{"id":"W4386553485","doi":"10.2196/45103","title":"The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Computer science; Wearable computer; Data collection; Missing data; Data quality; Wearable technology; Data science; Usability; Human–computer interaction; Engineering","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5514691,0.00140406,0.001909041,0.006813451,0.005141808,0.01376989,0.006929467,0.004389802,0.005827068],"category_scores_gemma":[0.8369839,0.001782892,0.002680084,0.01033613,0.009256999,0.01273311,0.007931497,0.007677084,0.00262898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006059288,"about_ca_system_score_gemma":0.02701742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01168005,"about_ca_topic_score_gemma":0.01330754,"domain_scores_codex":[0.289762,0.5360778,0.06548984,0.01503216,0.09085143,0.002786723],"domain_scores_gemma":[0.08971596,0.5909594,0.03395542,0.127376,0.1550809,0.002912344],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001693816,0.0007265285,0.09482147,0.01175174,0.001288586,0.0005861367,0.04975192,0.004620464,0.004206696,0.07206734,0.08225199,0.6762334],"study_design_scores_gemma":[0.001002783,0.002405134,0.11663,0.05779355,0.001344696,0.001627239,0.02723413,0.02414789,0.02038114,0.131336,0.6150502,0.00104723],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04824308,0.008137489,0.7682849,0.1039047,0.007130879,0.01882125,0.007688133,0.003556128,0.03423338],"genre_scores_gemma":[0.2604785,0.002810163,0.672346,0.02585208,0.001937718,0.02781267,0.003550208,0.001750663,0.003462129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4485309,"threshold_uncertainty_score":0.5531185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6675599645470226,"score_gpt":0.6681427682055714,"score_spread":0.0005828036585487917,"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."}}