Comparative Effectiveness of Digital Cognitive Behavioral Therapy vs Medication Therapy Among Patients With Insomnia

Menglin Lu; Yaoyun Zhang; Junhang Zhang; Songfang Huang; Fei Huang; Tingna Wang; Fei Wu; Hongjing Mao; Zhengxing Huang

doi:10.1001/jamanetworkopen.2023.7597

. 2023 Apr 11;6(4):e237597. doi: 10.1001/jamanetworkopen.2023.7597

Comparative Effectiveness of Digital Cognitive Behavioral Therapy vs Medication Therapy Among Patients With Insomnia

Menglin Lu ¹, Yaoyun Zhang ², Junhang Zhang ³, Songfang Huang ², Fei Huang ², Tingna Wang ⁴, Fei Wu ¹, Hongjing Mao ^1,^3,^✉, Zhengxing Huang ^1,^✉

¹Zhejiang University, Hangzhou, China

²Alibaba Damo Academy, Hangzhou, China

³Hangzhou Seventh People’s Hospital, Hangzhou, China

⁴Hangzhou slan-health Co, Ltd, Hangzhou, China

Accepted for Publication: February 22, 2023.

Published: April 11, 2023. doi:10.1001/jamanetworkopen.2023.7597

^✉

Corresponding Authors: Zhengxing Huang, PhD, Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, PR China (zhengxinghuang@zju.edu.cn); and Hongjing Mao, MD, Hangzhou Seventh People’s Hospital, 305 Tianmushan Rd, Hangzhou 310058, PR China (13777861972@163.com).

Author Contributions: Dr Huang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ms Lu and Dr Y Zhang are joint first authors.

Concept and design: Lu, Y. Zhang, J. Zhang, S. Huang, Mao, Z. Huang.

Acquisition, analysis, or interpretation of data: Lu, Y. Zhang, J. Zhang, F. Huang, Wang, Wu, Z. Huang.

Drafting of the manuscript: Lu, Y. Zhang, J. Zhang, Z. Huang.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Lu, Y. Zhang, Z. Huang.

Obtained funding: S. Huang, F. Huang, Z. Huang.

Administrative, technical, or material support: Y. Zhang, J. Zhang, S. Huang, F. Huang, Wu, Mao, Z. Huang.

Supervision: S. Huang, F. Huang, Wu, Mao, Z. Huang.

Conflict of Interest Disclosures: Ms Wang reported being the project manager and data coordinator of Hangzhou slan-health. No other disclosures were reported.

Funding/Support: This work was partially supported by the National Key Research and Development Program of China under grant No. 2022YFF1202400 and the National Nature Science Foundation of China under grant No. 82272129.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: Thanks to Wenyue Ma, MSC, Yunlong Gao, MSC, and others in Hangzhou slan-health Co, Ltd for their support in data access. They were compensated for their time.

^✉

Corresponding author.

PMCID: PMC10091171 PMID: 37040111

Key Points

Question

Can clinical data be used to assess the effectiveness, durability, engagement, and adaptability of digital cognitive behavioral therapy for insomnia (dCBT-I)?

Findings

In this cohort study of 4052 patients with insomnia, dCBT-I was superior to medication therapy at 6-month follow-up, although results were found to be unstable. The combination of dCBT-I and medication resulted in a sustained improvement in sleep quality compared with monotherapy modalities.

Meaning

These findings suggest that dCBT-I needs to be thoughtfully combined with medication for long-term benefits in the treatment of insomnia and that further research into the design, implementation, and delivery of dCBT-I in terms of engagement and stability is required.

This cohort study evaluates the clinical effectiveness, engagement, durability, and adaptability of digital cognitive behavioral therapy for insomnia.

Abstract

Importance

Although digital cognitive behavioral therapy for insomnia (dCBT-I) has been studied in many randomized clinical trials and is recommended as a first-line treatment option, few studies have systematically examined its effectiveness, engagement, durability, and adaptability in clinical settings.

Objective

To evaluate the clinical effectiveness, engagement, durability, and adaptability of dCBT-I.

Design, Setting, and Participants

This retrospective cohort study was conducted using longitudinal data collected via a mobile app named Good Sleep 365 between November 14, 2018, and February 28, 2022. Three therapeutic modes (ie, dCBT-I, medication, and their combination) were compared at month 1, month 3, and month 6 (primary). Inverse probability of treatment weighting (IPTW) using propensity scores was applied to enable homogeneous comparisons between the 3 groups.

Exposures

Treatment with dCBT-I, medication therapy, or combination therapy according to prescriptions.

Main Outcomes and Measures

The Pittsburgh Sleep Quality Index (PSQI) score and its essential subitems were used as the primary outcomes. Effectiveness on comorbid somnolence, anxiety, depression, and somatic symptoms were used as secondary outcomes. Cohen d effect size, P value, and standardized mean difference (SMD) were used to measure differences in treatment outcomes. Changes in outcomes and response rates (≥3 points change in PSQI score) were also reported.

Results

A total of 4052 patients (mean [SD] age, 44.29 [12.01] years; 3028 [74.7%] female participants) were selected for dCBT-I (n = 418), medication (n = 862), and their combination (n = 2772). Compared with the change in PSQI score at 6 months for participants receiving medication alone (from a mean [SD] of 12.85 [3.49] to 8.92 [4.03]), both dCBT-I (from a mean [SD] of 13.51 [3.03] to 7.15 [3.25]; Cohen d, −0.50; 95% CI, −0.62 to −0.38; P < .001; SMD = 0.484) and combination therapy (from a mean [SD] of 12.92 [3.49] to 6.98 [3.43]; Cohen d, 0.50; 95% CI, 0.42 to 0.58; P < .001; SMD = 0.518) were associated with significant reductions; dCBT-I had a comparable effect as combination therapy (Cohen d, 0.05; 95% CI, −0.05 to 0.15; P = .66; SMD = 0.05), but showed unstable durability. Outcomes of dCBT-I improved steadily and rapidly during the first 3 months, and then fluctuated. The response rates with dCBT-I and combination therapy were higher than with medication. Changes in secondary outcomes indicated statistically significant benefits from dCBT-I and combination therapy. The results of subgroup analysis were consistent with the main findings, demonstrating the superiority of dCBT-I vs medication therapy in various subpopulations.

Conclusions and Relevance

In this study, clinical evidence suggested that combination therapy was optimal, and dCBT-I was more effective than medication therapy, with long-term benefits for insomnia. Future studies are needed to analyze its clinical effectiveness and reliability in distinct subpopulations.

Introduction

Insomnia disorder, comprising reports of poor sleep with associated daytime effects occurring 3 or more nights per week for 3 or more months,¹ presents in 10% to 20% of adults worldwide.^2,3,4 Compared with before the COVID-19 pandemic,^5,6,7 the number of people experiencing insomnia is increasing rapidly, with the prevalence of clinically significant insomnia growing between 47% and 189%, leading to impaired work productivity and reduced quality of life.⁸ Insomnia is detrimental to human functions such as immunity and metabolism.⁹ It may exacerbate the cognitive and affective features of neurodegeneration and contribute to disease progression by accelerating pathogenic processes.⁹ In addition, insomnia has comorbid mental health disorders, such as depression and anxiety,^10,11 and physical disorders, such as cardiovascular disease, type 2 diabetes, and increased fatigue.¹² Therefore, it is urgent to identify an efficient intervention for insomnia with low-cost and wide dissemination.¹³

Digital cognitive behavioral therapy for insomnia (dCBT-I), by delivering the core content of cognitive behavioral therapy for insomnia (CBT-I) through the internet or mobile apps, includes 5 therapeutic sessions: stimulus control (SC), sleep restriction (SR), relaxation training (RT), cognitive reconstruction (CR), and sleep hygiene (SH).¹⁴ These 5 components can be used alone or in combination, and multicomponent cognitive behavioral therapy is strongly recommended in clinical practice guidelines.¹⁵ As a low-cost complement to CBT-I, dCBT-I has demonstrated efficacy in randomized clinical trials (RCTs) and meta-analyses for decreasing insomnia severity, enhancing sleep quality, reducing sleep medication intake, and mitigating comorbid mental disorders.^{16,17,18,19,20} It has been recommended as an effective and cost-saving alternative to sleeping pills by the National Institute for Health and Care Excellence of the US and England to reduce patients’ reliance on drugs, such as zolpidem and zopiclone, which can be dependence forming.²¹

However, existing studies exploring the efficacy of dCBT-I are mostly RCTs, mainly comparing the efficacy of dCBT-I with face-to-face CBT-I and usual care.^{19,22,23,24,25,26,27} There are also RCTs examining the efficacy of face-to-face CBT-I vs medication monotherapy and combination therapy.^28,29,30 Despite the advantages of RCTs in removing the influence of external factors, they are cumbersome and time-consuming and often do not match complex clinical scenarios.³¹ Practice-based evidence of the effectiveness of dCBT-I can be acquired through analysis of clinical data.^32,33,34 Notably, clinical data of health care delivery is routinely collected from a variety of settings, and the derived evidence can be more useful and generalizable than that from RCTs.³⁵ Although there are also some retrospective studies exploring dCBT-I, such as SHUTi, Sleepio, and DREAM, these studies analyzed the effectiveness of dCBT-I primarily by comparing outcomes before and after the intervention, or by comparing it with patient education or routine care.^{34,36,37,38,39,40} To our knowledge, few retrospective studies on insomnia have been conducted to compare dCBT-I, medication therapy, and their combination. Furthermore, there is a lack of systematic analysis regarding dCBT-I engagement, durability, and adaptability in the practice setting, which is essential to inform effective and widespread dissemination. In other words, the potential of clinical data to explore the clinical effectiveness of dCBT-I has been severely underestimated.⁴¹

In this work, we conducted a retrospective cohort study (eFigure 1 in Supplement 1) using longitudinal data of the Chinese population collected from a mobile app called Good Sleep 365 (eFigure 2 in Supplement 1).⁴² Based on 6-month follow-up, the effectiveness of 3 treatment modalities, including dCBT-I, medication therapy, and their combination, was measured and compared. Retrospective studies are prone to bias in the selection of case and control sources and may introduce factors that confound the association between treatments and outcomes.^43,44 To mitigate this problem, we first used inverse probability of treatment weighting (IPTW) to reduce the imbalance of measured confounders between treatment groups, which has already been proven effective in multiple clinical retrospective studies.^45,46,47,48 In addition, patient engagement and dCBT-I effectiveness in different subpopulations were also systematically examined.

Methods

Ethical Declaration

All data used in this study were deidentified according to Health Insurance Portability and Accountability Act guidelines. The requirement for informed consent was waived because all available data were anonymous. The use of clinical information and data collection protocols were approved by the ethical committee of College of Biomedical Engineering and Instrument Science, Zhejiang University. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Data Source

This retrospective study used data collected using Good Sleep 365 between November 14, 2018, and February 28, 2022. The app provided functions such as CBT-I training, medication records, and sleep quality assessment. Demographic characteristics (eg, sex, age) and clinical data related to insomnia (eg, family history of insomnia) were filled in at the time of enrollment. Therapeutic modes were allocated as prescribed by attending physicians (including 7 senior physicians with approximately 20 years of experience and 9 junior physicians with approximately 10 years of experience), and intervention details were documented in medication records, dCBT-I records, and sleep diaries. The participant’s treatment trajectory was remotely monitored through the app, and physicians regularly reminded the participant to complete the assessment scales, including PSQI, Epworth Sleepiness Sore (ESS), 7-item Generalized Anxiety Disorder (GAD-7), 9-item Patient Health Questionnaire (PHQ-9), and 15-item Patient Health Questionnaire (PHQ-15), to evaluate the treatment effectiveness. Overall, data available for analysis included cross-sectional data, such as patient demographic characteristics, insomnia-related clinical information, diagnoses, and complications, as well as longitudinal data documenting intervention details and assessment scores during follow-up.

Study Design and Population

We conducted a retrospective cohort study to assess the clinical effectiveness, engagement, durability, and adaptability of dCBT-I, medication therapy, and their combination in the treatment of insomnia, using IPTW to adjust for confounding in intervention mode selection. Participants were allocated to 3 treatment groups according to prescriptions. Taking the first outpatient visit as the baseline, the improvement of sleep quality and complications within 6 months after the baseline was analyzed.

The source population consisted of all outpatients with the main concern of insomnia between November 14, 2018, to February 28, 2022. Following previous studies,^19,49 we included patients (1) aged 18 to 80 years; (2) with no severe psychiatric disorders, such as restless legs syndrome, sleep apnea symptoms, epilepsy, or bipolar disorder; (3) with a PSQI score greater than 5 at baseline; (4) and with an ESS score of 10 or less at baseline. We excluded participants with incomplete demographic or treatment information. In addition, we retained participants who had completed at least 1 assessment during the 6-month follow-up period to ensure that the effectiveness of the intervention could be measured.

Exposure, Covariates, and Outcomes

The primary exposure of interest was treatment with dCBT-I, medication therapy, or combination therapy. Patients in the dCBT-I and medication groups received the corresponding monotherapy, respectively. Patients in the combination group received the combined therapy of dCBT-I and hypnotic medications. Baseline (ie, at time of outpatient visit) covariates included demographic characteristics, insomnia-related clinical information, insomnia severity, and comorbidities. Both PSQI and its subitems (except part F) were used as the primary outcomes to assess sleep quality, and the secondary outcomes included the ESS, GAD-7, PHQ-9 and PHQ-15 to assess the severity of comorbid disorders. In this study, both the primary and secondary outcomes were assessed at month 1, month 3, and month 6 (primary end point). More details are in eAppendix 1 in Supplement 1.

Statistical Analysis

We used IPTW to reduce imbalance in measured confounders between treatment groups, which has already been proved as an effective approach in multiple clinical retrospective studies (eMethods in Supplement 1).^{45,46,47,48,50} The propensity scores were based on the logistic regression models using multiple covariates as independent variables and each treatment group as the dependent variable. The mitigation of between-group differences by IPTW was fulfilled by using the R library ipw version 1.0-11. We calculated standardized mean differences (SMDs) for each covariate to determine whether there was significant difference between the 3 therapeutic groups. When the SMD was 0.1 or less after IPTW, the confounder was considered to have no between-group difference.^51,52 Outcomes were comparable when there were no between-group differences in all covariates.

Proportions of categorical variables and mean/SD of numerical variables were calculated to represent the composition of the demographic and insomnia-related clinical data of patients. We primarily analyzed the effectiveness, durability, and patient engagement of 3 interventions for insomnia. Intervention effectiveness was evaluated based on response rates, changes in outcomes, and effect sizes. Then we analyzed 3 modes for comorbidities including somnolence, anxiety, depression, and somatic symptoms. Additionally, we performed a subgroup analysis, comparing the effectiveness of the 3 intervention modes in subpopulations with different characteristics. A strict P value threshold of .01 was used to handle the potential for type I error due to multiple comparisons. More details are in eAppendix 2 in Supplement 1. Statistical analysis was implemented in Python version 3.8.0 (Python Software Foundation) and R version 4.1.3 (R Project for Statistical Computing). Result plotting was with Prism version 9.4.0 (GraphPad) and R.

Results

Cohort Characteristics

Between November 14, 2018, and February 28, 2022, 11 826 outpatients with the chief concern of insomnia disorder signed up for the app and obtained access. Patients used the app to receive dCBT-I, record their sleep and medication dairies, and fill out regular assessments for PSQI, ESS, GAD-7, PHQ-9, and PHQ-15. The detailed cohort selection procedure is illustrated in Figure 1. In total, 713 patients were excluded due to lack of complete demographic or treatment information; 786 patients were excluded due to inappropriate age or serious complications; 3104 patients with mild insomnia or somnolence at baseline were excluded; and 3171 patients were excluded without any outcome assessment within 6 months after baseline. As a result, the remaining 4052 eligible patients (34.26%) were included in the cohort, with a mean (SD) age of 44.29 (12.01) years and 3028 (74.7%) female participants. Specifically, there were 418 (10.32%), 862 (21.27%), and 2772 (68.41%) in the dCBT-I group, the medication group, and the combination group, respectively. The Table shows the baseline characteristics of this cohort. After IPTW adjustment, all baseline covariates met thresholds of SMD of 0.1 or less, indicating that between-group differences in covariates were eliminated and that differences in outcomes stemmed from different therapeutic modes (eTable 1 in Supplement 1).

Figure 1. — dCBT-I indicates digital cognitive behavioral therapy for insomnia; ESS, Epworth Sleepiness Score; and PSQI, Pittsburgh Sleep Quality Index.

Table. Demographic and Clinical Characteristics of the Cohort.

Characteristic	Patients, No. (%)				P value
Characteristic	Overall (N = 4052)	dCBT-I (n = 418)	Medication (n = 862)	Combination (n = 2772)	P value
Sex
Male	1024 (25.3)	118 (28.2)	220 (25.5)	686 (24.7)	.31
Female	3028 (74.7)	300 (71.8)	642 (74.5)	2086 (75.3)	.31
Age, mean (SD), y	44.29 (12.01)	40.23 (11.73)	42.55 (13.69)	45.45 (11.27)	<.001
Education
Primary	473 (11.7)	30 (7.2)	151 (17.5)	292 (10.5)	<.001
Middle and senior	1672 (41.3)	129 (30.9)	329 (38.2)	1214 (43.8)	<.001
College and undergraduate	1736 (42.8)	224 (53.6)	351 (40.7)	1161 (41.9)	<.001
Postgraduate	171 (4.2)	35 (8.4)	31 (3.6)	105 (3.8)	<.001
Employment
Employed	3427 (84.6)	379 (90.7)	728 (84.5)	2320 (83.7)	.001
Unemployed	625 (15.4)	39 (9.3)	134 (15.5)	452 (16.3)	.001
Insomnia duration
<1 mo	423 (10.4)	77 (18.4)	91 (10.6)	255 (9.2)	<.001
1-3 mos	573 (14.1)	69 (16.5)	124 (14.4)	380 (13.7)	.30
3-12 mos	771 (19.0)	61 (14.6)	192 (22.3)	518 (18.7)	.003
1-3 y	743 (18.3)	66 (15.8)	164 (19.0)	513 (18.5)	.34
3-5 y	487 (12.0)	52 (12.4)	111 (12.9)	324 (11.7)	.62
5-10 y	1055 (26.0)	93 (22.2)	180 (20.9)	782 (28.2)	<.001
Family history
Yes	1298 (32.0)	145 (34.7)	246 (28.5)	907 (32.7)	.03
No	2754 (68.0)	273 (65.3)	616 (71.5)	1865 (67.3)	.03
First onset
Yes	1004 (24.8)	99 (23.7)	215 (24.9)	690 (24.9)	.86
No	3048 (75.2)	319 (76.3)	647 (75.1)	2082 (75.1)	.86
Medication history
Yes	2380 (58.7)	178 (42.6)	550 (63.8)	1652 (59.6)	<.001
No	1672 (41.3)	240 (57.4)	312 (36.2)	1120 (40.4)	<.001
Current medication
Yes	2896 (71.5)	157 (37.6)	654 (75.9)	2085 (75.2)	<.001
No	1156 (28.5)	261 (62.4)	208 (24.1)	687 (24.8)	<.001
PSQI score at baseline, mean (SD)	14.99 (3.65)	12.60 (3.78)	15.09 (3.62)	15.32 (3.50)	<.001
ESS score, mean (SD)	3.45 (2.83)	4.21 (2.95)	3.50 (2.87)	3.32 (2.78)	<.001
GAD-7 score at baseline, mean (SD)	7.65 (5.83)	6.70 (5.49)	8.97 (6.23)	7.39 (5.69)	<.001
PHQ-9 score at baseline, mean (SD)	8.72 (6.19)	7.51 (5.64)	10.58 (7.01)	8.33 (5.87)	<.001
PHQ-15 score at baseline, mean (SD)	8.91 (4.58)	8.19 (4.59)	9.72 (4.78)	8.76 (4.49)	<.001

Open in a new tab

Abbreviations: dCBT-I, digital cognitive behavioral therapy for insomnia; ESS, Epworth Sleepiness Score; GAD-7, 7-item Generalized Anxiety Disorder; PHQ-9, 9-item Patient Health Questionnaire; PHQ-15, 15-item Patient Health Questionnaire; PSQI, Pittsburgh Sleep Quality Index.

Response Rates

Overall, 243 of 317 (77.30%), 200 of 244 (81.97%), and 92 of 120 (76.19%) participants developed dCBT-I responses at 1-, 3- and 6-month follow-up (eTable 2 in Supplement 1). In contrast, 275 of 496 (55.45%), 251 of 453 (55.45%), and 132 of 245 (54.08%) participants responded to medication therapy and 1630 of 2418 (67.40%), 1653 of 2224 (74.34%), and 1152 of 1510 (76.31%) participants responded to combination therapy at 1-, 3-, and 6-month follow-up, respectively. The secondary outcomes also demonstrated significant benefits of dCBT-I and combination therapy (GAD-7: 76 of 120 [63.44%] and 1088 of 1511 [72.03%]; PHQ-9: 73 of 120 [60.69%] and 1086 of 1507 [72.08%]; and PHQ-15: 55 of 119 [46.34%] and 809 of 1482 [54.56%]) at 6-month follow-up. The detailed results are presented in eAppendix 3 in Supplement 1.

Changes in Outcomes

Consistent findings were found on the basis of changes in primary and secondary outcomes. Improvements in PSQI with dCBT-I and combination therapy were observed from baseline to 6-month follow-up (from a mean [SD] of 13.51 [3.03] to 7.15 [3.25] for dCBT-I and from a mean [SD] of 12.92 [3.49] to 6.98 [3.43] for combination therapy), while pharmacological interventions (from a mean [SD] of 12.85 [3.49] to 8.92 [4.03]) were less effective (eTable 3 and eAppendix 3 in Supplement 1).

Treatment Effect Sizes

The comparison between dCBT-I and medication therapy showed that dCBT-I recipients had a more significant reduction in PSQI scores compared with the medication group at month 1 (small effect size: Cohen d, −0.26; 95% CI, −0.38 to −0.14; P = .005; SMD = 0.25), month 3 (moderate effect size: Cohen d, −0.57; 95% CI, −0.68 to −0.45; P < .001; SMD = 0.55), and month 6 (moderate effect size: Cohen d, −0.50; 95% CI, −0.62 to −0.38; P < .001; SMD = 0.484) (Figures 2 and 3). In particular, dCBT-I was superior to medication therapy in terms of subjective sleep quality, sleep onset latency, sleep efficiency, and daytime dysfunction at 6 months. Likewise, dCBT-I was more effective in improving all secondary outcomes. Taking depression as an example, dCBT-I produced a moderate effect size at month 1 (Cohen d, −0.63, 95% CI, −0.75 to −0.51; P < .001; SMD = 0.576); a moderate effect size at month 3 (Cohen d, −0.75; 95% CI, −0.87 to −0.63; P < .001; SMD = 0.667); and a small effect size at month 6 (Cohen d, −0.40; 95% CI, −0.51 to −0.28; P = .004; SMD = 0.381). Comparisons of primary and secondary outcomes across the 3 follow-up visits are presented in eTables 4 to 7 in Supplement 1. eFigures 3 and 4 in Supplement 1 show forest plots of medication vs combination therapy and dCBT-I vs combination therapy, respectively.

Figure 2. — Each horizontal line represents the 95% CI of outcome comparison (Cohen d effect size), and the result is plotted as a box in the middle of the horizontal line. The vertical line is a reference line, placed at the value where there is no difference between 2 interventions. PSQI indicates Pittsburgh Sleep Quality Index.

Figure 3. — Each horizontal line represents the 95% CI of outcome comparison (Cohen d effect size), and the results are plotted as a box in the middle of the horizontal line. The vertical line is a reference line, placed at the value where there is no difference between the 2 interventions. ESS indicates Epworth Sleepiness Score; GAD-7, 7-item Generalized Anxiety Disorder; PHQ-9, 9-item Patient Health Questionnaire; PHQ-15, 15-item Patient Health Questionnaire.

Durability

The 3 interventions showed significant change in symptoms after 1 month of intervention and then gradually stabilized on all outcomes (eTable 8 and eFigure 5 in Supplement 1). The symptoms of patients receiving combination therapy steadily improved, with sustainable changes on all outcomes. After 3 months of intervention, the performance on the primary outcomes gradually stabilized within a certain range. In contrast, outcomes of dCBT-I improved steadily and rapidly during the first 3 months and then fluctuated. The change with respect to each outcome for patients receiving medication leveled off between month 1 and month 2, and even worsened after 4 to 5 months, indicating unstable durability. Combination therapy also had persistent and steady downward trends (good durability) in improving the secondary outcomes in comparison with dCBT-I, which had a rebound trend after 5 months, whereas medication monotherapy was slower to improve these comorbid disorders. Notably, the score of sleep medication use increased from baseline to the first month for both medication and combination therapies. Likewise, ESS score increased dramatically in dCBT-I and combination therapy at month 1.

Engagement

Overall, 36.58% of patients participated in all components of dCBT-I. Almost all patients completed RT (98.40%) and SR (92.07%) during the 6-month period, while only 48.68% completed SC, and 44.33% completed CR. During the first month of treatment, 56.02%, 80.03%, and 94.14% of participants completed SH, SR, and RT, respectively, while only 8.78% and 8.93% participated in SC and CR (eFigure 6 and eTable 9 in Supplement 1). Since then, monthly engagements in SH (from 56.02% to 8.56%), SR (from 80.03% to 42.66%), and RT (from 94.14% to 49.91%) showed a downward trend, while participants in CR and SC increased gradually in the first 3 months. This observation is intuitive because in the dCBT-I program, the 5 sessions are organized in the order of SH, RT, SR, SC, and CR. Overall, patient adherence during the dCBT-I program (the first 3 months) was significantly better than after the program.

Subgroup Analysis

Three therapeutic modes had varied superiority in distinct subgroups (Figure 4 and eFigures 7 and 8 in Supplement 1). Consistent with the primary analysis, dCBT-I and combination therapy showed a greater advantage than medication treatment in all subpopulations, albeit with variations across subgroups. The detailed results are presented in eAppendix 4 in Supplement 1.

Discussion

Existing studies exploring the efficacy of dCBT-I are mostly RCTs, mainly comparing dCBT-I with face-to-face CBT-I and routine care.^22,23,36 There are also RCTs examining the efficacy of face-to-face CBT-I vs medication monotherapy and combination therapy.^26,28,29 Despite the advantages of RCTs in removing the influence of external factors, they are cumbersome and time-consuming and often do not match complex clinical scenarios.³¹ On the other hand, current retrospective studies primarily analyze the effectiveness of dCBT-I by comparing outcomes before and after the intervention or by comparing it with patient education or routine care.^{34,36,37,38,39,40} There is a lack of retrospective studies to compare the effectiveness of dCBT-I, medication, and their combination.^34,37,38,40 Furthermore, few studies have explored the engagement, durability, and adaptability of dCBT-I, which has great potential to provide supportive evidence for clinically targeted therapy. To our knowledge, this study is the first to systematically explore dCBT-I from multiple perspectives based on clinical data. Overall, clinical evidence suggests that combination therapy outperformed on most of the primary (PSQI score and its subitems) and secondary (GAD-7 score, PHQ-9 score, and PHQ-15 score) outcomes, and patients in distinct subgroups had varied adaptability under the 3 therapeutic modes. Those receiving dCBT-I achieved statistically significant improvement in the primary and secondary outcomes in comparison with those receiving medication alone (Figures 2 and 3). Combination therapy showed better durability of outcome improvement than the other 2 therapeutic modes. Overall, our main findings about effectiveness are consistent with previous findings that CBT-I can achieve better sleep health outcomes compared with medication therapy,^25,26,53 combinations of CBT-I and medications had the potential to optimize outcomes.^28,53,54,55

Our results add to considerable clinical evidence for insomnia treatment. At 6-month follow up, insomnia improvements for those receiving dCBT-I were promising (eTables 2 and 3 in Supplement 1). The response rate of using dCBT-I in practice settings was comparable with that reported in previous RCTs (around 45%-60%).^56,57,58 According to PSQI scores, 77.30%, 81.97%, 76.19% of patients receiving dCBT-I responded at 1-, 3-, and 6-month follow-up, respectively. dCBT-I was also more effective for comorbid disorders in comparison with medication, showing consistent superiority from the first month in relief of anxiety, depression, and somatic symptoms. Notably, the association of dCBT-I with reduction of ESS score started later than 1 month. One potential reason is that the abrupt implementation of the SR component in dCBT-I is associated with an increase in daytime somnolence, resulting in a sharp increase in ESS before stabilizing within reasonable limits.^59,60,61 Moreover, dCBT-I was associated with greater reductions in ESS than the other modes, probably because taking sleep medication would cause the symptoms of hypersomnia.

Overall, patient engagement in dCBT-I was relatively low (36.58% completed all dCBT-I sessions), and the proportions of patients who attended the different sessions varied. Most of patients completed RT (98.40%) and SR (92.07%), while SC (48.68%), SH (68.71%), and CR (44.33%) had lower adherence (eTable 9 in Supplement 1). Interestingly, nearly 60% of patients did not complete 5 dCBT-I sessions but spontaneously selected several of them, reflecting problems with dCBT-I implementation in a clinical setting. dCBT-I is a digital channel to deliver criterion-standard treatment for insomnia, and its advantage of providing evidence-based insomnia treatment at scale has been widely recognized in RCT studies.^62,63 However, it seems that populations are consistently less likely to initiate, continue, or complete all behavioral health treatments. Additionally, insignificant and unstable trends in outcome improvements with dCBT-I (ie, durability) were observed in the clinical data, indicating that the engagement challenge still exists when patients are asked to make major behavioral changes in their daily life. Many efforts have been spent on addressing this issue, while most research on dCBT-I and face-to-face CBT-I is based on therapist-guided interventions to improve patient engagement, albeit at a higher cost.^64,65 To increase patient engagement with self-help dCBT-I, innovational therapeutic content should be developed and consistently updated to intrigue patients to overhaul their sleep patterns, behaviors, and thoughts.⁶⁶ While existing studies show that culturally tailored health programming is associated with improved treatment outcomes,^67,68,69 there have been limited efforts for Chinese individuals with insomnia. As it appears that tailored dCBT-I content may be important to maximizing the treatment effect, future studies in how to successfully adapt this treatment approach in the practice setting are needed.

In addition, patients within different subgroups showed different adaptability under the 3 therapeutic modes (Figure 4 and eFigures 7 and 8 in Supplement 1). Both dCBT-I and combination therapy have shown effectiveness advantages over medication monotherapy in all subpopulations, indicating the generalizability of both interventions. For patients with a history of taking hypnotics before intervention, the advantage of dCBT-I over medication therapy was more pronounced, showing a moderate effect size. One potential reason is that long-term use of hypnotics might lead to drug resistance, as confirmed in previous studies.^25,26 Male patients gained more benefits with dCBT-I than female patients, possibly because female patients were prone to engage in unhelpful cognitive processes, such as rumination and worry, which made it more difficult to improve sleep quality.^70,71 The obtained results of most subgroup analyses showed that dCBT-I and combination therapy could offer tangible benefits to patients with insomnia disorder. Notably, the COVID-19 epidemic appeared to have a negative association with the effectiveness of dCBT-I. dCBT-I produced a medium effect size compared with medication therapy before the pandemic. However, the effect size decreased after COVID-19. Combination treatment showed no significant difference in effectiveness vs dCBT-I before COVID-19 but outperformed with a small effect size afterward. The reason might be the poor adaptability of the current dCBT-I design to the pandemic or that the pandemic aggravated anxiety and made it difficult to achieve better effectiveness through dCBT-I monotherapy.^7,72 dCBT-I should be optimized for use during pandemics for better insomnia management.^73,74

Limitations

This study has limitations. First, retrospective studies are not as standardized as prospective analyses owing to the nature of retrospective study design. Although common demographic and clinical factors that may potentially affect the outcomes were accounted for, and IPTW was used to balance between-group differences in this study, potential bias may be introduced from no blinding of participants and therapists or from any invisible confounder that may exist whose value cannot be well adjusted. Another limitation is the lack of study samples, which may explain the fluctuations in outcomes over time. We cannot draw firm conclusions about the drug resistance and instability of dCBT-I observed in this study. Further studies on a large scale of population with a large volume of clinical data are required. Third, patients with invisible outcomes were excluded from the study to ensure the accuracy of the effectiveness comparisons. However, bias may also be introduced. While retrospective studies have the potential to provide clinical evidence to evaluate the effectiveness of treatment modalities, reliable evidence stems from pragmatic RCTs with extensive inclusion criteria. In this sense, prospective studies are planned to further validate our findings.

Conclusions

The results of this retrospective cohort study conducted in China suggest that dCBT-I is effective and has long-term benefits in patients with insomnia and that the combination of medication and dCBT-I is associated with sustained improvement in sleep quality compared with medication monotherapy. These positive findings provide clinical evidence that dCBT-I contributes to meaningful sleep improvements. Given the unstable durability of dCBT-I at 6-month follow-up, the design, implementation, and delivery of dCBT-I in the practice setting warrants further investigation.

Supplement 1.

eAppendix 1. Exposure, Covariates, and Outcomes

eAppendix 2. Statistical Analysis

eAppendix 3. Results of Response Rates and Changes in Outcomes

eAppendix 4. Results of Subgroup Analysis

eMethods. Confounders Control by IPTW

eTable 1. Details of Covariates Before/After Adjustment by IPTW

eTable 2. Response Rates According to Treatment Group

eTable 3. Improvement of Primary and Secondary Outcomes According to Treatment Group

eTable 4. Primary and Secondary Outcomes According to Treatment Group

eTable 5. Comparison of Primary and Secondary Outcomes in the dCBT-I and Medication Groups

eTable 6. Comparison of Primary and Secondary Outcomes in the Medication and Combination Groups

eTable 7. Comparison of Primary and Secondary Outcomes in the dCBT-I and Combination Groups

eTable 8. Time Series of Primary and Secondary Outcomes

eTable 9. Patient Engagement in Each dCBT-I Session

eFigure 1. Study Design

eFigure 2. Interface of Good Sleep 365 App

eFigure 3. Forest Plots of Primary and Secondary Outcomes for Medication Therapy vs Combination Therapy

eFigure 4. Forest Plots of Primary and Secondary Outcomes for dCBT-I vs Combination Therapy

eFigure 5. Line Charts of Time Series of the Primary and Secondary Outcomes During a 6-Month Follow-up

eFigure 6. Patient Engagement in Each of the 5 dCBT-I Sessions

eFigure 7. Subgroup Analysis Comparing the Effectiveness of Medication Therapy and Combination Therapy

eFigure 8. Subgroup Analysis Comparing the Effectiveness of dCBT-I and Combination Therapy

eReferences.

Click here for additional data file.^{(2.4MB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(13.4KB, pdf)}

References

1.Souza JC, Magna LA, Reimao R. Insomnia and hypnotic use in Campo Grande general population, Brazil. Arq Neuropsiquiatr. 2002;60(3-B):702-707. doi: 10.1590/S0004-282X2002000500003 [DOI] [PubMed] [Google Scholar]
2.Pallesen S, Sivertsen B, Nordhus IH, Bjorvatn B. A 10-year trend of insomnia prevalence in the adult Norwegian population. Sleep Med. 2014;15(2):173-179. doi: 10.1016/j.sleep.2013.10.009 [DOI] [PubMed] [Google Scholar]
3.Garland SN, Rowe H, Repa LM, Fowler K, Zhou ES, Grandner MA. A decade’s difference: 10-year change in insomnia symptom prevalence in Canada depends on sociodemographics and health status. Sleep Health. 2018;4(2):160-165. doi: 10.1016/j.sleh.2018.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Qaseem A, Kansagara D, Forciea MA, Cooke M, Denberg TD; Clinical Guidelines Committee of the American College of Physicians . Management of chronic insomnia disorder in adults: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2016;165(2):125-133. doi: 10.7326/M15-2175 [DOI] [PubMed] [Google Scholar]
5.Cheng P, Casement MD, Cuellar R, et al. Sleepless in COVID-19: racial disparities during the pandemic as a consequence of structural inequity. Sleep. 2022;45(1):zsab242. doi: 10.1093/sleep/zsab242 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Voitsidis P, Gliatas I, Bairachtari V, et al. Insomnia during the COVID-19 pandemic in a Greek population. Psychiatry Res. 2020;289:113076. doi: 10.1016/j.psychres.2020.113076 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Kokou-Kpolou CK, Megalakaki O, Laimou D, Kousouri M. Insomnia during COVID-19 pandemic and lockdown: prevalence, severity, and associated risk factors in French population. Psychiatry Res. 2020;290:113128. doi: 10.1016/j.psychres.2020.113128 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Morin CM, Carrier J. The acute effects of the COVID-19 pandemic on insomnia and psychological symptoms. Sleep Med. 2021;77:346-347. doi: 10.1016/j.sleep.2020.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Voysey ZJ, Barker RA, Lazar AS. The treatment of sleep dysfunction in neurodegenerative disorders. Neurotherapeutics. 2021;18(1):202-216. doi: 10.1007/s13311-020-00959-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Roth T. Insomnia: definition, prevalence, etiology, and consequences. J Clin Sleep Med. 2007;3(5)(suppl):S7-S10. [PMC free article] [PubMed] [Google Scholar]
11.Morin CM, Jarrin DC, Ivers H, Mérette C, LeBlanc M, Savard J. Incidence, persistence, and remission rates of insomnia over 5 years. JAMA Netw Open. 2020;3(11):e2018782. doi: 10.1001/jamanetworkopen.2020.18782 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Kay-Stacey M, Attarian H. Advances in the management of chronic insomnia. BMJ. 2016;354:i2123. doi: 10.1136/bmj.i2123 [DOI] [PubMed] [Google Scholar]
13.van der Gaag M. The efficacy of CBT for severe mental illness and the challenge of dissemination in routine care. World Psychiatry. 2014;13(3):257-258. doi: 10.1002/wps.20162 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Perlis ML, Smith MT, Jungquist C, Nowakowski S, Orff H, Soeffing J. Cognitive behavioral therapy for insomnia. In: Attarian HP, Schuman C, eds. Clinical Handbook of Insomnia. Humana Press; 2010:281-296, doi: 10.1007/978-1-60327-042-7_22 [DOI] [Google Scholar]
15.Edinger JD, Arnedt JT, Bertisch SM, et al. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. 2021;17(2):255-262. doi: 10.5664/jcsm.8986 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Zachariae R, Lyby MS, Ritterband LM, O’Toole MS. Efficacy of internet-delivered cognitive-behavioral therapy for insomnia–a systematic review and meta-analysis of randomized controlled trials. Sleep Med Rev. 2016;30:1-10. doi: 10.1016/j.smrv.2015.10.004 [DOI] [PubMed] [Google Scholar]
17.Chan NY, Lam SP, Zhang J, et al. Efficacy of email-delivered versus face-to-face group cognitive behavioral therapy for insomnia in youths: a randomized controlled trial. J Adolesc Health. 2022;70(5):763-773. doi: 10.1016/j.jadohealth.2021.11.005 [DOI] [PubMed] [Google Scholar]
18.Kallestad H, Saksvik S, Vedaa Ø, et al. Digital cognitive-behavioural therapy for insomnia compared with digital patient education about insomnia in individuals referred to secondary mental health services in Norway: protocol for a multicentre randomised controlled trial. BMJ Open. 2021;11(6):e050661. doi: 10.1136/bmjopen-2021-050661 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Vedaa Ø, Kallestad H, Scott J, et al. Effects of digital cognitive behavioural therapy for insomnia on insomnia severity: a large-scale randomised controlled trial. Lancet Digit Health. 2020;2(8):e397-e406. doi: 10.1016/S2589-7500(20)30135-7 [DOI] [PubMed] [Google Scholar]
20.Trauer JM, Qian MY, Doyle JS, Rajaratnam SMW, Cunnington D. Cognitive behavioral therapy for chronic insomnia: a systematic review and meta-analysis. Ann Intern Med. 2015;163(3):191-204. doi: 10.7326/M14-2841 [DOI] [PubMed] [Google Scholar]
21.Wise J. Insomnia: NICE recommends digital app as treatment option. BMJ. 2022;377:o1268. doi: 10.1136/bmj.o1268 [DOI] [Google Scholar]
22.Kallestad H, Scott J, Vedaa Ø, et al. Mode of delivery of cognitive behavioral therapy for insomnia: a randomized controlled non-inferiority trial of digital and face-to-face therapy. Sleep. 2021;44(12):zsab185. doi: 10.1093/sleep/zsab185 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Soh HL, Ho RC, Ho CS, Tam WW. Efficacy of digital cognitive behavioural therapy for insomnia: a meta-analysis of randomised controlled trials. Sleep Med. 2020;75:315-325. doi: 10.1016/j.sleep.2020.08.020 [DOI] [PubMed] [Google Scholar]
24.Kjørstad K, Sivertsen B, Vedaa Ø, et al. The effect of reducing insomnia severity on work- and activity-related impairment. Behav Sleep Med. 2021;19(4):505-515. doi: 10.1080/15402002.2020.1799792 [DOI] [PubMed] [Google Scholar]
25.Mitchell MD, Gehrman P, Perlis M, Umscheid CA. Comparative effectiveness of cognitive behavioral therapy for insomnia: a systematic review. BMC Fam Pract. 2012;13:40. doi: 10.1186/1471-2296-13-40 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Sivertsen B, Omvik S, Pallesen S, et al. Cognitive behavioral therapy vs zopiclone for treatment of chronic primary insomnia in older adults: a randomized controlled trial. JAMA. 2006;295(24):2851-2858. doi: 10.1001/jama.295.24.2851 [DOI] [PubMed] [Google Scholar]
27.Taylor DJ, Peterson AL, Pruiksma KE, Young-McCaughan S, Nicholson K, Mintz J; STRONG STAR Consortium . Internet and in-person cognitive behavioral therapy for insomnia in military personnel: a randomized clinical trial. Sleep. 2017;40(6). doi: 10.1093/sleep/zsx075 [DOI] [PubMed] [Google Scholar]
28.Beaulieu-Bonneau S, Ivers H, Guay B, Morin CM. Long-term maintenance of therapeutic gains associated with cognitive behavioral therapy for insomnia delivered alone or combined with zolpidem. Sleep. 2017;40(3):zsx002. doi: 10.1093/sleep/zsx002 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Jan Y, Yang C, Huang S, Lee H. Treatment effect of cognitive-behavior therapy for insomnia combined with usual medication. Sleep Biol Rhythms. 2019;17:311-321. doi: 10.1007/s41105-019-00218-z [DOI] [Google Scholar]
30.Morin CM, Vallières A, Guay B, et al. Cognitive behavioral therapy, singly and combined with medication, for persistent insomnia: a randomized controlled trial. JAMA. 2009;301(19):2005-2015. doi: 10.1001/jama.2009.682 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Riccardo G, Falzarano R, Magnoni M, Benso G, Bocchino V, Squillante F. Randomized controlled trials versus real-life studies: bridging the gap. Minerva Respir Med. 2021;60(1):10-15. doi: 10.23736/S2784-8477.20.01868-4 [DOI] [Google Scholar]
32.Van Houdenhove L, Buyse B, Gabriëls L, Van den Bergh O. Treating primary insomnia: clinical effectiveness and predictors of outcomes on sleep, daytime function and health-related quality of life. J Clin Psychol Med Settings. 2011;18(3):312-321. doi: 10.1007/s10880-011-9250-7 [DOI] [PubMed] [Google Scholar]
33.Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA, Lichstein KL. Psychological and behavioral treatment of insomnia: update of the recent evidence (1998-2004). Sleep. 2006;29(11):1398-1414. doi: 10.1093/sleep/29.11.1398 [DOI] [PubMed] [Google Scholar]
34.Ritterband LM, Thorndike FP, Morin CM, et al. Real-world evidence from users of a behavioral digital therapeutic for chronic insomnia. Behav Res Ther. 2022;153:104084. doi: 10.1016/j.brat.2022.104084 [DOI] [PubMed] [Google Scholar]
35.Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med. 2016;375(23):2293-2297. doi: 10.1056/NEJMsb1609216 [DOI] [PubMed] [Google Scholar]
36.Stott R, Pimm J, Emsley R, Miller CB, Espie CA. Does adjunctive digital CBT for insomnia improve clinical outcomes in an improving access to psychological therapies service? Behav Res Ther. 2021;144:103922. doi: 10.1016/j.brat.2021.103922 [DOI] [PubMed] [Google Scholar]
37.Thorndike FP, Berry RB, Gerwien R, Braun S, Maricich YA. Protocol for Digital Real-world Evidence trial for Adults with insomnia treated via Mobile (DREAM): an open-label trial of a prescription digital therapeutic for treating patients with chronic insomnia. J Comp Eff Res. 2021;10(7):569-581. doi: 10.2217/cer-2021-0004 [DOI] [PubMed] [Google Scholar]
38.Koffel E, Farrell-Carnahan L. Feasibility and preliminary real-world promise of a manualized group-based cognitive behavioral therapy for insomnia protocol for veterans. Mil Med. 2014;179(5):521-528. doi: 10.7205/MILMED-D-13-00455 [DOI] [PubMed] [Google Scholar]
39.Davidson JR, Dawson S, Krsmanovic A. Effectiveness of group cognitive behavioral therapy for insomnia (CBT-I) in a primary care setting. Behav Sleep Med. 2019;17(2):191-201. doi: 10.1080/15402002.2017.1318753 [DOI] [PubMed] [Google Scholar]
40.Cassel M, Blom K, Gatzacis J, Renblad P, Kaldo V, Jernelöv S. Clinical feasibility of cognitive behavioural therapy for insomnia in a real-world mixed sample at a specialized psychiatric outpatient clinic. BMC Psychiatry. 2022;22(1):600. doi: 10.1186/s12888-022-04231-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Galbiati A, Sforza M, Fasiello E, Castronovo V, Ferini-Strambi L. Impact of phenotypic heterogeneity of insomnia on the patients’ response to cognitive-behavioral therapy for insomnia: current perspectives. Nat Sci Sleep. 2019;11:367-376. doi: 10.2147/NSS.S198812 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Good Sleep 365. Accessed March 6, 2023. https://www.haoshuimian365.com/
43.Schober P, Vetter TR. Confounding in observational research. Anesth Analg. 2020;130(3):635-635. doi: 10.1213/ANE.0000000000004627 [DOI] [PubMed] [Google Scholar]
44.Ali MS, Prieto-Alhambra D, Lopes LC, et al. Propensity score methods in health technology assessment: principles, extended applications, and recent advances. Front Pharmacol. 2019;10:973. doi: 10.3389/fphar.2019.00973 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi: 10.1080/00273171.2011.568786 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Nishida Y, Takahashi Y, Nakayama T, Soma M, Kitamura N, Asai S. Effect of candesartan monotherapy on lipid metabolism in patients with hypertension: a retrospective longitudinal survey using data from electronic medical records. Cardiovasc Diabetol. 2010;9:38. doi: 10.1186/1475-2840-9-38 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Feld E, Harton J, Meropol NJ, et al. Effectiveness of first-line immune checkpoint blockade versus carboplatin-based chemotherapy for metastatic urothelial cancer. Eur Urol. 2019;76(4):524-532. doi: 10.1016/j.eururo.2019.07.032 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Hatakeyama S, Tanaka T, Ikehata Y, et al. Axitinib versus sunitinib as first-line therapies for metastatic renal cell carcinoma: a multicenter retrospective analysis. J Clin Oncol. 2019;37(7):555. doi: 10.1200/JCO.2019.37.7_suppl.555 [DOI] [Google Scholar]
49.Smyth C. The Pittsburgh Sleep Quality Index (PSQI). J Gerontol Nurs. 1999;25(12):10-11. doi: 10.3928/0098-9134-19991201-10 [DOI] [PubMed] [Google Scholar]
50.van Duin D, Lok JJ, Earley M, et al. ; Antibacterial Resistance Leadership Group . Colistin versus ceftazidime-avibactam in the treatment of infections due to carbapenem-resistant enterobacteriaceae. Clin Infect Dis. 2018;66(2):163-171. doi: 10.1093/cid/cix783 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.El-Galaly A, Nielsen PT, Kappel A, Jensen SL. Reduced survival of total knee arthroplasty after previous unicompartmental knee arthroplasty compared with previous high tibial osteotomy: a propensity-score weighted mid-term cohort study based on 2,133 observations from the Danish Knee Arthroplasty Registry. Acta Orthop. 2020;91(2):177-183. doi: 10.1080/17453674.2019.1709711 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Zhang Z, Kim HJ, Lonjon G, Zhu Y; written on behalf of AME Big-Data Clinical Trial Collaborative Group . Balance diagnostics after propensity score matching. Ann Transl Med. 2019;7(1):16. doi: 10.21037/atm.2018.12.10 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Morin CM, Benca R. Chronic insomnia. Lancet. 2012;379(9821):1129-1141. doi: 10.1016/S0140-6736(11)60750-2 [DOI] [PubMed] [Google Scholar]
54.Mendelson WB. Combining pharmacologic and nonpharmacologic therapies for insomnia. J Clin Psychiatry. 2007;68(suppl 5):19-23. [PubMed] [Google Scholar]
55.Morin CM. Cognitive-behavioral approaches to the treatment of insomnia. J Clin Psychiatry. 2004;65(suppl 16):33-40. [PubMed] [Google Scholar]
56.Horsch CH, Lancee J, Griffioen-Both F, et al. Mobile phone-delivered cognitive behavioral therapy for insomnia: a randomized waitlist controlled trial. J Med internet Res. 2017;19(4):e70. doi: 10.2196/jmir.6524 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Ritterband LM, Thorndike FP, Ingersoll KS, et al. Effect of a web-based cognitive behavior therapy for insomnia intervention with 1-year follow-up: a randomized clinical trial. JAMA Psychiatry. 2017;74(1):68-75. doi: 10.1001/jamapsychiatry.2016.3249 [DOI] [PubMed] [Google Scholar]
58.Blom K, Tarkian Tillgren H, Wiklund T, et al. Internet-vs. group-delivered cognitive behavior therapy for insomnia: a randomized controlled non-inferiority trial. Behav Res Ther. 2015;70:47-55. doi: 10.1016/j.brat.2015.05.002 [DOI] [PubMed] [Google Scholar]
59.Xing J, Wu X, Liu H, et al. Effects of electroacupuncture therapy and cognitive behavioral therapy in chronic insomnia: a randomized controlled study. Evid Based Complement Alternat Med. 2020;2020:5630130. doi: 10.1155/2020/5630130 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Kyle SD, Miller CB, Rogers Z, Siriwardena AN, Macmahon KM, Espie CA. Sleep restriction therapy for insomnia is associated with reduced objective total sleep time, increased daytime somnolence, and objectively impaired vigilance: implications for the clinical management of insomnia disorder. Sleep. 2014;37(2):229-237. doi: 10.5665/sleep.3386 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Kyle SD, Morgan K, Spiegelhalder K, Espie CA. No pain, no gain: an exploratory within-subjects mixed-methods evaluation of the patient experience of sleep restriction therapy (SRT) for insomnia. Sleep Med. 2011;12(8):735-747. doi: 10.1016/j.sleep.2011.03.016 [DOI] [PubMed] [Google Scholar]
62.Christensen H, Batterham PJ, Gosling JA, et al. Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): a randomised controlled trial. Lancet Psychiatry. 2016;3(4):333-341. doi: 10.1016/S2215-0366(15)00536-2 [DOI] [PubMed] [Google Scholar]
63.Espie CA, Emsley R, Kyle SD, et al. Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial. JAMA Psychiatry. 2019;76(1):21-30. doi: 10.1001/jamapsychiatry.2018.2745 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.van der Zweerde T, van Straten A, Effting M, Kyle SD, Lancee J. Does online insomnia treatment reduce depressive symptoms? a randomized controlled trial in individuals with both insomnia and depressive symptoms. Psychol Med. 2019;49(3):501-509. doi: 10.1017/S0033291718001149 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Van der Zweerde T, Lancee J, Slottje P, Bosmans JE, Van Someren EJW, van Straten A. Nurse-guided internet-delivered cognitive behavior therapy for insomnia in general practice: results from a pragmatic randomized clinical trial. Psychother Psychosom. 2020;89(3):174-184. doi: 10.1159/000505600 [DOI] [PubMed] [Google Scholar]
66.van Straten A, Lancee J. Digital cognitive behavioural therapy for insomnia: the answer to a major public health issue? Lancet Digit Health. 2020;2(8):e381-e382. doi: 10.1016/S2589-7500(20)30167-9 [DOI] [PubMed] [Google Scholar]
67.Simon NM, Hofmann SG, Rosenfield D, et al. Efficacy of yoga vs cognitive behavioral therapy vs stress education for the treatment of generalized anxiety disorder: a randomized clinical trial. JAMA Psychiatry. 2021;78(1):13-20. doi: 10.1001/jamapsychiatry.2020.2496 [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Siu PM, Yu AP, Tam BT, et al. Effects of tai chi or exercise on sleep in older adults with insomnia: a randomized clinical trial. JAMA Netw Open. 2021;4(2):e2037199. doi: 10.1001/jamanetworkopen.2020.37199 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Okajima I, Akitomi J, Kajiyama I, Ishii M, Murakami H, Yamaguchi M. Effects of a tailored brief behavioral therapy application on insomnia severity and social disabilities among workers with insomnia in Japan: a randomized clinical trial. JAMA Netw Open. 2020;3(4):e202775. doi: 10.1001/jamanetworkopen.2020.2775 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Li SH, Graham BM, Werner-Seidler A. Gender differences in adolescent sleep disturbance and treatment response to smartphone app–delivered cognitive behavioral therapy for insomnia: exploratory study. JMIR Form Res. 2021;5(3):e22498. doi: 10.2196/22498 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Nolen-Hoeksema S, Aldao A. Gender and age differences in emotion regulation strategies and their relationship to depressive symptoms. Pers Individ Dif. 2011;51(6):704-708. doi: 10.1016/j.paid.2011.06.012 [DOI] [Google Scholar]
72.Song Y, Ta Park VM, Martin JL. Future directions to address sleep health disparity among Asian American immigrants. Sleep. 2022;45(4):zsac024. doi: 10.1093/sleep/zsac024 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Becker PM. Overview of sleep management during COVID-19. Sleep Med. 2022;91:211-218. doi: 10.1016/j.sleep.2021.04.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Cheng P, Casement MD, Kalmbach DA, Castelan AC, Drake CL. Digital cognitive behavioral therapy for insomnia promotes later health resilience during the coronavirus disease 19 (COVID-19) pandemic. Sleep. 2021;44(4):zsaa258. doi: 10.1093/sleep/zsaa258 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eAppendix 1. Exposure, Covariates, and Outcomes

eAppendix 2. Statistical Analysis

eAppendix 3. Results of Response Rates and Changes in Outcomes

eAppendix 4. Results of Subgroup Analysis

eMethods. Confounders Control by IPTW

eTable 1. Details of Covariates Before/After Adjustment by IPTW

eTable 2. Response Rates According to Treatment Group

eTable 3. Improvement of Primary and Secondary Outcomes According to Treatment Group

eTable 4. Primary and Secondary Outcomes According to Treatment Group

eTable 5. Comparison of Primary and Secondary Outcomes in the dCBT-I and Medication Groups

eTable 6. Comparison of Primary and Secondary Outcomes in the Medication and Combination Groups

eTable 7. Comparison of Primary and Secondary Outcomes in the dCBT-I and Combination Groups

eTable 8. Time Series of Primary and Secondary Outcomes

eTable 9. Patient Engagement in Each dCBT-I Session

eFigure 1. Study Design

eFigure 2. Interface of Good Sleep 365 App

eFigure 3. Forest Plots of Primary and Secondary Outcomes for Medication Therapy vs Combination Therapy

eFigure 4. Forest Plots of Primary and Secondary Outcomes for dCBT-I vs Combination Therapy

eFigure 5. Line Charts of Time Series of the Primary and Secondary Outcomes During a 6-Month Follow-up

eFigure 6. Patient Engagement in Each of the 5 dCBT-I Sessions

eFigure 7. Subgroup Analysis Comparing the Effectiveness of Medication Therapy and Combination Therapy

eFigure 8. Subgroup Analysis Comparing the Effectiveness of dCBT-I and Combination Therapy

eReferences.

Click here for additional data file.^{(2.4MB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(13.4KB, pdf)}

[zoi230249r1] 1.Souza JC, Magna LA, Reimao R. Insomnia and hypnotic use in Campo Grande general population, Brazil. Arq Neuropsiquiatr. 2002;60(3-B):702-707. doi: 10.1590/S0004-282X2002000500003 [DOI] [PubMed] [Google Scholar]

[zoi230249r2] 2.Pallesen S, Sivertsen B, Nordhus IH, Bjorvatn B. A 10-year trend of insomnia prevalence in the adult Norwegian population. Sleep Med. 2014;15(2):173-179. doi: 10.1016/j.sleep.2013.10.009 [DOI] [PubMed] [Google Scholar]

[zoi230249r3] 3.Garland SN, Rowe H, Repa LM, Fowler K, Zhou ES, Grandner MA. A decade’s difference: 10-year change in insomnia symptom prevalence in Canada depends on sociodemographics and health status. Sleep Health. 2018;4(2):160-165. doi: 10.1016/j.sleh.2018.01.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r4] 4.Qaseem A, Kansagara D, Forciea MA, Cooke M, Denberg TD; Clinical Guidelines Committee of the American College of Physicians . Management of chronic insomnia disorder in adults: a clinical practice guideline from the American College of Physicians. Ann Intern Med. 2016;165(2):125-133. doi: 10.7326/M15-2175 [DOI] [PubMed] [Google Scholar]

[zoi230249r5] 5.Cheng P, Casement MD, Cuellar R, et al. Sleepless in COVID-19: racial disparities during the pandemic as a consequence of structural inequity. Sleep. 2022;45(1):zsab242. doi: 10.1093/sleep/zsab242 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r6] 6.Voitsidis P, Gliatas I, Bairachtari V, et al. Insomnia during the COVID-19 pandemic in a Greek population. Psychiatry Res. 2020;289:113076. doi: 10.1016/j.psychres.2020.113076 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r7] 7.Kokou-Kpolou CK, Megalakaki O, Laimou D, Kousouri M. Insomnia during COVID-19 pandemic and lockdown: prevalence, severity, and associated risk factors in French population. Psychiatry Res. 2020;290:113128. doi: 10.1016/j.psychres.2020.113128 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r8] 8.Morin CM, Carrier J. The acute effects of the COVID-19 pandemic on insomnia and psychological symptoms. Sleep Med. 2021;77:346-347. doi: 10.1016/j.sleep.2020.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r9] 9.Voysey ZJ, Barker RA, Lazar AS. The treatment of sleep dysfunction in neurodegenerative disorders. Neurotherapeutics. 2021;18(1):202-216. doi: 10.1007/s13311-020-00959-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r10] 10.Roth T. Insomnia: definition, prevalence, etiology, and consequences. J Clin Sleep Med. 2007;3(5)(suppl):S7-S10. [PMC free article] [PubMed] [Google Scholar]

[zoi230249r11] 11.Morin CM, Jarrin DC, Ivers H, Mérette C, LeBlanc M, Savard J. Incidence, persistence, and remission rates of insomnia over 5 years. JAMA Netw Open. 2020;3(11):e2018782. doi: 10.1001/jamanetworkopen.2020.18782 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r12] 12.Kay-Stacey M, Attarian H. Advances in the management of chronic insomnia. BMJ. 2016;354:i2123. doi: 10.1136/bmj.i2123 [DOI] [PubMed] [Google Scholar]

[zoi230249r13] 13.van der Gaag M. The efficacy of CBT for severe mental illness and the challenge of dissemination in routine care. World Psychiatry. 2014;13(3):257-258. doi: 10.1002/wps.20162 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r14] 14.Perlis ML, Smith MT, Jungquist C, Nowakowski S, Orff H, Soeffing J. Cognitive behavioral therapy for insomnia. In: Attarian HP, Schuman C, eds. Clinical Handbook of Insomnia. Humana Press; 2010:281-296, doi: 10.1007/978-1-60327-042-7_22 [DOI] [Google Scholar]

[zoi230249r15] 15.Edinger JD, Arnedt JT, Bertisch SM, et al. Behavioral and psychological treatments for chronic insomnia disorder in adults: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep Med. 2021;17(2):255-262. doi: 10.5664/jcsm.8986 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r16] 16.Zachariae R, Lyby MS, Ritterband LM, O’Toole MS. Efficacy of internet-delivered cognitive-behavioral therapy for insomnia–a systematic review and meta-analysis of randomized controlled trials. Sleep Med Rev. 2016;30:1-10. doi: 10.1016/j.smrv.2015.10.004 [DOI] [PubMed] [Google Scholar]

[zoi230249r17] 17.Chan NY, Lam SP, Zhang J, et al. Efficacy of email-delivered versus face-to-face group cognitive behavioral therapy for insomnia in youths: a randomized controlled trial. J Adolesc Health. 2022;70(5):763-773. doi: 10.1016/j.jadohealth.2021.11.005 [DOI] [PubMed] [Google Scholar]

[zoi230249r18] 18.Kallestad H, Saksvik S, Vedaa Ø, et al. Digital cognitive-behavioural therapy for insomnia compared with digital patient education about insomnia in individuals referred to secondary mental health services in Norway: protocol for a multicentre randomised controlled trial. BMJ Open. 2021;11(6):e050661. doi: 10.1136/bmjopen-2021-050661 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r19] 19.Vedaa Ø, Kallestad H, Scott J, et al. Effects of digital cognitive behavioural therapy for insomnia on insomnia severity: a large-scale randomised controlled trial. Lancet Digit Health. 2020;2(8):e397-e406. doi: 10.1016/S2589-7500(20)30135-7 [DOI] [PubMed] [Google Scholar]

[zoi230249r20] 20.Trauer JM, Qian MY, Doyle JS, Rajaratnam SMW, Cunnington D. Cognitive behavioral therapy for chronic insomnia: a systematic review and meta-analysis. Ann Intern Med. 2015;163(3):191-204. doi: 10.7326/M14-2841 [DOI] [PubMed] [Google Scholar]

[zoi230249r21] 21.Wise J. Insomnia: NICE recommends digital app as treatment option. BMJ. 2022;377:o1268. doi: 10.1136/bmj.o1268 [DOI] [Google Scholar]

[zoi230249r22] 22.Kallestad H, Scott J, Vedaa Ø, et al. Mode of delivery of cognitive behavioral therapy for insomnia: a randomized controlled non-inferiority trial of digital and face-to-face therapy. Sleep. 2021;44(12):zsab185. doi: 10.1093/sleep/zsab185 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r23] 23.Soh HL, Ho RC, Ho CS, Tam WW. Efficacy of digital cognitive behavioural therapy for insomnia: a meta-analysis of randomised controlled trials. Sleep Med. 2020;75:315-325. doi: 10.1016/j.sleep.2020.08.020 [DOI] [PubMed] [Google Scholar]

[zoi230249r24] 24.Kjørstad K, Sivertsen B, Vedaa Ø, et al. The effect of reducing insomnia severity on work- and activity-related impairment. Behav Sleep Med. 2021;19(4):505-515. doi: 10.1080/15402002.2020.1799792 [DOI] [PubMed] [Google Scholar]

[zoi230249r25] 25.Mitchell MD, Gehrman P, Perlis M, Umscheid CA. Comparative effectiveness of cognitive behavioral therapy for insomnia: a systematic review. BMC Fam Pract. 2012;13:40. doi: 10.1186/1471-2296-13-40 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r26] 26.Sivertsen B, Omvik S, Pallesen S, et al. Cognitive behavioral therapy vs zopiclone for treatment of chronic primary insomnia in older adults: a randomized controlled trial. JAMA. 2006;295(24):2851-2858. doi: 10.1001/jama.295.24.2851 [DOI] [PubMed] [Google Scholar]

[zoi230249r27] 27.Taylor DJ, Peterson AL, Pruiksma KE, Young-McCaughan S, Nicholson K, Mintz J; STRONG STAR Consortium . Internet and in-person cognitive behavioral therapy for insomnia in military personnel: a randomized clinical trial. Sleep. 2017;40(6). doi: 10.1093/sleep/zsx075 [DOI] [PubMed] [Google Scholar]

[zoi230249r28] 28.Beaulieu-Bonneau S, Ivers H, Guay B, Morin CM. Long-term maintenance of therapeutic gains associated with cognitive behavioral therapy for insomnia delivered alone or combined with zolpidem. Sleep. 2017;40(3):zsx002. doi: 10.1093/sleep/zsx002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r29] 29.Jan Y, Yang C, Huang S, Lee H. Treatment effect of cognitive-behavior therapy for insomnia combined with usual medication. Sleep Biol Rhythms. 2019;17:311-321. doi: 10.1007/s41105-019-00218-z [DOI] [Google Scholar]

[zoi230249r30] 30.Morin CM, Vallières A, Guay B, et al. Cognitive behavioral therapy, singly and combined with medication, for persistent insomnia: a randomized controlled trial. JAMA. 2009;301(19):2005-2015. doi: 10.1001/jama.2009.682 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r31] 31.Riccardo G, Falzarano R, Magnoni M, Benso G, Bocchino V, Squillante F. Randomized controlled trials versus real-life studies: bridging the gap. Minerva Respir Med. 2021;60(1):10-15. doi: 10.23736/S2784-8477.20.01868-4 [DOI] [Google Scholar]

[zoi230249r32] 32.Van Houdenhove L, Buyse B, Gabriëls L, Van den Bergh O. Treating primary insomnia: clinical effectiveness and predictors of outcomes on sleep, daytime function and health-related quality of life. J Clin Psychol Med Settings. 2011;18(3):312-321. doi: 10.1007/s10880-011-9250-7 [DOI] [PubMed] [Google Scholar]

[zoi230249r33] 33.Morin CM, Bootzin RR, Buysse DJ, Edinger JD, Espie CA, Lichstein KL. Psychological and behavioral treatment of insomnia: update of the recent evidence (1998-2004). Sleep. 2006;29(11):1398-1414. doi: 10.1093/sleep/29.11.1398 [DOI] [PubMed] [Google Scholar]

[zoi230249r34] 34.Ritterband LM, Thorndike FP, Morin CM, et al. Real-world evidence from users of a behavioral digital therapeutic for chronic insomnia. Behav Res Ther. 2022;153:104084. doi: 10.1016/j.brat.2022.104084 [DOI] [PubMed] [Google Scholar]

[zoi230249r35] 35.Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med. 2016;375(23):2293-2297. doi: 10.1056/NEJMsb1609216 [DOI] [PubMed] [Google Scholar]

[zoi230249r36] 36.Stott R, Pimm J, Emsley R, Miller CB, Espie CA. Does adjunctive digital CBT for insomnia improve clinical outcomes in an improving access to psychological therapies service? Behav Res Ther. 2021;144:103922. doi: 10.1016/j.brat.2021.103922 [DOI] [PubMed] [Google Scholar]

[zoi230249r37] 37.Thorndike FP, Berry RB, Gerwien R, Braun S, Maricich YA. Protocol for Digital Real-world Evidence trial for Adults with insomnia treated via Mobile (DREAM): an open-label trial of a prescription digital therapeutic for treating patients with chronic insomnia. J Comp Eff Res. 2021;10(7):569-581. doi: 10.2217/cer-2021-0004 [DOI] [PubMed] [Google Scholar]

[zoi230249r38] 38.Koffel E, Farrell-Carnahan L. Feasibility and preliminary real-world promise of a manualized group-based cognitive behavioral therapy for insomnia protocol for veterans. Mil Med. 2014;179(5):521-528. doi: 10.7205/MILMED-D-13-00455 [DOI] [PubMed] [Google Scholar]

[zoi230249r39] 39.Davidson JR, Dawson S, Krsmanovic A. Effectiveness of group cognitive behavioral therapy for insomnia (CBT-I) in a primary care setting. Behav Sleep Med. 2019;17(2):191-201. doi: 10.1080/15402002.2017.1318753 [DOI] [PubMed] [Google Scholar]

[zoi230249r40] 40.Cassel M, Blom K, Gatzacis J, Renblad P, Kaldo V, Jernelöv S. Clinical feasibility of cognitive behavioural therapy for insomnia in a real-world mixed sample at a specialized psychiatric outpatient clinic. BMC Psychiatry. 2022;22(1):600. doi: 10.1186/s12888-022-04231-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r41] 41.Galbiati A, Sforza M, Fasiello E, Castronovo V, Ferini-Strambi L. Impact of phenotypic heterogeneity of insomnia on the patients’ response to cognitive-behavioral therapy for insomnia: current perspectives. Nat Sci Sleep. 2019;11:367-376. doi: 10.2147/NSS.S198812 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r42] 42.Good Sleep 365. Accessed March 6, 2023. https://www.haoshuimian365.com/

[zoi230249r43] 43.Schober P, Vetter TR. Confounding in observational research. Anesth Analg. 2020;130(3):635-635. doi: 10.1213/ANE.0000000000004627 [DOI] [PubMed] [Google Scholar]

[zoi230249r44] 44.Ali MS, Prieto-Alhambra D, Lopes LC, et al. Propensity score methods in health technology assessment: principles, extended applications, and recent advances. Front Pharmacol. 2019;10:973. doi: 10.3389/fphar.2019.00973 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r45] 45.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399-424. doi: 10.1080/00273171.2011.568786 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r46] 46.Nishida Y, Takahashi Y, Nakayama T, Soma M, Kitamura N, Asai S. Effect of candesartan monotherapy on lipid metabolism in patients with hypertension: a retrospective longitudinal survey using data from electronic medical records. Cardiovasc Diabetol. 2010;9:38. doi: 10.1186/1475-2840-9-38 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r47] 47.Feld E, Harton J, Meropol NJ, et al. Effectiveness of first-line immune checkpoint blockade versus carboplatin-based chemotherapy for metastatic urothelial cancer. Eur Urol. 2019;76(4):524-532. doi: 10.1016/j.eururo.2019.07.032 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r48] 48.Hatakeyama S, Tanaka T, Ikehata Y, et al. Axitinib versus sunitinib as first-line therapies for metastatic renal cell carcinoma: a multicenter retrospective analysis. J Clin Oncol. 2019;37(7):555. doi: 10.1200/JCO.2019.37.7_suppl.555 [DOI] [Google Scholar]

[zoi230249r49] 49.Smyth C. The Pittsburgh Sleep Quality Index (PSQI). J Gerontol Nurs. 1999;25(12):10-11. doi: 10.3928/0098-9134-19991201-10 [DOI] [PubMed] [Google Scholar]

[zoi230249r50] 50.van Duin D, Lok JJ, Earley M, et al. ; Antibacterial Resistance Leadership Group . Colistin versus ceftazidime-avibactam in the treatment of infections due to carbapenem-resistant enterobacteriaceae. Clin Infect Dis. 2018;66(2):163-171. doi: 10.1093/cid/cix783 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r51] 51.El-Galaly A, Nielsen PT, Kappel A, Jensen SL. Reduced survival of total knee arthroplasty after previous unicompartmental knee arthroplasty compared with previous high tibial osteotomy: a propensity-score weighted mid-term cohort study based on 2,133 observations from the Danish Knee Arthroplasty Registry. Acta Orthop. 2020;91(2):177-183. doi: 10.1080/17453674.2019.1709711 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r52] 52.Zhang Z, Kim HJ, Lonjon G, Zhu Y; written on behalf of AME Big-Data Clinical Trial Collaborative Group . Balance diagnostics after propensity score matching. Ann Transl Med. 2019;7(1):16. doi: 10.21037/atm.2018.12.10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r53] 53.Morin CM, Benca R. Chronic insomnia. Lancet. 2012;379(9821):1129-1141. doi: 10.1016/S0140-6736(11)60750-2 [DOI] [PubMed] [Google Scholar]

[zoi230249r54] 54.Mendelson WB. Combining pharmacologic and nonpharmacologic therapies for insomnia. J Clin Psychiatry. 2007;68(suppl 5):19-23. [PubMed] [Google Scholar]

[zoi230249r55] 55.Morin CM. Cognitive-behavioral approaches to the treatment of insomnia. J Clin Psychiatry. 2004;65(suppl 16):33-40. [PubMed] [Google Scholar]

[zoi230249r56] 56.Horsch CH, Lancee J, Griffioen-Both F, et al. Mobile phone-delivered cognitive behavioral therapy for insomnia: a randomized waitlist controlled trial. J Med internet Res. 2017;19(4):e70. doi: 10.2196/jmir.6524 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r57] 57.Ritterband LM, Thorndike FP, Ingersoll KS, et al. Effect of a web-based cognitive behavior therapy for insomnia intervention with 1-year follow-up: a randomized clinical trial. JAMA Psychiatry. 2017;74(1):68-75. doi: 10.1001/jamapsychiatry.2016.3249 [DOI] [PubMed] [Google Scholar]

[zoi230249r58] 58.Blom K, Tarkian Tillgren H, Wiklund T, et al. Internet-vs. group-delivered cognitive behavior therapy for insomnia: a randomized controlled non-inferiority trial. Behav Res Ther. 2015;70:47-55. doi: 10.1016/j.brat.2015.05.002 [DOI] [PubMed] [Google Scholar]

[zoi230249r59] 59.Xing J, Wu X, Liu H, et al. Effects of electroacupuncture therapy and cognitive behavioral therapy in chronic insomnia: a randomized controlled study. Evid Based Complement Alternat Med. 2020;2020:5630130. doi: 10.1155/2020/5630130 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r60] 60.Kyle SD, Miller CB, Rogers Z, Siriwardena AN, Macmahon KM, Espie CA. Sleep restriction therapy for insomnia is associated with reduced objective total sleep time, increased daytime somnolence, and objectively impaired vigilance: implications for the clinical management of insomnia disorder. Sleep. 2014;37(2):229-237. doi: 10.5665/sleep.3386 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r61] 61.Kyle SD, Morgan K, Spiegelhalder K, Espie CA. No pain, no gain: an exploratory within-subjects mixed-methods evaluation of the patient experience of sleep restriction therapy (SRT) for insomnia. Sleep Med. 2011;12(8):735-747. doi: 10.1016/j.sleep.2011.03.016 [DOI] [PubMed] [Google Scholar]

[zoi230249r62] 62.Christensen H, Batterham PJ, Gosling JA, et al. Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): a randomised controlled trial. Lancet Psychiatry. 2016;3(4):333-341. doi: 10.1016/S2215-0366(15)00536-2 [DOI] [PubMed] [Google Scholar]

[zoi230249r63] 63.Espie CA, Emsley R, Kyle SD, et al. Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial. JAMA Psychiatry. 2019;76(1):21-30. doi: 10.1001/jamapsychiatry.2018.2745 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r64] 64.van der Zweerde T, van Straten A, Effting M, Kyle SD, Lancee J. Does online insomnia treatment reduce depressive symptoms? a randomized controlled trial in individuals with both insomnia and depressive symptoms. Psychol Med. 2019;49(3):501-509. doi: 10.1017/S0033291718001149 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r65] 65.Van der Zweerde T, Lancee J, Slottje P, Bosmans JE, Van Someren EJW, van Straten A. Nurse-guided internet-delivered cognitive behavior therapy for insomnia in general practice: results from a pragmatic randomized clinical trial. Psychother Psychosom. 2020;89(3):174-184. doi: 10.1159/000505600 [DOI] [PubMed] [Google Scholar]

[zoi230249r66] 66.van Straten A, Lancee J. Digital cognitive behavioural therapy for insomnia: the answer to a major public health issue? Lancet Digit Health. 2020;2(8):e381-e382. doi: 10.1016/S2589-7500(20)30167-9 [DOI] [PubMed] [Google Scholar]

[zoi230249r67] 67.Simon NM, Hofmann SG, Rosenfield D, et al. Efficacy of yoga vs cognitive behavioral therapy vs stress education for the treatment of generalized anxiety disorder: a randomized clinical trial. JAMA Psychiatry. 2021;78(1):13-20. doi: 10.1001/jamapsychiatry.2020.2496 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r68] 68.Siu PM, Yu AP, Tam BT, et al. Effects of tai chi or exercise on sleep in older adults with insomnia: a randomized clinical trial. JAMA Netw Open. 2021;4(2):e2037199. doi: 10.1001/jamanetworkopen.2020.37199 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r69] 69.Okajima I, Akitomi J, Kajiyama I, Ishii M, Murakami H, Yamaguchi M. Effects of a tailored brief behavioral therapy application on insomnia severity and social disabilities among workers with insomnia in Japan: a randomized clinical trial. JAMA Netw Open. 2020;3(4):e202775. doi: 10.1001/jamanetworkopen.2020.2775 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r70] 70.Li SH, Graham BM, Werner-Seidler A. Gender differences in adolescent sleep disturbance and treatment response to smartphone app–delivered cognitive behavioral therapy for insomnia: exploratory study. JMIR Form Res. 2021;5(3):e22498. doi: 10.2196/22498 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r71] 71.Nolen-Hoeksema S, Aldao A. Gender and age differences in emotion regulation strategies and their relationship to depressive symptoms. Pers Individ Dif. 2011;51(6):704-708. doi: 10.1016/j.paid.2011.06.012 [DOI] [Google Scholar]

[zoi230249r72] 72.Song Y, Ta Park VM, Martin JL. Future directions to address sleep health disparity among Asian American immigrants. Sleep. 2022;45(4):zsac024. doi: 10.1093/sleep/zsac024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r73] 73.Becker PM. Overview of sleep management during COVID-19. Sleep Med. 2022;91:211-218. doi: 10.1016/j.sleep.2021.04.024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi230249r74] 74.Cheng P, Casement MD, Kalmbach DA, Castelan AC, Drake CL. Digital cognitive behavioral therapy for insomnia promotes later health resilience during the coronavirus disease 19 (COVID-19) pandemic. Sleep. 2021;44(4):zsaa258. doi: 10.1093/sleep/zsaa258 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Comparative Effectiveness of Digital Cognitive Behavioral Therapy vs Medication Therapy Among Patients With Insomnia

Menglin Lu, MSC

Yaoyun Zhang, PhD

Junhang Zhang, PhD

Songfang Huang, MSC

Fei Huang, PhD

Tingna Wang, MSC

Fei Wu, PhD

Hongjing Mao, MD

Zhengxing Huang, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Ethical Declaration

Data Source

Study Design and Population

Exposure, Covariates, and Outcomes

Statistical Analysis

Results

Cohort Characteristics

Figure 1. Cohort Selection Procedure.

Table. Demographic and Clinical Characteristics of the Cohort.

Response Rates

Changes in Outcomes

Treatment Effect Sizes

Figure 2. Forest Plots of Primary Outcomes of Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) vs Medication Therapy.

Figure 3. Forest Plots of Secondary Outcomes of Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) vs Medication Therapy.

Durability

Engagement

Subgroup Analysis

Figure 4. Subgroup Analysis Comparing the Effectiveness of Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) and Medication Therapy.

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases