{"id":"W2299215064","doi":"10.2196/mhealth.5027","title":"Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion","year":2016,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; Louisiana Clinical and Translational Science Center; University of California, Los Angeles; National Institutes of Health; Louisiana State University; U.S. Department of Agriculture","keywords":"mHealth; Weight management; Dashboard; Mobile phone; Computer science; Behavior change; Weight loss; Bluetooth; Multimedia; Medicine; Psychological intervention; Data science; Nursing; Wireless; Obesity; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.005174345,0.0005049497,0.001075213,0.0004570929,0.003418155,0.00002509905,0.0002193127,0.0003087754,0.0001877531],"category_scores_gemma":[0.00005337083,0.0003825233,0.0001380081,0.000423622,0.0002338977,0.000276027,0.0001563975,0.0005483883,0.00007855734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364675,"about_ca_system_score_gemma":0.001872748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000629083,"about_ca_topic_score_gemma":0.0005311494,"domain_scores_codex":[0.9923129,0.001062319,0.00236425,0.001300219,0.0004454725,0.002514803],"domain_scores_gemma":[0.9943509,0.0004832111,0.001573665,0.0006556889,0.0002406703,0.002695856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005050321,0.0005938898,0.01112081,0.02673161,0.00003253316,0.000001016337,0.001802291,1.097086e-7,0.000004211424,0.09890593,0.0501678,0.8101348],"study_design_scores_gemma":[0.01493053,0.00559298,0.06647223,0.00292585,0.00005877367,0.00002850677,0.002421634,0.0001299537,0.000002051266,0.00876402,0.8981509,0.0005226383],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2652102,0.06804622,0.09311341,0.3937244,0.004817129,0.1674588,0.002131907,0.002332412,0.003165522],"genre_scores_gemma":[0.6573278,0.1070048,0.01245763,0.03038906,0.001701537,0.1795252,0.0005292515,0.0002611461,0.01080355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8479831,"threshold_uncertainty_score":0.9998627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09655992409835487,"score_gpt":0.4787303019647725,"score_spread":0.3821703778664176,"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."}}