THE RELATIONSHIP BETWEEN INSTRUMENTAL ACTIVITIES OF DAILY LIVING AND NATURALISTIC DRIVING PERFORMANCE: INDICATIONS FOR MILD COGNITIVE IMPAIRMENT DETECTION
Notice bibliographique
Résumé
Rationale: A large body of literature has explored the ability of various assessment tools to determine the cognitive status of older adults, as well as the relationships between cognition and driving skills. However, few studies have linked occupational assessment tools and driving skills. Additionally, only a small selection of recent studies has explored both cognitive and driving skills using naturalistic driving data. Results of these studies suggest that cognitive assessments are not the strongest indicators of a person’s cognitive status prior to clinical presentation. Rather, naturalistic driving performance has been implicated as a tool to predict pre-clinical dementia. Due to the established links between occupation-based assessment and standardized driving performance tests, it is plausible that similar links may exist between functional cognition, as measured by occupational assessment, and naturalistic driving performance, implicating both for application in the early detection of dementia. Purpose: The study sought to determine what trends and/or relationships existed amongst participants’ driving aggression, amount of time driving at night, and frequency of drives based on performance in three clinical assessments (cognitive, occupational, driving). Research questions addressed included: 1) Is there a relationship between naturalistic driving performance and performance of IADLs?, 2) Is there a relationship between naturalistic driving performance and cognitive measures?, and 3) Is there a relationship between naturalistic driving performance and standardized driving assessment? Additional research questions investigated differences between age and gender groups. Design: This descriptive, exploratory study collected data for analysis over the course of one year, with naturalistic data collection lasting 20 weeks for each participant. Participants: Participants included 40 older adult drivers (65+ years). All participants were healthy, community-living adults obtained through convenience sampling. Methods: Instruments included the G2 data-logging chip by Azuga Industries, which tracked participants’ driving locations and velocity inside their personal vehicles. Data was computed into three “behavior� values: aggression, daylight driving, and number of trips. Other instruments included the Modified Driving Habits Questionnaire, the Assessment of Motor and Process Skills,and the Montreal Cognitive Assessment. Participants completed clinical assessment in the research lab within the 20-week driving period. Outcomes examined from the G2 chip included total instances of hard braking, total instances of speeding, weekly ratio of night to daylight driving time, and number of trips driven. Results: Analyses indicated that age, MoCA score, and P-Drive scores had significant relationships with one or more naturalistic driving behaviors. The distribution of aggressive driving behavior trended higher in drivers in their 60s and in drivers with low AMPS performance. Discussion: Naturalistic driving performance, as a single measure, was able to reflect differences in performance in all clinical assessments used. The trends in aggressive driving reflected in AMPS performance provide the only known link in the current literature between naturalistic driving and functional assessment. Therefore, the AMPS as a functional assessment may be implicated in the understanding of pre-clinical dementia.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».