Digital interventions to promote psychological resilience : a systematic review and meta-analysis

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Digital interventions to promote psychological resilience : a systematic review and meta-analysis © 2024 the Authors Published version Schäfer, Sarah K.; von Boros, Lisa; Schaubruch, Lea M.; Kunzler, Angela M.; Lindner, Saskia; Koehler, Friederike; Werner, Tabea; Zappalà, Federico; Helmreich, Isabella; Wessa, Michèle; Lieb, Klaus; Tüscher, Oliver Schäfer, S. K., von Boros, L., Schaubruch, L. M., Kunzler, A. M., Lindner, S., Koehler, F., Werner, T., Zappalà, F., Helmreich, I., Wessa, M., Lieb, K., & Tüscher, O. (2024). Digital interventions to promote psychological resilience : a systematic review and meta-analysis. npj digital medicine, 7, Article 30. https://doi.org/10.1038/s41746-024-01017-8 2024 REVIEW ARTICLE OPEN Digital interventions to promote psychological resilience: a systematic review and meta-analysis Sarah K. Schäfer 1,2 ✉, Lisa von Boros 1 , Lea M. Schaubruch 1 , Angela M. Kunzler 1,3 , Saskia Lindner 4 , Friederike Koehler 1,5,6 , Tabea Werner 1,5 , Federico Zappalà 1 , Isabella Helmreich 1 , Michèle Wessa 1,5 , Klaus Lieb 1,4,8 and Oliver Tüscher 1,4,7,8 Societies are exposed to major challenges at an increasing pace. This underscores the need for preventive measures such as resilience promotion that should be available in time and without access barriers. Our systematic review summarizes evidence on digital resilience interventions, which have the potential to meet these demands. We searched five databases for randomizedcontrolled trials in non-clinical adult populations. Primary outcomes were mental distress, positive mental health, and resilience factors. Multilevel meta-analyses were performed to compare intervention and control groups at post-intervention and follow-up assessments. We identified 101 studies comprising 20,010 participants. Meta-analyses showed small favorable effects on mental distress, SMD =–0.24, 95% CI [–0.31, –0.18], positive mental health, SMD =0.27, 95% CI [0.13, 0.40], and resilience factors, SMD =0.31, 95% CI [0.21, 0.41]. Among middle-aged samples, older age was associated with more beneficial effects at follow-up, and effects were smaller for active control groups. Effects were comparable to those of face-to-face interventions and underline the potential of digital resilience interventions to prepare for future challenges. npj Digital Medicine (2024) 7:30 ; https://doi.org/10.1038/s41746-024-01017-8 INTRODUCTION An increasing number of environmental and socioeconomic challenges and major disruptive events worldwide poses a significant threat to public mental health 1 . Recently, evidence for adverse mental health effects of stressors like the COVID-19 pandemic or armed conflicts has resulted in an increased interest in (psychological) resilience 2–4 . Resilience as an outcome describes the maintenance of stable good mental health or the quick recovery of mental health during or after stressor exposure 5 . However, rather than being a categorical outcome, resilience varies between different domains of life and fluctuates over time 6,7 . Promoting resilience at a population level may help societies to be better prepared for future disruptions 8 . Resilience-promoting interventions describe a heterogeneous category of interventions aiming to promote resilience as an outcome mostly by fostering so-called resilience factors and, less common though, higher-level, neurocognitive resilience mechanisms 9 . Resilience factors are internal and external resources that come into play when coping with various stressors 1 . These factors include dispositional variables such as resilience-promoting traits (e.g., optimism), beliefs (e.g., self-efficacy), and coping strategies (e.g., flexible or active coping) 9,10 , social and cultural factors (e.g., perceived social support, community cohesion) 6 . Recent approaches in resilience research suggest that these resilience factors show substantial interrelations 10 and may converge into a smaller number of higher-level resilience mechanisms (e.g., positive appraisal style 11 ; regulatory flexibility 12 ), which mediate their association with resilient outcomes 11 . Most resiliencepromoting interventions use approaches adapted from psychotherapy (e.g., cognitive-behavioral or mindfulness-based interventions) to enhance these factors with a broad set of exercises 13,14 (e.g., psychoeducation, relaxation, training of cognitive strategies). Previous research provided evidence for small to moderate favorable effects of resilience-promoting interventions in high-risk groups (e.g., healthcare workers, police staff 13,15,16 ), non-clinical (e.g., students 17,18 ) and clinical populations (e.g., diabetes or cancer patients 19,20 ). However, many of those interventions were delivered in face-to-face settings using individual or group trainings. Those interventions may have favorable effects, but at the same time they can only be delivered to a small number of people at a time, require substantial staff and financial resources and cannot be easily tailored to participants’needs, individual time constraints 21 , and demands in low-resource settings 22 . Moreover, especially the COVID-19 pandemic highlighted that stressor exposure itself may stop the availability of in-person interventions resulting in situations where resilience promotion would be of major importance but cannot be delivered 23 . Digital resilience interventions may help to address these shortcomings as they can be delivered to a large number of people at the same time, require a lot less staff and (in the long run) fewer financial resources 21 , making them promising for lowresource settings 24 . Digital resilience interventions may also be tailored to participants’needs and allow for flexible time plans (e.g., shift schedules) 25 . Moreover, digital resilience interventions may still be available when stressors like the pandemic prevent inperson meetings. Thus, developing effective digital resilience interventions might be a key component of preparedness for future pandemics and other types of disruptions and challenges. 1 Leibniz Institute for Resilience Research, Mainz, Germany. 2 Department of Clinical Psychology, Psychotherapy and Diagnostics - Child and Adolescent Psychology and Psychotherapy, Technische Universität Braunschweig, Braunschweig, Germany. 3 Institute for Evidence in Medicine, Medical Center –University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. 4 Department of Psychiatry and Psychotherapy, University Medical Center of Johannes Gutenberg University Mainz, Mainz, Germany. 5 Department of Clinical Psychology and Neuropsychology, Institute for Psychology, Johannes Gutenberg University Mainz, Mainz, Germany. 6 Centre of Excellence in Music, Mind, Body and Brain, University of Jyväskylä, Jyväskylä, Finland. 7 Institute for Molecular Biology, Johannes Gutenberg University Mainz, Mainz, Germany. 8 These authors contributed equally: Klaus Lieb, Oliver Tüscher. ✉email: [email protected] www.nature.com/npjdigitalmed Published in partnership with Seoul National University Bundang Hospital 1234567890():,; Previous systematic reviews combined digital and in-person resilience-promoting interventions 13,15,26 or examined a very small number of studies with highly restrictive inclusion criteria 21,27 (e.g., only studies that employed stand-alone online interventions). Moreover, those reviews primarily examined the effects of digital resilience interventions on self-reported resilience 21,27 . However, most of these measures fail to meet state-of-the-art resilience conceptualizations, which define resilient outcomes rather as a trajectory of stable good mental health in face of stress than a dispositional variable 5 . Thus, following these state-of-the-art approaches, effects on mental distress and positive mental health are even more important than changes in self-reported resilience 9 . The present systematic review and meta-analysis aim at addressing these gaps by applying a broader and more comprehensive definition of resilience-promoting interventions, as used in two recent Cochrane reviews 13,15,28 , also including interventions that build on the resilience concept or aim at enhancing hardiness or growth from stress exposure as related concepts. Moreover, we also examine interventions with blended designs that combine in-person interventions with digital components. In line with recent conceptualizations of resilience 1,5,9 , we examine mental distress and positive mental health as primary outcomes and study resilience factors as secondary outcomes. With this focus on resilience factors, we provide a proof of concept that has not been done in previous reviews on digital resilience interventions 21,27 . Based on a large number of studies, we also examine a broad range of potential moderators including sociodemographic sample characteristics (i.e., age, gender, population type), intervention characteristics (i.e., delivery format, theoretical foundation, availability of guidance, degree of individualization, intervention intensity, availability of in-person components), and aspects of study design (i.e., type of control group). We compare our findings with previous reviews on inperson resilience interventions and derive recommendations for the use of digital resilience interventions to prepare for future major disruptions. RESULTS Search outcomes Our search for primary studies in electronic databases yielded 2309 eligible records, with 590 duplicates being removed. Of 1719 records screened at title/abstract level, 498 were assessed at full text level, of which 49 were identified as eligible. Another 52 eligible records were identified by our search in systematic reviews, references cited in eligible primary studies and personal communication. Taken together, this resulted in 101 eligible primary studies for the quantitative synthesis (see Fig. 1). Study characteristics Supplementary Data 3 presented the characteristic of 101 included studies (comprising 20,010 participants) published between 2007 and 2023, with the vast majority being published from 2015 onwards (83.2%). Most studies were performed in the United States (31 studies, 30.7%), Germany (11 studies, 10.9%), and the United Kingdom (7 studies, 6.9%; see Supplementary Data 3 for all countries). The vast majority of studies was conducted in high-income countries, with only 11 studies (10.9%) being carried out in middle- or low-income countries that are more likely to represent low-resource settings. On average participants were 34.4 years old (SD 9.78; range: 17.9–57.6 years) and 71.5% of the participants were female (range: 0–100%). Thirty-six samples (35.6%) were recruited at their workplaces, 24 studies (23.8%) used student or university samples, 23 studies (22.8%) examined populations with increased stressor exposure in private life (e.g., informal caregivers), seven studies (6.9%) recruited samples from non-clinical military populations (e.g., military members and their partners), and 11 studies (10.9%) reported on not further specified non-clinical populations. Sixty-five studies (64.3%) reported on online interventions, 21 studies (20.8%) employed mobile-based interventions, seven studies (6.9%) used mixed interventions comprising both web- and app-based components, and four studies (4.0%) reported on Fig. 1 PRISMA flowchart. Note. Flowchart according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 77 .n= number of studies/records/reports. S.K. Schäfer et al. 2 npj Digital Medicine (2024) 30 Published in partnership with Seoul National University Bundang Hospital 1234567890():,; interventions using (at least partly) a lab-based delivery via computers. Only four studies (4.0%) used blended delivery formats combining digital and in-person components. Twenty-three studies (22.8%) combined different theoretical concepts for their interventions (e.g., CBT and mindfulness), 22 studies (21.8%) employed solely CBT-based interventions, followed by 17 studies (16.8%) reporting on mindfulness-based interventions, and 11 studies (10.9%) employing interventions that were based on positive psychology. The remaining 28 studies (27.7%) used other theoretical approaches (see Supplementary Data 3), with only three studies (3.0%) employing interventions grounded in specific resilience theories. Intervention duration ranged between 1-session interventions and delivery over 52 weeks (average duration: 6.2 weeks [SD 6.7]). Most interventions were unguided (69 studies; 68.3%), while 32 interventions (31.7%) were at least partly guided by trained lays or professionals. Fifty-eight studies (57.4%) used passive comparators (i.e., waitlist, no intervention), while 16 studies (15.8%) employed low-intensity active comparators (i.e., some kind of control intervention, but of shorter duration and less attention) and 27 studies used high-intensity active comparators (26.7%; i.e., interventions with similar duration and attention). Of the 43 studies reporting follow-up data, 29 studies (67.4%) reported on follow-up assessments 1-to-3 months after the end of the intervention, while 8 studies (18.6%) performed follow-ups between 3 and 6 months, and 6 studies (14.0%) between 6 and 12 months. Risk of bias There was a moderate to high risk of bias (see Fig. 2and Supplementary Data 4). Main flaws (≥20% of some concerns or high risk) across included effect estimates were found for measurement of outcome (post-intervention: 79% some concerns or high risk; follow-up: 55%), randomization (post-intervention: 60%; follow-up: 48%), selection of reported results (post-interven- tion: 56%; follow-up: 47%), missing outcome data (post-interven- tion: 50%; follow-up: 55%), and deviations from intended interventions (post-intervention: 27%; follow-up: 32%). The number of cRCTs was low, thus, the impact of bias from the recruitment of participants was low (post-intervention/follow-up: 3%). Publication bias Neither contour-enhanced funnel plots (see Supplementary Data 5) nor meta-regression models provided evidence for a publication bias at post-intervention assessments for mental distress, QM(1) =0.77, p=0.384, and positive mental health, QM(1) =1.08, p=0.302, while the meta-regression model on resilience factors suggested funnel plot asymmetry, QM(1) =4.52, p=0.039. Also, contour-enhanced funnel plots showed that some effect estimates fell into the significance border areas. At follow-up assessments, we found no evidence for a publication bias neither using a regression-based approach [mental distress: QM(1) =0.05, p=0.821; positive mental health: QM(1) =0.49 p=0.488; resilience factors: QM(1) =3.61, p=0.076], nor based on the inspection of contour-enhanced funnel plots (see Supplementary Data 6). Effects of post-intervention assessment Eighty-five studies (reporting 150 effect estimates) contributed to the meta-analysis on mental distress (see Supplementary Data 7 for forest plot). Across five outcome types, we found evidence for a small favorable effect of digital resilience interventions, SMD = –0.24, 95% CI [–0.31, –0.18], with substantial heterogeneity, Q(149) =366.22, p<0.001 (see Table 1). Heterogeneity was partly accounted for by between-outcome differences, QM(4) =5.12, p=0.001. Small favorable effects were found consistently across all outcome types ranging from SMD =–0.14, 95% CI [–0.25, –0.04], for general distress to SMD =–0.33, 95% CI [–0.41, –0.24], for stress symptoms, with moderate to substantial heterogeneity across outcome types (41.4 ≤I 2 ≥66.8). Heterogeneity remained significant after accounting for between-outcome differences, Q(145) =323.71, p<0.001. Seventy-seven studies (reporting 123 effect estimates) contributed to the meta-analysis on positive mental health outcomes (see Supplementary Data 8 for forest plot). Across nine outcome types, there was evidence for a small favorable effect of digital resilience interventions over comparators, SMD =0.27, 95% CI [0.13, 0.40], p< 0.001, with substantial heterogeneity, Q(122) =850.59, p<0.001. Heterogeneity was partly accounted for by between-outcome differences, QM(8) =4.37, p< 0.001. Except for happiness, SMD =0.07, 95% CI [–0.17, 0.30], and quality of life, SMD =0.28, 95% CI [–0.03, 0.59], there was evidence for favorable effects for all outcome types ranging from small effects for self-reported resilience, SMD =0.22, 95% CI [0.05, 0.39], to small-to-moderate effects for overall mental health, SMD =0.56, 95% CI [0.36, 0.76], with substantial heterogeneity for all outcome types (66.6 ≤I 2 ≥90.5), Q(114) =766.34, p< 0.001. Forty-five studies reported 64 effect estimates for resilience factors (see Supplementary Data 9 for forest plot). Across seven resilience factors, there was evidence for small favorable effects, SMD =0.31, 95% CI [0.21, 0.41], with substantial heterogeneity, Q(63) =229.66, p<0.001, which mostly derived from betweenstudy differences, while between-outcome differences accounted for a close-to-significant small proportion of heterogeneity, 20 40 60 80 100 6. Selection of re port ed re sults 5. Outcome measurement 4. Missing outcome data 3. Deviations from the intended interventions 2. Identification or recruitment of participants (only for cluster RCTs) 1. Randomization process % a. Post-intervention assessment 0 20406080100 6. Selection of re port ed re sults 5. Outcome measurement 4. Missing outcome data 3. Deviations from the intended interventions 2. Identification or recruitment of participants (only for cluster RCTs) 1. Randomization process Low risk Some concerns High risk Not applicable b. Follow-up assessment % % 0% Fig. 2 Risk of bias across effect estimates at post-intervention and follow-up assessments. Note. Risk of bias in percentages across effect estimates assessed using the Cochrane risk-of-bias tool for randomized trials (RoB2) 30 . Percentages at post-intervention assessment relate to 337 effect estimates from 101 studies included in our review (a), while percentages at follow-up assessment relate to 146 effect estimates from 43 studies (b). S.K. Schäfer et al. 3 Published in partnership with Seoul National University Bundang Hospital npj Digital Medicine (2024) 30 Table 1. Results of main analyses for primary outcomes comparing digital resilience interventions and comparators. Analysis nk SMDs 95% CI 95% PI p Q df p(Q) I 2 (A) Post-intervention assessment Mental distress 85 150 –0.24 [–0.31, –0.18] [–0.71, 0.22] <0.001 366.22 149 <0.001 Anxiety symptoms 34 34 –0.19 [–0.27, –0.11] [–0.63, 0.25] <0.001 62.8 Depressive symptoms 48 48 –0.25 [–0.31, –0.19] [–0.69, 0.19] <0.001 66.8 General distress 13 13 –0.14 [–0.25, –0.04] [–0.59, 0.30] 0.009 41.4 PTSD symptoms 15 15 –0.19 [–0.29, –0.09] [–0.64, 0.25] <0.001 42.2 Stress symptoms 40 40 –0.33 [–0.41, –0.24] [–0.77, 0.12] <0.001 60.9 Positive mental health 77 123 0.27 [0.13, 0.40] [–0.85, 1.38] <0.001 850.59 122 <0.001 Happiness 6 6 0.07 [–0.17, 0.30] –0.586 85.4 Life satisfaction 9 9 0.26 [0.10, 0.41] –0.002 82.2 Mental health 5 5 0.56 [0.36, 0.76] –<0.001 77.7 Positive emotions/affect 14 14 0.27 [0.10, 0.45] [–0.87, 1.42] 0.003 84.3 Stress-related/posttraumatic growth 8 8 0.31 [0.06, 0.56] –0.017 76.0 Quality of life 9 9 0.28 [–0.03, 0.59] [–0.89, 1.46] 0.073 80.6 Resilience 46 46 0.22 [0.05, 0.39] [–0.92, 1.37] 0.013 90.5 Vitality 3 3 0.39 [0.17, 0.61] –<0.001 66.6 Wellbeing 23 23 0.34 [0.17, 0.51] [–0.81, 1.48] <0.001 87.9 Resilience factors 45 64 0.31 [0.21, 0.41] [–0.22, 0.84] <0.001 229.66 63 <0.001 Active coping 8 8 0.43 [0.08, 0.77] –0.017 58.1 Mindfulness 15 15 0.27 [0.13, 0.41] [–0.28, 0.82] <0.001 68.2 Optimism 7 7 0.39 [0.17, 0.62] –0.001 71.6 Self-compassion 9 9 0.47 [0.21, 0.73] [–0.12, 1.06] <0.001 67.2 Self-efficacy 12 12 0.18 [–0.03, 0.40] [–0.39, 0.76] 0.090 74.5 Self-esteem 3 3 0.24 [0.04, 0.43] –0.019 73.3 Social support 10 10 0.26 [0.09, 0.44] [–0.30, 0.82] 0.005 67.7 B) Follow-up assessments Mental distress 38 64 –0.24 [–0.35, –0.13] [–0.82, 0.35] <0.001 205.32 63 <0.001 Anxiety symptoms 14 14 –0.21 [–0.34, –0.08] [–0.80, 0.38] 0.002 72.3 Depressive symptoms 25 25 –0.23 [–0.35, –0.07] [–0.81, 0.35] <0.001 78.4 PTSD symptoms 10 10 –0.21 [–0.35, –0.07] [–0.80, 0.38] 0.004 61.0 Stress symptoms 15 15 –0.29 [–0.46, –0.12] [–0.89, 0.31] 0.001 69.3 Positive mental health 36 57 0.19 [0.11, 0.26] [–0.20, 0.57] <0.001 139.66 56 <0.001 Happiness 3 3 0.10 [–0.04, 0.24] –0.169 80.7 Life satisfaction 5 5 0.15 [0.05, 0.25] –0.006 77.9 Mental health 3 3 0.31 [–0.08, 0.70] –0.118 68.9 Positive emotions/affect 5 5 0.25 [0.05, 0.46] –0.018 72.0 Stress-related/posttraumatic growth 5 5 –0.06 [–0.37, 0.26] –0.719 56.7 Quality of life 5 5 0.02 [–0.19, 0.23] –0.854 66.3 Resilience 20 20 0.23 [0.10, 0.36] [–0.17, 0.62] 0.001 63.3 Wellbeing 11 11 0.27 [0.08, 0.46] [–0.15, 0.69] 0.008 68.7 Resilience factors 18 25 0.19 [0.08, 0.30] [–0.25, 0.62] 0.002 83.62 24 <0.001 Mindfulness 4 4 0.10 [–0.13, 0.34] –0.350 43.2 Optimism 3 3 0.30 [–0.04, 0.64] –0.079 50.4 Self-compassion 6 6 0.33 [–0.02, 0.68] –0.063 42.7 Self-efficacy 4 4 0.01 [–0.39, 0.41] –0.938 42.4 Self-esteem 3 3 0.23 [0.10, 0.35] –0.002 40.3 Social support 5 5 0.15 [0.04, 0.25] –0.010 41.5 Note. The multilevel meta-analysis on distress indicators included anxiety symptoms, depressive symptoms, general distress, PTSD symptoms, and stress symptoms. Due to qualitative differences, positive mental health and resilience factors were analyzed separately. Positive mental health comprised measures of happiness, life satisfaction, mental health, positive emotions/affect, stress-related/posttraumatic growth, quality of life, resilience, vitality, and wellbeing. Resilience factors comprised active coping, mindfulness, optimism, self-compassion, self-efficacy, self-esteem, and social support. For distress indicators, negative SMDs indicate favorable effects of the intervention [i.e., lower distress in the digital resilience intervention group compared to the control group]. For positive mental health and resilience factors, positive SMDs indicate favorable intervention effects [i.e., higher levels of positive mental health and resilience factors in the digital resilience intervention group compared to the control group]. All tests and reported statistics use cluster-robust estimates to account for non-independent effect estimates within studies. df =degrees of freedom; I 2 =heterogeneity index in percentage (range: 0–100%). knumber of effect estimates, nnumber of studies, PTSD posttraumatic stress disorder, QCochran’s Q statistic with pvalue, SMD standardized mean difference, 95% CI 95% confidence interval, 95% PI 95% prediction interval. S.K. Schäfer et al. 4 npj Digital Medicine (2024) 30 Published in partnership with Seoul National University Bundang Hospital QM(6) =2.13, p=0.066. Effect estimates were non-significant for self-efficacy, SMD =0.18, 95% CI [–0.03, 0.40]. For the remaining resilience factors, estimates ranged between small favorable effects for self-esteem, SMD =0.24, 95% CI [0.04, 0.43], and small to moderate effects for self-compassion, SMD =0.47, 95% CI [0.21, 0.73]. For single outcome types, heterogeneity was substantial (58.1 ≤I 2 ≥74.5), Q(57) =203.95, p< 0.001. According to GRADE ratings 29 , certainty of evidence was very low for all outcome categories (see Supplementary Data 10). Effects of follow-up assessment Analyses on mental distress at follow-up assessments were based on 38 studies (comprising 64 effect estimates; see Supplementary Data 11 for forest plot) and yielded again evidence for a small favorable effect of digital resilience interventions over comparators, SMD =–0.24, 95% CI [–0.35, –0.13], with substantial heterogeneity, Q(63) =205.32, p< 0.001, mainly resulting from between-study differences, while between-outcome differences were of minor relevance, QM(3) =0.63, p=0.602. Across all outcomes, favorable effects were small ranging from SMD = –0.21, 95% CI [–0.34, –0.08], for anxiety symptoms to SMD =–0.29, 95% CI [–0.46, –0.12], for stress symptoms. Residual heterogeneity was significant, Q(60) =195.00, p< 0.001, and substantial across all outcome types (61.0 ≤I 2 ≥78.4). At follow-up assessments, based on 57 effect estimates from 36 studies (see Supplementary Data 12 for forest plot), there was evidence for small favorable effects of digital resilience interventions on positive mental health, SMD =0.19, 95% CI [0.11, 0.26], with substantial heterogeneity, Q(56) =139.66, p< 0.001, mainly resulting from between-study differences, while betweenoutcome differences were of minor relevance, QM(7) =1.68, p=0.352. At single outcome level, no effects emerged for happiness, mental health, stress-related/posttraumatic growth, and quality of life, while small favorable effects were found for other outcomes ranging from SMD =0.15, 95% CI [0.05, 0.25], for life satisfaction to SMD =0.27, 95% CI [0.08, 0.46], for wellbeing. Heterogeneity remained significant, Q(49) =117.65, p< 0.001, and substantial across all outcome types (56.7 ≤I 2 ≥80.7). Eighteen studies (reporting 25 effect estimates) assessed resilience factors at follow-up assessments (see Supplementary Data 13 for forest plot), finding an overall small favorable effect, SMD =0.19, 95% CI [0.08, 0.30], with substantial heterogeneity, Q(24) =83.62, p< 0.001, which derived mostly from betweenstudy differences, while between-outcome differences were nonsignificant, QM(5)==1.11, p=0.405. At single outcome level, there were small favorable effects for social support, SMD =0.15, 95% CI [0.04, 0.25], and self-esteem, SMD =0.23, 95% CI [0.10, 0.35], while no effects emerged for other outcome types. Residual heterogeneity was significant, Q(19) =62.41, p< 0.001, but moderate across all outcome types (40.3 ≤I 2 ≥50.4). Also for follow-up assessments, GRADE ratings 29 indicated a very low certainty of evidence for all outcome categories (see Supplementary Data 14). Stability of intervention effects at follow-up assessment Overall, there was an almost perfect stability of intervention effects from post-intervention to follow-up assessments, ICC = 0.88, 95% CI [0.86, 0.90]. Effect estimates showed moderate to good stability for mental distress, ICC =0.63, 95% CI [0.53, 0.72], and resilience factors, ICC =0.81, 95% CI [0.71, 0.88], and were even more stable for positive mental health, ICC =0.94, 95% CI [0.92, 0.96]. Moderator analyses Based on the substantial to considerable heterogeneity identified by our main analyses mainly resulting from between-study differences, we performed several moderator analyses (see Table 2). At post-intervention assessment for both mental distress and positive mental health, the type of control condition impacted effect estimates with favorable effects being larger for no intervention/waitlist controls and low-intensity controls than for high-intensity controls. There was a trend towards more favorable intervention effects for guided compared to unguided interventions for resilience factors, but no evidence for other moderator effects. For follow-up assessments, the results of the moderator analysis are presented in Supplementary Data 15. For samples with higher mean age, there was evidence for more favorable effects on mental distress, QM(1) =4.32, p=0.045, and positive mental health, QM(1) =7.59, p=0.010. Moreover, favorable effects on positive mental health were larger for guided compared to unguided interventions, QM(1) =5.21, p=0.029. For positive mental health, there was a trend towards larger favorable effects when studies employed passive compared to active control groups, QM(2) =2.94, p=0.067. For resilience factors, there was evidence for more favorable effects for standardized compared to individualized interventions, QM(1) =6.60, p=0.021. Sensitivity analyses We examined whether the use of smaller or larger betweenoutcome correlations (ρ=0.40, ρ=0.80) impacted on our results (see Supplementary Data 16). Neither for post- nor follow-up assessments were the results significantly different. To account for a potential impact of risk of bias within studies, we re-ran our analyses limiting included studies to those with low risk of bias for the respective category of RoB2 30 . Neither at postintervention nor follow-up assessment these analyses yielded contrary results. However, in some cases, previously significant effects were non-significant as those analyses were based on a smaller number of effect estimates (see Supplementary Data 17). When we excluded one outlier 31 from our analyses on positive mental health at post-intervention, which showed a strong favorable effect on self-reported resilience (SMD =4.90), results remained largely unchanged, SMD =0.21, 95% CI [0.13, 0.29], while heterogeneity decreased, Q(121) =467.29, p< 0.001. We found no evidence for differences between studies examining digital resilience interventions during the COVID-19 pandemic and pre-pandemic studies, p≥0.281 (see Table 2and Supplementary Data 15). We examined whether effect estimates were different for studies with small digital component and found no evidence for a difference neither at post-intervention nor follow-up assessment, p≥0.350 (see Table 2and Supplementary Data 15). As we found evidence for a publication bias in the analyses on resilience factors at post-intervention, we performed additional sensitivity analyses for this model. When we included only nonaffirmative results in our meta-analyses, the analyses still yielded small favorable effects, SMD =0.10, 95% CI [0.04, 0.15], suggesting that no amount of publication bias under the assumed model would suffice to shift the point estimate to null. DISCUSSION The present systematic review comprehensively summarized evidence on digital resilience interventions. We found small favorable effects of digital resilience interventions over control groups for all outcome types, that is, mental distress, positive mental health, and resilience factors, which remained stable at (mostly short-term) follow-up assessments and robust in sensitivity analyses. At post-intervention assessment, favorable intervention effects for mental distress and positive mental health were larger when studies used no intervention/waitlist controls and S.K. Schäfer et al. 5 Published in partnership with Seoul National University Bundang Hospital npj Digital Medicine (2024) 30 low-intensity active comparators than for high-intensity active comparators (i.e., with similar duration and attention). At follow-up assessments, a moderator effect of age indicated more favorable intervention effects on mental distress and positive mental health in older samples. Our findings tie in with a recent review on online interventions to promote resilience 27 , which found small to moderate favorable effects, SMD =0.54, 95% CI [0.28, 0.78], on self-reported resilience, but contradict results from Díaz-García et al. 21 , who found no evidence for favorable effects on self-reported resilience, SMD = 0.12, 95% CI [–0.14, 0.38]. Beyond previous reviews, our systematic review relied on a much larger number of studies (101 in our analyses vs. 11 21 vs. 22 27 ) including more participants (20,010 participants in our analyses vs. 1174 21 vs. 2876 27 ). Moreover, by applying a state-of-the-art definition of resilience 1,5,9 , we examined a broader range of outcomes showing small favorable effects on mental distress, positive mental health (including self-reports of resilience), and resilience factors. Thereby, we provide a proof of concept showing that digital resilience interventions promote not only mental health but also resilience factors and likely, in turn, resilient outcomes (i.e., stable good mental health or a quick regain of mental health during or after stressor exposure). However, future high-quality effectiveness studies will have to further examine this mediating mechanism and also study why favorable effects were absent for active coping and self-efficacy as well-established resilience factors 1,32,33 . Intervention effects were comparable in size to those (mostly) found for in-person interventions. For example, Kunzler et al. 13,15 reported small to moderate effects of primarily in-person resilience interventions in healthcare professionals (44 studies) and healthcare students (22 studies) on self-reported resilience and mental distress, while there were little to no effects on positive mental health. Similarly, Joyce et al. 34 reported small to moderate favorable effects of mostly in-person resilience interventions based on 11 studies. Numerically, effects on self-reported resilience in previous systematic reviews were larger (range of Table 2. Results of moderator analyses at post-intervention assessment. Mental distress Positive mental health Resilience factors n/k M(SMD) [95% CI], p n/k M(SMD) [95% CI], p n/k M(SMD) [95% CI], p Sociodemographic characteristics Mean age 74/132 QM(1) =1.98, p=0.164 69/110 QM(1) =1.67, p=.0.201 40/57 QM(1) =0.00, p=0.950 Gender (% women) 82/144 QM(1) =0.00, p=0.978 74/121 QM(1) =0.17, p=.0.681 42/59 QM(1) =0.49, p=0.486 Population type (Military vs. University/College vs. Workplace) Omnibus moderator test 55/98 QM(2) =1.88, p=0.163 47/72 QM(1) =0.06, p=0.811 32/41 QM(1) =0.46, p=0.638 Delivery format (eHealth vs. mHealth vs. mixed) Omnibus moderator test 85/150 QM(2) =2.18, p=0.120 77/123 QM(2) =2.27, p=0.111 45/64 QM(1) =1.51, p=0.233 Theoretical foundation (CBT vs. Coping Literature vs. Mindfulness vs. Positive Psychology vs. mixed) Omnibus moderator test 56/101 QM(4) =1.19, p=0.329 56/95 QM(4) =0.50, p=0.734 37/56 QM(4) =0.84, p=.511 Guidance Unguided 0.24 [0.16, 0.34], p< 0.001 Guided 0.44 [0.25, 0.64], p< 0.001 Omnibus moderator test 84/148 QM(1) =0.95, p=0.332 77/123 QM(1) =2.72, p=0.103 45/64 QM(1) =3.81, p=0.058 Intervention type (standalone vs. blended interventions) Omnibus moderator test 84/149 QM(1) =0.26, p=0.610 76/122 QM(1) =0.02, p=0.897 45/64 QM(1) =0.20, p=0.653 Degree of individualization (individualized vs. standardized) Omnibus moderator test 85/150 QM(1) =2.43, p=0.123 77/123 QM(1) =2.25, p=0.138 45/64 QM(1) =0.09, p=0.769 Intervention intensity in weeks 82/142 QM(1) =0.12, p=0.729 77/123 QM(1) =0.98, p=0.325 45/64 QM(1) =1.71, p=0.198 Improvement over time Publication year 85/150 QM(1) =0.86, p=0.355 77/123 QM(1) =3.23, p=0.076 45/64 QM(1) =0.39, p=0.538 Type of control group No intervention/ waitlist –0.30 [–0.39, –0.21], p< 0.001 0.38 [0.15, 0.60], p=0.001 Low-intensity active control –0.31 [–0.45, –0.17], p< 0.001 0.22 [0.04, 0.40], p=0.019 High-intensity active control –0.08 [–0.18, 0.01], p=0.086 0.07 [–0.01, 0.15], p=0.102 Omnibus moderator test 85/150 QM(2) =6.51, p=0.002* 77/123 QM(2) =4.16, p=0.019* 45/64 QM(2) =0.18, p=0.835 COVID-19 context (before COVID-19 vs. during COVID-19) Omnibus moderator test 85/150 QM(1) =0.49, p=0.488 77/123 QM(1) =1.12, p=0.293 45/64 QM(1) =0.09, p=0.763 Small digital component (small digital component vs. other) Omnibus moderator test 84/149 QM(1) =0.51, p=0.479 76/122 QM(1) =0.18, p=0.675 45/64 QM(1) =0.22, p=0.641 Note. As results were at high risk of being biased by single studies, we did not report on moderation tests when three or less effect estimates were available per subgroup. QM(df) omnibus test for moderators, which follows approximately a χ 2 distribution, df degrees of freedom, knumber of effect estimates, SMD standardized mean difference, pvalue, 95% CI 95% confidence interval. * highlights significant results at p< .05. S.K. Schäfer et al. 6 npj Digital Medicine (2024) 30 Published in partnership with Seoul National University Bundang Hospital SMDs: 0.43–0.45) 13,15,34 than those obtained by our analyses (SMDs =0.22–0.23). However, based on Wald tests no significant differences emerged for both, self-reported resilience and mental distress. By contrast, effects on positive mental health were even larger and found more consistently than in previous reviews 13,15 . Due to the large number of studies included in our review, we were able to perform more analyses on follow-up effects than previous reviews 13,15 . For short to medium follow-up intervals (≤12 months), we found evidence for stable effects (ICC =0.88) across all outcome categories. However, for single outcome types (e.g., mindfulness, mental health), effect estimates were smaller at follow-up assessment. A potential decrease of intervention effects over time should therefore be examined in future studies with longer follow-up periods. In sum, the current review provides evidence for digital resilience interventions having the potential to effectively promote resilience - potentially even in the long-term. However, we also found that favorable effects at postintervention assessments were smaller (and non-significant) for mental distress and positive mental health in the subgroup of studies using high-intensity active comparators (e.g., intense psychoeducation 35 , comparable unspecific narratives 36 ). Thereby, our analyses were more nuanced than those of previous reviews that—at the most—contrasted passive and active controls 13,15 . This finding suggests that the favorable effects of the included interventions may (at least to some degree) result from unspecific effects of attention and/or engagement—an effect well-known from face-to-face interventions 9 . At the same time, one may ask for suitable comparators for digital resilience interventions. Even though active controls can be seen as the gold standard of intervention evaluation 37 , some of those comparators may have been (unintendedly) less intense resilience interventions. For example, many studies 38,39 used psychoeducation as key component of their interventions, while other studies 35,40 used psychoeducation as an active comparator. Moreover, in real-world settings, the most realistic comparator to digital resilience intervention is no intervention as most people have no access to resilience interventions (but see for a critical reflection on the validity of waitlist controls: Cuijpers et al. 37 ). Thus, our finding of reduced intervention effects in studies using high-intensity comparators may also point to vague definition of resilience-promoting interventions, which might have also negatively impacted on the choice of comparators. Interestingly, we found evidence for more favorable intervention effects on mental distress and positive mental health in older samples at follow-up assessment. This may reflect a larger proportion of older adults going on to use intervention components in their everyday life after the end of the intervention period (e.g., they use another meditation app after being included in a RCT on a mindfulness app). So far, there is only little evidence on the determinants of continuance intention for digital health interventions, however, previous studies showed that older adults use digital interventions more consequently 41 and that the link between satisfaction with mHealth interventions and continuance intentions is particularly strong in older adults 42 . Moreover, older adults may be less ‘overdosed’by the constant use of mobile and web-based services or take their participation in the study more seriously than younger samples (e.g., students who participate in a study to receive course credit 43 ). Older adults may also, somewhat counterintuitively, make better use of digital tools and offers compared to younger people, as shown in the COVID-19 pandemic 44 . To note, our meta-analyses do not include many older people (highest mean age: 57.6 years 45 ) and our findings apply to the age range from young to middle-aged adulthood. Future studies will have to examine age as effect modulator in greater detail also including older adults (≥60 years) and may determine additional variables that impact on age-related differences in intervention effects (e.g., eHealth literacy 46 , attitudes towards e/mHealth services 47 ). We found more favorable effects on resilience factors for standardized compared to individualized interventions at followup assessments. This finding was surprising at first sight and may partly be accounted for by the small number of studies included in our analyses employing individualized interventions. Moreover, individualized interventions that adapt intervention intensity, delivery and/or contents based on participants’responses and behaviors may be more complex in terms of design and delivery. Thus, this finding may rather point to the potentials of individualized interventions being not used yet in the research on digital resilience interventions. Therefore, the result should not be misinterpreted as a valid evaluation of the impact of individualization on intervention effects. More high-quality studies examining the add-on effects of individualized interventions on a broad range of outcomes including acceptability 48 and user engagement 49 , which may be more sensitive to individualization, are strongly needed. Other moderator effects did not emerge consistently across outcomes. For example, our analyses on resilience factors at postintervention assessment and on positive mental health outcomes at follow-up assessments pointed to an impact of guidance favoring guided over unguided interventions, however, the effect was only close-to-significance for resilience factors and did not emerge consistently between outcome types and over time. Future studies will have to explore these effects in greater detail. Although included studies were rather heterogeneous with respect to intervention content, intervention delivery, formats and intensity, we found no evidence for moderator effects of these intervention characteristics. For some of these variables, low reporting standards in primary studies did not allow for more indepth analyses. Future studies complying with higher reporting standards 50 will help to shed light on these potential effect modulators. Such studies will allow us to derive more concrete recommendations on ideal intervention design and delivery. The findings of the present review have to be interpreted in the light of their limitations, which arise from both, the included studies and the review process itself. A major shortcoming in the field is that a precise definition of resilience interventions is still missing 9 . In line with two recent Cochrane reviews 13,15 , we included studies that either explicitly state to promote resilience (or resilience-related concepts like hardiness and stress-related/posttraumatic growth) or that refer to resilience as a key background of their intervention. However, even between two studies examining the same intervention 36,51 , the theoretical framing may differ, with interventions being referred to as either resilience or (mental) health promoting or focusing on the treatment of mental distress. Thus, there is a huge need for a more elaborated definition of resilience-promoting interventions which goes beyond authors’labeling. As for most inperson resilience interventions 13,15 , overall risk of bias was moderate to high, and certainty of evidence was very low across all outcomes. This may reflect that high-quality research requires resources that are often not available for researchers designing and evaluating resilience interventions. However, such research is needed to validly examine intervention effects and tailor interventions to participants’(likely) heterogeneous needs. Other limitations arise from our review process. We searched five databases from 2019 to 2022 and identified studies published before 2019 by means of systematic reviews on resilience and health-promoting interventions (see Supplementary Data 2 for our search rationale). Moreover, we performed extensive citation searching. However, we cannot exclude that we have missed relevant studies. Moreover, we made minor changes from the preregistration of the review, which are described in Supplementary Data 1. In line with recommendations of the Cochrane collaboration 52 , we refrained from studying pre-to-post changes as pre-to-post value correlations were only available for a very small number of studies and the reliance on pre-to-post changes S.K. Schäfer et al. 7 Published in partnership with Seoul National University Bundang Hospital npj Digital Medicine (2024) 30 may also unintendedly ‘correct’flaws in study design (like unsuccessful randomization) 53 . However, analyses based on the between-group comparison of pre-to-post changes may have provided divergent results (see Liu et al. 26 for a review on pre-to- post changes during resilience interventions). Moreover, we were not able to perform (component) network meta-analysis. Such analyses are highly needed to rank intervention components according to their efficacy and to derive recommendations for ideal interventions. In the case of our review, the requirements for network meta-analyses 54 were not met (e.g., we found nonrandom differences between effect modifiers). However, future reviews based on more homogeneous studies may use the potential of network meta-analysis to shed further light on the relative importance of intervention components and to identify the “active ingredients”of those interventions 55 . These may include the promotion of higher-level resilience mechanisms (e.g., positive appraisal style 11 ; regulatory flexibility 12 ) or competencies like self-reflection 56 , with preliminary evidence suggesting that intervention effects of health-promoting interventions are mediated via positive appraisal style 57 . For regulatory flexibility, first training programs are about to be tested empirically 58,59 . Future studies will have to address these research gaps and compare the relative importance of different mechanisms. Moreover, some of the included studies also aimed at targeting other outcomes than those included in the present review (e.g., fatigue and pain 45 , parental acceptance 60 , work engagement 61 ). These outcomes were not considered in our analyses. Thus, our results only allow for conclusions on mental health and resilience factors and should not be misinterpreted as overall evaluation of the included interventions as they may be (more or less) effective in targeting other outcomes. The present review provides preliminary evidence for the efficacy of digital interventions to enhance resilience. Evidence for favorable intervention effects was comparably strong (or weak) as for mostly non-digital interventions 13,15,26,34 . Favorable effects were found to be stable during short- and medium-time follow-up periods. At the same time, we found substantial to considerable heterogeneity mostly coming from between-study differences and no strong evidence in favor of any specific digital resilience intervention. By contrast, most interventions were only examined in single trials with limited statistical power, which need replications. Thus, the current review should not be misunderstood as a prediction of concrete intervention effects of any resilience intervention, which is also indicated by wide prediction intervals that consistently included null effects. Preparing for future crises in terms of resilience interventions would require more coordinated international research effort. One may learn from recent advances in the field of transdiagnostic psychosocial interventions for the treatment of mental distress in stress-exposed populations 62,63 . Initiated by the World Health Organization, a series of scalable psychosocial interventions (e.g., Problem Management Plus 64 , Step-by-Step 65 ) was developed to address the high care need in stress-exposed populations. Most importantly, those interventions are examined in a series of highquality RCTs for their feasibility and effectiveness 63,66 . Such an approach may also be useful for the development and evaluation of digital resilience interventions. The present review may provide an evidence base for intervention development, and later, individual-participant-data meta-analyses based on international effectiveness RCTs may help to shed light on the effectiveness of those interventions and participant-level effect modulators. Digital interventions may also have the potential to improve health promotion and prevention in low-resource settings 67 , which resulted in a very optimistic initial view of digital interventions as potential ‘game changers’in global health care 68 . So far, evidence on digital resilience interventions in low-resource settings is still rare with only 10.9% of the studies included in our review being conducted in middle-income countries and no intervention being delivered in a low-income country. While our review does not allow for strong conclusions on low-resource settings, implementation studies of other digital interventions in those settings pointed to substantial barriers 69 (e.g., problems due to non-participative intervention development and delivery). Future research needs to examine whether and how digital resilience interventions can help to deliver mental health promotion and prevention in settings with limited resources 70 . Moreover, the current review focused on digital resilience interventions in mostly middle-aged adult populations. However, preparing for future crises will need a lifespan approach also including children and adolescents as well as older people. Evidence on resilience interventions for those age groups is still rare 18,71,72 . Especially for children and adolescents, that were found to be particularly burdened by increases in stressor exposure 73,74 , digital resilience interventions may constitute an important component of stressor preparedness 75 . Future studies will have to examine whether resilience promotion in those age groups requires interventions that are more sensitive to developmental processes 76 . The present review found small favorable effects of digital resilience interventions on mental distress, positive mental health, and resilience factors, which remained stable at least at short-term follow-up assessments. Those effects were comparable between online and mobile interventions and to those found for in-person interventions. For some but not all outcomes, we found older age to be associated with more favorable effects at follow-up assessments. So far, only a small number of studies made use of potential advantages of digital interventions (i.e., flexible time schedules, individualization) with mixed results. Digital resilience interventions have the potential to contribute to preparedness for future major disruptions. However, there is no strong evidence for any particular intervention, with the majority of interventions being only examined in single studies. Future research should focus on the evidence-based development of digital resilience interventions, which should be examined in fully powered effectiveness studies in both low- and high-resource settings. These studies may pave the way for digital resilience interventions being used to prepare and manage major disruptions at a societal level. METHODS This systematic review adheres to the standards of the Cochrane Collaboration 52 and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 77 (PRISMA). Differences between the preregistration of the review (PROSPERO preregistration-ID: CRD42021286780) and the final review are presented as Supplementary Data 1. Search strategy The search strategy was developed based on two previous Cochrane reviews 13,15,28 . First, we searched for systematic reviews examining health- and resilience-promoting interventions irrespective of their delivery mode. This approach was chosen to efficiently identify randomized-controlled trials (RCTs) that had been published between 2000 and 2018. Second, we searched for primary studies to cover the period from January 1, 2019 to August 15, 2022 (see Supplementary Data 2 for the rationale of this two-step search strategy). Searches were performed in the Cochrane Central Register of Controlled Trials (CENTRAL), Embase (incl. Pubmed and Medline), PsycINFO and PsycArticles via EbscoHost, Scopus, and Web of Science. For both searches, search terms comprised three clusters that were searched in title, abstract, and keywords: terms related to i) resilience and adaptation processes (e.g., “resilien*”, hardiness), ii) interventions S.K. Schäfer et al. 8 npj Digital Medicine (2024) 30 Published in partnership with Seoul National University Bundang Hospital