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Cardiorespiratory Fitness and Physical Activity in Pediatric Diabetes

2024· review· en· W4392096989 on OpenAlexafffund
Hannah Steiman De Visser, Isaak Fast, Nicole Brunton, E. Arévalo, Nicole Askin, Rasheda Rabbani, Ahmed M Abou-Setta, Jonathan McGavock

Bibliographic record

VenueJAMA Network Open · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsDiabetes CanadaResearch ManitobaGeorge & Fay Yee Centre for Healthcare InnovationUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersCanadian Institutes of Health Research
KeywordsCardiorespiratory fitnessCINAHLMedicineObservational studyType 2 diabetesMeta-analysisDiabetes mellitusPhysical fitnessPhysical activityMEDLINEPhysical therapyInternal medicineEndocrinologyPsychological interventionPsychiatry

Abstract

fetched live from OpenAlex

Importance: It is unclear whether cardiorespiratory fitness (CRF) and physical activity are lower among youths with type 1 diabetes (T1D) and type 2 diabetes (T2D) compared with youths without diabetes. Objective: To describe the magnitude, precision, and constancy of the differences in CRF and physical activity among youths with and without diabetes. Data Sources: MEDLINE, Embase, CINAHL, and SPORTDiscus were searched from January 1, 2000, to May 1, 2022, for eligible studies. Study Selection: Observational studies with measures of CRF and physical activity in children and adolescents aged 18 years or younger with T1D or T2D and a control group were included. Data Extraction and Synthesis: Data extraction was completed by 2 independent reviewers. A random-effects meta-analysis model was used to estimate differences in main outcomes. The pooled effect estimate was measured as standardized mean differences (SMDs) with 95% CIs. The Preferred Reporting Items for Systematic Review and Meta-Analyses guideline was followed. Main Outcomes and Measures: The main outcomes were objectively measured CRF obtained from a graded maximal exercise test and subjective or objective measures of physical activity. Subgroup analyses were performed for weight status and measurement type for outcome measures. Results: Of 7857 unique citations retrieved, 9 studies (755 participants) with measures of CRF and 9 studies (1233 participants) with measures of physical activity for youths with T2D were included; for youths with T1D, 23 studies with measures of CRF (2082 participants) and 36 studies with measures of PA (12 196 participants) were included. Random-effects models revealed that directly measured CRF was lower in youths with T2D (SMD, -1.06; 95% CI, -1.57 to -0.56; I2 = 84%; 9 studies; 755 participants) and in youths with T1D (SMD, -0.39; 95% CI, -0.70 to -0.09; I2 = 89%; 22 studies; 2082 participants) compared with controls. Random-effects models revealed that daily physical activity was marginally lower in youths with T1D (SMD, -0.29; 95% CI, -0.46 to -0.11; I2 = 89%; 31 studies; 12 196 participants) but not different among youths with T2D (SMD, -0.56; 95% CI, -1.28 to 0.16; I2 = 91%; 9 studies; 1233 participants) compared with controls. When analyses were restricted to studies with objective measures, physical activity was significantly lower in youths with T2D (SMD, -0.71; 95% CI, -1.36 to -0.05; I2 = 23%; 3 studies; 332 participants) and T1D (SMD, -0.67; 95% CI, -1.17 to -0.17; I2 = 93%; 12 studies; 1357 participants) compared with controls. Conclusions and Relevance: These findings suggest that deficits in CRF may be larger and more consistent in youths with T2D compared with youths with T1D, suggesting an increased risk for cardiovascular disease-related morbidity in adolescents with diabetes, particularly among those with T2D. The findings reinforce calls for novel interventions to empower youths living with diabetes to engage in regular physical activity and increase their CRF.

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)0.010
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0040.006
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.068
GPT teacher head0.393
Teacher spread0.325 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations16
Published2024
Admission routes2
Has abstractyes

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