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Kaczmarek-Majer K, Dominiak M, Antosik AZ, Hryniewicz O, Kamińska O, Opara K, Owsiński J, Radziszewska W, Sochacka M, Święcicki Ł. Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study. Acta Psychiatr Scand 2024. [PMID: 39118422 DOI: 10.1111/acps.13735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/14/2024] [Accepted: 07/06/2024] [Indexed: 08/10/2024]
Abstract
INTRODUCTION Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD. AIMS OF THE STUDY We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase. METHODS A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models. RESULTS The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found. CONCLUSIONS Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.
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Affiliation(s)
- Katarzyna Kaczmarek-Majer
- Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
| | - Monika Dominiak
- Department of Pharmacology and Physiology of the Nervous System, Institute of Psychiatry and Neurology, Warsaw, Poland
- Section of Biological Psychiatry, Polish Psychiatric Association, Warsaw, Poland
| | - Anna Z Antosik
- Section of Biological Psychiatry, Polish Psychiatric Association, Warsaw, Poland
- Department of Psychiatry, Faculty of Medicine, Collegium Medicum, Cardinal Wyszynski University in Warsaw, Warsaw, Poland
| | - Olgierd Hryniewicz
- Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
| | - Olga Kamińska
- Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
| | - Karol Opara
- Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
| | - Jan Owsiński
- Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
| | - Weronika Radziszewska
- Department of Stochastic Methods, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland
| | | | - Łukasz Święcicki
- Department of Affective Disorders, II Psychiatric Clinic, Institute of Psychiatry and Neurology, Warsaw, Poland
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Dominiak M, Gędek A, Antosik AZ, Mierzejewski P. Mobile health for mental health support: a survey of attitudes and concerns among mental health professionals in Poland over the period 2020-2023. Front Psychiatry 2024; 15:1303878. [PMID: 38559395 PMCID: PMC10978719 DOI: 10.3389/fpsyt.2024.1303878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Mobile health (mHealth) has emerged as a dynamic sector supported by technological advances and the COVID-19 pandemic and have become increasingly applied in the field of mental health. Aim The aim of this study was to assess the attitudes, expectations, and concerns of mental health professionals, including psychiatrists, psychologists, and psychotherapists, towards mHealth, in particular mobile health self-management tools and telepsychiatry in Poland. Material and methods This was a survey conducted between 2020 and 2023. A questionnaire was administered to 148 mental health professionals, covering aspects such as telepsychiatry, mobile mental health tools, and digital devices. Results The majority of professionals expressed readiness to use telepsychiatry, with a peak in interest during the COVID-19 pandemic, followed by a gradual decline from 2022. Concerns about telepsychiatry were reported by a quarter of respondents, mainly related to difficulties in correctly assessing the patient's condition, and technical issues. Mobile health tools were positively viewed by professionals, with 86% believing they could support patients in managing mental health and 74% declaring they would recommend patients to use them. Nevertheless, 29% expressed concerns about the effectiveness and data security of such tools. Notably, the study highlighted a growing readiness among mental health professionals to use new digital technologies, reaching 84% in 2023. Conclusion These findings emphasize the importance of addressing concerns and designing evidence-based mHealth solutions to ensure long-term acceptance and effectiveness in mental healthcare. Additionally, the study highlights the need for ongoing regulatory efforts to safeguard patient data and privacy in the evolving digital health landscape.
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Affiliation(s)
- Monika Dominiak
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Adam Gędek
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
- Praski Hospital, Warsaw, Poland
| | - Anna Z. Antosik
- Department of Psychiatry, Faculty of Medicine, Collegium Medicum, Cardinal Wyszynski University, Warsaw, Poland
| | - Paweł Mierzejewski
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
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Dominiak M, Gędek A, Antosik AZ, Mierzejewski P. Prevalence, attitudes and concerns toward telepsychiatry and mobile health self-management tools among patients with mental disorders during and after the COVID-19 pandemic: a nationwide survey in Poland from 2020 to 2023. Front Psychiatry 2024; 14:1322695. [PMID: 38260801 PMCID: PMC10801431 DOI: 10.3389/fpsyt.2023.1322695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Mobile Health (mHealth) is a rapidly growing field of medicine that has the potential to significantly change everyday clinical practice, including in psychiatry. The COVID-19 pandemic and technological developments have accelerated the adoption of telepsychiatry and mobile solutions, but patient perceptions and expectations of mHealth remain a key factor in its implementation. Aim The aim of this study was to assess (1) the prevalence, (2) attitudes, preferences and (3) concerns about mobile mental health, including telepsychiatry and self-management tools, among patients with mental disorders over the period 2020-2023, i.e., at the onset, peak and after the expiration of the COVID-19 pandemic. Materials and methods A semi-structured survey was administrated to 354 patients with mental disorders in Poland. The questions were categorized into three section, addressing prevalence, attitudes, and concerns about telepsychiatry and mobile health self-management tools. The survey was conducted continuously from May 2020 to the end of May 2023. Result As many as 95.7% of patients with mental disorders used mobile devices at least once a week. Over the course of 3 years (from 2020 to 2023), there was a significant increase in the readiness of patients to embrace new technologies, with the percentage rising from 20% to 40%. In particular, a remarkable growth in patient preferences for telepsychiatry was observed, with a significant increase from 47% in 2020 to a substantial 96% in 2023. Similarly, mHealth self-management tools were of high interest to patients. In 2020, 62% of patients like the idea of using mobile apps and other mobile health tools to support the care and treatment process. This percentage also increased during the pandemic, reaching 66% in 2023. At the same time, the percentage of patients who have concerns about using m-health solutions has gradually decreased, reaching 35% and 28% in 2023 for telepsychiatry and for the reliability and safety of m-health self-management tools, respectively. Conclusion This study highlights the growing acceptance of modern technologies in psychiatric care, with patients showing increased readiness to use telepsychiatry and mobile health self-management tools, in particular mobile applications, after the COVID-19 pandemic. This was triggered by the pandemic, but continues despite its expiry. In the face of patient readiness, the key issue now is to ensure the safety and efficacy of these tools, along with providing clear guidelines for clinicians. It is also necessary to draw the attention of health systems to the widespread implementation of these technologies to improve the care of patients with mental disorders.
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Affiliation(s)
- Monika Dominiak
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Adam Gędek
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
| | - Anna Z. Antosik
- Department of Psychiatry, Faculty of Medicine, Collegium Medicum, Cardinal Wyszynski University in Warsaw, Warsaw, Poland
| | - Paweł Mierzejewski
- Department of Pharmacology, Institute of Psychiatry and Neurology, Warsaw, Poland
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Leibold A, Mansoor Ali D, Harrop J, Sharan A, Vaccaro AR, Sivaganesan A. Smartphone-based activity tracking for spine patients: Current technology and future opportunities. World Neurosurg X 2024; 21:100238. [PMID: 38221955 PMCID: PMC10787294 DOI: 10.1016/j.wnsx.2023.100238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 09/26/2023] [Indexed: 01/16/2024] Open
Abstract
Activity trackers and wearables allow accurate determination of physical activity, basic vital parameters, and tracking of complex medical conditions. This review attempts to provide a roadmap for the development of these applications, outlining the basic tools available, how they can be combined, and what currently exists in the marketplace for spine patients. Various types of sensors currently exist to measure distinct aspects of user movement. These include the accelerometer, gyroscope, magnetometer, barometer, global positioning system (GPS), Bluetooth and Wi-Fi, and microphone. Integration of data from these sensors allows detailed tracking of location and vectors of motion, resulting in accurate mobility assessments. These assessments can have great value for a variety of healthcare specialties, but perhaps none more so than spine surgery. Patient-reported outcomes (PROMs) are subject to bias and are difficult to track frequently - a problem that is ripe for disruption with the continued development of mobility technology. Currently, multiple mobile applications exist as an extension of clinical care. These include Manage My Surgery (MMS), SOVINITY-e-Healthcare Services, eHealth System, Beiwe Smartphone Application, QS Access, 6WT, and the TUG app. These applications utilize sensor data to assess patient activity at baseline and postoperatively. The results are evaluated in conjunction with PROMs. However, these applications have not yet exploited the full potential of available sensors. There is a need to develop smartphone applications that can accurately track the functional status and activity of spine patients, allowing a more quantitative assessment of outcomes, in contrast to legacy PROMs.
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Affiliation(s)
- Adam Leibold
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Daniyal Mansoor Ali
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - James Harrop
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Ashwini Sharan
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Alexander R. Vaccaro
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
- Rothman Orthopaedic Institute, Jefferson Health, Philadelphia, PA, USA
| | - Ahilan Sivaganesan
- Department of Neurological Surgery, Thomas Jefferson University Hospital, Philadelphia, PA, USA
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Kark SM, Worthington MA, Christie RH, Masino AJ. Opportunities for digital health technology: identifying unmet needs for bipolar misdiagnosis and depression care management. Front Digit Health 2023; 5:1221754. [PMID: 37771820 PMCID: PMC10523347 DOI: 10.3389/fdgth.2023.1221754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 08/22/2023] [Indexed: 09/30/2023] Open
Abstract
Introduction Digital health technologies (DHTs) driven by artificial intelligence applications, particularly those including predictive models derived with machine learning methods, have garnered substantial attention and financial investment in recent years. Yet, there is little evidence of widespread adoption and scant proof of gains in patient health outcomes. One factor of this paradox is the disconnect between DHT developers and digital health ecosystem stakeholders, which can result in developing technologies that are highly sophisticated but clinically irrelevant. Here, we aimed to uncover challenges faced by psychiatrists treating patients with major depressive disorder (MDD). Specifically, we focused on challenges psychiatrists raised about bipolar disorder (BD) misdiagnosis. Methods We conducted semi-structured interviews with 10 United States-based psychiatrists. We applied text and thematic analysis to the resulting interview transcripts. Results Three main themes emerged: (1) BD is often misdiagnosed, (2) information crucial to evaluating BD is often occluded from clinical observation, and (3) BD misdiagnosis has important treatment implications. Discussion Using upstream stakeholder engagement methods, we were able to identify a narrow, unforeseen, and clinically relevant problem. We propose an organizing framework for development of digital tools based upon clinician-identified unmet need.
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Affiliation(s)
| | | | | | - Aaron J. Masino
- AiCure, New York, NY, United States
- Department of Biostatistics, Epidemiology, and Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Frank E, Wallace ML, Matthews MJ, Kendrick J, Leach J, Moore T, Aranovich G, Choudhury T, Shah NR, Framroze Z, Posey G, Burgess SA, Kupfer DJ. Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment. Front Digit Health 2022; 4:870522. [PMID: 36120713 PMCID: PMC9478192 DOI: 10.3389/fdgth.2022.870522] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 08/09/2022] [Indexed: 11/16/2022] Open
Abstract
We conducted a 16-week randomized controlled trial in psychiatric outpatients with a lifetime diagnosis of a mood and/or anxiety disorder to measure the impact of a first-of-its-kind precision digital intervention software solution based on social rhythm regulation principles. The full intent-to-treat (ITT) sample consisted of 133 individuals, aged 18-65. An exploratory sub-sample of interest was those individuals who presented with moderately severe to severe depression at study entry (baseline PHQ-8 score ≥15; N = 28). Cue is a novel digital intervention platform that capitalizes on the smartphone's ability to continuously monitor depression-relevant behavior patterns and use each patient's behavioral data to provide timely, personalized "micro-interventions," making this the first example of a precision digital intervention of which we are aware. Participants were randomly allocated to receive Cue plus care-as-usual or digital monitoring only plus care as usual. Within the full study and depressed-at-entry samples, we fit a mixed effects model to test for group differences in the slope of depressive symptoms over 16 weeks. To account for the non-linear trajectory with more flexibility, we also fit a mixed effects model considering week as a categorical variable and used the resulting estimates to test the group difference in PHQ change from baseline to 16 weeks. In the full sample, the group difference in the slope of PHQ-8 was negligible (Cohen's d = -0.10); however, the Cue group demonstrated significantly greater improvement from baseline to 16 weeks (p = 0.040). In the depressed-at-entry sample, we found evidence for benefit of Cue. The group difference in the slope of PHQ-8 (Cohen's d = -0.72) indicated a meaningfully more rapid rate of improvement in the intervention group than in the control group. The Cue group also demonstrated significantly greater improvement in PHQ-8 from baseline to 16 weeks (p = 0.009). We are encouraged by the size of the intervention effect in those who were acutely ill at baseline, and by the finding that across all participants, 80% of whom were receiving pharmacotherapy, we observed significant benefit of Cue at 16 weeks of treatment. These findings suggest that a social rhythm-focused digital intervention platform may represent a useful and accessible adjunct to antidepressant treatment (https://clinicaltrials.gov/ct2/show/NCT03152864?term=ellen+frank&draw=2&rank=3).
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Affiliation(s)
- Ellen Frank
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- HealthRhythms, Inc., Long Island City, NY, United States
| | - Meredith L. Wallace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Mark J. Matthews
- HealthRhythms, Inc., Long Island City, NY, United States
- School of Computer Science, University College, Dublin, Ireland
| | - Jeremy Kendrick
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Jeremy Leach
- HealthRhythms, Inc., Long Island City, NY, United States
| | - Tara Moore
- HealthRhythms, Inc., Long Island City, NY, United States
| | | | - Tanzeem Choudhury
- HealthRhythms, Inc., Long Island City, NY, United States
- Department of Computing and Information Science, Cornell Tech, New York, NY, United States
| | - Nirav R. Shah
- Department of Medicine - Clinical Excellence Research Center, Stanford University School of Medicine, Palo Alto, CA, United States
| | | | - Greg Posey
- HealthRhythms, Inc., Long Island City, NY, United States
| | | | - David J. Kupfer
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- HealthRhythms, Inc., Long Island City, NY, United States
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Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Comput Sci 2022; 8:e1042. [PMID: 36092018 PMCID: PMC9455148 DOI: 10.7717/peerj-cs.1042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Mental health issues are a serious consequence of the COVID-19 pandemic, influencing about 700 million people worldwide. These physiological issues need to be consistently observed on the people through non-invasive devices such as smartphones, and fitness bands in order to remove the burden of having the conciseness of continuously being monitored. On the other hand, technological improvements have enhanced the abilities and roles of conventional mobile phones from simple communication to observations and improved accessibility in terms of size and price may reflect growing familiarity with the smartphone among a vast number of consumers. As a result of continuous monitoring, together with various embedded sensors in mobile phones, raw data can be converted into useful information about the actions and behaviors of the consumers. Thus, the aim of this comprehensive work concentrates on the literature work done so far in the prediction of mental health issues via passive monitoring data from smartphones. This study also explores the way users interact with such self-monitoring technologies and what challenges they might face. We searched several electronic databases (PubMed, IEEE Xplore, ACM Digital Libraries, Soups, APA PsycInfo, and Mendeley Data) for published studies that are relevant to focus on the topic and English language proficiency from January 2015 to December 2020. We identified 943 articles, of which 115 articles were eligible for this scoping review based on the predetermined inclusion and exclusion criteria carried out manually. These studies provided various works regarding smartphones for health monitoring such as Physical activity (26.0 percent; 30/115), Mental health analysis (27.8 percent; 32/115), Student specific monitoring (15.6 percent; 18/115) are the three analyses carried out predominantly.
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Affiliation(s)
- Abinaya Gopalakrishnan
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Revathi Venkataraman
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - Rohan Genrich
- School of Business, University of Southern Queensland, Toowoomba, Australia
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Polhemus A, Simblett S, Dawe-Lane E, Gilpin G, Elliott B, Jilka S, Novak J, Nica R, Temesi G, Wykes T. Health tracking via mobile apps for depression self-management: a qualitative content analysis of user reviews (Preprint). JMIR Hum Factors 2022; 9:e40133. [DOI: 10.2196/40133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/19/2022] [Accepted: 08/06/2022] [Indexed: 11/13/2022] Open
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Miller ML, Raugh IM, Strauss GP, Harvey PD. Remote digital phenotyping in serious mental illness: Focus on negative symptoms, mood symptoms, and self-awareness. Biomark Neuropsychiatry 2022. [DOI: 10.1016/j.bionps.2022.100047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Polhemus A, Novak J, Majid S, Simblett S, Morris D, Bruce S, Burke P, Dockendorf MF, Temesi G, Wykes T. Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives. JMIR Ment Health 2022; 9:e25249. [PMID: 35482368 PMCID: PMC9100378 DOI: 10.2196/25249] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 03/29/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions. OBJECTIVE The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health. METHODS In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis. RESULTS We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users' experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions. CONCLUSIONS When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not "one-size-fits-all," and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.
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Affiliation(s)
- Ashley Polhemus
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Zurich, Switzerland.,Epidemiology, Biostatistics and Prevention Institute, University of Zürich, Zürich, Switzerland
| | - Jan Novak
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Prague, Czech Republic
| | - Shazmin Majid
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Prague, Czech Republic.,School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Sara Simblett
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daniel Morris
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Stuart Bruce
- RADAR-CNS Patient Advisory Board, London, United Kingdom
| | - Patrick Burke
- RADAR-CNS Patient Advisory Board, London, United Kingdom
| | - Marissa F Dockendorf
- Global Digital Analytics and Technologies, Merck, Sharpe, & Dohme, Kenilworth, NJ, United States
| | - Gergely Temesi
- Merck Research Labs, Information Technology, Merck, Sharpe, & Dohme, Vienna, Austria
| | - Til Wykes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley National Health Services Foundation Trust, London, United Kingdom
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Vega J, Bell BT, Taylor C, Xie J, Ng H, Honary M, McNaney R. Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating. JMIR Ment Health 2022; 9:e32146. [PMID: 35086064 PMCID: PMC9086876 DOI: 10.2196/32146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Binge eating is a subjective loss of control while eating, which leads to the consumption of large amounts of food. It can cause significant emotional distress and is often accompanied by purging behaviors (eg, meal skipping, overexercising, or vomiting). OBJECTIVE The aim of this study was to explore the potential of mobile sensing to detect indicators of binge-eating episodes, with a view toward informing the design of future context-aware mobile interventions. METHODS This study was conducted in 2 stages. The first involved the development of the DeMMI (Detecting Mental health behaviors using Mobile Interactions) app. As part of this, we conducted a consultation session to explore whether the types of sensor data we were proposing to capture were useful and appropriate, as well as to gather feedback on some specific app features relating to self-reporting. The second stage involved conducting a 6-week period of data collection with 10 participants experiencing binge eating (logging both their mood and episodes of binge eating) and 10 comparison participants (logging only mood). An optional interview was conducted after the study, which discussed their experience using the app, and 8 participants (n=3, 38% binge eating and n=5, 63% comparisons) consented. RESULTS The findings showed unique differences in the types of sensor data that were triangulated with the individuals' episodes (with nearby Bluetooth devices, screen and app use features, mobility features, and mood scores showing relevance). Participants had a largely positive opinion about the app, its unobtrusive role, and its ease of use. Interacting with the app increased participants' awareness of and reflection on their mood and phone usage patterns. Moreover, they expressed no privacy concerns as these were alleviated by the study information sheet. CONCLUSIONS This study contributes a series of recommendations for future studies wishing to scale our approach and for the design of bespoke mobile interventions to support this population.
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Affiliation(s)
- Julio Vega
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | | | | | - Jue Xie
- Department of Human Centred Computing, Monash University, Clayton, Australia
| | - Heidi Ng
- Department of Human Centred Computing, Monash University, Clayton, Australia
| | | | - Roisin McNaney
- Department of Human Centred Computing, Monash University, Clayton, Australia
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Morton E, Nicholas J, Yang L, Lapadat L, Barnes SJ, Provencher MD, Depp C, Chan M, Kulur R, Michalak EE. Evaluating the quality, safety, and functionality of commonly used smartphone apps for bipolar disorder mood and sleep self-management. Int J Bipolar Disord 2022; 10:10. [PMID: 35368207 PMCID: PMC8977125 DOI: 10.1186/s40345-022-00256-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 02/01/2022] [Indexed: 12/03/2022] Open
Abstract
Background Individuals with bipolar disorder (BD) are increasingly turning to smartphone applications (apps) for health information and self-management support. While reviews have raised concerns regarding the effectiveness and safety of publicly available apps for BD, apps surveyed may not reflect what individuals with BD are using. The present study had two aims: first, to characterize the use of health apps to support mood and sleep amongst people with BD, and second, to evaluate the quality, safety and functionality of the most commonly used self-management apps. Methods A web-based survey was conducted to explore which apps people with BD reported using to support self-management of mood and sleep. The characteristics of the most commonly nominated apps were described using a standardized framework, including their privacy policy, clinical foundations, and functionality. Results Respondents (n = 919) were 77.9% female with a mean age of 36.9 years. 41.6% of participants (n = 382) reported using a self-management app to support mood or sleep. 110 unique apps were nominated in relation to mood, and 104 unique apps nominated in relation to sleep; however, most apps were only mentioned once. The nine most frequently nominated apps related to mood and sleep were subject to further evaluation. All reviewed apps offered a privacy policy, however user control over data was limited and the complexity of privacy policies was high. Only one app was developed for BD populations. Half of reviewed apps had published peer-reviewed evidence to support their claims of efficacy, but little research was specific to BD. Conclusion Findings illustrate the potential of smartphone apps to increase the reach of psychosocial interventions amongst people with BD. Apps were largely created by commercial developers and designed for the general population, highlighting a gap in the development and dissemination of evidence-informed apps for BD. There may be risks in using generic health apps for BD self-management; clinicians should enquire about patients’ app use to foster conversations about their particular benefits and limitations. Supplementary Information The online version contains supplementary material available at 10.1186/s40345-022-00256-6.
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Majid S, Reeves S, Figueredo G, Brown S, Lang A, Moore M, Morriss R. The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review. JMIR Ment Health 2021; 8:e27991. [PMID: 34931992 PMCID: PMC8726024 DOI: 10.2196/27991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 06/29/2021] [Accepted: 08/11/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND The number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The application of models of involvement, such as user-centered design, is becoming standardized to optimize the reach, adoption, and sustained use of this type of technology. OBJECTIVE This paper aims to examine the current ways in which users are involved in the design and evaluation of self-monitoring apps for BD by investigating 3 specific questions: are users involved in the design and evaluation of technology? If so, how does this happen? And what are the best practice ingredients regarding the design of mental health technology? METHODS We reviewed the available literature on self-tracking technology for BD and make an overall assessment of the level of user involvement in design. The findings were reviewed by an expert panel, including an individual with lived experience of BD, to form best practice ingredients for the design of mental health technology. This combines the existing practices of patient and public involvement and HCI to evolve from the generic guidelines of user-centered design and to those that are tailored toward mental health technology. RESULTS For the first question, it was found that out of the 11 novel smartphone apps included in this review, 4 (36%) self-monitoring apps were classified as having no mention of user involvement in design, 1 (9%) self-monitoring app was classified as having low user involvement, 4 (36%) self-monitoring apps were classified as having medium user involvement, and 2 (18%) self-monitoring apps were classified as having high user involvement. For the second question, it was found that despite the presence of extant approaches for the involvement of the user in the process of design and evaluation, there is large variability in whether the user is involved, how they are involved, and to what extent there is a reported emphasis on the voice of the user, which is the ultimate aim of such design approaches. For the third question, it is recommended that users are involved in all stages of design with the ultimate goal of empowering and creating empathy for the user. CONCLUSIONS Users should be involved early in the design process, and this should not just be limited to the design itself, but also to associated research ensuring end-to-end involvement. Communities in health care-based design and HCI design need to work together to increase awareness of the different methods available and to encourage the use and mixing of the methods as well as establish better mechanisms to reach the target user group. Future research using systematic literature search methods should explore this further.
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Affiliation(s)
- Shazmin Majid
- School of Computer Science, Horizon Centre for Doctoral Training, University of Nottingham, Nottingham, United Kingdom
| | - Stuart Reeves
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Grazziela Figueredo
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Susan Brown
- National Institute for Health Research MindTech Medtech Co-operative, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Alexandra Lang
- Human Factors Research Group, Faculty of Engineering, University of Nottingham, Nottingham, United Kingdom
| | - Matthew Moore
- Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
| | - Richard Morriss
- National Institute for Health Research Applied Research Collaboration East Midlands, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
- Nottingham National Institute for Health Research Biomedical Research Centre, Institute of Mental Health, University of Nottingham, Nottingham, United Kingdom
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Ryan KA, Smith SN, Yocum AK, Carley I, Liebrecht C, Navis B, Vest E, Bertram H, McInnis MG, Kilbourne AM. The Life Goals Self-Management Mobile App for Bipolar Disorder: Consumer Feasibility, Usability, and Acceptability Study. JMIR Form Res 2021; 5:e32450. [PMID: 34898452 PMCID: PMC8713087 DOI: 10.2196/32450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/08/2021] [Accepted: 11/05/2021] [Indexed: 12/12/2022] Open
Abstract
Background Life Goals is an evidence-based self-management intervention that assists individuals with bipolar disorder (BD) by aligning BD symptom coping strategies with their personal goals. The intervention can be availed via in-person and telephonic sessions, and it has been recently developed as an individualized, customizable mobile app. Objective We examined the feasibility, usability, and acceptability of the Life Goals self-management app among individuals diagnosed with BD who used the app for up to 6 months. Methods A total of 28 individuals with BD used the Life Goals app on their personal smartphone for 6 months. They completed key clinical outcome measurements of functioning, disability, and psychiatric symptoms at baseline, 3 months, and 6 months, in addition to a poststudy survey about usability and satisfaction. Results Participants used the app for a median of 25 times (IQR 13-65.75), and for a longer time during the first 3 months of the study. The modules on depression and anxiety were the most frequently used, accounting for 35% and 22% of total usage, respectively. Overall, the study participants found the app useful (15/25, 60%) and easy to use (18/25, 72%), and they reported that the screen displayed the material adequately (22/25, 88%). However, less than half of the participants found the app helpful in managing their health (10/25, 40%) or in making progress on their wellness goals (9/25, 36%). Clinical outcomes showed a trend for improvements in mental and physical health and mania-related well-being. Conclusions The Life Goals app showed feasibility of use among individuals with BD. Higher user engagement was observed in the initial 3 months with users interested more frequently in the mood modules than other wellness modules. Participants reported acceptability with the ease of app use and satisfaction with the app user interface, but the app showed low success in encouraging self-management within this small sample. The Life Goals app is a mobile health technology that can provide individuals with serious mental illness with more flexible access to evidence-based treatments.
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Affiliation(s)
- Kelly A Ryan
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Shawna N Smith
- Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Anastasia K Yocum
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Isabel Carley
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Celeste Liebrecht
- VA Ann Arbor Healthcare System, United States Department of Veterans Affairs, Ann Arbor, MI, United States.,Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Bethany Navis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Erica Vest
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Holli Bertram
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Melvin G McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Amy M Kilbourne
- VA Ann Arbor Healthcare System, United States Department of Veterans Affairs, Ann Arbor, MI, United States.,Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States
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Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digit Health 2021; 3:662811. [PMID: 34713137 PMCID: PMC8521964 DOI: 10.3389/fdgth.2021.662811] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/02/2021] [Indexed: 12/21/2022] Open
Abstract
Collecting and analyzing data from sensors embedded in the context of daily life has been widely employed for the monitoring of mental health. Variations in parameters such as movement, sleep duration, heart rate, electrocardiogram, skin temperature, etc., are often associated with psychiatric disorders. Namely, accelerometer data, microphone, and call logs can be utilized to identify voice features and social activities indicative of depressive symptoms, and physiological factors such as heart rate and skin conductance can be used to detect stress and anxiety disorders. Therefore, a wide range of devices comprising a variety of sensors have been developed to capture these physiological and behavioral data and translate them into phenotypes and states related to mental health. Such systems aim to identify behaviors that are the consequence of an underlying physiological alteration, and hence, the raw sensor data are captured and converted into features that are used to define behavioral markers, often through machine learning. However, due to the complexity of passive data, these relationships are not simple and need to be well-established. Furthermore, parameters such as intrapersonal and interpersonal differences need to be considered when interpreting the data. Altogether, combining practical mobile and wearable systems with the right data analysis algorithms can provide a useful tool for the monitoring and management of mental disorders. The current review aims to comprehensively present and critically discuss all available smartphone-based, wearable, and environmental sensors for detecting such parameters in relation to the treatment and/or management of the most common mental health conditions.
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Affiliation(s)
- Mahsa Sheikh
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - M Qassem
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, School of Mathematics, Computer Science & Engineering, City, University of London, London, United Kingdom
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16
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Gica Ş, Selvı Y. Sleep Interventions in the Treatment of Schizophrenia and Bipolar Disorder. Noro Psikiyatr Ars 2021; 58:S53-S60. [PMID: 34658636 PMCID: PMC8498809 DOI: 10.29399/npa.27467] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 05/25/2021] [Indexed: 11/07/2022] Open
Abstract
Due to the effects of sleep on the central nervous system, it is thought that sleep disorders have a special importance in the onset, course and treatment of psychiatric diseases. Although the negative effects of sleep problems on the occurrence, recurrence and clinical course of psychiatric disorders are well known, it is reported that clinicians do not spend enough time for sleep problems in practice. This may be related to the fact that patients underreport their complaints for various reasons, insufficient examination time, and clinicians' lack of knowledge about the importance of the subject. Pharmacotherapy, psychological and behavioral interventions are options among the therapeutic approaches to sleep problems. But, it seems that clinicians tend to prefer pharmacological approaches for the treatment of sleep problems. However, it is important to choose the appropriate treatment option with considering the method preferred by the patients, who already use many and high doses of pharmacological agents, the nature of the psychiatric disorder and the sleep problem. In this context, chronotherapeutic approaches such as bright light, sleep deprivation, interpersonal relations and social rhythm therapy, and cognitive behavioral therapy techniques adapted for patients with bipolar disorder can be used in the treatment of suitable patients. In this article, the current literature about sleep-related problems observed in patients with schizophrenia and bipolar disorder is reviewed comprehensively with presenting clinical phenotypes and treatment approaches.
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Affiliation(s)
- Şakir Gica
- Necmettin Erbakan University, Meram Medical Faculty, Department of Psychiatry, Konya, Turkey
| | - Yavuz Selvı
- Selçuk University, Selçuklu Medical Faculty, Department of Psychiatry, Konya, Turkey
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Fellendorf FT, Hamm C, Platzer M, Lenger M, Dalkner N, Bengesser SA, Birner A, Queissner R, Sattler M, Pilz R, Kapfhammer HP, Lackner HK, van Poppel M, Reininghaus E. [Symptom Monitoring and Detection of Early Warning Signs in Bipolar Episodes Via App - Views of Patients and Relatives on e-Health Need]. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2021; 90:268-279. [PMID: 34359094 DOI: 10.1055/a-1503-4986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
BACKGROUND The onset and early warning signs of episodes of bipolar disorder are often realized late by those affected. The earlier an incipient episode is treated, the more prognostically favorable the course will be. Symptom monitoring via smartphone application (app) could be an innovative way to recognize and react to early warning signs more swiftly. The aim of this study was to find out whether patients and their relatives consider technical support through an app to be useful and practical in the early warning sign detection and treatment. METHODS In the present study, 51 patients with bipolar disorder and 28 relatives were interviewed. We gathered information on whether participants were able to perceive early warning signs in form of behavioral changes sufficiently and in a timely fashion and also whether they would use an app as treatment support tool. RESULTS Although 94.1% of the surveyed patients and 78.6% of their relatives felt that they were well informed about the disease, 13.7% and 35.7%, respectively were not fully satisfied with the current treatment options. Early warning signs of every depressive development were noticed by 25.5% of the patients (relatives 10.7%). Every (hypo)manic development was only noticed by 11.8% of the patients (relatives 7.1%); 88.2% of the patients and 85.7% of the relatives noticed the same symptoms recurrently at the beginning of a depression and 70.6% and 67.9%, respectively, at the beginning of a (hypo)manic episode (in particular changes in physical activity, communication behavior and the sleep-wake rhythm). 84.3% of the patients and 89.3% of the relatives stated that they considered technical support that draws attention to mood and activity changes as useful and that they would use such an app for the treatment. DISCUSSION The current options for perceiving early warning signs of a depressive or (hypo)manic episode in bipolar disorder are clinically inadequate. Those affected and their relatives desire innovative, technical support. Early detection of symptoms, which often manifest themselves in changes in behavior or activity patterns, is essentiell for managing the course of bipolar disorder. In the future, smartphone apps could be used for clinical treatment and research through objective, continuous and.
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Affiliation(s)
- Frederike T Fellendorf
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Carlo Hamm
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Martina Platzer
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Melanie Lenger
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Nina Dalkner
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Susanne A Bengesser
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Armin Birner
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Robert Queissner
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Matteo Sattler
- Institut für Sportwissenschaften, Karl-Franzens-Universität Graz, Graz, Austria
| | - Rene Pilz
- Universitätsklinik für Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Hans-Peter Kapfhammer
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
| | - Helmut K Lackner
- Otto Loewi Forschungszentrum, Lehrstuhl für Physiologie, Medizinische Universität Graz Zentrum für Physiologische Medizin, Graz, Austria
| | - Mireille van Poppel
- Institut für Sportwissenschaften, Karl-Franzens-Universität Graz, Graz, Austria
| | - Eva Reininghaus
- Psychiatrie und Psychotherapeutische Medizin, Medizinische Universität Graz, Graz, Austria
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18
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Morton E, Torous J, Murray G, Michalak EE. Using apps for bipolar disorder - An online survey of healthcare provider perspectives and practices. J Psychiatr Res 2021; 137:22-28. [PMID: 33647725 DOI: 10.1016/j.jpsychires.2021.02.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 01/18/2021] [Accepted: 02/17/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND Smartphone apps have recognized potential for improving access to evidence-based care in the treatment of bipolar disorder (BD). Healthcare providers are well-positioned to play a role in guiding patients to access safe, evidence-supported, and trustworthy apps. However, little is known about whether and how clinicians use apps with people with BD: understanding practices and attitudes of healthcare providers is essential to support the implementation of mHealth interventions in a real-world context. METHODS A web-based survey was used to explore clinicians' attitudes towards, and use of apps when working with people with BD. Descriptive statistics were used to summarize quantitative findings. Free text responses were investigated using qualitative content analysis. RESULTS Eighty healthcare providers completed the survey. Approximately half of the respondents reported discussing or recommending apps in clinical practice with BD populations. Recommended apps were most commonly related to mood, sleep, and exercise. Barriers to discussing apps included a lack of healthcare provider knowledge/confidence, concerns about patients' ability to access apps, and beliefs that patients lacked interest in apps. CONCLUSION Although research suggests that people with BD are interested in using apps, uptake of such technology among clinicians is more limited. A lack of clinician knowledge regarding apps, combined with concerns about the digital divide and patient interest, may account for this relatively limited integration of apps into the management of BD. These findings emphasise the importance of considering the information needs of healthcare providers when planning dissemination strategies for app-based interventions for BD.
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Affiliation(s)
- Emma Morton
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada.
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, USA
| | - Greg Murray
- Centre for Mental Health, Swinburne University, Melbourne, Australia
| | - Erin E Michalak
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; Department of Psychology, University of British Columbia, Vancouver, BC, Canada
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19
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Fraccaro P, Beukenhorst A, Sperrin M, Harper S, Palmier-Claus J, Lewis S, Van der Veer SN, Peek N. Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review. J Am Med Inform Assoc 2021; 26:1412-1420. [PMID: 31260049 PMCID: PMC6798569 DOI: 10.1093/jamia/ocz043] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/11/2019] [Accepted: 03/27/2019] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. RESULTS Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.
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Affiliation(s)
- Paolo Fraccaro
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.,Hartree Centre STFC Laboratory, IBM Research UK, Warrington, United Kingdom
| | - Anna Beukenhorst
- Centre for Epidemiology, Division of Musculoskeletal & Dermatological Sciences, University of Manchester, Manchester, United Kingdom
| | - Matthew Sperrin
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom
| | - Simon Harper
- School of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Jasper Palmier-Claus
- Division of Psychology & Mental Health, University of Manchester, Manchester, United Kingdom.,Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom
| | - Shôn Lewis
- Division of Psychology & Mental Health, University of Manchester, Manchester, United Kingdom
| | - Sabine N Van der Veer
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.,Centre for Epidemiology, Division of Musculoskeletal & Dermatological Sciences, University of Manchester, Manchester, United Kingdom.,National Institute of Health Research Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester, United Kingdom
| | - Niels Peek
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, United Kingdom.,National Institute of Health Research Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester, United Kingdom.,National Institute of Health Research Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom
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20
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Mood, activity, and sleep measured via daily smartphone-based self-monitoring in young patients with newly diagnosed bipolar disorder, their unaffected relatives and healthy control individuals. Eur Child Adolesc Psychiatry 2021; 30:1209-1221. [PMID: 32743692 PMCID: PMC8310852 DOI: 10.1007/s00787-020-01611-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/27/2020] [Indexed: 02/07/2023]
Abstract
Diagnostic evaluations and early interventions of patients with bipolar disorder (BD) rely on clinical evaluations. Smartphones have been proposed to facilitate continuous and fine-grained self-monitoring of symptoms. The present study aimed to (1) validate daily smartphone-based self-monitored mood, activity, and sleep, against validated questionnaires and clinical ratings in young patients with newly diagnosed BD, unaffected relatives (UR), and healthy controls persons (HC); (2) investigate differences in daily smartphone-based self-monitored mood, activity, and sleep in young patients with newly diagnosed BD, UR, and HC; (3) investigate associations between self-monitored mood and self-monitored activity and sleep, respectively, in young patients with newly diagnosed BD. 105 young patients with newly diagnosed BD, 24 UR and 77 HC self-monitored 2 to 1077 days (median [IQR] = 65 [17.5-112.5]). There was a statistically significantly negative association between the mood item on Hamilton Depression Rating Scale (HAMD) and smartphone-based self-monitored mood (B = - 0.76, 95% CI - 0.91; - 0.63, p < 0.001) and between psychomotor item on HAMD and self-monitored activity (B = - 0.44, 95% CI - 0.63; - 0.25, p < 0.001). Smartphone-based self-monitored mood differed between young patients with newly diagnosed BD and HC (p < 0.001), and between UR and HC (p = 0.008) and was positively associated with smartphone-based self-reported activity (p < 0.001) and sleep duration (p < 0.001). The findings support the potential of smartphone-based self-monitoring of mood and activity as part of a biomarker for young patients with BD and UR. Smartphone-based self-monitored mood is better to discriminate between young patients with newly diagnosed BD and HC, and between UR and HC, compared with smartphone-based activity and sleep.Trial registration clinicaltrials.gov NCT0288826.
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21
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Lagan S, Ramakrishnan A, Lamont E, Ramakrishnan A, Frye M, Torous J. Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder. Int J Bipolar Disord 2020; 8:39. [PMID: 33259047 PMCID: PMC7704602 DOI: 10.1186/s40345-020-00202-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 09/08/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Although a growing body of literature highlights the potential benefit of smartphone-based mobile apps to aid in self-management and treatment of bipolar disorder, it is unclear whether such evidence-based apps are readily available and accessible to a user of the app store. RESULTS Using our systematic framework for the evaluation of mental health apps, we analyzed the accessibility, privacy, clinical foundation, features, and interoperability of the top-returned 100 apps for bipolar disorder. Only 56% of the apps mentioned bipolar disorder specifically in their title, description, or content. Only one app's efficacy was supported in a peer-reviewed study, and 32 apps lacked privacy policies. The most common features provided were mood tracking, journaling, and psychoeducation. CONCLUSIONS Our analysis reveals substantial limitations in the current digital environment for individuals seeking an evidence-based, clinically usable app for bipolar disorder. Although there have been academic advances in development of digital interventions for bipolar disorder, this work has yet to be translated to the publicly available app marketplace. This unmet need of digital mood management underscores the need for a comprehensive evaluation system of mental health apps, which we have endeavored to provide through our framework and accompanying database (apps.digitalpsych.org).
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Affiliation(s)
- Sarah Lagan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA, 02446, USA
| | - Abinaya Ramakrishnan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA, 02446, USA
- Vanderbilt University, Nashville, TN, USA
| | - Evan Lamont
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA, 02446, USA
- Boston Graduate School of Psychoanalysis, Boston, MA, USA
| | - Aparna Ramakrishnan
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA, 02446, USA
| | | | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, 75 Fenwood Road, Boston, MA, 02446, USA.
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Stanislaus S, Vinberg M, Melbye S, Frost M, Busk J, Bardram JE, Kessing LV, Faurholt-Jepsen M. Smartphone-based activity measurements in patients with newly diagnosed bipolar disorder, unaffected relatives and control individuals. Int J Bipolar Disord 2020; 8:32. [PMID: 33135120 PMCID: PMC7604277 DOI: 10.1186/s40345-020-00195-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND In DSM-5 activity is a core criterion for diagnosing hypomania and mania. However, there are no guidelines for quantifying changes in activity. The objectives of the study were (1) to investigate daily smartphone-based self-reported and automatically-generated activity, respectively, against validated measurements of activity; (2) to validate daily smartphone-based self-reported activity and automatically-generated activity against each other; (3) to investigate differences in daily self-reported and automatically-generated smartphone-based activity between patients with bipolar disorder (BD), unaffected relatives (UR) and healthy control individuals (HC). METHODS A total of 203 patients with BD, 54 UR, and 109 HC were included. On a smartphone-based app, the participants daily reported their activity level on a scale from -3 to + 3. Additionally, participants owning an android smartphone provided automatically-generated data, including step counts, screen on/off logs, and call- and text-logs. Smartphone-based activity was validated against an activity questionnaire the International Physical Activity Questionnaire (IPAQ) and activity items on observer-based rating scales of depression using the Hamilton Depression Rating scale (HAMD), mania using Young Mania Rating scale (YMRS) and functioning using the Functional Assessment Short Test (FAST). In these analyses, we calculated averages of smartphone-based activity measurements reported in the period corresponding to the days assessed by the questionnaires and rating scales. RESULTS (1) Smartphone-based self-reported activity was a valid measure according to scores on the IPAQ and activity items on the HAMD and YMRS, and was associated with FAST scores, whereas the majority of automatically-generated smartphone-based activity measurements were not. (2) Daily smartphone-based self-reported and automatically-generated activity correlated with each other with nearly all measurements. (3) Patients with BD had decreased daily self-reported activity compared with HC. Patients with BD had decreased physical (number of steps) and social activity (more missed calls) but a longer call duration compared with HC. UR also had decreased physical activity compared with HC but did not differ on daily self-reported activity or social activity. CONCLUSION Daily self-reported activity measured via smartphone represents overall activity and correlates with measurements of automatically generated smartphone-based activity. Detecting activity levels using smartphones may be clinically helpful in diagnosis and illness monitoring in patients with bipolar disorder. Trial registration clinicaltrials.gov NCT02888262.
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Affiliation(s)
- Sharleny Stanislaus
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Maj Vinberg
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Sigurd Melbye
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Mads Frost
- Monsenso ApS, Langelinie Allé 47, Copenhagen, Denmark
| | - Jonas Busk
- Copenhagen Center for Health Technology (CACHET), Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jakob E Bardram
- Copenhagen Center for Health Technology (CACHET), Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Lars Vedel Kessing
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Maria Faurholt-Jepsen
- The Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Department O, 6243, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark
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23
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Murray G, Gottlieb J, Hidalgo MP, Etain B, Ritter P, Skene DJ, Garbazza C, Bullock B, Merikangas K, Zipunnikov V, Shou H, Gonzalez R, Scott J, Geoffroy PA, Frey BN. Measuring circadian function in bipolar disorders: Empirical and conceptual review of physiological, actigraphic, and self-report approaches. Bipolar Disord 2020; 22:693-710. [PMID: 32564457 DOI: 10.1111/bdi.12963] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Interest in biological clock pathways in bipolar disorders (BD) continues to grow, but there has yet to be an audit of circadian measurement tools for use in BD research and practice. PROCEDURE The International Society for Bipolar Disorders Chronobiology Task Force conducted a critical integrative review of circadian methods that have real-world applicability. Consensus discussion led to the selection of three domains to review-melatonin assessment, actigraphy, and self-report. RESULTS Measurement approaches used to quantify circadian function in BD are described in sufficient detail for researchers and clinicians to make pragmatic decisions about their use. A novel integration of the measurement literature is offered in the form of a provisional taxonomy distinguishing between circadian measures (the instruments and methods used to quantify circadian function, such as dim light melatonin onset) and circadian constructs (the biobehavioral processes to be measured, such as circadian phase). CONCLUSIONS Circadian variables are an important target of measurement in clinical practice and biomarker research. To improve reproducibility and clinical application of circadian constructs, an informed systematic approach to measurement is required. We trust that this review will decrease ambiguity in the literature and support theory-based consideration of measurement options.
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Affiliation(s)
- Greg Murray
- Centre for Mental Health, Swinburne University of Technology, Victoria, Australia
| | - John Gottlieb
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.,Chicago Psychiatry Associates, Chicago, IL, USA
| | - Maria Paz Hidalgo
- Laboratorio de Cronobiologia e Sono, Hospital de Porto Alegre, Porto Alegre, Brazil.,Graduate Program in Psychiatry and Behavioral Sciences, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Bruno Etain
- Département de Psychiatrie et de Médecine Addictologique and INSERM UMRS 1144, Université de Paris, AP-HP, Groupe Hospitalo-universitaire AP-HP Nord, Paris, France
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Debra J Skene
- Chronobiology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Corrado Garbazza
- Centre for Chronobiology, University of Basel, Basel, Switzerland.,Transfaculty Research Platform Molecular and Cognitive Neurosciences, University of Basel, Basel, Switzerland
| | - Ben Bullock
- Centre for Mental Health, Swinburne University of Technology, Victoria, Australia
| | - Kathleen Merikangas
- Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, USA
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert Gonzalez
- Department of Psychiatry and Behavioral Health, Penn State Health Milton S. Hershey Medical Center, Hershey, PA
| | - Jan Scott
- Institute of Neuroscience, Newcastle University, Newcastle, UK
| | - Pierre A Geoffroy
- Département de psychiatrie et d'addictologie, AP-HP, Hopital Bichat - Claude Bernard, Paris, France.,Université de Paris, NeuroDiderot, France
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.,Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada
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24
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An update on sleep in bipolar disorders: presentation, comorbidities, temporal relationships and treatment. Curr Opin Psychol 2020; 34:1-6. [DOI: 10.1016/j.copsyc.2019.08.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 08/18/2019] [Accepted: 08/19/2019] [Indexed: 10/26/2022]
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25
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Daus H, Bloecher T, Egeler R, De Klerk R, Stork W, Backenstrass M. Development of an Emotion-Sensitive mHealth Approach for Mood-State Recognition in Bipolar Disorder. JMIR Ment Health 2020; 7:e14267. [PMID: 32618577 PMCID: PMC7367525 DOI: 10.2196/14267] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 11/30/2019] [Accepted: 01/26/2020] [Indexed: 01/16/2023] Open
Abstract
Internet- and mobile-based approaches have become increasingly significant to psychological research in the field of bipolar disorders. While research suggests that emotional aspects of bipolar disorders are substantially related to the social and global functioning or the suicidality of patients, these aspects have so far not sufficiently been considered within the context of mobile-based disease management approaches. As a multiprofessional research team, we have developed a new and emotion-sensitive assistance system, which we have adapted to the needs of patients with bipolar disorder. Next to the analysis of self-assessments, third-party assessments, and sensor data, the new assistance system analyzes audio and video data of these patients regarding their emotional content or the presence of emotional cues. In this viewpoint, we describe the theoretical and technological basis of our emotion-sensitive approach and do not present empirical data or a proof of concept. To our knowledge, the new assistance system incorporates the first mobile-based approach to analyze emotional expressions of patients with bipolar disorder. As a next step, the validity and feasibility of our emotion-sensitive approach must be evaluated. In the future, it might benefit diagnostic, prognostic, or even therapeutic purposes and complement existing systems with the help of new and intuitive interaction models.
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Affiliation(s)
- Henning Daus
- Institute of Clinical Psychology, Centre for Mental Health, Klinikum Stuttgart, Stuttgart, Germany.,Faculty of Science, Eberhard-Karls-University Tübingen, Tübingen, Germany
| | - Timon Bloecher
- Embedded Systems and Sensors Engineering, Research Center for Information Technology, Karlsruhe, Germany
| | | | | | - Wilhelm Stork
- Institute for Information Processing Technologies, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Matthias Backenstrass
- Institute of Clinical Psychology, Centre for Mental Health, Klinikum Stuttgart, Stuttgart, Germany.,Department of Clinical Psychology and Psychotherapy, Institute of Psychology, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
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26
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Antosik-Wójcińska AZ, Dominiak M, Chojnacka M, Kaczmarek-Majer K, Opara KR, Radziszewska W, Olwert A, Święcicki Ł. Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling. Int J Med Inform 2020; 138:104131. [DOI: 10.1016/j.ijmedinf.2020.104131] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 03/15/2020] [Accepted: 03/22/2020] [Indexed: 01/06/2023]
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27
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Towards clinically actionable digital phenotyping targets in schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:13. [PMID: 32372059 PMCID: PMC7200667 DOI: 10.1038/s41537-020-0100-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/12/2020] [Indexed: 12/12/2022]
Abstract
Digital phenotyping has potential to quantify the lived experience of mental illness and generate real-time, actionable results related to recovery, such as the case of social rhythms in individuals with bipolar disorder. However, passive data features for social rhythm clinical targets in individuals with schizophrenia have yet to be studied. In this paper, we explore the relationship between active and passive data by focusing on temporal stability and variance at an individual level as well as large-scale associations on a population level to gain clinically actionable information regarding social rhythms. From individual data clustering, we found a 19% cluster overlap between specific active and passive data features for participants with schizophrenia. In the same clinical population, two passive data features in particular associated with social rhythms, "Circadian Routine" and "Weekend Day Routine," and were negatively associated with symptoms of anxiety, depression, psychosis, and poor sleep (Spearman ρ ranged from -0.23 to -0.30, p < 0.001). Conversely, in healthy controls, more stable social rhythms were positively correlated with symptomatology (Spearman ρ ranged from 0.20 to 0.44, p < 0.05). Our results suggest that digital phenotyping in schizophrenia may offer clinically relevant information for understanding how daily routines affect symptomatology. Specifically, negative correlations between smartphone reported anxiety, depression, psychosis, and poor sleep in individuals with schizophrenia, but not in healthy controls, offer an actionable clinical target and area for further investigation.
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28
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Torous J, Brady R. Advancing care for bipolar disorder today and breakthroughs in access and treatments tomorrow with mobile health and smartphone apps. Bipolar Disord 2020; 22:211-212. [PMID: 32445218 DOI: 10.1111/bdi.12928] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Roscoe Brady
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
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29
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Osotsi A, Oravecz Z, Li Q, Smyth J, Brick TR. Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2020; 4:91-109. [DOI: 10.1007/s41666-019-00064-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 09/30/2019] [Accepted: 11/27/2019] [Indexed: 10/25/2022]
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30
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Gershon A, Kaufmann CN, Torous J, Depp C, Ketter TA. Electronic Ecological Momentary Assessment (EMA) in youth with bipolar disorder: Demographic and clinical predictors of electronic EMA adherence. J Psychiatr Res 2019; 116:14-18. [PMID: 31176107 DOI: 10.1016/j.jpsychires.2019.05.026] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 05/26/2019] [Accepted: 05/31/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Ecological momentary assessment (EMA) is increasingly used to characterize patients' daily lives, monitor mood, and test efficacy of treatment interventions. However, few studies have examined patient characteristics impacting adherence with EMA protocols, and to our knowledge, no such study has been conducted in youth with bipolar disorder (BD). METHODS As part of a larger observational study, 14- to 21-year-olds diagnosed with BD, and who were between episodes of illness (n = 39, 19.0 ± 2.05 Mean ± Standard Deviation years old, 74.4% female) and psychiatrically healthy controls (n = 47, 18.3 ± 2.40 years old, 66.0% female) completed baseline diagnostic and symptom severity interviews, and were instructed to complete diary assessments of mood, sleep, and behavior electronically three times per day for 21 consecutive days (i.e., in total 5418 (or 63 per person) diary entries). Multiple regression was used to examine effects of BD participants' demographic and clinical characteristics on diary completion rates. RESULTS 53.8 ± 9.3 diary entries per person were actually completed. Adherence rates were high (87.5% of healthy controls and 80.4% of adolescents with BD), but were still significantly poorer in youth with BD. Adequate adherence (≥80%) rates were also significantly poorer in youth with BD relative to healthy controls (56.4% versus 83.0%). Among youth with BD, more lifetime suicide attempts and higher current mood elevation symptom severity predicted significantly poorer adherence. LIMITATIONS Limited sample size/generalizability. CONCLUSIONS Findings highlight the importance of considering the impact of patient characteristics on adherence with EMA protocols among youth with severe mental illness.
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Affiliation(s)
- Anda Gershon
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Christopher N Kaufmann
- Division of Geriatrics and Gerontology, Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Colin Depp
- Department of Psychiatry, University of California San Diego, La Jolla, CA, United States; Sam and Rose Stein Institute for Research on Aging, University of California San Diego, La Jolla, CA, United States
| | - Terence A Ketter
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States.
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31
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Brietzke E, Hawken ER, Idzikowski M, Pong J, Kennedy SH, Soares CN. Integrating digital phenotyping in clinical characterization of individuals with mood disorders. Neurosci Biobehav Rev 2019; 104:223-230. [DOI: 10.1016/j.neubiorev.2019.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 07/08/2019] [Accepted: 07/15/2019] [Indexed: 12/26/2022]
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32
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Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR Mhealth Uhealth 2019; 7:e12649. [PMID: 31444874 PMCID: PMC6729117 DOI: 10.2196/12649] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 05/24/2019] [Accepted: 05/28/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Technological advancements, together with the decrease in both price and size of a large variety of sensors, has expanded the role and capabilities of regular mobile phones, turning them into powerful yet ubiquitous monitoring systems. At present, smartphones have the potential to continuously collect information about the users, monitor their activities and behaviors in real time, and provide them with feedback and recommendations. OBJECTIVE This systematic review aimed to identify recent scientific studies that explored the passive use of smartphones for generating health- and well-being-related outcomes. In addition, it explores users' engagement and possible challenges in using such self-monitoring systems. METHODS A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, to identify recent publications that explore the use of smartphones as ubiquitous health monitoring systems. We ran reproducible search queries on PubMed, IEEE Xplore, ACM Digital Library, and Scopus online databases and aimed to find answers to the following questions: (1) What is the study focus of the selected papers? (2) What smartphone sensing technologies and data are used to gather health-related input? (3) How are the developed systems validated? and (4) What are the limitations and challenges when using such sensing systems? RESULTS Our bibliographic research returned 7404 unique publications. Of these, 118 met the predefined inclusion criteria, which considered publication dates from 2014 onward, English language, and relevance for the topic of this review. The selected papers highlight that smartphones are already being used in multiple health-related scenarios. Of those, physical activity (29.6%; 35/118) and mental health (27.9; 33/118) are 2 of the most studied applications. Accelerometers (57.7%; 67/118) and global positioning systems (GPS; 40.6%; 48/118) are 2 of the most used sensors in smartphones for collecting data from which the health status or well-being of its users can be inferred. CONCLUSIONS One relevant outcome of this systematic review is that although smartphones present many advantages for the passive monitoring of users' health and well-being, there is a lack of correlation between smartphone-generated outcomes and clinical knowledge. Moreover, user engagement and motivation are not always modeled as prerequisites, which directly affects user adherence and full validation of such systems.
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Affiliation(s)
- Alina Trifan
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - Maryse Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
| | - José Luís Oliveira
- Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal
- Institute of Electronics and Informatics Engineering of Aveiro, University of Aveiro, Aveiro, Portugal
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33
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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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34
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Aref-Adib G, McCloud T, Ross J, O'Hanlon P, Appleton V, Rowe S, Murray E, Johnson S, Lobban F. Factors affecting implementation of digital health interventions for people with psychosis or bipolar disorder, and their family and friends: a systematic review. Lancet Psychiatry 2019; 6:257-266. [PMID: 30522979 DOI: 10.1016/s2215-0366(18)30302-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 07/31/2018] [Accepted: 08/01/2018] [Indexed: 12/20/2022]
Abstract
Digital health interventions present an important opportunity to improve health care for people with psychosis or bipolar disorder, but despite their potential, integrating and implementing them into clinical settings has been difficult worldwide. This Review aims to identify factors affecting implementation of digital health interventions for people affected by psychosis or bipolar disorder. We searched seven databases and synthesised data from 26 studies using the Consolidated Framework for Implementation Research. Attitudes and beliefs about interventions were crucial factors for both staff and service users, with negative attitudes and scepticism resulting in a lack of motivation to engage with interventions or complete them. The complexity of the interventions was a barrier for people with psychiatric symptoms, low premorbid intelligence quotient, or minimal information technology skills. The accessibility and adaptability of interventions were key facilitators, but insufficient resources, finances, and staff time were barriers to implementation. Interventions need to be user friendly and adaptable to the needs and capabilities of people with psychosis or bipolar disorder, and the staff who support their implementation. Service users and staff should cofacilitate the process of developing and implementing the interventions.
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Affiliation(s)
- Golnar Aref-Adib
- Division of Psychiatry, University College London, London, UK; Camden and Islington National Health Service Foundation Trust, St Pancras Hospital, London, UK.
| | - Tayla McCloud
- Division of Psychiatry, University College London, London, UK
| | - Jamie Ross
- Research Department of Primary Care and Population Health, University College London, London, UK
| | - Puffin O'Hanlon
- Division of Psychiatry, University College London, London, UK
| | - Victoria Appleton
- Faculty of Health and Medicine, Furness College, Lancaster University, Lancaster, UK
| | - Sarah Rowe
- Division of Psychiatry, University College London, London, UK
| | - Elizabeth Murray
- Research Department of Primary Care and Population Health, University College London, London, UK
| | - Sonia Johnson
- Division of Psychiatry, University College London, London, UK; Camden and Islington National Health Service Foundation Trust, St Pancras Hospital, London, UK
| | - Fiona Lobban
- Faculty of Health and Medicine, Furness College, Lancaster University, Lancaster, UK
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35
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Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Ment Health 2019; 6:e9819. [PMID: 30785404 PMCID: PMC6401668 DOI: 10.2196/mental.9819] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 06/30/2018] [Accepted: 12/15/2018] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. OBJECTIVE To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. METHODS A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. RESULTS Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. CONCLUSIONS Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.
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Affiliation(s)
- Jussi Seppälä
- Center for Life Course of Health Research, University of Oulu, Oulu, Finland.,Department of Mental and Substance Use Services, Eksote, Lappeenranta, Finland
| | | | - Timo Jämsä
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Jouko Miettunen
- Center for Life Course of Health Research, University of Oulu, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Matti Isohanni
- Center for Life Course of Health Research, University of Oulu, Oulu, Finland
| | - Katya Rubinstein
- The Gertner Institute for Epidemiology and Health Policy Research, Tel Aviv, Israel
| | - Yoram Feldman
- The Gertner Institute for Epidemiology and Health Policy Research, Tel Aviv, Israel
| | - Eva Grasa
- Department of Psychiatry, Biomedical Research Institute Sant Pau (IIB-SANT PAU), Hospital Sant Pau, Barcelona, Spain.,Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.,CIBERSAM, Madrid, Spain
| | - Iluminada Corripio
- Department of Psychiatry, Biomedical Research Institute Sant Pau (IIB-SANT PAU), Hospital Sant Pau, Barcelona, Spain.,Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.,CIBERSAM, Madrid, Spain
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- m-RESIST, Barcelona, Spain
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Park H, Park MS. Capturing the trend of mHealth research using text mining. Mhealth 2019; 5:48. [PMID: 31728383 PMCID: PMC6851422 DOI: 10.21037/mhealth.2019.09.06] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 08/30/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND With the increasing development and use of mobile technologies, an increasing amount of research on mobile health is being conducted. The purpose of the study was to capture the trends in mHealth research by mining terms related to medical conditions, interventions, study populations, and the relationships between these terms. METHODS This study analyzed 5,600 journal articles published in Web of Science from 2008 to 2018. Using text mining techniques, a total of 39,292 terms extracted from the titles and abstracts of the journal articles were independently reviewed to identify meaningful terms related to medical conditions, interventions, and study populations. RESULTS A total of 48 different types of medical conditions were identified in the dataset. Mood disorders appeared to be the most frequently identified medical condition in mHealth research. Thirty interventions were identified. Cell phone-, SMS-, and Internet-based interventions appeared to be the most prominent types, and "female" appeared to be the most frequently identified term related to the studied population. Females appeared to have been studied in the widest range of medical conditions, including pregnancy issues, overnutrition, neoplasms, and AIDS. Older adults were the least studied population in mHealth. CONCLUSIONS Knowledge gaps that have not been explored in previous studies in mHealth research were identified, which should be addressed by researchers.
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Affiliation(s)
- Hyejin Park
- College of Nursing, Florida State University, Tallahassee, FL, USA
| | - Min Sook Park
- School of Information Studies, University of Wisconsin Milwaukee, Milwaukee, WI, USA
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Disease management apps and technical assistance systems for bipolar disorder: Investigating the patients´ point of view. J Affect Disord 2018; 229:351-357. [PMID: 29331693 DOI: 10.1016/j.jad.2017.12.059] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/16/2017] [Accepted: 12/30/2017] [Indexed: 01/08/2023]
Abstract
BACKGROUND Smartphone-based disease management has become increasingly interesting for research in the field of bipolar disorders. This article investigates the attitudes of persons affected by this disorder towards the appropriation of mobile apps or assistance systems for the management of their disease. METHODS We conducted two separate studies. Study 1 was an online survey with 88 participants. In study 2 we consulted 15 participants during a semi-structured interview. All the participants had formerly been diagnosed with bipolar disorder. RESULTS More than half of the participants of study 1 and most participants of study 2 agreed with the use of an app or assistance system for self-ratings, third party ratings and an objective symptom monitoring. Potential interventions that were popular in both groups included a regular feedback, the visualization of monitored data and advice in crises. LIMITATIONS With study 1 we were not able to ensure correct diagnoses or to interact in a flexible way. In Study 2 those issues were resolved, but the small number of participants raises the question of a possible generalisability of the results. Furthermore, for both studies a selection bias could not be excluded. CONCLUSIONS Our results indicate positive attitudes of bipolar patients towards disease management apps and assistance systems. Even new and innovative features such as partner apps or the analysis of facial expressions in video data were appreciated and daily interactions were favoured. However, the variety of answers calls for flexible systems which allow activating or deactivating certain features.
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Abstract
A vast majority of psychological science focuses on snapshots of individuals. Clinical outcome studies may integrate multiple snapshots, typically with yearly intervals. However, there is much to learn about psychological processes as they unfold over real time, including minutes, days, weeks, and months. This special issue contains several articles that make significant advances in real-time assessment of psychological processes using state-of-the-art measurement. This is a brief summary of the specific innovations of this special issue. The summary includes suggestions for applying these measurement innovations to the study of functional dynamics that lead to clinically and socially significant events. Several examples provide ways in which a dynamical analysis of function may be revealing. Most important, the assessment of dynamic mechanisms underlying the amplification of psychopathology seems especially useful for tailoring clinical interventions to meet clients' specific needs.
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Affiliation(s)
- Thomas J Dishion
- 1 Arizona State University, Tempe, AZ, USA.,2 Oregon Research Institute, Eugene, OR, USA
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Abstract
Most commonly used clinical assessment tools cannot fully capture the dynamic psychological processes often hypothesized as core mechanisms of psychopathology and psychotherapy. There is therefore a gap between our theories of problems and interventions for those problems and the tools we use to understand clients. The purpose of this special issue is to connect theory about clinical dynamics to practice by focusing on methods for collecting dynamic data, statistical models for analyzing dynamic data, and conceptual schemes for implementing dynamic data in applied settings. In this introductory article, we argue for the importance of assessing dynamic processes, highlight recent advances in assessment science that enable their measurement, review challenges in using these advances in applied practice, and adumbrate the articles in this issue.
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Allega OR, Leng X, Vaccarino A, Skelly M, Lanzini M, Hidalgo MP, Soares CN, Kennedy SH, Frey BN. Performance of the biological rhythms interview for assessment in neuropsychiatry: An item response theory and actigraphy analysis. J Affect Disord 2018; 225:54-63. [PMID: 28787704 DOI: 10.1016/j.jad.2017.07.047] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 07/21/2017] [Accepted: 07/24/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Biological rhythm disturbances are widely associated with the pathophysiology of mood disorders. The Biological Rhythms Interview for Assessment in Neuropsychiatry (BRIAN) is a self-report that indexes rhythm disturbance in sleep, activity, social and eating patterns. The aim of this study was to perform an Item Response Theory (IRT) analysis of the BRIAN and investigate its associations with objective sleep and rhythm disturbance measures. METHODS 103 subjects (31 bipolar, 32 major depression and 40 healthy volunteers) wore an actiwatch for fifteen days, and completed a first morning urine sample and the BRIAN on day 15. IRT analysis assessed individual BRIAN items and their relationship to total score. Individual actiwatch records were processed to produce a sequence of transitions between rest/activity, and a likelihood of transitioning between states was calculated to investigate sleep-wake dynamics. Cosinor analysis produced daily activity rhythms (DARs). Spearman correlations were used to assess the association between sleep/DAR variables and the BRIAN. RESULTS IRT analyses showed that 11 of 18 BRIAN items displayed a high level of discrimination between item options across a range of BRIAN total scores. Total BRIAN score correlated with wake after sleep onset, total activity count during sleep, and urinary 6-sulphatoxymelatonin. BRIAN Activity domain correlated with the daytime transition probability from rest to activity. LIMITATIONS The sample size may have been underpowered for the graded-response model employed in IRT. The study lacked an objective comparison for BRIAN eating and social domain. CONCLUSION The present study reveals the BRIAN displays promising external validity compared to objective parameters of circadian rhythmicity.
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Affiliation(s)
- Olivia R Allega
- Mood Disorders Program, St. Joseph's Healthcare Hamilton, ON, Canada
| | | | | | - Matthew Skelly
- Department of Health Sciences, McMaster University, Canada
| | - Mariana Lanzini
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Canada
| | - Maria Paz Hidalgo
- Department of Psychiatry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Laboratorio de Chronobiologia HCPA/UFRGS, Brazil
| | - Claudio N Soares
- Department of Psychiatry at Queen's University School of Medicine, Canada
| | | | - Benicio N Frey
- Mood Disorders Program, St. Joseph's Healthcare Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, ON, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
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Dogan E, Sander C, Wagner X, Hegerl U, Kohls E. Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review. J Med Internet Res 2017; 19:e262. [PMID: 28739561 PMCID: PMC5547249 DOI: 10.2196/jmir.7006] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 03/31/2017] [Accepted: 05/15/2017] [Indexed: 02/06/2023] Open
Abstract
Background Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones. Objective We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes. Methods We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. Results A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage. Conclusions The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.
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Affiliation(s)
- Ezgi Dogan
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany
| | - Christian Sander
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Xenija Wagner
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany
| | - Ulrich Hegerl
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Elisabeth Kohls
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany
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Attwood S, Parke H, Larsen J, Morton KL. Using a mobile health application to reduce alcohol consumption: a mixed-methods evaluation of the drinkaware track & calculate units application. BMC Public Health 2017; 17:394. [PMID: 28511698 PMCID: PMC5434584 DOI: 10.1186/s12889-017-4358-9] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Accepted: 04/28/2017] [Indexed: 01/22/2023] Open
Abstract
Background Smartphone applications (“apps”) offer promise as tools to help people monitor and reduce their alcohol consumption. To date, few evaluations of alcohol reduction apps exist, with even fewer considering apps already available to the public. The aim of this study was to evaluate an existing publically available app, designed by Drinkaware, a UK-based alcohol awareness charity. Methods We adopted a mixed-methods design, analysing routinely collected app usage data to explore user characteristics and patterns of usage. Following this, in-depth interviews were conducted with a sub-sample of app users to examine perceptions of acceptability, usability and perceived effectiveness, as well as to provide recommendations on how to improve the app. Results One hundred nineteen thousand seven hundred thirteen people downloaded and entered data into the app over a 13-month period. High attrition was observed after 1 week. Users who engaged with the app tended to be “high risk” drinkers and to report being motivated “to reduce drinking” at the point of first download. In those who consistently engaged with the app over time, self-reported alcohol consumption levels reduced, with most change occurring in the first week of usage. Our qualitative findings indicate satisfaction with the usability of the app, but mixed feedback was given regarding individual features. Users expressed conflicting views concerning the type of feedback and notifications that the app currently provides. A common preference was expressed for more personalised content. Conclusions The Drinkaware app is a useful tool to support behaviour change in individuals who are already motivated and committed to reducing their alcohol consumption. The Drinkaware app would benefit from greater personalisation and tailoring to promote longer term use. This evaluation provides insight into the usability and acceptability of various app features and contains a number of recommendations for improving user satisfaction and the potential effectiveness of apps designed to encourage reductions in alcohol consumption. Electronic supplementary material The online version of this article (doi:10.1186/s12889-017-4358-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sophie Attwood
- Nuffield Health Research Group, Nuffield Health, London, UK.
| | | | | | - Katie L Morton
- Health and Well-being Services, School of Sport, Health and Applied Science, St. Mary's University, Twickenham, UK
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