{"id":"W4414430914","doi":"10.2196/75629","title":"Optimizing Self-Monitoring in a Digital Weight Loss Intervention (Spark): Protocol for a Factorial Randomized Trial","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Protocol (science); Randomized controlled trial; Weight loss; Digital health; Intervention (counseling); Randomization","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":[0.03744385,0.006084967,0.009469057,0.002694714,0.003567109,0.003612191,0.002987539,0.005775798,0.06476994],"category_scores_gemma":[0.03044365,0.003435523,0.00433672,0.00336477,0.00399869,0.002928582,0.002273728,0.007060588,0.01254475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005006074,"about_ca_system_score_gemma":0.01732315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00259087,"about_ca_topic_score_gemma":0.004197109,"domain_scores_codex":[0.9784112,0.01276517,0.00207092,0.00205245,0.002394432,0.002305858],"domain_scores_gemma":[0.9841372,0.004417358,0.003103135,0.002559735,0.003716174,0.002066368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.8755507,0.03320126,0.00101599,0.01102148,0.001617274,0.0002471164,0.0006239678,0.003978711,0.002834479,0.004956482,0.0198888,0.04506366],"study_design_scores_gemma":[0.9304792,0.03368136,0.001764018,0.001732422,0.0005046685,0.00004391417,0.0001073725,0.002732466,0.0009868179,0.00266443,0.02518707,0.0001161544],"study_design_candidate":"randomized_trial","study_design_consensus":"randomized_trial","genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.001225209,0.00007521337,0.001011323,0.00006714825,0.0001579768,0.9967244,0.0003421651,0.00007123769,0.0003252769],"genre_scores_gemma":[0.0009116514,0.0000324812,0.001203711,0.00004823157,0.00002075118,0.997579,0.00004099044,0.00000302675,0.000160106],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.06476994,"threshold_uncertainty_score":0.216677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2388909063895475,"score_gpt":0.6514962615283265,"score_spread":0.4126053551387789,"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."}}