The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement – ScienceOpen
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      The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

      research-article
      , BSc, MSc 1 , 2 , , , BSc 3 , , MSc 4 , , PhD 4 , , BSc 5 , , BSc 1 , 6 , , MSc 7 , 8 , , CPsychol, PhD 1 , 9 , , PhD 4 , , PhD 2 , 4 , 10 , 11 , 12 , , MSc 13 , , BSc 13 , , PhD 13 , , PhD 1 , 2 , , PhD 1 , 2
      (Reviewer), (Reviewer)
      JMIR Mental Health
      JMIR Publications
      depression, anxiety, digital health, wearable devices, smartphone, passive sensing, mobile health, mHealth, digital phenotyping, mobile phone

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          Abstract

          Background

          Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment.

          Objective

          A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement.

          Methods

          A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device.

          Results

          The overall retention rate was 60%. Higher-intensity treatment ( χ 2 1=4.6; P=.03) and higher baseline anxiety ( t 56.28=−2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not ( t 50.4=−0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition ( U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list.

          Conclusions

          Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.

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          Most cited references42

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Generalized anxiety disorder (GAD) is one of the most common mental disorders; however, there is no brief clinical measure for assessing GAD. The objective of this study was to develop a brief self-report scale to identify probable cases of GAD and evaluate its reliability and validity. A criterion-standard study was performed in 15 primary care clinics in the United States from November 2004 through June 2005. Of a total of 2740 adult patients completing a study questionnaire, 965 patients had a telephone interview with a mental health professional within 1 week. For criterion and construct validity, GAD self-report scale diagnoses were compared with independent diagnoses made by mental health professionals; functional status measures; disability days; and health care use. A 7-item anxiety scale (GAD-7) had good reliability, as well as criterion, construct, factorial, and procedural validity. A cut point was identified that optimized sensitivity (89%) and specificity (82%). Increasing scores on the scale were strongly associated with multiple domains of functional impairment (all 6 Medical Outcomes Study Short-Form General Health Survey scales and disability days). Although GAD and depression symptoms frequently co-occurred, factor analysis confirmed them as distinct dimensions. Moreover, GAD and depression symptoms had differing but independent effects on functional impairment and disability. There was good agreement between self-report and interviewer-administered versions of the scale. The GAD-7 is a valid and efficient tool for screening for GAD and assessing its severity in clinical practice and research.
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              The PHQ-9: validity of a brief depression severity measure.

              While considerable attention has focused on improving the detection of depression, assessment of severity is also important in guiding treatment decisions. Therefore, we examined the validity of a brief, new measure of depression severity. The Patient Health Questionnaire (PHQ) is a self-administered version of the PRIME-MD diagnostic instrument for common mental disorders. The PHQ-9 is the depression module, which scores each of the 9 DSM-IV criteria as "0" (not at all) to "3" (nearly every day). The PHQ-9 was completed by 6,000 patients in 8 primary care clinics and 7 obstetrics-gynecology clinics. Construct validity was assessed using the 20-item Short-Form General Health Survey, self-reported sick days and clinic visits, and symptom-related difficulty. Criterion validity was assessed against an independent structured mental health professional (MHP) interview in a sample of 580 patients. As PHQ-9 depression severity increased, there was a substantial decrease in functional status on all 6 SF-20 subscales. Also, symptom-related difficulty, sick days, and health care utilization increased. Using the MHP reinterview as the criterion standard, a PHQ-9 score > or =10 had a sensitivity of 88% and a specificity of 88% for major depression. PHQ-9 scores of 5, 10, 15, and 20 represented mild, moderate, moderately severe, and severe depression, respectively. Results were similar in the primary care and obstetrics-gynecology samples. In addition to making criteria-based diagnoses of depressive disorders, the PHQ-9 is also a reliable and valid measure of depression severity. These characteristics plus its brevity make the PHQ-9 a useful clinical and research tool.
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                Author and article information

                Contributors
                Journal
                JMIR Ment Health
                JMIR Ment Health
                JMH
                JMIR Mental Health
                JMIR Publications (Toronto, Canada )
                2368-7959
                2023
                24 January 2023
                : 10
                : e42866
                Affiliations
                [1 ] Department of Psychological Medicine Institute of Psychiatry, Psychology and Neuroscience King's College London London United Kingdom
                [2 ] NIHR Maudsley Biomedical Research Centre  South London and Maudsley NHS Foundation Trust London United Kingdom
                [3 ] Department of Psychology King's College London London United Kingdom
                [4 ] Department of Biostatistics & Health Informatics Institute of Psychiatry, Psychology and Neuroscience King's College London London United Kingdom
                [5 ] Lewisham Talking Therapies South London and Maudsley NHS Foundation Trust London United Kingdom
                [6 ] Department of Psychology University of Bath Bath United Kingdom
                [7 ] Biomedical Signal Interpretation and Computational Simulation Group Aragón Institute of Engineering Research (I3A), IIS Aragón University of Zaragoza Zaragoza Spain
                [8 ] Centro de Investigación Biomédica en Red of Bioengineering Biomaterials and Nanomedicine (CIBER-BBN) Madrid Spain
                [9 ] School of Psychology University of Sussex Brighton United Kingdom
                [10 ] Institute of Health Informatics University College London London United Kingdom
                [11 ] Health Data Research UK London University College London London United Kingdom
                [12 ] NIHR Biomedical Research Centre at University College London Hospitals University College London Hospitals NHS Foundation Trust London United Kingdom
                [13 ] Institute of Psychiatry, Psychology and Neuroscience King's College London London United Kingdom
                Author notes
                Corresponding Author: Valeria de Angel valeria.de_angel@ 123456kcl.ac.uk
                Author information
                https://orcid.org/0000-0002-5109-3636
                https://orcid.org/0000-0002-9959-427X
                https://orcid.org/0000-0003-4817-3835
                https://orcid.org/0000-0002-1178-917X
                https://orcid.org/0000-0001-6818-9856
                https://orcid.org/0000-0002-4962-9673
                https://orcid.org/0000-0001-6776-8522
                https://orcid.org/0000-0002-4055-904X
                https://orcid.org/0000-0003-3652-5266
                https://orcid.org/0000-0002-0333-1927
                https://orcid.org/0000-0003-3079-3120
                https://orcid.org/0000-0003-0513-0915
                https://orcid.org/0000-0002-6843-9920
                https://orcid.org/0000-0003-4224-9245
                https://orcid.org/0000-0002-3980-4466
                Article
                v10i1e42866
                10.2196/42866
                9906314
                36692937
                de573ad1-68d5-43af-9734-8584e14a15ca
                ©Valeria de Angel, Fadekemi Adeleye, Yuezhou Zhang, Nicholas Cummins, Sara Munir, Serena Lewis, Estela Laporta Puyal, Faith Matcham, Shaoxiong Sun, Amos A Folarin, Yatharth Ranjan, Pauline Conde, Zulqarnain Rashid, Richard Dobson, Matthew Hotopf. Originally published in JMIR Mental Health (https://mental.jmir.org), 24.01.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.

                History
                : 21 September 2022
                : 5 November 2022
                : 10 November 2022
                : 26 November 2022
                Categories
                Original Paper
                Original Paper

                depression,anxiety,digital health,wearable devices,smartphone,passive sensing,mobile health,mhealth,digital phenotyping,mobile phone

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