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Cited by 14Digital Mental Health InterventionsMental Health Research TopicsTreatment of Major Depression

A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms

Open access
Vignapiano, Annarita, Monaco, Francesco, Pagano, Claudio, Piacente, Martina, Farina, Federica, Petrillo, Gianvito, Sica, Raffaella, Marenna, Alessandra +3 more·2023·Frontiers in Psychiatry
DOI 10.3389/fpsyt.2023.1321345PMC PMC10701678
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Abstract

Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients’ state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression’s stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.

AI snapshot

Written from the paper’s full text.

TL;DR

This narrative review synthesizes 12 studies on digital biomarkers from wearables and smartphone apps for major depressive disorder (MDD) and treatment-resistant depression (TRD). It finds promise for predicting diagnostic status, assessing symptom severity, monitoring treatment response, and real-world monitoring, but concludes more research is needed before routine clinical translation.

Key findings

  • The review included 12 articles and organized digital biomarker research into four core areas: predicting diagnostic status, assessing symptom severity/progression, identifying treatment response, and monitoring real-world/ecological validity.
  • Jacobson et al. reported that movement-pattern data predicted diagnostic group status with 89% accuracy; other studies linked passive movement, light exposure, heart-rate variation, smartphone use, and nearby Bluetooth device count (NBDC) to depression severity or symptoms.
  • In treatment studies, Abbas et al. found digital facial, vocal, and movement markers tracked depression severity and antidepressant treatment response over 4 weeks; Winkler et al. observed that TRD patients achieving remission after ECT had increases in light activity, overall activity, and circadian amplitude; Nishida et al. found rTMS improved depression symptoms and sleep quality on rating scales, but actigraphy-based sleep measures did not show substantial changes.
  • Wearable and smartphone data showed potential for real-world monitoring: Cormack et al. found daily mood/cognitive assessments were practical and correlated with established measures, and Zhang et al. reported NBDC data correlated with depressive symptoms, with lower PHQ-8 scores associated with increased social activities.

Methods

  • Narrative review searching PubMed and APA PsycINFO for articles published from January 1, 2013, to July 30, 2023, using terms combining depression or resistant depression with device, digital tools, digital biomarkers, smartwatch, or actigraphy.
  • Inclusion criteria included randomized controlled trial, retrospective study, cohort study, open study, or expert opinion; MDD diagnosis by DSM-5 or ICD-10; English; human studies; and journals indexed in Embase or Medline. Exclusion criteria included meta-analysis, review, duplicates, comments, editorials, case reports/case series, theses, proceedings, letters, short surveys, notes, irrelevant studies, and unavailable full text.
  • Two independent reviewers screened titles/abstracts and full texts, with a third reviewer resolving discrepancies; data extraction used a standardized Excel sheet by two independent researchers. The search produced 215 records, plus 6 from reference checking; after exclusions, 12 articles were included.

Limitations

  • The paper states that the review “falls short of reporting the effectiveness measure values, and therefore does not assess performance.”
  • The paper states that the validity of digital phenotyping measurements “need more strong confirmations” and that more studies are needed to translate digital biomarkers into actionable interventions.
  • Evident from the text: the review is narrative rather than meta-analytic, includes heterogeneous devices and small studies, and reports some inconsistent sample details between the narrative and Table 1 (e.g., Siddi et al. table reports 510 MDD while text says RADAR-MDD involved 600 individuals; Winkler et al. table reports 14 TRD while text says 15).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/fpsyt.2023.1321345

Cites

Statistics

Nothing to recompute.

Claims the paper makes

0 of 69 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    The review collected 12 studies that used wearable devices to assess or monitor depressive symptoms and TRD or to predict MDD.

    “We collected a total of 12 studies that made use of wearable devices to assess or monitor depressive symptoms and TRD or to predict MDD (Table 1).”
    Quote in the paperSubstantial claim
  • Findingnone given

    The reviewed studies focused on gathering physiological, activity/sleep, or subjective data from individuals through digital devices to explore relationships with depression.

    “The reviewed studies focused on gathering specific physiological, activity/sleep, or subjective data from individuals through digital devices, with the aim of exploring the relationship between these parameters and depression.”
    Quote in the paperSubstantial claim
  • Premisecitation

    Combining MDD and BD data revealed potential transdiagnostic traits.

    “Combining MDD and BD data revealed potential transdiagnostic traits.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [28] Jacobson, 2019, Digital biomarkers of mood disorders and symptom change, NPJ Digit Med source

      Not checked in the free run.

  • Premisecitation

    Approximately one-third of individuals with major depressive disorder do not achieve full remission of symptoms even after trying two suitable trials of antidepressants without adequate response.

    “Approximately one-third of individuals with major depressive disorder (MDD) do not achieve full remission of symptoms even after trying two suitable trials of antidepressants without adequate response (11, 12).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [11] Rush, 2006, Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report, Am J Psychiatry source

      Not checked in the free run.

    • cites [12] Voineskos, 2020, Management of treatment-resistant depression: challenges and strategies, Neuropsychiatr Dis Treat source

      Not checked in the free run.

  • Premisecitation

    Despite increased accessibility of treatment over the last four decades, prevalence rates of depressive disorders have remained static.

    “Despite increased accessibility of treatment over the last four decades prevalence rates have remained static (7).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [7] Ormel, 2019, Depression: more treatment but no drop in prevalence: how effective is treatment? And can we do better?, Curr Opin Psychiatry source

      Not checked in the free run.

  • May combine two claims. 24
  • Classified as probably not a substantial claim. 18
  • The quote only partly states this claim. 2

  • May combine two claims.

    Findingnone given

    Among the included studies examining wearable devices for individuals with MDD, approximately one-third employed actigraph units, while the rest used commercial wearable devices not originally designed for medical purposes.

    “Among the various studies examining wearable devices for individuals with MDD, approximately one- third employed actigraph units, while the rest utilized commercial wearable devices not originally designed for medical purposes.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Findingnone given

    Research on digital biomarkers confronts four core aspects: predicting diagnostic status, assessing symptom severity and progression, identifying treatment response, and monitoring real-world and ecological validity.

    “Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    The search across PubMed and APA PsycINFO produced 215 records, plus 6 from reference checking, and 12 articles were included in the synthesis.

    “The search across PubMed and APA PsycINFO bibliographic database produced a total of 215 records. Additionally, by conducting backward reference list checking and forward reference list checking, we discovered 6 new studies. After initial screening based on their titles and abstracts, 180 records were excluded. Out of the initial 39 references, the synthesis now comprises a total of 12 articles.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Siddi et al. found that during resting periods, decreases in heart rate variation during the day were related to increased severity of depression, and heart rate at night was higher in participants with more severe depressive symptoms.

    “During resting periods: decreases in HR variation during the day were related with an increased severity of depression.An HR at night was higher in participants with more severe depressive symptoms”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Siddi, 2023, The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity, Psychol Med source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Movement and light data are relevant for detecting mood disorders and correlate with energy levels, psychomotor activity, and sleep disturbances during mood episodes.

    “Movement and light data were found relevant for detecting mood disorders and correlated with behaviors like energy levels, psychomotor activity, and sleep disturbances during mood episodes.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [28] Jacobson, 2019, Digital biomarkers of mood disorders and symptom change, NPJ Digit Med source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Several digital biomarkers have been investigated for MDD characterization and diagnosis, including physical activity patterns, voice features, light exposure measurements, mobile phone GPS, and normal smartphone usage duration and frequency.

    “In the recent years, several digital biomarkers have been investigated for MDD characterization and diagnosis such as measure patterns of physical activity (24, 25) features from voice samples (26, 27), light exposure measurements (25), mobile phone global positioning systems (GPS) and normal usage of smartphones such as usage duration and frequency (22).”
    Quote in the paperSubstantial claim0 of 5 sources checked
    • cites [24] Jacobson, 2019, Using digital phenotyping to accurately detect depression severity, J Nerv Ment Dis source

      Not checked in the free run.

    • cites [25] Tonon, 2017, Nocturnal motor activity and light exposure: objective actigraphy-based marks of melancholic and non-melancholic depressive disorder. Brief report, Psychiatry Res source

      Not checked in the free run.

    • cites [26] Mundt, 2007, Voice acoustic measures of depression severity and treatment response collected via interactive voice response (IVR) technology, J Neurolinguistics source

      Not checked in the free run.

    • cites [27] Zhang, 2020, Automated voice biomarkers for depression symptoms using an online cross-sectional data collection initiative, Depress Anxiety source

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    • cites [22] Torous, 2018, Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study, JMIR Ment Health source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Kim et al. found that MDD participants traveled longer distances than controls.

    “MDD participants traveled longer distances than controls.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Kim, 2023, Prediction of diagnosis and treatment response in adolescents with depression by using a smartphone app and deep learning approaches: usability study, JMIR Form Res source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    MDD is poorly defined and diagnosed because its diagnosis is mostly based on data subjectively reported by patients themselves and lacks objective, clinically relevant outcome measures.

    “Despite its medical importance MDD is poorly defined and diagnosed since its diagnosis is mostly based on data subjectively reported by the patients themselves (13–15) and lack of objective, clinically relevant outcome measure.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [13] Fried, 2017, Moving forward: how depression heterogeneity hinders progress in treatment and research, Expert Rev Neurother source

      Not checked in the free run.

    • cites [14] Fried, 2017, The 52 symptoms of major depression: lack of content overlap among seven common depression scales, J Affect Disord source

      Not checked in the free run.

    • cites [15] Taliaz, 2021, Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data, Transl Psychiatry source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Sverdlov et al. found that selected digital biomarker features were able to predict individual MADRS total scores and be used as classifiers.

    “Selected digital biomarker features (PHQ2 of Cambridge cognition; behavioral tracker features of BeHapp; neurophysiological features of Neurocart; EEG—resting state features of ElMindA Ltd. and EEG—BNA features of ElMindA Ltd.) were able to predict individual MADRS total scores, and use these models as classifiers”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Sverdlov, 2021, A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression, Front Psychiatry source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Zhang et al. found that increased time at home, inability to work or study, and diminished social interactions are reflected in reduced amount of the NBDC sequence.

    “Increased time at home, inability to work or study, and diminished social interactions are reflected in the reduced amount of the NBDC sequence.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Zhang, 2021, Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study, JMIR Mhealth Uhealth source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Digital technologies enable remote collection of large volumes of clinically relevant data that may be less burdensome than traditional in-clinic visits and more reflective of clinically relevant changes.

    “These technologies enable the remote collection of large volumes of clinically relevant data, which may be less burdensome than traditional in-clinic visits and more reflective of clinically relevant changes (20, 21).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [20] Ahmed, 2023, Wearable devices for anxiety & depression: a scoping review, Comput Methods Programs Biomed Update source

      Not checked in the free run.

    • cites [21] Ahmed, 2023, Chatbot features for anxiety and depression: a scoping review, Health Informatics J source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Zhang et al. suggested that depression may lead to misalignment of the circadian rhythm and make life rhythms such as sleep rhythms and social rhythms more irregular.

    “Depression also may lead to misalignment of the circadian rhythm and make people’s life rhythms (such as sleep rhythms and social rhythms) more irregular”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [34] Zhang, 2021, Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study, JMIR Mhealth Uhealth source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Jacobson et al. used extreme gradient boosting on movement patterns to achieve 89% accuracy in predicting diagnostic groups and monitoring symptom changes over 2 weeks.

    “Jacobson et al. (28) conducted a study to identify digital biomarkers for MDD and bipolar disorder (BD) and track symptom changes over 2 weeks. By analyzing movement patterns, they used extreme gradient boosting to achieve an 89% accuracy in predicting diagnostic groups and monitoring symptom changes.”
    • Also stated in: § Search strategy and study eligibility criteria · plain
      “Participants’ diagnostic group status can be predicted with a high degree of accuracy (predicted correctly 89% of the time)”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [28] Jacobson, 2019, Digital biomarkers of mood disorders and symptom change, NPJ Digit Med source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationnone given

    Through narrative synthesis, the review pinpointed specific domains where integration of tools and digital markers significantly enhances clinicians' capabilities in predicting, diagnosing, and providing care and treatment for individuals with MDD and TRD.

    “Through a narrative synthesis of various reviews and in agreement with several authors’ perspectives, we have pinpointed specific domains where the integration of tools and digital markers significantly enhances clinicians’ capabilities in predicting, diagnosing, and providing care and treatment for individuals grappling with MDD and TRD.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Abbas et al. found that monoamine antidepressants such as SSRIs and SNRIs had a significant impact on digital biomarkers, with reduction in symptom severity as assessed by MADRS, indicating improvement in motor functioning and decrease in depression severity.

    “The study found that monoamine ADTs, such as SSRIs and SNRIs, had a significant impact on digital biomarker with a reduction in symptom severity as assessed by the MADRS evaluation, indicating an improvement in motor functioning and a decrease in depression severity due to these treatments.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Abbas, 2021, Remote digital measurement of facial and vocal markers of major depressive disorder severity and treatment response: a pilot study, Front Digit Health source

      Not checked in the free run.

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    Premisecitation

    Mahendran et al. found that an ensemble of logistic regression and random forest models performed better than individual models, with random forest outperforming logistic regression.

    “The results indicated that the weighted average ensemble performed better than the individual models, with random forest outperforming logistic regression”
    • Also stated in: § General description of included studies · plain
      “the results showed that the ensemble of models performed better than individual implementations, with random forest standing out.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Mahendran, 2019, Sensor-assisted weighted average ensemble model for detecting major depressive disorder, Sensors source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Cormack et al. observed high correspondence between frequent assessments and established measures, showing moderate alignment between daily mood evaluations and validated depression questionnaires, and similar correlation for cognitive assessments with depression-sensitive tests.

    “High correspondence was observed between frequent assessments and established measures, showing moderate alignment between daily mood evaluations and validated depression questionnaires, and similar correlation for cognitive assessments with depression-sensitive tests”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Cormack, 2019, Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study, JMIR Ment Health source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Abbas et al. found that digitally measured facial, vocal, and movement behaviors can measure depression severity and treatment response across 4 weeks of antidepressant treatment.

    “Ability of digitally measured facial, vocal, and movement behaviors to measure depression severity and treatment response across 4 weeks of antidepressant treatment”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Abbas, 2021, Remote digital measurement of facial and vocal markers of major depressive disorder severity and treatment response: a pilot study, Front Digit Health source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Kim et al. found that MDD participants exhibited extended smartphone usage not oriented towards social communication, marking a distinction from controls.

    “MDD participants exhibited extended smartphone usage, yet their usage wasn’t oriented towards social communication, marking a distinction from controls.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Kim, 2023, Prediction of diagnosis and treatment response in adolescents with depression by using a smartphone app and deep learning approaches: usability study, JMIR Form Res source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Nishida et al. found that a daytime physical activity response to rTMS occurred in early sessions.

    “A daytime physical activity response to rTMS occurred in early sessions”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Nishida, 2017, Actigraphy in patients with major depressive disorder undergoing repetitive transcranial magnetic stimulation: an open label pilot study, J ECT source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The integration of movement, light exposure, and smartphone data has enabled accurate predictions and revealed common features across mood disorders, indicating transdiagnostic traits.

    “The integration of movement, light exposure, and smartphone data has not only enabled accurate predictions but has also revealed common features across mood disorders, highlighting the existence of transdiagnostic traits.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [35] Mahendran, 2019, Sensor-assisted weighted average ensemble model for detecting major depressive disorder, Sensors source

      Not checked in the free run.

    • cites [39] McNamara, 2021, Multifactorial prediction of depression diagnosis and symptom dimensions, Psychiatry Res source

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    • Rests on an assumption: Digital technologies offer objective tools for depression symptom detection.

  • May combine two claims.

    Interpretationnone given

    Digital phenotyping may significantly enhance assessment of symptom severity and progression, but the validity of these measurements needs stronger confirmation.

    “Assessing symptom severity and progression may be significantly enhanced by digital phenotyping, although this kind of technology is rapidly improving and the validity of these measurements need more strong confirmations.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Digital technologies offer objective tools for depression symptom detection.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    McNamara et al. found that psychiatric control groups can help distinguish specific factors in the diagnosis of interest.

    “Psychiatric control groups can help to distinguish specific factors in the diagnosis of interest.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Abbas, 2021, Remote digital measurement of facial and vocal markers of major depressive disorder severity and treatment response: a pilot study, Front Digit Health source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Daily assessments using wearable technology over 6 weeks in individuals with MDD were practical and showed meaningful correlations with established measures of mood and cognition.

    “The study found that daily assessments were practical and showed meaningful correlations with established measures of mood and cognition.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [32] Cormack, 2019, Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study, JMIR Ment Health source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    McNamara et al. found that perceived sleep quality and impairment are important predictors of depression.

    “Additionally, perceived sleep quality and impairment are also important predictors”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Abbas, 2021, Remote digital measurement of facial and vocal markers of major depressive disorder severity and treatment response: a pilot study, Front Digit Health source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The focus of research in mood disorders has shifted to more quantifiable metrics, while behavioral aspects have diminished markedly in importance.

    “The focus of research in mood disorders has shifted to more quantifiable metrics, while behavioral aspects have diminished markedly in importance (17).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [17] Insel, 2017, Digital phenotyping: technology for a new science of behavior, JAMA source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The impact of antidepressant therapies on digital biomarkers related to motor function may provide valuable insights into the mechanisms of action of these treatments.

    “Moreover, the impact of antidepressant therapies on digital biomarkers related to motor function may provides valuable insights into the mechanisms of action of these treatments.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Depression's stable rates and high relapse risk necessitate innovative approaches.

    “Depression’s stable rates and high relapse risk necessitate innovative approaches.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation

    Artificial intelligence advances offer new diagnostic and therapeutic possibilities.

    “Artificial intelligence advances offer new diagnostic and therapeutic possibilities.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    McNamara et al. found that low positive emotionality is a strong differentiator of depression.

    “Low positive emotionality is a strong differentiator of depression.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Abbas, 2021, Remote digital measurement of facial and vocal markers of major depressive disorder severity and treatment response: a pilot study, Front Digit Health source

      Not checked in the free run.

  • May combine two claims. The quote only partly states this claim.

    Premisecitation

    In medication-resistant MDD patients, 10 rTMS sessions targeting the bilateral dorsolateral prefrontal cortex resulted in significant improvements in depression symptoms and sleep quality measured by rating scales.

    “The results showed significant improvements in depression symptoms and sleep quality measured by rating scales”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [37] Nishida, 2017, Actigraphy in patients with major depressive disorder undergoing repetitive transcranial magnetic stimulation: an open label pilot study, J ECT source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    The review's study selection used two independent reviewers for title/abstract and full-text screening, with discrepancies resolved by a third reviewer.

    “Initially, two independent reviewers assessed the titles and abstracts of all the retrieved papers. In the subsequent stage, these same reviewers individually examined the full texts of the papers identified in the first phase. Any discrepancies between the two reviewers were resolved by involving a third reviewer.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Winkler et al. found an increase in light activity and circadian amplitude in patients with remission after ECT.

    “Increase in light activity and circadian amplitude in patients with remission after ECT”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Winkler, 2014, Actigraphy in patients with treatment-resistant depression undergoing electroconvulsive therapy, J Psychiatr Res source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    Smartphone data aids in tracking depression severity and treatment responses.

    “Smartphone data aids in tracking depression severity and treatment responses.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Premisecitation

    Call duration was especially significant in predicting treatment responses in adolescents with MDD.

    “Call duration was especially significant in predicting treatment responses.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Kim, 2023, Prediction of diagnosis and treatment response in adolescents with depression by using a smartphone app and deep learning approaches: usability study, JMIR Form Res source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    In that wearable-technology study, mood improved somewhat but varied among participants.

    “While there was some improvement in mood, it varied among participants, highlighting the complexity of depression.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [32] Cormack, 2019, Wearable technology for high-frequency cognitive and mood assessment in major depressive disorder: longitudinal observational study, JMIR Ment Health source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Passive behaviors indicative of depression are more common in individuals with greater depression severity, particularly during the nighttime when heart rate may be elevated due to sleep problems often seen in MDD.

    “The research indicates that passive behaviors, which are indicative of depression, are more common in individuals with greater depression severity, particularly during the nighttime when HR may be elevated”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Siddi, 2023, The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity, Psychol Med source

      Not checked in the free run.

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  • The paper relies on this without support.

    There is a strong need in the depression field for monitoring clinical evolution and treatment responses in a more efficient manner to identify treatment-resistant depression forms.

    “In the depression field there is a strong need for monitoring clinical evolution and treatment responses in a more efficient manner to identify the treatment-resistant depression (TRD) forms.”

  • The paper relies on this without support.

    Digital technologies offer objective tools for depression symptom detection.

    “Digital technologies offer objective tools for depression symptom detection.”

  • The paper relies on this without support.

    Mobile apps and wearables can generate digital biomarkers in real-world settings.

    “Mobile apps and wearables can generate digital biomarkers in real-world settings.”

  • The paper relies on this without support.

    Wearable technology allows for ongoing and unbiased observation of individuals in their everyday environments.

    “Wearable technology allows for ongoing and unbiased observation of individuals in their everyday environments.”

  • The paper relies on this without support.

    Wearable technology enables objective tracking of real-time changes and enhances the precision of monitoring treatment outcomes.

    “This enables the objective tracking of real-time changes and enhances the precision of monitoring treatment outcomes.”
5 of 5

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Winkler study has two different participant counts

    Minor

    Table 1 lists 14 participants with TRD for Winkler et al., whereas the treatment-response section says the study included 15.

  • Study-selection counts do not reconcile

    Minor

    The database search yielded 215 records and 180 were excluded, leaving 35, not 39. Adding the six newly discovered studies would instead leave 41; neither calculation yields the stated 39 references.

    Reported 39 · recomputed 35

  • McNamara study is assigned two reference numbers

    Minor

    Table 1 cites McNamara et al. as reference 31, while the results text cites it as reference 39. The reference list identifies 31 as Abbas et al. and 39 as McNamara et al.

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The citation lists haven’t been fetched yet

References 39 · Cited by 14

Research works printed but not linked: 1

  1. 2022, World mental health report: transforming mental health for all

Authors
Vignapiano, A., Monaco, F., Pagano, C., Piacente, M., Farina, F., Petrillo, G., Sica, R., Marenna, A., Shin, J. I., Solmi, M., & Corrivetti, G.
Affiliations
  • European Biomedical Research Institute of Salerno (IT)
  • University of Salerno (IT)
  • Yonsei University (KR)
  • University of Ottawa (CA)
Corresponding author
Vignapiano, Annarita
ORCID
Venue
Frontiers in Psychiatry
Publisher
Frontiers Media SA
Published
2023-11-23
Volume
14
Pages
1321345-1321345
ISSN
1664-0640 (ISSN-L)
DOI
10.3389/fpsyt.2023.1321345
PMC
PMC10701678
OpenAlex
W4388945421
Cited by
14
References
39
Citations by year
2026: 6 · 2025: 5 · 2024: 3
FWCI
0.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Psychiatry (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
Funders
European Regional Development Fund (FESR 2014-2020, 2014-2020’)
Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
Keywords
digital biomarkers, major depressive disorder, treatment-resistant depression, wearable devices, symptom severity assessment, treatment response prediction, depression diagnosis, continuous monitoring, personalized medicine, relapse risk

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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