The impact of a novel mimicry task for increasing emotion recognition in adults with autism spectrum disorder and alexithymia : protocol for a randomized controlled trial
- Caine, Joshua, Klein, Britt, Edwards, Stephen
- Authors: Caine, Joshua , Klein, Britt , Edwards, Stephen
- Date: 2021
- Type: Text , Journal article
- Relation: JMIR Research Protocols Vol. 10, no. 6 (2021), p.
- Full Text:
- Reviewed:
- Description: Background: Impaired facial emotion expression recognition (FEER) has typically been considered a correlate of autism spectrum disorder (ASD). Now, the alexithymia hypothesis is suggesting that this emotion processing problem is instead related to alexithymia, which frequently co-occurs with ASD. By combining predictive coding theories of ASD and simulation theories of emotion recognition, it is suggested that facial mimicry may improve the training of FEER in ASD and alexithymia. Objective: This study aims to evaluate a novel mimicry task to improve FEER in adults with and without ASD and alexithymia. Additionally, this study will aim to determine the contributions of alexithymia and ASD to FEER ability and assess which of these 2 populations benefit from this training task. Methods: Recruitment will primarily take place through an ASD community group with emphasis put on snowball recruiting. Included will be 64 consenting adults equally divided between participants without an ASD and participants with an ASD. Participants will be screened online using the Kessler Psychological Distress Scale (K-10; cut-off score of 22), Autism Spectrum Quotient (AQ-10), and Toronto Alexithymia Scale (TAS-20) followed by a clinical interview with a provisional psychologist at the Federation University psychology clinic. The clinical interview will include assessment of ability, anxiety, and depression as well as discussion of past ASD diagnosis and confirmatory administration of the Autism Mental Status Exam (AMSE). Following the clinical interview, the participant will complete the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and then undertake a baseline assessment of FEER. Consenting participants will then be assigned using a permuted blocked randomization method into either the control task condition or the mimicry task condition. A brief measure of satisfaction of the task and a debriefing session will conclude the study. Results: The study has Federation University Human Research Ethics Committee approval and is registered with the Australian New Zealand Clinical Trials. Participant recruitment is predicted to begin in the third quarter of 2021. Conclusions: This study will be the first to evaluate the use of a novel facial mimicry task condition to increase FEER in adults with ASD and alexithymia. If efficacious, this task could prove useful as a cost-effective adjunct intervention that could be used at home and thus remove barriers to entry. This study will also explore the unique effectiveness of this task in people without an ASD, with an ASD, and with alexithymia. © 2021 JMIR Research Protocols.
- Authors: Caine, Joshua , Klein, Britt , Edwards, Stephen
- Date: 2021
- Type: Text , Journal article
- Relation: JMIR Research Protocols Vol. 10, no. 6 (2021), p.
- Full Text:
- Reviewed:
- Description: Background: Impaired facial emotion expression recognition (FEER) has typically been considered a correlate of autism spectrum disorder (ASD). Now, the alexithymia hypothesis is suggesting that this emotion processing problem is instead related to alexithymia, which frequently co-occurs with ASD. By combining predictive coding theories of ASD and simulation theories of emotion recognition, it is suggested that facial mimicry may improve the training of FEER in ASD and alexithymia. Objective: This study aims to evaluate a novel mimicry task to improve FEER in adults with and without ASD and alexithymia. Additionally, this study will aim to determine the contributions of alexithymia and ASD to FEER ability and assess which of these 2 populations benefit from this training task. Methods: Recruitment will primarily take place through an ASD community group with emphasis put on snowball recruiting. Included will be 64 consenting adults equally divided between participants without an ASD and participants with an ASD. Participants will be screened online using the Kessler Psychological Distress Scale (K-10; cut-off score of 22), Autism Spectrum Quotient (AQ-10), and Toronto Alexithymia Scale (TAS-20) followed by a clinical interview with a provisional psychologist at the Federation University psychology clinic. The clinical interview will include assessment of ability, anxiety, and depression as well as discussion of past ASD diagnosis and confirmatory administration of the Autism Mental Status Exam (AMSE). Following the clinical interview, the participant will complete the Bermond-Vorst Alexithymia Questionnaire (BVAQ) and then undertake a baseline assessment of FEER. Consenting participants will then be assigned using a permuted blocked randomization method into either the control task condition or the mimicry task condition. A brief measure of satisfaction of the task and a debriefing session will conclude the study. Results: The study has Federation University Human Research Ethics Committee approval and is registered with the Australian New Zealand Clinical Trials. Participant recruitment is predicted to begin in the third quarter of 2021. Conclusions: This study will be the first to evaluate the use of a novel facial mimicry task condition to increase FEER in adults with ASD and alexithymia. If efficacious, this task could prove useful as a cost-effective adjunct intervention that could be used at home and thus remove barriers to entry. This study will also explore the unique effectiveness of this task in people without an ASD, with an ASD, and with alexithymia. © 2021 JMIR Research Protocols.
Virtual care initiatives for older adults in Australia : scoping review
- Savira, Feby, Gupta, Adyya, Gilbert, Cecily, Huggins, Catherine, Browning, Colette, Chapman, Wendy, Haines, Terry, Peeters, Anna
- Authors: Savira, Feby , Gupta, Adyya , Gilbert, Cecily , Huggins, Catherine , Browning, Colette , Chapman, Wendy , Haines, Terry , Peeters, Anna
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 25, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: There has been a rapid shift toward the adoption of virtual health care services in Australia. It is unknown how widely virtual care has been implemented or evaluated for the care of older adults in Australia. Objective: We aimed to review the literature evaluating virtual care initiatives for older adults across a wide range of health conditions and modalities and identify key challenges and opportunities for wider adoption at both patient and system levels in Australia. Methods: A scoping review of the literature was conducted. We searched MEDLINE, Embase, PsycINFO, CINAHL, AgeLine, and gray literature (January 1, 2011, to March 8, 2021) to identify virtual care initiatives for older Australians (aged
- Authors: Savira, Feby , Gupta, Adyya , Gilbert, Cecily , Huggins, Catherine , Browning, Colette , Chapman, Wendy , Haines, Terry , Peeters, Anna
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 25, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: There has been a rapid shift toward the adoption of virtual health care services in Australia. It is unknown how widely virtual care has been implemented or evaluated for the care of older adults in Australia. Objective: We aimed to review the literature evaluating virtual care initiatives for older adults across a wide range of health conditions and modalities and identify key challenges and opportunities for wider adoption at both patient and system levels in Australia. Methods: A scoping review of the literature was conducted. We searched MEDLINE, Embase, PsycINFO, CINAHL, AgeLine, and gray literature (January 1, 2011, to March 8, 2021) to identify virtual care initiatives for older Australians (aged
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.
A fully automated self-help biopsychosocial transdiagnostic digital intervention to reduce anxiety and/or depression and improve emotional regulation and well-being: pre-follow-up single-arm feasibility trial
- Klein, Britt, Nguyen, Huy, McLaren, Suzanne, Andrews, Brooke, Shandley, Kerrie
- Authors: Klein, Britt , Nguyen, Huy , McLaren, Suzanne , Andrews, Brooke , Shandley, Kerrie
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Formative Research Vol. 7, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Anxiety disorders and depression are prevalent disorders with high comorbidity, leading to greater chronicity and severity of symptoms. Given the accessibility to treatment issues, more evaluation is needed to assess the potential benefits of fully automated self-help transdiagnostic digital interventions. Innovating beyond the current transdiagnostic one-size-fits-all shared mechanistic approach may also lead to further improvements. Objective: The primary objective of this study was to explore the preliminary effectiveness and acceptability of a new fully automated self-help biopsychosocial transdiagnostic digital intervention (Life Flex) aimed at treating anxiety and/or depression, as well as improving emotional regulation; emotional, social, and psychological well-being; optimism; and health-related quality of life. Methods: This was a real-world pre-during-post-follow-up feasibility trial design evaluation of Life Flex. Participants were assessed at the preintervention time point (week 0), during intervention (weeks 3 and 5), at the postintervention time point (week 8), and at 1- and 3-month follow-ups (weeks 12 and 20, respectively). Results: The results provided early support for the Life Flex program in reducing anxiety (Generalized Anxiety Disorder 7), depression (Patient Health Questionnaire 9), psychological distress (Kessler 6), and emotional dysregulation (Difficulties in Emotional Regulation 36) and increasing emotional, social, and psychological well-being (Mental Health Continuum-Short Form); optimism (Revised Life Orientation Test); and health-related quality of life (EQ-5D-3L Utility Index and Health Rating; all false discovery rate [FDR] < .001). Large within-group treatment effect sizes (range |d|=0.82 to 1.33) were found for most variables from pre- to postintervention assessments and at the 1- and 3-month follow-up. The exceptions were medium treatment effect sizes for EQ-5D-3L Utility Index (range Cohen d=
- Authors: Klein, Britt , Nguyen, Huy , McLaren, Suzanne , Andrews, Brooke , Shandley, Kerrie
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Formative Research Vol. 7, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Anxiety disorders and depression are prevalent disorders with high comorbidity, leading to greater chronicity and severity of symptoms. Given the accessibility to treatment issues, more evaluation is needed to assess the potential benefits of fully automated self-help transdiagnostic digital interventions. Innovating beyond the current transdiagnostic one-size-fits-all shared mechanistic approach may also lead to further improvements. Objective: The primary objective of this study was to explore the preliminary effectiveness and acceptability of a new fully automated self-help biopsychosocial transdiagnostic digital intervention (Life Flex) aimed at treating anxiety and/or depression, as well as improving emotional regulation; emotional, social, and psychological well-being; optimism; and health-related quality of life. Methods: This was a real-world pre-during-post-follow-up feasibility trial design evaluation of Life Flex. Participants were assessed at the preintervention time point (week 0), during intervention (weeks 3 and 5), at the postintervention time point (week 8), and at 1- and 3-month follow-ups (weeks 12 and 20, respectively). Results: The results provided early support for the Life Flex program in reducing anxiety (Generalized Anxiety Disorder 7), depression (Patient Health Questionnaire 9), psychological distress (Kessler 6), and emotional dysregulation (Difficulties in Emotional Regulation 36) and increasing emotional, social, and psychological well-being (Mental Health Continuum-Short Form); optimism (Revised Life Orientation Test); and health-related quality of life (EQ-5D-3L Utility Index and Health Rating; all false discovery rate [FDR] < .001). Large within-group treatment effect sizes (range |d|=0.82 to 1.33) were found for most variables from pre- to postintervention assessments and at the 1- and 3-month follow-up. The exceptions were medium treatment effect sizes for EQ-5D-3L Utility Index (range Cohen d=
The diagnostic validity and reliability of an internet-based clinical assessment program for mental disorders
- Nguyen, David, Klein, Britt, Meyer, Denny, Austin, David, Abbott, Jo-Anne
- Authors: Nguyen, David , Klein, Britt , Meyer, Denny , Austin, David , Abbott, Jo-Anne
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 17, no. 9 (2015), p.
- Full Text:
- Reviewed:
- Description: Background: Internet-based assessment has the potential to assist with the diagnosis of mental health disorders and overcome the barriers associated with traditional services (eg, cost, stigma, distance). Further to existing online screening programs available, there is an opportunity to deliver more comprehensive and accurate diagnostic tools to supplement the assessment and treatment of mental health disorders. Objective: The aim was to evaluate the diagnostic criterion validity and test-retest reliability of the electronic Psychological Assessment System (e-PASS), an online, self-report, multidisorder, clinical assessment and referral system. Methods: Participants were 616 adults residing in Australia, recruited online, and representing prospective e-PASS users. Following e-PASS completion, 158 participants underwent a telephone-administered structured clinical interview and 39 participants repeated the e-PASS within 25 days of initial completion. Results: With structured clinical interview results serving as the gold standard, diagnostic agreement with the e-PASS varied considerably from fair (eg, generalized anxiety disorder:kappa=.37) to strong (eg, panic disorder:kappa=.62). Although the e-PASS' sensitivity also varied (0.43-0.86) the specificity was generally high (0.68-1.00). The e-PASS sensitivity generally improved when reducing the e-PASS threshold to a subclinical result. Test-retest reliability ranged from moderate (eg, specific phobia:kappa=.54) to substantial (eg, bulimia nervosa:kappa=.87). Conclusions: The e-PASS produces reliable diagnostic results and performs generally well in excluding mental disorders, although at the expense of sensitivity. For screening purposes, the e-PASS subclinical result generally appears better than a clinical result as a diagnostic indicator. Further development and evaluation is needed to support the use of online diagnostic assessment programs for mental disorders.
- Authors: Nguyen, David , Klein, Britt , Meyer, Denny , Austin, David , Abbott, Jo-Anne
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 17, no. 9 (2015), p.
- Full Text:
- Reviewed:
- Description: Background: Internet-based assessment has the potential to assist with the diagnosis of mental health disorders and overcome the barriers associated with traditional services (eg, cost, stigma, distance). Further to existing online screening programs available, there is an opportunity to deliver more comprehensive and accurate diagnostic tools to supplement the assessment and treatment of mental health disorders. Objective: The aim was to evaluate the diagnostic criterion validity and test-retest reliability of the electronic Psychological Assessment System (e-PASS), an online, self-report, multidisorder, clinical assessment and referral system. Methods: Participants were 616 adults residing in Australia, recruited online, and representing prospective e-PASS users. Following e-PASS completion, 158 participants underwent a telephone-administered structured clinical interview and 39 participants repeated the e-PASS within 25 days of initial completion. Results: With structured clinical interview results serving as the gold standard, diagnostic agreement with the e-PASS varied considerably from fair (eg, generalized anxiety disorder:kappa=.37) to strong (eg, panic disorder:kappa=.62). Although the e-PASS' sensitivity also varied (0.43-0.86) the specificity was generally high (0.68-1.00). The e-PASS sensitivity generally improved when reducing the e-PASS threshold to a subclinical result. Test-retest reliability ranged from moderate (eg, specific phobia:kappa=.54) to substantial (eg, bulimia nervosa:kappa=.87). Conclusions: The e-PASS produces reliable diagnostic results and performs generally well in excluding mental disorders, although at the expense of sensitivity. For screening purposes, the e-PASS subclinical result generally appears better than a clinical result as a diagnostic indicator. Further development and evaluation is needed to support the use of online diagnostic assessment programs for mental disorders.
Methods and applications of social media monitoring of mental health during disasters : scoping review
- Teague, Samantha, Shatte, Adrian, Weller, Emmelyn, Fuller-Tyszkiewicz, Matthew, Hutchinson, Delyse
- Authors: Teague, Samantha , Shatte, Adrian , Weller, Emmelyn , Fuller-Tyszkiewicz, Matthew , Hutchinson, Delyse
- Date: 2022
- Type: Text , Journal article , Review
- Relation: JMIR Mental Health Vol. 9, no. 2 (2022), p.
- Full Text:
- Reviewed:
- Description: Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. © 2022 Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson.
- Authors: Teague, Samantha , Shatte, Adrian , Weller, Emmelyn , Fuller-Tyszkiewicz, Matthew , Hutchinson, Delyse
- Date: 2022
- Type: Text , Journal article , Review
- Relation: JMIR Mental Health Vol. 9, no. 2 (2022), p.
- Full Text:
- Reviewed:
- Description: Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. © 2022 Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson.
Consumer perspectives on the use of artificial intelligence technology and automation in crisis support services : mixed methods study
- Ma, Jennifer, O’Riordan, Megan, Mazzer, Kelly, Batterham, Philip, Bradford, Sally, Kõlves, Kairi, Titov, Nickolai, Klein, Britt, Rickwood, Debra
- Authors: Ma, Jennifer , O’Riordan, Megan , Mazzer, Kelly , Batterham, Philip , Bradford, Sally , Kõlves, Kairi , Titov, Nickolai , Klein, Britt , Rickwood, Debra
- Date: 2022
- Type: Text , Journal article
- Relation: JMIR Human Factors Vol. 9, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Background: Emerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area. Objective: This study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline’s crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline’s crisis support services if technology and automation were implemented in the future. Methods: A mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline’s crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives. Results: One-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline’s crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services. Conclusions: Although Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue. © Jennifer S Ma, Megan O’Riordan, Kelly Mazzer, Philip J Batterham, Sally Bradford, Kairi Kõlves, Nickolai Titov, Britt Klein, Debra J Rickwood.
- Authors: Ma, Jennifer , O’Riordan, Megan , Mazzer, Kelly , Batterham, Philip , Bradford, Sally , Kõlves, Kairi , Titov, Nickolai , Klein, Britt , Rickwood, Debra
- Date: 2022
- Type: Text , Journal article
- Relation: JMIR Human Factors Vol. 9, no. 3 (2022), p.
- Full Text:
- Reviewed:
- Description: Background: Emerging technologies, such as artificial intelligence (AI), have the potential to enhance service responsiveness and quality, improve reach to underserved groups, and help address the lack of workforce capacity in health and mental health care. However, little research has been conducted on the acceptability of AI, particularly in mental health and crisis support, and how this may inform the development of responsible and responsive innovation in the area. Objective: This study aims to explore the level of support for the use of technology and automation, such as AI, in Lifeline’s crisis support services in Australia; the likelihood of service use if technology and automation were implemented; the impact of demographic characteristics on the level of support and likelihood of service use; and reasons for not using Lifeline’s crisis support services if technology and automation were implemented in the future. Methods: A mixed methods study involving a computer-assisted telephone interview and a web-based survey was undertaken from 2019 to 2020 to explore expectations and anticipated outcomes of Lifeline’s crisis support services in a nationally representative community sample (n=1300) and a Lifeline help-seeker sample (n=553). Participants were aged between 18 and 93 years. Quantitative descriptive analysis, binary logistic regression models, and qualitative thematic analysis were conducted to address the research objectives. Results: One-third of the community and help-seeker participants did not support the collection of information about service users through technology and automation (ie, via AI), and approximately half of the participants reported that they would be less likely to use the service if automation was introduced. Significant demographic differences were observed between the community and help-seeker samples. Of the demographics, only older age predicted being less likely to endorse technology and automation to tailor Lifeline’s crisis support service and use such services (odds ratio 1.48-1.66, 99% CI 1.03-2.38; P<.001 to P=.005). The most common reason for reluctance, reported by both samples, was that respondents wanted to speak to a real person, assuming that human counselors would be replaced by automated robots or machine services. Conclusions: Although Lifeline plans to always have a real person providing crisis support, help-seekers automatically fear this will not be the case if new technology and automation such as AI are introduced. Consequently, incorporating innovative use of technology to improve help-seeker outcomes in such services will require careful messaging and assurance that the human connection will continue. © Jennifer S Ma, Megan O’Riordan, Kelly Mazzer, Philip J Batterham, Sally Bradford, Kairi Kõlves, Nickolai Titov, Britt Klein, Debra J Rickwood.
Evaluation of various support intensities of digital mental health treatment for reducing anxiety and depression in adults : protocol for a mixed methods, adaptive, randomized clinical trial
- Andrews, Brooke, Klein, Britt, McLaren, Suzanne, Watson, Shaun, Corboy, Denise
- Authors: Andrews, Brooke , Klein, Britt , McLaren, Suzanne , Watson, Shaun , Corboy, Denise
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Research Protocols Vol. 12, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Anxiety and depression are leading causes of disease worldwide, requiring timely access to evidence-based treatment. Digital mental health (dMH) interventions increase accessibility to evidence-based psychological services delivered in a variety of web-based formats (eg, self-help and therapist-assisted interventions). Robust and rigorous studies of adaptive web-based intervention designs are scarce. No identified randomized clinical trial has investigated the efficacy of a 2-stage adaptive design, whereby the program-only condition or no support dMH treatment program is augmented by either low or high therapist assistance, if a participant does not improve or engage in the program-only condition. Objective: The primary objective is to assess whether low or high therapist-assisted support delivered via video chat is more effective in reducing anxiety and depressive symptoms compared with a dMH program–only condition. The secondary objective is to evaluate the role of motivation; self-efficacy; and preferences in participant engagement, adherence, and clinical outcomes (anxiety and depression symptoms) among the 3 treatment conditions (program only, low-intensity therapist assistance, and high-intensity therapist assistance). A mixed methods analysis of factors affecting participant attrition, participant reasons for nonengagement and withdrawal, and therapist training and implementation of dMH interventions will be completed. Qualitative data regarding participant and therapist experiences and satisfaction with video chat assessment and treatment will also be analyzed. Methods: Australian adults (N=137) with symptoms or a diagnosis of anxiety or depression will be screened for eligibility and given access to the 8-module Life Flex dMH treatment program. On day 15, participants who meet the augmentation criteria will be stepped up via block randomization to receive therapist assistance delivered via video chat for either 10 minutes (low intensity) or 50 minutes (high intensity) per week. This adaptive trial will implement a mixed methods design, with outcomes assessed before the intervention (week 0), during the intervention (weeks 3 and 6), after the intervention (week 9), and at the 3-month follow-up (week 21). Results: The primary outcome measures are for anxiety (Generalized Anxiety Disorder–7) and depression severity (Patient Health Questionnaire–9). Measures of working alliance, health status, health resources, preferences, self-efficacy, and motivation will be used for secondary outcomes. Qualitative methods will be used to explore participant and therapist experiences of video chat assessment and treatment, participant reasons for withdrawal and nonengagement, and therapist training and implementation experiences. Data collection commenced in November 2020 and was completed at the end of March 2022. Conclusions: This is the first mixed methods adaptive trial to explore the comparative efficacy of different intensity levels of self-help and a therapist-assisted dMH intervention program delivered via video chat for adults with anxiety or depression. Anticipated results may have implications for the implementation of dMH interventions. © Brooke Andrews, Britt Klein, Suzanne McLaren, Shaun Watson, Denise Corboy. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.04.2023.
- Authors: Andrews, Brooke , Klein, Britt , McLaren, Suzanne , Watson, Shaun , Corboy, Denise
- Date: 2023
- Type: Text , Journal article
- Relation: JMIR Research Protocols Vol. 12, no. (2023), p.
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- Description: Background: Anxiety and depression are leading causes of disease worldwide, requiring timely access to evidence-based treatment. Digital mental health (dMH) interventions increase accessibility to evidence-based psychological services delivered in a variety of web-based formats (eg, self-help and therapist-assisted interventions). Robust and rigorous studies of adaptive web-based intervention designs are scarce. No identified randomized clinical trial has investigated the efficacy of a 2-stage adaptive design, whereby the program-only condition or no support dMH treatment program is augmented by either low or high therapist assistance, if a participant does not improve or engage in the program-only condition. Objective: The primary objective is to assess whether low or high therapist-assisted support delivered via video chat is more effective in reducing anxiety and depressive symptoms compared with a dMH program–only condition. The secondary objective is to evaluate the role of motivation; self-efficacy; and preferences in participant engagement, adherence, and clinical outcomes (anxiety and depression symptoms) among the 3 treatment conditions (program only, low-intensity therapist assistance, and high-intensity therapist assistance). A mixed methods analysis of factors affecting participant attrition, participant reasons for nonengagement and withdrawal, and therapist training and implementation of dMH interventions will be completed. Qualitative data regarding participant and therapist experiences and satisfaction with video chat assessment and treatment will also be analyzed. Methods: Australian adults (N=137) with symptoms or a diagnosis of anxiety or depression will be screened for eligibility and given access to the 8-module Life Flex dMH treatment program. On day 15, participants who meet the augmentation criteria will be stepped up via block randomization to receive therapist assistance delivered via video chat for either 10 minutes (low intensity) or 50 minutes (high intensity) per week. This adaptive trial will implement a mixed methods design, with outcomes assessed before the intervention (week 0), during the intervention (weeks 3 and 6), after the intervention (week 9), and at the 3-month follow-up (week 21). Results: The primary outcome measures are for anxiety (Generalized Anxiety Disorder–7) and depression severity (Patient Health Questionnaire–9). Measures of working alliance, health status, health resources, preferences, self-efficacy, and motivation will be used for secondary outcomes. Qualitative methods will be used to explore participant and therapist experiences of video chat assessment and treatment, participant reasons for withdrawal and nonengagement, and therapist training and implementation experiences. Data collection commenced in November 2020 and was completed at the end of March 2022. Conclusions: This is the first mixed methods adaptive trial to explore the comparative efficacy of different intensity levels of self-help and a therapist-assisted dMH intervention program delivered via video chat for adults with anxiety or depression. Anticipated results may have implications for the implementation of dMH interventions. © Brooke Andrews, Britt Klein, Suzanne McLaren, Shaun Watson, Denise Corboy. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.04.2023.
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.
Efficacy of a digital mental health biopsychosocial transdiagnostic intervention with or without therapist assistance for adults with anxiety and depression : adaptive randomized controlled trial
- Andrews, Brooke, Klein, Britt, Nguyen, Huy, Corboy, Denise, McLaren, Suzanne, Watson, Shaun
- Authors: Andrews, Brooke , Klein, Britt , Nguyen, Huy , Corboy, Denise , McLaren, Suzanne , Watson, Shaun
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 25, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Digital mental health (DMH) interventions incorporating elements that adapt to the evolving needs of consumers have the potential to further our understanding of the optimal intensity of therapist assistance and inform stepped-care models. Objective: The primary objective was to compare the efficacy of a transdiagnostic biopsychosocial DMH program, with or without therapist assistance for adults with subthreshold symptoms or a diagnosis of anxiety or depression. Methods: In a randomized adaptive clinical trial design, all participants had access to the DMH program, with eligibility to have their program augmented with therapist assistance determined by program engagement or symptom severity. Participants who met stepped-care criteria were randomized to have their treatment program augmented with either low-intensity (10 min/week of video chat support for 7 weeks) or high-intensity (50 min/week of video chat support for 7 weeks) therapist assistance. A total of 103 participants (mean age 34.17, SD 10.50 years) were assessed before (week 0), during (weeks 3 and 6), and after the intervention (week 9) and at the 3-month follow-up (week 21). The effects of 3 treatment conditions (DMH program only, DMH program+low-intensity therapist assistance, and DMH program+high-intensity therapist assistance) on changes in the 2 primary outcomes of anxiety (7-item Generalized Anxiety Disorder Scale [GAD-7]) and depression (9-item Patient Health Questionnaire [PHQ-9]) were assessed using the Cohen d, reliable change index, and mixed-effects linear regression analyses. Results: There were no substantial differences in the outcome measures among intervention conditions. However, there were significant time effect changes in most outcomes over time. All 3 intervention conditions demonstrated strong and significant treatment effect changes in GAD-7 and PHQ-9 scores, with absolute Cohen d values ranging from 0.82 to 1.79 (all P<.05). The mixed-effects models revealed that, in the Life Flex program–only condition at week 3, mean GAD-7 and PHQ-9 scores significantly decreased from baseline by 3.54 and 4.38 (all P<.001), respectively. At weeks 6, 9, and 21, GAD-7 and PHQ-9 scores significantly decreased from baseline by at least 6 and 7 points (all P<.001), respectively. Nonresponders at week 3 who were stepped up to therapist assistance increased program engagement and treatment response. At the postintervention time point and 3-month follow-up, 67% (44/65) and 69% (34/49) of the participants, respectively, no longer met diagnostic criteria for anxiety or depression. Conclusions: The findings highlight that early detection of low engagement and non–treatment response presents an opportunity to effectively intervene by incorporating an adaptive design. Although the study findings indicate that therapist assistance was no more effective than the DMH intervention program alone for reducing symptoms of anxiety or depression, the data highlight the potential influence of participant selection bias and participant preferences within stepped-care treatment models. ©Brooke Andrews, Britt Klein, Huy Van Nguyen, Denise Corboy, Suzanne McLaren, Shaun Watson.
- Authors: Andrews, Brooke , Klein, Britt , Nguyen, Huy , Corboy, Denise , McLaren, Suzanne , Watson, Shaun
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 25, no. (2023), p.
- Full Text:
- Reviewed:
- Description: Background: Digital mental health (DMH) interventions incorporating elements that adapt to the evolving needs of consumers have the potential to further our understanding of the optimal intensity of therapist assistance and inform stepped-care models. Objective: The primary objective was to compare the efficacy of a transdiagnostic biopsychosocial DMH program, with or without therapist assistance for adults with subthreshold symptoms or a diagnosis of anxiety or depression. Methods: In a randomized adaptive clinical trial design, all participants had access to the DMH program, with eligibility to have their program augmented with therapist assistance determined by program engagement or symptom severity. Participants who met stepped-care criteria were randomized to have their treatment program augmented with either low-intensity (10 min/week of video chat support for 7 weeks) or high-intensity (50 min/week of video chat support for 7 weeks) therapist assistance. A total of 103 participants (mean age 34.17, SD 10.50 years) were assessed before (week 0), during (weeks 3 and 6), and after the intervention (week 9) and at the 3-month follow-up (week 21). The effects of 3 treatment conditions (DMH program only, DMH program+low-intensity therapist assistance, and DMH program+high-intensity therapist assistance) on changes in the 2 primary outcomes of anxiety (7-item Generalized Anxiety Disorder Scale [GAD-7]) and depression (9-item Patient Health Questionnaire [PHQ-9]) were assessed using the Cohen d, reliable change index, and mixed-effects linear regression analyses. Results: There were no substantial differences in the outcome measures among intervention conditions. However, there were significant time effect changes in most outcomes over time. All 3 intervention conditions demonstrated strong and significant treatment effect changes in GAD-7 and PHQ-9 scores, with absolute Cohen d values ranging from 0.82 to 1.79 (all P<.05). The mixed-effects models revealed that, in the Life Flex program–only condition at week 3, mean GAD-7 and PHQ-9 scores significantly decreased from baseline by 3.54 and 4.38 (all P<.001), respectively. At weeks 6, 9, and 21, GAD-7 and PHQ-9 scores significantly decreased from baseline by at least 6 and 7 points (all P<.001), respectively. Nonresponders at week 3 who were stepped up to therapist assistance increased program engagement and treatment response. At the postintervention time point and 3-month follow-up, 67% (44/65) and 69% (34/49) of the participants, respectively, no longer met diagnostic criteria for anxiety or depression. Conclusions: The findings highlight that early detection of low engagement and non–treatment response presents an opportunity to effectively intervene by incorporating an adaptive design. Although the study findings indicate that therapist assistance was no more effective than the DMH intervention program alone for reducing symptoms of anxiety or depression, the data highlight the potential influence of participant selection bias and participant preferences within stepped-care treatment models. ©Brooke Andrews, Britt Klein, Huy Van Nguyen, Denise Corboy, Suzanne McLaren, Shaun Watson.
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