ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Factors Contributing to Dropping-out in an Online Health Community: Static and Longitudinal Analyses.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Assessing Real-Time Moderation for Developing Adaptive Mobile Health Interventions for Medical Interns: Micro-Randomized Trial.Journal of medical Internet research · 2020Trial
- A feasibility study on the virtual adaptation of Child Adult Relationship Enhancement in Primary Care.Journal of technology in behavioral science · 2024Article
- Online health communities influence people's health behaviors in the context of COVID-19.PloS one · 2023Article
- To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App.JMIR mHealth and uHealth · 2018Article
- Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions.Journal of participatory medicine · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Dropping-out, which refers to when an individual abandons an intervention, is common in Internet-based studies as well as in online health communities. Community facilitators and health researchers are interested in this phenomenon because it usually indicates dissatisfaction towards the community and/or its failure to deliver expected benefits. In this study, we propose a method to identify dropout members from a large public online breast cancer community. We then study quantitatively what longitudinal factors of participation are correlated with dropping-out. Our experimental results suggest that dropout members discuss diagnosis- and treatment-related topics more than other topics. Furthermore, in the time before withdrawing from the community, dropout members tend to initiate more discussions but do not receive adequate response from the other members. We also discuss implications of our results and challenges in dropout-member identification. This study contributes to further understanding community participation and opens up a number of future research questions.
Indexed as
Identifiers
28269969PMC5333218What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.