Associations between smartphone keystroke metadata and mental health symptoms in adolescents: findings from the future proofing study
- Braund, Taylor, O'Dea, Bridianne, Bal, Debopriyo, Maston, Kate, Larsen, Mark, Werner-Seidler, Aliza, Tillman, Gabriel, Christensen, Helen
- Authors: Braund, Taylor , O'Dea, Bridianne , Bal, Debopriyo , Maston, Kate , Larsen, Mark , Werner-Seidler, Aliza , Tillman, Gabriel , Christensen, Helen
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Mental Health Vol. 10, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ©Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen.
- Authors: Braund, Taylor , O'Dea, Bridianne , Bal, Debopriyo , Maston, Kate , Larsen, Mark , Werner-Seidler, Aliza , Tillman, Gabriel , Christensen, Helen
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Mental Health Vol. 10, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ©Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen.
Disordered social media use during COVID-19 predicts perceived stress and depression through indirect effects via fear of COVID-19
- Tillman, Gabriel, March, Evita, Lavender, Andrew, Braund, Taylor, Mesagno, Christopher
- Authors: Tillman, Gabriel , March, Evita , Lavender, Andrew , Braund, Taylor , Mesagno, Christopher
- Date: 2023
- Type: Text , Journal article
- Relation: Behavioral Sciences Vol. 13, no. 9 (2023), p.
- Full Text:
- Reviewed:
- Description: The 2019 novel coronavirus disease (COVID-19) is a global threat that can have an adverse effect on an individuals’ physical and mental health. Here, we investigate if disordered social media use predicts user stress and depression symptoms indirectly via fear of COVID-19. A total of 359 (timepoint 1 = 171, timepoint 2 = 188) participants were recruited via social media and snowball sampling. They completed an online survey that measured disordered social media use, fear of COVID-19, perceived stress, and depression symptomatology at two cross-sectional timepoints. We found that disordered social media use predicts depression indirectly through fear of COVID-19 at both timepoints. We also found that disordered social media use predicts perceived stress indirectly through fear of COVID-19, but only at timepoint 1. Taken together with previous research, our findings indicate that disordered social media use may lead to increased fear of COVID-19, which in turn may lead to poorer psychological wellbeing outcomes. Overall, there is evidence that the impact of the COVID-19 pandemic is affecting the physical, psychological, and emotional health of individuals worldwide. Moreover, this impact may be exacerbated by disordered use of social media. © 2023 by the authors.
- Authors: Tillman, Gabriel , March, Evita , Lavender, Andrew , Braund, Taylor , Mesagno, Christopher
- Date: 2023
- Type: Text , Journal article
- Relation: Behavioral Sciences Vol. 13, no. 9 (2023), p.
- Full Text:
- Reviewed:
- Description: The 2019 novel coronavirus disease (COVID-19) is a global threat that can have an adverse effect on an individuals’ physical and mental health. Here, we investigate if disordered social media use predicts user stress and depression symptoms indirectly via fear of COVID-19. A total of 359 (timepoint 1 = 171, timepoint 2 = 188) participants were recruited via social media and snowball sampling. They completed an online survey that measured disordered social media use, fear of COVID-19, perceived stress, and depression symptomatology at two cross-sectional timepoints. We found that disordered social media use predicts depression indirectly through fear of COVID-19 at both timepoints. We also found that disordered social media use predicts perceived stress indirectly through fear of COVID-19, but only at timepoint 1. Taken together with previous research, our findings indicate that disordered social media use may lead to increased fear of COVID-19, which in turn may lead to poorer psychological wellbeing outcomes. Overall, there is evidence that the impact of the COVID-19 pandemic is affecting the physical, psychological, and emotional health of individuals worldwide. Moreover, this impact may be exacerbated by disordered use of social media. © 2023 by the authors.
- Braund, Taylor, Breukelaar, Isabella, Griffiths, Kristi, Tillman, Gabriel, Palmer, Donna, Bryant, Richard, Phillips, Mary, Harris, Anthony, Korgaonkar, Mayuresh
- Authors: Braund, Taylor , Breukelaar, Isabella , Griffiths, Kristi , Tillman, Gabriel , Palmer, Donna , Bryant, Richard , Phillips, Mary , Harris, Anthony , Korgaonkar, Mayuresh
- Date: 2022
- Type: Text , Journal article
- Relation: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging Vol. 7, no. 3 (2022), p. 276-284
- Full Text: false
- Reviewed:
- Description: Background: Antidepressant efficacy in people with major depressive disorder remains modest, yet identifying treatment-predictive neurobiological markers may improve outcomes. While disruptions in functional connectivity within and between large-scale brain networks predict poorer treatment outcome, it is unclear whether higher trait neuroticism, which has been associated with generally poorer outcomes, contributes to these disruptions and to antidepressant-specific treatment outcomes. Here, we used whole-brain functional connectivity analysis to identify a neural connectomic signature of neuroticism and tested whether this signature predicted antidepressant treatment outcome. Methods: Participants were 226 adults with major depressive disorder and 68 healthy control subjects who underwent functional magnetic resonance imaging and were assessed on clinical features at baseline. Participants with major depressive disorder were then randomized to 1 of 3 commonly prescribed antidepressants and after 8 weeks completed a second functional magnetic resonance imaging and were reassessed for depressive symptom remission/response. Baseline intrinsic functional connectivity between each pair of 436 brain regions was analyzed using network-based statistics to identify connectomic features associated with neuroticism. Features were then assessed on their ability to predict treatment outcome and whether they changed after 8 weeks of treatment. Results: Higher baseline neuroticism was associated with greater connectivity within and between the salience, executive control, and somatomotor brain networks. Greater connectivity across these networks predicted poorer treatment outcome that was not mediated by baseline neuroticism, and connectivity strength decreased after antidepressant treatment. Conclusions: Our findings demonstrate that neuroticism is associated with organization of intrinsic neural networks that predict treatment outcome, elucidating its biological underpinnings and opportunity for better treatment personalization. © 2021 Society of Biological Psychiatry
Smartphone sensor data for identifying and monitoring symptoms of mood disorders : a longitudinal observational study
- Braund, Taylor, Zin, May, Boonstra, Tjeerd, Wong, Quincy, Larsen, Mark, Christensen, Helen, Tillman, Gabriel, O'Dea, Bridianne
- Authors: Braund, Taylor , Zin, May , Boonstra, Tjeerd , Wong, Quincy , Larsen, Mark , Christensen, Helen , Tillman, Gabriel , O'Dea, Bridianne
- Date: 2022
- Type: Text , Journal article
- Relation: JMIR Mental Health Vol. 9, no. 5 (2022), p.
- Full Text:
- Reviewed:
- Description: 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. © Taylor A Braund, May The Zin, Tjeerd W Boonstra, Quincy J J Wong, Mark E Larsen, Helen Christensen, Gabriel Tillman, Bridianne O'Dea.
- Authors: Braund, Taylor , Zin, May , Boonstra, Tjeerd , Wong, Quincy , Larsen, Mark , Christensen, Helen , Tillman, Gabriel , O'Dea, Bridianne
- Date: 2022
- Type: Text , Journal article
- Relation: JMIR Mental Health Vol. 9, no. 5 (2022), p.
- Full Text:
- Reviewed:
- Description: 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. © Taylor A Braund, May The Zin, Tjeerd W Boonstra, Quincy J J Wong, Mark E Larsen, Helen Christensen, Gabriel Tillman, Bridianne O'Dea.
Antidepressant side effects and their impact on treatment outcome in people with major depressive disorder : an iSPOT-D report
- Braund, Taylor, Tillman, Gabriel, Palmer, Donna, Gordon, Evian, Rush, A., Harris, Anthony
- Authors: Braund, Taylor , Tillman, Gabriel , Palmer, Donna , Gordon, Evian , Rush, A. , Harris, Anthony
- Date: 2021
- Type: Text , Journal article
- Relation: Translational Psychiatry Vol. 11, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Side effects to antidepressant medications are common and can impact the prognosis of successful treatment outcome in people with major depressive disorder (MDD). However, few studies have investigated the severity of side effects over the course of treatment and their association with treatment outcome. Here we assessed the severity of side effects and the impact of treatment type and anxiety symptoms over the course of treatment, as well as whether side effects were associated with treatment outcome. Participants were N = 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic MDD. Participants were randomised to receive escitalopram, sertraline, or venlafaxine-extended release with equal probability and reassessed at 8 weeks regarding Hamilton Rating Scale Depression (HRSD17) and Quick Inventory of Depressive Symptomatology (QIDS-SR16) remission and response. Severity of side effects were assessed using the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale and assessed at day 4 and weeks 2, 4, 6, and 8. Frequency, intensity, and burden of side effects were greatest at week 2, then only frequency and intensity of side effects gradually decreased up to week 6. Treatment type and anxiety symptoms did not impact the severity of side effects. A greater burden—but not frequency or intensity—of side effects was associated with poorer treatment outcome and as early as 4 days post-treatment. Together, this work provides an informative mapping of the progression of side effects throughout the treatment course and their association with treatment outcome. Importantly, the burden of side effects that are present as early as 4 days post-treatment predicts poorer treatment outcome and should be monitored closely. iSPOT-D: Registry name: ClinicalTrials.gov. Registration number: NCT00693849. © 2021, The Author(s).
- Authors: Braund, Taylor , Tillman, Gabriel , Palmer, Donna , Gordon, Evian , Rush, A. , Harris, Anthony
- Date: 2021
- Type: Text , Journal article
- Relation: Translational Psychiatry Vol. 11, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Side effects to antidepressant medications are common and can impact the prognosis of successful treatment outcome in people with major depressive disorder (MDD). However, few studies have investigated the severity of side effects over the course of treatment and their association with treatment outcome. Here we assessed the severity of side effects and the impact of treatment type and anxiety symptoms over the course of treatment, as well as whether side effects were associated with treatment outcome. Participants were N = 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic MDD. Participants were randomised to receive escitalopram, sertraline, or venlafaxine-extended release with equal probability and reassessed at 8 weeks regarding Hamilton Rating Scale Depression (HRSD17) and Quick Inventory of Depressive Symptomatology (QIDS-SR16) remission and response. Severity of side effects were assessed using the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale and assessed at day 4 and weeks 2, 4, 6, and 8. Frequency, intensity, and burden of side effects were greatest at week 2, then only frequency and intensity of side effects gradually decreased up to week 6. Treatment type and anxiety symptoms did not impact the severity of side effects. A greater burden—but not frequency or intensity—of side effects was associated with poorer treatment outcome and as early as 4 days post-treatment. Together, this work provides an informative mapping of the progression of side effects throughout the treatment course and their association with treatment outcome. Importantly, the burden of side effects that are present as early as 4 days post-treatment predicts poorer treatment outcome and should be monitored closely. iSPOT-D: Registry name: ClinicalTrials.gov. Registration number: NCT00693849. © 2021, The Author(s).
Sequential sampling models without random between-trial variability : the racing diffusion model of speeded decision making
- Tillman, Gabriel, Van Zandt, Trish, Logan, Gordon
- Authors: Tillman, Gabriel , Van Zandt, Trish , Logan, Gordon
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Psychonomic Bulletin and Review Vol. 27, no. 5 (2020), p. 911-936
- Full Text:
- Reviewed:
- Description: Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models. © 2020, The Psychonomic Society, Inc.
- Authors: Tillman, Gabriel , Van Zandt, Trish , Logan, Gordon
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Psychonomic Bulletin and Review Vol. 27, no. 5 (2020), p. 911-936
- Full Text:
- Reviewed:
- Description: Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models. © 2020, The Psychonomic Society, Inc.
- «
- ‹
- 1
- ›
- »