The Internet-Based Conversational Engagement Trial (I-CONECT): Theoretical Framework and Latest Findings.
Bibliographic record
Abstract
Abstract Social isolation is a risk factor for dementia. In the recently completed randomized controlled trial, I-CONECT (www.i-conect.org; ClinicalTrials.gov: NCT02871921), we investigated the impact of frequent conversational interactions—a key component of social interactions—on cognitive functions. We hypothesized that engaging in frequent conversations enhances compensatory neural activity and helps maintain cognitive function, a concept framed by Park et al. (2013) as “scaffolding,” similar to Stern’s (2012) cognitive reserve theory. To specifically target the benefits of social bridging (Perry, 2012) separate from social bonding, we rotated interviewers each week. Participants in the experimental group engaged in semi-structured conversations with trained interviewers, prompted by daily themes and pictures, four times a week (30 minutes/session) for 6 months using user-friendly video-chat devices. The control group received brief (∼10-minute) weekly phone check-ins. A total of 186 socially isolated older adults aged 75 and older (86 with normal cognition, 100 with mild cognitive impairment [MCI]) were randomized. We previously reported that global cognitive function (the primary outcome), measured by the Montreal Cognitive Assessment (MoCA), improved by nearly 2 points in the MCI experimental group compared to the control group at 6 months (effect size: Cohen’s d = 0.73) (Dodge, 2024, doi: 10.1093/geront/gnad147). Functional MRI data suggested a trend toward increased connectivity in the dorsal attention network favoring the experimental group. Social satisfaction improved in both groups. During the symposium, we will explain the theoretical framework, recruitment, intervention approach, and findings to date, which can serve as a foundation for future behavioral trials of this kind.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".