Stages of use: consideration, initiation, utilization, and outcomes of an internet-mediated intervention
Bibliographic record
Abstract
BACKGROUND: Attrition, or nonuse of the intervention, is a significant problem in e-health. However, the reasons for this phenomenon are poorly understood. Building on Eysenbach's "Law of Attrition", this study aimed to explore the usage behavior of users of e-health services. We used two theoretical models, Andersen's Behavioral Model of Health Service Utilization and Venkatesh's Unified Theory of Acceptance and Use of Technology, to explore the factors associated with uptake and use of an internet-mediated intervention for caregivers taking care of a family member with dementia. METHODS: A multiphase, longitudinal design was used to follow a convenience sample of 46 family caregivers who received an e-health intervention. Applying the two theories, usage behavior was conceptualized to form four stages: consideration, initiation, utilization (attrition or continuation), and outcome. The variables and measurement scales were selected based on these theories to measure the sociodemographic context, technology aptitudes, and clinical needs of the caregivers. RESULTS: In the Consideration Stage, caregivers who felt that the information communication technology (ICT)-mediated service was easy to use were more likely to consider participating in the study (p = 0.04). In the Initiation Stage, caregivers who showed greater technology acceptance were more likely to initiate service earlier (p = 0.02). In the Utilization Stage, the frequent users were those who had a more positive attitude toward technology (p = 0.04) and a lower perceived caregiver competence (p = 0.04) compared with nonusers. In the Outcome Stage, frequent users experienced a decline in perceived burden compared with an escalation of perceived burden by nonusers (p = 0.02). CONCLUSIONS: We illustrate a methodological framework describing how to develop and expand a theory on attrition. The proposed framework highlighted the importance of conceptualizing e-health "use" and "adoption" as dynamic, continuous, longitudinal processes occurring in different stages, influenced by different factors to predict advancement to the next stage. Although usage behavior was influenced mainly by technological factors in the initial stages, both clinical and technological factors were equally important in the later stages. Frequency of use was associated with positive clinical outcomes. A plausible explanation was that intervention benefits motivated the caregivers to continue the service and regular use led to more positive clinical outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".