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Enregistrement W2744066693 · doi:10.1093/gerona/glx078

Response to “Motor Output Variability Impairs Driving Ability in Older Adults”

2017· letter· en· W2744066693 sur OpenAlexaff
Arne Stinchcombe, Anne E. Dickerson, Bruce Weaver, Michel Bédard

Notice bibliographique

RevueThe Journals of Gerontology Series A · 2017
Typeletter
Langueen
DomaineHealth Professions
ThématiqueOlder Adults Driving Studies
Établissements canadiensNOSM UniversityLakehead University
Organismes subventionnairesnon disponible
Mots-clésPhysical medicine and rehabilitationPsychologyMedicine

Résumé

récupéré en direct d'OpenAlex

We read the article by Lodha and colleagues, titled Motor Output Variability Impairs Driving Ability in Older Adults, with great interest. In their study, a small number of younger (n = 12) and older (n = 16) drivers completed a series of three laboratory tasks aimed at quantifying foot and ankle motor output force and variability, visual tracking, and reactive responses. In the reactive response task, participants sat in front of a computer monitor and were instructed to track a visual target using a gas pedal and react to a sudden visual stimulus (ie, brake lights appearing) by swiftly moving their foot to the brake pedal and exerting force. Their analysis showed that, compared to younger participants, older participants showed poorer performance on the reactive response task and showed greater motor output variability. Motor output variability was shown to be associated with performance on the reactive response task, leading the authors to conclude that their “study provides novel evidence that age-related declines in motor control but not strength impair reactive driving” (1). Identification of tasks that can accurately predict driving impairment would prove useful to health care providers who often have to make decisions regarding driving fitness. However, driving is a complex and multifactorial behavior, drawing upon multiple physical, cognitive, and sensory systems. Driving is further complicated by factors such as behavioral adaptation (eg, maintaining slower speeds), allowing drivers to compensate for deficits in one or more domains. The complexity of the driving task itself as well as the many factors that can contribute to performance may help explain why attempts to identify tasks that predict impaired driving performance among older adults with sufficient accuracy have proven unsuccessful (2,3). In our reading of the article, we noted several methodological inconsistencies. In particular, recruitment of participants relied upon self-reported health, which may not screen out cognitively impaired drivers. The inclusion of individuals with some cognitive deficits may help explain that some older drivers exhibited a greater number of brake pedal errors. In their analysis, the authors reported a statistically significant t-value (t = 1.87) when comparing positional variability of the gas pedal between groups. With 26 degrees of freedom, the t-value needed to reach statistical significance (assuming two-tailed with α = .05) is t = 2.056. The authors did not correct for family-wise error rate associated with multiple contrasts. Moreover, using four independent variables and stepwise selection with a small sample size (n = 28), will likely lead to “over-fitting” of the regression model (4). Thus, the large R-square is not particularly surprising nor is likely to be a true reflection of the association in the population. Our greatest concerns with the article, however, are related to the interpretation of the data given the paradigm used. The study did not measure a driving outcome but instead used a surrogate measure (ie, a laboratory-based reactive response task), thereby ignoring factors such as behavioral adaptation. It is clear from research evidence that brake reaction time alone is not predictive of fitness to drive for older adults; the activity of driving has much more complexity than moving a foot from an accelerator to a brake quickly. Even driving simulators do not demonstrate perfect correspondence with real-world performance. Similarly, it is not clear whether the statistically significant differences observed are clinically meaningful. Conclusions regarding driving impairment among older adults necessitate the examination of task performance relative to a driving outcome (eg, on-road test, at-fault collisions). Because the study does not assess driving, our interpretation of the data suggests that there are age-based differences in physical strength and response time, findings which have been well-documented in the literature, but which do not necessarily translate into a greater crash risk. While we commend the authors for their enthusiasm in seeking to identify physical factors that impair driving performance among older drivers (and particularly factors that may be amenable to remediation), we strongly advise that the authors and readers temper their conclusions to be consistent with the data and the nature of the dependent variable. The article does not demonstrate that motor output variability impairs driving ability, as presented in the title, but instead shows that motor output variables are associated with a computer-based task assessing response time, accuracy, visual attention, executive functions, and motor output; such a task does not account for the complexity of driving and the role of behavioral factors in promoting safe driving. Findings from this study cannot be used to inform clinical decisions regarding driving fitness nor should they be used to oversimplify the complex and dynamic nature of the driving task, especially considering the importance of driving to mobility, independence, and quality of life among older adults.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,038
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,048
Score d'incertitude au seuil0,027

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,038
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,002
Communication savante0,0030,002
Science ouverte0,0020,002
Intégrité de la recherche0,0480,027
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

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 Opus0,063
Tête enseignante GPT0,406
Écart entre enseignants0,343 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentnon

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