{"doi":"10.1111/ijpo.12889","title":"Measuring <scp>BMI</scp> change among children and adolescents","abstract":"BACKGROUND: Weight control programs for children monitor BMI changes using BMI z-scores that adjust BMI for the sex and age of the child. It is, however, uncertain if BMIz is the best metric for assessing BMI change. OBJECTIVE: To identify which of 6 BMI metrics is optimal for assessing change. We considered a metric to be optimal if its short-term variability was consistent across the entire BMI distribution. SUBJECTS: 285 643 2- to 17-year-olds with BMI measured 3 times over a 10- to 14-month period. METHODS: We summarized each metric's variability using the within-child standard deviation. RESULTS: Most metrics' initial or mean value correlated with short-term variability (|r| ~ 0.3 to 0.5). The metric for which the within-child variability was largely independent (r = 0.13) of the metric's initial or mean value was the percentage of the 50th expressed on a log scale. However, changes in this metric between the first and last visits were highly (r ≥ 0.97) correlated with changes in %95th and %50th. CONCLUSIONS: Log %50 was the metric for which the short-term variability was largely independent of a child's BMI. Changes in log %50th, %95th, and %50th are strongly correlated.","journal":"Pediatric Obesity","year":2022,"id":251600,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":24,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9353,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":895024,"name":"Amy J. Goodwin Davies","orcid":"0000-0002-3697-5120","position":1,"is_corresponding":false},{"id":632283,"name":"Thao-Ly T. Phan","orcid":"0000-0003-4616-6869","position":2,"is_corresponding":false},{"id":308565,"name":"F. Sessions Cole","orcid":"0000-0002-3797-9369","position":3,"is_corresponding":false},{"id":675067,"name":"Nathan M. Pajor","orcid":"0000-0002-1637-4942","position":4,"is_corresponding":false},{"id":516763,"name":"Suchitra Rao","orcid":"0000-0002-0334-6301","position":5,"is_corresponding":false},{"id":722028,"name":"Ihuoma Eneli","orcid":"0000-0002-4436-7141","position":6,"is_corresponding":false},{"id":410876,"name":"Lyudmyla Kompaniyets","orcid":"0000-0003-0634-689X","position":7,"is_corresponding":false},{"id":256768,"name":"Samantha J. Lange","orcid":null,"position":8,"is_corresponding":false},{"id":675064,"name":"Dimitri Christakis","orcid":"0000-0003-0726-7253","position":9,"is_corresponding":false},{"id":302148,"name":"Christopher B. Forrest","orcid":"0000-0003-1252-068X","position":10,"is_corresponding":false},{"id":303652,"name":"David S. Freedman","orcid":"0000-0001-8014-0289","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T00:24:41.993282Z","pmid":"35064761","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}