Journal of Clinical Nursing - 2025 - Huang - Effectiveness of Mobile Health‐Delivered Cognitive Behavioural Therapy for

(1)

META- ANALYSIS

OPEN ACCESS

Effectiveness of Mobile Health- Delivered Cognitive

Behavioural Therapy for Insomnia in Adults: A Systematic Review and Meta- Analysis of Randomised Controlled Trials

1School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China | ²Center on Behavioral Health, Faculty of Social Sciences, The University of Hong Kong, Hong Kong SAR, China | ³Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, UK | ⁴The George Institute for Global Health, Barangaroo, New South Wales, Australia

Correspondence: Jung Jae Lee ([email protected])

Received: 16 July 2024 | Revised: 3 May 2025 | Accepted: 2 June 2025 Funding: The authors received no specific funding for this work.

Keywords: cognitive behavioural therapy | insomnia | meta- analysis | mobile health | systematic review

ABSTRACT

Aims: To determine the treatment effectiveness associated with mobile health- delivered cognitive behavioural therapy for insomnia (mCBT- I) interventions for adults with insomnia and to identify the potential characteristics associated with better treatment outcomes.

Design: A systematic review and meta- analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA 2020) guidelines.

Methods: Seven English- and two Chinese- language databases were searched, without restrictions on publication dates, up to July 2024. Reference lists of relevant reviews and grey literature were included in the search. Randomised controlled trials evaluating mCBT- I in adults with insomnia and published in either English or Chinese were included in this meta- analysis. A random- effects model was used for data analysis, accompanied by additional subgroup analyses and meta- regression.

Results: Sixteen studies involving 2146 participants were included in this meta- analysis. mCBT- I interventions were associated with significantly reduced insomnia symptoms and improved sleep quality at post intervention, at 1–3- month follow- up, and at 4–6- month follow- up. Interventions that included five components of CBT- I, were delivered for 6 weeks or longer, and were conducted in a group format were linked to better treatment outcomes; the differences in other subgroup categories were not statistically significant. Studies involving participants with comorbid conditions showed a greater effect in reducing insomnia symptoms than those without such participants. In addition, mCBT- I interventions delivered by healthcare professionals resulted in statistically larger effect sizes for improving sleep quality than self- help regimens.

Conclusions: The systematic review and meta- analysis identified the effectiveness of mCBT- I in reducing insomnia symptoms and improving sleep quality and offered practical implications for the development of effective mCBT- I interventions in clinical practice. However, future robust studies are needed to explore the long- term effects of mCBT- I interventions.

Patient or Public Contribution: No patient or public contribution.

Trail Registration: PROSPERO CRD: 42023454647

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

(2)

1 | Introduction

Insomnia is a prevalent sleep disorder, affecting up to 33% of the general adult population worldwide (Perlis et al. 2021).

Characterised by difficulties in initiating and/or maintain- ing sleep, or experiencing non- restorative sleep despite ade- quate opportunities and circumstances, insomnia can result in daytime impairment or distress (American Psychiatric Association 2013). In addition, insomnia is associated with ad- verse health outcomes such as poor physical (Hasan et al. 2023;

Javaheri and Redline 2017) and mental health (Li et al. 2016;

Zhou, Li, et al. 2022), poor health- related quality of life (Lucena et al. 2020), and suicidal ideation and behaviour (Woznica et al. 2015). Given the prevalence and negative impact of insomnia, the importance of evidence- based treatments specifically targeting insomnia is evident.

Cognitive behavioural therapy for insomnia (CBT- I) is a multi- component and evidence- based psychotherapy that addresses maladaptive sleep behaviours and beliefs and typically com- prises five components: sleep hygiene education, cognitive therapy, sleep restriction, stimulus control and relaxation exercise (Sutton 2021). Meta- analyses demonstrate that CBT- I has moderate to large and sustainable effects on insomnia symptoms and sleep quality (Ma et al. 2020; van der Zweerde et al. 2019; Xu et al. 2021). The American Academy of Sleep Medicine (Edinger et al. 2021) and American College of Physicians (Qaseem et al. 2016) recommend CBT- I as a first- line treatment for insomnia due to its durability and effectiveness. However, traditional face- to- face CBT- I is not widely available owing to a shortage of CBT- I trained therapists and the time- consuming process of delivering in- person CBT- I (Koffel et al. 2018). Thus, novel and viable CBT- I delivery modalities are urgently needed.

With the advent of digital technologies, mobile health (mHealth)—defined as the use of mobile technologies in healthcare and public health—may represent a viable modality for delivering CBT- I (World Health Organization 2010). Over the past decade, the popularity of CBT- I delivered via mHealth platforms (mCBT- I) has increased (Chan et al. 2023; Yang et al. 2023; Zhang et al. 2023). Several factors contribute to the rapid growth of mCBT- I interventions. First, the high ownership of mobile devices, coupled with the extensive internet penetra- tion globally, means that mCBT- I interventions can be delivered to a large number of people (International Telecommunication

ing mCBT- I interventions (International Telecommunication Union 2022; Yang et al. 2022). Third, people spend an average of 3.5 h daily on their mobile devices and keep them in close prox- imity (Howarth 2024), allowing mCBT- I interventions to cap- ture participants' attention at the most relevant moments (e.g., in the case of bedtime relaxation reminders; Chan et al. 2023; Perlis et al. 2005). Fourth, data collected from sensors (e.g., actigraphy and smart bracelets) in participants' natural environments enhance researchers' ability to tailor CBT- I interventions in real time, facilitating changes in maladaptive sleep behaviours (e.g., by prompting “avoid napping” when prolonged daytime inactiv- ity is detected; Zhang et al. 2023).

Although mCBT- I appears to be an effective alternative and in- novative modality for managing insomnia, there has been no comprehensive evidence synthesis regarding the effectiveness of mCBT- I for adults with insomnia. Therefore, this systematic review and meta- analysis aimed to (1) evaluate the effectiveness of mCBT- I in terms of insomnia symptoms and sleep quality in adults and (2) identify study- , population- and intervention- level characteristics associated with better treatment outcomes.

2 | Methods

This systematic review and meta- analysis followed the Preferred Reporting Items for Systematic Reviews and Meta- analyses (PRISMA) guideline (File S1) and was preregistered at PROSPERO (CRD 42023454647; Page et al. 2021).

2.1 | Search Strategy and Eligibility Criteria

Searches were made in seven English databases (PubMed, Embase, Web of Science, Cochrane Library, PsycINFO, CINAHL, and Clini calTr ials. gov) and two Chinese databases (Wan Fang and China National Knowledge Infrastructure [CNKI]) from inception to July 2, 2024, using combined vari- ations of keywords in three categories: insomnia, cognitive behavioural therapy for insomnia and mobile health. The search strategy was developed in consultation with a medical librarian at the authors' university (FileS2). In addition, the reference lists of relevant reviews and grey literature were screened for additional potentially eligible studies. The Population, Intervention, Comparison, Outcome and Study design (PICOS) framework was followed: (1) Population: adults with insomnia symptoms as determined by a standardised diagnostic system (e.g., International Statistical Classification of Diseases, ICD;

World Health Organization 1992) and Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association 2013), or other valid assessment measure (e.g., Insomnia Severity Index, ISI; Morin et al. 2011) and Pittsburgh Sleep Quality Index (PSQI; Buysse et al. 1989); (2) Intervention:

mCBT- I, an mHealth intervention was defined as one that uti- lised any mobile intervention technologies (e.g., mobile phones and tablets) by using the mobile functions (e.g., short messaging service and applications; World Health Organization 2010).

CBT- I included, at minimum, three components (Hertenstein clinical community?

○ mCBT- I can significantly reduce insomnia symptoms and enhance sleep quality among adults with insomnia.

○ Incorporating all components of CBT- I, extending the intervention delivery period to at least 6 weeks, utilising a group format, and involving healthcare professionals (e.g., nurses) can potentially improve mCBT- I treatment outcomes.

○ Future studies should explore the long- term effects of mCBT- I in the management of insomnia.

(3)

et al. 2022). A behavioural treatment component of CBT- I, either sleep restriction or stimulus control, was mandatory for inclusion in this meta- analysis because both have been identified as effective stand- alone treatments for insomnia (Sutton 2021).

Other components of CBT- I, such as relaxation training, sleep hygiene education and cognitive therapy, could be included but were not mandatory (Hertenstein et al. 2022); (3) Comparison:

at least one control condition in the study had to be placebo, waitlist, care as usual (CAU), no intervention, or patient education (PE); (4) Outcome: the primary outcomes were evaluated using at least one empirically validated measure for insomnia symptoms, specifically the ISI (Morin et al. 2011), and for sleep quality, the PSQI (Buysse et al. 1989). These measures were chosen due to their widely reported clinical relevance (Buysse et al. 1989; Morin et al. 2011). Secondary outcomes encompass sleep parameters recorded through sleep diaries or smart bracelets, including sleep onset latency (SOL), number of awakenings, time in bed (TIB), total sleep time (TST) and sleep efficiency (SE); (5) Study design: only randomised controlled trials (RCTs) were included.

2.2 | Study Selection and Data Extraction

The searched records were imported into Endnote 21 for screening, and duplicates were removed automatically and manually after all studies were entered. Two independent reviewers (Y.H.

and Y.Y.) screened the titles and abstracts based on pre- defined selection criteria to identify potentially eligible studies. Any discrepancies between the reviewers during this process were resolved through discussion. If the title or abstract lacked suffi- cient information, the full text was reviewed to assess eligibility.

In cases where consensus could not be reached, a third reviewer (J.J.L.) was consulted to make the final decision, adhering to the pre- defined selection criteria. After full- text screening, studies were either included or excluded, with the reasons for exclusion provided in Figure 1. The following information was extracted from each included study: (1) study and population characteristics (i.e., first author, year of publication, country, study population, insomnia diagnosis, sample size, mean age, female (%) of each intervention and control groups, and dropout rates); (2) intervention characteristics (i.e., CBT- I components, intervention format, mode of delivery, provider and intervention duration);

(3) data on primary and secondary outcomes (i.e., outcome measurements, number of participants, post- intervention/follow- up scores of both intervention and control groups). Data were extracted by two reviewers (YH and YY). Any disagreements were resolved through discussion until a consensus was reached.

2.3 | Quality Assessment

The revised Cochrane risk- of- bias tool for randomised trials (RoB 2), covering five domains (i.e., bias from the randomiza- tion process, deviations from intended interventions, missing outcome data, outcome measurement and selection of reported results), was used to assess the methodological quality of each eligible study (Sterne et al. 2019). Two independent reviewers (Y.H. and Y.Y.) evaluated the risk of bias for each study, with discrepancies resolved through discussion. A study was cat- egorised as “low risk of bias” if it was judged to have a low risk of bias in all domains for the assessed result. A study was classified as having “some concerns” if there was concern in at least one domain but not a high risk of bias in any domain. A FIGURE 1 | PRISMA flow diagram. Search and study selection process.

(4)

confidence in the result (Sterne et al. 2019). The certainty of evidence for each outcome was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, considering five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias (Schünemann et al. 2013). Based on these domains, the certainty of evidence was classified as high, moderate, low, or very low.

2.4 | Statistical Analyses

Statistical analyses were performed using the meta and “meta- for” packages in R, version 4.2.2 (Viechtbauer 2010). Effect sizes were calculated using Hedges' g, with values of 0.2, 0.5 and 0.8 interpreted as small, medium and large effect size, respectively (Cohen 2013). A Cochrane Q test (with p- value) and I² were adopted to assess the statistical heterogeneity. In established conventions for I² interpretation, a value of 0%

indicates no heterogeneity whereas values of 25%, 50% and 75% represent low, moderate and high heterogeneity, respectively (Higgins and Thompson 2002). A random- effects model was selected for the meta- analysis (Borenstein et al. 2010).

Publication bias was evaluated by inspecting the asymmetry of funnel plots visually and statistically tested by conducting Egger's regression tests (Sterne and Egger 2005). Sensitivity analysis was performed by the leave- one- out approach (Higgins et al. 2022). A p- value of less than 0.05 (two- sided) was regarded as statistically significant.

Subgroup analyses and univariate meta- regression were performed to explore the source of heterogeneity and to evaluate the relationships between characteristics at the study, population and intervention levels and the effects on primary outcomes (insomnia symptoms and sleep quality). Subgroup analyses were conducted for (1) study- level characteristics:

Control condition type (waitlist vs. CAU vs. PE), methodological quality of included studies (low risk of bias vs. some concerns vs. high risk of bias), statistical method (intention- to- treat [ITT] vs. per protocol [PP]) and insomnia diagnosis (standardised diagnostic system vs. only validated questionnaire); (2) population- level characteristics: Comorbid condition (yes vs. no), ethnicity (western ethnicity: yes vs. no) and economic classification (World Bank Organization 2023) (High- income country [HIC] vs. upper middle- income country [UMIC] vs. lower middle- income country [LMIC]); (3) intervention- level characteristics: The provider of the mCBT- I interventions (healthcare professionals vs. self- help), five components of CBT- I (yes vs. no), the inclusion of cognitive therapy (yes vs. no), mode of delivery (instant messaging application vs. CBT- I mobile application vs. text messages vs.

videoconference application), format of delivery (group- based vs. individual- based) and intervention duration ≥ 6 weeks (yes vs. no). Univariate meta- regression was conducted for (1) study- level characteristics: Publication date and dropout rate;

(2) population- level characteristics: Age and gender ratio and (3) intervention- level characteristics: Intervention duration.

These factors were identified from previous research (Bae

secondary outcomes, neither subgroup analyses nor meta- regression were conducted due to the insufficient number of studies included.

3 | Results

3.1 | Study Selection

Figure 1 outlines the process of study selection. A total of 14,742 studies were identified. Duplicate studies (n = 5321) were removed from this set. After screening titles and abstracts, 177 full texts were assessed for eligibility. Ultimately, 16 studies (Abdelaziz et al. 2022; Ahorsu et al. 2020; Chan et al. 2023; Horsch et al. 2017; Krieger et al. 2019; Kuhn et al. 2022; Li et al. 2022; Okajima et al. 2020; Oswald et al. 2022; Rajabi Majd et al. 2020; Yang et al. 2023; Zhang et al. 2019, 2023; Zheng et al. 2022; Zhong et al. 2019; Zhou, Kong, et al. 2022) published between 2017 and 2023 were included in the analysis.

3.2 | Characteristics of Included Studies

Table 1 outlines the study characteristics. A total of 2146 participants from seven countries (FileS3) were included, with sample sizes ranging from 30 to 320. Seven studies were conducted in HICs (Abdelaziz et al. 2022; Chan et al. 2023; Horsch et al. 2017; Krieger et al. 2019; Kuhn et al. 2022; Okajima et al. 2020; Oswald et al. 2022), seven in upper- middle- income countries (UMICs; Li et al. 2022; Yang et al. 2023;

Zhang et al. 2019; Zhang et al. 2023; Zheng et al. 2022;

Zhong et al. 2019; Zhou, Kong, et al. 2022), and two in low- middle- income countries (LMICs) (FileS4; Ahorsu et al. 2020;

Rajabi Majd et al. 2020). Six studies (Abdelaziz et al. 2022;

Krieger et al. 2019; Yang et al. 2023; Zheng et al. 2022; Zhong et al. 2019; Zhou, Kong, et al. 2022) used CAU control groups, another six studies (Chan et al. 2023; Horsch et al. 2017; Kuhn et al. 2022; Okajima et al. 2020; Oswald et al. 2022; Zhang et al. 2019) used the waitlist as the control, and the remaining four studies (Ahorsu et al. 2020; Li et al. 2022; Rajabi Majd et al. 2020; Zhang et al. 2023) compared the mCBT- I intervention with a PE control condition. The overall mean (stan- dard deviation) age of mCBT- I and control groups was 37.01 (7.75) and 37.27 (7.75) years, respectively. The female propor- tion in the mCBT- I intervention group ranged from 23.1% (Li et al. 2022) to 100% (Oswald et al. 2022), and that in the control group ranged from 24.3% (Li et al. 2022) to 100% (Oswald et al. 2022).

Of the included studies, 11 studies (Abdelaziz et al. 2022;

Ahorsu et al. 2020; Chan et al. 2023; Li et al. 2022; Rajabi Majd et al. 2020; Yang et al. 2023; Zhang et al. 2019, 2023;

Zheng et al. 2022; Zhong et al. 2019; Zhou, Kong, et al. 2022) incorporated all five components of CBT- I, whereas five studies (Horsch et al. 2017; Krieger et al. 2019; Kuhn et al. 2022;

Okajima et al. 2020; Oswald et al. 2022) incorporated only some components. For example, two of the studies (Horsch

(5)

TABLE 1 | Characteristics of included studies. Author (year)Country (economic class)Study populationInsomnia diagnosisSample sizeMean age, years (SD)Female (%)Intervention characteristicsIntervention durationOutcome measurements

Dropout rate, % (Intervention group vs. control group) Abdelaziz et al. (2022)Saudi Arabia (HIC)Menopausal women with insomnia symptoms

PSQI, ≥ 5 + ISI ≥7mCBT- I: 40 CAU: 40mCBT- I: 53.90 (4.14) CAU: 52.23 (4.31)

mCBT- I: 100.0 CAU: 100.0

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: Instant messaging application Provider: Healthcare professional (Nurse)

6 weeks

ISI PSQI SOL Number of awakenings

SE TST

8.2 vs. 8.2 Ahorsu et al. (2020)Iran (LMIC)Epilepsy patients with insomnia symptoms

ISI ≥15mCBT- I: 160 PE: 160

mCBT- I: 38.37(13.45) PE: 37.99 (9.88)

mCBT- I: 60.6 PE: 56.2

CBT- I components: SHE, CT, SR, SC, RE, Format: Individual Mode of delivery: CBT- I mobile application Provider: Self- help

6 weeks

ISI PSQI

SOL SE TST

13.8 vs. 9.4 (Continues

(6)

Author (year)Country (economic class)Study populationInsomnia diagnosisSample sizeMean age, years (SD)Female (%)Intervention characteristicsIntervention durationOutcome measurements

Drop rate, % (Interve grou cont grou Chan et al. (2023)Hong Kong (HIC)Adults with major depression and insomnia

ISI ≥10 + ICD- 10 + DSM- 5

mCBT- I:167 Waitlist: 153

mCBT- I: 27.28 (7.25) Waitlist: 27.26 (7.22)

mCBT- I: 65.0 Waitlist: 81.0

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: CBT- I mobile application Provider: Self- help

6 weeks

ISI PS37.7 vs. 2 QI HorschThe Netherlands Adults with et al. (2017)(HIC)insomnia symptoms

DSM- 5mCBT- I: 74 Waitlist: 77mCBT- I: 39 (13.0) Waitlist: 41 (13.9)

CBT- I components: SHE, SR, RE Format: Individual Mode of delivery: CBT- I mobile application Provider: Self- help

6–7 weeks

TIB SE TST

39 v (C

TABLE 1 | (Continued)

(7)

Dropout rate, % (Intervention group vs. control group) Krieger et al. (2019)Switzerland (HIC)Adults with insomnia symptoms

ICSD- 3mCBT- I: 42 CAU: 21mCBT- I: 42.17 (12.4) CAU: 45.24 (12.4)

mCBT- I: 61.9 CAU: 81.0

CBT- I components: SHE, CT, SR, RE Format: Individual Mode of delivery: Text messages Provider: Self- help

8 weeks

ISI PS11.9 vs. 4.8 QI KuhnUnited States of Military et al. (2022)America (HIC)veterans with insomnia symptoms

ISI ≥ 10+ report ≥ 3 months with insomnia symptoms occurring ≥ 3 days/ week with ≥ 30- min sleep loss

mCBT- I:25 Waitlist: 25mCBT- I: 44.00 (7.64) Waitlist: 44.96 (8.07)

CBT- I components: CT, SR, SC, RE Format: Individual Mode of delivery: CBT- I mobile application Provider: Self- help

6 weeks

SE TST

16 vs. 12 (Continues

(8)

Drop rate, % (Interve grou cont grou

Li et a

l. (2022)China (UMIC)College students with insomnia symptoms

PSQI≥ 5mCBT- I: 39 PE: 37mCBT- I: 20.56 (1.79) PE: 21.38 (2.13)

mCBT- I: 23.1 PE: 24.3

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: Instant messaging application Provider: Self- help

3 weeksPSQI0 v Rajabi Majd et al. (2020)Iran (LMIC)Adults with insomnia symptoms

DSM- 5 + ISI ≥10mCBT- I: 156 PE: 156

mCBT- I: 36.21 (5.81) PE: 35.29 (5.76)

mCBT- I: 53.9 PE: 57.7

6 weeks

ISI PS5.7 v QI (C

(9)

Dropout rate, % (Intervention group vs. control group) Okajima et al. (2020)Japan (HIC)Workers with insomnia symptoms

ISI ≥8mCBT- I:24 Waitlist: 22mCBT- I: 43.4 (11.9) Waitlist: 40.7 (10.9)

CBT- I components: SHE, SR, SC, RE, Format: Individual Mode of delivery: CBT- I mobile application Provider: Self- help

2 weeksISI66.7 vs. 40.1 Oswald et al. (2022)United States of America (HIC)Breast cancer survivors with insomnia symptoms

Reported clinically significant insomnia symptoms (i.e., ISI I ≥8)

mCBT- I:15 Waitlist: 15mCBT- I: 56.90 (8.91) Waitlist: 59.98 (9.58)

CBT- I components: SHE, CT, SR, SC Format: Group Mode of delivery: Videoconference application Provider: Healthcare professional (Psychologist)

6 weeks

ISI PSQI SE

0 vs. 6.7 (Continues

(10)

Drop rate, % (Interve grou cont grou Yang et al. (2023)China (UMIC)Adults with insomnia symptoms

DSM- 5mCBT- I: 95 CAU: 97mCBT- I: 35.6 (11.2) CAU: 35.0 (10.6)

mCBT- I: 57.9 CAU: 64.9

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: Instant messaging application Provider: Healthcare professionals (Researchers and Clinicians)

1 weekISI18.9 v Zhang et al. (2019)China (UMIC)Adults with insomnia symptoms

DSM- 5mCBT- I: 95 Waitlist: 95mCBT- I: 34.8 (6.7) Waitlist: 35.1 (8.0)

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: Instant messaging application Provider: Healthcare professional (Psychiatrist)

4 weeks

ISI SOL

Number of awakenings

TIB SE TST

42.1 v (C

(11)

Dropout rate, % (Intervention group vs. control group) Zhang et al. (2023)China (UMIC)Adults with insomnia symptoms

ICSD- 3+ ISI ≥14mCBT- I: 38 PE: 39mCBT- I: 49.6 (13.0) PE: 50.6 (14.1)

mCBT- I: 78.9 PE: 66.7

6 weeks

ISI SOL

Number of awakenings

SE TST

9.8 vs. 12.2 Zheng et al. (2022)China (UMIC)College students with insomnia symptoms

PSQI≥8mCBT- I: 100 CAU: 100

mCBT- I: 20.04 (1.42) CAU: 20.12 (1.37)

mCBT- I: 46.0 CAU: 43.0

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: Instant messaging application Provider: Healthcare professional (Doctor)

4 weeksPSQI0 vs. 0 (Continues

(12)

Drop rate, % (Interve grou cont grou Zhong et al. (2019)China (UMIC)Medical students with insomnia symptoms

PSQI≥7mCBT- I: 30 CAU: 30mCBT- I: 19.37(1.00) CAU: 19.83 (1.21)

mCBT- I: 86.7 CAU: 80.0

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: Instant messaging application Provider: Healthcare professional (Nurse)

4 weeksPSQI0 v Zhou, Kong, et al. (2022)

China (UMIC)Nurses with insomnia symptom

ICSD- 3mCBT- I: 60 CAU: 58mCBT- I: 31.0 (4.4) CAU: 29.6 (4.5)

mCBT- I: 98.3 CAU: 98.3

CBT- I components: SHE, CT, SR, SC, RE Format: Individual Mode of delivery: CBT- I mobile Application Provider: Healthcare professional (Medical staff)

6 weeks

ISI PS0 v QI Abbreviations: CAU, care as usual; CT, cognitive therapy; DSM- 5, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; HIC, high- income country; ICD- 10, International Statistical Classification of Disease Related Health Problems—Tenth Revision; ICSD- 3, International Classification of Sleep Disorders, Third Edition; LMIC, low- middle- income country; mCBT- I, mHealth- delivered cognitive behavioural therapy for insom patients education; RE, relaxation exercise; SC, stimulus control; SE, sleep efficiency; SHE, sleep hygiene education; SM, sleep diary monitoring; SOL, sleep onset latency; SR, sleep restriction; TIB, time in bed; TST, total s UMIC, upper- middle- income country.

(13)

tive therapy component. Only one study (Oswald et al. 2022) used a group format for mCBT- I delivery, with the remaining 15 studies (Abdelaziz et al. 2022; Ahorsu et al. 2020; Chan et al. 2023; Horsch et al. 2017; Krieger et al. 2019; Kuhn et al. 2022; Li et al. 2022; Okajima et al. 2020; Rajabi Majd et al. 2020; Yang et al. 2023; Zhang et al. 2019, 2023; Zheng et al. 2022; Zhong et al. 2019; Zhou, Kong, et al. 2022) using an individual format. The mode of delivery was CBT- I mobile application in eight studies (Ahorsu et al. 2020; Chan et al. 2023; Horsch et al. 2017; Kuhn et al. 2022; Okajima et al. 2020; Rajabi Majd et al. 2020; Zhang et al. 2023; Zhou, Kong, et al. 2022), instant messaging application in six studies (Abdelaziz et al. 2022; Li et al. 2022; Yang et al. 2023; Zhang et al. 2019; Zheng et al. 2022; Zhong et al. 2019), text messages in one study (Krieger et al. 2019), and videoconference application in one study (Oswald et al. 2022). In seven studies (Abdelaziz et al. 2022; Oswald et al. 2022; Yang et al. 2023;

Zhang et al. 2019; Zheng et al. 2022; Zhong et al. 2019; Zhou, Kong, et al. 2022), mCBT- I was delivered by healthcare professionals (e.g., a nurse Abdelaziz et al. 2022; Zhong et al. 2019) and doctor (Zhang et al. 2019; Zheng et al. 2022), whereas in the other nine studies (Ahorsu et al. 2020; Chan et al. 2023;

Horsch et al. 2017; Krieger et al. 2019; Kuhn et al. 2022; Li et al. 2022; Okajima et al. 2020; Rajabi Majd et al. 2020; Zhang et al. 2023), self- help regimens were adopted. The intervention durations varied considerably, ranging from 1 (Yang et al. 2023) to 8 (Krieger et al. 2019) weeks. All included studies (Abdelaziz et al. 2022; Ahorsu et al. 2020; Chan et al. 2023;

Horsch et al. 2017; Krieger et al. 2019; Kuhn et al. 2022; Li et al. 2022; Okajima et al. 2020; Oswald et al. 2022; Rajabi Majd et al. 2020; Yang et al. 2023; Zhang et al. 2019, 2023;

Zheng et al. 2022; Zhong et al. 2019; Zhou, Kong, et al. 2022) included a post- intervention assessment. Furthermore, six studies (Ahorsu et al. 2020; Kuhn et al. 2022; Okajima et al. 2020; Rajabi Majd et al. 2020; Yang et al. 2023; Zhang et al. 2023) had a 1–3- month follow- up, and three studies (Ahorsu et al. 2020; Rajabi Majd et al. 2020; Zhang et al. 2023) also had a 4–6- month follow- up.

3.3 | Quality Assessment

Six of the included studies had a low risk of bias (Ahorsu et al. 2020; Chan et al. 2023; Krieger et al. 2019; Okajima et al. 2020; Rajabi Majd et al. 2020; Zhang et al. 2023), seven had some concern (Abdelaziz et al. 2022; Kuhn et al. 2022;

Li et al. 2022; Oswald et al. 2022; Yang et al. 2023; Zhong et al. 2019; Zhou, Kong, et al. 2022), and three had a high risk of bias (Horsch et al. 2017; Zhang et al. 2019; Zheng et al. 2022).

The major sources of risk of bias were deviations from the intended interventions, missing data from dropouts throughout the RCT study design, and the selective reporting of results stemming from deviations in the original study protocol without a justified reason (FilesS5andS6).

According to GRADE which assessed 18 outcomes, 6 outcomes were graded as very low, 4 as low, 5 as moderate, and 3 as high (FileS7). The primary reason for the very low to low grades in most outcomes (n = 10) was the inconsistency, reflected by significant variability in study results, and imprecision, indicated

studies.

3.4 | Overall Association of mCBT- I With Insomnia- Related Outcomes

Figures 2 and 3 show that mCBT- I interventions were associated with significantly reduced insomnia symptoms and improved sleep quality at the post- intervention (ISI: Hedges' g, −0.76 [95%

CI, −0.92, −0.60]; PSQI: −0.81 [−1.04, −0.58]), 1–3- month follow- up (ISI: −0.91 [−1.11, −0.71]; PSQI: −0.77 [−0.93, −0.62]), and 4–6- month follow- up assessments (ISI: −0.81 [−1.14,

−0.48]; PSQI: −0.77 [−0.93, −0.61]), with low to considerable heterogeneity (I² = 0%–75%). No publication bias was found for insomnia symptoms or sleep quality, as indicated by funnel plot inspections and Egger's tests (FilesS8andS9). Leave- one- out analyses revealed that the outcomes for insomnia symptoms and sleep quality remained robust across all assessments, with no significant differences observed following the removal of any single study. Sensitivity analyses excluding studies with a high risk of bias found no significant changes in the overall effect size (FileS10).

The effect was significant, with a medium effect size favouring mCBT- I for SOL, number of awakenings and TIB, and SE at post intervention, whereas no significant differences were found in the TST. At the 1–3- month follow- up, no significant differences were identified. At the 4–6- month follow- up, the reported data only presented a statistically significant difference in SOL (FilesS11−S13). The leave- one- out analyses indicated the robustness of SOL, number of awakenings, TIB and SE, but not of the TST, at post- intervention. Similarly, sensitivity analyses that excluded studies with a high risk of bias revealed no significant changes in the overall effect size (FileS10).

3.5 | Subgroup and Meta Regression Analyses of Study- Level Characteristics

No significant subgroup differences were found in study- level characteristics, including the control condition type (waitlist vs.

CAU vs. PE), methodological quality (low risk of bias vs. some concern vs. high risk of bias), statistical methods (ITT vs. PP), and the method of insomnia diagnosis (standardised diagnostic system vs. only validated questionnaires). However, each subgroup category exhibited statistically significant reductions in insomnia symptoms and improvements in sleep quality (Tables 2 and 3). In addition, meta- regression results demonstrated that the publication date and dropout rate did not significantly moderate the effect sizes (FileS14).

3.6 | Subgroup and Meta Regression Analyses of Population- Level Characteristics

Subgroup analysis showed that participants with comorbid conditions had significantly larger reductions in insomnia symptoms than those without (Q = 7.79, p < 0.01), whereas sleep quality improvements were consistent across subgroups, regard- less of comorbidity (Q = 0.37, p = 0.54). No significant subgroup

(14)

differences in effect sizes were observed for ethnicity (Western ethnicity: yes vs. no) or economic classification (HIC vs. UMIC vs. LMIC). However, statistically significant reductions in insomnia symptoms and improvements in sleep quality were observed across each subgroup category (Tables 2 and 3). In addition, meta- regression analyses indicated that age and the gender ratio did not significantly affect the effect sizes (File S14).

3.7 | Subgroup and Meta Regression Analyses of Intervention- Level Characteristics

Subgroup analysis revealed a significant difference in improved sleep quality when comparing mCBT- I interventions delivered

by healthcare professionals versus self- help regimens (Q = 5.39, p = 0.02), with effect sizes being larger for a professionally delivered mCBT- I. However, no significant differences were observed in reducing insomnia symptoms between these groups (Q = 0.03, p = 0.87), although both groups demonstrated significant reductions in insomnia symptoms. There were no significant subgroup differences in the effect sizes relating to the inclusion of all components of CBT- I (yes vs. no), the inclusion of cognitive therapy (yes vs. no), the mode of delivery (instant messaging application vs. CBT- I mobile application vs. text messages vs.

videoconference application), or the format of delivery (group vs. individual). However, each subgroup category showed statistically significant reductions in insomnia symptoms and improvements in sleep quality. Interventions delivered for 6 weeks FIGURE 2 | Effect of mCBT- I interventions compared with control conditions on insomnia symptoms.

(15)

or longer significantly improved insomnia symptoms and sleep quality (Tables 2 and 3). Furthermore, meta- regression analysis indicated that studies with longer intervention durations reported higher effect sizes in reducing insomnia symptoms (File S14).

4 | Discussion

This systematic review is the first evaluation of the effectiveness of mCBT- I for reducing insomnia symptoms and improving sleep quality. The systematic search yielded 16 RCTs, involving a total of 2126 participants from seven countries. Furthermore, 12 of the 16 included studies were published in the last 3 years, reflecting both increased research interest in mCBT- I interventions for the management of insomnia and the increased

adoption and utilisation of mHealth technologies by individuals with insomnia and healthcare professionals.

4.1 | Overall Association of mCBT- I With Insomnia- Related Outcomes

This review found that mCBT- I had significant effects in the management of insomnia. This finding is consistent with the findings of previous meta- analyses that explored the effects of CBT- I on insomnia symptoms (post- intervention: g = −0.78;

Squires et al. 2022); 3 months follow- up: g = −0.64 (van der Zweerde et al. 2019); 6 months follow- up: g = −0.40 (van der Zweerde et al. 2019) and sleep quality (post- intervention:

g = −0.65; van Straten et al. 2018; 3 months follow- up:

g = −0.78; van der Zweerde et al. 2019); 6 months follow- up:

FIGURE 3 | Effect of mCBT- I interventions compared with control conditions on sleep quality.

(16)

Characteristic Studies, no.

Sample size, intervention/

control, No.

Meta- analysis Heterogeneity groups

SMD (95%

CI) p Q p I², % Q p

Insomnia symptoms Study- level characteristics Control condition type

Waitlist 6 295/354 −0.75 (−1.01,

−0.48) < 0.01 11.39 0.04 56

CAU 4 236/216 −0.75 (−1.10,

−0.39) < 0.01 9.24 0.03 68 0.08 0.96

PE 3 354/355 −0.80 (−1.12,

−0.49) < 0.01 7.23 0.03 72 Methodological quality of included studies

Low risk of bias 6 521/536 −0.80 (−1.04,

−0.57) < 0.01 14.39 0.01 65

Some concerns 5 235/234 −0.64 (−0.94,

−0.35) < 0.01 8.59 0.07 53 1.04 0.59

High risk of bias 2 129/155 −0.86 (−1.18,

−0.53) < 0.01 1.76 0.18 43 Statistical method

Intention to treat 11 815/833 −0.72 (−0.90,

−0.54) < 0.01 28.52 < 0.01 65 2.47 1.12

Per protocol 2 70/92 −1.03 (−1.36,

−0.69) < 0.01 0.01 0.94 0 Insomnia diagnosis

Standardised

diagnostic system 8 621/664 −0.77 (−0.95,

−0.58) < 0.01 17.45 0.01 60 0.06 0.80 Only validated

questionnaire 5 264/261 −0.71 (−1.09,

−0.34) < 0.01 12.14 0.02 67 Population- level characteristics

Comorbid condition

Yes 4 317/352 −1.02 (−1.18,

−0.85) < 0.01 0.83 0.84 0 7.79 < 0.01

No 9 568/573 −0.66 (−0.85,

−0.46) < 0.01 18.26 0.02 56 Western ethnicity

Yes 4 155/137 −0.74 (−1.02,

−0.46) < 0.01 3.7 0.30 19 0.01 0.91

No 9 730/788 −0.76 (−0.96,

−0.56) < 0.01 26.7 < 0.01 70 Economic classification

HIC 7 321/337 −0.75 (−0.99,

−0.52) < 0.01 10.8 0.09 44 1.11 0.57 (Continues)

(17)

Characteristic Studies, no.

control, No.

Meta- analysis Heterogeneity Between

groups SMD (95%

CI) p Q p I², % Q p

UMIC 4 248/272 −0.67 (−1.03,

−0.32) < 0.01 11.37 < 0.01 74

LMIC 2 316/316 −0.91 (−1.23,

−0.60) < 0.01 3.6 0.06 72 Intervention- level characteristics

Provider of mCBT- I interventions Healthcare-

professionals 6 235/233 −0.86 (−1.09,

−0.62) < 0.01 7.17 0.21 30 0.84 0.36

Self- help 7 650/692 −0.70 (−0.93,

−0.48) < 0.01 22.6 < 0.01 73 Five components of CBT- I

Yes 8 706/766 −0.81 (−1.00,

−0.61) < 0.01 21.83 < 0.01 68 0.77 0.38

No 5 179/159 −0.64 (−0.95,

−0.33) < 0.01 6.89 < 0.01 42 Involve cognitive therapy

Yes 11 787/826 −0.80 (−0.98,

−0.63) < 0.01 25.53 < 0.01 61 1.31 0.25

No 2 98/99 −0.48 (−1.00,

−0.03) 0.07 2.51 0.11 60 The mode of delivery

Instant messaging application

3 190/215 −0.72 (−1.18,

−0.26) < 0.01 9.58 < 0.01 79 1.50 0.68 CBT- I mobile

application 8 639/675 −0.75 (−0.94,

−0.56) < 0.01 17.54 0.01 60

Text messages 1 41/21 −1.07 (−1.63,

−0.51) < 0.01 NA NA NA Videoconference

application 1 15/14 −1.00 (−1.78,

−0.22) 0.01 NA NA NA

The format of delivery

Group- based 1 15/14 1.00 (−1.78,

−0.22) 0.01 NA NA NA 0.38 0.54

Individual- based 12 870/911 −0.75 (−0.92,

−0.58) < 0.01 30.38 < 0.01 64 Intervention duration (≥ 6 weeks)

Yes 10 711/728 −0.84 (−0.98,

−0.70) < 0.01 13.09 0.16 31 1.27 0.26

No 3 174/197 −0.63 (−1.05,

−0.01) 0.05 10.6 < 0.01 81

(18)

Characteristic Studies, No.

control, No.

Meta- analysis Heterogeneity group tests SMD (95%

CI) p Q p I², % Q p

Study- level characteristics Control condition type

Waitlist 4 208/240 −0.76 (−1.06,

−0.46) < 0.01 5.78 0.12 48 6.62 0.05

CAU 5 272/249 −1.08 (−1.39,

−0.78) < 0.01 9.95 0.04 60

PE 3 355/353 −0.50 (−0.86,

−0.14) < 0.01 9.73 < 0.01 79 Methodological quality of included studies

Low risk of bias 4 452/461 −0.77 (−0.90,

−0.63) < 0.01 2.96 0.40 0 1.15 0.56

Some concerns 6 209/204 −0.66 (−1.05,

−0.27) < 0.01 18.04 < 0.01 72

High risk of bias 2 174/177 −1.13 (−1.90,

−0.36) < 0.01 11.26 < 0.01 91 Statistical method

Intention to treat 11 820/828 −0.79 (−1.10,

−0.56) < 0.01 42.78 < 0.01 77 1.04 0.31

Per protocol 1 15/14 −1.22 (−2.02,

−0.42) < 0.01 43.81 < 0.01 75 Insomnia diagnosis

Standardised

diagnostic system 5 426/436 −0.78 (−0.92,

−0.64) < 0.01 2.87 < 0.58 0 0 0.98 Only validated

questionnaires 7 409/406 −0.79 (−1.20,

−0.37) < 0.01 40.67 < 0.01 85 Population- level characteristics

Comorbid condition

Yes 4 309/338 −0.85 (−1.02,

−0.69) < 0.01 3.07 0.38 2 0.37 0.54

No 8 526/504 −0.74 (−1.06,

−0.41) < 0.01 40.36 < 0.01 83 Economic classification

HIC 6 290/301 −0.85 (−1.08,

−0.62) < 0.01 7.64 0.18 35 1.22 0.54

UMIC 4 229/225 −0.79 (−1.45,

−0.12) 0.02 32.54 < 0.01 91

LMIC 2 316/316 −0.69 (−0.85,

−0.53) < 0.01 0.14 0.70 0 Intervention- level characteristics

Provider of mCBT- I interventions

(Continues)

(19)

g = −0.48 (van der Zweerde et al. 2019), but mCBT- I demonstrated similar or even greater effect sizes for both insomnia symptoms (post- intervention: g = −0.76; 1–3- month follow- up:

g = −0.91; 4–6- month follow- up: g = −0.81) and sleep quality (post- intervention: g = −0.81; 1–3- month follow- up: g = −0. 77;

4–6- month follow- up: g = −0.77). Sensitivity analyses and no indication of publication bias underscore the robustness of these findings.

Furthermore, mCBT- I was associated with significant improvements in SE, TIB, SOL and the number of awakenings (Hedges' g: 0.52–0.68). Consistent with a recent meta- analysis of CBT- I, no significant effect on TST was identified (Maurer et al. 2021). This may reflect an initial reduction in sleep time due to sleep restriction, aimed at consolidating sleep, which dampens the chronic state of hyperarousal in insomniacs and reduces maladaptive conditioning (Maurer et al. 2018). The Characteristic Studies,

No.

control, No.

Meta- analysis Heterogeneity Between- group tests SMD (95%

CI) p Q p I², % Q p

Healthcare-

professionals 6 287/263 −1.10 (−1.37,

−0.83) < 0.01 9.99 0.08 50 7.66 < 0.01

Self- help 6 548/579 −0.60 (−0.82,

−0.37) < 0.01 15.7 < 0.01 68 Five components of CBT- I

Yes 8 679/705 −0.82 (−1.09,

−0.55) < 0.01 37.66 < 0.01 81 0.05 0.82

No 4 156/137 −0.76 (−1.13,

−0.40) < 0.01 5.84 0.12 49 Involve cognitive therapy

Yes 11 761/765 −0.81 (−1.05,

−0.57) < 0.01 43.62 < 0.01 77 0.13 0.72

No 1 74/77 −0.74 (−1.07,

−0.41) < 0.01 NA NA NA The mode of delivery

Instant messaging application

4 209/207 −0.85 (−1.56,

−0.14) 0.02 32.28 < 0.01 91 2.65 0.45

CBT- I mobile

application 6 569/600 −0.73 (−0.85,

−0.61) < 0.01 5.21 0.39 4

Text messages 1 42/21 −1.06 (−1.61,

−0.50) < 0.01 NA NA NA Videoconference

application 1 15/14 −1.22 (−2.02,

−0.42) < 0.01 NA NA NA The format of delivery

Group- based 1 15/14 −1.22 (−2.02,

−0.42) < 0.01 NA NA NA 1.04 0.31

Individual- based 11 820/828 −0.79 (−1.01,

−0.56) < 0.01 42.78 < 0.01 77 Intervention duration (≥ 6 weeks)

Yes 9 666/675 −0.77 (−0.88,

−0.66) < 0.01 9.54 0.30 16 0.00 1.00

No 3 169/167 −0.77 (−1.68,

0.14) 0.10 32.1 < 0.01 94

(20)

4.2 | Population- Level Characteristics

This study revealed that mCBT- I interventions had significantly stronger effects in reducing insomnia symptoms in participants with comorbid conditions than in participants without comorbid conditions. These results align with the findings of meta- analyses of other CBT- I modalities involving participants with insomnia and comorbid conditions (g = −0.76; Lee et al. 2023).

However, mCBT- I showed a larger effect size in participants with comorbid conditions (g = −1.02). This suggests that mCBT- I is a viable modality for individuals with comorbid conditions (e.g., depression and cancer), especially considering the high prevalence of insomnia among such populations (Al Maqbali et al. 2022; Nutt et al. 2008).

In addition, no statistically significant differences in treatment effectiveness were observed among subgroups for several other population characteristics, including ethnicity or economic classification. It is noteworthy that across each subgroup category, statistically significant reductions in insomnia symptoms and improvements in sleep quality were consistently observed.

Furthermore, the age and gender of the study participants were not significant moderators of the treatment effectiveness associated with the mCBT- I interventions. These results suggest that mCBT- I interventions may be broadly effective for a diverse population of individuals with insomnia. However, it is important to acknowledge that certain age groups, particularly older adults, were underrepresented in the included studies. Given that insomnia is more prevalent among older individuals and that the global population is rapidly aging, future research should specifically investigate the effectiveness of mCBT- I interventions in this age group (Patel et al. 2018).

4.3 | Intervention- Level Characteristics

First, in comparison with a previous meta- analysis of healthcare professional- delivered psychological interventions (Ekers et al. 2013), mCBT- I interventions administered by healthcare professionals showed statistically significant improvements in sleep quality in this review. This improvement would be due to professional oversight, which includes supervision, reminders and personalised advice to alter maladaptive sleep behaviours and beliefs—elements often missing in self- help regimes (Okajima et al. 2020). The limited variety of healthcare professional backgrounds in this review restricted the exploration of the specific impacts of healthcare professional backgrounds on insomnia- related outcomes. Nonetheless, individual studies have suggested that mCBT- I can be effectively delivered by trained nurses (Abdelaziz et al. 2022; Zhong et al. 2019) and doctors (Zheng et al. 2022). It is worth noting that in LMICs, where doctors and nurses are often in short supply, community healthcare workers (CHWs) play a crucial role in delivering care (Feroz et al. 2020). Future research should explore the potential of CHWs in delivering mCBT- I interventions in LMICs, which might help address the shortage of healthcare professionals and

Second, mCBT- I interventions that included cognitive therapy had larger effect sizes on reduced insomnia symptoms and improved sleep quality than mCBT- I interventions without cognitive therapy, although no statistical subgroup differences were identified. This could be due to cognitive therapy helping participants change negative beliefs and thoughts about sleep to reduce fear, anxiety, and effort around sleep (Perlis et al. 2005).

Furthermore, in accordance with a current insomnia management guideline (Sutton 2021), this study suggests that CBT- I, when incorporating all its components, tends to yield a larger effect size. These findings suggest that future mCBT- I interventions that include all components will enhance treatment effectiveness.

Third, the text message delivery mode and videoconference delivery mode were each associated with larger effect size improvements in insomnia symptoms and sleep quality, respectively, although no statistical differences with other modes were identified. Text message- based interventions may allow for more personalised support, as participants can seek tailored suggestions by texting back when encountering sleep problems (Dobson et al. 2018). However, a previous study has shown that some participants are reluctant to use videoconferencing due to privacy concerns (Mozer et al. 2015). Given that several mCBT- I interventions were delivered through mHealth apps, it is essential to address their quality and safety (Akbar et al. 2020). Involving patients in the development of mHealth apps via co- design may help ensure future mCBT- I interventions are tailored to their needs and good quality (Noorbergen et al. 2021).

Fourth, the results of this study suggest that group- format mCBT- I interventions have an advantage in improving insomnia symptoms and sleep quality. Incorporating a group format encourages the sharing of experiences, social support and in- teraction, and peer learning, and can enhance motivation and commitment to therapy (Loughan et al. 2024). This aligns with previous meta- analyses underscoring the benefits of group CBT- I (Koffel et al. 2015). Importantly, this delivery format may be particularly valuable in contexts where individual therapy is not feasible due to a scarcity of trained healthcare professionals or a high demand for therapy services. However, it is also worth considering whether videoconferencing- based group formats would be equally effective across different cultures.

The Oswald et al. study (Oswald et al. 2022), which demonstrated the benefits of group- format mCBT- I, was conducted in the United States of America, a country with a more open, discursive, and inclusive culture. Countries with different cultural norms and social dynamics might experience different outcomes with group- format interventions. Therefore, future research should investigate the impact of cultural differences on the effectiveness of group mCBT- I interventions to ensure that these interventions are tailored to the specific needs and contexts of diverse populations.

Fifth, the study found that longer durations of mCBT- I interventions are associated with significantly greater reductions in insomnia symptoms and improvements in sleep quality. This

(21)

found that longer intervention durations are correlated with better treatment outcomes (Xu et al. 2021). This study specifically advocates for the development of mCBT- I interventions lasting more than 6 weeks to maximise the treatment effectiveness. The use of mobile technologies in CBT- I allows for greater flexibility in terms of time and distance, making it a more feasible option for delivering longer interventions than traditional face- to- face regimens (Forsythe and Venter 2019).

4.4 | Implications for Nursing Practice

The findings from this meta- analysis have several important implications for nursing practice. The integration of mCBT- I interventions into routine nursing care provides a scalable and cost- effective solution to bridge the gap caused by the shortage of trained CBT- I therapists, aligning with World Health Organization's recommendations to train nurses in basic psychological interventions (World Health Organization 2024).

To maximise the effectiveness of mCBT- I, nurses should: (1) incorporate all five CBT- I components; (2) plan interventions lasting at least 6 weeks and (3) consider group- format delivery where feasible to enhance peer support. Healthcare organisa- tions should also support nurses through comprehensive training in mCBT- I delivery and provide necessary technological infrastructure for implementation. Existing RCTs have demonstrated significant reductions in insomnia severity with nurse- supported self- help CBT- I (Ulmer et al. 2024; Van der Zweerde et al. 2020). Given the high workload that nurses face in clinical settings, nurse- supported self- help mCBT- I might offer a flexi- ble alternative.

4.5 | Limitations

This meta- analysis has some limitations. First, we restricted our search to studies published in English and Chinese, potentially limiting the comprehensiveness of our findings. Second, the included studies were mainly conducted in HICs, UMICs, with only two studies from LMICs. This may bias our findings toward countries in higher economic classes. Third, the incon- sistent reporting of detailed information on the dose of mCBT- I interventions, such as the number of sessions, duration and in- tensity, in the included studies limits our ability to analyse and draw conclusions about the potential dose–response relation- ship between mCBT- I interventions and sleep outcomes. Fourth, certain subgroup categories were represented by only a single study, making the results prone to type- I error. Fifth, there was considerable heterogeneity. A series of analyses, such as subgroup analysis and meta- regression, were conducted to explore the sources of heterogeneity. These analyses could not com- pletely explain the sources of heterogeneity, which may limit the interpretability and generalisability of the overall findings.

Sixth, seven studies were rated as having some concerns, and three studies were rated as having a high risk of bias, potentially affecting the reliability of the pooled estimates. Seventh, most outcomes were graded as low or very low certainty by GRADE, reducing confidence in the findings' robustness. Lastly, the long- term effects of mCBT- I remained unclear due to the limited follow- up data in the included studies.

This systematic review and meta- analysis synthesised available empirical evidence, demonstrating that mCBT- I has beneficial effects on insomnia symptoms, sleep quality and sleep parameters.

In addition, the study highlights the importance of proactively considering population and intervention characteristics to further enhance the effectiveness of mCBT- I. In particular, participants' comorbid conditions, the provider delivering the intervention, the components included, and the duration and format of mCBT- I interventions should be carefully considered for more effective intervention design. These findings are expected to provide healthcare professionals and researchers in the rapidly developing field of mHealth with practical insights for the development, prescription, and implementation of mCBT- I interventions. However, future research is needed to confirm or refute these findings and to exam- ine the long- term effects of mCBT- I on insomnia- related outcomes.

Author Contributions

Yangxi Huang: conceptualisation, methodology, software, validation, formal analysis, investigation, data curation, writing – original draft, writing – review and editing, visualisation. Yongyang Yan: conceptualisation, methodology, validation, formal analysis, writing – review and editing. Jojo Yan Yan Kwok: methodology, writing – reviewing and editing, supervision. Pui Hing Chau: methodology, writing – reviewing and editing, supervision. Mu- Hsing Ho: methodology, writing – reviewing and editing, supervision. Siobhán O'Connor: methodology, writing – reviewing and editing, supervision. Jung Jae Lee: conceptualisation, methodology, writing – review and editing, visualisation, supervision.

Acknowledgements

We would like to extend our heartfelt appreciation to the librarian, Ms.

Sherry Ng from The University of Hong Kong, for her invaluable as- sistance and expertise in formulating the search terms for our meta- analysis. This research received no specific grant from any funding agency in the public, commercial, or not- for- profit sectors.

Disclosure

Declaration of Generative Al and Al- Assisted Technologies in the Writing Process: During the preparation of this manuscript, the authors used ChatGPT 4.0 to improve the clarity and consistency of the writing style and language across different sections of the manuscript after completing the initial draft. The first and last authors reviewed and edited the content after using this AI tool, and all authors take full responsibility for the content of the final draft submitted for publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

All data analysed in this meta- analysis were extracted from published studies cited in the reference list. The datasets generated during the current study (e.g., extracted outcome measures, risk- of- bias assessments, and coding sheets) can be obtained from the corresponding author upon reasonable request.

References

Abdelaziz, E. M., N. B. Elsharkawy, and S. M. Mohamed. 2022.

“Efficacy of Internet- Based Cognitive Behavioral Therapy on Sleeping