Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T20:21:33.493394Z
Paper Citation Record · LEDGER
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
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.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T19:30:22.782910Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
Reference resolution
64 of 64 outbound references displayed
- verified exact2
- verified fuzzy57
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
Observation 8f5b0b97-4a73-44f7-9ff0-70acfce557e2 · outbound
This paper cites The mental health of young people: the view from primary care.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The mental health of young people: the view from primary care
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.362657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 89b5d0ef-ab61-4c38-99f3-31fcf8731937 · outbound
This paper cites Lifetime prevalence and age -of-onset distributions of DSM -IV disorders in the National Comorbidity Survey Replication.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Lifetime prevalence and age -of-onset distributions of DSM -IV disorders in the National Comorbidity Survey Replication
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.350660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 29369a45-6845-4036-8e3a-1b6f82007073 · outbound
This paper cites Annual research review: A meta‐analysis of the worldwide prevalence of mental disorders in children and adolescents.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Annual research review: A meta‐analysis of the worldwide prevalence of mental disorders in children and adolescents
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.338617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9cf27853-c15d-460f-b408-b79a9fdb7e47 · outbound
This paper cites The global burden of disease study at 30 years.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The global burden of disease study at 30 years
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.326918Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e8d93176-7486-4836-8ddd-bbc75d2663a5 · outbound
This paper cites Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.315246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation fe0c0571-0c56-49c3-9e68-ef425065d5cf · outbound
This paper cites Childhood and adolescent psychiatric disorders as predictors of young adult disorders.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Childhood and adolescent psychiatric disorders as predictors of young adult disorders
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.303561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4c4126eb-7099-4ea4-8ed8-33c4c442b388 · outbound
This paper cites The nature and predictors of undercontrolled and internalizing problem trajectories across early childhood.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The nature and predictors of undercontrolled and internalizing problem trajectories across early childhood
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.291658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 20a7ecc0-a042-4bac-afb9-8ae2df466fc7 · outbound
This paper cites Externalizing disorders and environmental risk: Mechanisms of gene -environment interplay and strategies for intervention.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Externalizing disorders and environmental risk: Mechanisms of gene -environment interplay and strategies for intervention
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.280069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ec34a62c-c909-422b-9520-e740c96bb254 · outbound
This paper cites Systematic review of the effects of schools and school environment interventions on health: evidence mapping and synthesis.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Systematic review of the effects of schools and school environment interventions on health: evidence mapping and synthesis
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.269048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation d8e31f9b-1bcf-4529-9290-b22db59835d5 · outbound
This paper cites an unresolved cited work.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T20:21:34.257038Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 53818071-34af-4a9b-a7ad-3f51b457418c · outbound
This paper cites Using science to sell apps: evaluation of mental health app store quality claims.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Using science to sell apps: evaluation of mental health app store quality claims
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.245274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e14b027b-195f-47bb-8ce9-10480b875330 · outbound
This paper cites Smartphones for smarter delivery of mental health programs: a systematic review.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Smartphones for smarter delivery of mental health programs: a systematic review
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.233535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5e3687ab-5fdc-491c-83df-4771f4f47247 · outbound
This paper cites Mobile apps that promote emotion regulation, positive mental health, and well -being in the general population: systematic review and meta-analysis.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Mobile apps that promote emotion regulation, positive mental health, and well -being in the general population: systematic review and meta-analysis
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.221916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 71fd28c2-f5f5-4d34-bf43-c6753cf7c932 · outbound
This paper cites lifestyle psychiatry.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data lifestyle psychiatry
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.209829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 768cba7c-c50c-4f02-9926-f288f8742f1e · outbound
This paper cites Development of a mobile phone app to support self-monitoring of emotional well-being: a mental health digital innovation.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Development of a mobile phone app to support self-monitoring of emotional well-being: a mental health digital innovation
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.197692Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a7af932c-7b93-47d6-be45-0ce503877b11 · outbound
This paper cites Digital phenotyping: data-driven psychiatry to redefine mental health.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Digital phenotyping: data-driven psychiatry to redefine mental health
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.185739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f39eaaed-de9c-450a-8c5c-ae08c93b7095 · outbound
This paper cites Digital phenotyping: technology for a new science of behavior.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Digital phenotyping: technology for a new science of behavior
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.172888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 38d11934-9c6c-4c35-a705-0a5475a05bb4 · outbound
This paper cites Harnessing smartphone -based digital phenotyping to enhance behavioral and mental health.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Harnessing smartphone -based digital phenotyping to enhance behavioral and mental health
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.160934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation a26b1538-8ee5-4c01-8ad4-87b4baeab4f8 · outbound
This paper cites Next -generation psychiatric assessment: Using smartphone sensors to mo nitor behavior and mental health.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Next -generation psychiatric assessment: Using smartphone sensors to mo nitor behavior and mental health
Reference 19
- Resolution
- verified exact
- doi, observed 2026-08-10T20:21:33.531044Z
Source-reported events for the cited work
correction dated 2015-12-21. Source: crossref record 10.1037/prj0000169->10.1037/prj0000130:correction, observed 2026-07-11T03:04:59.247043+00:00. This notice travels one citation hop only.
Observation a059e501-dac8-425f-a12f-32bb0391d65f · outbound
This paper cites Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.149400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 381a88fe-7838-4d34-9e42-3b6500850510 · outbound
This paper cites Next -generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Next -generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.137893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 9fa43473-d618-4fc6-a7b4-b1146da403f4 · outbound
This paper cites an unresolved cited work.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Unresolved cited work
Reference 22
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T20:21:34.122568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 505e571a-1b52-4cd6-bf10-2286c16fe19a · outbound
This paper cites an unresolved cited work.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Unresolved cited work
Reference 23
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T20:21:34.110661Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8330e2c5-4dd7-48fc-9699-adcbde8d0a28 · outbound
This paper cites an unresolved cited work.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Unresolved cited work
Reference 24
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T20:21:34.098010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b6e5e420-e39e-4ccb-b74c-700eef8dbae9 · outbound
This paper cites CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.086606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bc22dede-e809-426b-9bc6-34aa5cd6ae7d · outbound
This paper cites Using smartphones to monitor bipolar disorder symptoms: a pilot study.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Using smartphones to monitor bipolar disorder symptoms: a pilot study
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.075348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 15dde3c7-8cd2-4aa4-9628-fe69da520d38 · outbound
This paper cites Passive sensing of prediction of moment -to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Passive sensing of prediction of moment -to-moment depressed mood among undergraduates with clinical levels of depression sample using smartphones
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.063149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation dc3aebe4-17a8-4037-a30c-66a3bf0455c9 · outbound
This paper cites Towards early detection of depression through smartphone sensing.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Towards early detection of depression through smartphone sensing
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.050977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation da4a703e-0cb3-4b5b-9b30-8722b82b77cf · outbound
This paper cites Toi Même, a mobile health platform for measuring bipolar illness activity: protocol for a feasibility study.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Toi Même, a mobile health platform for measuring bipolar illness activity: protocol for a feasibility study
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.038949Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 065d99d3-5fd9-46f5-9aec-ddc7695d0116 · outbound
This paper cites Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning –Based Exploratory Study.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning –Based Exploratory Study
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.026792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 1bde7c43-c86f-4771-888b-30e0b572eb64 · outbound
This paper cites an unresolved cited work.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Unresolved cited work
Reference 31
- Resolution
- unresolved
- raw_fallback, observed 2026-08-10T20:21:34.015458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ee57c192-9f1f-4881-8a08-1c5f27052abe · outbound
This paper cites Predicting depressive symptoms using smartphone data.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Predicting depressive symptoms using smartphone data
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:34.002930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5edc3825-6f6f-4b8d-be0b-1659b8bf7166 · outbound
This paper cites A mobile sensing app to monitor youth mental health: observational pilot study.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data A mobile sensing app to monitor youth mental health: observational pilot study
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.991436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 89a187de-5403-4f79-ad2a-df9964c38d3b · outbound
This paper cites Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.979977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5ec059ef-da6c-4d0b-a066-8690855d7549 · outbound
This paper cites A linguistic analysis of suicide-related Twitter posts.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data A linguistic analysis of suicide-related Twitter posts
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.967818Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 78cb1690-54bd-4761-ac92-0e2c08d6284a · outbound
This paper cites Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.955285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4da7771c-9624-40b1-a523-551837907ff2 · outbound
This paper cites The Strengths and Difficulties Questionnaire: a research note.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The Strengths and Difficulties Questionnaire: a research note
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.943422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation e0f25bdf-d6bc-409b-bc11-14b81d1d5eae · outbound
This paper cites Development, psychometric properties and preliminary clinical validation of a brief, session‐by‐session measure of eating disorder cognitions and behaviors: The ED‐15.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Development, psychometric properties and preliminary clinical validation of a brief, session‐by‐session measure of eating disorder cognitions and behaviors: The ED‐15
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.930592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 6b09f7c4-5d82-4d12-9fec-6a79c62ec560 · outbound
This paper cites The PHQ‐9: validity of a brief depression severity measure.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The PHQ‐9: validity of a brief depression severity measure
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.917812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 962b95e3-fc81-4ea8-9b4b-ad0f07fbef96 · outbound
This paper cites Youth screening depression: Validation of the Patient Health Questionnaire-9 (PHQ-9) in a representative sample of adolescents.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Youth screening depression: Validation of the Patient Health Questionnaire-9 (PHQ-9) in a representative sample of adolescents
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.903570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 93d23bbf-c630-44b0-bf05-e88c114225c2 · outbound
This paper cites The Sleep Condition Indicator: a clinical screening tool to evaluate insomnia disorder.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The Sleep Condition Indicator: a clinical screening tool to evaluate insomnia disorder
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.890965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bb50dd51-b292-4d94-b07b-ae9aa450b5bb · outbound
This paper cites The Sleep Condition Indicator: reference values derived from a sample of 200 000 adults.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The Sleep Condition Indicator: reference values derived from a sample of 200 000 adults
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.877543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7b2e9ea0-56b1-4518-9e5f-f4502c6662eb · outbound
This paper cites Using the Strengths and Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Using the Strengths and Difficulties Questionnaire (SDQ) to screen for child psychiatric disorders in a community sample
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.864092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 056d08b7-1f59-4232-aade-2b41d6898637 · outbound
This paper cites Eating Disorder‐15 (ED‐15): Factor structure, psychometric properties, and clinical validation.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Eating Disorder‐15 (ED‐15): Factor structure, psychometric properties, and clinical validation
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.852326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bd3b16f5-463f-4780-ba42-c96256902d40 · outbound
This paper cites A unified approach to interpreting model predictions.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data A unified approach to interpreting model predictions
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.840298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation ac563339-65ae-4be7-a308-2bb41c3a44d5 · outbound
This paper cites From local explanations to global understanding with explainable AI for trees.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data From local explanations to global understanding with explainable AI for trees
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.828230Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation b30bde64-a442-4f98-a607-79da76de2201 · outbound
This paper cites CatBoost: unbiased boosting with categorical features.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data CatBoost: unbiased boosting with categorical features
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.816874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4d5bccce-59ce-434e-bd62-28c7b6a63ab9 · outbound
This paper cites CatBoost for big data: an interdisciplinary review.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data CatBoost for big data: an interdisciplinary review
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.805307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f0c92957-e358-45b0-86b7-bf206891a432 · outbound
This paper cites Smartphone -based self- monitoring, treatment, and automatically generated data in children, adolescents, and young adults with psychiatric disorders: systematic review.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Smartphone -based self- monitoring, treatment, and automatically generated data in children, adolescents, and young adults with psychiatric disorders: systematic review
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.793869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation bb7347f9-eaa0-47db-a59a-a9d3af1c2679 · outbound
This paper cites Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.781784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 7e200523-0273-4ed9-99a4-926b37bda12b · outbound
This paper cites Digital phenotyping for monitoring mental disorders: systematic review.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Digital phenotyping for monitoring mental disorders: systematic review
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.768808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 14174993-492d-474c-8856-125c69e85f6f · outbound
This paper cites Digital phenotyping for mental health of college students: a clinical review.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Digital phenotyping for mental health of college students: a clinical review
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.756936Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 8b3d395b-0707-414a-9523-a7dc1d715c60 · outbound
This paper cites Brief School - Based Interventions Targeting Student Mental Health or Well-Being: A Systematic Review and Meta-Analysis.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Brief School - Based Interventions Targeting Student Mental Health or Well-Being: A Systematic Review and Meta-Analysis
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.745002Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 10a2f8b4-ee36-4ba9-a9a2-570ea380f437 · outbound
This paper cites The Patient Experience of the Future is Personalized: Using Technology to Scale an N of 1 Approach.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The Patient Experience of the Future is Personalized: Using Technology to Scale an N of 1 Approach
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.732708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation f00246a9-5e3a-4d21-8f82-94d6a883f3c5 · outbound
This paper cites Ethical development of digital phenotyping tools for mental health applications: Delphi study.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Ethical development of digital phenotyping tools for mental health applications: Delphi study
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.721309Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 45d9042a-1c80-4bcd-9bc4-2188b7b608ae · outbound
This paper cites Digital mental health for young people: a scoping review of ethical promises and challenges.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Digital mental health for young people: a scoping review of ethical promises and challenges
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.709395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 03d26ffe-760f-4f5c-9239-45c2103605ae · outbound
This paper cites 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.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data 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
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.697242Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 827bc353-acd9-476e-8e7c-b9c56eccf168 · outbound
This paper cites Healthcare information systems: data mining methods in the creation of a clinical recommender system.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Healthcare information systems: data mining methods in the creation of a clinical recommender system
Reference 58
- Resolution
- verified exact
- raw_fallback, observed 2026-08-10T20:21:33.604668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 586635e9-5ece-4755-beb9-a5a53ee616c9 · outbound
This paper cites MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.684917Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 647ff500-2908-4b30-bb30-17bb6e83a77e · outbound
This paper cites Recommender systems in the healthcare domain: state-of-the-art and research issues.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Recommender systems in the healthcare domain: state-of-the-art and research issues
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.672811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3c8c67bb-45fc-4d4e-9679-557fbd7e8c0c · outbound
This paper cites The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.658520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 898bf591-4100-4a3f-b5fc-d8ada2a570c4 · outbound
This paper cites A survey of recommendation systems: recommendation models, techniques, and application fields.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data A survey of recommendation systems: recommendation models, techniques, and application fields
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.644677Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 115ddc68-c10f-4b7d-9d58-50bc30b8ae3d · outbound
This paper cites Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Personalised Recommendations in Mental Health Apps: The Impact of Autonomy and Data Sharing
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.631599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 3ca44d10-d7a4-4699-8618-c70e05ec72c1 · outbound
This paper cites Personality and Engagement with Digital Mental Health Interventions.
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data Personality and Engagement with Digital Mental Health Interventions
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T20:21:33.618519Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Pith citing papers
Observation 727039fa-b965-4140-bc02-1542d1a78633 · inbound
A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health cites this paper.
A Computational Ethical Framework for Financial Digital Phenotyping for Mental Health Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data
Reference 265
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-31T19:31:29.576342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.