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Record W2969686486 · doi:10.1024/1662-9647/a000208

Technology, Physical Activity, Loneliness, and Cognitive Functioning in Old Age

2019· article· en· W2969686486 on OpenAlexaff
Theresa Pauly, Jennifer C. Lay, Pavel Kozik, Peter Graf, Atiya Mahmood, Christiane A. Hoppmann

Bibliographic record

VenueGeroPsych · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsLonelinessPsychologyCognitionPhysical activityDevelopmental psychologyGerontologyClinical psychologyMedicineSocial psychologyPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Abstract. Information and communication technology (ICT) has the potential to benefit aging processes. This study examined portable ICT usage and associated changes in physical activity, loneliness, and cognitive functioning. Ninety-two mostly-novice tablet-users aged 51–85 years participated in technology workshops and then reported on their portable ICT use biweekly for 6+ months. Physical activity, loneliness, and executive functioning were assessed before and after this period. More frequent use of exercise functions was associated with more moderate-intensity physical activity and less sitting, controlling for pretracking levels. More frequent use of social functions was associated with more social loneliness and a tendency toward less emotional loneliness, controlling for pretracking levels of loneliness. The use of exercise and social functions showed no associations with executive functioning. Portable ICT thus may bring both risks and benefits for physical and social functioning in older adulthood.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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