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Record W2943644714 · doi:10.5167/uzh-165200

Healthy cognition in old age: effects of an engaged lifestyle and cognitive training

2018· dissertation· en· W2943644714 on OpenAlexfundno aff
Sabrina Guye

Bibliographic record

VenueZurich Open Repository and Archive (University of Zurich) · 2018
Typedissertation
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsnot available
FundersFreie Universität BerlinUniversität ZürichHumboldt-Universität zu BerlinJacobs FoundationInternational Max Planck Research School for Environmental, Cellular and Molecular MicrobiologySaskatoon City Hospital Foundation
KeywordsCognitionCognitive trainingPsychologyPsychological interventionEffects of sleep deprivation on cognitive performanceCognitive declineDevelopmental psychologyCognitive InterventionCognitive reserveGerontologyIntervention (counseling)Cognitive skillMedicineCognitive impairmentDementiaPsychiatryDisease

Abstract

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Healthy cognitive functioning is a key aspect of successful aging and a crucial component of the well-being of older adults. On the group level, crystallized abilities (e.g., factual knowledge) remain relatively stable until old age, fluid cognitive abilities (e.g., working memory), however, decline gradually across the lifespan. Therefore, and in light of the projected demographic changes, the identification of modifiable lifestyle factors and the development of interventions that promote successful cognitive aging have become increasingly important. Thus, the main question of this thesis was if an engaged lifestyle and cognitive training interventions have a positive impact on cognitive ability, cognitive plasticity, and functional ability in everyday life in older adults. In Article 1, we found a positive association between measures of an engaged lifestyle and functional ability everyday life, which was mediated through cognitive ability. In Article 2 cognitive training studies in older adults were reviewed with regards to training, transfer and maintenance effects, as well as training-related structural and functional brain changes. In Article 3, we found evidence supporting the absence of generalization effects to untrained cognitive abilities after an intensive cognitive training intervention. In Article 4, we found that baseline cognitive performance predicted change in training performance, confirming the magnification account of cognitive change. Intakte kognitive Fähigkeiten sind ein grundlegender Aspekt des erfolgreichen Alterns und ein wesentlicher Bestandteil des Wohlbefindens älterer Menschen. Auf Gruppenebene zeigt sich, dass kristalline Fähigkeiten (z. B. Faktenwissen) bis ins hohe Alter stabil bleiben, während fluide Fähigkeiten (z. B. Arbeitsgedächtnis) sukzessive über die Lebensspanne abnehmen. Deshalb, und in Anbetracht der vorhergesagten demographischen Veränderungen, ist die Identifikation modifizierbarer Lifestyle-Faktoren und die Entwicklung von Interventionen die das erfolgreiche kognitive Altern fördern von grösster Bedeutung. Diese Arbeit ging der Frage nach, ob ein aktiver Lebensstil und kognitive Trainingsinterventionen einen positiven Effekt auf die kognitiven Fähigkeiten, die kognitive Plastizität und die funktionelle Fähigkeit im Alltag älterer Menschen haben. In Artikel 1 konnte ein positiver Zusammenhang zwischen einem aktiven Lebensstil und funktioneller Fähigkeit gezeigt werden, welcher durch die kognitive Fähigkeit mediiert wird. In Artikel 2 wurden kognitive Trainingsstudien mit älteren Menschen hinsichtlich ihrer Wirksamkeit evaluiert. In Artikel 3 konnte Evidenz für die Abwesenheit von Generalsierungseffekten zu untrainerten kognitiven Fähigkeiten nach einer Trainingsintervention gefunden werden. In Artikel 4 konnte gezeigt werden, dass die kognitive Leistung zur Baseline die Trainingsperformanz vorhersagt.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.252
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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