数字心理健康平台中临床结果的改善与维持:一项纵向观察性真实世界研究的结果。

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Lydia G Roos, Sara J Sagui-Henson, Cynthia Castro Sweet, Camille E Welcome Chamberlain, Brooke J Smith
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引用次数: 0

摘要

背景介绍数字心理健康服务作为一项健康福利计划,越来越多地由雇主提供,它可以改善心理健康护理的可及性并消除障碍。分层护理模式尤其能提供个性化的护理建议,在节约资源的同时提供临床有效的干预措施。然而,要了解这些模式对心理健康的益处以及在实际环境中的应用,还需要进行临床评估:本研究旨在检查临床结果(即抑郁和焦虑症状及幸福感)的变化,并评估分层混合护理在雇主赞助的数字心理健康福利成员中的使用情况:在一项大型前瞻性观察研究中,我们对 509 名参与者(平均年龄 33.9, SD 8.7 years; women: n=312, 61.3%; men: n=175, 34.4%; nonbinary: n=22, 4.3%),他们新注册并参与了雇主赞助的数字心理健康平台(Modern Health Inc)的护理。我们还调查了参与者在多大程度上遵循了通过分层混合护理模式向他们提供的建议:结果:基线抑郁和焦虑症状升高的参与者症状明显改善,抑郁得分提高了 37%,焦虑得分提高了 29%(P 值 结论:基线抑郁和焦虑症状升高的参与者症状明显改善,抑郁得分提高了 37%,焦虑得分提高了 29%:基线抑郁或焦虑症状升高的参与者从基线到随访期间的心理健康状况都有明显改善,大多数没有症状或基线症状轻微的参与者随着时间的推移保持了心理健康。此外,参与模式表明,分层混合护理模式能够有效地为个人匹配最有效、成本最低的护理服务,同时也允许他们自主决定护理服务,并使用最适合其需求的服务组合。总之,本研究的结果证明了该平台在改善和保护心理健康方面的临床有效性,并支持分层混合护理模式在改善数字心理健康服务的获取和使用方面的实用性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improvement and Maintenance of Clinical Outcomes in a Digital Mental Health Platform: Findings From a Longitudinal Observational Real-World Study.

Background: Digital mental health services are increasingly being provided by employers as health benefit programs that can improve access to and remove barriers to mental health care. Stratified care models, in particular, offer personalized care recommendations that can offer clinically effective interventions while conserving resources. Nonetheless, clinical evaluation is needed to understand their benefits for mental health and their use in a real-world setting.

Objective: This study aimed to examine the changes in clinical outcomes (ie, depressive and anxiety symptoms and well-being) and to evaluate the use of stratified blended care among members of an employer-sponsored digital mental health benefit.

Methods: In a large prospective observational study, we examined the changes in depressive symptoms (9-item Patient Health Questionnaire), anxiety symptoms (7-item Generalized Anxiety Disorder scale), and well-being (5-item World Health Organization Well-Being Index) for 3 months in 509 participants (mean age 33.9, SD 8.7 years; women: n=312, 61.3%; men: n=175, 34.4%; nonbinary: n=22, 4.3%) who were newly enrolled and engaged in care with an employer-sponsored digital mental health platform (Modern Health Inc). We also investigated the extent to which participants followed the recommendations provided to them through a stratified blended care model.

Results: Participants with elevated baseline symptoms of depression and anxiety exhibited significant symptom improvements, with a 37% score improvement in depression and a 29% score improvement in anxiety (P values <.001). Participants with baseline scores indicative of poorer well-being also improved over the study period (90% score improvement; P=.002). Furthermore, over half exhibited clinical improvement or recovery for depressive symptoms (n=122, 65.2%), anxiety symptoms (n=127, 59.1%), and low well-being (n=82, 64.6%). Among participants with mild or no baseline symptoms, we found high rates of maintenance for low depressive (n=297, 92.2%) and anxiety (n=255, 86.7%) symptoms and high well-being (n=344, 90.1%). In total, two-thirds of the participants (n=343, 67.4%) used their recommended care, 16.9% (n=86) intensified their care beyond their initial recommendation, and 15.7% (n=80) of participants underused care by not engaging with the highest level of care recommended to them.

Conclusions: Participants with elevated baseline depressive or anxiety symptoms improved their mental health significantly from baseline to follow-up, and most participants without symptoms or with mild symptoms at baseline maintained their mental health over time. In addition, engagement patterns indicate that the stratified blended care model was efficient in matching individuals with the most effective and least costly care while also allowing them to self-determine their care and use combinations of services that best fit their needs. Overall, the results of this study support the clinical effectiveness of the platform for improving and preserving mental health and support the utility and effectiveness of stratified blended care models to improve access to and use of digitally delivered mental health services.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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