{"doi":"10.1093/gerona/glaa262","title":"Variability in Hourly Activity Levels: Statistical Noise or Insight Into Older Adult Frailty?","abstract":"BACKGROUND: Frailty is associated with lower mean activity; however, hourly activity is highly variable among older individuals. We aimed to relate frailty to hourly activity variance beyond frailty's association with mean activity. METHOD: Using the 2010-2011 National Social Life, Health and Aging Project wrist accelerometry data (n = 647), we employed a mixed-effects location scale model to simultaneously determine whether an adapted phenotypic frailty scale (0-4) was associated with the log10-mean hourly counts per minute (cpm) and between-and within-subject hourly activity variability, adjusting for demographics, health characteristics, season, day-of-week, and time-of-day. We tested the significance of a Frailty × Time-of-day interaction and whether adjusting for sleep time altered relationships. RESULTS: Each additional frailty point was associated with a 7.6% (10-0.0343, β = -0.0343; 95% CI: -0.05, -0.02) lower mean hourly cpm in the morning, mid-day, and late afternoon but not evening. Each frailty point was also associated with a 24.5% (e0.219, β = 0.219; 95% CI: 0.09, 0.34) greater between-subject hourly activity variance across the day; a 7% (e0.07, β = 0.07; 95% CI: 0.01¸ 0.13), 6% (e0.06, β = 0.06; 95% CI: 0, 0.12), and 10% (e0.091, β = 0.091; 95% CI: 0.03, 0.15) greater within-subject hourly activity variance in the morning, mid-day, and late afternoon, respectively; and a 6% (e-0.06, β = -0.06; 95% CI: -0.12, -0.003) lower within-subject hourly activity variance in the evening. Adjusting for sleep time did not alter results. CONCLUSIONS: Frail adults have more variable hourly activity levels than robust adults, a potential novel marker of vulnerability. These findings suggest a need for more precise activity assessment in older adults.","journal":"The Journals of Gerontology Series A","year":2020,"id":89431,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9427,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":326654,"name":"Kristen Wroblewski","orcid":"0000-0001-9297-0394","position":1,"is_corresponding":false},{"id":447299,"name":"Linda J. Waite","orcid":"0000-0002-8997-5802","position":2,"is_corresponding":false},{"id":271500,"name":"Elbert S. Huang","orcid":"0000-0002-4628-2061","position":3,"is_corresponding":false},{"id":353456,"name":"L. Philip Schumm","orcid":"0000-0002-3050-2429","position":4,"is_corresponding":false},{"id":305561,"name":"Donald Hedeker","orcid":"0000-0001-8134-6094","position":5,"is_corresponding":false},{"id":364317,"name":"Megan Huisingh‐Scheetz","orcid":"0000-0002-3997-5791","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T22:01:50.225871Z","pmid":"33049032","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":[]}