Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Rohani, Darius Adam; Faurholt‐Jepsen, Maria; Kessing, Lars Vedel; Bardram, Jakob E.

doi:10.2196/preprints.9691

Cited by 3 publications

(4 citation statements)

References 72 publications

(116 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices is referred to as "digital phenotyping" (13,14). There is now a growing body of research demonstrating that digital phenotyping data may enable the identification of people suffering from or at risk of developing mental disorders, in some cases even before symptoms are visible (or detectable) using traditional methods (11,(15)(16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

“…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). Finally, in a meta-analytic review of studies assessing the correlation between smartphone and wearable device data and affective disorders, Rohani et al (15) revealed that the association between time spent at home and depressive symptoms was the most consistently significant finding of any smartphone-derived feature in the analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In a study by Edwards et al [23], they found a significant increase in depression scores (F = 11.85, p = .001) in the group of healthy young adults that were asked to minimize physical steps for one week. In an extensive systematic review, Rohani et al [21] revealed a consistent negative correlation among studies between vigorous activity and depressive symptoms.…”

Section: Activity Category On 'Good-days'mentioning

confidence: 96%

“…To keep a circadian rhythm, denoted here as 'diurnal rhythm', have shown a positive statistically significant correlation with mood [21]. This relation is explored by defining 'diurnal rhythm' as the most common AC within each hour.…”

Section: Circadian Rhythmmentioning

confidence: 99%

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study (Preprint)

Rohani¹,

Tuxen²,

Lopategui³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Behavioral Activation (BA) is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance BA. Objective: The objectives of this paper were to 1) automatically identify behavioral patterns through statistical analysis of the paper-based activity schedules, and 2) determine whether it is feasible to move the BA therapy format to a digital solution. Methods: We collected activity schedules from seven patients, covering in total 2,480 hours of self-reported activities. A pleasure score, on a 1-10 rating scale, was reported for each activity. The activities were digitalized into seven activity categories, and statistical analyses were conducted. Results: Across all patients, movement-related activities were associated with the highest pleasure score followed by social activities. On an individual level, through a non-parametric Wilcoxon Signed-Rank test, one patient had a statistically significant larger amount of spare time activities when feeling bad (z = -2.045, p = .041). Through a within-subject ANCOVA, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm ( = .265, p = .0288). Furthermore, a second-order trend indicated that ρ two hours of daily social activity was the optimal for the patients (β 2 = -0.08, t(63) = -1.22, p = .23). Conclusions:The data-driven statistical approach was able to find patterns within the behavioral traits that could help to assist, the therapist in a future application.

show abstract

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

Rohani¹,

Tuxen²,

Lopategui³

et al. 2018

JMIR Ment Health

Self Cite

View full text Add to dashboard Cite

BackgroundBehavioral activation is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance behavioral activation.ObjectiveThe objectives of this paper were to (1) automatically identify behavioral patterns through statistical analysis of the paper-based activity diaries, and (2) determine whether it is feasible to move the behavioral activation therapy format to a digital solution.MethodsWe collected activity diaries from seven patients with bipolar depression, covering in total 2,480 hours of self-reported activities. A pleasure score, on a 1-10 rating scale, was reported for each activity. The activities were digitalized into 6 activity categories, and statistical analyses were conducted.ResultsAcross all patients, movement-related activities were associated with the highest pleasure score followed by social activities. On an individual level, through a nonparametric Wilcoxon Signed-Rank test, one patient had a statistically significant larger amount of spare time activities when feeling bad (z=–2.045, P=.041). Through a within-subject analysis of covariance, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm (ρ=.265, P=.029). Furthermore, a second-order trend indicated that two hours of daily social activity was optimal for the patients (β2=–0.08, t (63)=–1.22, P=.23).ConclusionsThe data-driven statistical approach was able to find patterns within the behavioral traits that could assist the therapist in as well as help design future technologies for behavioral activation.

show abstract

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

Cited by 3 publications

References 72 publications

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study (Preprint)

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

Contact Info

Product

Resources

About