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Integrating human support into behavioral intervention technologies: The efficiency model of support.
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“Recent evidence suggests that the nature of that support is an important element in improving outcomes.”
From Feasibility and Acceptability of the Aboriginal and Islander Mental Health Initiative for Youth App: Nonrandomized Pilot With First Nations Young People (2023) · cites this paper as [8]
In the citing paper
“A focus on both increasing engagement and furthering knowledge and implementation of the skills learned in the dMH intervention is needed [8].”
From Feasibility and Acceptability of the Aboriginal and Islander Mental Health Initiative for Youth App: Nonrandomized Pilot With First Nations Young People (2023) · cites this paper as [8]
In the citing paper
“Support that targets engagement alone is not sufficient to improve clinical outcomes beyond those of unsupported interventions [8].”
From Feasibility and Acceptability of the Aboriginal and Islander Mental Health Initiative for Youth App: Nonrandomized Pilot With First Nations Young People (2023) · cites this paper as [8]
In the citing paper
“On the other hand, support that addresses the reasons people might fail to benefit from dMH interventions, such as deficiencies in usability, engagement, fit, knowledge, or implementation, is likely to be more effective.”
From Feasibility and Acceptability of the Aboriginal and Islander Mental Health Initiative for Youth App: Nonrandomized Pilot With First Nations Young People (2023) · cites this paper as [8]
In the citing paper
“Short text-based reminders may be suitable for in-the-moment interventions [251] and may be useful to address barriers to forgetting to use an intervention.”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [251]
In the citing paper
“Some of the themes identified in this review overlap with previous models conceptualizing engagement with digital health interventions, as well as general technology acceptance and health behavior, such as the Efficiency Model of Support [251], Technology Acceptance Model [253], and Health Belief Model [249].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [251]
In the citing paper
“For instance, the Efficiency Model of Support [251] states that human support increases engagement in the context of the use of digital health interventions when it addresses 1 of 5 failure points: usability, implementation, fit, engagement, and knowledge.”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [251]
In the citing paper
“The implementation failure point in the efficiency model pertained to whether the user can apply knowledge gained from an intervention in their lives.”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [251]
In the citing paper
“Similarly, the content and model of support vary, and this may be an additional factor that impacts effectiveness and engagement [62].”
From The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review (2020) · cites this paper as [62]
In the citing paper
“It is also useful for providers to be able to see usage of the technology so that patient engagement is supported (35, 65).”
From Banbury forum consensus statement on the path forward for digital mental health treatment (2021) · cites this paper as [35]
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“…themes identified in this review overlap with previous models conceptualizing engagement with digital health interventions, as well as general technology acceptance and health behavior, such as the Efficiency Model of Support [251], Technology Acceptance Model [253], and Health Belief Model [249].”
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Baumel, A., Brandes, O., Brendryen, H., Muench, F. J., Kane, J. M., & Saar, C. R. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100680 · Cited by 17
Chow, P. I., Showalter, S. L., Gerber, M. S., Kennedy, E. M., Brenin, D. R., Schroen, A. T., Mohr, D. C., Lattie, E. G., & Cohn, W. F. (2018) · JMIR Research Protocols · doi:10.2196/11452 · Cited by 16
Self-citationShaping Blended Care: Adapting an Instrument to Support Therapists in Using eMental Health
Kip, H., Wentzel, J., & Kelders, S. M. (2020) · JMIR Mental Health · doi:10.2196/24245 · Cited by 19
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Boß, L., Angerer, P., Dragano, N., Ebert, D. D., Engels, M., Heber, E., Kuhlmann, R., Ruhle, S. A., Schwens, C., Wulf, I. C., et al. (2021) · BMC Public Health · doi:10.1186/s12889-021-12229-y · Cited by 13
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A Randomized Clinical Trial of Clinician-Supported PTSD Coach in VA Primary Care Patients
Possemato, K., Johnson, E., Barrie, K. A., Ghaus, S. J., Noronha, D. O., Wade, M. J., Greenbaum, M. A., Rosen, C. S., Cloître, M., Owen, J. E., et al. (2023) · Journal of General Internal Medicine · doi:10.1007/s11606-023-08130-6 · Cited by 15
Metadata
- Authors
- Schueller, S. M., Tomasino, K. N., & Mohr, D. C.
- Venue
- Clinical Psychology: Science and Practice
- Published
- 2017-03
- Volume
- 24
- Issue
- 1
- Pages
- 27-45
- ISSN
- 0969-5893 (ISSN-L)
- 1468-2850
- OpenAlex
- W4240249117
- Cited by
- 70
- References
- 0
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 10 · 2023: 7 · 2022: 14 · 2021: 10 · 2020: 3 · 2019: 3 · 2018: 3
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Psychology Science and Practice (publishedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- human support, human-technology interaction, technology-based interventions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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