The DOI matches its Crossref record; 85 of the 85 references it cites were found in a registry or an index; 3 of the 30 sources its claims cite were checked for support.
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
Open accessAbstract
Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods. Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety. Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study. Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression. Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In a 30-day observational study of 55 adults using an iPhone and Oura Ring during COVID-19 restrictions, smartphone GPS and wearable sleep/HRV data predicted depression and anxiety symptoms in multilevel models. A combined model of passive sensing plus daily self-reported mood best predicted depression, while EMA-only models were strongest for anxiety and stress.
Key findings
- Location variance was negatively associated with depression (beta = −0.21, p = 0.037); no other GPS features were significantly associated with depression, anxiety, or stress.
- Wearable sleep/HRV measures predicted symptoms: total sleep time (beta = 0.24, p = 0.023) and time in bed (beta = 0.26, p = 0.020) were positively associated with depression; wake after sleep onset (beta = 0.23, p = 0.035) and HRV (beta = 0.26, p = 0.035) were positively associated with anxiety.
- EMA valence was related to depression (beta = −0.39, p = 0.001), anxiety (beta = −0.30, p = 0.014), and stress (beta = −0.39, p < 0.001); the combined EMA plus digital phenotyping model had the strongest prediction of depression, while EMA-only models were strongest for anxiety and stress.
- Baseline DASS-21 scores were depression M = 3.78, SD = 3.48; anxiety M = 2.73, SD = 2.68; stress M = 6.00, SD = 3.82, with 67.3% normal depression, 70.9% normal anxiety, and 65.5% normal stress.
Methods
- Longitudinal observational study with repeated measurements over 30 days; N = 60 recruited online (55 in final analysis), ages 24–68, mean age 42.8 (SD 11.6), required to own an iPhone and Oura Ring.
- Data sources: Delphi smartphone app continuously monitored GPS and smartphone usage; Oura Ring measured activity (steps, MET), sleep (TST, SOL, WASO, TIB), and HRV; EMA mood was prompted 3 times daily (valence and arousal); DASS-21 was completed at baseline, day 16, and day 31.
- Analysis: Spearman correlations with cluster-wise Bonferroni-Holm adjustment; multilevel models with random intercepts and slopes, z-standardized predictors, multiple imputation (20 datasets, 15 iterations), and likelihood ratio tests for combined models.
Limitations
- Stated: observational design cannot establish causality or direction; pooling data prevented examining high-frequency processes and temporal precedence; small sample likely underpowered for small effects; sample was skewed toward white, employed, educated, iOS-using, non-clinical participants; no pre-pandemic movement data were available to confirm restricted movement.
- Stated: missingness occurred in DASS-21 (10%) and sensing variables (9.1%), assumed missing at random and handled with multiple imputation.
- Evident: the results text states no significant relationship between arousal and depression, anxiety, or stress, but tables report arousal in the combined depression model and the EMA stress model, indicating inconsistent reporting.
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Claims the paper makes
27 Own result · 20 Not checked yet · 16 Own method · 6 Own interpretation · 2 No basis given · 1 Partly supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, contrary to previous findings (19, 67–69), we did not find a significant relationship between the other smartphone features (total distance, location entropy, normalized location entropy and time spent at home) and mental health measures.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
What the sources say
- Partly supportedcites [19] Saeb, 2015, Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study, J Med Internet Res. sourceExperimental — accuracy not yet measured
The citing authors’ lack of significant relationships is their own finding; this citation is offered for the contrasting previous findings. In “Relationship Between Features and Levels of Depression,” the source reports significant associations with depressive symptoms for normalized location entropy and time spent at home, but not for total distance or location entropy. It therefore supports only part of the implied contrast across the four listed features. (LLM, conf=0.86)
- Not checked yetcites [67] Farhan, 2016, Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data source
- Partly supportedcites [68] Saeb, 2016, The relationship between mobile phone location sensor data and depressive symptom severity, PeerJ. sourceExperimental — accuracy not yet measured
The citation is attached to “previous findings,” not to the citing authors’ own null result. In Results (2-week features), the source reports significant correlations with PHQ-9 scores for home stay, entropy, and normalized entropy; Results (10-week features, Table 2) also lists total distance, but does not establish a significant relationship for it. Thus the contrast is supported for three of the four named features, not all four. (LLM, conf=0.85)
- Partly supportedcites [69] Ben-Zeev, 2015, Next-Generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health, Psychiatr Rehabil J. sourceExperimental — accuracy not yet measured
The citing authors describe the absence of significant relationships as their own finding; they cite this paper as a contrasting previous finding. In Method > Measures, the paper defines Geospatial Activity as total distance covered daily, and in Results > Relationship between Smartphone Sensor Data and Average Daily Stress it reports a significant association with daily stress. Our copy does not report corresponding findings for location entropy, normalized location entropy, or time spent at home. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“No significant association between the other GPS-derived features (total distance, location entropy, normalized location entropy and time at home) and symptoms of depression, anxiety or stress were found.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Predicting Symptom Severity From Smartphone and Wearable Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Depression could be predicted by EMA and smartphone and wearable data.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Combined Predictions
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For anxiety and stress, EMA-only data models were the strongest predictors (see Tables 4, 5).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Combined Predictions
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A multi-level model predicting the daily distance traveled (using categorized country Finland vs. other as predictor) revealed no significant distance between the average daily distance traveled between participants from Finland and those from other countries (β = −922.9 [in meters], p = 0.943).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Participant Characteristics and Adherence
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“time in bed and depression (beta = 0.26, p = 0.020)”
- Also stated in: § Predicting Symptom Severity From Smartphone and Wearable Data · plain
“time in bed and depression [beta = 0.26, SE = 0.11, t(59) = 2.39, p = 0.020]”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Predicting Symptom Severity From Smartphone and Wearable Data · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was no significant relationship between arousal and depression, anxiety or stress.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Predicting Symptom Severity From Smartphone and Wearable Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we found no significant relationship between smartphone usage duration or smartphone usage frequency and symptoms of depression, anxiety and stress.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Predicting Symptom Severity From Smartphone and Wearable Data
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“anxiety [beta = −0.30, SE = 0.12, t(57) = −2.54, p = 0.014]”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Predicting Symptom Severity From Smartphone and Wearable Data
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that GPS (location variance on depression) and wearable device data (total sleep time and time in bed on depression; wake after sleep onset and HRV on anxiety), were able to predict mental health.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 11
- Probably not a substantial claim. 18
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.”
- Also stated in: § Combined Predictions · plain
“EMA performed better than smartphone and wearable data models, but the combination yielded the best fit (see Table 3).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § Combined Predictions · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All other GPS features as well as variance and entropy for anxiety and stress were non-significant (see Table 2 for more details).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Correlations Between Smartphone and Wearable Data and Mental Health Symptoms
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The present study used methods from a previous study (19) investigating the correlation between GPS variables and depressive symptom severity with a sample size of N = 40.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Participants and Procedure
What the sources say
- Not checked yetcites [19] Saeb, 2015, Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study, J Med Internet Res. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The DASS21 has demonstrated high internal consistency for the three subscales of depression, anxiety, and stress in previous administrations (42).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Assessment
What the sources say
- Not checked yetcites [42] Antony, 1998, Psychometric properties of the 42-item and 21-item versions of the depression anxiety stress scales in clinical groups and a community sample, Psychol Assess. source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“MLMs take into account that data is nested within persons, i.e., the observations are not independent (56) and reduce the likelihood of Type I errors (57).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
What the sources say
- Not checked yetcites [56] Nezlek, 2001, multilevel random coefficient analyses of event- and interval-contingent data in social and personality psychology research, Personal Soc Psychol Bull. source
- Not checked yetcites [57] Musca, 2011, Data with hierarchical structure: impact of intraclass correlation and sample size on type-i error, Front Psychol. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Beiwinkel et al. (20) found that the total distance traveled had a significant negative relationship with clinical manic symptoms in patients diagnosed with bipolar disorder (beta = −0.37).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Introduction
What the sources say
- Not checked yetcites [20] Beiwinkel, 2016, Using smartphones to monitor bipolar disorder symptoms: a pilot study, JMIR Ment Heal. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 60 participants at intake, 1 participant (1.7%) dropped out of the study due to concerns over privacy, 2 participants (3.4%) dropped out due to burden of self-report and 2 (3.4%) participants dropped out for unknown reasons.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Participant Characteristics and Adherence
plainn = 60- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“However, p-value adjustment methods are sensitive and since the present study is of an exploratory nature, adjustment was only performed cluster-wise (e.g., separately for GPS features and wearable features) to avoid an overcorrection by p-value adjustment leading to a false-rejection of findings.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as dropout rates in longitudinal observation studies using digital phenotyping data are typically high, recruitment was continued for 2 weeks after reaching the required sample size to compensate for dropout.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Participants and Procedure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To monitor data collection and compliance during the study period, a secure web-based dashboard was developed displaying the data gathered for all participants, updated at 15-min intervals.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Participants and Procedure
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“P-values were adjusted for multiple testing based on the Bonferroni Holm method (50) with a false discovery rate of 0.05.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
What the sources say
- Not checked yetcites [50] Holm, 1979, Board of the foundation of the scandinavian journal of statistics a simple sequentially rejective multiple test procedure a simple sequentially rejective multiple test procedure, Source Scand J Stat Scand J Stat.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To avoid bias introduced by missingness we used multiple imputations to handle missing values in the correlation and regression analysis.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- The authors state this as an assumption: Missingness in the DASS-21 assessment and sensing variables was missing at random (MAR), meaning missingness depended on observed data.
In the paper
“The imputation model took the nested structure of the data into account and followed guidelines for multilevel multiple imputations (58).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
What the sources say
- Not checked yetcites [58] van Buuren, 2011, mice: Multivariate imputation by chained equations in R, J Stat Softw. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Missingness in the DASS-21 assessment and sensing variables was missing at random (MAR), meaning missingness depended on observed data.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the remaining 55 participants, 47 (85.5%) completed the midpoint questionnaire and 54 (98.2%) completed the endpoint questionnaire.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Participant Characteristics and Adherence
plainn = 55- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Convergence was visually assessed and confirmed (58).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
What the sources say
- Not checked yetcites [58] van Buuren, 2011, mice: Multivariate imputation by chained equations in R, J Stat Softw. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Saeb et al. (19), e.g., found that regularity of participants' 24-h movement patterns (r = −0.63), the variance of locations visited (r = −0.58) and the proportion of time spent at home (r = 0.49) were related to depressive symptom severity in a non-clinical population (19).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Introduction
What the sources say
- Not checked yetcites [19] Saeb, 2015, Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study, J Med Internet Res. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“To avoid biases introduced by missing values we used full information maximum likelihood as the estimator (51, 52).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
What the sources say
- Not checked yetcites [51] EndersCK Applied Missing Data Analysis2010
- Not checked yetcites [52] RevelleW 19012653Procedures for Psychological, Psychometric, and Personality Research2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Ethics Statement
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“as pooling data prevented us from exploring high-frequency processes (e.g., the relationship between movement and mood), we were unable to establish temporal precedence (74).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
What the sources say
- Not checked yetcites [74] Kazdin, 2007, Mediators and mechanisms of change in psychotherapy research, Annu Rev Clin Psychol. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As the study commenced after the onset of the pandemic, we were unable to provide any data comparing participants' movement before and during the study to confirm that their movement was indeed more limited during the study period.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Given the issues quantifying explained variance in multilevel models (86, 87)”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“thus the current findings do not necessarily represent a causal relationship between the behavior measured by smartphone and wearable devices and symptoms of depression and anxiety, nor can they explain the direction between them.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“sleep data from consumer wearables may thus provide valuable tools to identify early warning signs of mental disorders, thereby facilitating time-sensitive preventative measures (72).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
What the sources say
- Not checked yetcites [72] Baglioni, 2011, Insomnia as a predictor of depression: A meta-analytic evaluation of longitudinal epidemiological studies, J Affect Disord. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Research has shown that sociodemographic status and smartphone usage behavior may differ between iOS and Android users (75, 76).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
What the sources say
- Not checked yetcites [75] Ubhi, 2017, A comparison of the characteristics of iOS and Android users of a smoking cessation app, Transl Behav Med. source
- Not checked yetcites [76] Pryss, 2019, Ecological momentary assessment based differences between Android and iOS Users of the trackyourhearing mhealth crowdsensing platform source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Related to this, the study was open only to participants with an Apple (iOS) smartphone.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The patients/participants provided their written informed consent to participate in this study.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Ethics Statement
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The authors state this as an assumption.
Missingness in the DASS-21 assessment and sensing variables was missing at random (MAR), meaning missingness depended on observed data.
“Missingness only occurred in the DASS-21 assessment (10%) and the sensing variables (9.1%) and was assumed to be missing at random (MAR), meaning missingness depended on observed data (51, 52).”
The paper relies on this without support.
Participants' movement was indeed more limited during the study period than before the pandemic.
“we were unable to provide any data comparing participants' movement before and during the study to confirm that their movement was indeed more limited during the study period.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions [14,22,25-28].”
From Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021) · cites this paper as [28]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“One such method could be smart sensing, with the goal of achieving scalable, precise, and time-effective detection of symptoms (27–29).”
In the citing paper
“The findings in this study are also supported by prior research that investigated the relationship between screen interactions and mental health [3,27,28,33].”
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Consistentt(81) = -2.13reported p = .037 · recomputed p = .036
“t(81) = −2.13, p = .037”
- Consistentt(73) = 2.33reported p = .023 · recomputed p = .023
“t(73) = 2.33, p = .023”
- Consistentt(59) = 2.39reported p = .02 · recomputed p = .020
“t(59) = 2.39, p = .020”
- Consistentt(90) = 2.13reported p = .035 · recomputed p = .036
“t(90) = 2.13, p = .035”
- Consistentt(71) = 2.15reported p = .035 · recomputed p = .035
“t(71) = 2.15, p = .035”
- Consistentt(55) = -3.43reported p = .001 · recomputed p = .001
“t(55) = −3.43, p = .001”
- Consistentt(57) = -2.54reported p = .014 · recomputed p = .014
“t(57) = −2.54, p = .014”
- Consistentt(74) = -3.64reported p < .001 · recomputed p < .001
“t(74) = −3.64, p < .001”
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.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Discussion
“our finding that adding smartphone and wearable data to EMA data had the highest predictive power of all models in the analysis suggests that a combination of passive sensing and active assessment may provide the greatest predictive power in identifying people suffering from symptoms of depression and anxiety.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Combined Predictions
“For anxiety and stress, EMA-only data models were the strongest predictors (see Tables 4, 5).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“the mean stress level was M = 6.00, SD = 3.82 (normal: 65.5%, mild-moderate: 29.1%, severe: 4.4%).”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Participant Characteristics and Adherence
Reported 4.4 · recomputed 5.454545
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“Of the 60 participants at intake, 1 participant (1.7%) dropped out of the study due to concerns over privacy, 2 participants (3.4%) dropped out due to burden of self-report and 2 (3.4%) participants dropped out for unknown reasons.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Participant Characteristics and Adherence
Reported 3.4 · recomputed 3.333333
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Combined Predictions
“| Arousal | −0.39 | 0.11 | −3.71 | 74 | <0.001 | | | |”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Predicting Symptom Severity From Smartphone and Wearable Data
“There was no significant relationship between arousal and depression, anxiety or stress.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Materials and Methods > Statistical Analysis
“Correlations were calculated using the Spearman's correlation coefficient, since the data was not normally distributed.”
§ Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data > Results > Correlations Between Smartphone and Wearable Data and Mental Health Symptoms
“Table 2 presents Pearson's correlation matrixes of the four sets of independent variables (GPS, smartphone-usage, wearable, and EMA features) and mental health symptom severity.”
Citations
Cites
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334
Lovibond, P. F., & Lovibond, S. H. (1995) · Behaviour Research and Therapy · doi:10.1016/0005-7967(94)00075-u · Cited by 14,523
mice: Multivariate Imputation by Chained Equations in R
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870
(2007) · doi:10.5555/1283383.1283494 · Cited by 7,364
Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998) · Psychological Assessment · doi:10.1037/1040-3590.10.2.176 · Cited by 4,874
Mental health and the COVID-19 pandemic
Pfefferbaum, B., & North, C. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmp2008017 · Cited by 4,378
Network analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359
Research works printed but not linked: 2
- Holm, 1979, Board of the foundation of the scandinavian journal of statistics a simple sequentially rejective multiple test procedure a simple sequentially rejective multiple test procedure, Source Scand J Stat Scand J Stat., 6, 65
- Cummins, 2019, Mobile sensing and digital phenotyping in psychoinformatics
Cited by
Sorted by citations
Items 201–260 of 260
Alt, A. K., Pascher, A., Seizer, L., Finkbeiner, M., Conzelmann, A., & Renner, T. J. (2024) · doi:10.2196/preprints.58561
Digitale Phänotypisierung – Integration alltagsnah erhobener Daten in die Einzelfallbeurteilung
Garatva, P., & Baumeister, H. (2024) · Springer eBooks · doi:10.1007/978-3-662-64801-8_143-1
Self-citationUnveiling Depression: Monitoring Daily Activity Changes for Anomalies
Shaker, R., Ibrahim, N., & Abdennadher, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-65522-7_57
Langener, A. M. (2024) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.1069770227 · Cited by 1
Devices, Mobile Health, and Digital Phenotyping
Volpe, U., Elkholy, H. A., Gargot, T., da Costa, M. P., & Orsolini, L. (2024) · Springer eBooks · doi:10.1007/978-3-030-51366-5_151
Study and Analysis of IoT-Based Telemedicine and Remote Patient Monitoring
Kumar, R. K., & Sobin, C. C. (2024) · Advances in IT standards and standardization research (AISSR) book series/Advances in IT standards and standardization research series · doi:10.4018/979-8-3693-1686-3.ch005 · Cited by 1
Ahmed, A., Ramesh, J., Ganguly, S., Aburukba, R., Sagahyroon, A. A., & Aloul, F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5035-1_38
Beikzadeh, M., Holloway, I. W., Kärkkäinen, K., Hong, C., Cascalheira, C. J., Wu, E. S. C., Boka, C., Avandaño, A., & Sarrafzadeh, M. (2024) · doi:10.2196/preprints.68013
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Self-citationPassive sensing with psyche: utilizing data from wearable technology to predict emotion statesPreprint
Karim, H. T., Matin, A., & Goel, M. (2025) · medRxiv · doi:10.1101/2025.02.13.25322254
Lee, H., Kang, S., & Lee, S. H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5216287
Ni, S.-Y., Lu, C., Wu, C., Hsieh, M.-H., Chen, I., Lin, C., Lai, F., & Chien, Y. (2025) · doi:10.2196/preprints.76381
Leveraging Machine Learning to Detect and Alleviate Natural Anxiety
Kumbhar, M. F., Zafar, H., & Ghosh, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-88042-1_22
Kalyanasundaram, S., Phanord, C. S., Ruzic, L. L., Kaiser, R. H., & Chaspari, T. (2025) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi64645.2025.00046
Narwaria, M., & Jaiswal, S. (2025) · International Conference on Networks and Cryptology (NETCRYPT) · doi:10.1109/netcrypt65877.2025.11102435
Meßner, E.-M., Ustahüseyin, B., & Portenhauser, A. A. (2025) · European Family Therapy Association series · doi:10.1007/978-3-031-92225-1_6
Hoang, H., Ha, H., Nguyen, H. T., Watton, P. N., & Ngo, L. T. (2025) · Biomedical Engineering Letters · doi:10.1007/s13534-025-00505-4 · Cited by 2
Multidisciplinary perspectives on personalised prevention in youth mental health
Löchner, J., Bolivar, M., Booth, L., Canella, S., Calobro, M., Firth, J., García‐Palacios, A., Kyritsaka, A., Sander, L., Seiferth, C., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1568472 · Cited by 1
Self-citationWang, Y., Zhou, K., Lin, Z., Shen, H., Liu, R., Su, Z., Womer, F. Y., Wang, Y., Wei, Y., Zhu, R., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07572-y · Cited by 1
Eto, A., Mochizuki, K., Fukami, T., Sakakibara, W., & Izumi, K. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1695158
Caruso, A., Vázquez, C. G., Eicher, C., Huber, R., Kronenberg, G., Landolt, H., Seifritz, E., & Poian, G. D. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11251685
Banerjee, D., & Sarkar, S. (2025) · Journal of Development Policy and Practice · doi:10.1177/24551333251394459
Dine, C., Rekkas, Z., Frey, B. N., Krishnan, S., Duarte, D. G. G., Gomes, F. A., Mak, M. S. B., & Espinola, C. W. (2026) · JMIR Research Protocols · doi:10.2196/89466
Venturo-Conerly, K. E., Johnson, N. E., Osborn, T. L., Puffer, E. S., Rusch, T., Ndetei, D. M., Wasanga, C. M., Mutiso, V., Musyimi, C. W., & Weisz, J. R. (2022) · Figshare · doi:10.6084/m9.figshare.19881377
Venturo-Conerly, K. E., Johnson, N. E., Osborn, T. L., Puffer, E. S., Rusch, T. W., Ndetei, D. M., Wasanga, C. M., Mutiso, V., Musyimi, C. W., & Weisz, J. R. (2022) · Figshare · doi:10.6084/m9.figshare.19881380
Hall, L. S., Eastwood, C. R., Dela Rue, B. T., Dale, K., Kuhn-Sherlock, B., & Edwards, J. P. (2026) · Animal Production Science · doi:10.1071/an25382
Supporting Self-Awareness of Smartphone Use with Passive Sensing and LLM-Driven Feedback
Grech, N. S., & Trasciatti, G. (2025) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3714394.3756242
Digital Monitoring of Micro- and Macro-Movement Regularity in Psychiatric Inpatients With Depression
Shin, J., Lee, J., Joo, S. W., Park, H. G., Shin, H., Lim, H., Park, J. H., & Kim, S. M. (2026) · Psychiatry Investigation · doi:10.30773/pi.2025.0130
Mehta, S., Bansal, R., & Sood, P. (2025) · Interdisciplinary Research in Medical Sciences Specialty · doi:10.51847/lwbba590nm
Digital biomarkers in the diagnosis of social anxiety disorder: a scoping review
Qiao, F., Dong, W.-Q., Wang, X.-N., & Sun, Q. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-07875-8
Lee, H., Kang, S., & Lee, S. H. (2026) · Journal of Medical Internet Research · doi:10.2196/76432 · Cited by 1
Telemedicine in deep brain stimulation
Bogdan, I. D., Kuijf, M. L., Pathak, Y. J., & Kubben, P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-323-99742-3.00008-4
Wang, M., Peng, F., Mu, W., Tao, Y., Zhao, H., Yin, Q., Yan, Z., & Shi, C. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1637109
Zhu, H., You, H., Nie, Y., Sun, Y., Duan, L., Yan, P., Chen, Y., & Zhou, M. K. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1729303 · Cited by 2
Schwientek, A.-K., Drüge, M., Terhorst, Y., Kabrel, M., Sahan, F., & Apolinário-Hagen, J. (2026) · Advances in experimental medicine and biology · doi:10.1007/978-981-95-6872-7_29
Self-citationTowards detection of depression and anxiety in adolescents in social media
Magdaleno-Gatica, S., & López-Nava, I. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-333496-2.00011-3
AI-Based Early Prediction of Stress and Mental Health Issues Using Wearable Sensor Data
Bisht, A., Saxena, K., & Anjum, S. (2026) · International Conference on Emerging Computational Intelligence (ICECI) · doi:10.1109/iceci69159.2026.11519445
Zhu, N., Schoedel, R., Sust, L. N. N., Bühner, M., & Terhorst, Y. (2026) · doi:10.2196/preprints.101850
Self-citationLeimhofer, J., Schreynemackers, S., Hegerl, U., Reich, H., Dominik, A., Heider, D., & Allenhof, C. (2026) · doi:10.2196/preprints.101756
Sameh, A., Nauha, L., Seppänen, M., Niemelä, M., Oussalah, M., Korpelainen, R., & Farrahi, V. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117242 · Cited by 2
Graph-Based Early Warning from Therapy Narratives: A System-of-Systems Copilot for Behavioral Health
AlMakinah, R., & Canbaz, M. A. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536450
How has the Oura Ring been used in research? A Scoping Review (Preprint)Preprint
Jackson, J., Hwang, I.-W., Hurtado, A., Bustamante, E. E., Quinn, L. T., Varady, K. A., Zejnullahi, R., & Hibbing, P. R. (2026) · doi:10.2196/preprints.102169
Interdisciplinary Workshop on XR and AI Interfaces for Stress Management
Dong, Z., Zhang, J., Siriaraya, P., Hirai, T., Liew, K., Thanyadit, S., Intharah, T., Yomaboot, P., Ens, B. M., Clark, A., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7195-6_34
Smartphone sensor-based mobility and social behavioral correlates of anxiety severity in adults
Lan, Y., Roberts, H., & Helbich, M. (2026) · Wellbeing Space and Society · doi:10.1016/j.wss.2026.100428
Kim, S. M., Park, H. G., Shin, J. W., Park, J. H., Joo, S. W., Park, S., Jung, D., Lee, S., Lee, S., Kim, H., et al. (2026) · JMIR Formative Research · doi:10.2196/88083
Hattori, M., & Hasunuma, N. (2025) · Methods of Information in Medicine · doi:10.1055/a-2895-5222
Mendes, N. K. N., Andrade, R., & Oliveira, P. A. M. (2026) · doi:10.5753/sbcas.2026.20388
Marzorati, D., Rossi, A. D., Švihrová, R., Baldassari, A., Kochergin, V., Grossenbacher, M., & Faraci, F. D. (2026) · Digital Health · doi:10.1177/20552076261427103
Zhu, N., Schoedel, R., Sust, L. N. N., Bühner, M., & Terhorst, Y. (2026) · Journal of Medical Internet Research · doi:10.2196/101850
Self-citationNaim‐Feil, J., Stirling, R. E., Silva, R., Cook, M. J., Zalesky, A., Kang, M. J., Hopwood, M., & Karoly, P. J. (2026) · medRxiv · doi:10.64898/2026.07.22.26358741
Towards Monitoring Acute Opioid Withdrawal Severity Using a Multimodal Wearable Chest Patch
Abbaraju, V., Rahman, F. N., Cho, M. J., Liu, J. C., Yang, Y., Murray, C. E., Nawar, A., Berkebile, J. A., Chiok, J., Choi, J., et al. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3833425
Schnittstelle Technologie und Wissenschaft
Mundorf, A., Peterburs, J., & Ocklenburg, S. (2026) · Springer eBooks · doi:10.1007/978-3-662-73259-5_14
What Data Modalities are Participants Willing to Share for Mental Health Screening
Shrestha, A., Hickey, K., Maerker, C., Dixon–Gordon, K. L., & Rundensteiner, E. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00251
Wearable sensors offer opportunities for phenotyping perinatal mood disorders: a narrative review
Carrioli, O., Bruce, L. K., Kriplani, S. M., Schaap, T., Stewart, C., & Smarr, B. L. (2026) · Reproduction · doi:10.1093/reprod/xaag115
López, N. L., Cabada, R. Z., Estrada, M. L. B., & Beltrán, V. M. B. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-37438-7_46
Predicting Developer Burnout Using Temporal Stress Features in AI-Augmented Workplaces
Sahay, H., Mishra, V., & Prakash, P. G. O. (2026) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc69558.2026.11681714
Ren, Q., Chi, J., Li, Y., Zhao, P., Luo, M., Sun, J., He, Y., Liu, J., Luo, L., & Wu, Y. (2026) · Journal of Medical Internet Research · doi:10.2196/101329
Azadifar, S., Sameh, A., Nauha, L., Kärmeniemi, M., Niemelä, M., & Farrahi, V. (2026) · BMC Medicine · doi:10.1186/s12916-026-05276-y
Yıldız, O., Subasi, M. E., & Subasi, A. (2026) · Algorithms · doi:10.3390/a19100872
Omigbodun, F. T. (2026) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2026.100258
Metadata
- Authors
- Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L.
- Affiliations
- University of Helsinki (FI)
- Universität Ulm (DE)
- University of Freiburg (DE)
- University of Oulu (FI)
- Northwestern University (US)
- Corresponding author
- Moshe, Isaac
- ORCID
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2021-01-28
- Volume
- 12
- Pages
- 625247-625247
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC7876288
- OpenAlex
- W3126710441
- Semantic Scholar
- 5668ebac231d637ec29a731be26a949b7b73b20c
- Cited by
- 261
- References
- 83
- Citations by year
- 2026: 45 · 2025: 50 · 2024: 50 · 2023: 50 · 2022: 47 · 2021: 19
- FWCI
- 36.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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)
- Työväentutkimus Vuosikirja (submittedVersion)
- OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) (submittedVersion, other-oa)
- Universität Ulm, Kommunikations- und Informationszentrum (kiz) (cc-by)
- Funders
- Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- depression, anxiety, smartphone data, wearable devices, Oura Ring, heart rate variability, sleep, GPS location data, smartphone usage behavior, mood, mental health screening, stress, DASS-21
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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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