Improvement in executive function for older adults through smartphone apps: a randomized clinical trial comparing language learning and brain training
Bibliographic record
Abstract
Bilingualism has been linked to improved executive function and delayed onset of dementia, but it is unknown whether similar benefits can be obtained later in life through deliberate intervention. Given the logistical hurdles of second language acquisition in a randomized trial for older adults, few interventional studies have been done thus far. However, recently developed smartphone apps offer a convenient means to acquire skills in a second language and can be compared with brain training apps specifically designed to improve executive function. In a randomized clinical trial, 76 adults aged 65-75 were assigned to either 16 weeks of Spanish learning using the app Duolingo 30 minutes a day, an equivalent amount of brain training using the app BrainHQ, or a waitlist control condition. Executive function was assessed before and after the intervention with preregistered (NCT03638882) tests previously linked to better performance in bilinguals. For two of the primary measures: incongruent Stroop color naming and 2-back accuracy, Duolingo provided equivalent benefits as BrainHQ compared to a control group. On reaction time for N-back and Simon tests, the BrainHQ group alone experienced strong gains over the other two groups. Duolingo was rated as more enjoyable. These results suggest that app-based language learning may provide some similar benefits as brain training in improving executive function in seniors but has less impact on processing speed. However, future advancements in app design may optimize not only the acquisition of the target language but also the side benefits of the language learning experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".