{"as_of":"2026-08-11T12:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e3770145f9e00369d9669fbdd8aaf1863b29c411bdd98db19b1b7c2607fc23aa","coverage":[{"denominator":64,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":64,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:21:33.493394Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T19:30:22.782910Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"cited_work":{"arxiv_id":"2501.08851","doi":"10.48550/arxiv.2501.08851","metadata_source":"pith","pith_arxiv_id":"2501.08851","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","venue":"cs.LG","work_id":"1c8371f4-3113-4c20-b23c-a5a45bc5f3be","year":2025},"citing_paper":{"arxiv_id":"2607.24275","last_updated":"2026-07-27T11:19:52Z","snapshot_observed_at":"2026-08-09T11:06:25.864557Z","submitted_at":"2026-07-27T11:19:52Z","title":"A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health","version":1},"reference_index":265,"source":"arxiv_source","source_observed_at":"2026-07-31T19:30:22.782910Z"},"links":{"cited_paper":"/paper/2501.08851","citing_paper":"/paper/2607.24275"},"observation_digest":"sha256:e4e83d68a85e8c9e87ac2fe4a176d1fb9abaf49e0df87da1b753154019da4b58","observation_id":"727039fa-b965-4140-bc02-1542d1a78633","resolution":{"observed_at":"2026-07-31T19:31:29.576342Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.08851/citation-record","integrity":"/paper/2501.08851/integrity","json":"/paper/2501.08851/citation-record.json","paper":"/paper/2501.08851"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.358433Z","title":"The mental health of young people: the view from primary care","venue":null,"work_id":"a2b8ddda-4fd0-4fc0-90c4-c0bcc46235c3","year":2016},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.241266Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:e71389dbe35043ecea8e1df4d69b690f4ce7698809e9cec0bbcdeff172d285fb","observation_id":"8f5b0b97-4a73-44f7-9ff0-70acfce557e2","resolution":{"observed_at":"2026-08-10T20:21:34.362657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.346334Z","title":"Lifetime prevalence and age -of-onset distributions of DSM -IV disorders in the National Comorbidity Survey Replication","venue":null,"work_id":"b9cb88f3-12ac-4f47-8232-162fe3904741","year":2005},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.245964Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:21d1389a0982e270c7bfc8c89c551f3fea2335b6a3cf28b10bbfa5f1fccac218","observation_id":"89b5d0ef-ab61-4c38-99f3-31fcf8731937","resolution":{"observed_at":"2026-08-10T20:21:34.350660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.334602Z","title":"Annual research review: A meta‐analysis of the worldwide prevalence of mental disorders in children and adolescents","venue":null,"work_id":"57c1ae2f-3c10-4c14-a3a1-d9a7d249cf42","year":2015},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.250320Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:7a2cdaceb37b81fd6805f31587d12490a20cadc7dac49f86fa97d694d9cff6ea","observation_id":"29369a45-6845-4036-8e3a-1b6f82007073","resolution":{"observed_at":"2026-08-10T20:21:34.338617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.322509Z","title":"The global burden of disease study at 30 years","venue":null,"work_id":"f3cb3bec-5906-4be6-8c00-83487a8170f4","year":2022},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.255014Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:4dc8522e02a147adf6e505722897329fd8ed8af27b534c45ac94324429b84f1d","observation_id":"9cf27853-c15d-460f-b408-b79a9fdb7e47","resolution":{"observed_at":"2026-08-10T20:21:34.326918Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.310922Z","title":"Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review","venue":null,"work_id":"6d5c6a66-4765-4acd-9877-4d07adb1432e","year":2010},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.258934Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:8cd0b0b32794112f65ff73683dfaafa0fc86505c017486647253f28a9436ecdd","observation_id":"e8d93176-7486-4836-8ddd-bbc75d2663a5","resolution":{"observed_at":"2026-08-10T20:21:34.315246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.299592Z","title":"Childhood and adolescent psychiatric disorders as predictors of young adult disorders","venue":null,"work_id":"59c2b77e-8d7b-4728-b908-3aee5b95fd59","year":2009},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.262803Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:06f20e39a1df6ba2e7b0e23dd659fb12ce052a1c372de2190a6f634917514350","observation_id":"fe0c0571-0c56-49c3-9e68-ef425065d5cf","resolution":{"observed_at":"2026-08-10T20:21:34.303561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.287569Z","title":"The nature and predictors of undercontrolled and internalizing problem trajectories across early childhood","venue":null,"work_id":"18f3d973-3640-4cb6-8f5e-a3f8a2300cb7","year":2009},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.267109Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:13c44e7329833adafd24857cb070966bce8df51e3ad53ec341891143089c6571","observation_id":"4c4126eb-7099-4ea4-8ed8-33c4c442b388","resolution":{"observed_at":"2026-08-10T20:21:34.291658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.276021Z","title":"Externalizing disorders and environmental risk: Mechanisms of gene -environment interplay and strategies for intervention","venue":null,"work_id":"b6e72846-05d2-4d80-9c9d-5df16c7110f2","year":2014},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.270997Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:0c7156a5dc97fafb33cbd0f62f9ed958c25d13054dc2dd9694cdfb4085bcff4a","observation_id":"20a7ecc0-a042-4bac-afb9-8ae2df466fc7","resolution":{"observed_at":"2026-08-10T20:21:34.280069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.265082Z","title":"Systematic review of the effects of schools and school environment interventions on health: evidence mapping and synthesis","venue":null,"work_id":"05653455-6c28-445f-a975-58831559ac89","year":2013},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.274681Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:a56d381262ac477c2481bd101c2ab0e93bd46a8c4cc937c1356d71b2c91ed91d","observation_id":"ec34a62c-c909-422b-9520-e740c96bb254","resolution":{"observed_at":"2026-08-10T20:21:34.269048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.253070Z","title":null,"venue":null,"work_id":"2ee176c2-6737-43f0-87cf-199e8c4781bf","year":2019},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.278451Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:adb248084c5625c1710b066339e89bf03a60510730f183576bb3aafaa0d92364","observation_id":"d8e31f9b-1bcf-4529-9290-b22db59835d5","resolution":{"observed_at":"2026-08-10T20:21:34.257038Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.241422Z","title":"Using science to sell apps: evaluation of mental health app store quality claims","venue":null,"work_id":"613a6e8d-fbcf-42c4-ad99-5031a999667a","year":2019},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.282071Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:4781da053e354ae95e92f87b69a62de6394221a6333724e0f0bae2aca55453b4","observation_id":"53818071-34af-4a9b-a7ad-3f51b457418c","resolution":{"observed_at":"2026-08-10T20:21:34.245274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.229268Z","title":"Smartphones for smarter delivery of mental health programs: a systematic review","venue":null,"work_id":"46f53304-ab1f-44e3-a313-7c882b906269","year":2013},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.286095Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:f10bef18dd7b239ab3e56122b2a273e28a216f6881628b386e46a4d51a53b31c","observation_id":"e14b027b-195f-47bb-8ce9-10480b875330","resolution":{"observed_at":"2026-08-10T20:21:34.233535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.217757Z","title":"Mobile apps that promote emotion regulation, positive mental health, and well -being in the general population: systematic review and meta-analysis","venue":null,"work_id":"44377c24-fff6-425b-af00-a8669f2242ef","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.289820Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:e0629069139777c32e13ba6b7374793237c62c4fb49e218e83e2ee18dceef31c","observation_id":"5e3687ab-5fdc-491c-83df-4771f4f47247","resolution":{"observed_at":"2026-08-10T20:21:34.221916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.205847Z","title":"lifestyle psychiatry","venue":null,"work_id":"2a0b9bc7-fb52-4192-b8e2-9c5a68e1355a","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.293832Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:863c22a8e8880b554e464f8e5f6c175e5bdedd5a331da2ae8d7fce6d15462e9b","observation_id":"71fd28c2-f5f5-4d34-bf43-c6753cf7c932","resolution":{"observed_at":"2026-08-10T20:21:34.209829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.193304Z","title":"Development of a mobile phone app to support self-monitoring of emotional well-being: a mental health digital innovation","venue":null,"work_id":"55859986-52a3-4c00-aad6-591494234b1f","year":2016},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.298040Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:507580fe0516b759717fca632c395ca5cc098ac155b2b471277fe2247557ce7d","observation_id":"768cba7c-c50c-4f02-9926-f288f8742f1e","resolution":{"observed_at":"2026-08-10T20:21:34.197692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.181423Z","title":"Digital phenotyping: data-driven psychiatry to redefine mental health","venue":null,"work_id":"dbd695a0-c51a-4f9c-ad4b-9660cd670278","year":2023},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.301551Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:9e86c96dbdcf9871b8d8a6fa13af532f8f71f905426b738573534a56fea3052b","observation_id":"a7af932c-7b93-47d6-be45-0ce503877b11","resolution":{"observed_at":"2026-08-10T20:21:34.185739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.168827Z","title":"Digital phenotyping: technology for a new science of behavior","venue":null,"work_id":"3f43cf53-a466-4326-b0f6-bac227161c11","year":2017},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.305263Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:d43f574db544bb3ef64132bb5691bcdd7f40bb865e6ff7c60b4700a05e20119e","observation_id":"f39eaaed-de9c-450a-8c5c-ae08c93b7095","resolution":{"observed_at":"2026-08-10T20:21:34.172888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.156667Z","title":"Harnessing smartphone -based digital phenotyping to enhance behavioral and mental health","venue":null,"work_id":"d0491c80-a94b-40b3-969e-1c96d5875449","year":2016},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.309032Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:f9aec18c72ea10636bc8ac2667bec324a8a9ebc30128f31cc5703074099bd70e","observation_id":"38d11934-9c6c-4c35-a705-0a5475a05bb4","resolution":{"observed_at":"2026-08-10T20:21:34.160934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1037/prj0000130","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.524640Z","title":"Next -generation psychiatric assessment: Using smartphone sensors to mo nitor behavior and mental health","venue":null,"work_id":"35209360-d05a-4c1d-823e-1c4b46af65a0","year":2015},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.312943Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:27953ce891595285b243adb92525b81dbe3f718995bdb5e214d2e55bac0c003e","observation_id":"a26b1538-8ee5-4c01-8ad4-87b4baeab4f8","resolution":{"observed_at":"2026-08-10T20:21:33.531044Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-08-11T00:18:05.477456+00:00","source":"paper_reference_links","state":"open"},"event_date":"2015-12-21","event_type":"correction","notice_doi":"10.1037/prj0000169","provenance":{"observed_at":"2026-07-11T03:04:59.247043+00:00","source":"crossref","source_record_id":"10.1037/prj0000169->10.1037/prj0000130:correction"}}],"reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.145248Z","title":"Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis","venue":null,"work_id":"9bc9f76d-2bd6-4e64-8593-55a27974bc46","year":2015},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.316996Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:fd4fdc712adaa8b96d700f37669843cf4b64518cd2ef4da85f6b4cf82e4d846f","observation_id":"a059e501-dac8-425f-a12f-32bb0391d65f","resolution":{"observed_at":"2026-08-10T20:21:34.149400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.133380Z","title":"Next -generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health","venue":null,"work_id":"55fcc050-c6ad-43d9-a921-7e186088b760","year":2015},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.320827Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:67893befa73f730af92a4dccd8c44b9fa99002ace627d2b8b5e2da4fb8c8781c","observation_id":"381a88fe-7838-4d34-9e42-3b6500850510","resolution":{"observed_at":"2026-08-10T20:21:34.137893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.118543Z","title":null,"venue":null,"work_id":"16653c62-8bc8-4798-ab14-af3c62d1bbf7","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.325356Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:3a66fb4c1fc5abb0a4052277192dd6c44e6223fbde6aa6fa47eb3f7ab69f1a37","observation_id":"9fa43473-d618-4fc6-a7b4-b1146da403f4","resolution":{"observed_at":"2026-08-10T20:21:34.122568Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.106516Z","title":null,"venue":null,"work_id":"b430263a-13db-4e9f-a204-cb670583c3cb","year":2014},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.329155Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:f97a21aa6ca03e819f50e58598997c419ff177274020b8e65c18293143542863","observation_id":"505e571a-1b52-4cd6-bf10-2286c16fe19a","resolution":{"observed_at":"2026-08-10T20:21:34.110661Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.094115Z","title":null,"venue":null,"work_id":"c987ad50-9e44-4262-b6a3-85ca4482f0a7","year":2018},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.333237Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:05a6cf4541f4f0e85b7194ad666c7090e40fcb5fc17cdf87e1e3ef2cc72c8a08","observation_id":"8330e2c5-4dd7-48fc-9699-adcbde8d0a28","resolution":{"observed_at":"2026-08-10T20:21:34.098010Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.082593Z","title":"CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia","venue":null,"work_id":"e5b6453e-a736-4e0e-9f5b-39eb3742fa82","year":2016},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.337258Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:0ae7879af8ea87a9e386fd9f5e2eee385352eaebc7dac8570cdf3512046c6c6b","observation_id":"b6e5e420-e39e-4ccb-b74c-700eef8dbae9","resolution":{"observed_at":"2026-08-10T20:21:34.086606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.071166Z","title":"Using smartphones to monitor bipolar disorder symptoms: a pilot study","venue":null,"work_id":"60994ad9-dbc4-45d0-8556-b463836fa1c2","year":2016},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.340912Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:08a3722065d947229752ea9f13dab5c38624254712617629e0e671d20297da9a","observation_id":"bc22dede-e809-426b-9bc6-34aa5cd6ae7d","resolution":{"observed_at":"2026-08-10T20:21:34.075348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.059017Z","title":"Passive sensing of prediction of moment -to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones","venue":null,"work_id":"5c5d3f09-aeff-4e6f-ad74-e76a0e3905eb","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.344721Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:958e56237743b4d6afe04e622807cee405e0e230227500f309a7ea7263440069","observation_id":"15dde3c7-8cd2-4aa4-9628-fe69da520d38","resolution":{"observed_at":"2026-08-10T20:21:34.063149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.046505Z","title":"Towards early detection of depression through smartphone sensing","venue":null,"work_id":"169144e1-719e-4b25-8ee8-cfcae3e4816e","year":2019},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.348766Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:ba872cf56317ee66ed8501f0fd4aa1f5d75c3dd9341e2176057734c4c581a879","observation_id":"dc3aebe4-17a8-4037-a30c-66a3bf0455c9","resolution":{"observed_at":"2026-08-10T20:21:34.050977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.034358Z","title":"Toi Même, a mobile health platform for measuring bipolar illness activity: protocol for a feasibility study","venue":null,"work_id":"2d4fd2f2-6abd-46cf-ac4f-65d30ce319a7","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.352778Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:b5ed5be45d50386d2bab009ec65d3941cd503b3848543d3ac8f6c76e6393e1e3","observation_id":"da4a703e-0cb3-4b5b-9b30-8722b82b77cf","resolution":{"observed_at":"2026-08-10T20:21:34.038949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.022763Z","title":"Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning –Based Exploratory Study","venue":null,"work_id":"e989a1ab-0ccd-4add-87c0-f0ada57d5f06","year":2022},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.356722Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:70be50193fec1b3156648d606f3fdcd1563b4311f4129cf6d17561881ca997ce","observation_id":"065d99d3-5fd9-46f5-9aec-ddc7695d0116","resolution":{"observed_at":"2026-08-10T20:21:34.026792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:34.011512Z","title":null,"venue":null,"work_id":"d3bd6964-ea78-43ea-932d-d596e91f355a","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.360429Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:61045ba0e19c88a50196d03702c42b736c24c42599a75f93f7a9c652488db46d","observation_id":"1bde7c43-c86f-4771-888b-30e0b572eb64","resolution":{"observed_at":"2026-08-10T20:21:34.015458Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.998836Z","title":"Predicting depressive symptoms using smartphone data","venue":null,"work_id":"7650f7f9-7b05-412c-bb3a-d793da74f200","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.364188Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:18064d6b4260fd7cc481560bcc8257a8f39baa79c31c3c520a5c8ab11698a099","observation_id":"ee57c192-9f1f-4881-8a08-1c5f27052abe","resolution":{"observed_at":"2026-08-10T20:21:34.002930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.987262Z","title":"A mobile sensing app to monitor youth mental health: observational pilot study","venue":null,"work_id":"352e8e8b-3226-474d-b2b2-487296e65492","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.368262Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:e62c44495d876c14ad586b55adbe5bb88cbd348fc860242b413388c4a8017ac0","observation_id":"5edc3825-6f6f-4b8d-be0b-1659b8bf7166","resolution":{"observed_at":"2026-08-10T20:21:33.991436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.975747Z","title":"Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps","venue":null,"work_id":"7cc14968-7bf1-427b-8352-d66660b7c6b7","year":2018},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.372349Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:1888a706c515c1b92dc2d22373a37d89780b6aa874945b4895084f4a7350d01c","observation_id":"89a187de-5403-4f79-ad2a-df9964c38d3b","resolution":{"observed_at":"2026-08-10T20:21:33.979977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.963511Z","title":"A linguistic analysis of suicide-related Twitter posts","venue":null,"work_id":"35ef9794-ca2d-4448-bf99-8383ccc99a5f","year":2017},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.376080Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:c155ad19a1b0a92b2b82ae07b6288d0d393a995011e9785170a6c9ac9b91bacc","observation_id":"5ec059ef-da6c-4d0b-a066-8690855d7549","resolution":{"observed_at":"2026-08-10T20:21:33.967818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.951339Z","title":"Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study","venue":null,"work_id":"41f04fdf-f6ae-4140-9cf2-ba007783d551","year":2023},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.380476Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:01932b78d01646c717b388ee4c48bed452a34235ccd2278eab63c777bf37d596","observation_id":"78cb1690-54bd-4761-ac92-0e2c08d6284a","resolution":{"observed_at":"2026-08-10T20:21:33.955285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.938551Z","title":"The Strengths and Difficulties Questionnaire: a research note","venue":null,"work_id":"83f4dcb8-abb2-4ab7-a8b3-8c398048df8b","year":1997},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.384416Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:c97e8a0c7edb5e8ddf91909600ac0159bebc5ee850709821a93acb587350ad20","observation_id":"4da7771c-9624-40b1-a523-551837907ff2","resolution":{"observed_at":"2026-08-10T20:21:33.943422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.926253Z","title":"Development, psychometric properties and preliminary clinical validation of a brief, session‐by‐session measure of eating disorder cognitions and behaviors: The ED‐15","venue":null,"work_id":"2a4af18d-bbb5-42c0-b731-a0b07e051a68","year":2015},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.388432Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:b45fcb2263b83d46bfd22ddd0bfed86d4c24811e13bc16d7e7bab112f92d5ccb","observation_id":"e0f25bdf-d6bc-409b-bc11-14b81d1d5eae","resolution":{"observed_at":"2026-08-10T20:21:33.930592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.913431Z","title":"The PHQ‐9: validity of a brief depression severity measure","venue":null,"work_id":"0bb7779d-f596-425a-bbaa-b6174c553aa1","year":2001},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.392638Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:bdbe2b6db1cfecc098b2825f9cbeea0e0ef56d8b221b191616979df87ce59ee8","observation_id":"6b09f7c4-5d82-4d12-9fec-6a79c62ec560","resolution":{"observed_at":"2026-08-10T20:21:33.917812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.899189Z","title":"Youth screening depression: Validation of the Patient Health Questionnaire-9 (PHQ-9) in a representative sample of adolescents","venue":null,"work_id":"ecc63a14-2cfc-493f-a231-de9c4168720b","year":2023},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.396607Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:8f99c8e3544f50df560d2dcb3ce448bf4cb20ef391ed38b48bc9a0c6c566554a","observation_id":"962b95e3-fc81-4ea8-9b4b-ad0f07fbef96","resolution":{"observed_at":"2026-08-10T20:21:33.903570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.885394Z","title":"The Sleep Condition Indicator: a clinical screening tool to evaluate insomnia disorder","venue":null,"work_id":"873f1cd8-67d1-4e28-aed7-85193d71ff3d","year":2014},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.400359Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:7e4c7f8497247db2cb82b32069f5a7f695b7bc83f756751a47e155ee8eaff26e","observation_id":"93d23bbf-c630-44b0-bf05-e88c114225c2","resolution":{"observed_at":"2026-08-10T20:21:33.890965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.873074Z","title":"The Sleep Condition Indicator: reference values derived from a sample of 200 000 adults","venue":null,"work_id":"4a9937cf-5ade-4c63-8804-51695e03e070","year":2018},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.405106Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:ddb1e362e49fed16b30b7fb6dccbc7dbf092af21e4eac569aac99f5fe5334205","observation_id":"bb50dd51-b292-4d94-b07b-ae9aa450b5bb","resolution":{"observed_at":"2026-08-10T20:21:33.877543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.859873Z","title":"Using the Strengths and Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample","venue":null,"work_id":"c717d611-eb1f-4762-be2a-6bc4a745a4e5","year":2000},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.408755Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:42e514b3ec9e559cf052256157a15d7e2e4e3712141be2113660c84098a1daa8","observation_id":"7b2e9ea0-56b1-4518-9e5f-f4502c6662eb","resolution":{"observed_at":"2026-08-10T20:21:33.864092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.847964Z","title":"Eating Disorder‐15 (ED‐15): Factor structure, psychometric properties, and clinical validation","venue":null,"work_id":"d2c9b60d-757d-4009-9fdf-1824b48fb78d","year":2019},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.412814Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:fc3a73668b02185dc96bb164ace59ff4e15dcc5f8da81c99165ef0dfb6ebc6d4","observation_id":"056d08b7-1f59-4232-aade-2b41d6898637","resolution":{"observed_at":"2026-08-10T20:21:33.852326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.836099Z","title":"A unified approach to interpreting model predictions","venue":null,"work_id":"923c0612-5d07-4c3b-ad0f-ae3c8ab41936","year":2017},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.416641Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:54c49eac219e5744787e02da6f51f34604cf67d76932c5e484bf81171a32503c","observation_id":"bd3b16f5-463f-4780-ba42-c96256902d40","resolution":{"observed_at":"2026-08-10T20:21:33.840298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.824200Z","title":"From local explanations to global understanding with explainable AI for trees","venue":null,"work_id":"97d55636-8bfa-4490-aa57-4646e10cacdb","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.420595Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:0273aa0048b8a9f294f1a6156f4363ccac3746a1fdb9ac6cd611e4d0d38dd667","observation_id":"ac563339-65ae-4be7-a308-2bb41c3a44d5","resolution":{"observed_at":"2026-08-10T20:21:33.828230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.812949Z","title":"CatBoost: unbiased boosting with categorical features","venue":null,"work_id":"eca95336-0a46-42ba-a1b8-fc6fc422901d","year":2018},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.424655Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:47f0aeda6949def730f7e6c0e696cc734b0f744f13680665aec615cae7495694","observation_id":"b30bde64-a442-4f98-a607-79da76de2201","resolution":{"observed_at":"2026-08-10T20:21:33.816874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.800755Z","title":"CatBoost for big data: an interdisciplinary review","venue":null,"work_id":"a14ddc42-dcb2-4ca4-9ad9-0fe2b0ba667f","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.428735Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:95f52292c06ab02e9b9d3757aa1d45219388072ae153ff7a5c9c1f63a1480120","observation_id":"4d5bccce-59ce-434e-bd62-28c7b6a63ab9","resolution":{"observed_at":"2026-08-10T20:21:33.805307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.789372Z","title":"Smartphone -based self- monitoring, treatment, and automatically generated data in children, adolescents, and young adults with psychiatric disorders: systematic review","venue":null,"work_id":"40ca2a5f-9417-4d7e-ad78-2d4afeb08932","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.432688Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:feb8155712c635d6239b733dde1ab025f9ef363c39dc902639626fbbbf2982e5","observation_id":"f0c92957-e358-45b0-86b7-bf206891a432","resolution":{"observed_at":"2026-08-10T20:21:33.793869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.777124Z","title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","venue":null,"work_id":"915df8ec-904b-4d13-b0e1-2b46dcbefae5","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.437243Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:a4339242ff6946237a5cdc5ae799349a079235e0c9e0b2d56d8fa982e00a83bb","observation_id":"bb7347f9-eaa0-47db-a59a-a9d3af1c2679","resolution":{"observed_at":"2026-08-10T20:21:33.781784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.764607Z","title":"Digital phenotyping for monitoring mental disorders: systematic review","venue":null,"work_id":"f69de52a-d5d5-4249-bb88-dfa101d25316","year":2023},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.441330Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:d029a88bc4b3498a6223128bde3b1a3e661f03ba793fcf3bf753b3708e0fa75e","observation_id":"7e200523-0273-4ed9-99a4-926b37bda12b","resolution":{"observed_at":"2026-08-10T20:21:33.768808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.752847Z","title":"Digital phenotyping for mental health of college students: a clinical review","venue":null,"work_id":"8f0fc61a-d20d-427c-98cd-8ffe48e55733","year":2020},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.445341Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:66b5e9c30cebe6003cba5a51256b34c3c72d1f4a63fa557c35740899441fd950","observation_id":"14174993-492d-474c-8856-125c69e85f6f","resolution":{"observed_at":"2026-08-10T20:21:33.756936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.740148Z","title":"Brief School - Based Interventions Targeting Student Mental Health or Well-Being: A Systematic Review and Meta-Analysis","venue":null,"work_id":"e4b84074-c815-4688-9010-9924d47aad90","year":2024},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.449394Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:351ef7bc66647ed041a2398e9e93fcb8e0753cc9c2edad0796141670fbc2e653","observation_id":"8b3d395b-0707-414a-9523-a7dc1d715c60","resolution":{"observed_at":"2026-08-10T20:21:33.745002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.728708Z","title":"The Patient Experience of the Future is Personalized: Using Technology to Scale an N of 1 Approach","venue":null,"work_id":"10d39096-a1ef-4b14-ad2b-96b0bc77e940","year":2023},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.453369Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:db89ad4ce3c7bc951d08516dff9600fa73bf8d3bdd48a9858ab1cff5840d361a","observation_id":"10a2f8b4-ee36-4ba9-a9a2-570ea380f437","resolution":{"observed_at":"2026-08-10T20:21:33.732708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.717011Z","title":"Ethical development of digital phenotyping tools for mental health applications: Delphi study","venue":null,"work_id":"f1750c09-3bbf-43e8-922a-73dd1b9b4d4c","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.457494Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:c6d7b0bc5bccbcb6d134480b221dd9497172bd74d6dfda81df9fbbc0e3473830","observation_id":"f00246a9-5e3a-4d21-8f82-94d6a883f3c5","resolution":{"observed_at":"2026-08-10T20:21:33.721309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.705443Z","title":"Digital mental health for young people: a scoping review of ethical promises and challenges","venue":null,"work_id":"d29d41c2-d5d6-46c5-acf0-f45ff2251f79","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.461180Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:81c3eafe6965ffd2e1c3b393f86bae73c3ed89fb67762cd570d55184e5172e2e","observation_id":"45d9042a-1c80-4bcd-9bc4-2188b7b608ae","resolution":{"observed_at":"2026-08-10T20:21:33.709395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.693080Z","title":"A systematic review of reviews on the advantages of mHealth utilization in mental health services: A viable option for large populations in low-resource settings","venue":null,"work_id":"d3c190bd-b256-43be-8fcf-15084b1c2f96","year":2024},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.465500Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:11b70269d3efd61f739dd029e4908db178b6f0d9e20bda45bb29a07202a1eb71","observation_id":"03d26ffe-760f-4f5c-9239-45c2103605ae","resolution":{"observed_at":"2026-08-10T20:21:33.697242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7575.2010","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.598053Z","title":"Healthcare information systems: data mining methods in the creation of a clinical recommender system","venue":null,"work_id":"9cfd89b2-a383-4dd6-8c1d-6942e2cd7a16","year":2011},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.469141Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:3d532e33d6891666d630bc9a0eec43bf4fd465bdc532652a0f4d4d9b2f3b1728","observation_id":"827bc353-acd9-476e-8e7c-b9c56eccf168","resolution":{"observed_at":"2026-08-10T20:21:33.604668Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.680727Z","title":"MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones","venue":null,"work_id":"2fbbf5a8-106d-401f-9e68-082e9989dd8f","year":2015},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.473370Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:893ee179dcddbd5a3b8739352659a654262204b575c79b277d68cf6bf5f90859","observation_id":"586635e9-5ece-4755-beb9-a5a53ee616c9","resolution":{"observed_at":"2026-08-10T20:21:33.684917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.668161Z","title":"Recommender systems in the healthcare domain: state-of-the-art and research issues","venue":null,"work_id":"bd5e9c50-8370-417f-aa80-71ffe3919910","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.477585Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:ba7d4352b9d12c705c8395add9dc6e5cfc631e31d3b34f6826ec4d8c6a37d1b0","observation_id":"647ff500-2908-4b30-bb30-17bb6e83a77e","resolution":{"observed_at":"2026-08-10T20:21:33.672811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.653787Z","title":"The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care","venue":null,"work_id":"509ae5c5-db08-40d8-8f64-3d04d96fc4ac","year":2018},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.481545Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:d79230b36a26dd58db5cb77530ff209eeaa90a2d0236ec46184daf8f2a57ebf9","observation_id":"3c8c67bb-45fc-4d4e-9679-557fbd7e8c0c","resolution":{"observed_at":"2026-08-10T20:21:33.658520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.639887Z","title":"A survey of recommendation systems: recommendation models, techniques, and application fields","venue":null,"work_id":"8e1230df-5902-42a1-9cc9-9e7398d9f2dd","year":2022},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.485152Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:282459d443ee45e3dc87236da9ebcaa9c2fa297f8cd96210343b7b90c3f7842f","observation_id":"898bf591-4100-4a3f-b5fc-d8ada2a570c4","resolution":{"observed_at":"2026-08-10T20:21:33.644677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.627352Z","title":"Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing","venue":null,"work_id":"a25081ab-a2b5-4f4a-acb8-42e71d0cc1e8","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.489058Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:c6a0154d89bcfed33945ad2a4aad322ee113cb262b7d17b93d189331e0c68a41","observation_id":"115ddc68-c10f-4b7d-9d58-50bc30b8ae3d","resolution":{"observed_at":"2026-08-10T20:21:33.631599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:21:33.613948Z","title":"Personality and Engagement with Digital Mental Health Interventions","venue":null,"work_id":"c831b9a5-3dea-4754-9968-197cd7e1392e","year":2021},"citing_paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-10T20:21:33.493394Z"},"links":{"citing_paper":"/paper/2501.08851"},"observation_digest":"sha256:6d5a82e8edcf88cda53f32a594327f5fd2accaad3fe06cba35fe48392500fe86","observation_id":"3ca44d10-d7a4-4699-8618-c70e05ec72c1","resolution":{"observed_at":"2026-08-10T20:21:33.618519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.08851","last_updated":"2025-01-15T15:05:49Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T20:13:55.758460Z","submitted_at":"2025-01-15T15:05:49Z","title":"Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data"},"reference_resolution":{"displayed":64,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":2,"verified_fuzzy":57},"total_outbound_references":64},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2501.08851."}