| Title |
A computational cognitive model of self-efficacy and daily adherence in mHealth
|
|---|---|
| Published in |
Translational Behavioral Medicine, February 2016
|
| DOI | 10.1007/s13142-016-0391-y |
| Pubmed ID | |
| Authors |
Peter Pirolli |
| Abstract |
Mobile health (mHealth) applications provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting day-to-day health behavior change dynamics. A computational predictive model (ACT-R-DStress) is presented and fit to individual daily adherence in 28-day mHealth exercise programs. The ACT-R-DStress model refines the psychological construct of self-efficacy. To explain and predict the dynamics of self-efficacy and predict individual performance of targeted behaviors, the self-efficacy construct is implemented as a theory-based neurocognitive simulation of the interaction of behavioral goals, memories of past experiences, and behavioral performance. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Russia | 1 | <1% |
| Switzerland | 1 | <1% |
| Unknown | 101 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 18% |
| Student > Ph. D. Student | 18 | 17% |
| Student > Master | 15 | 14% |
| Student > Bachelor | 11 | 11% |
| Student > Doctoral Student | 6 | 6% |
| Other | 23 | 22% |
| Unknown | 12 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 21 | 20% |
| Computer Science | 16 | 15% |
| Medicine and Dentistry | 13 | 13% |
| Nursing and Health Professions | 8 | 8% |
| Social Sciences | 7 | 7% |
| Other | 22 | 21% |
| Unknown | 17 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 February 2017.
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#14,847,187
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Outputs from Translational Behavioral Medicine
#702
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Outputs of similar age
#167,272
of 298,757 outputs
Outputs of similar age from Translational Behavioral Medicine
#15
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