Connecting screens: the effect of online playful interactions on social, emotional, and cognitive functioning among older adults
Bibliographic record
Abstract
Abstract Studies have shown that engaging in playful social activities offers valuable opportunities for development throughout one’s lifespan and is associated with enhanced physical and psychological well-being in older age. However, research that examined playful interactions in the online setting is still scarce. The aim of the present study was to assess the impact of online playful interaction on emotional, social, and cognitive functions among the older population. Thirty-four older adults (aged 74–91, Mage = 85) participated in a within-group study design. Participants took part in two 15-minute online sessions of playful interaction and a control condition, personal conversation and an exercise class. Cognitive and subjective measures were taken before and after the sessions to assess socio-emotional and cognitive functions. A significant interaction (Time X Type of Activity) was found, validating the playful interaction’s positive effect on increasing Digit Span score (logarithmic scale). A significant increase was also found in the social measures (closeness and affiliation) following the playful interaction but not following the control condition. There was no significant interaction for the Stroop (selective-attention test) or for positive and negative affect. In sum, a short and focused online playful interaction with older adults had significant effects on cognitive and social functioning, even in an online platform. Healthcare providers working with older individuals can consider incorporating online playful activities into their daily routines to enhance cognitive functioning and social connectedness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".