Evidence map›Paper›PMID 39984168›Full record

Observational studyJMIR mental health2025

Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study.

Talayeh Aledavood, Nguyen Luong, Ilya Baryshnikov, Richard Darst, Roope Heikkilä, Joel Holmén, Arsi Ikäheimonen, Annasofia Martikkala, Kirsi Riihimäki, Outi Saleva and 2 more

Abstract readObservational Study
In one paragraph

Observational study in JMIR mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Talayeh AledavoodDepartment of Computer Science, Aalto University, Espoo, Finland.ORCID 0000-0002-0110-5694
Nguyen LuongDepartment of Computer Science, Aalto University, Espoo, Finland.ORCID 0000-0003-0122-0286
Ilya BaryshnikovDepartment of Psychiatry, University of Helsinki, Helsinki, Finland.ORCID 0000-0001-6229-6116
Richard DarstSchool of Science, Aalto University, Espoo, Finland.ORCID 0000-0002-0402-7994
Roope HeikkiläCity of Helsinki Mental Health Services, Helsinki, Finland.ORCID 0000-0002-2125-577X
Joel HolménUniversity of Turku and Turku University Central Hospital, Turku, Finland.ORCID 0000-0002-6598-1810
Arsi IkäheimonenDepartment of Computer Science, Aalto University, Espoo, Finland.ORCID 0000-0002-1617-6911
Annasofia MartikkalaDepartment of Psychiatry, University of Helsinki, Helsinki, Finland.ORCID 0009-0003-9523-2700
Kirsi RiihimäkiHelsinki and Uusimaa Hospital District, Helsinki, Finland.ORCID 0000-0003-3913-5968
Outi SalevaHelsinki and Uusimaa Hospital District, Helsinki, Finland.ORCID 0009-0005-3454-9282
Ana Maria TrianaDepartment of Computer Science, Aalto University, Espoo, Finland.ORCID 0000-0002-5006-3021
Erkki IsometsäDepartment of Psychiatry, University of Helsinki, Helsinki, Finland.ORCID 0000-0001-5956-2399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMood disorders are among the most common mental health conditions worldwide. Wearables and consumer-grade personal digital devices create digital traces that can be collected, processed, and analyzed, offering a unique opportunity to quantify and monitor individuals with mental disorders in their natural living environments.

objectiveThis study comprised (1) 3 subcohorts of patients with a major depressive episode, either with major depressive disorder, bipolar disorder, or concurrent borderline personality disorder, and (2) a healthy control group. We investigated whether differences in behavioral patterns could be observed at the group level, that is, patients versus healthy controls. We studied the volume and temporal patterns of smartphone screen and app use, communication, sleep, mobility, and physical activity. We investigated whether patients or controls exhibited more homogenous temporal patterns of activity when compared with other individuals in the same group. We examined which variables were associated with the severity of depression.

methodsIn total, 188 participants were recruited to complete a 2-phase study. In the first 2 weeks, data from bed sensors, actigraphy, smartphones, and 5 sets of daily questions were collected. In the second phase, which lasted up to 1 year, only passive smartphone data and biweekly 9-item Patient Health Questionnaire data were collected. Survival analysis, statistical tests, and linear mixed models were performed.

resultsSurvival analysis showed no statistically significant difference in adherence. Most participants did not stay in the study for 1 year. Weekday location variance showed lower values for patients (control: mean -10.04, SD 2.73; patient: mean -11.91, SD 2.50; Mann-Whitney U [MWU] test P=.004). Normalized entropy of location was lower among patients (control: mean 2.10, SD 1.38; patient: mean 1.57, SD 1.10; MWU test P=.05). The temporal communication patterns of controls were more diverse compared to those of patients (MWU test P<.001). In contrast, patients exhibited more varied temporal patterns of smartphone use compared to the controls. We found that the duration of incoming calls (β=-0.08, 95% CI -0.12 to -0.04; P<.001) and the SD of activity magnitude (β=-2.05, 95% CI -4.18 to -0.20; P=.02) over the 14 days before the 9-item Patient Health Questionnaire records were negatively associated with depression severity. Conversely, the duration of outgoing calls showed a positive association with depression severity (β=0.05, 95% CI 0.00-0.09; P=.02).

conclusionsOur work shows the important features for future analyses of behavioral markers of mood disorders. However, among outpatients with mild to moderate depressive disorders, the group-level differences from healthy controls in any single modality remain relatively modest. Therefore, future studies need to combine data from multiple modalities to detect more subtle differences and identify individualized signatures. The high dropout rates for longer study periods remain a challenge and limit the generalizability.

Indexed as

AffectMajor Depressive DisorderActigraphyAdultBipolar DisorderCase-Control StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedMobile ApplicationsPhenotypeSmartphonedepressiondigital healthdigital phenotypingmental disordersmobile devicesmobile phonemultisensorsmartphones

Identifiers

PMID39984168
PMCPMC11890149

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.