评估基于app的睡眠程序以改善临床失眠人群的睡眠结果:随机对照试验。

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Walter Staiano, Christine Callahan, Michelle Davis, Leah Tanner, Chelsea Coe, Sarah Kunkle, Ulrich Kirk
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引用次数: 0

摘要

背景:失眠是最常见的睡眠障碍,严重影响心理健康和生活质量。失眠的传统治疗方法包括药物干预或失眠认知行为疗法(CBT-I),但这些选择可能不是每个需要治疗的人都能获得。目的:本研究旨在评估基于应用程序的顶空睡眠项目对临床失眠成人患者睡眠障碍和心理健康结果的有效性,并与等待列表对照组进行比较。方法:这项随机对照试验包括132名患有临床失眠症的成年人,他们被分配到顶空睡眠计划(一个18期的自我指导,利用CBT-I技术增强正念的应用程序内计划)或候补对照组。通过失眠症状(使用失眠严重指数测量)和睡眠效率(通过睡眠日记和活动记录仪测量)的变化来评估睡眠障碍的结果。心理健康结果包括感知压力(由10项感知压力量表测量)、抑郁症状(由8项患者健康问卷测量)、睡眠质量(由匹兹堡睡眠质量指数测量)、焦虑症状(由7项广泛性焦虑障碍量表测量)和正念(由正念注意力意识量表测量)。从基线到干预后和随访的变化对每个结果进行评估。结果:参与者的平均(SD)年龄为37.2(10.6)岁,132人中有69人(52.3%)为女性。与等候名单对照组相比,随机分配到顶空睡眠计划组的患者从基线到干预后和随访期间失眠症状的改善明显更大(结论:顶空睡眠计划是改善临床失眠成人睡眠障碍的有效干预措施。试验注册:ClinicalTrials.gov NCT05872672;https://clinicaltrials.gov/ct2/show/NCT05872672。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of an App-Based Sleep Program to Improve Sleep Outcomes in a Clinical Insomnia Population: Randomized Controlled Trial.

Background: Insomnia is the most commonly reported sleep disturbance and significantly impacts mental health and quality of life. Traditional treatments for insomnia include pharmacological interventions or cognitive behavioral therapy for insomnia (CBT-I), but these options may not be accessible to everyone who needs treatment.

Objective: This study aims to assess the effectiveness of the app-based Headspace Sleep Program in adults with clinical insomnia on sleep disturbance and mental health outcomes, compared with a waitlist control group.

Methods: This randomized controlled trial included 132 adults with clinical insomnia who were assigned to either the Headspace Sleep Program (an 18-session self-guided, in-app program utilizing CBT-I techniques augmented by mindfulness) or a waitlist control group. Sleep disturbance outcomes were assessed by changes in insomnia symptoms (measured using the Insomnia Severity Index) and sleep efficiency (measured via sleep diary and actigraphy). Mental health outcomes included perceived stress (measured by the 10-item Perceived Stress Scale), depressive symptoms (measured by the 8-item Patient Health Questionnaire), sleep quality (measured by the Pittsburgh Sleep Quality Index), anxiety symptoms (measured by the 7-item Generalized Anxiety Disorder Scale), and mindfulness (measured by the Mindful Attention Awareness Scale). Changes from baseline to postintervention and follow-up were assessed for each outcome.

Results: Participants had a mean (SD) age of 37.2 (10.6) years, with 69 out of 132 (52.3%) identifying as female. Those randomized to the Headspace Sleep Program group experienced significantly greater improvements in insomnia symptoms from baseline to postintervention and follow-up compared with participants in the waitlist control group (P<.001, η²p=0.107). Improvements from baseline to postintervention and follow-up were also observed in the Headspace Sleep Program group for sleep efficiency, as measured by both sleep diary (P=.01, η²p=.03) and actigraphy outcomes (P=.01, η²p=.03). Participants in the Headspace Sleep Program group showed greater improvements in insomnia remission rates (8/66, 12%, at postintervention and 9/66, 14%, at follow-up) and treatment response (11/66, 17%, at postintervention and 15/66, 23%, at follow-up) compared with the control group (remission rate 2/66, 3%, at postintervention and 0/66, 0%, at follow-up; treatment response 3/66, 5%, at postintervention and 1/66, 2%, at follow-up). The results suggest significant improvements in depressive symptoms (P=.01, η²p=.04), anxiety symptoms (P=.02, η²p=.02), and mindfulness (P=.01, η²p=.03) in the Headspace Sleep Program group.

Conclusions: The Headspace Sleep Program is an effective intervention for improving sleep disturbances in adults with clinical insomnia.

Trial registration: ClinicalTrials.gov NCT05872672; https://clinicaltrials.gov/ct2/show/NCT05872672.

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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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