The DOI matches its Crossref record; 113 of the 114 references it cites were found in a registry or an index.
Applying and advancing behavior change theories and techniques in the context of a digital health revolution: proposals for more effectively realizing untapped potential
Open accessAbstract
As more behavioral health interventions move from traditional to digital platforms, the application of evidence-based theories and techniques may be doubly advantageous. First, it can expedite digital health intervention development, improving efficacy, and increasing reach. Second, moving behavioral health interventions to digital platforms presents researchers with novel (potentially paradigm shifting) opportunities for advancing theories and techniques. In particular, the potential for technology to revolutionize theory refinement is made possible by leveraging the proliferation of "real-time" objective measurement and "big data" commonly generated and stored by digital platforms. Much more could be done to realize this potential. This paper offers proposals for better leveraging the potential advantages of digital health platforms, and reviews three of the cutting edge methods for doing so: optimization designs, dynamic systems modeling, and social network analysis.
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“Furthermore, to conform to the complexity of behavior change, eHealth technologies often consist of multiple components that may interact in reaching a certain effect and that people can use in many different ways in terms of the elements they use as well as the frequency, time, and place of use [7,8].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [8]
In the citing paper
“However, a technology can be designed for multiple target groups and, depending on the individual user goals and the desired outcomes, technology can be used in many different ways in terms of the features that are used, as well as the frequency, time, and place of use [13,14].”
From Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence (2017) · cites this paper as [14]
In the citing paper
“Such analyses are a promising approach to explain the outcomes of the more traditional methods, such as RCTs, by gaining insight into the mediating mechanisms that contribute to the found effects [8,9] and have also the potential to identify unexpected effects of a technology.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [8]
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“The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [8]
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“However, the possibilities of log data in eHealth research have not been exploited to their fullest extent.”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [8]
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“Log data analyses can also provide answers to more fundamental research questions related to existing models and theories [8].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [8]
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Cited by
Sorted by citations
Objective user engagement with mental health apps: systematic search and panel-based usage analysis
Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019) · Journal of Medical Internet Research · doi:10.2196/14567 · Cited by 965
Sieverink, F., Kelders, S. M., & van Gemert-Pijnen, J. E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8578 · Cited by 331
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Digital Behaviour Change Interventions to Break and Form Habits
Pinder, C., Vermeulen, J., Cowan, B. R., & Beale, R. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3196830 · Cited by 235
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Metadata
- Authors
- Moller, A. C., Merchant, G., Conroy, D. E., West, R., Hekler, E., Kugler, K. C., & Michie, S.
- Affiliations
- Northwestern University (US)
- University of California San Diego (US)
- Pennsylvania State University (US)
- University College London (GB)
- Arizona State University (US)
- Corresponding author
- Moller, Arlen C.
- Venue
- Journal of Behavioral Medicine
- Published
- 2017-01-05
- Volume
- 40
- Issue
- 1
- Pages
- 85-98
- ISSN
- 0160-7715 (ISSN-L)
- 1573-3521
- PMC
- PMC5532801
- OpenAlex
- W2570133882
- Cited by
- 151
- References
- 114
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 17 · 2023: 16 · 2022: 23 · 2021: 16 · 2020: 16 · 2019: 21 · 2018: 14 · 2017: 9
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Behavioral Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Cancer Research UK (14135)
- National Institute for Health and Care Research (NF-SI-0512-10002)
- Medical Research Council (MR/K023195/1)
- Economic and Social Research Council (ES/G007489/1)
- National Institute on Drug Abuse (P50DA039838)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Behavioral Health and Interventions
- Keywords
- behaviour change theory, digital health interventions, behavioral health, theory refinement, big data, design optimization, dynamic system modeling, social network analysis, intervention development
- MeSH terms
- Behavior Therapy, Health Promotion, Health Services, Humans, Health Behavior, Behavioral Research
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