Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment

on behalf of the RADAR-CNS consortium

doi:10.1016/j.jad.2023.03.030

Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment

on behalf of the RADAR-CNS consortium

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

Background: In time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption. Method: In a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality. Results: Higher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background. Conclusion: For people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.

Original language	English
Pages (from-to)	334-341
Number of pages	8
Journal	Journal of Affective Disorders
Volume	331
DOIs	https://doi.org/10.1016/j.jad.2023.03.030
Publication status	Published - 15 Jun 2023

Keywords

Depression
Discrete choice experiment
Mobile technology

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10.1016/j.jad.2023.03.030

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@article{3c44ab7cea29415e9827f151205ef6b3,

title = "Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment",

abstract = "Background: In time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption. Method: In a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality. Results: Higher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background. Conclusion: For people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.",

keywords = "Depression, Discrete choice experiment, Mobile technology",

author = "{on behalf of the RADAR-CNS consortium} and Simblett, {S. K.} and M. Pennington and M. Quaife and S. Siddi and F. Lombardini and Haro, {J. M.} and Pe{\~n}arrubia-Maria, {M. T.} and S. Bruce and R. Nica and S. Zorbas and A. Polhemus and J. Novak and E. Dawe-Lane and D. Morris and M. Mutepua and C. Odoi and E. Wilson and F. Matcham and White, {K. M.} and M. Hotopf and T. Wykes",

note = "Funding Information: This paper was written as part of the development of useful mHealth and remote measurement technology systems in the RADAR-CNS project. The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115902. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA) (www.imi.europa.eu) with one pharmaceutical company supporting the preparation of this manuscript as part of this pre-competitive public and private partnership. This communication reflects the views of the RADAR-CNS consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. This paper also represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Professor Til Wykes would specifically like to acknowledge support from the NIHR for their Senior Investigator Awards. The authors thank Stephen Kelly for his help with the back-translation from English to Spanish of the survey. Funding Information: This paper was written as part of the development of useful mHealth and remote measurement technology systems in the RADAR-CNS project. The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115902 . This Joint Undertaking receives support from the European Union 's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA) ( www.imi.europa.eu ) with one pharmaceutical company supporting the preparation of this manuscript as part of this pre-competitive public and private partnership. This communication reflects the views of the RADAR-CNS consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. This paper also represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London . The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Professor Til Wykes would specifically like to acknowledge support from the NIHR for their Senior Investigator Awards. The authors thank Stephen Kelly for his help with the back-translation from English to Spanish of the survey. Funding Information: This work was supported by the Innovative Medicines Initiative 2 Joint Undertaking (grant number 115902— RADAR-CNS). Publisher Copyright: {\textcopyright} 2023",

year = "2023",

month = jun,

day = "15",

doi = "10.1016/j.jad.2023.03.030",

language = "English",

volume = "331",

pages = "334--341",

journal = "Journal of Affective Disorders",

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TY - JOUR

T1 - Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression

T2 - A discrete choice experiment

AU - on behalf of the RADAR-CNS consortium

AU - Simblett, S. K.

AU - Pennington, M.

AU - Quaife, M.

AU - Siddi, S.

AU - Lombardini, F.

AU - Haro, J. M.

AU - Peñarrubia-Maria, M. T.

AU - Bruce, S.

AU - Nica, R.

AU - Zorbas, S.

AU - Polhemus, A.

AU - Novak, J.

AU - Dawe-Lane, E.

AU - Morris, D.

AU - Mutepua, M.

AU - Odoi, C.

AU - Wilson, E.

AU - Matcham, F.

AU - White, K. M.

AU - Hotopf, M.

AU - Wykes, T.

N1 - Funding Information: This paper was written as part of the development of useful mHealth and remote measurement technology systems in the RADAR-CNS project. The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115902. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA) (www.imi.europa.eu) with one pharmaceutical company supporting the preparation of this manuscript as part of this pre-competitive public and private partnership. This communication reflects the views of the RADAR-CNS consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. This paper also represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Professor Til Wykes would specifically like to acknowledge support from the NIHR for their Senior Investigator Awards. The authors thank Stephen Kelly for his help with the back-translation from English to Spanish of the survey. Funding Information: This paper was written as part of the development of useful mHealth and remote measurement technology systems in the RADAR-CNS project. The RADAR-CNS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115902 . This Joint Undertaking receives support from the European Union 's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA) ( www.imi.europa.eu ) with one pharmaceutical company supporting the preparation of this manuscript as part of this pre-competitive public and private partnership. This communication reflects the views of the RADAR-CNS consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein. This paper also represents independent research part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London . The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Professor Til Wykes would specifically like to acknowledge support from the NIHR for their Senior Investigator Awards. The authors thank Stephen Kelly for his help with the back-translation from English to Spanish of the survey. Funding Information: This work was supported by the Innovative Medicines Initiative 2 Joint Undertaking (grant number 115902— RADAR-CNS). Publisher Copyright: © 2023

PY - 2023/6/15

Y1 - 2023/6/15

N2 - Background: In time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption. Method: In a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality. Results: Higher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background. Conclusion: For people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.

AB - Background: In time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption. Method: In a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality. Results: Higher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background. Conclusion: For people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.

KW - Depression

KW - Discrete choice experiment

KW - Mobile technology

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U2 - 10.1016/j.jad.2023.03.030

DO - 10.1016/j.jad.2023.03.030

M3 - Article

C2 - 36934854

AN - SCOPUS:85150904877

SN - 0165-0327

VL - 331

SP - 334

EP - 341

JO - Journal of Affective Disorders

JF - Journal of Affective Disorders

ER -

Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment

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Keywords

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