Simblett S K, Pennington M, Quaife M, Siddi S, Lombardini F, Haro J M, Peñarrubia-Maria M T, Bruce S, Nica R, Zorbas S, Polhemus A, Novak J, Dawe-Lane E, Morris D, Mutepua M, Odoi C, Wilson E, Matcham F, White K M, Hotopf M, Wykes T
Department of Psychology, King's College London, London, UK.
King's Health Economics, King's College London, London, UK.
J Affect Disord. 2023 Jun 15;331:334-341. doi: 10.1016/j.jad.2023.03.030. Epub 2023 Mar 17.
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.
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.
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.
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.
假以时日，我们或许能够利用通过移动技术收集的行为数据来检测抑郁症症状的早期发作，甚至预测复发情况。然而，技术应用存在障碍，了解这些因素对用户的重要性对于确保最大程度的应用至关重要。
在一项离散选择实验中，要求有抑郁症病史的人（N = 171）从一系列包含四个特征的小场景中选择他们偏好的技术，这四个特征分别是隐私、临床支持、既定益处和设备准确性（即检测症状的能力），每个特征有不同的水平。使用混合逻辑模型来确定最有可能影响技术应用的因素。亚组分析探讨了年龄、性别、教育程度、技术接受度和熟悉程度以及国籍的影响。
更高水平的隐私、更多的临床支持、更高的感知益处和更好的设备准确性很重要。准确性是最重要的，人们只愿意在一定程度上妥协以增加其他因素，如隐私。既定益处是最不受重视的属性，参与者对具有可能但未知益处的技术感到满意。偏好受到技术接受度、年龄、国籍和教育背景的影响。
对于有抑郁症病史的人来说，采用技术可能是出于对准确检测症状的渴望。然而，技术接受度较低、教育程度较低、年龄较小的人以及特定国籍的人可能愿意为了更多的隐私和临床支持而在一定程度的准确性上做出妥协。这些偏好应有助于塑造移动健康工具的设计。
