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Record W2626810785 · doi:10.17886/rki-gbe-2017-040

Health-enhancing physical activity during leisure time among adults in Germany

2017· article· en· W2626810785 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityLeisure timeQuarter (Canadian coin)Aerobic exerciseGerontologyPsychologyMedicineDemographyPhysical therapy

Abstract

fetched live from OpenAlex

Self-reported data from the GEDA 2014/2015-EHIS study was used to calculate the level of compliance among adults in Germany with the World Health Organization's (WHO) recommendations on physical activity. The WHO's recommendations distinguish between 'aerobic activity' and 'muscle-strengthening activity'. In Germany, 42.6% of women and 48.0% of men reported that they conduct at least 2.5 hours of aerobic physical activity per week, and therefore meet the WHO's recommendation on this form of activity. A higher level of education among women and men of all ages is associated with a higher frequency of meeting the WHO's recommendations on aerobic activity. In addition, 27.6% of women and 31.2% of men conduct muscle-strengthening activity at least twice a week, thereby meeting the WHO's recommendations on this form of activity. About one fifth of women (20.5%) and one quarter of men in Germany (24.7%) meet both of these recommendations. In summary, the results point to the value of encouraging people to conduct more physical activity during their leisure time. In fact, inactive people who begin to follow the WHO's recommendations can significantly reduce their long-term risk of premature mortality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.690
Threshold uncertainty score0.553

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.293
Teacher spread0.268 · 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 teacher head, 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

Citations52
Published2017
Admission routes1
Has abstractyes

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