavioral therapy mobile health app to support people experiencing gambling problems: a randomized control trial protocol. BMC Public Health 2020;20(1) View
- Holmes W, Moorhead S, Coates V, Bond R, Zheng H. Impact of digital technologies for communicating messages on weight loss maintenance: a systematic literature review. European Journal of Public Health 2019;29(2):320 View
- Ni Mhurchu C, Eyles H, Jiang Y, Blakely T. Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial. Appetite 2018;121:360 View
- Fuchs K, Haldimann M, Grundmann T, Fleisch E. Supporting food choices in the Internet of People: Automatic detection of diet-related activities and display of real-time interventions via mixed reality headsets. Future Generation Computer Systems 2020;113:343 View
- Dunford E, Poti J, Xavier D, Webster J, Taillie L. Color-Coded Front-of-Pack Nutrition Labels—An Option for US Packaged Foods?. Nutrients 2017;9(5):480 View
- Maramis C, Moulos I, Ioakimidis I, Papapanagiotou V, Langlet B, Lekka I, Bergh C, Maglaveras N. A smartphone application for semi-controlled collection of objective eating behavior data from multiple subjects. Computer Methods and Programs in Biomedicine 2020;194:105485 View
- Vogel M, Combs S, Kessel K. mHealth and Application Technology Supporting Clinical Trials: Today’s Limitations and Future Perspective of smartRCTs. Frontiers in Oncology 2017;7 View
- Zhang M, Ho R. Smartphone application for multi-phasic interventional trials in psychiatry: Technical design of a smart server. Technology and Health Care 2017;25(2):373 View
- Volkova E, Michie J, Corrigan C, Sundborn G, Eyles H, Jiang Y, Mhurchu C. Effectiveness of recruitment to a smartphone-delivered nutrition intervention in New Zealand: analysis of a randomised controlled trial. BMJ Open 2017;7(6):e016198 View
- Calegari L, Barbosa J, Marodin G, Fettermann D. A conjoint analysis to consumer choice in Brazil: Defining device attributes for recognizing customized foods characteristics. Food Research International 2018;109:1 View
- Chen R, Santo K, Wong G, Sohn W, Spallek H, Chow C, Irving M. Mobile Apps for Dental Caries Prevention: Systematic Search and Quality Evaluation. JMIR mHealth and uHealth 2021;9(1):e19958 View
- Lo B, Shi J, Wong H, Abi-Jaoudé A, Johnson A, Hollenberg E, Chaim G, Cleverley K, Henderson J, Levinson A, Robb J, Sanches M, Voineskos A, Wiljer D. Considerations for evaluating digital mental health tools remotely- reflections after a randomized trial of Thought Spot. General Hospital Psychiatry 2021;70:76 View
- Tonkin E, Brimblecombe J, Wycherley T. Characteristics of Smartphone Applications for Nutrition Improvement in Community Settings: A Scoping Review. Advances in Nutrition 2017;8(2):308 View
- Ni Mhurchu C, Volkova E, Jiang Y, Eyles H, Michie J, Neal B, Blakely T, Swinburn B, Rayner M. Effects of interpretive nutrition labels on consumer food purchases: the Starlight randomized controlled trial ,. The American Journal of Clinical Nutrition 2017;105(3):695 View
- Razavi R, Xue G. Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach. Journal of Medical Internet Research 2023;25:e45332 View