Smartphone‐based virtual agents and insomnia management: A proof‐of‐concept study for new methods of autonomous screening and management of insomnia symptoms in the general population
Bibliographic record
Abstract
Summary Insomnia is the most frequent sleep disorder, and the COVID‐19 crisis has massively increased its prevalence in the population, due to psychosocial stress or direct viral contamination. KANOPEE_2 is a smartphone‐based application that provides interactions with a virtual agent to autonomously screen and alleviate insomnia symptoms through an intervention programme giving personalized advices regarding sleep hygiene, relaxation techniques and stimulus‐control. In this proof‐of‐concept study, we tested the effects of KANOPEE_2 among users from all over the country (France) who downloaded the app between 1 June and 26 October 2020 (to focus on effects after the end of COVID‐19 confinement). Outcome measures include insomnia severity (Insomnia Severity Index) and sleep/wake schedules measured by a sleep diary. One‐thousand and thirty‐four users answered the screening interview (Mage = 43.76 years; SD = 13.14), and 108 completed the two‐step programme (Mage = 46.64 years; SD = 13.63). Of those who answered the screening, 42.8% did not report sleep complaints, while 57.2% presented mild‐to‐severe insomnia symptoms. At the end of the intervention, users reported significantly fewer sleep complaints compared with the beginning of the intervention (Insomnia Severity Indexbeginning = 13.58; Insomnia Severity Indexend = 11.30; p < 0.001), and significantly increased their sleep efficiency (sleep efficiencybeginning = 76.46%; sleep efficiencyend = 80.17%; p = 0.013). KANOPEE_2 is a promising solution both to provide autonomous evaluation of individuals’ sleep hygiene and reduce insomnia symptoms over a brief and simple intervention. These results are very encouraging for addressing the issue of insomnia management in people exposed to major psychosocial stress and the consequences of COVID‐19 infection.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".