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Henderson K, Reihm J, Koshal K, Wijangco J, Sara N, Miller N, Doyle M, Mallory A, Sheridan J, Guo CY, Oommen L, Rankin KP, Sanders S, Feinstein A, Mangurian C, Bove R. A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care. JMIR Form Res 2024; 8:e52809. [PMID: 38488827 PMCID: PMC10980989 DOI: 10.2196/52809] [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: 09/18/2023] [Revised: 10/27/2023] [Accepted: 11/24/2023] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND People living with multiple sclerosis (MS) face a higher likelihood of being diagnosed with a depressive disorder than the general population. Although many low-cost screening tools and evidence-based interventions exist, depression in people living with MS is underreported, underascertained by clinicians, and undertreated. OBJECTIVE This study aims to design a closed-loop tool to improve depression care for these patients. It would support regular depression screening, tie into the point of care, and support shared decision-making and comprehensive follow-up. After an initial development phase, this study involved a proof-of-concept pilot randomized controlled trial (RCT) validation phase and a detailed human-centered design (HCD) phase. METHODS During the initial development phase, the technological infrastructure of a clinician-facing point-of-care clinical dashboard for MS management (BRIDGE) was leveraged to incorporate features that would support depression screening and comprehensive care (Care Technology to Ascertain, Treat, and Engage the Community to Heal Depression in people living with MS [MS CATCH]). This linked a patient survey, in-basket messages, and a clinician dashboard. During the pilot RCT phase, a convenience sample of 50 adults with MS was recruited from a single MS center with 9-item Patient Health Questionnaire scores of 5-19 (mild to moderately severe depression). During the routine MS visit, their clinicians were either asked or not to use MS CATCH to review their scores and care outcomes were collected. During the HCD phase, the MS CATCH components were iteratively modified based on feedback from stakeholders: people living with MS, MS clinicians, and interprofessional experts. RESULTS MS CATCH links 3 features designed to support mood reporting and ascertainment, comprehensive evidence-based management, and clinician and patient self-management behaviors likely to lead to sustained depression relief. In the pilot RCT (n=50 visits), visits in which the clinician was randomized to use MS CATCH had more notes documenting a discussion of depressive symptoms than those in which MS CATCH was not used (75% vs 34.6%; χ21=8.2; P=.004). During the HCD phase, 45 people living with MS, clinicians, and other experts participated in the design and refinement. The final testing round included 20 people living with MS and 10 clinicians including 5 not affiliated with our health system. Most scoring targets for likeability and usability, including perceived ease of use and perceived effectiveness, were met. Net Promoter Scale was 50 for patients and 40 for clinicians. CONCLUSIONS Created with extensive stakeholder feedback, MS CATCH is a closed-loop system aimed to increase communication about depression between people living with MS and their clinicians, and ultimately improve depression care. The pilot findings showed evidence of enhanced communication. Stakeholders also advised on trial design features of a full year long Department of Defense-funded feasibility and efficacy trial, which is now underway. TRIAL REGISTRATION ClinicalTrials.gov NCT05865405; http://tinyurl.com/4zkvru9x.
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Affiliation(s)
- Kyra Henderson
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jennifer Reihm
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Kanishka Koshal
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Jaeleene Wijangco
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Narender Sara
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Nicolette Miller
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Marianne Doyle
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Alicia Mallory
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Judith Sheridan
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Chu-Yueh Guo
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Lauren Oommen
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Katherine P Rankin
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Stephan Sanders
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Anthony Feinstein
- Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Christina Mangurian
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
| | - Riley Bove
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, United States
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Arévalo Avalos MR, Xu J, Figueroa CA, Haro-Ramos AY, Chakraborty B, Aguilera A. The effect of cognitive behavioral therapy text messages on mood: A micro-randomized trial. PLOS DIGITAL HEALTH 2024; 3:e0000449. [PMID: 38381747 PMCID: PMC10880955 DOI: 10.1371/journal.pdig.0000449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 01/15/2024] [Indexed: 02/23/2024]
Abstract
The StayWell at Home intervention, a 60-day text-messaging program based on Cognitive Behavioral Therapy (CBT) principles, was developed to help adults cope with the adverse effects of the global pandemic. Participants in StayWell at Home were found to show reduced depressive and anxiety symptoms after participation. However, it remains unclear whether the intervention improved mood and which intervention components were most effective at improving user mood during the pandemic. Thus, utilizing a micro-randomized trial (MRT) design, we examined two intervention components to inform the mechanisms of action that improve mood: 1) text messages delivering CBT-informed coping strategies (i.e., behavioral activation, other coping skills, or social support); 2) time at which messages were sent. Data from two independent trials of StayWell are included in this paper. The first trial included 303 adults aged 18 or older, and the second included 266 adults aged 18 or older. Participants were recruited via online platforms (e.g., Facebook ads) and partnerships with community-based agencies aiming to reach diverse populations, including low-income individuals and people of color. The results of this paper indicate that participating in the program improved and sustained self-reported mood ratings among participants. We did not find significant differences between the type of message delivered and mood ratings. On the other hand, the results from Phase 1 indicated that delivering any type of message in the 3 pm-6 pm time window improved mood significantly over sending a message in the 9 am-12 pm time window. The StayWell at Home program increases in mood ratings appeared more pronounced during the first two to three weeks of the intervention and were maintained for the remainder of the study period. The current paper provides evidence that low-burden text-message interventions may effectively address behavioral health concerns among diverse communities.
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Affiliation(s)
- Marvyn R. Arévalo Avalos
- School of Social Welfare, University of California Berkeley, Berkeley, California, United States of America
| | - Jing Xu
- Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Caroline Astrid Figueroa
- School of Social Welfare, University of California Berkeley, Berkeley, California, United States of America
- Faculty of Technology, Policy, and Management, Delft Technical University, Delft, The Netherlands
| | - Alein Y. Haro-Ramos
- School of Public Health, Health Policy and Management, University of California Berkeley, Berkeley, California, United States of America
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, United States of America
| | - Adrian Aguilera
- School of Social Welfare, University of California Berkeley, Berkeley, California, United States of America
- Department of Psychiatry and Behavioral Sciences, University of California–San Francisco, San Francisco, California, United States of America
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Kraft R, Reichert M, Pryss R. Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:472. [PMID: 38257567 PMCID: PMC10820952 DOI: 10.3390/s24020472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/13/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.
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Affiliation(s)
- Robin Kraft
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
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Gruichich TS, Gomez JCD, Zayas-Cabán G, McInnis MG, Cochran AL. A digital self-report survey of mood for bipolar disorder. Bipolar Disord 2021; 23:810-820. [PMID: 33587813 PMCID: PMC8364560 DOI: 10.1111/bdi.13058] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/13/2020] [Accepted: 02/02/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVES Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity. METHODS We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure. RESULTS We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R2 = 0.47) and SIGH-D (R2 = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores. CONCLUSIONS While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.
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Denecke K, Gabarron E, Petersen C, Merolli M. Defining participatory health informatics - a scoping review. Inform Health Soc Care 2021; 46:234-243. [PMID: 33622168 DOI: 10.1080/17538157.2021.1883028] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Healthcare has been shifting toward individuals participating in decision-making and empowered to be active in their treatment, and health monitoring. The term "participatory health informatics" (PHI) started to appear in literature. A clear definition of PHI is missing, and facets of PHI still have to be shaped. The objective of this paper is to offer a definition of PHI considering themes and technologies that make healthcare participatory. We searched Pubmed, ACM Digital Library, IEEE Xplore, EMBASE, and conference proceedings for articles that reported about use of information technology or informatics in the context of PHI. We performed qualitative synthesis and reported summary statistics. 39 studies were eligible after screening 382 titles and abstracts and reviewing 82 full texts. The top 5 person-centered key themes related to PHI included empowerment, decision-making, informed patient, collaboration, and disease management. Finally, we propose to define PHI as multidisciplinary field that uses information technology as provided through the web, smartphones, or wearables to increase participation of individuals in their care process and to enable them in self-care and shared decision-making. Goals to be achieved through PHI include maintaining health and well-being; improving the healthcare system and health outcomes; sharing experiences; achieving life goals; and self-education.
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Affiliation(s)
- Kerstin Denecke
- Institute for Medical Informatics, Bern University of Applied Sciences, Bern, Switzerland
| | - Elia Gabarron
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
| | - Carolyn Petersen
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark Merolli
- Centre for Digital Transformation of Health, Melbourne Medical School, the University of Melbourne, Melbourne, Australia
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Schueller SM, Neary M, Lai J, Epstein DA. Understanding People's Use of and Perspectives on Mood-Tracking Apps: Interview Study. JMIR Ment Health 2021; 8:e29368. [PMID: 34383678 PMCID: PMC8387890 DOI: 10.2196/29368] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 06/09/2021] [Accepted: 06/24/2021] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Supporting mental health and wellness is of increasing interest due to a growing recognition of the prevalence and burden of mental health issues. Mood is a central aspect of mental health, and several technologies, especially mobile apps, have helped people track and understand it. However, despite formative work on and dissemination of mood-tracking apps, it is not well understood how mood-tracking apps used in real-world contexts might benefit people and what people hope to gain from them. OBJECTIVE To address this gap, the purpose of this study was to understand motivations for and experiences in using mood-tracking apps from people who used them in real-world contexts. METHODS We interviewed 22 participants who had used mood-tracking apps using a semistructured interview and card sorting task. The interview focused on their experiences using a mood-tracking app. We then conducted a card sorting task using screenshots of various data entry and data review features from mood-tracking apps. We used thematic analysis to identify themes around why people use mood-tracking apps, what they found useful about them, and where people felt these apps fell short. RESULTS Users of mood-tracking apps were primarily motivated by negative life events or shifts in their own mental health that prompted them to engage in tracking and improve their situation. In general, participants felt that using a mood-tracking app facilitated self-awareness and helped them to look back on a previous emotion or mood experience to understand what was happening. Interestingly, some users reported less inclination to document their negative mood states and preferred to document their positive moods. There was a range of preferences for personalization and simplicity of tracking. Overall, users also liked features in which their previous tracked emotions and moods were visualized in figures or calendar form to understand trends. One gap in available mood-tracking apps was the lack of app-facilitated recommendations or suggestions for how to interpret their own data or improve their mood. CONCLUSIONS Although people find various features of mood-tracking apps helpful, the way people use mood-tracking apps, such as avoiding entering negative moods, tracking infrequently, or wanting support to understand or change their moods, demonstrate opportunities for improvement. Understanding why and how people are using current technologies can provide insights to guide future designs and implementations.
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Affiliation(s)
- Stephen M Schueller
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
- Department of Informatics, University of California, Irvine, Irvine, CA, United States
| | - Martha Neary
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Jocelyn Lai
- Department of Psychological Science, University of California, Irvine, Irvine, CA, United States
| | - Daniel A Epstein
- Department of Informatics, University of California, Irvine, Irvine, CA, United States
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