Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of resilience‐enhancing interventions in clinical and non‐clinical populations.
Background
Description of the condition
Since the introduction of a salutogenic orientation (focusing on factors that promote health and well‐being), as a basis for health promotion (Antonovsky 1979), and the Ottawa Charter for Health Promotion (WHO 1986), the concept of resilience has stimulated extensive research. Resilience describes the empirically observable phenomenon under which an individual does not or only temporarily, experiences mental health problems despite being subjected to psychological or physical stressors of short (acute) or long (chronic) duration (Kalisch 2015). By definition, resilience always presupposes the exposure to substantial risk or adversity (Earvolino‐Ramirez 2007; Jackson 2007; Luthar 2000; Masten 2001). Thus, the psychological resilience of a person can only be determined if the individual was exposed to previous or current stress or trauma.
In the literature, three different resilience definitions are discussed: trait resilience, resilience as an outcome and resilience as a process (Hu 2015; Kalisch 2015). Trait resilience refers to resilience defined as personal resources or static, positive personality characteristics that enhance individual adaptation (Block 1996; Nowack 1989; Wagnild 1993). This approach has largely been superceded by a view of resilience as an outcome rather than a static personality trait (Kalisch 2015; Mancini 2009), that is, psychological adaptation (for example, mental health, well‐being, quality of life), despite significant stress or trauma. According to this outcome‐oriented definition, the positive outcome resilience is partially determined by several resilience factors (Kalisch 2015). To date, a large range of genetic, psychological, social and environmental factors have been discussed in resilience research that often overlap or may interact (Bengel 2012; Bonanno 2013; Carver 2010; Connor 2006; Earvolino‐Ramirez 2007; Feder 2011; Forgeard 2012; Haglund 2007; Iacoviello 2014; Kuiper 2012; Mancini 2009; Michael 2003; Ozbay 2007; Rutten 2013; Sapienza 2011; Sarkar 2014; Southwick 2005; Southwick 2012; Stewart 2011; Wu 2013; Zauszniewski 2010). Psychosocial resilience factors that are well‐evidenced according to the current state of knowledge and are thought to be modifiable include meaning or purpose in life, sense of coherence, positive emotions, hardiness, self‐esteem, active coping, self‐efficacy, optimism, social support, cognitive flexibility (including positive reappraisal and acceptance) and religiosity or spirituality or religious coping (see Appendix 1; level 1). Most recently, resilience has even been conceptualised as a multidimensional, dynamic and variable process (Johnston 2015; Kalisch 2015; Kent 2014; Mancini 2009; Norris 2009; Rutten 2013; Sapienza 2011; Southwick 2012). This resilient process is characterised by either a trajectory of undisturbed mental health during or after adversities or temporary dysfunctions followed by successful recovery (Kalisch 2015). In general, resilience is viewed as the outcome of an interaction between the individual and his or her environment (Cicchetti 2012; Rutten 2013), which may be influenced through personal (e.g. optimism) as well as environmental (e.g. social support) resources (Haglund 2007; Iacoviello 2014; Kalisch 2015; Southwick 2005; Wu 2013). As such, resilience is modifiable and can be improved by interventions (Bengel 2012; Connor 2006; Southwick 2011).
The development and evaluation of interventions that aim to foster or enhance psychological resilience and prevent stress‐related mental dysfunctions are the focus of the third wave of resilience research (Bengel 2012; Waite 2004). Resilience‐training programmes have been developed for, and conducted in, a variety of clinical and non‐clinical populations using various formats, such as multimedia programmes or face‐to‐face settings, and delivered in a group or individual context (see Bengel 2012 and Southwick 2011 for an overview). However, the empirical evidence regarding the efficacy of these interventions is still unclear and requires further research.
Description of the intervention
Despite increasing interest worldwide in the development and evaluation of resilience interventions for different groups, there is little consensus about when to consider a programme as ‘resilience training’ or what components are needed for effective programmes (Leppin 2014). The diversity across resilience‐training programmes in their theoretical assumptions, the operationalisation of their construct, and inclusion of core components reflect the current state of knowledge (Leppin 2014; Macedo 2014; Robertson 2015). Leading guidelines on definition, conceptualisation, intervention design and assessment of resilience are still under discussion (compare Kalisch 2015; Robertson 2015).
Most training programmes, whether individual‐ or group‐based, are implemented face‐to‐face. Alternatively used formats include online‐based interventions or multimodal training combining different formats (e.g. face‐to‐face and coaching via telephone). Resilience‐training programmes often use methods such as discussions, role plays, practical exercises and homework to reinforce training contents. Moreover, they mostly contain a psychoeducative element to provide information on the concept of resilience or specific training elements (e.g. cognitive restructuring).
Different psychotherapeutic procedures and methods provide the basis for resilience interventions: cognitive‐behavioural therapy (e.g. Abbott 2009; Songprakun 2012), acceptance and commitment therapy (e.g. Ryan 2014), mindfulness‐based therapy (e.g. Geschwind 2011), attention and interpretation therapy (e.g. Loprinzi 2011; Sood 2014), problem‐solving therapy (e.g. Bekki 2013; Sahler 2013), as well as stress inoculation (e.g. Farchi 2010). Besides, a number of training programmes focus on fostering single or multiple psychosocial resilience factors (e.g. Kanekar 2009; Sadow 1993), without being assignable to a certain approach. Few interventions base their work on a defined resilience model (e.g. Schachman 2004; Steinhardt 2008).
How the intervention might work
Depending on the underlying resilience concept, resilience interventions target different resources and competences. The theoretical foundations of resilience‐training programmes and the hypotheses on how they might maintain or regain mental health are as diverse as their contents. Currently, no empirically validated theoretical framework exists that outlines the mode of action of resilience interventions (Bengel 2012; Leppin 2014).
As resilience as an outcome is determined by several, potentially modifiable resilience factors (see Description of the condition), resilience interventions might work by strengthening these factors in interventions. Appendix 2 presents examples of possible training methods to foster well‐evidenced resilience factors. However, depending on the theoretical foundation of resilience training programmes, there are different theories of change on how certain resilience factors and hence resilience might be affected.
From the 'cognitive‐behavioural perspective', stress‐related mental dysfunctions (e.g. depression, anxiety disorder, substance abuse) can be considered as a result of dysfunctional thinking (Beck 2011; Benjamin 2011). When confronted with stress or adversity, people show maladaptive behavioural responses or experience negative mood states, or both, due to irrational cognitions (Beck 1976; Ellis 1975). This is in line with other stress and resilience theories assuming that not the stressor itself, but its cognitive appraisal may lead to stress reactions (e.g. Kalisch 2015; Lazarus 1987). Therefore, modifying cognitive processes into more adaptive patterns of thought will probably produce more adaptive emotional and behavioural responses to stress (Beck 1964). By challenging an individual’s maladaptive thoughts and by teaching new problem‐solving coping strategies, resilience interventions based on cognitive behavioural therapy might be beneficial in promoting the resilience factors of cognitive flexibility and active coping, for example.
'Stress inoculation therapy', as a form of cognitive behavioural therapy, is based on the assumption that exposing individuals to milder forms of stress can strengthen coping strategies and the individual’s confidence in using his or her coping repertoire (Meichenbaum 2007). Therefore, resilience training programmes grounded in stress inoculation therapy might foster resilience by enhancing factors such as self‐efficacy.
According to 'acceptance and commitment therapy' (Hayes 2004; Hayes 2006), psychopathology is primarily the consequence of psychological inflexibility (i.e. inability to persist or change behaviour according to long‐term values due to language and cognition skills) (Hayes 2006), which is also relevant when an individual is confronted with stress or adversity. By teaching acceptance and mindfulness skills on the one hand (e.g. being in contact with the present moment, acceptance, cognitive defusion), and commitment and behavior‐change skills on the other hand (e.g. values, committed action), several resilience factors might be fostered in resilience interventions based on acceptance and commitment therapy (e.g. cognitive flexibility, purpose in life). In particular, the acceptance of a full range of emotions taught in acceptance and commitment therapy might result in a better adjustment to stressful conditions (i.e. resilience).
In 'mindfulness‐based therapy' (e.g. mindfulness‐based stress reduction (e.g. Stahl 2010); attention and interpretation therapy (Sood 2010)), mindfulness is characterised by the non‐judging awareness of the present moment and its accompanying mental phenomena (i.e. body sensations, perceptions, thoughts and emotions). Since practitioners learn to accept whatever occurs in the present moment, they are thought to adapt more efficiently to stress (Grossman 2004; Shapiro 2005). As being more aware of the 'here and now' possibly enhances the sensitivity for positive aspects in life, mindfulness‐based resilience interventions might help participants to gain a brighter outlook for the future (i.e. the resilience factor of optimism) or to experience positive emotions more regularly. Besides, teaching mindfulness might also increase the participants’ cognitive flexibility by learning to accept negative situations and emotions.
Based on the 'problem‐solving' model of stress and adaptation, effective problem‐solving can attenuate the negative effects of stress and adversity on well‐being by moderating or mediating (or both) the effects of stressors on emotional distress (Nezu 2013). For example, deficient problem‐solving was found to be related to psychological maladaptation to stress in several populations, whereas other studies identified a moderator or mediator function of efficient problem‐solving (Nezu 2013). Resilience interventions based on problem‐solving that enhance an individual’s positive problem orientation as well as his or her planful problem solving (i.e. analysing the problem and setting goals, generating possible solutions, choosing the best solution and creating an action plan, implementing the solution and reviewing the problem‐solving process) might foster the participants’ psychological adaptation to stress (i.e. resilience) by increasing the resilience factor of active coping, especially.
Independent of the underlying theory, resilience training might work differently depending on the respective 'delivery format' and 'intervention setting' (Robertson 2015; Vanhove 2015). For example, interventions implemented face‐to‐face could work better than online interventions in increasing resilience due to the more direct contact between trainers and participants (Vanhove 2015), which might also increase the compliance of participants. Resilience training in an individual setting could be more efficient than group‐based interventions as trainers might be better able to attend to participants’ individual needs and provide feedback more easily (Vanhove 2015). On the other hand, group‐based interventions could also enhance the participants’ social resources.
Vanhove 2015 already hypothesised on varying effects of resilience interventions in different populations. Although different target groups (e.g. employees, patient populations, military or police, general population) may experience similar daily stressors, they could, nevertheless, differ in other sources of stress exposure (e.g. combat experience in the military, organisational restructuring in employees) (Vanhove 2015). Moreover, the stressor load (i.e. number of experienced stressors) might vary between groups. As populations at a greater risk of experiencing stress or with a higher stressor load could require more resilience factors to overcome adversities, they might profit more from resilience training programmes.
Why it is important to do this review
To date, two systematic reviews (Macedo 2014; Robertson 2015) and two meta‐analyses (Leppin 2014; Vanhove 2015) have investigated the efficacy of resilience interventions in adults, each concluding that resilience interventions can improve personal resilience, mental health and performance.
However, all four publications suffer from methodological weaknesses, which the present review seeks to address. Each publication focused on different aspects of resilience training, using different definitions of resilience, different samples and settings, as well as different inclusion and exclusion criteria for studies. Each review varies in the extent to which it describes the search strategy used, and the reporting of 'Risk of bias' assessments also differs for those studies that are common amongst the publications (Leppin 2014; Macedo 2014; Robertson 2015). One review reports no 'Risk of bias' assessment (Vanhove 2015). The absence of a published protocol for these reviews also reduces the transparency and comparability in the reviews' procedures, leads to possible biases and potentially restricts the evidence found. In addition, to date, only Leppin 2014 and Vanhove 2015 were able to perform a meta‐analysis, whereby Vanhove 2015 focused on resilience‐building programmes for the workplace only.
In the present review, we are particularly interested in psychological resilience interventions offered to clinical as well as to non‐clinical populations in different contexts (i.e. the workplace as well as a student or military context). The interventions have to be scientifically founded, that is, they have to address one or more of the resilience factors stated above that are known to be associated with resilience in adults according to current state of research (compare Appendix 1; level 1). In addition, the trained population has to fulfil the condition of stress or trauma exposure (concept implication of resilience), in order to clearly distinguish genuine resilience interventions from other interventions focused on fostering associated constructs such as mental health (Windle 2011a). Since resilience as a prevention concept is highly up‐to‐date, and there is increasing interest worldwide in promoting mental health and preventing disease (WHO 1986; WHO 2004), the present review will provide further and more detailed evidence on which interventions are most likely to foster resilience and to prevent stress‐related mental health problems. In this way, practitioners as well as policy makers will profit from the present work.
Objectives
To assess the effects of resilience‐enhancing interventions in clinical and non‐clinical populations.
Methods
Criteria for considering studies for this review
Types of studies
Randomised controlled trials (RCT), including cluster‐randomised trials.
Types of participants
Adults aged 18 years and older, irrespective of employment or health status, who have been exposed to stress or trauma in the past, or who are facing substantial stress or trauma currently or will be in the future (see Data synthesis).
We will include studies involving participants less than 18 years of age, as well as those aged 18 years and older, if data for participants aged 18 years and above are reported separately or can be obtained by contacting the study authors.
Types of interventions
Any psychological resilience intervention, irrespective of content, duration, setting or delivery model.
For the purpose of this review, psychological resilience interventions are defined as follows: interventions focused on fostering resilience or the related concepts of hardiness or post‐traumatic growth by strengthening well‐evidenced resilience factors that are thought to be modifiable by training (see above and Appendix 1; level 1).
We will only consider studies on pharmacological (e.g. treatment with antidepressants) and physical (e.g. exercise) interventions, as well as relaxation techniques (e.g. progressive muscle relaxation), if these interventions are part of psychological resilience training.
We will not include studies that merely examine the efficacy of disorder‐specific psychotherapy (e.g. cognitive behavioural therapy for depression). We will include broader, health‐promoting interventions (e.g. well‐being therapy) providing they focus on resilience and they address any of the resilience factors described above.
Types of outcome measures
Due to the different ways in which resilience has been operationalised, as well as the possible inclusion of broader, health‐promoting interventions, resilience as an intervention outcome cannot always be guaranteed in trials. Therefore, we will also define assessments on psychological adaptation (e.g. mental health) as primary outcomes.
Secondary outcomes include a range of psychological factors associated with resilience according to the current state of knowledge that are selected based on conceptual clarity and measurability (level 1a and 1b; see Appendix 1). We may include additional secondary outcomes after the full literature review has been conducted.
Measures for the assessment of psychological resilience and psychological adaptation as well as resilience factors are specified on the basis of previous reviews on resilience interventions (Leppin 2014; Macedo 2014; Robertson 2015; Vanhove 2015) and reviews on resilience measurements (Pangallo 2015; Windle 2011b) (see Appendix 3, Appendix 4, Appendix 5, respectively). We will examine the influence of the differing underlying concept in resilience scales in a sensitivity analysis (intervention outcome versus personality characteristic) (see Sensitivity analysis).
We will consider self‐rated and observer‐ or clinician‐rated measures as well as study outcomes at all time frames. The missing reporting of the above described primary or secondary outcomes is not an exclusion criterion in this review.
Primary outcomes
Resilience*, as measured by improvements in specific resilience scales (Bengel 2012; Earvolino‐Ramirez 2007; Pangallo 2015; Windle 2011b) such as the Resilience Scale for Adults (Friborg 2003).
Mental health and well‐being, subsumed into the categories below, and as measured by improvements in the respective assessment scales such as the Depression Anxiety and Stress Scale (DASS‐21) (Lovibond 1995) (see Appendix 4 for further examples).
Anxiety*.
Depression*.
Stress or stress perception*.
Well‐being or quality of life* (e.g. well‐being, life satisfaction, (health‐related) quality of life, vitality, vigour).
Secondary outcomes
Resilience factors* (e.g. Bengel 2012; Haglund 2007; Iacoviello 2014; Southwick 2005; Southwick 2012; Wu 2013), whenever they are available as outcomes, as assessed by an increase in the respective instruments (e.g. Life Orientation Test ‐ Revised (LOT‐R); Scheier 1994) (for further examples see Appendix 5).
Social support.
Optimism.
Self‐efficacy.
Active coping.
Self‐esteem.
Hardiness1.
Positive emotions.
We will extract and report secondary outcomes whenever they are assessed. If possible, we will calculate and report effect sizes.
We will note any adverse outcomes reported in a trial. Where data are available, we will use outcomes marked by an asterisk (*) to generate a ‘Summary of findings’ table. In case of insufficient information, we will provide a narrative description of the evidence.
1Although hardiness is often used as synonym for resilience in the literature, we will conceptualise it as resilience factor (see Appendix 1).
Search methods for identification of studies
Electronic searches
We will retrieve relevant trials from the electronic sources listed below.
Cochrane Central Register of Controlled Trials (CENTRAL; current issue) in the Cochrane Library, which includes the Cochrane Developmental, Psychosocial and Learning Problems Specialised Register.
MEDLINE Ovid (1946 to present).
Embase Ovid (1980 to present).
PsycINFO EBSCOhost (1840 to present).
CINAHL EBSCOhost (Cumulative Index to Nursing and Allied Health Literature; 1981 to present).
PSYNDEX EBSCOhost (1977 to present).
Science Citation Index Web of Science (SCI; 1970 to present).
Social Science Citation Index Web of Science (SSCI; 1970 to present).
Conference Proceedings Citation Index ‐ Social Science & Humanities Web of Science (CPCI‐SSH; 1990 to present).
Conference Proceedings Citation Index ‐ Science Web of Science (CPCI‐S; 1990 to present).
International Bibliography of the Social Sciences ProQuest (IBSS; 1951 to present).
Applied Social Sciences Index & Abstracts ProQuest (ASSIA; 1987 to present).
ProQuest Dissertations & Theses (PQDT; 1743 to present).
Cochrane Database of Systematic Reviews (CDSR; current issue) in the Cochrane Library.
Database of Abstracts of Reviews of Effects (DARE; current issue) in the Cochrane Library.
Epistemonikos (epistemonikos.org; all available years).
ERIC EBSCOhost (Education Resources Information Center Institute of Education Sciences; 1966 to present).
Current Controlled Trials (controlled‐trials.com; all available years).
ClinicalTrials.gov (clinicaltrials.gov; all available years).
World Health Organization International Clinical Trials Registry Platform (WHO ICTRP; who.int/trialsearch; all available years).
The search strategy for MEDLINE is presented in Appendix 6, and we will adapt the search terms and syntax for other databases. We will not restrict the searches to language, publication status or publication format. We will limit our search to the period 1 January 1990 to present, to account for the fact that the resilience concept and its operationalisation have developed significantly over the past decades (Fletcher 2013; Hu 2015; Kalisch 2015; Pangallo 2015). Because of the lack of homogeneity for the period 1990 to 2014 (Robertson 2015), it is likely that using a broader time frame would make it even more difficult to detect resilience‐training studies with similar resilience concepts and assessments. Moreover, it appears plausible to concentrate on the period 1990 to present since the idea of resilience as an outcome and modifiable process only emerged in recent years, and paved the way for the development of resilience‐promoting interventions (Bengel 2009; Southwick 2011). Therefore, the idea of promoting resilience by specific training is relatively new (Leppin 2014), which can also be seen in the review of Macedo 2014, who searched for studies on resilience‐enhancing interventions every year until 2013 but only found RCTs published after 1990.
As resilience‐training programmes should be adapted to scientific findings on a regular basis, and with the current research focusing on the detection of general resilience mechanisms (Kalisch 2015; Luthar 2000), the last two years will be especially important in synthesising the evidence on newly developed resilience training.
Searching other resources
In addition to the electronic search, we will inspect the reference lists of all identified RCTs and reviews, and contact researchers in the field as well as the authors of selected trials to check if there are any unpublished or ongoing studies. If data are missing or unclear, we will contact the respective author. We will also search for grey literature (for example, conference proceedings) in appropriate databases (see Electronic searches).
Data collection and analysis
Selection of studies
Two review authors (AK, IH) will independently screen titles and abstracts in order to determine eligible studies. Clearly irrelevant papers will be excluded immediately. At full‐text level, eligibility will be also inspected in duplicate by the same two review authors (AK, IH) working independently. We will calculate inter‐rater reliability at both stages of screening (title and abstract screening and full‐text screening). We will record our decisions in a PRISMA flow diagram (Moher 2009).
We will assess the feasibility of the selection criteria a priori by screening a small number (50) of studies in order to attain acceptable inter‐rater reliability. In case screening results in poor feasibility of the eligibility criteria, we will revise the criteria based on a mutual team discussion.
Data extraction and management
We will develop a data extraction sheet (Appendix 7), based on Cochrane guidelines (Higgins 2011c), and test it on 10 randomly‐selected included studies. If the initial test of the data extraction sheet fails (e.g. insufficient agreement between review authors AK and IH), we will adapt the extraction sheet on the basis of a mutual team discussion. Review authors AK and IH will independently extract the data in duplicate. The extraction sheet will contain the following aspects: source and eligibility, study methods (e.g. design), allocation process, participant characteristics, interventions and comparators, outcomes and assessment instruments (means and standard deviations in any standardised scale), results and miscellaneous aspects. Both review authors will resolve any disagreements in data collection by discussion; where they cannot reach a consensus, a third review author (AC or KL) will arbitrate. If necessary, we will contact the study authors to seek additional information.
Assessment of risk of bias in included studies
Two review authors (AK, IH) will independently assess the risk of bias of the included studies. We will check the risk of bias for each trial using the criteria presented in the Cochrane Handbook for Systematic Reviews of Interventions, hereafter referred to as the CochraneHandbook (Higgins 2011d) (see Appendix 8). Any disagreements will be resolved by discussion or by consulting a third review author. In accordance with Cochrane’s tool for assessing risk of bias (Higgins 2011b), we will critically assess the following domains: sequence generation and allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective outcome reporting (reporting bias). In the first part of the assessment, we will describe what was reported to have happened in the study for each domain before assigning a judgment regarding the risk of bias (low, high or unclear) for that entry.
Measures of treatment effect
Dichotomous data
We will analyse dichotomous outcomes by calculating the risk ratio (RR) of a successful outcome (i.e. improvement in relevant variables) for each trial. We will express uncertainty in each result using 95% confidence intervals (CIs).
Continous data
Because it is unlikely that most resilience‐training studies use the same measurement scale to assess resilience and related constructs (Leppin 2014; Macedo 2014; Robertson 2015), we will use standardised mean difference (SMD) effect sizes (Cohen's d) and their 95% CIs for continuous data in pair‐wise meta‐analysis. We will calculate effect sizes on the basis of means, standard deviations and sample sizes for each trial condition. In case respective data are not provided, we will compute Cohen's d from alternative statistics (e.g. t test).
Unit of analysis issues
Cluster‐randomised trials
As allocation of individuals to different conditions in resilience intervention studies partly occurs by groups (e.g. work sites, army platoons), we intend to include cluster‐randomised trials along with individually‐randomised trials. If the clustering is ignored and the unit of analysis is different from the unit of allocation (‘unit‐of‐analysis error’) (Whiting‐O'Keefe 1984), P values may be artificially small and result in false positive conclusions (Higgins 2011e). Therefore, we will account for the clustering in the data and follow the recommendations given in the literature (Higgins 2011e; White 2005). For those cluster‐randomised trials that do not report correct standard errors, we will first try to recover correct standard errors by applying the usual formula for the variance inflation factor 1 + (M – 1) ICC, where M is the average cluster size and ICC the intracluster correlation coefficient (Higgins 2011e). If it is not possible to extract ICC values from the study, we will use the ICC of all cluster‐randomised trials in our review that investigate the same primary outcome scale in a similar setting. If this is not available, we use the average ICC of all other cluster‐randomised trials in our review. If no such studies are available, we will use ICC = 0.05 as a mildly conservative guess for the primary analysis, and add a sensitivity analysis using ICC = 0.10. We will conduct sensitivity analyses based on the unit of randomisation as well as the ICC estimate in cluster‐randomised trials (see Sensitivity analysis).
Repeated observations on participants
If there are longitudinal designs with repeated observations on participants, we will define several outcomes based on different periods of follow‐up and conduct separate analyses, as recommended in the Cochrane Handbook (Higgins 2011e). One analysis will include all studies with measurement at the end of intervention (post‐test), other analyses will be based on the period of follow‐up (short‐term: three months or less; medium‐term: more than three to six months; and long‐term follow‐up: more than six months).
Studies with multiple treatment groups
If selected studies contain more than two intervention groups, two review authors will determine which group is relevant to the systematic review and the particular meta‐analysis based on the inclusion criteria for interventions (see Types of interventions). In case multiple groups in a study are relevant, we will account for the correlation between the effect sizes from multi‐arm studies in a pair‐wise meta‐analysis (Higgins 2011e).
We will formally treat each comparison between a control group and a treatment group as an independent study. We will multiply the standard errors of the effect estimates by an adjustment factor to account for correlation between effect estimates. In doing so, we acknowledge heterogeneity between different treatment groups. If there is an adequate evidence base, we will consider performing a network meta‐analysis (see Data synthesis).
Dealing with missing data
If there are missing data within the RCTs, we will contact the original researchers to provide the missing information (e.g. outcome data). We will compute missing standard deviations of continuous outcomes on the basis of other statistical information (e.g. CIs, standard errors, t values, P values, F values) (Higgins 2011e).
If standard deviations can neither be recovered from reported results nor obtained from the authors, we will consider single imputation by the means of pooled within‐treatment standard deviations from all other studies, providing less than five studies have missing standard deviations. If more than five studies have missing standard deviations, we will perform multiple imputation on the basis of the hierarchical model fitted to the non‐missing standard deviations. We expect to find enough information in all papers to restore standard deviations from the reported results.
We will record missing data and attrition levels for each included trial in the ‘Risk of bias’ tables (beneath the 'Characteristics of included studies' tables). Moreover, we will conduct a sensitivity analysis to examine the consequences of excluding trials with high levels of missing data on the conclusions of the review (see Sensitivity analysis).
Assessment of heterogeneity
We will assess the presence of clinical heterogeneity by comparing the trial and study population characteristics across all eligible trials (e.g. by generating descriptive statistics). In accordance with the Cochrane Handbook (Deeks 2011), we will explore if studies are sufficiently homogenous in terms of participant characteristics, interventions and outcomes.
We will assess methodological diversity by inspecting included studies for variability in study design and risk of bias. In accordance with previous reviews, which have already described the great heterogeneity in resilience intervention studies (Leppin 2014; Macedo 2014; Robertson 2015; Vanhove 2015), we will also discuss different forms of diversity in full in our review.
To assess statistical heterogeneity between included trials within each pair‐wise meta‐analysis (i.e. heterogeneity in observed treatment effects that exceeds sampling error alone), we will rely on forest plots, Chi² test, tau² statistic and I² statistic, as suggested by Deeks 2011. In addition, we will consider G², to take small‐study effects into account (Rücker 2011). Significant statistical heterogeneity will be indicated by a P value on the Chi² test lower than 0.10. Since resilience‐training studies are often conducted with relatively small sample sizes (e.g. Loprinzi 2011; Sood 2014), we acknowledge that the Chi² test has only limited power in such cases. The I² is a descriptive statistic, which equally reflects the percentage of total variation across studies that is due to heterogeneity rather than chance. In accordance with the guidelines of Deeks 2011, we will suppose substantial heterogeneity if an I² is greater than 50%. G² indicates the proportion of unexplained variance, after having allowed for possible small‐study effects (Rücker 2011). No statistical heterogeneity is indicated by a G² near zero.
Assessment of reporting biases
We will assess potential publication bias by inspecting funnel plots (plotting the effect estimates of trials against their standard errors on reversed scales) (Sterne 2011). We acknowledge the fact that funnel plot asymmetry does not necessarily reflect publication bias, but can stem from a number of reasons (Sterne 2011). To differ between real asymmetry and chance, we will follow the recommendations in Sterne 2011 and use Egger’s test (Egger 1997) to test for funnel plot asymmetry, providing there are at least 10 studies included in the meta‐analysis.
Data synthesis
We will synthesise results by describing the resilience interventions, their theoretical concept (when possible), as well as the populations and outcomes studied. We will summarise results in narrative and tabular form. We will perform statistical analyses either in RevMan 2014 or R (R 3.2.2 2015), when appropriate. We will attempt to combine the outcome measures of trials through a pair‐wise meta‐analysis (any resilience training versus control), in order to determine summary (pooled) intervention effects of resilience‐training programmes. The decision to summarise numerical results of RCTs in a pair‐wise meta‐analysis will depend on the number of studies found as well as the heterogeneity of included trials with regard to content or components of resilience interventions, outcomes measured as well as the methodological quality (risk of bias) of selected studies. If intervention studies differ excessively regarding their content, outcomes are too diverse or individual studies are predominantly at high risk of bias, we will not perform a meta‐analysis.
In case a trial reports more than one resilience scale, we will use the scale with better psychometric qualities (as specified in Appendix 3) to calculate effect sizes. If a study reports results for more than one instrument for mental health and well‐being outcomes or for a specific resilience factor, we will select the measure used most often among included studies to calculate effect sizes. In case a study provides data of two instruments used equally frequently in the included RCTs, two review authors (AK, IH) will identify the appropriate measure through discussion (compare Stoffers 2013).
For interventions conducted as preparation for a pre‐defined upcoming stressor or trauma (e.g. military deployment), the stress exposure has to be finished when intervention outcomes are assessed (post‐test or follow‐up) or the stress exposure has to be simulated (e.g. scenarios, video simulation, laboratory stress test) in order to include these studies in the meta‐analysis. This guarantees that the study can be considered as the evaluation of a resilience training and not an intervention fostering related constructs such as mental health.
Since we expect a certain degree of heterogeneity between trials, as indicated by the results of previous reviews (Leppin 2014; Macedo 2014; Robertson 2015), we intend to perform a random‐effects, pair‐wise meta‐analysis using an inverse variance approach, specifically the restricted maximum likelihood method (Veroniki 2015), which is implemented in R (Schwarzer 2015; Viechtbauer 2010). As part of our sensitivity analyses, we will perform both fixed‐effect and random‐effects analyses (see Sensitivity analysis).
Once we have produced a summary of the evidence to date, and only if a pair‐wise meta‐analysis (any resilience training versus control) is possible, we will examine if data are also suitable for a network meta‐analysis (NMA). Network meta‐analyses will be merely exploratory and will only be conducted if the review results in a sufficient and adequate evidence base.
Network meta‐analyses offer the possibility of comparing multiple treatments simultaneously (Caldwell 2005). They combine both direct (head‐to‐head) and indirect evidence (Caldwell 2005; Mills 2012), by using direct comparisons of interventions within RCTs, as well as indirect comparisons across trials on the basis of a common reference group (e.g. an identical control group) (Li 2011). Up to now, a network meta‐analysis on resilience‐training programmes does not exist.
According to Mills 2012, Linde 2016 and the Cochrane Handbook (Higgins 2011e), there are three important conditions for the conduction of NMAs (transitivity, homogeneity, consistency). If a NMA is possible (i.e. the three conditions are fulfilled), we will conduct an analysis ‐ with expert statistical support as suggested by Cochrane (Higgins 2011e) – using a frequentist approach in R (Rücker 2015; Viechtbauer 2015). For sensitivity analyses, the same models will be fitted by the restricted maximum likelihood method (Piepho 2012; Piepho 2014; Rücker 2015). We will consider categorising resilience training into seven groups, based on the underlying training concept: (1) cognitive behavioural therapy, (2) acceptance and commitment therapy, (3) mindfulness‐based therapy, (4) attention and interpretation therapy, (5) problem‐solving therapy, (6) stress inoculation therapy and (7) multimodal resilience training. We may include additional groups after the full literature search has been conducted. Reference groups that will possibly be included in the network meta‐analysis are: attention control, wait‐list, treatment as usual or no intervention. We will investigate inconsistency and flow of evidence in accordance with recommendations in the literature (e.g. Dias 2008; Higgins 2011a; König 2013; Krahn 2013; Krahn 2014; Lu 2006; Lumley 2002; Rücker 2015; Salanti 2008; White 2012a).
Summary of findings
In the review, we will create a ‘Summary of findings’ table per comparison using the software developed by the GRADE Working Group: GRADEpro: Guideline Development Tool (GRADEpro GDT 2015). To create the table, we will consider the comparison between resilience‐training programmes and control group. We will include in the ‘Summary of findings’ table all primary outcomes (resilience, anxiety, depression, stress or stress perception, well‐being or quality of life). Depending on the assessment of heterogeneity and possible effect modifiers (see Subgroup analysis and investigation of heterogeneity), we will create several ‘Summary of findings’ tables, for example, with regard to the clinical status of study populations or the comparator group. We will assess the quality of the body of evidence using the GRADE approach proposed by the GRADE working group (Schünemann 2011; Schünemann 2013).
We will assess the quality of the evidence using the five GRADE considerations: limitations in the design and implementation of available studies (i.e. high risk of bias of studies contributing to the respective outcome), indirectness of evidence (i.e. indirect population, intervention, control, outcomes), unexplained heterogeneity or inconsistency of results (i.e. heterogeneity exists but the subgroup analyses fail to identify a plausible explanation), imprecision of results (i.e. wide CIs) and high probability of publication bias (i.e. high risk of selective outcome reporting bias for studies contributing to the outcome) (Schünemann 2011). The quality assessment will be performed in duplicate, by two review authors (AK, IH), working independently. They will resolve any disagreements by discussion or by consulting a third review author.
Subgroup analysis and investigation of heterogeneity
If substantial heterogeneity is detected, we will examine characteristics of studies that may be associated with this diversity (Deeks 2011). The selection of potential effect modifiers is based on experiences from previous reviews (Leppin 2014; Robertson 2015; Vanhove 2015). We plan to perform the following subgroup analyses:
setting of resilience interventions (group setting versus individual setting versus combined setting);
delivery format of resilience interventions (face‐to‐face versus online versus bibliotherapy versus multimodal delivery);
target group of resilience‐training programmes (employees versus patient populations versus military or police versus general population)³;
theoretical foundation of resilience‐training programmes (cognitive behavioural therapy versus acceptance and commitment therapy versus mindfulness‐based therapy versus attention and interpretation therapy versus problem‐solving training versus stress inoculation versus multimodal resilience training)³; and
comparator group in intervention studies (attention control versus wait‐list control versus treatment as usual versus no intervention).
We will only conduct subgroup analyses if we identify 10 or more studies in the review process (Deeks 2011). Moreover, we will restrict the subgroup analyses to our primary outcomes.
³We will provide details in the ‘Differences between protocol and review’ section of the review if the literature search reveals further relevant groups.
Sensitivity analysis
Comparable to the planned subgroup analyses, we will perform sensitivity analyses on the condition that more than 10 RCTs are included in the review. We will also restrict the sensitivity analyses to the primary outcomes.
With regard to intervention studies assessing resilience via resilience scales, we will perform a sensitivity analysis on the basis of the underlying concept (state versus trait) in these measures and limit the analysis to scales assessing resilience as an outcome of an intervention.
In order to examine the impact of the risk of bias of included trials, we will limit the studies to be included in the sensitivity analysis to those whose risk of bias was rated as low or unclear. We will exclude studies assessed at high risk of bias. For studies with low or unclear risk of bias, we will conduct subgroup analyses.
We also plan to consider the restriction to registered studies. We will identify registration both by recording whether we found a study in a trial registry and by noting whether the author claimed to have registered it.
We will perform sensitivity analyses moreover by limiting analysis to those studies with low levels of missing data (less than 10% missing primary outcome). With regards to coping with missing data, we will limit the analysis to studies where missing data were imputed or accounted for by fitting a model for longitudinal data, or where the proportion of missing primary outcome data was less than 10%.
In addition, we intend to check the robustness of our findings, by performing both fixed‐effect and random‐effects analyses in our sensitivity analyses.
We also plan to perform sensitivity analyses based on the ICC estimate in cluster‐randomised trials without adjustment for clustering by excluding cluster‐RCTs where standard errors were not corrected or corrected only on the basis of an externally‐estimated ICC. In an additional sensitivity analysis, we will replace all externally‐estimated ICCs that were less than 0.10, by 0.10.
Finally, we will conduct a sensitivity analysis with regard to the unit of randomisation by limiting the analysis to individually‐randomised trials.
Acknowledgements
The authors wish to acknowledge the support of the Cochrane Developmental, Psychosocial and Learning Problems Group (CDPLPG), based at Queen's University Belfast. We would like to thank especially Professor Geraldine Macdonald, the Co‐ordinating Editor, Dr Joanne Wilson, the Managing Editor, Gemma O'Loughlin, the Assistant Managing Editor, and Margaret Anderson, the Information Specialist of CDPLPG, for advice and assistance in the editorial process. Furthermore, we would like to acknowledge the support and recommendations of the external referees, the statistician and the members of the Cochrane Editorial Unit.
Appendices
Appendix 1. Evidence rating of modifiable resilience factors
Although an immense number of factors have been discussed in the literature, only a set of psychosocial factors has been scientifically validated as being appropriate determinants of resilience by cross‐sectional and longitudinal (frequently observational) studies in different populations (e.g. patients affected by physical diseases like cancer, diabetes, spinal cord injury, coronary heart disease, etc.; different caregiver groups; individuals after trauma exposure). Upon closer examination, only some of the discussed resilience factors may be viewed as well‐evidenced factors that have also been found to be protective factors in systematic reviews and meta‐analyses (level 1). These factors are most likely to be related to adult resilience, as they were proven in different populations facing various adversities and stressors. However, it has to be kept in mind that the chosen factors represent the current state of knowledge on psychosocial resilience‐promoting factors, and that other factors, which are not yet well researched, could also contribute to resilience.
| Level of evidence and criteria | Resilience factors |
Level 1: strong evidence (SRs and MAs)
| |
| Level 1a: there is evidence for this factor from several SRs AND several MAs (both across different populations) |
|
| Level 1b: there is evidence for this factor from several SRs AND a single MA (both across different populations) |
|
| Level 1c: there is evidence for this factor from several SRs (across different populations) AND a single MA (in the same population) |
|
Level 2: moderate evidence (only SRs or single MA)
| |
| Level 2a: there is evidence for this factor from several SRs (across different populations) OR there is no evidence from SRs, but from a MA (across different populations) |
|
| Level 2b: there is evidence for this factor from several SRs (in the same population) |
|
| Level 2c: there is evidence for this factor from a single SR (in the same population) |
|
Level 3: weak evidence (no SR or MA)
| |
|
|
Footnotes
MA: Meta‐analysis; SR: Systematic review.
Results on systematic reviews and meta‐analyses based on a literature search for potentially modifiable resilience factors in MEDLINE (search strategy: respective resilience factor.tw. AND (review or meta‐analy$).tw.; search limited to “All adults (19 plus years)” and 1990‐2016).
1Cognitive flexibility and religiosity or spirituality are multidimensional concepts resulting in highly ambiguous operationalisations. Cognitive flexibility comprises several concepts, such as positive reappraisal and acceptance (Southwick 2005). Religiosity or spirituality combines affective, behavioural and cognitive dimensions, each measured differently (Ano 2005; Pargament 2000; Salsman 2015).
Appendix 2. Examples of training methods to address resilience factors
| Evidence‐based resilience factor | Examples of training methods to address the resilience factor |
| Meaning in life or purpose in life | Introduce the benefits of purpose in life; support individuals in identifying important sources of meaning (e.g. social relationships, work) as well as in setting priorities and guiding values for their life (e.g. Sood 2011) |
| Sense of coherence (comprehensibility, meaningfulness, manageability) | Promote the understanding of external life challenges, personal beliefs and emotions; encourage participants to reflect on personal (internal or external) resources and to use them more frequently (e.g. Tan 2016) |
| Positive emotions or positive affect | Psychoeducation on emotions; mindfulness techniques; support individuals in identifying pleasant activities to enhance positive emotions (e.g. Jennings 2013) |
| Hardiness (challenge, commitment, control) | Situational reconstruction (i.e. imagination of stressful circumstances); focusing (i.e. reflection on bodily sensations of emotional upset) (e.g. Maddi 1998; Maddi 2009) |
| Self‐esteem | Support participants in identifying personal strengths |
| Active coping (e.g. problem‐solving, planning) | Introduce the problem‐solving model and familiarise participants with the use of active coping strategies in stressful situations (e.g. making action plans) (e.g. Abbott 2009; Bekki 2013; Sahler 2013) |
| Self‐efficacy | Support participants in identifying personal strengths and other sources of self‐efficacy (e.g. social connections); support individuals in realising previous successes (e.g. coping of negative situations) |
| Optimism or positive attributional style | Teach participants to adapt a more positive attributional style for stressful (i.e. external, unstable, specific) and pleasant events (i.e. internal, stable, global); encourage individuals to gain a brighter outlook for the future by enhancing their attention for and the discovery of positive aspects in their lives (e.g. Carver 2010; Sadow 1993) |
| Social support | Encourage the individual’s reflection on his or her current network (i.e. magnitude of social network, positive or negative aspects in social relationships); enhance the individual’s support network by providing them with communication techniques (e.g. Kent 2011; Schachman 2004; Sood 2011; Steinhardt 2008) |
| Cognitive flexibility (e.g. positive reappraisal, acceptance of negative situations and emotions) | Positive reappraisal: introduction of ABC (Activating Event, Belief, Consequence) Technique of Irrational Beliefs (Ellis 1957) of cognitive therapy; train participants in identifying and challenging maladaptive thoughts and replacing them by more positive ones (e.g. Abbott 2009; Farchi 2010; Songprakun 2012; Steinhardt 2008) Acceptance: relaxation or mindfulness techniques |
| Religiosity or spirituality or religious coping (e.g. frequent religious attendance) | Spiritual exercises like meditation or yoga; psychoeducation on coping strategies like regular praying or participating in religious community activities (e.g. worship) (e.g. Sood 2011) |
Appendix 3. Potential instruments for the measurement of psychological resilience based on previous reviews (Leppin 2014; Macedo 2014; Robertson 2015) and additional literature searches
| N° | Measure | Theory and item selection | Internal consistency | Validity | Rating |
| 1 | Resilience Scale (RS‐25) (Wagnild 1993)4 |
+ | +++ | +++ | 6✦ |
| 2 | Brief Resilience Scale (BRS) (Smith 2008) |
+ | +++ | +++ | 6✦ |
| 3 | Ego Resiliency (Klohnen 1996)4 |
+ | ++ | +++ | 5✦ |
| 4 | Connor ‐ Davidson Resilience Scale (CD‐RISC) (Connor 2003) |
+ | ++ | +++ | 5✦ |
| 5 | Resilience Scale for Adults (RSA33) (Friborg 2005) |
+ | ++ | +++ | 5✦ |
| 6 | Trauma Resilience Scale (TRS37) (Madsen 2010) |
+ | +++ | ++ | 5✦ |
| 7 | Ego ‐ Resiliency Scale (ER89) (Block 1996)4 |
‐ | ++ | +++ | 5✧ |
| 8 | Resilience Scale (RS‐14) (Wagnild 2010)4 |
+ | +++ | + | 4✦ |
| 9 | Resilience Scale for Adults (RSA37) (Friborg 2003) |
+ | ++ | ++ | 4✦ |
| 10 | Resilience at Work Scale (Winwood 2013) |
+ | ++ | ++ | 4✦ |
| 11 | Workplace Resilience Inventory (WRI) (McLarnon 2013) |
+ | ++ | ++ | 4✦ |
| 12 | Multidimensional Trauma Recovery and Resiliency Scale (MTRR) (Harvey 2003) |
+ | +++ | + | 4✦ |
| 13 | Resiliency Attitudes and Skills Profile (RASP) (Hurtes 2001) |
+ | +++ | + | 4✦ |
| 14 | Resilience Appraisals Scale (RAS) (Johnson 2010) |
‐ | +++ | + | 4✧ |
| 15 | Revised Ego Resiliency 89 Scale (ER89‐R) (Alessandri 2007)4 |
+ | ++ | + | 3✦ |
| 16 | Ego Resiliency (Bromley 2006)4 |
+ | ++ | + | 3✦ |
| 17 | Connor ‐ Davidson Resilience Scale (CD‐RISC‐10) (Campbell‐Sills 2007) |
+ | ++ | + | 3✦ |
| 18 | Resilience Scale for Adults (RSA45) (Hjemdal 2001) |
+ | +++ | ‐ | 3✦ |
| 19 | Brief Resilient Coping Scale (BRCS) (Sinclair 2004) |
+ | + | ++ | 3✦ |
| 20 | Trauma Resilience Scale (TRS48) (Madsen 2010) |
+ | +++ | ‐ | 3✦ |
| 21 | Child and Youth Resilience Measure ‐ 28 (CYRM‐28) (Liebenberg 2012; Ungar 2008) |
+ | +++ | ‐ | 3✦ |
| 22 | Post‐traumatic Growth Inventory (PTGI) (Tedeschi 1996)5 |
+ | ++ | + | 3✦ |
| 23 | Adolescent Resilience Scale (Oshio 2002; Oshio 2003) |
‐ | ++ | + | 3✧ |
| 24 | Resilience and Reintegration (20 items drawn from Spirit Core Scale) (Waite 2004) |
‐ | +++ | ‐ | 3✧ |
| 25 | Psychological resilience (Windle 2008) |
+ | ++ | ‐ | 2✦ |
| 26 | Child and Youth Resilience Measure ‐ 12 (CYRM‐12) (Liebenberg 2013) |
+ | ++ | ‐ | 2✦ |
| 27 | Resilience scale (Bekki 2013) |
+ | ++ | ‐ | 2✦ |
| 28 | Perceived resilience (Van der Kleij 2011) |
‐ | ++ | ‐ | 2✧ |
| 29 | Romanian Scale of Resilience to Occupational Stress (SROS) (Aniţei 2012) |
‐ | ‐ | ‐ | 0✧ |
Footnotes
The resilience scales are specified hierarchically according to psychometric quality criteria.
Theory & item selection: ‐ (✧): no description of theory or item selection process available; and + (✦): description of theory or item selection process available.
Internal consistency (Cronbach’s alpha): ‐ (0): no information; + (1): α < 0.70; ++ (2): α ≥ 0.70; and +++ (3): α > 0.90.
Validity (convergent/divergent or criterion validity): ‐ (0): no information; + (1): correlations (r) with construct‐related measures or criterions available, all correlations < 0.50 or resilience measure only correlated with original instrument/long‐form or no correlations but alternative results reported (e.g. odds ratio); ++ (2): correlations (r) with construct‐related measures or criterions available, ≤ 50% of correlations ≥ 0.50; and +++ (3): correlations (r) with construct‐related measures or criterions available, > 50% of correlations ≥ 0.50.
4Scales assessing resilience as personality characteristic.
5Scale assessing post‐traumatic growth.
Appendix 4. Possible assessment instruments for the measurement of mental health and well‐being based on intervention studies included in previous reviews and meta‐analyses (Leppin 2014; Macedo 2014; Robertson 2015; Vanhove 2015)
-
Anxiety
Depression Anxiety and Stress Scales (DASS‐21) (Lovibond 1995)
Smith Anxiety Scale (SAS) (Smith 2007)
Beck Anxiety Inventory (BAI) (Beck 1993)
State‐Trait Anxiety Inventory (STAI) (Spielberger 1970)
-
Depression
Depression Anxiety and Stress Scales (DASS‐21) (Lovibond 1995)
Center for Epidemiological Studies ‐ Depression Scale (CES‐D) (Radloff 1977)
Maslach Burnout Inventory (MBI) (Maslach 1997)
Oldenburg Burnout Inventory (Demerouti 2010)
Beck Depression Inventory (BDI) (Beck 1961)
Beck Depression Inventory ‐ II (BDI‐II) (Beck 1996)
Visual Analog Scale ‐ Fatigue (VAS‐Fatigue) (Wolfe 2004)
Patient Health Questionnaire for Depression (PHQ‐D) (Spitzer 1999)
Hospital Anxiety and Depression Scale (HADS) (Zigmond 1983)
Time Urgency Scale (TUS) (Landy 1991)
-
Stress or stress perception
Depression Anxiety and Stress Scales (DASS‐21) (Lovibond 1995)
Perceived Stress Scale (PSS) (Cohen 1988)
Personal Stress Scale (PSS) (self‐developed) (Petree 2012)
Subjective Units of Distress (SUDS) (Wolpe 1958)
Visual Analog Scale (VAS) (Arnetz 1985; Hasson 2005)
Stress and Perception of Control Scale (SPOCS) (unpublished instrument) (Rose 2013)
-
Well‐being or life satisfaction or quality of life or vitality or vigour
-
Well‐being
-
Life satisfaction:
Satisfaction with Life Scale (Diener 1985)
-
(Health‐related) Quality of life (QOL):
Linear Analog Self‐Assessment Scale (QOL‐LASA) (Locke 2007)
Medical Outcomes Study (MOS) 36‐item short‐form health survey (SF‐36) (Ware 1994)
World Health Organization Quality of Life – BREF (WHOQOL‐BREF) (WHOQOL Group 1998)
-
Vitality
Subscale of the MOS 36‐item short‐form health survey (SF‐36) (Ware 1994)
-
Vigour
Work Vigour subscale of the Utrecht Work Engagement scale (Schaufeli 2002)
-
Appendix 5. Possible assessment instruments for the measurement of resilience factors based on intervention studies included in previous reviews and meta‐analyses (Leppin 2014; Macedo 2014; Robertson 2015; Vanhove 2015)
-
Social support
Interpersonal Support Evaluation List ‐ 12 (ISEL‐12) (Cohen 1983)
Personal Resources Questionnaire (PRQ‐85) (Brandt 1981)
Social Provisions Scale (Cutrona 1987)
Subscale Interpersonal relations of the Health‐Promoting Lifestyle Profile II (Walker 1987)
Interpersonal Relationship Inventory (IPR) (Tilden 1990)
Support questionnaire (Cushway 1996)
MOS Social Support Survey (Sherbourne 1991)
Total of four scales devised by Moos (1979) for perceived social support (Maddi 1998)
-
Optimism
Life Orientation Test ‐ Revised (LOT‐R) (Scheier 1994)
-
Self‐efficacy
Coping self‐efficacy (CSE) (Chesney 2003)
Self‐efficacy scale (Sherer 1982)
Teachers’ Sense of Efficacy Questionnaire (TSES) (Tschannen‐Moran 2001)
New General Self‐Efficacy Scale (NGSE) (Chen 2004)
Coping Efficacy Scale (self‐developed) (Bekki 2013)
-
Active coping
Brief Coping Orientations to Problems Experienced scale (Brief COPE) (Carver 1997)
Ways of Coping Questionnaire (WOC) (Folkman 1988)
Coping Styles Questionnaire (CSQ) (Williams 1997)
Coping Styles (self‐developed) (Bekki 2013)
-
Self‐esteem
Rosenberg Self‐Esteem Scale (RSES) (Rosenberg 1965)
Self‐Esteem Rating Scale (SERS) (Nugent 1993)
-
Hardiness
HardiSurvey III ‐ R (Maddi 2001)
Personal Views Survey (Maddi 1987)
Hardiness Scale or College Student Hardiness Measure (CSHM) (Atri 2007a; Atri 2007b; Kanekar 2009)
Cognitive Hardiness Scale (Nowack 1990)
-
Positive emotions or positive affect
Positive and Negative Affect Schedule (PANAS) (Watson 1988)
Positive and Negative Affect Schedule Expanded Form (PANAS‐X) (Watson 1994)
Authentic Happiness Inventory (AHI; unpublished measure) (Abbott 2009)
Appendix 6. MEDLINE search strategy (January 1990 to present)
In order to get a comprehensive understanding of the evidence in the field of psychological resilience interventions, and to identify training programmes that can really be assumed to enhance resilience in adults based on scientific findings, we will perform a literature search that combines and complements the search approaches from previous reviews and meta‐analyses.
In contrast to the search strategy of Leppin 2014, Robertson 2015 and Vanhove 2015, who used very narrow search terms (e.g. ‘resilience programme’ or 'hardiness training’), we will also search for broader intervention terms. These broader search terms will be based on the search performed by Macedo 2014, but will also be supplemented by new terms (e.g. ‘acceptance and commitment therapy’, ‘stress management’, ‘mindfulness’).
1 Resilience, Psychological/ 2 social adjustment/ 3 Adaptation, Psychological/ 4 (post‐traumatic growth or posttraumatic growth or stress‐related growth).tw,kf. 5 (positiv$ adj1 (adapt$ or adjust$)).tw,kf. 6 (psychol$ adj1 (adapt$ or adjust$)).tw,kf. 7 (resilien$ or hardiness$).tw,kf. 8 (cope or coping).tw,kf. 9 ((withstand$ or overcom$ or resist$ or recover$ or thriv$ or adapt$ or adjust$ or bounc$ back) adj5 (stress$ or trauma$ or adversit$)).tw,kf. 10 or/1‐9 11 exp psychotherapy/ 12 Stress, Psychological/th 13 (psychotherap$ or psycho‐therap$).tw,kf. 14 (behav$ adj3 (intervention$ or program$ or therap$)).tw,kf. 15 ((cognit$ or cognitive behavior$ or CBT) adj3 (intervention$ or program$ or therap$)).tw,kf. 16 (psycho$ adj3 (intervention$ or program$ or therap$)).tw,kf. 17 relaxation.tw,kf. 18 mindful$.tw,kf. 19 (counsel?ing or coaching).tw,kf. 20 (third wave adj (psycho$ or therap$)).tw,kf. 21 cognit$ restructur$.tw,kf. 22 positive psychology.tw,kf. 23 (refram$ or re‐fram$ or reapprais$).tw,kf. 24 (stress adj1 (inoculation or manag$ or reduc$ or resist$)).tw,kf. 25 (anxiety adj3 manage$).tw,kf. 26 "acceptance and commitment ".tw,kf. 27 Combined Modality Therapy/ 28 (multimodal or multi‐modal or combined modal$).tw,kf. 29 exp Health promotion/ 30 (health adj3 (educat$ or promot$)).tw,kf. 31 or/11‐30 32 10 and 31 33 (resilien$ adj5 (train$ or program$ or intervention$ or promot$ or prevent$ or enhanc$ or learn$ or teach$ or educat$ or increas$ or develop$ or manag$ or therap$ or protocol$ or treat$)).tw,kf. 34 (hardiness$ adj5 (train$ or program$ or intervention$ or promot$ or prevent$ or enhanc$ or learn$ or teach$ or educat$ or increas$ or develop$ or manag$ or therap$ or protocol$ or treat$)).tw,kf. 35 or/32‐34 36 randomized controlled trial.pt. 37 controlled clinical trial.pt. 38 randomi#ed.ab. 39 placebo$.ab. 40 drug therapy.fs. 41 randomly.ab. 42 trial.ab. 43 groups.ab. 44 or/36‐43 45 exp animals/ not humans.sh. 46 44 not 45 47 35 and 46 48 limit 47 to yr="1990 ‐Current"
Appendix 7. Data collection/extraction sheet (items according to Higgins 2011c)
| Source |
|
| Eligibility |
|
| Methods |
|
| Participants |
|
| Interventions |
|
| Outcomes |
|
| Results |
|
| Miscellaneous aspects |
|
*Full description required for standard items in ‘Risk of bias’ tool.
CI: confidence interval; ID: identifier; SD: standard deviation.
Appendix 8. Criteria for 'Risk of bias' assessment in included RCTs (according to Higgins 2011d)
| Item | Judgment | Description |
| 1. Random sequence generation (selection bias). We will describe the method used to generate the allocation sequence in sufficient detail for each included trial to allow an assessment of whether it should produce comparable groups. | Low risk | The investigators describe a random component in the sequence generation process such as:
*Minimisation may be implemented without a random element (treatment sums are equal), and this is considered to be equivalent to being random. |
| High risk | The researchers describe a (systematic or non‐systematic) non‐random component in the sequence generation process such as:
|
|
| Unclear risk | Insufficient information to permit a judgment of ‘Low risk’ or ‘High risk’. | |
| 2. Allocation concealment (selection bias). For each RCT we will describe the method used to conceal the allocation sequence in sufficient detail to determine whether intervention allocations could have been foreseen in advance of, or during, enrolment. | Low risk | Participants and investigators enrolling participants could not foresee assignment because one of the following, or an equivalent method, was used to conceal allocation:
|
| High risk | Participants or investigators enrolling participants could possibly foresee assignment and thus introduce selection bias because one of the following methods was used:
|
|
| Unclear risk | Insufficient information to permit judgement of ‘Low risk’ or ‘High risk’. This is usually the case if the method of concealment is not described or not described in sufficient detail to allow a definite judgment (e.g. if the use of assignment envelopes is described, but it remains unclear whether envelopes were sequentially numbered, opaque and sealed). | |
| 3. Blinding of participants and personnel (performance bias): objective outcomes. For each included trial, we will describe all methods used to blind trial participants and personnel from knowledge of which intervention a participant received. We will provide any information relating to whether the intended blinding was effective. We will assess blinding separately for different classes of outcomes. Outcomes will be divided into objective (e.g. cortisol) and subjective (e.g. self‐reported resilience and other psychological outcomes). We will consider the same outcomes at different time points. | Low risk | Any one of the following:
|
| 4. Blinding of participants and personnel (performance bias): subjective outcomes. For each included trial we will describe all methods used to blind trial participants and personnel from knowledge of which intervention a participant received. We will provide any information relating to whether the intended blinding was effective. We will assess blinding separately for different classes of outcomes. Outcomes will be divided into objective (e.g. cortisol) and subjective (e.g. self‐reported resilience and other psychological outcomes). We will consider the same outcomes at different time points. | Low risk | Blinding of participants and intervention providers, and unlikely that the blinding could have been broken. |
| High risk | Any one of the following:
|
|
| Unclear risk | Insufficient information to permit a judgment of ‘Low risk’ or ‘High risk’. | |
| 5. Blinding of outcome assessors (detection bias): objective outcomes. For each included trial we will describe all methods used to blind outcome assessors from knowledge of which intervention a participant received. We will provide any information relating to whether the intended blinding was effective. We will assess blinding separately for different classes of outcomes. Outcomes will be divided into objective (e.g. cortisol) and subjective (e.g. self‐reported resilience and other psychological outcomes). We will consider the same outcomes at different time points. | Low risk | Any one of the following:
|
| 6. Blinding of outcome assessors (detection bias): subjective outcomes. For each included trial we will describe all methods used to blind outcome assessors from knowledge of which intervention a participant received. We will provide any information relating to whether the intended blinding was effective. We will assess blinding separately for different classes of outcomes. Outcomes will be divided into objective (e.g. cortisol) and subjective (e.g. self‐reported resilience and other psychological outcomes). We will consider the same outcomes at different time points. | Low risk | Any one of the following:
|
| High risk | Any one of the following:
|
|
| Unclear risk | Insufficient information to permit a judgment of ‘Low risk’ or ‘High risk’. | |
| 7. Incomplete outcome data (attrition bias). For each RCT we will describe the completeness of outcome data for each main outcome, including attrition and exclusions from the analysis. We will state whether attrition and exclusions were reported, the numbers included at each stage (compared with the total number of participants randomised), reasons for attrition or exclusions (where reported), and whether missing data were balanced across groups or were related to outcomes. Where sufficient data are reported, or can be provided by the trial authors, we will re‐include missing data in the analyses. | Low risk | Any one of the following:
|
| High risk | Any one of the following:
|
|
| Unclear risk | Insufficient reporting of attrition or exclusions to permit a judgement of ‘Low risk’ or ‘High risk’ (e.g. number randomised not stated, no reasons for missing data provided, number of dropouts not reported for each group). | |
| 8. Selective outcome reporting (reporting bias). For each included trial we will describe how the possibility of selective outcome reporting was examined and what was found. | Low risk | Any of the following:
|
| High risk | Any one of the following:
|
|
| Unclear risk | Insufficient information to permit a judgment of ‘Low risk’ or ‘High risk’. |
Footnotes
RCT: randomised controlled trial.
Contributions of authors
All seven review authors contributed to the development of this protocol. The first authorship as well as the overall responsibility for the review is shared by IH and AK. IH, AK, AC and KL conceived the initial review design and developed the protocol. JK and HB provided expert statistical support for the planned data analysis. JK, HB, MW and KL critically commented on the protocol. All protocol authors agreed on this version before publication.
Sources of support
Internal sources
-
German Resilience Center, Langenbeckstr. 1, Geb. 701, 55131 Mainz, Germany.
Home institution of IH, AK, AC, MW and KL; support provided in the form of salary and resources
-
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Germany.
Home institution of JK and HB; support provided in the form of salary and resources
External sources
None, Other.
Declarations of interest
Isabella Helmreich ‐ none known. Angela Kunzler ‐ none known. Andrea Chmitorz ‐ none known. Jochem König ‐ none known. Harald Binder ‐ none known. Michèle Wessa ‐ none known. Klaus Lieb ‐ is a board‐certified cognitive‐behaviour therapist with a special interest in schema therapy. Klaus Lieb is an Editor with the Cochrane Developmental, Psychosocial and Learning Problems Group.
New
References
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