Factors associated with persistent participation in an online diet intervention
Abstract
In recent years, much work has been carried out in interface design and service quality in order to maximise user experience and sustain engagement. We are often unsure, however, what factors really influence user interactions with the technologies. Here we report on an ongoing examination of the relationships between user demographics, self reported attitudes, efficacy, and system feature, and participation on an online diet support site. Our findings indicate that not only the characteristics of the users themselves are associated with sustained engagement with a weight loss site, but also that usage of particular features on the site results in higher return rates. These findings support a push for designers to understand their users and features of their site, in order to maximise engagement with their target audiences.
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High usage of the intervention during the first weeks (early interaction) was highly correlated with retention of the user to the intervention.
“For example, log data from a lifestyle (weight loss) Web-based intervention that combines social networking tools (blogs, discussion forum) with an online dietary intervention program [8] showed that high usage of the intervention during the first weeks (early interaction) was highly correlated with retention of the user to the intervention.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [8]
Usage was a stronger predictor for a return than behavior intentions or demographics.
“Usage was a stronger predictor for a return than behavior intentions or demographics.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [8]
The usage of persuasive features, such as social support, influenced the return and adherence to the intervention.
“The usage of persuasive features, such as social support, influenced the return and adherence to the intervention.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [8]
Adherence to a Web-based intervention can be predicted on the usage patterns of an intervention.
“Recent studies have shown that adherence to a Web-based intervention can be predicted on the usage patterns of an intervention [8,9].”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [8]
The number of logins in the first week of a treatment process or the number of active days in the first week can be used as predictors for adherence to the interventions.
“For example, the number of logins in the first week of a treatment process or the number of active days in the first week can be used as predictors for adherence to the interventions.”
From Understanding the usage of content in a mental health intervention for depression: an analysis of log data. (2014) · cites this paper as [8]
Freyne et al found that uploading a profile picture on a diet support site in the first week resulted in higher return rates.
“In another example, Freyne et al [25] found that uploading a profile picture on a diet support site in the first week resulted in higher return rates.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [25]
Individuals' use of specific elements of a technology in the first week influences their use of that technology in the second week.
“Freyne et al [25] found that individuals’ use of specific elements of a technology in the first week influences their use of that technology in the second week.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [25]
Cites
E-LOYALTY: YOUR SECRET WEAPON ON THE WEB
Reichheld, F. F., & Schefter, P. (2003) · Cited by 2,073 · see citing works
(2004) · City Research Online (City University London) · Cited by 1,385 · see citing works
Proactive Coping and Quality of Life Management
Greenglass, E. R. (2002) · Oxford University Press eBooks · doi:10.1093/med:psych/9780198508144.003.0003 · Cited by 238 · see citing works
Personalized Techniques for Lifestyle Change
Freyne, J., Berkovsky, S., Baghaei, N., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22218-4_18 · Cited by 14 · see citing works
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Van Gemert-Pijnen, J. E., Kelders, S. M., & Bohlmeijer, E. T. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2991 · Cited by 84 · see citing works
Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data
Sieverink, F., Kelders, S., Poel, M., & van Gemert-Pijnen, L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6452 · Cited by 88 · see citing works
- Authors
- Freyne, J., Saunders, I., Brindal, E., Berkovsky, S., & Smith, G.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- CHI '12 Extended Abstracts on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2012-05-05
- Pages
- 2375-2380
- OpenAlex
- W2091028569
- Cited by
- 19
- References
- 4
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 1 · 2018: 1 · 2017: 2 · 2016: 1 · 2015: 3 · 2014: 3 · 2012: 1
- FWCI
- 1.57
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Funders
- Australian Government
- Commonwealth Scientific and Industrial Research Organisation
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- weight loss support, user engagement, participant retention, user demographics, self-efficacy, user attitudes, system characteristics, return rate, health technology adoption
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