• Je něco špatně v tomto záznamu ?

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

SK. Simblett, M. Pennington, M. Quaife, S. Siddi, F. Lombardini, JM. Haro, MT. Peñarrubia-Maria, S. Bruce, R. Nica, S. Zorbas, A. Polhemus, J. Novak, E. Dawe-Lane, D. Morris, M. Mutepua, C. Odoi, E. Wilson, F. Matcham, KM. White, M. Hotopf, T....

. 2023 ; 331 (-) : 334-341. [pub] 20230317

Jazyk angličtina Země Nizozemsko

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc23011047

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.

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc23011047
003      
CZ-PrNML
005      
20230801132758.0
007      
ta
008      
230718s2023 ne f 000 0|eng||
009      
AR
024    7_
$a 10.1016/j.jad.2023.03.030 $2 doi
035    __
$a (PubMed)36934854
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a ne
100    1_
$a Simblett, S K $u Department of Psychology, King's College London, London, UK. Electronic address: sara.simblett@kcl.ac.uk
245    10
$a Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment / $c SK. Simblett, M. Pennington, M. Quaife, S. Siddi, F. Lombardini, JM. Haro, MT. Peñarrubia-Maria, S. Bruce, R. Nica, S. Zorbas, A. Polhemus, J. Novak, E. Dawe-Lane, D. Morris, M. Mutepua, C. Odoi, E. Wilson, F. Matcham, KM. White, M. Hotopf, T. Wykes, RADAR-CNS consortium
520    9_
$a 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.
650    _2
$a lidé $7 D006801
650    12
$a deprese $x diagnóza $x terapie $7 D003863
650    _2
$a pacientova volba $7 D057240
650    12
$a telemedicína $7 D017216
650    _2
$a stupeň vzdělání $7 D004522
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Pennington, M $u King's Health Economics, King's College London, London, UK
700    1_
$a Quaife, M $u Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
700    1_
$a Siddi, S $u Parc Sanitari Sant Joan de Déu, Fundación Sant Joan de Déu, Centro de Investigación Biomedica en Red (CIBER), Barcelona, Spain
700    1_
$a Lombardini, F $u Parc Sanitari Sant Joan de Déu, Fundación Sant Joan de Déu, Centro de Investigación Biomedica en Red (CIBER), Barcelona, Spain
700    1_
$a Haro, J M $u Fundació Idiap Jordi Gol i Gurina, Barcelona, Spain
700    1_
$a Peñarrubia-Maria, M T $u Fundació Idiap Jordi Gol i Gurina, Barcelona, Spain
700    1_
$a Bruce, S $u RADAR-CNS Patient Advisory Board, King's College London, UK
700    1_
$a Nica, R $u RADAR-CNS Patient Advisory Board, King's College London, UK
700    1_
$a Zorbas, S $u RADAR-CNS Patient Advisory Board, King's College London, UK
700    1_
$a Polhemus, A $u Merck Research Labs IT, Merck Sharpe, & Dohme, Prague, Czech Republic; Human Genetics, Charles University, Faculty of Science, Prague, Czech Republic
700    1_
$a Novak, J $u Merck Research Labs IT, Merck Sharpe, & Dohme, Prague, Czech Republic; Department of Anthropology and Human Genetics, Charles University, Faculty of Science, Prague, Czech Republic
700    1_
$a Dawe-Lane, E $u Department of Psychology, King's College London, London, UK
700    1_
$a Morris, D $u Department of Psychology, King's College London, London, UK
700    1_
$a Mutepua, M $u Department of Psychology, King's College London, London, UK
700    1_
$a Odoi, C $u Department of Psychology, King's College London, London, UK; Department of Anthropology and Human Genetics, Charles University, Faculty of Science, Prague, Czech Republic
700    1_
$a Wilson, E $u Department of Psychology, King's College London, London, UK
700    1_
$a Matcham, F $u Department of Psychological Medicine, King's College London, London, UK
700    1_
$a White, K M $u Department of Psychological Medicine, King's College London, London, UK
700    1_
$a Hotopf, M $u NIHR South London and Maudsley Biomedical Research Centre, London, UK; Department of Psychological Medicine, King's College London, London, UK
700    1_
$a Wykes, T $u Department of Psychology, King's College London, London, UK; NIHR South London and Maudsley Biomedical Research Centre, London, UK
710    2_
$a RADAR-CNS consortium
773    0_
$w MED00002501 $t Journal of affective disorders $x 1573-2517 $g Roč. 331, č. - (2023), s. 334-341
856    41
$u https://pubmed.ncbi.nlm.nih.gov/36934854 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y p $z 0
990    __
$a 20230718 $b ABA008
991    __
$a 20230801132755 $b ABA008
999    __
$a ok $b bmc $g 1963450 $s 1197312
BAS    __
$a 3
BAS    __
$a PreBMC-MEDLINE
BMC    __
$a 2023 $b 331 $c - $d 334-341 $e 20230317 $i 1573-2517 $m Journal of affective disorders $n J Affect Disord $x MED00002501
LZP    __
$a Pubmed-20230718

Najít záznam

Citační ukazatele

Možnosti archivace