The genetic basis of major depressive disorder

Molecular Psychiatry, Journal Year: 2023, Volume and Issue: 28(6), P. 2254 - 2265

Published: Jan. 26, 2023

The genetic dissection of major depressive disorder (MDD) ranks as one the success stories psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 risk loci and proposing more than 200 candidate genes. However, GWAS results derive from analysis cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating there is large component unique to MDD remains inaccessible phenotyping strategies majority identified approaches unlikely be loci. show inventive uses biobank data, novel imputation methods, combined interviewer cases, can identify contribute episodic severe shifts mood, neurovegetative cognitive changes central MDD. Furthermore, new theories about nature causes MDD, drawing upon advances neuroscience psychology, provide handles on how best interpret exploit mapping results.

Language: Английский

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Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

BMC Psychiatry, Journal Year: 2022, Volume and Issue: 22(1)

Published: Feb. 21, 2022

Abstract Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response treatment identify early indicators relapse. Remote Measurement Technologies (RMT) provide an opportunity transform the measurement management MDD, via data collected from inbuilt smartphone sensors wearable devices alongside app-based questionnaires tasks. A key question for field extent which participants can adhere research protocols completeness collected. We aimed describe drop out in a naturalistic multimodal longitudinal RMT study, people with history recurrent MDD. further determine whether those experiencing depressive relapse at baseline contributed less complete data. Methods Assessment Disease Relapse – (RADAR-MDD) multi-centre, prospective observational cohort study conducted as part Central Nervous System (RADAR-CNS) program. People MDD were provided wrist-worn device, apps designed to: a) collect sensors; b) deliver questionnaires, speech tasks, cognitive assessments. Participants followed-up minimum 11 months maximum 24 months. Results Individuals ( n = 623) enrolled study,. report 80% completion rates primary outcome assessments across all follow-up timepoints. 79.8% participated amount time available 20.2% withdrew prematurely. found no evidence association between severity depression availability In total, 110 had > 50% types. Conclusions RADAR-MDD largest mental health. Here, we have shown that collecting clinical population feasible. comparable levels active passive forms collection, demonstrating both are feasible this patient group.

Language: Английский

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Evaluating Conversational Agents for Mental Health: Scoping Review of Outcomes and Outcome Measurement Instruments

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e44548 - e44548

Published: March 31, 2023

Rapid proliferation of mental health interventions delivered through conversational agents (CAs) calls for high-quality evidence to support their implementation and adoption. Selecting appropriate outcomes, instruments measuring assessment methods are crucial ensuring that evaluated effectively with a high level quality.We aimed identify the types outcome measurement instruments, used assess clinical, user experience, technical outcomes in studies effectiveness CA health.We undertook scoping review relevant literature health. We performed comprehensive search electronic databases, including PubMed, Cochrane Central Register Controlled Trials, Embase (Ovid), PsychINFO, Web Science, as well Google Scholar Google. included experimental evaluating interventions. The screening data extraction were independently by 2 authors parallel. Descriptive thematic analyses findings performed.We 32 targeted promotion well-being (17/32, 53%) treatment monitoring symptoms (21/32, 66%). reported 203 measure clinical (123/203, 60.6%), experience (75/203, 36.9%), (2/203, 1.0%), other (3/203, 1.5%). Most only 1 study (150/203, 73.9%) self-reported questionnaires (170/203, 83.7%), most electronically via survey platforms (61/203, 30.0%). No validity was cited more than half (107/203, 52.7%), which largely created or adapted they (95/107, 88.8%).The diversity choice employed on CAs point need an established minimum core set greater use validated instruments. Future should also capitalize affordances made available smartphones streamline evaluation reduce participants' input burden inherent self-reporting.

Language: Английский

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Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: Feb. 17, 2023

Abstract Recent growth in digital technologies has enabled the recruitment and monitoring of large diverse populations remote health studies. However, generalizability inference drawn from remotely collected data could be severely impacted by uneven participant engagement attrition over course study. We report findings on long-term retention patterns a multinational observational study for depression containing active (surveys) passive sensor via Android smartphones, Fitbit devices 614 participants up to 2 years. Majority (67.6%) continued remain engaged after 43 weeks. Unsupervised clustering participants’ apps usage showed 3 distinct subgroups each stream. found: (i) least group had highest severity (4 PHQ8 points higher) across all streams; (ii) (completed 4 bi-weekly surveys) took significantly longer respond survey notifications (3.8 h more) were 5 years younger compared most 20 surveys); (iii) considerable proportion (44.6%) who stopped completing surveys 8 weeks share (average 42 weeks). Additionally, multivariate survival models age, ownership brand sites associated with Together these inform design future studies enable equitable balanced collection populations.

Language: Английский

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Wearable Technology in Clinical Practice for Depressive Disorder

New England Journal of Medicine, Journal Year: 2023, Volume and Issue: 389(26), P. 2457 - 2466

Published: Dec. 27, 2023

Sleep patterns and physical activity can be monitored by wearable technology. The authors describe the state of art for using data from devices in diagnosing managing depression.

Language: Английский

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From neural circuits to communities: an integrative multidisciplinary roadmap for global mental health

Nature Mental Health, Journal Year: 2023, Volume and Issue: 1(1), P. 12 - 24

Published: Jan. 17, 2023

Language: Английский

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Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies

BMJ Mental Health, Journal Year: 2023, Volume and Issue: 26(1), P. e300718 - e300718

Published: Feb. 1, 2023

Digital phenotyping methods present a scalable tool to realise the potential of personalised medicine. But underlying this is need for digital data represent accurate and precise health measurements.To assess impact population, clinical, research technological factors on quality as measured by rates missing data.This study analyses retrospective cohorts mindLAMP smartphone application studies run at Beth Israel Deaconess Medical Center between May 2019 March 2022 involving 1178 participants (studies college students, people with schizophrenia depression/anxiety). With large combined set, we report sampling frequency, active engagement application, phone type (Android vs Apple), gender protocol features missingness/data quality.Missingness from sensors in related user application. After 3 days no engagement, there was 19% decrease average coverage both Global Positioning System accelerometer. Data sets high degrees missingness can generate incorrect behavioural that may lead faulty clinical interpretations.Digital requires ongoing technical efforts minimise missingness. Adding run-in periods, education hands-on support tools easily monitor are all productive strategies use today.While it feasible capture diverse populations, clinicians should consider degree before using them decision-making.

Language: Английский

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Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: July 8, 2024

Abstract Bioelectronic implantable devices are adept at facilitating continuous monitoring of health and enabling the early detection diseases, offering insights into physiological conditions various bodily organs. Furthermore, these advanced systems have therapeutic capabilities in neuromodulation, demonstrating their efficacy addressing diverse medical through precise delivery stimuli directly to specific targets. This comprehensive review explores developments applications bioelectronic within biomedical field. Special emphasis is placed on evolution closed‐loop systems, which stand out for dynamic treatment adjustments based real‐time feedback. The integration Artificial Intelligence (AI) edge computing technologies discussed, significantly bolster diagnostic functions devices. By elemental analyses, current challenges, future directions devices, aims guide pathway advances

Language: Английский

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Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study

npj Mental Health Research, Journal Year: 2024, Volume and Issue: 3(1)

Published: Jan. 4, 2024

Abstract While studies show links between smartphone data and affective symptoms, we lack clarity on the temporal scale, specificity (e.g., to depression vs. anxiety), person-specific (vs. group-level) nature of these associations. We conducted a large-scale ( n = 1013) smartphone-based passive sensing study identify within- between-person digital markers anxiety symptoms over time. Participants (74.6% female; M age 40.9) downloaded LifeSense app, which facilitated continuous collection GPS, app device use, communication) across 16 weeks. Hierarchical linear regression models tested associations 2-week windows passively sensed with (PHQ-8) or generalized (GAD-7). used shifting window understand time scale at features relate mental health predicting 2 weeks in future (distal prediction), 1 week (medial 0 (proximal prediction). Spending more home relative one’s average was an early signal PHQ-8 severity β 0.219, p 0.012) continued medial 0.198, 0.022) proximal 0.183, 0.045) windows. In contrast, circadian movement proximally related −0.131, 0.035) but did not predict 0.034, 0.577; −0.089, 0.138) PHQ-8. Distinct communication (i.e., call/text app-based messaging) GAD-7. Findings have implications for identifying novel treatment targets, personalizing interventions, enhancing traditional patient-provider interactions. Certain movement) may represent correlates true prospective indicators symptoms. Conversely, other like duration be such signals intra-individual symptom change, indicating potential utility prophylactic intervention behavioral activation) response increases signals.

Language: Английский

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Personalized mood prediction from patterns of behavior collected with smartphones

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: Feb. 28, 2024

Abstract Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones wearable devices, to infer depressive mood. However, most phenotype studies suffer poor replicability, often fail detect clinically relevant events, use measures of depression that are not validated or suitable for collecting large longitudinal data. Here, we report high-quality assessments mood computerized adaptive testing paired with continuous behavior smartphone sensors up 40 weeks on 183 individuals experiencing mild severe symptoms depression. We apply a combination cubic spline interpolation idiographic models generate individualized predictions future achieving high prediction accuracy severity three advance ( R 2 ≥ 80%) 65.7% reduction error over baseline model which predicts based past alone. Finally, our study verified feasibility obtaining clinical population predicting symptom collected Our results indicate possibility expanding repertoire patient-specific enable psychiatric research.

Language: Английский

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