{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2276815,0.003558091,0.004095868,0.006094662,0.007176733,0.009558085,0.01255154,0.01655031,0.01713787],"category_scores_gemma":[0.3394383,0.002500043,0.006610563,0.007815937,0.006094901,0.02568426,0.008119826,0.01753034,0.008203153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01058776,"about_ca_system_score_gemma":0.09868317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07254624,"about_ca_topic_score_gemma":0.1684696,"domain_scores_codex":[0.8042189,0.1528398,0.01545135,0.004748825,0.0165218,0.006219246],"domain_scores_gemma":[0.4874172,0.2525701,0.01676808,0.0296659,0.160072,0.05350679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"qualitative","study_design_scores_codex":[0.0007411608,0.004604894,0.03494919,0.01084602,0.0003727855,0.001250811,0.0161339,0.001336921,0.0007699466,0.01154481,0.5022444,0.4152051],"study_design_scores_gemma":[0.003907735,0.006466111,0.05282572,0.1110823,0.002032228,0.001924191,0.2187363,0.007192911,0.003635313,0.07060107,0.5196492,0.001947031],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01114213,0.01467473,0.0476175,0.8655402,0.01777501,0.02419353,0.002900015,0.002153976,0.01400284],"genre_scores_gemma":[0.05218353,0.02187903,0.588082,0.2147259,0.004517167,0.1017607,0.00207918,0.0004438986,0.01432849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2276815,"threshold_uncertainty_score":0.9524062,"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."}}