This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (2939) mHealth for Data Collection and Research (955) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (565) Text-messaging (SMS, WeChat etc)-Based Interventions (650)

Published on in Vol 15, No 12 (2013): December

Text Messaging Data Collection for Monitoring an Infant Feeding Intervention Program in Rural China: Feasibility Study

Text Messaging Data Collection for Monitoring an Infant Feeding Intervention Program in Rural China: Feasibility Study

Text Messaging Data Collection for Monitoring an Infant Feeding Intervention Program in Rural China: Feasibility Study

Authors of this article:

Ye Li1 ;   Wei Wang1 ;   Michelle Helena van Velthoven2 ;   Li Chen1 ;   Josip Car2 ;   Igor Rudan3 ;   Yanfeng Zhang1 ;   Qiong Wu1 ;   Xiaozhen Du1 ;   Robert W Scherpbier4
Article Authors Cited by (21) Tweetations (16) Metrics

Journals

  1. Firchow P, Mac Ginty R. Including Hard-to-Access Populations Using Mobile Phone Surveys and Participatory Indicators. Sociological Methods & Research 2020;49(1):133 View
  2. Gibby C, Palacios C, Campos M, Graulau R, Banna J. Acceptability of a text message-based intervention for obesity prevention in infants from Hawai‘i and Puerto Rico WIC. BMC Pregnancy and Childbirth 2019;19(1) View
  3. Udtha M, Nomie K, Yu E, Sanner J. Novel and Emerging Strategies for Longitudinal Data Collection. Journal of Nursing Scholarship 2015;47(2):152 View
  4. Berrouiguet S, Baca-García E, Brandt S, Walter M, Courtet P. Fundamentals for Future Mobile-Health (mHealth): A Systematic Review of Mobile Phone and Web-Based Text Messaging in Mental Health. Journal of Medical Internet Research 2016;18(6):e135 View
  5. Hong Y, Zhou Z, Fang Y, Shi L. The Digital Divide and Health Disparities in China: Evidence From a National Survey and Policy Implications. Journal of Medical Internet Research 2017;19(9):e317 View
  6. Gibson D, Pereira A, Farrenkopf B, Labrique A, Pariyo G, Hyder A. Mobile Phone Surveys for Collecting Population-Level Estimates in Low- and Middle-Income Countries: A Literature Review. Journal of Medical Internet Research 2017;19(5):e139 View
  7. Iribarren S, Cato K, Falzon L, Stone P, Mihalopoulos C. What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLOS ONE 2017;12(2):e0170581 View
  8. Banna J, Campos M, Gibby C, Graulau R, Meléndez M, Reyes A, Lee J, Palacios C. Multi-site trial using short mobile messages (SMS) to improve infant weight in low-income minorities: Development, implementation, lessons learned and future applications. Contemporary Clinical Trials 2017;62:56 View
  9. van Velthoven M, Li Y, Wang W, Chen L, Du X, Wu Q, Zhang Y, Rudan I, Car J, Operario D. Prevalence of Mobile Phones and Factors Influencing Usage by Caregivers of Young Children in Daily Life and for Health Care in Rural China: A Mixed Methods Study. PLOS ONE 2015;10(3):e0116216 View
  10. Greenleaf A, Gibson D, Khattar C, Labrique A, Pariyo G. Building the Evidence Base for Remote Data Collection in Low- and Middle-Income Countries: Comparing Reliability and Accuracy Across Survey Modalities. Journal of Medical Internet Research 2017;19(5):e140 View
  11. Shimoni N, Nippita S, Castaño P. Best practices for collecting repeated measures data using text messages. BMC Medical Research Methodology 2020;20(1) View
  12. van Velthoven M, Car J, Zhang Y, Marušić A. mHealth series: New ideas for mHealth data collection implementation in low– and middle–income countries. Journal of Global Health 2013;3(2) View
  13. Sridhar D, Car J, Chopra M, Campbell H, Woods N, Rudan I. Improving health aid for a better planet: The planning, monitoring and evaluation tool (PLANET). Journal of Global Health 2015;5(2) View
  14. Taki S, Lymer S, Russell C, Campbell K, Laws R, Ong K, Elliott R, Denney-Wilson E. Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index. JMIR mHealth and uHealth 2017;5(6):e89 View
  15. Taki S, Russell C, Lymer S, Laws R, Campbell K, Appleton J, Ong K, Denney-Wilson E. A Mixed Methods Study to Explore the Effects of Program Design Elements and Participant Characteristics on Parents' Engagement With an mHealth Program to Promote Healthy Infant Feeding: The Growing Healthy Program. Frontiers in Endocrinology 2019;10 View
  16. van Velthoven M, Wang W, Wu Q, Li Y, Scherpbier R, Du X, Chen L, Zhang Y, Car J, Rudan I. Comparison of text messaging data collection vs face-to-face interviews for public health surveys: a cluster randomized crossover study of care-seeking for childhood pneumonia and diarrhoea in rural China. Journal of Global Health 2018;8(1) View
  17. Shah N, Mohan D, Agarwal S, Scott K, Chamberlain S, Bhatnagar A, Labrique A, Indurkar M, Ved R, LeFevre A, Evans D. Novel approaches to measuring knowledge among frontline health workers in India: Are phone surveys a reliable option?. PLOS ONE 2020;15(6):e0234241 View
  18. Li Y, Wang W, Wu Q, van Velthoven M, Chen L, Du X, Zhang Y, Rudan I, Car J. Increasing the response rate of text messaging data collection: a delayed randomized controlled trial. Journal of the American Medical Informatics Association 2015;22(1):51 View
  19. Ceballos F, Hernandez M, Olivet F, Paz C, Hodges M. Assessing the use of cell phones to monitor health and nutrition interventions: Evidence from rural Guatemala. PLOS ONE 2020;15(11):e0240526 View
  20. Nuruliawati , Mardhiah U, Muktamarianti A, Muttaqin E, Sheherazade , Surya S, Nugroho A, Rahmadi C, Widiyanto D, Leggett M, Mardiah S, Veríssimo D. Using SMS surveys to understand songbird ownership and shark product consumption in Indonesia. Oryx 2024;58(4):437 View
  21. Jimenez-Arberas E, Casais-Suarez Y, Fernandez-Mendez A, Menendez-Espina S, Rodriguez-Menendez S, Llosa J, Prieto-Saborit J. Evidence-Based Implementation of the Family-Centered Model and the Use of Tele-Intervention in Early Childhood Services: A Systematic Review. Healthcare 2024;12(1):112 View