Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Taylor A Braund; May The Zin; Tjeerd W. Boonstra; Quincy J J Wong; Mark E Larsen; Helen Christensen; Gabriel Tillman; Bridianne O'Dea

doi:10.2196/35549

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

Taylor A Braund^*, May The Zin, Tjeerd W. Boonstra, Quincy J J Wong, Mark E Larsen, Helen Christensen, Gabriel Tillman, Bridianne O'Dea

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

BACKGROUND: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.

OBJECTIVE: Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.

METHODS: A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.

RESULTS: Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5.

CONCLUSIONS: Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.

Original language	English
Article number	e35549
Number of pages	13
Journal	JMIR Mental Health
Volume	9
Issue number	5
DOIs	https://doi.org/10.2196/35549
Publication status	Published - 2 May 2022

Keywords

BIPOLAR
CIRCADIAN-RHYTHM
COMMUNITY
CONNECTEDNESS
DEPRESSIVE SYMPTOMS
LIFE
SATISFACTION
SLEEP
SOCIAL RHYTHM REGULARITY
TRAJECTORIES
bipolar disorder
circadian rhythm
depression
mobile app
mobile phone
sensors

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@article{2ca6ff36db35422d89cf34015160241d,

title = "Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study",

abstract = "BACKGROUND: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.OBJECTIVE: Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.METHODS: A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.RESULTS: Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5.CONCLUSIONS: Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.",

keywords = "BIPOLAR, CIRCADIAN-RHYTHM, COMMUNITY, CONNECTEDNESS, DEPRESSIVE SYMPTOMS, LIFE, SATISFACTION, SLEEP, SOCIAL RHYTHM REGULARITY, TRAJECTORIES, bipolar disorder, circadian rhythm, depression, mobile app, mobile phone, sensors",

author = "Braund, {Taylor A} and Zin, {May The} and Boonstra, {Tjeerd W.} and Wong, {Quincy J J} and Larsen, {Mark E} and Helen Christensen and Gabriel Tillman and Bridianne O'Dea",

note = "{\textcopyright}Taylor A Braund, May The Zin, Tjeerd W Boonstra, Quincy J J Wong, Mark E Larsen, Helen Christensen, Gabriel Tillman, Bridianne O{\textquoteright}Dea. Originally published in JMIR Mental Health (https://mental.jmir.org), 04.05.2022.",

year = "2022",

month = may,

day = "2",

doi = "10.2196/35549",

language = "English",

volume = "9",

journal = "JMIR Mental Health",

issn = "2368-7959",

publisher = "JMIR Publications Inc.",

number = "5",

}

TY - JOUR

T1 - Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders

T2 - A Longitudinal Observational Study

AU - Braund, Taylor A

AU - Zin, May The

AU - Boonstra, Tjeerd W.

AU - Wong, Quincy J J

AU - Larsen, Mark E

AU - Christensen, Helen

AU - Tillman, Gabriel

AU - O'Dea, Bridianne

N1 - ©Taylor A Braund, May The Zin, Tjeerd W Boonstra, Quincy J J Wong, Mark E Larsen, Helen Christensen, Gabriel Tillman, Bridianne O’Dea. Originally published in JMIR Mental Health (https://mental.jmir.org), 04.05.2022.

PY - 2022/5/2

Y1 - 2022/5/2

N2 - BACKGROUND: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.OBJECTIVE: Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.METHODS: A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.RESULTS: Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5.CONCLUSIONS: Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.

AB - BACKGROUND: Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.OBJECTIVE: Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.METHODS: A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.RESULTS: Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R2=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5.CONCLUSIONS: Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.

KW - BIPOLAR

KW - CIRCADIAN-RHYTHM

KW - COMMUNITY

KW - CONNECTEDNESS

KW - DEPRESSIVE SYMPTOMS

KW - LIFE

KW - SATISFACTION

KW - SLEEP

KW - SOCIAL RHYTHM REGULARITY

KW - TRAJECTORIES

KW - bipolar disorder

KW - circadian rhythm

KW - depression

KW - mobile app

KW - mobile phone

KW - sensors

U2 - 10.2196/35549

DO - 10.2196/35549

M3 - Article

C2 - 35507385

SN - 2368-7959

VL - 9

JO - JMIR Mental Health

JF - JMIR Mental Health

IS - 5

M1 - e35549

ER -

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

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Keywords

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