This paper is in the following e-collection/theme issue:

Reviews in Digital Mental Health (243) Depression and Mood Disorders; Suicide Prevention (1363) Web-based and Mobile Health Interventions (3297) Anxiety and Stress Disorders (1053) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514) Mobile Health (mhealth) (2939) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (565) Health Services in Resource-Poor Settings and LMICs (414)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43066, first published .
Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis

Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis

Effectiveness of Digital Mental Health Tools to Reduce Depressive and Anxiety Symptoms in Low- and Middle-Income Countries: Systematic Review and Meta-analysis

Authors of this article:

Jiyeong Kim1 Author Orcid Image ;   Lois M D Aryee2 Author Orcid Image ;   Heejung Bang3 Author Orcid Image ;   Steffi Prajogo4 Author Orcid Image ;   Yong K Choi5 Author Orcid Image ;   Jeffrey S Hoch6 Author Orcid Image ;   Elizabeth L Prado7 Author Orcid Image
Article Authors Cited by (17) Tweetations (9) Metrics

    Review

    1Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA, United States

    2Department of Nutrition and Food Science, University of Ghana, Accra, Ghana

    3Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA, United States

    4Johns Hopkins Bayview Medical Center, Baltimore, MD, United States

    5Department of Health Information Management, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, United States

    6Division of Health Policy and Management, Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, CA, United States

    7Department of Nutrition, Institute for Global Nutrition, University of California, Davis, Davis, CA, United States

    Corresponding Author:

    Jiyeong Kim, BPharm, MPH

    Department of Public Health Sciences

    School of Medicine

    University of California, Davis

    1 Shields Avenue

    Davis, CA, 95616

    United States

    Phone: 1 5307522793

    Email: jiykim@ucdavis.edu


    Background: Depression and anxiety contribute to an estimated 74.6 million years of life with disability, and 80% of this burden occurs in low- and middle-income countries (LMICs), where there is a large gap in care.

    Objective: We aimed to systematically synthesize available evidence and quantify the effectiveness of digital mental health interventions in reducing depression and anxiety in LMICs.

    Methods: In this systematic review and meta-analysis, we searched PubMed, Embase, and Cochrane databases from the inception date to February 2022. We included randomized controlled trials conducted in LMICs that compared groups that received digital health interventions with controls (active control, treatment as usual, or no intervention) on depression or anxiety symptoms. Two reviewers independently extracted summary data reported in the papers and performed study quality assessments. The outcomes were postintervention measures of depression or anxiety symptoms (Hedges g). We calculated the pooled effect size weighted by inverse variance.

    Results: Among 11,196 retrieved records, we included 80 studies in the meta-analysis (12,070 participants n=6052, 50.14% in the intervention group and n=6018, 49.85% in the control group) and 96 studies in the systematic review. The pooled effect sizes were −0.61 (95% CI −0.78 to −0.44; n=67 comparisons) for depression and −0.73 (95% CI −0.93 to −0.53; n=65 comparisons) for anxiety, indicating that digital health intervention groups had lower postintervention depression and anxiety symptoms compared with controls. Although heterogeneity was considerable (I2=0.94 for depression and 0.95 for anxiety), we found notable sources of variability between the studies, including intervention content, depression or anxiety symptom severity, control type, and age. Grading of Recommendations, Assessments, Development, and Evaluation showed that the evidence quality was overall high.

    Conclusions: Digital mental health tools are moderately to highly effective in reducing depression and anxiety symptoms in LMICs. Thus, they could be effective options to close the gap in depression and anxiety care in LMICs, where the usual mental health care is minimal.

    Trial Registration: PROSPERO CRD42021289709; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=289709

    JMIR Ment Health 2023;10:e43066

    doi:10.2196/43066

    Keywords

    digital mental health; mHealth; mobile health; digital health; low- and middle-income country; depression; anxiety; mobile phone 


    Background

    Depressive and anxiety disorders are a leading cause of the global burden of disease [1]. Depression and anxiety contribute to an estimated 74.6 million years lived with disability, and 80% of this burden occurs in low- and middle-income countries (LMICs) [2]. However, the investment in mental health disorder prevention and treatment is substantially lower in LMICs than in high-income countries (HICs; $2 vs $50 per person in HICs) [3]. In LMICs, 80% to 95% of people with depression and anxiety do not receive the necessary mental health care, mainly because of the limited availability of service providers [4,5]. This large treatment gap could lead to detrimental consequences for the overall health of individuals with mental disorders, social and economic burden on their families, and large-scale societal loss in terms of decreased economic productivity owing to missed work (absenteeism) or reduced efficiency at work (presenteeism) [4,6].

    In recent decades, the use of digital mental health (DMH) services to deliver mental health care has been increasing through the internet and other forms of technologies [7,8]. In this paper, we use the term DMH tools to refer to the following: either a digital platform as a tool to deliver mental health care (eg, cognitive behavioral therapy [CBT] provided by a service provider remotely via computer) or a digital platform itself as a main mental health intervention (eg, smartphone apps for depression). DMH tools have shown effectiveness among various populations in HICs, including youth, adults, older adults, and antenatal and postpartum women, in reducing conditions, such as mild to moderate or severe depression, social anxiety, panic disorder, suicidal ideation, posttraumatic stress disorder, attention-deficit/hyperactivity disorder, or insomnia [7,8]. A growing body of literature has started investigating barriers and facilitators for the successful implementation [9], usability, and acceptability to enhance user engagement with DMH tools [10,11]. Economic evaluations of DMH tools have reported that they are cost-effective [12,13]. These investigations informed us that well-designed DMH interventions could enhance access to quality mental health care with low-cost investment, which is ideal for LMICs. Indeed, the World Health Organization recognized the potential role of technology-supported mental health care tools in closing mental health treatment gaps in LMICs [14]. As the technology-enabling environment is expanding in LMICs (eg, 90% mobile phone penetration rate and 40% average internet connectivity) [15-17], diverse DMH interventions have been tested [18].

    Prior Work

    A few systematic reviews examined digital health interventions for mental disorders in LMICs and reported several limitations [19], including that the quality of studies was suboptimal [20] and most studies reported short-term follow-ups and a low retention rate [21]. Thus, the findings were inconclusive in determining the clinical impact of DMH interventions. A recent meta-analysis reported a moderate effect of digital psychological interventions on mental disorders in LMICs [22]. However, the results were not explicit for depression or anxiety, which are the most prevalent forms of mental health issues [1]. Of note, this study was restricted to adult populations. This is a notable limitation given that (1) adolescents and children have escalating mental health care needs and (2) technology-enabled mental health care delivery is a promising strategy for these age groups [23,24]. Moreover, under the unprecedented SARS/COVID-19 pandemic, technology-based mental health interventions have surged, and overall mental distress was elevated in LMICs [25]. Hence, we need to investigate the effectiveness of DMH tools on depression and anxiety in acquiring comprehensive knowledge of this promising strategy in depression and anxiety care in low-resource settings. Therefore, this study aimed to quantify the effectiveness of digital health tools in reducing depression and anxiety in adults, adolescents, and children in LMICs.


    Search Strategy and Study Selection

    We searched the PubMed, Embase, and Cochrane databases for papers without language restrictions from database inception to February 22, 2022. We adapted our search strategy from previous reviews and settings [22,26] and refined it further to tailor it to our review purpose. Search terms included text, keywords, and Medical Subject Headings for PubMed/Medline and Cochrane databases, and the expansion (/exp) function was applied in Embase in the following three main areas: (1) technology; (2) mental health (depression or anxiety); and (3) LMICs, based on the World Bank Country Classification of the year the study was conducted. The full search terms for each database are provided in Multimedia Appendix 1.

    During the full-text review, a snowball search was applied to find relevant studies from the references of previous systematic reviews. In addition, papers suggested by the citation program (Mendeley) were screened. We used the Google Translate software for data extraction and quality assessment of papers published in non-English languages. We also screened papers on ClinicalTrials.gov and the International Clinical Trials Registry Platform via the Cochrane Library to find unpublished clinical trials.

    Studies were included if they (1) were randomized controlled trials (RCTs), (2) used technology (eg, computer, tablet PC, internet, mobile app, telephone, texting, or video or audio files) either as a tool to deliver traditional mental health care or the technology itself was a main mental health intervention, (3) measured depression or anxiety as either a primary or secondary outcome, (4) targeted people with low to moderate severity of depression or anxiety (eg, from lightly symptomatic to moderate disorder), (5) were conducted in an LMIC, and (6) used any comparison group (eg, active control, treat as usual, no intervention, or waitlist control).

    Widely used standardized programs were used for abstract screening (Covidence) and full-text review (Microsoft Excel). Two reviewers (JK and SP) independently screened the abstracts. JK extracted and double-checked the data. SP independently extracted the data from 20% of randomly selected papers. If there were disagreements during screening and full-text review for inclusion, a third reviewer (HB) resolved the conflict. We applied the same inclusion and exclusion criteria to both the systematic review and the meta-analysis. The primary reviewer (JK) imported the extracted data into the summary table, and another reviewer (LMDA) checked the data in the summary table for accuracy. All the coauthors reviewed the data in the summary table. The biostatistician (HB) checked a random sample of the final data for effect size calculation.

    Data Analysis

    The authors extracted the study population (demographics), study aims, settings, region, inclusion and exclusion criteria, interventions and controls (duration and details of procedures), outcome means, SDs, and measurement instruments. We contacted the corresponding authors to secure necessary data that were missing from published studies and excluded studies from the meta-analysis if the requested data were not provided. We evaluated an individual study’s methodological quality and risk of bias using the Effective Public Health Practice Project quality assessment tool in the following domains: selection bias, study design, confounders, blinding, data collection methods, withdrawal and dropout, intervention integrity, and analyses. Three reviewers assessed the listed domains independently (JK, LMDA, and SP), and the assessments were compared between the two assessors. Each domain was rated as 1=strong, 2=moderate, and 3=weak, and a global rating was assigned based on the section ratings as follows: 1=strong (no weak ratings), 2=moderate (1 weak rating), and 3=weak (2 or more weak ratings). In case of conflict, assessors discussed the discrepancies and reconciled the ratings. See Multimedia Appendix 2 [26-121] for our Effective Public Health Practice Project assessment. The quality of evidence for the outcome across all studies was assessed using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) criteria—risk of bias, inconsistency of effect, imprecision, indirectness, and publication bias (JK and LMDA). See Multimedia Appendix 3 for our GRADE assessment.

    Stata 17 (StataCorp) was used to compute pooled effect sizes with 95% CIs. As the studies reported outcomes with different measurement tools, we calculated the Hedges g values with 95% CI as a standardized mean difference index to estimate the effect size. We chose Hedges g over Cohen d, because it is less prone to bias for the small sample studies, some of which were contained in our review [122]. Postintervention means and SDs were used for pooled effect size calculation. Weights were assigned to each study by calculating the inverse variance of the outcome scores. If more than one mental health measurement was reported, we included the one that was reported in the largest number of other studies. In the multiple arm studies, digital intervention was compared with each arm, and the effect size for each comparison was estimated. In this case, the frequency (N) of the control was divided by the number of comparisons to avoid overweighting the studies with multiple arms. We selected a random effect model to calculate pooled effect sizes, because we expected the studies to be dissimilar, whereas we wanted to generalize the results to other populations. Higgins and Thompson I2 values were calculated to evaluate the heterogeneity across studies. We conducted subgroup analyses to investigate the variations in effect sizes by the following study characteristics: intervention content, type of technology use, mode, intervention duration, outcome measurement, depression or anxiety severity, control type, participant age, and study region. Furthermore, we performed a univariate, random-effect meta-regression using prespecified study characteristics (eg, study quality and blinding, in addition to the characteristics examined for the subgroup analysis) to look for notable sources of heterogeneity.

    We conducted preplanned sensitivity analyses to test the robustness of the results for the studies where (1) the quality was poor, (2) more than one depression or anxiety score was reported, (3) the baseline psychometric scores were considerably different between the intervention and control groups, and (4) the digital tool was adjunct to the main nondigital intervention. We performed a post hoc sensitivity analysis, excluding outliers defined as the estimates’ 95% CI values that did not overlap with the 95% CI values of the pooled effect size [123]. To assess small-study effects and publication bias, we visually examined funnel plots and performed Egger weighted regression test to quantitatively evaluate the degree of asymmetry. To calculate the bias-corrected overall effect sizes after accounting for the publication bias and small-study effects, the Duval and Tweedie trim and fill method was used. The study’s review protocol was registered in PROSPERO (CRD42021289709), and the review process was compliant with the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines, which can be found in Multimedia Appendix 4.


    Characteristics of the Included Studies

    Initially, 11,196 records were retrieved and 1158 records were left after the RCT filter. When the records were imported to Covidence, 239 duplicates were removed automatically. We screened 919 studies for titles and abstracts and excluded 741 records as irrelevant. In addition, 12 studies were added from other sources; hence, a total of 190 studies were full-text reviewed and 94 studies were further excluded. We included 10 studies out of 22 from the recent meta-analysis, whereas the studies that were conducted in HICs and targeted posttraumatic stress disorder or substance abuse without depression or anxiety measurements were excluded [22]. A total of 96 studies were selected for the systematic review, and 80 studies were included for the meta-analysis, because data for 16 studies were not available to calculate the postintervention effect size (Figure 1).

    ‎
    Figure 1. Study selection. CT.gov: ClinicalTrials.gov; ICTRP: International Clinical Trials Registry Platform; LMIC: low- and middle-income country; RCT: randomized controlled trial.

    Multimedia Appendix 5 [26-121] shows the characteristics of collected data from each study included in this systematic review. More information about the included studies is available in Multimedia Appendix 6 [26-121]. The meta-analysis contained a total of 12,070 participants (n=6052, 50.14% in the technology intervention group and n=6018, 49.85% in the control group). The participants’ age range was broad (4-75 years). The average number of participants per study was 144, ranging from 19 to 954. There were 59 and 54 studies reporting depression and anxiety outcomes, respectively. The measurement instruments were various, with 11 types for depression and 17 for anxiety. Symptoms were self-reported by the study participants. All 96 studies included in the systematic review were conducted between 2011 and 2021 in LMICs, including Asia (n=68), Africa (n=6), Europe (n=15), and Latin America (n=7). Studies published in Chinese and Portuguese were translated into English (n=3). In most studies (80/96, 83%), technology itself was a primary intervention, whereas technology was a tool to deliver intervention contents in 16 studies. Mobile apps and internet were the most common technology formats (80/96, 83%). Multiple contents (eg, psychotherapy plus peer support) were most frequently provided (33/96, 34%), followed by psychotherapy, including CBT (24/96, 25%). Playful distraction was primarily observed in relieving preprocedure anxiety (eg, dental treatment, venipuncture, and bone marrow aspiration), which was usually based on a single session and performed for children. The median intervention duration was 6 weeks, ranging from 1 day to 72 weeks. Some studies (13/96, 14%) had more than 2 study groups (eg, internet-based CBT vs in-person CBT vs no intervention). Usual in-person care or active control (65/96, 68%) was more common than no intervention as a control. Only 15% (14/96) of the studies provided adverse event information.

    The study quality assessment results revealed that 80% (77/96) of the studies were considered moderate-to-high quality. The randomization method was described in most reports (72/96, 75%), and 91% (87/96) were considered likely to have representative sample populations. Hence, the possibility of selection bias seemed low. Approximately 77% (74/96) showed less than 20% attrition rates, and 65% (62/96) performed intention-to-treat analysis to account for the missing data. We rated the overall quality of evidence as high because we only included RCTs and did not downgrade it in the key 5 criteria—risk of bias, inconsistency, indirectness, imprecision, and publication bias. First, risk of bias was not considered high regarding randomization, blinding, attrition, and selective reporting. Second, outcomes were considered consistent because the study conclusions were consistent, and pooled effect sizes have narrow CIs. Although heterogeneity was considerable, the likely sources of heterogeneity were comprehensively suggested. Third, indirectness was low, as all the outcomes can directly answer our research question. Fourth, imprecision was considered low because the effect estimate was calculated from a large number of total participants (n=12,070) and was precise according to the GRADE guidelines. Finally, the possibility of publication bias was low to moderate.

    Outcomes of the Included Studies

    We assessed the effects of DMH intervention compared with control groups with a mean (SD) from 80 studies (67 records for depression and 65 records for anxiety; Figure 2 [26, 28, 29, 32, 33, 35-39, 44-47, 54, 55, 57, 59-61, 63, 65, 68-78, 80, 82-84, 86-88, 90, 91, 93, 95, 97-101, 106, 107, 109, 111, 113, 115, 117, 119-121] and Figure 3 [27, 29, 30, 32, 35-38, 40, 42, 43, 48-50, 52, 55-59, 61, 62, 68, 69, 72-74, 78, 80, 82-84, 86, 88, 89, 93, 99, 101-103, 105-107, 109, 110, 112-118, 120, 121]). The pooled effect sizes were −0.61 (95% CI −0.78 to −0.44) for depression and −0.73 (95% CI −0.93 to −0.53) for anxiety, indicating that groups that used DMH tools had lower postintervention depression and anxiety symptoms compared with the controls. Considerable heterogeneity was observed (I2=0.94 for depression and 0.95 for anxiety). However, we were able to find the likely sources of heterogeneity, because the effect sizes were considerably different between prespecified subgroups (eg, by intervention content, technology type, mode, depression or anxiety level, outcome, and age) in both depression and anxiety (Table 1). For example, by intervention content, the largest effects on depression and anxiety were found among studies that provided CBT and other types of psychotherapy. By mode of delivery, internet- or mobile app–based interventions showed larger effect sizes compared with telephone- or text message–based approaches. By age, effect sizes on depression were larger among adults than among children, whereas effects on anxiety were larger among children than among adults. Although effect sizes were notably different among subgroups of studies, DMH interventions showed remarkably consistent pooled effects in reducing depression and anxiety symptoms among all subgroups of studies examined.

    Notably, the effect size was larger (−0.70, 95% CI −0.90 to −0.51 for depression and −0.85, 95% CI −1.08 to −0.62 for anxiety) when we removed studies having considerable baseline score differences between groups in the sensitivity analysis. However, excluding outliers resulted in decreased effect sizes both in depression (−0.57, 95% CI −0.66 to −0.44) and anxiety (−0.66, 95% CI −0.77 to −0.56). Other sensitivity analyses did not change the effect sizes (Multimedia Appendix 7). Funnel plots appeared to be symmetrical for both anxiety and depression (Multimedia Appendix 8). Egger test results were not significant for depression (P=.19) but significant for anxiety (P=.03), suggesting potential publication bias for anxiety. However, when we ran the Duval and Tweedie trim and fill analysis to statistically assess the publication bias, no imputation was necessary to adjust for the publication bias in both depression and anxiety, and the effect sizes stayed the same.

    ‎
    Figure 2. Effect of digital mental health interventions on depression. *Studies with more than 2 arms. The frequency (N) of the control group was divided by the number of comparisons to avoid being overweight. REML: restricted maximum likelihood.
    ‎
    Figure 3. Effect of digital mental health interventions on anxiety. *Studies with more than 2 arms. The frequency (N) of the control group was divided by the number of comparisons to avoid being overweight. REML: restricted maximum likelihood.
    Table 1. Subgroup analyses of digital mental health interventions.
    SubgroupaDepressionAnxiety

    		Frequency, nHedges g (95% CI)I2 (%)P valueFrequency, nHedges g (95% CI)I2 (%)P value
    By content<.001


    		<.001

    		CBTb18−0.93 (−1.33 to −0.53)94
    		10−0.78 (−1.20 to −0.35)94

    		Other psychotherapy7−0.58 (−1.64 to 0.48)96
    		7−0.82 (−2.15 to 0.52)98

    		Psychoeducation7−0.30 (−0.45 to −0.16)42
    		11−0.27 (−0.61 to 0.07)89

    		Multiple content21−0.62 (−0.85 to −0.39)90
    		19−0.75 (−0.98 to −0.53)87

    		Physical activity7−0.29 (−0.47 to −0.11)40
    		1−0.53 (−0.98 to −0.07)N/Ac

    		Mindfulness4−0.36 (−0.84 to 0.11)84
    		4−0.26 (−0.73 to 0.21)84

    		Social support3−0.59 (−1.02 to −0.15)42
    		1−0.64 (−1.13 to −0.16)N/A

    		Playful distractionN/AN/AN/A
    		12−1.22 (−1.67 to −0.76)86
    By type of technology use<.001


    		.01

    		Tech intervention56−0.58 (−0.73 to −0.44)90
    		54−0.74 (−0.94 to −0.55)93

    		As a delivery tool11−0.74 (−1.52 to 0.05)96
    		11−0.65 (−1.41 to 0.11)96
    By mode.03


    		<.001

    		Internet-based37−0.61 (−0.84 to −0.38)92
    		19−0.65 (−0.96 to −0.35)91

    		Mobile apps21−0.75 (−0.50 to −1.00)92
    		36−0.92 (−1.18 to −0.66)95

    		Telephone or Texting8−0.24 (−1.00 to −0.50)96
    		9−0.10 (−0.79 to 0.59)96

    		Audio or video files1−0.58 (−0.74 to −0.42)N/A
    		1−1.05 (−1.61 to −0.49)N/A
    By duration.49


    		.005

    		Multiple session66−0.62 (−0.79 to −0.44)94
    		50−0.71 (−0.96 to −0.46)96

    		Single session1−0.35 (−0.74 to 0.04)N/A
    		15−0.80 (−1.06 to −0.53)71
    By depression or anxiety severity<.001


    		<.001

    		Disorder19−0.91 (−1.25 to −0.56)95
    		10−0.96 (−1.30 to −0.62)82

    		Symptomatic48−0.50 (−0.69 to −0.31)93
    		55−0.70 (−0.93 to −0.47)95
    By outcome<.001


    		.007

    		Primary60−0.57 (−0.76 to −0.39)94
    		58−0.73 (−0.95 to −0.51)94

    		Secondary7−0.94 (−1.24 to −0.65)85
    		7−0.73 (−1.27 to −0.19)96
    By control<.001


    		.91

    		No intervention22−0.48 (−0.74 to −0.22)89
    		22−0.65 (−0.85 to −0.44)73

    		Usual care or active45−0.67 (−0.90 to −0.45)95
    		43−0.77 (−1.06 to −0.48)97
    By age<.001


    		<.001

    		Children6−0.32 (−0.76 to 0.12)82
    		15−1.05 (−1.46 to −0.63)91

    		Adult60−0.63 (−0.82 to −0.45)94
    		50−0.64 (−0.87 to −0.41)95

    		Older adult1−0.78 (−1.23 to −0.34)N/A
    		N/AN/AN/A
    By region.67


    		<.001

    		Asia53−0.63 (−0.85 to −0.42)95
    		46−0.72 (−0.97 to −0.47)96

    		Africa3−0.48 (−0.61 to −0.35)29
    		2−0.08 (−0.26 to 0.10)0

    		Europe8−0.57 (−0.84 to −0.38)35
    		13−0.82 (−1.27 to −0.37)91

    		Latin America3−0.62 (−1.14 to −0.10)23
    		4−0.93 (−1.54 to −0.32)75

    aIntervention content (CBT, psychotherapy, psychoeducation, multiple contents, physical activity, mindfulness, social support, and playful distraction). Although CBT and mindfulness-based cognitive therapy are types of psychotherapy, we separated these from other types of psychotherapy. As CBT was the single most frequent, and mindfulness was emerging content for digital mental health tools, we intended to evaluate the effects separately: type of technology use (technology itself as an intervention or technology as a delivery tool); mode (internet-based, mobile apps, telephone or text messages, or audio or video files); intervention duration (multiple session or single session); depression or anxiety severity (depression or anxiety disorder or depression or anxiety at risk or simply symptomatic); outcome measurement (depression or anxiety is primary outcome or depression or anxiety is secondary outcome); control type (usual care or active control, no intervention, or waitlist control); participant age (children, adults, or older adults); and study region (Asia, Africa, Europe, or Latin America).

    bCBT: cognitive behavioral therapy.

    cN/A: not applicable.


    Principal Findings and Comparisons With Previous Work

    With our 96 studies for systematic review and 80 studies for meta-analysis, we found that DMH interventions showed moderate to high effectiveness in reducing depression and anxiety symptoms in LMICs. Our findings contribute to knowledge-building in the effectiveness of DMH tools in LMICs, especially on depression and anxiety, which are the 2 most common mental disorders. The aggregated results from 80 RCTs with moderate to high effect sizes (Hedges g=−0.61 for depression and −0.73 for anxiety) provide a comprehensive up-to-date review (up to February 2022), with some promising evidence. Moreover, our findings are well aligned with previous systematic reviews in that DMH tools improved mental health outcomes in low-resource settings [20] with a moderate overall effect size (Hedges g=0.60) [22]. Our results are also comparable with the outcomes of digital psychological interventions in HICs that were found to be moderately effective in reducing depression (Hedges g=0.51-0.58) [124,125] and highly effective for anxiety (Hedges g=0.80) [126].

    This study has some clinical implications for patient care. First, when psychotherapy, including CBT, was delivered via digital formats, it effectively relieved depression and anxiety symptoms compared with the usual care. Moreover, perinatal and postpartum depression was the single most frequent target for DMH intervention, followed by depression or anxiety care for the caregivers of children with chronic or congenital health conditions. Patients with chronic diseases were also often targeted. Thus, mobile app– or internet-based psychotherapy could be one way to care for the mental health of perinatal or postpartum women, patients with chronic disease, or their caregivers, who may be marginalized in mental health care in low-resource settings. Finally, our findings showed that digital tools (eg, social media and audiovisual materials) effectively alleviated preprocedure-related anxiety in the clinical setting.

    Limitations

    Substantial heterogeneity should be acknowledged as a limitation of this study. It was anticipated because the main components of the study varied, including intervention formats, control types, and participants’ ages, which could lead to the large variance in effect sizes between studies. As I2 is an indicator of inconsistency across the study outcomes, high I2 signals that the observed variability could be real. We are also aware of the possibility of bias, including the small-study effect, because half of the studies had small sample sizes (eg, the total participants were <100). Small and pilot studies tend to include participants whose symptoms are likely more prominent or who are likely proactive and compliant with the study instructions. This could allow the studies to be well controlled and managed, potentially leading to a large overall effect size compared with what may occur in a real-world setting. Considering that DMH interventions tested through RCTs are still at an early stage in LMICs, it is not surprising that the sample size tends to be small, and the contents and study protocols vary substantially between studies. Moreover, the Egger test detected the possibility of publication bias (P=.03) for anxiety, although the trim and fill test revealed that no imputation was necessary for both outcomes to account for the publication bias. There is a possibility of duplication bias as we observed some studies conducted by the same research team, using similar study designs and interventions but for different target populations. We also need to recognize citation bias, as the notable findings are likely to be cited and included in our study. However, we believe that the possibility of language bias and availability or cost bias is minimal; we included non-English studies published in their local journals, and no papers were excluded owing to journal unavailability for full-text review. Finally, we were unable to adjust for the baseline scores, which could lead to the biased effect size or conservative results. For example, we witnessed some preintervention score differences between groups exceeding the postintervention score differences, meaning that the postintervention score differences could not correctly reflect the genuine effect of the intervention. In this study, this made the pooled effect size conservative for both depression and anxiety, resulting in increased effect sizes when these studies were excluded for sensitivity analysis.

    Study Strengths and Future Directions

    This study has several strengths that can be acknowledged. First, our findings are aggregated results of 96 RCTs conducted in LMICs from 2011 to 2021. Hence, the moderate and high pooled effect sizes for depression and anxiety are supported by a large number of participants included. Second, our study focused on the most common mental disorders—depression and anxiety. The participants included those with both light depression or anxiety symptoms and moderate depression or anxiety disorders. The participants’ age range was broad, from preschool children to older adults, and study settings varied, including hospitals, schools, or fully web based. Thus, the diversity of study populations and treatment delivery settings may contribute to the generalizability of our results and qualitative and quantitative comparisons between various subgroups. Third, we were able to detect potential sources of variability, which could possibly be the intervention content, control type, baseline depression or anxiety symptom severity, and participant age. Notable effect size differences by subgroup signaled that the effectiveness of DMH tools could be more effective in reducing depressive or anxiety symptoms when a certain content was applied (eg, CBT) or it was administered for a certain level of baseline depression or anxiety (eg, depression disorder). Notably, our findings showed that using mobile apps could be more effective than non–mobile app–based DMH tools in relieving depressive and anxiety symptoms, although all these interpretations need caution. The evidence of the source of variability for both depression and anxiety is novel, as this information was not available in previous reviews. Fourth, noticeably, the effect size was higher when the DMH tools were compared with usual care or active control than no intervention for depression. This could highlight that DMH could be as effective as usual in-person care, not just better than nothing. Finally, studies conducted during the COVID-19 pandemic were included, showing that DMH tools could be useful to deal with depression and anxiety even when in-person social connections were reduced during an unprecedented worldwide infectious disease outbreak. Our findings suggest that DMH tools could be viable options when infectious diseases increase the risk of in-person–based care. This could be encouraging, as we are still in the extended COVID-19 pandemic era, and reportedly, the overall mental distress had been elevated nearly universally among all populations, especially in LMICs. For future studies, we would suggest that more effort would be needed for economic evaluation of DMH tools to gauge the feasibility of implementation and economic consequences and implications in LMICs.

    Conclusions

    Our findings from a systematic review and meta-analysis of 96 RCTs showed that DMH tools could be an effective method to care for depression and anxiety in low-resource settings where usual care is minimally available or feasible despite the care necessity and urgency. This study provides ample evidence-based insight regarding future directions of DMH use for depression and anxiety in LMICs, given the anticipated increasing demand, development, and implementation of DMH tools.

    Data Availability

    All data extracted from the reported papers for this systematic review and meta-analysis will be made available with publication on reasonable request to the corresponding author.

    Authors' Contributions

    JK conceptualized the study, and JK and ELP designed the methods and protocol. JK, SP, and HB contributed to the literature review and study selection for inclusion. JK, SP, LMDA, and HB contributed to data extraction (JK did data double extraction), data check, and study quality assessment. JK and HB were responsible for statistical analysis and data interpretation. JK prepared the first draft of the manuscript, and HB, ELP, JSH, and YKC substantially contributed to manuscript revision. All authors had full access to all the data, read the final manuscript, and approved its submission for publication.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Search strategy.

    PDF File (Adobe PDF File), 101 KB
    ‎
    Multimedia Appendix 2

    Study quality assessment (Effective Public Health Practice Project).

    PDF File (Adobe PDF File), 230 KB
    ‎
    Multimedia Appendix 3

    Overall quality of evidence (Grading of Recommendations, Assessment, and Evaluation).

    PDF File (Adobe PDF File), 103 KB
    ‎
    Multimedia Appendix 4

    PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) 2020 Checklist.

    PDF File (Adobe PDF File), 145 KB
    ‎
    Multimedia Appendix 5

    Characteristics of studies included in the systematic review.

    PDF File (Adobe PDF File), 301 KB
    ‎
    Multimedia Appendix 6

    Supplemental information of the studies for the systematic review.

    PDF File (Adobe PDF File), 250 KB
    ‎
    Multimedia Appendix 7

    Supplemental information on the sensitivity analysis.

    PDF File (Adobe PDF File), 71 KB
    ‎
    Multimedia Appendix 8

    Funnel plots for depression and anxiety.

    PDF File (Adobe PDF File), 237 KB
    1. GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry 2022 Feb;9(2):137-150 [FREE Full text] [CrossRef] [Medline]
    2. Depression and other common mental disorders: global health estimates. World Health Organization. 2017.   URL: https://apps.who.int/iris/bitstream/handle/10665/254610/W?sequence=1 [accessed 2023-01-19]
    3. Mental health atlas 2014. World Health Organization. 2015.   URL: https://apps.who.int/iris/handle/10665/178879 [accessed 2022-07-08]
    4. Chisholm D, Sweeny K, Sheehan P, Rasmussen B, Smit F, Cuijpers P, et al. Scaling-up treatment of depression and anxiety: a global return on investment analysis. Lancet Psychiatry 2016 May;3(5):415-424 [FREE Full text] [CrossRef] [Medline]
    5. Pathare S, Brazinova A, Levav I. Care gap: a comprehensive measure to quantify unmet needs in mental health. Epidemiol Psychiatr Sci 2018 Oct;27(5):463-467 [FREE Full text] [CrossRef] [Medline]
    6. Fit Mind, Fit Job: From Evidence to Practice in Mental Health and Work. Organisation for Economic Co-operation and Development. Paris, France: OECD Publishing; 2016.   URL: https://read.oecd-ilibrary.org/employment/fit-mind-fit-job_9789264228283-en#page1 [accessed 2022-07-08]
    7. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: a meta-analysis of randomized controlled trials. World Psychiatry 2017 Oct;16(3):287-298 [FREE Full text] [CrossRef] [Medline]
    8. Karyotaki E, Efthimiou O, Miguel C, Bermpohl FM, Furukawa TA, Cuijpers P, Individual Patient Data Meta-Analyses for Depression (IPDMA-DE) Collaboration, et al. Internet-based cognitive behavioral therapy for depression: a systematic review and individual patient data network meta-analysis. JAMA Psychiatry 2021 Apr 01;78(4):361-371 [FREE Full text] [CrossRef] [Medline]
    9. Cross SP, Karin E, Staples LG, Bisby MA, Ryan K, Duke G, et al. Factors associated with treatment uptake, completion, and subsequent symptom improvement in a national digital mental health service. Internet Interv 2022 Mar;27:100506 [FREE Full text] [CrossRef] [Medline]
    10. Kaveladze BT, Wasil AR, Bunyi JB, Ramirez V, Schueller SM. User experience, engagement, and popularity in mental health apps: secondary analysis of app analytics and expert app reviews. JMIR Hum Factors 2022 Jan 31;9(1):e30766 [FREE Full text] [CrossRef] [Medline]
    11. Povey J, Sweet M, Nagel T, Lowell A, Shand F, Vigona J, et al. Determining priorities in the aboriginal and islander mental health initiative for youth app second phase participatory design project: qualitative study and narrative literature review. JMIR Form Res 2022 Feb 18;6(2):e28342 [FREE Full text] [CrossRef] [Medline]
    12. Gerhards SA, de Graaf LE, Jacobs LE, Severens JL, Huibers MJ, Arntz A, et al. Economic evaluation of online computerised cognitive-behavioural therapy without support for depression in primary care: randomised trial. Br J Psychiatry 2010 Apr;196(4):310-318. [CrossRef] [Medline]
    13. Hedman E, Andersson E, Ljótsson B, Andersson G, Rück C, Lindefors N. Cost-effectiveness of internet-based cognitive behavior therapy vs. cognitive behavioral group therapy for social anxiety disorder: results from a randomized controlled trial. Behav Res Ther 2011 Nov;49(11):729-736. [CrossRef] [Medline]
    14. Carswell K, Harper-Shehadeh M, Watts S, Van't Hof E, Abi Ramia J, Heim E, et al. Step-by-step: a new WHO digital mental health intervention for depression. Mhealth 2018 Aug 13;4:34 [FREE Full text] [CrossRef] [Medline]
    15. Bergström A, Fottrell E, Hopkins H, Lloyd D, Stevenson O, Willats P, et al. mHealth: can mobile technology improve health in low-and middle-income countries? University College London. 2015 Jul.   URL: https://www.ucl.ac.uk/public-policy/sites/public-policy/files/migrated-files/mhealth_briefing.pdf [accessed 2022-07-15]
    16. Mobile for Development: The State of Mobile Internet Connectivity Report 2019. GSM Association. 2019 Jul 16.   URL: https:/​/www.​gsma.com/​mobilefordevelopment/​resources/​the-state-of-mobile-internet-connectivity-report-2019/​#targetText=The [accessed 2022-07-15]
    17. Burki T. Developing countries in the digital revolution. Lancet 2018 Feb 03;391(10119):417. [CrossRef] [Medline]
    18. Stephani V, Opoku D, Quentin W. A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries. BMC Public Health 2016 Jul 15;16:572 [FREE Full text] [CrossRef] [Medline]
    19. Kaonga NN, Morgan J. Common themes and emerging trends for the use of technology to support mental health and psychosocial well-being in limited resource settings: a review of the literature. Psychiatry Res 2019 Nov;281:112594. [CrossRef] [Medline]
    20. Naslund JA, Aschbrenner KA, Araya R, Marsch LA, Unützer J, Patel V, et al. Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature. Lancet Psychiatry 2017 Jun;4(6):486-500 [FREE Full text] [CrossRef] [Medline]
    21. Jiménez-Molina Á, Franco P, Martínez V, Martínez P, Rojas G, Araya R. Internet-based interventions for the prevention and treatment of mental disorders in Latin America: a scoping review. Front Psychiatry 2019 Sep 13;10:664 [FREE Full text] [CrossRef] [Medline]
    22. Fu Z, Burger H, Arjadi R, Bockting CL. Effectiveness of digital psychological interventions for mental health problems in low-income and middle-income countries: a systematic review and meta-analysis. Lancet Psychiatry 2020 Oct;7(10):851-864 [FREE Full text] [CrossRef] [Medline]
    23. Grist R, Porter J, Stallard P. Mental health mobile apps for preadolescents and adolescents: a systematic review. J Med Internet Res 2017 May 25;19(5):e176 [FREE Full text] [CrossRef] [Medline]
    24. Clarke AM, Kuosmanen T, Barry MM. A systematic review of online youth mental health promotion and prevention interventions. J Youth Adolesc 2015 Jan;44(1):90-113. [CrossRef] [Medline]
    25. Dragioti E, Li H, Tsitsas G, Lee KH, Choi J, Kim J, et al. A large-scale meta-analytic atlas of mental health problems prevalence during the COVID-19 early pandemic. J Med Virol 2022 May;94(5):1935-1949 [FREE Full text] [CrossRef] [Medline]
    26. Arjadi R, Nauta MH, Scholte WF, Hollon SD, Chowdhary N, Suryani AO, et al. Internet-based behavioural activation with lay counsellor support versus online minimal psychoeducation without support for treatment of depression: a randomised controlled trial in Indonesia. Lancet Psychiatry 2018 Sep;5(9):707-716. [CrossRef] [Medline]
    27. Abbasi H, Saqib M, Jouhar R, Lal A, Ahmed N, Ahmed MA, et al. The efficacy of little lovely dentist, dental song, and tell-show-do techniques in alleviating dental anxiety in paediatric patients: a clinical trial. Biomed Res Int 2021 May 23;2021:1119710 [FREE Full text] [CrossRef] [Medline]
    28. Adewuya AO, Momodu O, Olibamoyo O, Adegbaju A, Adesoji O, Adegbokun A. The effectiveness and acceptability of mobile telephone adherence support for management of depression in the Mental Health in Primary Care (MeHPriC) project, Lagos, Nigeria: a pilot cluster randomised controlled trial. J Affect Disord 2019 Jun 15;253:118-125. [CrossRef] [Medline]
    29. Ahorsu DK, Lin CY, Imani V, Carlbring P, Nygårdh A, Broström A, et al. Testing an app-based intervention to improve insomnia in patients with epilepsy: a randomized controlled trial. Epilepsy Behav 2020 Nov;112:107371. [CrossRef] [Medline]
    30. Alessi J, de Oliveira GB, Franco DW, Becker AS, Knijnik CP, Kobe GL, et al. Telehealth strategy to mitigate the negative psychological impact of the COVID-19 pandemic on type 2 diabetes: a randomized controlled trial. Acta Diabetol 2021 Jul;58(7):899-909 [FREE Full text] [CrossRef] [Medline]
    31. Araya R, Menezes PR, Claro HG, Brandt LR, Daley KL, Quayle J, et al. Effect of a digital intervention on depressive symptoms in patients with comorbid hypertension or diabetes in Brazil and Peru: two randomized clinical trials. JAMA 2021 May 11;325(18):1852-1862 [FREE Full text] [CrossRef] [Medline]
    32. Asadzadeh L, Jafari E, Kharaghani R, Taremian F. Effectiveness of midwife-led brief counseling intervention on post-traumatic stress disorder, depression, and anxiety symptoms of women experiencing a traumatic childbirth: a randomized controlled trial. BMC Pregnancy Childbirth 2020 Mar 06;20(1):142 [FREE Full text] [CrossRef] [Medline]
    33. Baruah U, Varghese M, Loganathan S, Mehta KM, Gallagher-Thompson D, Zandi D, et al. Feasibility and preliminary effectiveness of an online training and support program for caregivers of people with dementia in India: a randomized controlled trial. Int J Geriatr Psychiatry 2021 Apr;36(4):606-617. [CrossRef] [Medline]
    34. Byonanebye DM, Nabaggala MS, Naggirinya AB, Lamorde M, Oseku E, King R, et al. An interactive voice response software to improve the quality of life of people living with HIV in Uganda: randomized controlled trial. JMIR Mhealth Uhealth 2021 Feb 11;9(2):e22229 [FREE Full text] [CrossRef] [Medline]
    35. Chan KL, Leung WC, Tiwari A, Or KL, Ip P. Using smartphone-based psychoeducation to reduce postnatal depression among first-time mothers: randomized controlled trial. JMIR Mhealth Uhealth 2019 May 14;7(5):e12794 [FREE Full text] [CrossRef] [Medline]
    36. Chavooshi B, Mohammadkhani P, Dolatshahee B. Telemedicine vs. in-person delivery of intensive short-term dynamic psychotherapy for patients with medically unexplained pain: a 12-month randomized, controlled trial. J Telemed Telecare 2017 Jan;23(1):133-141. [CrossRef] [Medline]
    37. Chavooshi B, Mohammadkhani P, Dolatshahi B. A randomized double-blind controlled trial comparing Davanloo intensive short-term dynamic psychotherapy as internet-delivered vs treatment as usual for medically unexplained pain: a 6-month pilot study. Psychosomatics 2016;57(3):292-300. [CrossRef] [Medline]
    38. Chiang VC, Lee RL, Ho MF, Leung CK, Tang PY, Wong SW, et al. Fulfilling the psychological and information need of the family members of critically ill patients using interactive mobile technology: a randomised controlled trial. Intensive Crit Care Nurs 2017 Aug;41:77-83. [CrossRef] [Medline]
    39. Ciuca AM, Berger T, Crişan LG, Miclea M. Internet-based treatment for panic disorder: a three-arm randomized controlled trial comparing guided (via real-time video sessions) with unguided self-help treatment and a waitlist control. PAXPD study results. J Anxiety Disord 2018 May;56:43-55. [CrossRef] [Medline]
    40. Constant D, de Tolly K, Harries J, Myer L. Mobile phone messages to provide support to women during the home phase of medical abortion in South Africa: a randomised controlled trial. Contraception 2014 Sep;90(3):226-233. [CrossRef] [Medline]
    41. Craveiro MA, Caldeira CL. Influence of an audiovisual resource on the preoperative anxiety of adult endodontic patients: a randomized controlled clinical trial. J Endod 2020 Jul;46(7):909-914. [CrossRef] [Medline]
    42. Cumino DO, Vieira JE, Lima LC, Stievano LP, Silva RA, Mathias LA. Smartphone-based behavioural intervention alleviates children's anxiety during anaesthesia induction: a randomised controlled trial. Eur J Anaesthesiol 2017 Mar;34(3):169-175. [CrossRef] [Medline]
    43. Dığın F, Özkan ZK, Şahin A. Effect of sending SMS, which reminds about the intake of medication, on reducing postoperative anxiety in patients undergoing cataract surgery: a randomized controlled study. J Perianesth Nurs 2022 Feb;37(1):75-79. [CrossRef] [Medline]
    44. Duan YP, Liang W, Guo L, Wienert J, Si GY, Lippke S. Evaluation of a web-based intervention for multiple health behavior changes in patients with coronary heart disease in home-based rehabilitation: pilot randomized controlled trial. J Med Internet Res 2018 Nov 19;20(11):e12052 [FREE Full text] [CrossRef] [Medline]
    45. Duan YP, Wienert J, Hu C, Si GY, Lippke S. Web-based intervention for physical activity and fruit and vegetable intake among Chinese university students: a randomized controlled trial. J Med Internet Res 2017 Apr 10;19(4):e106 [FREE Full text] [CrossRef] [Medline]
    46. Duan Y, Liang W, Wang Y, Lippke S, Lin Z, Shang B, et al. The effectiveness of sequentially delivered web-based interventions on promoting physical activity and fruit-vegetable consumption among Chinese college students: mixed methods study. J Med Internet Res 2022 Jan 26;24(1):e30566 [FREE Full text] [CrossRef] [Medline]
    47. Duruturk N, Özköslü MA. Effect of tele-rehabilitation on glucose control, exercise capacity, physical fitness, muscle strength and psychosocial status in patients with type 2 diabetes: a double blind randomized controlled trial. Prim Care Diabetes 2019 Dec;13(6):542-548. [CrossRef] [Medline]
    48. Erdogan B, Aytekin Ozdemir A. The effect of three different methods on venipuncture pain and anxiety in children: distraction cards, virtual reality, and Buzzy® (randomized controlled trial). J Pediatr Nurs 2021;58:e54-e62. [CrossRef] [Medline]
    49. Özalp Gerçeker G, Karayağız Muslu G, Yardimci F. Children's postoperative symptoms at home through nurse-led telephone counseling and its effects on parents' anxiety: a randomized controlled trial. J Spec Pediatr Nurs 2016 Oct;21(4):189-199. [CrossRef] [Medline]
    50. Ghanbari E, Yektatalab S, Mehrabi M. Effects of psychoeducational interventions using mobile apps and mobile-based online group discussions on anxiety and self-esteem in women with breast cancer: randomized controlled trial. JMIR Mhealth Uhealth 2021 May 18;9(5):e19262 [FREE Full text] [CrossRef] [Medline]
    51. Ghawadra SF, Lim Abdullah K, Choo WY, Danaee M, Phang CK. The effect of mindfulness-based training on stress, anxiety, depression and job satisfaction among ward nurses: a randomized control trial. J Nurs Manag 2020 Jul;28(5):1088-1097. [CrossRef] [Medline]
    52. Gu S, Ping J, Xu M, Zhou Y. TikTok browsing for anxiety relief in the preoperative period: a randomized clinical trial. Complement Ther Med 2021 Aug;60:102749 [FREE Full text] [CrossRef] [Medline]
    53. Guo L, Zhang J, Mu L, Ye Z. Preventing postpartum depression with mindful self-compassion intervention: a randomized control study. J Nerv Ment Dis 2020 Feb;208(2):101-107. [CrossRef] [Medline]
    54. Guo Y, Hong YA, Cai W, Li L, Hao Y, Qiao J, et al. Effect of a WeChat-based intervention (Run4Love) on depressive symptoms among people living with HIV in China: a randomized controlled trial. J Med Internet Res 2020 Feb 09;22(2):e16715 [FREE Full text] [CrossRef] [Medline]
    55. Hamedi V, Hamid N, Beshlideh K, Marashi SA, Hashemi Sheikh Shabani SE. Effectiveness of conventional cognitive-behavioral therapy and its computerized version on reduction in pain intensity, depression, anger, and anxiety in children with cancer: a randomized, controlled trial. Iran J Psychiatry Behav Sci 2020 Dec 5;14(4):e83110. [CrossRef]
    56. Hatipoglu Z, Gulec E, Lafli D, Ozcengiz D. Effects of auditory and audiovisual presentations on anxiety and behavioral changes in children undergoing elective surgery. Niger J Clin Pract 2018 Jun;21(6):788-794 [FREE Full text] [CrossRef] [Medline]
    57. Heim E, Ramia JA, Hana RA, Burchert S, Carswell K, Cornelisz I, et al. Step-by-step: feasibility randomised controlled trial of a mobile-based intervention for depression among populations affected by adversity in Lebanon. Internet Interv 2021 Apr;24:100380 [FREE Full text] [CrossRef] [Medline]
    58. Hua Y, Qiu R, Yao W, Zhang Q, Chen X. The effect of virtual reality distraction on pain relief during dressing changes in children with chronic wounds on lower limbs. Pain Manage Nursing 2015 Oct 18;16(5):685-691 [FREE Full text] [CrossRef]
    59. Huang LX. Effect of WeChat platform-based nursing care on negative emotion and quality of life in women with chronic hepatitis B during late pregnancy. World Chinese J Dig 2018;26:126-130. [CrossRef]
    60. Huang L, Shen Q, Fang Q, Zheng X. Effects of internet-based support program on parenting outcomes for primiparous women: a pilot study. Int J Environ Res Public Health 2021 Apr 21;18(9):4402 [FREE Full text] [CrossRef] [Medline]
    61. Imamura K, Tran TT, Nguyen HT, Sasaki N, Kuribayashi K, Sakuraya A, et al. Effect of smartphone-based stress management programs on depression and anxiety of hospital nurses in Vietnam: a three-arm randomized controlled trial. Sci Rep 2021 May 31;11(1):11353 [FREE Full text] [CrossRef] [Medline]
    62. İnangil D, Şendir M, Büyükyılmaz F. Efficacy of cartoon viewing devices during phlebotomy in children: a randomized controlled trial. J Perianesth Nurs 2020 Aug;35(4):407-412. [CrossRef] [Medline]
    63. Jannati N, Mazhari S, Ahmadian L, Mirzaee M. Effectiveness of an app-based cognitive behavioral therapy program for postpartum depression in primary care: a randomized controlled trial. Int J Med Inform 2020 Sep;141:104145. [CrossRef] [Medline]
    64. Jareethum R, Titapant V, Chantra T, Sommai V, Chuenwattana P, Jirawan C. Satisfaction of healthy pregnant women receiving short message service via mobile phone for prenatal support: a randomized controlled trial. J Med Assoc Thai 2008 Apr;91(4):458-463. [Medline]
    65. Khushnood K, Altaf S, Sultan N, Ali Awan MM, Mehmood R, Qureshi S. Role Wii Fit exer-games in improving balance confidence and quality of life in elderly population. J Pak Med Assoc 2021 Sep;71(9):2130-2134 [FREE Full text] [CrossRef] [Medline]
    66. Korkmaz S, Iyigun E, Tastan S. An evaluation of the influence of web-based patient education on the anxiety and life quality of patients who have undergone mammaplasty: a randomized controlled study. J Cancer Educ 2020 Oct;35(5):912-922. [CrossRef] [Medline]
    67. Li J, Mo PK, Kahler CW, Lau JT. A three-arm randomised controlled trial to evaluate the efficacy of a positive psychology and social networking intervention in promoting mental health among HIV-infected men who have sex with men in China. Epidemiol Psychiatr Sci 2021 Mar 19;30:e24 [FREE Full text] [CrossRef] [Medline]
    68. Liu H, Peng H, Song X, Xu C, Zhang M. Using AI chatbots to provide self-help depression interventions for university students: a randomized trial of effectiveness. Internet Interv 2022 Mar;27:100495 [FREE Full text] [CrossRef] [Medline]
    69. Liu Z, Qiao D, Xu Y, Zhao W, Yang Y, Wen D, et al. The efficacy of computerized cognitive behavioral therapy for depressive and anxiety symptoms in patients with COVID-19: randomized controlled trial. J Med Internet Res 2021 May 14;23(5):e26883 [FREE Full text] [CrossRef] [Medline]
    70. Luo Y, Xia W, Cheung AT, Ho LL, Zhang J, Xie J, et al. Effectiveness of a mobile device-based resilience training program in reducing depressive symptoms and enhancing resilience and quality of life in parents of children with cancer: randomized controlled trial. J Med Internet Res 2021 Nov 29;23(11):e27639 [FREE Full text] [CrossRef] [Medline]
    71. Luo YJ, Jackson T, Stice E, Chen H. Effectiveness of an internet dissonance-based eating disorder prevention intervention among body-dissatisfied young Chinese women. Behav Ther 2021 Jan;52(1):221-233. [CrossRef] [Medline]
    72. Rajabi Majd N, Broström A, Ulander M, Lin CY, Griffiths MD, Imani V, et al. Efficacy of a theory-based cognitive behavioral technique app-based intervention for patients with insomnia: randomized controlled trial. J Med Internet Res 2020 Apr 01;22(4):e15841 [FREE Full text] [CrossRef] [Medline]
    73. Mak WW, Chan AT, Cheung EY, Lin CL, Ngai KC. Enhancing web-based mindfulness training for mental health promotion with the health action process approach: randomized controlled trial. J Med Internet Res 2015 Jan 19;17(1):e8 [FREE Full text] [CrossRef] [Medline]
    74. Mehri M, Chehrzad MM, Maleki M, Kousha M, Akhlaghi E, Mardani A. The effect of behavioral parent training of children with attention deficit hyperactivity disorder on parents’ mental health. Neurol Psychiatry Brain Res 2020 Sep;37:53-59. [CrossRef]
    75. Shamshiri Milani H, Azargashb E, Beyraghi N, Defaie S, Asbaghi T. Effect of telephone-based support on postpartum depression: a randomized controlled trial. Int J Fertil Steril 2015;9(2):247-253 [FREE Full text] [CrossRef] [Medline]
    76. Moeini B, Bashirian S, Soltanian AR, Ghaleiha A, Taheri M. Examining the effectiveness of a web-based intervention for depressive symptoms in female adolescents: applying social cognitive theory. J Res Health Sci 2019 Aug 19;19(3):e00454 [FREE Full text] [Medline]
    77. Mogoaşe C, Brăilean A, David D. Can concreteness training alone reduce depressive symptoms? A randomized pilot study using an internet-delivered protocol. Cogn Ther Res 2013 Jan 3;37(4):704-712. [CrossRef]
    78. Newman MG, Kanuri N, Rackoff GN, Jacobson NC, Bell MJ, Taylor CB. A randomized controlled feasibility trial of internet-delivered guided self-help for generalized anxiety disorder (GAD) among university students in India. Psychotherapy (Chic) 2021 Dec;58(4):591-601 [FREE Full text] [CrossRef] [Medline]
    79. Ngai FW, Wong PW, Leung KY, Chau PH, Chung KF. The effect of telephone-based cognitive-behavioral therapy on postnatal depression: a randomized controlled trial. Psychother Psychosom 2015;84(5):294-303. [CrossRef] [Medline]
    80. Nobakht Z, Rassafiani M, Hosseini SA, Hosseinzadeh S. A web-based daily care training to improve the quality of life of mothers of children with cerebral palsy: a randomized controlled trial. Res Dev Disabil 2020 Oct;105:103731 [FREE Full text] [CrossRef] [Medline]
    81. Ofoegbu TO, Asogwa U, Otu MS, Ibenegbu C, Muhammed A, Eze B. Efficacy of guided internet-assisted intervention on depression reduction among educational technology students of Nigerian universities. Medicine (Baltimore) 2020 Feb;99(6):e18774 [FREE Full text] [CrossRef] [Medline]
    82. Osborn TL, Rodriguez M, Wasil AR, Venturo-Conerly KE, Gan J, Alemu RG, et al. Single-session digital intervention for adolescent depression, anxiety, and well-being: outcomes of a randomized controlled trial with Kenyan adolescents. J Consult Clin Psychol 2020 Jul;88(7):657-668. [CrossRef] [Medline]
    83. Pakrad F, Ahmadi F, Grace SL, Oshvandi K, Kazemnejad A. Traditional vs extended hybrid cardiac rehabilitation based on the continuous care model for patients who have undergone coronary artery bypass surgery in a middle-income country: a randomized controlled trial. Arch Phys Med Rehabil 2021 Nov;102(11):2091-101.e3. [CrossRef] [Medline]
    84. Peng X, Su Y, Hu Z, Sun X, Li X, Dolansky M, et al. Home-based telehealth exercise training program in Chinese patients with heart failure: a randomized controlled trial. Medicine (Baltimore) 2018 Aug;97(35):e12069 [FREE Full text] [CrossRef] [Medline]
    85. Esmaeili Rad M, Ahmadi F. A new method to measure and decrease the online social networking addiction. Asia Pac Psychiatry 2018 Dec;10(4):e12330. [CrossRef] [Medline]
    86. Rahimi R, Hasanpour S, Mirghafourvand M, Esmaeilpour K. Effect of Hope-oriented group counseling on mental health of infertile women with failed IVF cycles: a randomized controlled trial. BMC Psychiatry 2021 Jun 02;21(1):286 [FREE Full text] [CrossRef] [Medline]
    87. Salamanca-Sanabria A, Richards D, Timulak L, Connell S, Mojica Perilla M, Parra-Villa Y, et al. A culturally adapted cognitive behavioral internet-delivered intervention for depressive symptoms: randomized controlled trial. JMIR Ment Health 2020 Jan 31;7(1):e13392 [FREE Full text] [CrossRef] [Medline]
    88. Shahdosti H, Mazlom SR, Vaghee S, Amini S. Evaluating the effect of planned online video visitations on anxiety and depression of patients at open heart intensive care unit: a randomized controlled trial. Iran Red Crescent Med J 2020 Aug 04;22(7):e102578 [FREE Full text] [CrossRef]
    89. Sivrikaya EC, Yilmaz O, Sivrikaya P. Dentist-patient communication on dental anxiety using the social media: a randomized controlled trial. Scand J Psychol 2021 Dec;62(6):780-786. [CrossRef] [Medline]
    90. Song Y, Xie X, Chen Y, Wang Y, Yang H, Nie A, et al. The effects of WeChat-based educational intervention in patients with ankylosing spondylitis: a randomized controlled trail. Arthritis Res Ther 2021 Mar 04;23(1):72 [FREE Full text] [CrossRef] [Medline]
    91. Srivastava P, Mehta M, Sagar R, Ambekar A. Smartteen- a computer assisted cognitive behavior therapy for Indian adolescents with depression- a pilot study. Asian J Psychiatr 2020 Apr;50:101970. [CrossRef] [Medline]
    92. Stamm B, Girardon-Perlini NM, Pasqualoto AS, Beuter M, Magnago TS. Telephone intervention for anxiety management in oncology patients: a randomized clinical trial. Acta Paul Enferm 2018 Mar;31(2):137-143 [FREE Full text] [CrossRef]
    93. Su JJ, Yu DS. Effects of a nurse-led eHealth cardiac rehabilitation programme on health outcomes of patients with coronary heart disease: a randomised controlled trial. Int J Nurs Stud 2021 Oct;122:104040. [CrossRef] [Medline]
    94. Taleban R, Zamani A, Moafi M, Jiryaee N, Khadivi R. Applications of text messaging, and bibliotherapy for treatment of patients affected by depressive symptoms. Int J Prev Med 2016 Mar 1;7:46 [FREE Full text] [CrossRef] [Medline]
    95. Tam CC, Li X, Benotsch EG, Lin D. A resilience-based intervention programme to enhance psychological well-being and protective factors for rural-to-urban migrant children in China. Appl Psychol Health Well Being 2020 Mar;12(1):53-76. [CrossRef] [Medline]
    96. Thitipitchayanant K, Somrongthong R, Kumar R, Kanchanakharn N. Effectiveness of self-empowerment-affirmation-relaxation (Self-EAR) program for postpartum blues mothers: a randomize controlled trial. Pak J Med Sci 2018;34(6):1488-1493 [FREE Full text] [CrossRef] [Medline]
    97. Tiburcio M, Lara MA, Martínez N, Fernández M, Aguilar A. Web-based intervention to reduce substance abuse and depression: a three arm randomized trial in Mexico. Subst Use Misuse 2018 Nov 10;53(13):2220-2231. [CrossRef] [Medline]
    98. Tol WA, Leku MR, Lakin DP, Carswell K, Augustinavicius J, Adaku A, et al. Guided self-help to reduce psychological distress in South Sudanese female refugees in Uganda: a cluster randomised trial. Lancet Glob Health 2020 Feb;8(2):e254-e263 [FREE Full text] [CrossRef] [Medline]
    99. Torabizadeh C, Rousta S, Gholamzadeh S, Kojouri J, Jamali K, Parvizi MM. Efficacy of education delivery through multimedia and text messaging on the psychological parameters of patients scheduled for coronary angiography: a single-blind randomized controlled clinical trial. BMC Cardiovasc Disord 2021 Jan 04;21(1):3 [FREE Full text] [CrossRef] [Medline]
    100. Tulbure BT, Andersson G, Sălăgean N, Pearce M, Koenig HG. Religious versus conventional internet-based cognitive behavioral therapy for depression. J Relig Health 2018 Oct;57(5):1634-1648. [CrossRef] [Medline]
    101. Tulbure BT, Szentagotai A, David O, Ștefan S, Månsson KN, David D, et al. Internet-delivered cognitive-behavioral therapy for social anxiety disorder in Romania: a randomized controlled trial. PLoS One 2015 May 4;10(5):e0123997 [FREE Full text] [CrossRef] [Medline]
    102. Wang H, Zhao Q, Mu W, Rodriguez M, Qian M, Berger T. The effect of shame on patients with social anxiety disorder in internet-based cognitive behavioral therapy: randomized controlled trial. JMIR Ment Health 2020 Jul 20;7(7):e15797 [FREE Full text] [CrossRef] [Medline]
    103. Wantanakorn P, Harintajinda S, Chuthapisith J, Anurathapan U, Rattanatamrong P. A new mobile application to reduce anxiety in pediatric patients before bone marrow aspiration procedures. Hosp Pediatr 2018 Oct;8(10):643-650. [CrossRef] [Medline]
    104. Wei N, Huang B, Lu SJ, Hu JB, Zhou XY, Hu CC, et al. Efficacy of internet-based integrated intervention on depression and anxiety symptoms in patients with COVID-19. J Zhejiang Univ Sci B 2020 May;21(5):400-404 [FREE Full text] [CrossRef] [Medline]
    105. Xia L. The effects of continuous care model of information-based hospital-family integration on colostomy patients: a randomized controlled trial. J Cancer Educ 2020 Apr;35(2):301-311. [CrossRef] [Medline]
    106. Yan Z, Liu F, Lin XP, Chen J, Huang QL, Chen SL, et al. WeChat-based remote follow-up management alleviates the home care burden and anxiety of parents of premature infants: randomized controlled study. Child Care Health Dev 2022 Jul;48(4):651-657. [CrossRef] [Medline]
    107. Yang B, Liu JF, Xie WP, Cao H, Chen Q. The effects of WeChat follow-up management to improve the parents' mental status and the quality of life of premature newborns with patent ductus arteriosus. J Cardiothorac Surg 2021 Aug 21;16(1):235 [FREE Full text] [CrossRef] [Medline]
    108. Yang L, Wang X, Cui X. Patients' intensive telephone-based care program reduces depression in coronary artery disease patients and may contribute to favorable overall survival by decreasing depression. J Cardiovasc Nurs 2019;34(3):236-243. [CrossRef] [Medline]
    109. Yang M, Jia G, Sun S, Ye C, Zhang R, Yu X. Effects of an online mindfulness intervention focusing on attention monitoring and acceptance in pregnant women: a randomized controlled trial. J Midwifery Womens Health 2019 Jan;64(1):68-77. [CrossRef] [Medline]
    110. Yardımcı T, Mert H. Web-based intervention to improve implantable cardioverter defibrillator patients' shock-related anxiety and quality of life: a randomized controlled trial. Clin Nurs Res 2019 Feb;28(2):150-164. [CrossRef] [Medline]
    111. Yeung A, Wang F, Feng F, Zhang J, Cooper A, Hong L, et al. Outcomes of an online computerized cognitive behavioral treatment program for treating Chinese patients with depression: a pilot study. Asian J Psychiatr 2018 Dec;38:102-107. [CrossRef] [Medline]
    112. Zengin M, Başoğul C, Yayan EH. The effect of online solution-focused support program on parents with high level of anxiety in the COVID-19 pandemic: a randomised controlled study. Int J Clin Pract 2021 Dec;75(12):e14839 [FREE Full text] [CrossRef] [Medline]
    113. Zhang QL, Lei YQ, Liu JF, Cao H, Chen Q. Using telemedicine to improve the quality of life of parents of infants with CHD surgery after discharge. Int J Qual Health Care 2021 Sep 25;33(3):mzab133. [CrossRef] [Medline]
    114. Zhang QL, Xu N, Huang ST, Cao H, Chen Q. WeChat-assisted pre-operative health education improves the quality of life of parents of children with ventricular septal defects: a prospective randomised controlled study. J Paediatr Child Health 2021 May;57(5):664-669. [CrossRef] [Medline]
    115. Zhang X, Lin P, Sun J, Sun Y, Shao D, Cao D, et al. Prenatal stress self-help mindfulness intervention via social media: a randomized controlled trial. J Ment Health (forthcoming) 2021 Jul 15:1-10. [CrossRef] [Medline]
    116. Zhang Y, Zhang B, Gan L, Ke L, Fu Y, Di Q, et al. Effects of online bodyweight high-intensity interval training intervention and health education on the mental health and cognition of sedentary young females. Int J Environ Res Public Health 2021 Jan 03;18(1):302 [FREE Full text] [CrossRef] [Medline]
    117. Zhao M, You Y, Chen S, Li L, Du X, Wang Y. Effects of a web-based parent-child physical activity program on mental health in parents of children with ASD. Int J Environ Res Public Health 2021 Dec 07;18(24):12913 [FREE Full text] [CrossRef] [Medline]
    118. Zheng Y, Wang W, Zhong Y, Wu F, Zhu Z, Tham YC, et al. A peer-to-peer live-streaming intervention for children during COVID-19 homeschooling to promote physical activity and reduce anxiety and eye strain: cluster randomized controlled trial. J Med Internet Res 2021 Apr 30;23(4):e24316 [FREE Full text] [CrossRef] [Medline]
    119. Zhianfar L, Nadrian H, Asghari Jafarabadi M, Espahbodi F, Shaghaghi A. Effectiveness of a multifaceted educational intervention to enhance therapeutic regimen adherence and quality of life amongst Iranian hemodialysis patients: a randomized controlled trial (MEITRA study). J Multidiscip Healthc 2020 Apr 14;13:361-372 [FREE Full text] [CrossRef] [Medline]
    120. Zhou K, Li J, Li X. Effects of cyclic adjustment training delivered via a mobile device on psychological resilience, depression, and anxiety in Chinese post-surgical breast cancer patients. Breast Cancer Res Treat 2019 Nov;178(1):95-103. [CrossRef] [Medline]
    121. Zhuang JX. Psychosomatic care plus WeChat communication relaxation therapy in patients with functional dyspepsia. World Chin J Dig 2017 May;25(13):1191 [FREE Full text] [CrossRef]
    122. Hedges LV. Distribution theory for Glass's estimator of effect size and related estimators. J Educ Stat 1981;6(2):107-128. [CrossRef]
    123. Harrer M, Cuijpers P, Furukawa TA, Ebert DD. Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL, USA: Chapman and Hall; 2021.
    124. Serrano-Ripoll MJ, Zamanillo-Campos R, Fiol-DeRoque MA, Castro A, Ricci-Cabello I. Impact of smartphone app-based psychological interventions for reducing depressive symptoms in people with depression: systematic literature review and meta-analysis of randomized controlled trials. JMIR Mhealth Uhealth 2022 Jan 27;10(1):e29621 [FREE Full text] [CrossRef] [Medline]
    125. Wang Y, Lin Y, Chen J, Wang C, Hu R, Wu Y. Effects of internet-based psycho-educational interventions on mental health and quality of life among cancer patients: a systematic review and meta-analysis. Support Care Cancer 2020 Jun;28(6):2541-2552. [CrossRef] [Medline]
    126. Barak A, Hen L, Boniel-Nissim M, Shapira N. A comprehensive review and a meta-analysis of the effectiveness of internet-based psychotherapeutic interventions. In: Database of Abstracts of Reviews of Effects (DARE): Quality-Assessed Reviews. York, UK: Centre for Reviews and Dissemination; 1995.

    ‎
    CBT: cognitive behavioral therapy
    DMH: digital mental health
    GRADE: Grading of Recommendations Assessment, Development, and Evaluation
    HIC: high-income country
    LMIC: low- and middle-income country
    PRISMA: Preferred Reporting Item for Systematic Reviews and Meta-Analyses
    RCT: randomized controlled trial

    Edited by J Torous; submitted 29.09.22; peer-reviewed by P Watson, A Daros; comments to author 14.01.23; revised version received 27.01.23; accepted 30.01.23; published 20.03.23

    Copyright

    ©Jiyeong Kim, Lois M D Aryee, Heejung Bang, Steffi Prajogo, Yong K Choi, Jeffrey S Hoch, Elizabeth L Prado. Originally published in JMIR Mental Health (https://mental.jmir.org), 20.03.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.