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Enregistrement W2517713646 · doi:10.1111/jgs.14219

Drivers with Amnestic Mild Cognitive Impairment Can Benefit from a Multiple‐Session Driving Simulator Automated Training Program

2016· letter· en· W2517713646 sur OpenAlexafffundabout
Normand Teasdale, Martin Simoneau, Lisa Hudon, Thierry Moszkowicz, Denis Laurendeau, Mathieu Robitaille, Louis Bherer, Simon Duchesne, Carol Hudon

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2016
Typeletter
Langueen
DomaineHealth Professions
ThématiqueOlder Adults Driving Studies
Établissements canadiensInstitut Universitaire en Santé Mentale de QuébecInstitut Universitaire de Gériatrie de MontréalUniversité LavalConcordia UniversityCentre hospitalier universitaire de Québec
Organismes subventionnairesRéseau québécois de recherche sur le vieillissement
Mots-clésMedicineAffect (linguistics)Session (web analytics)CognitionCognitive psychologyDriving simulatorCognitive trainingImplicit learningPopulationPhysical medicine and rehabilitationCognitive impairmentPsychologySimulationPsychiatryComputer scienceCommunication

Résumé

récupéré en direct d'OpenAlex

To the Editor: Amnestic mild cognitive impairment (aMCI) may affect as much as 30% of the elderly population.1 Most of these individuals will progress to Alzheimer's disease. A large proportion of older individuals with aMCI are active drivers.2 It is often mentioned that most of these individuals can drive safely but that their driving is characterized by subtle functional decrements.3 Because their driving abilities are expected to worsen with the progression of the disease,4 there is debate as to whether these individuals should continue to drive,5 but identifying at-risk drivers is problematic.6 An alternative approach could be to provide training programs. Procedural memory (implicit learning) is preserved in individuals with aMCI and those with Alzheimer' disease.7 Implicit learning, contrary to explicit learning, takes place without awareness, often by repetition, and without reference to explicit knowledge learned previously. With driving, knowledge of road safety rules is explicit knowledge, but some maneuvers involve procedural learning. For example, people explicitly know that they should brake at intersections with stop signs, but releasing the accelerator and controlling the pressure applied on the brake pedal when braking is implicit learning that takes place with practice. The current study examined whether individuals with aMCI can benefit from an automated 5-week training program in a driving simulator. Elderly individuals (six men, two women, aged 71.1 ± 7.2) with a diagnosis of aMCI were recruited from memory clinics. Healthy elderly adults without memory or cognitive problems also participated (five men, three women, aged 71.1 ± 7.2). They all drove regularly and reported similar driving habits. The ethics committee of the Institut Universitaire en santé mentale de Québec approved this study (File 362). The experiment was conducted using an instrumented fixed-based simulator (STISIM Drive 3.0, System Technology, Inc., Hawthorne, CA).8 Participants received five training sessions over a 21-day period. For each session, they first drove a 6-km practice run during which general explanations were provided. The experimenter made sure that the driver understood all recorded messages. The main scenario (27.5 km of naturalistic driving) included messages to inform the driver about requested maneuvers (e.g., instructions to safely overtake a slower-moving vehicle) and conditional recorded feedback about six driving maneuvers when an error was detected (speeding, tailgating, not indicating a lane change, absence of blind spot verification, incomplete stop at an intersection, running a yellow or red light). For instance, exceeding the speed limit by more than 10 km/h triggered the recorded feedback: “Your current speed exceeds the speed limit. You should slow down.” For all maneuvers, the driver had to respond positively to the feedback within 10 seconds to avoid another warning for the same event. No additional information was provided. No summary performance was given at the end or before any session. Both groups showed significant gradual improvements for several maneuvers (speeding, not using the turn signal, verification of the blind spot, tailgating) (Figure 1). Individuals with aMCI also showed implicit learning, with their braking requiring fewer alternate pedal responses, leading to shorter and more uniform deceleration phases with training (Figure 1). An alternation involves moving the right foot from one pedal to the other. Moving the right foot from the accelerator to the brake pedal (one alternation) is the expected behavior when driving. The learning observed suggests that proper training could help to modify unsafe behaviors and contribute to maintaining the driving performance of individuals with aMCI. Previous research has shown that individuals undergoing passive training (classroom-like training) had no improvement in their driving performance. In contrast, participants receiving specific driving feedback in a simulator allowed the learning to be transferred to safer on-road driving.8, 9 Hence, there is a strong possibility that the learning observed in the present study can transfer to safer on-road driving. In addition to conducting a large-scale study to test specifically whether this learning transfers to better on-road performance for individuals with aMCI, a follow-up longitudinal study is essential to determine the long-term persistence of training. An understanding of how driving degrades with progression of the disease would also contribute to identifying at-risk drivers.10 Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. This research received support from the Réseau Québécois de Recherche sur le Vieillissement, le Centre d'Excellence sur le Vieillissement de Québec et le Réseau de Bio-Imagerie du Québec. Author Contributions: All authors contributed to all aspects of this study. Sponsor's Role: The sponsors played no role in any aspect of this study.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,484
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,001
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,005
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,353
Écart entre enseignants0,321 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2016
Routes d'admission3
Résumé présentoui

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