{"id":"W2978760291","doi":"10.2196/13305","title":"Lessons Learned: Recommendations For Implementing a Longitudinal Study Using Wearable and Environmental Sensors in a Health Care Organization","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Intelligence Advanced Research Projects Activity; Office of the Director of National Intelligence","keywords":"Wearable computer; Troubleshooting; Data collection; Computer science; Wearable technology; Health care; mHealth; Data science; Scale (ratio); Human–computer interaction; Nursing; Medicine; Psychological intervention; Embedded system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009227796,0.0001562164,0.0003243414,0.000231985,0.0005421433,0.0001297968,0.0001171076,0.00004872107,0.0000135658],"category_scores_gemma":[0.00001824357,0.0001711955,0.00001920188,0.0003569755,0.0000159764,0.0004620547,0.000148268,0.0001726718,0.000004578192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003809341,"about_ca_system_score_gemma":0.0003515902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001405568,"about_ca_topic_score_gemma":0.001070696,"domain_scores_codex":[0.9978842,0.0002957642,0.0005084064,0.0005995338,0.0001706248,0.0005415122],"domain_scores_gemma":[0.9990084,0.000146289,0.0003287169,0.0002480425,0.00004349803,0.0002250728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003608118,0.0003874338,0.7963209,0.001362098,0.00001922781,0.000002019056,0.03831986,0.0000214076,0.00005893624,0.0005449075,0.0001242049,0.1628029],"study_design_scores_gemma":[0.01027093,0.002877143,0.8181024,0.0006591609,0.00004375913,0.0001730766,0.1197758,0.03875038,0.00004318236,0.0003220776,0.007963569,0.001018602],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839441,0.0004164969,0.007932556,0.0052695,0.0001498558,0.0021708,0.00004892465,0.00005481179,0.00001293408],"genre_scores_gemma":[0.9968048,0.0001515284,0.002569806,0.0002897887,0.00004013901,0.00005524777,0.0000372183,0.0000189926,0.00003244065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1617844,"threshold_uncertainty_score":0.6981149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1457812092861262,"score_gpt":0.4154230142283649,"score_spread":0.2696418049422387,"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."}}