{"id":"W4387786719","doi":"10.2196/50663","title":"Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Physical Activity and Health","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center","keywords":"Wearable computer; Smartwatch; Categorical variable; Cluster analysis; Logistic regression; Wearable technology; Physical activity; Computer science; Machine learning; Physical medicine and rehabilitation; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001401388,0.0003788735,0.0005236561,0.002242746,0.0003297762,0.0005074822,0.0004372754,0.0003217244,0.001018394],"category_scores_gemma":[0.003579041,0.0001255032,0.001020823,0.002177875,0.0001743309,0.000378727,0.0004189434,0.0003560939,0.00020386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000430723,"about_ca_system_score_gemma":0.0004756409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01190954,"about_ca_topic_score_gemma":0.01010958,"domain_scores_codex":[0.9992816,0.0002365902,0.00008557763,0.0002087535,0.0001320668,0.00005545504],"domain_scores_gemma":[0.9988717,0.0003592227,0.0002658457,0.0001177852,0.0002820381,0.0001032663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005876214,0.000207514,0.9592969,0.0001274988,0.0005877335,0.0001437867,0.000454374,0.005038305,0.0008863604,0.0002703413,0.001112853,0.03128679],"study_design_scores_gemma":[0.00002311131,0.0002533353,0.9207844,0.00004723077,0.0002654729,0.0002410423,0.0009344833,0.07546481,0.000431243,0.0006405043,0.000880455,0.00003398377],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885173,0.0003964924,0.008833849,0.0001607061,0.00002936869,0.000126671,0.001412619,0.00004957089,0.0004734296],"genre_scores_gemma":[0.994821,0.0001672849,0.003012078,0.00001626623,0.00001604457,0.00009555536,0.001668671,0.000006910082,0.0001962644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01190954,"threshold_uncertainty_score":0.02368045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03409136740421127,"score_gpt":0.3513190872034616,"score_spread":0.3172277197992504,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}