Published on in Vol 17, No 6 (2015): June

Mapping Power Law Distributions in Digital Health Social Networks: Methods, Interpretations, and Practical Implications

Mapping Power Law Distributions in Digital Health Social Networks: Methods, Interpretations, and Practical Implications

Mapping Power Law Distributions in Digital Health Social Networks: Methods, Interpretations, and Practical Implications

Journals

  1. Joglekar S, Sastry N, Coulson N, Taylor S, Patel A, Duschinsky R, Anand A, Jameson Evans M, Griffiths C, Sheikh A, Panzarasa P, De Simoni A. How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British Lung Foundation Online Communities. Journal of Medical Internet Research 2018;20(7):e238 View
  2. Carron-Arthur B, Ali K, Cunningham J, Griffiths K. From Help-Seekers to Influential Users: A Systematic Review of Participation Styles in Online Health Communities. Journal of Medical Internet Research 2015;17(12):e271 View
  3. Liqing Q, Jinfeng Y, Xin F, Wei J, Wenwen G. Analysis of Influence Maximization in Temporal Social Networks. IEEE Access 2019;7:42052 View
  4. Yuan W, He K, Guan D, Han G. Edge-Dual Graph Preserving Sign Prediction for Signed Social Networks. IEEE Access 2017;5:19383 View
  5. De Simoni A, Shah A, Fulton O, Parkinson J, Sheikh A, Panzarasa P, Pagliari C, Coulson N, Griffiths C. Superusers’ Engagement in Asthma Online Communities: Asynchronous Web-Based Interview Study. Journal of Medical Internet Research 2020;22(6):e18185 View
  6. van Mierlo T, Hyatt D, Ching A. Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks. Network Modeling Analysis in Health Informatics and Bioinformatics 2016;5(1) View
  7. Urbanoski K, van Mierlo T, Cunningham J. Investigating Patterns of Participation in an Online Support Group for Problem Drinking: a Social Network Analysis. International Journal of Behavioral Medicine 2017;24(5):703 View
  8. van Mierlo T, Li X, Hyatt D, Ching A. Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups. Journal of Medical Internet Research 2017;19(2):e40 View
  9. Gopalsamy R, Semenov A, Pasiliao E, McIntosh S, Nikolaev A. Engagement as a Driver of Growth of Online Health Forums: Observational Study. Journal of Medical Internet Research 2017;19(8):e304 View
  10. van Mierlo T, Hyatt D, Ching A, Fournier R, Dembo R. Behavioral Economics, Wearable Devices, and Cooperative Games: Results From a Population-Based Intervention to Increase Physical Activity. JMIR Serious Games 2016;4(1):e1 View
  11. Sharma A, Colonna G. System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration. Molecular Diagnosis & Therapy 2021;25(1):9 View
  12. Rapanos T. What makes an opinion leader: Expertise vs popularity. Games and Economic Behavior 2023;138:355 View
  13. Rondina R, van Mierlo T, Fournier R. Testing Behavioral Nudges and Prompts in Digital Courses for the Self-guided Treatment of Depression and Anxiety: Protocol for a 3-Arm Randomized Controlled Trial. JMIR Research Protocols 2022;11(8):e37231 View
  14. van Mierlo T, Rondina R, Fournier R. Nudges and Prompts Increase Engagement in Self-Guided Digital Health Treatment for Depression and Anxiety: Results From a 3-Arm Randomized Controlled Trial. JMIR Formative Research 2024;8:e52558 View
  15. Teckchandani T, Shields R, Andrews K, Maguire K, Jamshidi L, Nisbet J, Afifi T, Lix L, Stewart S, Sauer-Zavala S, Krakauer R, Neary J, Krätzig G, Carleton R. Trouble with the curve: the 90–9-1 rule to measure volitional participation inequalities among Royal Canadian Mounted Police cadets during training. Frontiers in Psychiatry 2024;15 View

Books/Policy Documents

  1. Iorliam A. Cybersecurity in Nigeria. View