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Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder
Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote...
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Published in: | Journal of affective disorders 2022-08, Vol.310, p.106-115 |
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creator | Matcham, F. Carr, E. White, K.M. Leightley, D. Lamers, F. Siddi, S. Annas, P. de Girolamo, G. Haro, J.M. Horsfall, M. Ivan, A. Lavelle, G. Li, Q. Lombardini, F. Mohr, D.C. Narayan, V.A. Penninx, B.W.H.J. Oetzmann, C. Coromina, M. Simblett, S.K. Weyer, J. Wykes, T. Zorbas, S. Brasen, J.C. Myin-Germeys, I. Conde, P. Dobson, R.J.B. Folarin, A.A. Ranjan, Y. Rashid, Z. Cummins, N. Dineley, J. Vairavan, S. Hotopf, M. |
description | Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics.
The Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.
A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.
Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.
These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
•Depression severity does not predict engagement with remote sensing in major depression.•Increased perceived ease of use predicts higher questionnaire completion and FitBit wear-time.•Remote sensing is robust across depression, anxiety and functional disability severity. |
doi_str_mv | 10.1016/j.jad.2022.05.005 |
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The Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.
A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.
Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.
These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
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The Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.
A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.
Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.
These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
•Depression severity does not predict engagement with remote sensing in major depression.•Increased perceived ease of use predicts higher questionnaire completion and FitBit wear-time.•Remote sensing is robust across depression, anxiety and functional disability severity.</description><subject>Cohort study</subject><subject>Engagement</subject><subject>Major Depressive Disorder</subject><subject>Predictors</subject><subject>Remote sensing</subject><issn>0165-0327</issn><issn>1573-2517</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kDFvFDEQhS1ERC6BH0CDXNLsMrbP3ltRoQQCUqJQQG15veOLV7f24fEF5d9nwwVKqine9540H2NvBbQChPkwtZMbWwlStqBbAP2CrYTuVCO16F6y1cLoBpTsTtkZ0QQApu_gFTtVWkutoVux9L3gGH3NhXgOHNPWbXHGVPnvWO94wTlX5ISJYtryiv4u5V3eRiQecuH0MO9rnvmMjg7lWIyJ37hpCS9xX5Ao3iO_jJTLiOU1OwluR_jm-Z6zn18-_7j42lzfXn27-HTdeNWb2vjQbzZm7YwIo-pVNzgZpNLC97oTmwHVIIewXoPrBzmCDh0YBc4PDrTp9XpU5-z9cXdf8q8DUrVzJI-7nUuYD2SlMQI2nQa9oOKI-pKJCga7L3F25cEKsE-a7WQXzfZJswVt4U_n3fP8YZhx_Nf463UBPh4BXJ68j1gs-YjJL64L-mrHHP8z_wiEiY81</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Matcham, F.</creator><creator>Carr, E.</creator><creator>White, K.M.</creator><creator>Leightley, D.</creator><creator>Lamers, F.</creator><creator>Siddi, S.</creator><creator>Annas, P.</creator><creator>de Girolamo, G.</creator><creator>Haro, J.M.</creator><creator>Horsfall, M.</creator><creator>Ivan, A.</creator><creator>Lavelle, G.</creator><creator>Li, Q.</creator><creator>Lombardini, F.</creator><creator>Mohr, D.C.</creator><creator>Narayan, V.A.</creator><creator>Penninx, B.W.H.J.</creator><creator>Oetzmann, C.</creator><creator>Coromina, M.</creator><creator>Simblett, S.K.</creator><creator>Weyer, J.</creator><creator>Wykes, T.</creator><creator>Zorbas, S.</creator><creator>Brasen, J.C.</creator><creator>Myin-Germeys, I.</creator><creator>Conde, P.</creator><creator>Dobson, R.J.B.</creator><creator>Folarin, A.A.</creator><creator>Ranjan, Y.</creator><creator>Rashid, Z.</creator><creator>Cummins, N.</creator><creator>Dineley, J.</creator><creator>Vairavan, S.</creator><creator>Hotopf, M.</creator><general>Elsevier B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20220801</creationdate><title>Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder</title><author>Matcham, F. ; Carr, E. ; White, K.M. ; Leightley, D. ; Lamers, F. ; Siddi, S. ; Annas, P. ; de Girolamo, G. ; Haro, J.M. ; Horsfall, M. ; Ivan, A. ; Lavelle, G. ; Li, Q. ; Lombardini, F. ; Mohr, D.C. ; Narayan, V.A. ; Penninx, B.W.H.J. ; Oetzmann, C. ; Coromina, M. ; Simblett, S.K. ; Weyer, J. ; Wykes, T. ; Zorbas, S. ; Brasen, J.C. ; Myin-Germeys, I. ; Conde, P. ; Dobson, R.J.B. ; Folarin, A.A. ; Ranjan, Y. ; Rashid, Z. ; Cummins, N. ; Dineley, J. ; Vairavan, S. ; Hotopf, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-cf98864a61fd3937ba2f2351c95718be3b2bf440a9b2d05f70630acba056954d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cohort study</topic><topic>Engagement</topic><topic>Major Depressive Disorder</topic><topic>Predictors</topic><topic>Remote sensing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Matcham, F.</creatorcontrib><creatorcontrib>Carr, E.</creatorcontrib><creatorcontrib>White, K.M.</creatorcontrib><creatorcontrib>Leightley, D.</creatorcontrib><creatorcontrib>Lamers, F.</creatorcontrib><creatorcontrib>Siddi, S.</creatorcontrib><creatorcontrib>Annas, P.</creatorcontrib><creatorcontrib>de Girolamo, G.</creatorcontrib><creatorcontrib>Haro, J.M.</creatorcontrib><creatorcontrib>Horsfall, M.</creatorcontrib><creatorcontrib>Ivan, A.</creatorcontrib><creatorcontrib>Lavelle, G.</creatorcontrib><creatorcontrib>Li, Q.</creatorcontrib><creatorcontrib>Lombardini, F.</creatorcontrib><creatorcontrib>Mohr, D.C.</creatorcontrib><creatorcontrib>Narayan, V.A.</creatorcontrib><creatorcontrib>Penninx, B.W.H.J.</creatorcontrib><creatorcontrib>Oetzmann, C.</creatorcontrib><creatorcontrib>Coromina, M.</creatorcontrib><creatorcontrib>Simblett, S.K.</creatorcontrib><creatorcontrib>Weyer, J.</creatorcontrib><creatorcontrib>Wykes, T.</creatorcontrib><creatorcontrib>Zorbas, S.</creatorcontrib><creatorcontrib>Brasen, J.C.</creatorcontrib><creatorcontrib>Myin-Germeys, I.</creatorcontrib><creatorcontrib>Conde, P.</creatorcontrib><creatorcontrib>Dobson, R.J.B.</creatorcontrib><creatorcontrib>Folarin, A.A.</creatorcontrib><creatorcontrib>Ranjan, Y.</creatorcontrib><creatorcontrib>Rashid, Z.</creatorcontrib><creatorcontrib>Cummins, N.</creatorcontrib><creatorcontrib>Dineley, J.</creatorcontrib><creatorcontrib>Vairavan, S.</creatorcontrib><creatorcontrib>Hotopf, M.</creatorcontrib><creatorcontrib>RADAR-CNS consortium</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of affective disorders</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Matcham, F.</au><au>Carr, E.</au><au>White, K.M.</au><au>Leightley, D.</au><au>Lamers, F.</au><au>Siddi, S.</au><au>Annas, P.</au><au>de Girolamo, G.</au><au>Haro, J.M.</au><au>Horsfall, M.</au><au>Ivan, A.</au><au>Lavelle, G.</au><au>Li, Q.</au><au>Lombardini, F.</au><au>Mohr, D.C.</au><au>Narayan, V.A.</au><au>Penninx, B.W.H.J.</au><au>Oetzmann, C.</au><au>Coromina, M.</au><au>Simblett, S.K.</au><au>Weyer, J.</au><au>Wykes, T.</au><au>Zorbas, S.</au><au>Brasen, J.C.</au><au>Myin-Germeys, I.</au><au>Conde, P.</au><au>Dobson, R.J.B.</au><au>Folarin, A.A.</au><au>Ranjan, Y.</au><au>Rashid, Z.</au><au>Cummins, N.</au><au>Dineley, J.</au><au>Vairavan, S.</au><au>Hotopf, M.</au><aucorp>RADAR-CNS consortium</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder</atitle><jtitle>Journal of affective disorders</jtitle><addtitle>J Affect Disord</addtitle><date>2022-08-01</date><risdate>2022</risdate><volume>310</volume><spage>106</spage><epage>115</epage><pages>106-115</pages><issn>0165-0327</issn><eissn>1573-2517</eissn><abstract>Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics.
The Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.
A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.
Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.
These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.
•Depression severity does not predict engagement with remote sensing in major depression.•Increased perceived ease of use predicts higher questionnaire completion and FitBit wear-time.•Remote sensing is robust across depression, anxiety and functional disability severity.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>35525507</pmid><doi>10.1016/j.jad.2022.05.005</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Cohort study Engagement Major Depressive Disorder Predictors Remote sensing |
title | Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder |
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