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Qian Y, Solano MJ, Kreindler D. Grouping of mood symptoms by time series dynamics. J Affect Disord 2022; 309:186-192. [PMID: 35461820 DOI: 10.1016/j.jad.2022.04.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 03/12/2022] [Accepted: 04/16/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND Understanding how symptoms of mood disorders vary over time in relation to each other is potentially valuable for diagnosis and predicting episodes of illness. In this paper, we characterize the degree of similarity of time series of different mood disorder symptoms. METHODS We collected 32,215 mood disorder symptom questionnaires, administered twice-daily over 18 months to (n = 19) subjects with rapidly cycling bipolar disorder and (n = 20) healthy control subjects, using visual analog scales to rate 11 sets of symptom severity ratings plus a control item. We used Dynamic Time Warping to calculate similarity ratings between all within-subject pairs of severity ratings followed by Exploratory Factor Analysis (EFA) to identify latent factors of symptom time series across all subjects. RESULTS Two latent factors were identified: one with depression and anxiety; and a second, with concentration, energy, irritability, fatigue, appetite, euphoria/elation and overall mood. Restlessness, racing thoughts, and the control item (daily hours of daylight) did not cluster with any of the others. LIMITATIONS Limited sample size dictated that we pool bipolar and healthy patients and use an iterative EFA procedure. CONCLUSION This analysis suggests that, in a pooled sample of individuals with bipolar disorder and in healthy controls, severity ratings of overall depression and overall anxiety vary jointly as one dynamic factor, while some but not all other DSM mood symptoms vary jointly along with overall mood rating as a second dynamic factor. Further investigation may determine if these findings can simplify subjective symptom reporting in mood-monitoring studies.
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Affiliation(s)
- Yuxin Qian
- Applied Mathematics Program, University of California Los Angeles, Los Angeles, California, USA
| | - Maria José Solano
- Mathematics and Computer Science Program, McGill University, Montreal, Quebec, Canada
| | - David Kreindler
- Division of Child and Youth Mental Health, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada, M5T 1R8; Centre for Mobile Computing in Mental Health, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, M4N 3M5; Division of Youth Psychiatry, Department of Psychiatry, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, M4N 3M5.
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2
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Rabin JS, Davidson B, Giacobbe P, Hamani C, Cohn M, Illes J, Lipsman N. Neuromodulation for major depressive disorder: innovative measures to capture efficacy and outcomes. Lancet Psychiatry 2020; 7:1075-1080. [PMID: 33129374 DOI: 10.1016/s2215-0366(20)30187-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 04/02/2020] [Accepted: 04/14/2020] [Indexed: 12/13/2022]
Abstract
Major depressive disorder is a common and debilitating disorder. Although most patients with this disorder benefit from established treatments, a subset of patients have symptoms that remain treatment resistant. Novel treatment approaches, such as deep brain stimulation, are urgently needed for patients with treatment-resistant major depressive disorder. These novel treatments are currently being tested in clinical trials in which success hinges on how accurately and comprehensively the primary outcome measure captures the treatment effect. In this Personal View, we argue that current measures used to assess outcomes in neurosurgical trials of major depressive disorder might be missing clinically important treatment effects. A crucial problem of continuing to use suboptimal outcome measures is that true signals of efficacy might be missed, thereby disqualifying potentially effective treatments. We argue that a re-evaluation of how outcomes are measured in these trials is much overdue and describe several novel approaches that attempt to better capture meaningful change.
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Affiliation(s)
- Jennifer S Rabin
- Sunnybrook Research Institute, Toronto, ON, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
| | - Benjamin Davidson
- Sunnybrook Research Institute, Toronto, ON, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medicine, Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Peter Giacobbe
- Sunnybrook Research Institute, Toronto, ON, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Clement Hamani
- Sunnybrook Research Institute, Toronto, ON, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medicine, Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Melanie Cohn
- Department of Psychology, University of Toronto, Toronto, ON, Canada; Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | - Judy Illes
- Neuroethics Canada, Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Nir Lipsman
- Sunnybrook Research Institute, Toronto, ON, Canada; Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medicine, Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
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3
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Taylor RW, Marwood L, Greer B, Strawbridge R, Cleare AJ. Predictors of response to augmentation treatment in patients with treatment-resistant depression: A systematic review. J Psychopharmacol 2019; 33:1323-1339. [PMID: 31526204 DOI: 10.1177/0269881119872194] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Treatment-resistant depression is an important contributor to the global burden of depression. Antidepressant augmentation is a recommended treatment strategy for treatment-resistant patients, but outcomes remain poor. Identifying factors that are predictive of response to augmentation treatments may improve outcomes. AIMS This review aimed to synthesise the existing literature examining predictors of response to augmentation treatments in patients who had insufficiently responded to initial treatment. METHODS A systematic search was conducted identifying 2241 unique manuscripts. 24 examining predictors of outcome to pharmacological or psychological augmentation treatment were included in this review. RESULTS Atypical antipsychotics were the most frequently assessed treatment class (nine studies), closely followed by mood stabilisers (eight studies). Only one eligible psychological augmentation study was identified. Early response to treatment (week 2) was the best-supported predictor of subsequent treatment outcome, reported by six studies. Many predictor variables were only assessed by one report and others such as pre-treatment severity yielded contradictory results, both within and across treatment classes. CONCLUSIONS This review highlights the importance of early response as a predictor of pharmacological augmentation outcome, with implications for both the monitoring and treatment of resistant unipolar patients. Further replication is needed across specific interventions to fully assess the generalisability of this finding. However, the clear lack of consistent evidence for other predictive factors both within and across treatments, and the scarce examination of psychological augmentation, demonstrates the need for much more research of a high quality if response prediction is to improve outcomes for patients with treatment-resistant depression.
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Affiliation(s)
- Rachael W Taylor
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
| | - Lindsey Marwood
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ben Greer
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rebecca Strawbridge
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
| | - Anthony J Cleare
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
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Aledavood T, Torous J, Triana Hoyos AM, Naslund JA, Onnela JP, Keshavan M. Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders. Curr Psychiatry Rep 2019; 21:49. [PMID: 31161412 PMCID: PMC6546650 DOI: 10.1007/s11920-019-1043-y] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
PURPOSE OF REVIEW Sleep is an important feature in mental illness. Smartphones can be used to assess and monitor sleep, yet there is little prior application of this approach in depressive, anxiety, or psychotic disorders. We review uses of smartphones and wearable devices for sleep research in patients with these conditions. RECENT FINDINGS To date, most studies consist of pilot evaluations demonstrating feasibility and acceptability of monitoring sleep using smartphones and wearable devices among individuals with psychiatric disorders. Promising findings show early associations between behaviors and sleep parameters and agreement between clinic-based assessments, active smartphone data capture, and passively collected data. Few studies report improvement in sleep or mental health outcomes. Success of smartphone-based sleep assessments and interventions requires emphasis on promoting long-term adherence, exploring possibilities of adaptive and personalized systems to predict risk/relapse, and determining impact of sleep monitoring on improving patients' quality of life and clinically meaningful outcomes.
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Affiliation(s)
- Talayeh Aledavood
- Department of Psychiatry, University of Helsinki, P.O. Box 22, Välskärinkatu 12 A, FI-00014, Helsinki, Finland.
- Department of Computer Science, Aalto University, Espoo, Finland.
| | - John Torous
- Division of Digital Psychiatry Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - John A Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Jukka-Pekka Onnela
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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Colombo D, Fernández-Álvarez J, Patané A, Semonella M, Kwiatkowska M, García-Palacios A, Cipresso P, Riva G, Botella C. Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review. J Clin Med 2019; 8:E465. [PMID: 30959828 PMCID: PMC6518287 DOI: 10.3390/jcm8040465] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 03/26/2019] [Accepted: 04/01/2019] [Indexed: 12/20/2022] Open
Abstract
Ecological momentary assessment (EMA) and ecological momentary intervention (EMI) are alternative approaches to retrospective self-reports and face-to-face treatments, and they make it possible to repeatedly assess patients in naturalistic settings and extend psychological support into real life. The increase in smartphone applications and the availability of low-cost wearable biosensors have further improved the potential of EMA and EMI, which, however, have not yet been applied in clinical practice. Here, we conducted a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to explore the state of the art of technology-based EMA and EMI for major depressive disorder (MDD). A total of 33 articles were included (EMA = 26; EMI = 7). First, we provide a detailed analysis of the included studies from technical (sampling methods, duration, prompts), clinical (fields of application, adherence rates, dropouts, intervention effectiveness), and technological (adopted devices) perspectives. Then, we identify the advantages of using information and communications technologies (ICTs) to extend the potential of these approaches to the understanding, assessment, and intervention in depression. Furthermore, we point out the relevant issues that still need to be addressed within this field, and we discuss how EMA and EMI could benefit from the use of sensors and biosensors, along with recent advances in machine learning for affective modelling.
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Affiliation(s)
- Desirée Colombo
- Department of Basic Psychology, Clinic and Psychobiology, Universitat Jaume I, Av. Sos Baynat, s/n, 12071 Castellón, Spain.
| | - Javier Fernández-Álvarez
- Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli, 1, 20100 Milan, Italy.
| | - Andrea Patané
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Rd, Oxford, OX1 3QD, UK.
| | - Michelle Semonella
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy.
| | - Marta Kwiatkowska
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Rd, Oxford, OX1 3QD, UK.
| | - Azucena García-Palacios
- Department of Basic Psychology, Clinic and Psychobiology, Universitat Jaume I, Av. Sos Baynat, s/n, 12071 Castellón, Spain.
- CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, 28029 Madrid, Spain.
| | - Pietro Cipresso
- Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli, 1, 20100 Milan, Italy.
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy.
| | - Giuseppe Riva
- Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli, 1, 20100 Milan, Italy.
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy.
| | - Cristina Botella
- Department of Basic Psychology, Clinic and Psychobiology, Universitat Jaume I, Av. Sos Baynat, s/n, 12071 Castellón, Spain.
- CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, 28029 Madrid, Spain.
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Prada P, Zamberg I, Bouillault G, Jimenez N, Zimmermann J, Hasler R, Aubry JM, Nicastro R, Perroud N. EMOTEO: A Smartphone Application for Monitoring and Reducing Aversive Tension in Borderline Personality Disorder Patients, a Pilot Study. Perspect Psychiatr Care 2017; 53:289-298. [PMID: 27439663 DOI: 10.1111/ppc.12178] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 06/16/2016] [Accepted: 06/17/2016] [Indexed: 11/27/2022] Open
Abstract
PURPOSE We developed a smartphone application (App; EMOTEO: emotion-meteo [weather forecast]) to help borderline personality disorder (BPD) patients to monitor and regulate their inner tension. The App proposes targeted mindfulness-based exercises. DESIGN AND METHODS We assessed the usability and efficiency of this App for monitoring and reduction of aversive tension in 16 BPD participants over a 6-month period. FINDINGS We recorded a mean of 318.1 sessions (SD = 166.7) per participants, with a high level of satisfaction. There was a significant decrease in aversive tension (p < .05) and the App was mainly used around 10 a.m. and 9 p.m. PRACTICE IMPLICATIONS EMOTEO was user-friendly and efficient in reducing aversive tension in BPD patients.
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Affiliation(s)
- Paco Prada
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Ido Zamberg
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Gérald Bouillault
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Naya Jimenez
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Julien Zimmermann
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Roland Hasler
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Jean-Michel Aubry
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Rosetta Nicastro
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Nader Perroud
- Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
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Van Ameringen M, Turna J, Khalesi Z, Pullia K, Patterson B. There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depress Anxiety 2017; 34:526-539. [PMID: 28569409 DOI: 10.1002/da.22657] [Citation(s) in RCA: 174] [Impact Index Per Article: 24.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 04/04/2017] [Accepted: 04/09/2017] [Indexed: 02/06/2023] Open
Abstract
Mental health apps are viewed as a promising modality to extend the reach of mental health care beyond the clinic. They do so by providing a means of assessment, tracking, and treatment through a smartphone. Given that nearly 2/3 of the American population owns a smartphone, mental health apps offer the possibility of overcoming treatment barriers such as geographic location or financial barriers. Unfortunately, the excitement surrounding mental health apps may be premature as the current supporting literature regarding their efficacy is limited. The app marketplace is littered with apps claiming to treat or assess symptoms, but even those created by reputable organizations or those incorporating components of evidence-based treatments have not yet been validated in terms of their efficacy. This review aims to provide a comprehensive review of the current state of the mental health app literature by examining published reports of apps designed for DSM-5 anxiety and mood disorders, OCD, and PTSD. The breadth of apps reviewed includes those oriented around assessment, symptom tracking, and treatment as well as "multipurpose" apps, which incorporate several of these components. This review will also present some of the most popular mental health apps which may have clinical utility and could be prescribed to clients. While we discuss many potential benefits of mental health apps, we focus on a number of issues that the current state of the app literature presents. Overall there is a significant disconnect between app developers, the scientific community and health care, leaving the utility of existing apps questionable.
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Affiliation(s)
- Michael Van Ameringen
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.,MacAnxiety Research Centre, McMaster University, Hamilton, ON, Canada.,Hamilton Health Sciences, Hamilton, ON, Canada
| | - Jasmine Turna
- MacAnxiety Research Centre, McMaster University, Hamilton, ON, Canada.,MiNDS Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Zahra Khalesi
- MacAnxiety Research Centre, McMaster University, Hamilton, ON, Canada
| | - Katrina Pullia
- MacAnxiety Research Centre, McMaster University, Hamilton, ON, Canada
| | - Beth Patterson
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.,MacAnxiety Research Centre, McMaster University, Hamilton, ON, Canada
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Abstract
Mobile health (mHealth) apps are becoming much more widely available. As more patients learn about and download apps, clinicians are sure to face more questions about the role these apps can play in treatment. Clinicians thus need to familiarize themselves with the clinical and legal risks that apps may introduce. Regulatory rules and organizations that oversee the safety and efficacy of mHealth apps are currently fragmentary in nature and clinicians should pay special attention to categories of apps which are currently exempt from significant regulation. Uniform HIPAA protection does not apply to personal health data that are shared with apps in many contexts which creates a number of clinically relevant privacy and security concerns. Clinicians should also consider several relatively novel potential adverse clinical outcomes and liability concerns that may be relevant to specific categories of apps, including apps that target (i) medication adherence, (ii) collection of self-reported data, (iii) collection of passive data, and (iv) generation of treatment recommendations for psychotherapeutic and behavioral interventions. Considering these potential pitfalls (and disclosing them to patients as a part of obtaining informed consent) is necessary as clinicians consider incorporating apps into treatment.
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Huguet A, Rao S, McGrath PJ, Wozney L, Wheaton M, Conrod J, Rozario S. A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression. PLoS One 2016; 11:e0154248. [PMID: 27135410 PMCID: PMC4852920 DOI: 10.1371/journal.pone.0154248] [Citation(s) in RCA: 181] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 04/11/2016] [Indexed: 12/21/2022] Open
Abstract
Depression is a common mental health condition for which many mobile apps aim to provide support. This review aims to identify self-help apps available exclusively for people with depression and evaluate those that offer cognitive behavioural therapy (CBT) or behavioural activation (BA). One hundred and seventeen apps have been identified after searching both the scientific literature and the commercial market. 10.26% (n = 12) of these apps identified through our search offer support that seems to be consistent with evidence-based principles of CBT or BA. Taking into account the non existence of effectiveness/efficacy studies, and the low level of adherence to the core ingredients of the CBT/BA models, the utility of these CBT/BA apps are questionable. The usability of reviewed apps is highly variable and they rarely are accompanied by explicit privacy or safety policies. Despite the growing public demand, there is a concerning lack of appropiate CBT or BA apps, especially from a clinical and legal point of view. The application of superior scientific, technological, and legal knowledge is needed to improve the development, testing, and accessibility of apps for people with depression.
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Affiliation(s)
- Anna Huguet
- Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada
- Department of Community Health & Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Sanjay Rao
- Annapolis Valley Health, Kentville, Nova Scotia, Canada
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Patrick J. McGrath
- Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada
- Departments of Pediatrics and Science, Dalhousie University, Halifax, Nova Scotia, Canada
- Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | - Lori Wozney
- Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada
| | - Mike Wheaton
- Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada
| | - Jill Conrod
- Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada
| | - Sharlene Rozario
- Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada
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Macias C, Panch T, Hicks YM, Scolnick JS, Weene DL, Öngür D, Cohen BM. Using Smartphone Apps to Promote Psychiatric and Physical Well-Being. Psychiatr Q 2015; 86:505-19. [PMID: 25636496 DOI: 10.1007/s11126-015-9337-7] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
This pilot study tested the acceptability and usability of a prototype app designed to promote the physical well-being of adults with psychiatric disorders. The application under evaluation, WellWave, promoted walking as a physical exercise, and offered a variety of supportive non-physical activities, including confidential text-messaging with peer staff, and a digital library of readings and videos on recovery from psychiatric illness. Study participants engaged strongly in the app throughout the 4-week study, showing a 94 % mean daily usage rate, and a 73 % mean response rate across all electronic messages and prompts, which approximates the gold standard of 75 % for momentary ecological assessment studies. Seven of the ten study participants averaged two or more walks per week, beginning with 5-min walks and ending with walks lasting 20 min or longer. This responsiveness to the walking prompts, and the overall high rate of engagement in other app features, suggest that adults with psychiatric conditions would welcome and benefit from similar smartphone interventions that promote healthy behaviours in life domains other than exercise. Pilot study results also suggest that smartphone applications can be useful as research tools in the development and testing of theories and practical strategies for encouraging healthy lifestyles. Participants were prompted periodically to rate their own health quality, perceived control over their health, and stage-of-change in adopting a walking routine, and these electronic self-ratings showed acceptable concurrent and discriminant validity, with all participants reporting moderate to high motivation to exercise by the end of the study.
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Affiliation(s)
- Cathaleene Macias
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA. .,McLean Hospital, Belmont, MA, USA.
| | | | - Yale M Hicks
- Waverley Place at Waverley Square, McLean Hospital, Belmont, MA, USA.
| | - Jason S Scolnick
- Waverley Place at Waverley Square, McLean Hospital, Belmont, MA, USA.
| | - David Lyle Weene
- Waverley Place at Waverley Square, McLean Hospital, Belmont, MA, USA.
| | - Dost Öngür
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA. .,Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA.
| | - Bruce M Cohen
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA. .,Program for Neuropsychiatric Research, McLean Hospital, Belmont, MA, USA.
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Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, Onnela JP. Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder. JMIR Ment Health 2015; 2:e8. [PMID: 26543914 PMCID: PMC4607379 DOI: 10.2196/mental.3889] [Citation(s) in RCA: 156] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 01/12/2015] [Accepted: 01/22/2015] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Accurate reporting of patient symptoms is critical for diagnosis and therapeutic monitoring in psychiatry. Smartphones offer an accessible, low-cost means to collect patient symptoms in real time and aid in care. OBJECTIVE To investigate adherence among psychiatric outpatients diagnosed with major depressive disorder in utilizing their personal smartphones to run a custom app to monitor Patient Health Questionnaire-9 (PHQ-9) depression symptoms, as well as to examine the correlation of these scores to traditionally administered (paper-and-pencil) PHQ-9 scores. METHODS A total of 13 patients with major depressive disorder, referred by their clinicians, received standard outpatient treatment and, in addition, utilized their personal smartphones to run the study app to monitor their symptoms. Subjects downloaded and used the Mindful Moods app on their personal smartphone to complete up to three survey sessions per day, during which a randomized subset of PHQ-9 symptoms of major depressive disorder were assessed on a Likert scale. The study lasted 29 or 30 days without additional follow-up. Outcome measures included adherence, measured by the percentage of completed survey sessions, and estimates of daily PHQ-9 scores collected from the smartphone app, as well as from the traditionally administered PHQ-9. RESULTS Overall adherence was 77.78% (903/1161) and varied with time of day. PHQ-9 estimates collected from the app strongly correlated (r=.84) with traditionally administered PHQ-9 scores, but app-collected scores were 3.02 (SD 2.25) points higher on average. More subjects reported suicidal ideation using the app than they did on the traditionally administered PHQ-9. CONCLUSIONS Patients with major depressive disorder are able to utilize an app on their personal smartphones to self-assess their symptoms of major depressive disorder with high levels of adherence. These app-collected results correlate with the traditionally administered PHQ-9. Scores recorded from the app may potentially be more sensitive and better able to capture suicidality than the traditional PHQ-9.
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Affiliation(s)
- John Torous
- Harvard Longwood Psychiatry Residency Training Prorgam Boston, MA United States ; Beth Israel Deaconess Medical Center Department of Psychiatry Harvard Medical School Boston, MA United States
| | - Patrick Staples
- Department of Biostatistics Harvard School of Public Health Harvard University Boston, MA United States
| | - Meghan Shanahan
- Beth Israel Deaconess Medical Center Department of Psychiatry Harvard Medical School Boston, MA United States
| | | | - Pamela Peck
- Beth Israel Deaconess Medical Center Department of Psychiatry Harvard Medical School Boston, MA United States
| | - Matcheri Keshavan
- Beth Israel Deaconess Medical Center Department of Psychiatry Harvard Medical School Boston, MA United States
| | - Jukka-Pekka Onnela
- Department of Biostatistics Harvard School of Public Health Harvard University Boston, MA United States
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Abend R, Dan O, Maoz K, Raz S, Bar-Haim Y. Reliability, validity and sensitivity of a computerized visual analog scale measuring state anxiety. J Behav Ther Exp Psychiatry 2014; 45:447-53. [PMID: 24978117 DOI: 10.1016/j.jbtep.2014.06.004] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2013] [Revised: 03/24/2014] [Accepted: 06/06/2014] [Indexed: 11/24/2022]
Abstract
BACKGROUND AND OBJECTIVES Assessment of state anxiety is frequently required in clinical and research settings, but its measurement using standard multi-item inventories entails practical challenges. Such inventories are increasingly complemented by paper-and-pencil, single-item visual analog scales measuring state anxiety (VAS-A), which allow rapid assessment of current anxiety states. Computerized versions of VAS-A offer additional advantages, including facilitated and accurate data collection and analysis, and applicability to computer-based protocols. Here, we establish the psychometric properties of a computerized VAS-A. METHODS Experiment 1 assessed the reliability, convergent validity, and discriminant validity of the computerized VAS-A in a non-selected sample. Experiment 2 assessed its sensitivity to increase in state anxiety following social stress induction, in participants with high levels of social anxiety. RESULTS Experiment 1 demonstrated the computerized VAS-A's test-retest reliability (r = .44, p < .001); convergent validity with the State-Trait Anxiety Inventory's state subscale (STAI-State; r = .60, p < .001); and discriminant validity as indicated by significantly lower correlations between VAS-A and different psychological measures relative to the correlation between VAS-A and STAI-State. Experiment 2 demonstrated the VAS-A's sensitivity to changes in state anxiety via a significant pre- to during-stressor rise in VAS-A scores (F(1,48) = 25.13, p < .001). LIMITATIONS Set-order administration of measures, absence of clinically-anxious population, and gender-unbalanced samples. CONCLUSIONS The adequate psychometric characteristics, combined with simple and rapid administration, make the computerized VAS-A a valuable self-rating tool for state anxiety. It may prove particularly useful for clinical and research settings where multi-item inventories are less applicable, including computer-based treatment and assessment protocols. The VAS-A is freely available: http://people.socsci.tau.ac.il/mu/anxietytrauma/visual-analog-scale/.
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Affiliation(s)
- Rany Abend
- School of Psychological Sciences, Tel Aviv University, P.O. Box 39040, Tel Aviv 69978, Israel.
| | - Orrie Dan
- Department of Psychology, The Center for Psychobiological Research, The Max Stern Yezreel Valley College, Israel
| | - Keren Maoz
- School of Psychological Sciences, Tel Aviv University, P.O. Box 39040, Tel Aviv 69978, Israel
| | - Sivan Raz
- Department of Psychology, The Center for Psychobiological Research, The Max Stern Yezreel Valley College, Israel
| | - Yair Bar-Haim
- School of Psychological Sciences, Tel Aviv University, P.O. Box 39040, Tel Aviv 69978, Israel
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