Background
Online self-management of depression and anxiety has evolved as a popular, clinically effective and cost-efficient public health solution to reducing the personal and societal burden associated with unmet treatment need [
1],[
2]. Grounded predominantly in cognitive behaviour therapy (CBT), and increasingly incorporating other therapeutic approaches [
3],[
4], online psychological interventions help people with symptom management by teaching skills to regain control over and change problematic thoughts and behaviours (including cognitive restructuring, problem solving techniques and behavioural activation [
5]). Whereas effect sizes in studies of online interventions compare well with face-to-face treatments [
6], the psychological mechanisms that explain these findings are largely unknown. Understanding how, why and for whom interventions affect symptom change is critical for maximising the clinical potency and cost effectiveness of online public health interventions for common mental disorders. Furthermore, rates of adherence with these interventions, which are characteristically low [
7], may be improved by incorporating program content and functions that increase therapeutic efficiency by targeting intervening processes directly [
8].
A potential framework for understanding the effects of online interventions for mental health problems is provided by Bandura’s Social Learning Theory (SLT; [
9]), a theory that specifies multiple interacting determinants of behaviour and behaviour change. According to Bandura, a putative contributor to therapeutic outcomes in psychological interventions is perceived self-efficacy, that is, the degree to which an individual believes that he or she can perform a specific behaviour or set of behaviours. In support of Bandura, self-efficacy has been identified as a key factor explaining treatment gains and behavioural change in several studies of health promoting behaviours, including smoking cessation, reducing alcohol and drug use, weight loss, and chronic disease self-management e.g., [
10]-[
14]. Findings show that higher levels of pre-treatment self-efficacy and increased self-efficacy over the course of treatment are important predictors of therapeutic success, and suggest that precise targeting of self-efficacy antecedent processes and information cues may assist in honing treatment efficiency and efficacy [
9].
Theoretical models posit that self-efficacy impacts therapeutic outcomes by affecting individuals’ decisions to change their behaviour, and by influencing “
how much effort people will expend and how long they will persist in the face of obstacles and aversive experiences” ([
9] p. 194). Self-efficacy is likely, therefore, to be an important factor contributing to symptom and functional gains within the context of online interventions, particularly those with minimal therapist input. This is because such interventions require the active cognitive and behavioural involvement of the individual [
15],[
16], as well as the ongoing practice and implementation of therapeutic skills (e.g., self-monitoring, activity scheduling, and problem solving; [
15],[
16]), often in the face of challenges and difficult experiences.
Previous reviews support a mediating role of cognitive variables (including dysfunctional attitudes, automatic thoughts and attributional styles) in recovery from mental health problems [
17],[
18] and, more recently, a construct related to self-efficacy, namely ‘perceived control’, has been shown to predict outcomes of online therapist-assisted CBT for depression [
4]. Increased self-efficacy beliefs have also been linked with more effective emotion regulation and psychosocial functioning [
19]. However, to our knowledge, no research has examined whether symptom and functional outcomes in online self-help interventions are associated with changes in self-efficacy beliefs over the course of treatment (i.e., that improvements in self-efficacy account for treatment gains), and pre-treatment self-efficacy remains largely unexplored as a potential determinant of therapeutic gains in online CBT interventions (that is, self-efficacy as moderator of treatment outcomes).
Randomised controlled trials (RCTs) provide the ideal context in which to examine possible determinants of psychotherapy outcomes [
20]. In a recently conducted large scale RCT, we showed that a fully-automated public health intervention combining mobile phone and web technology,
myCompass, effectively reduced symptoms of depression, anxiety and stress and improved work and social functioning for people with symptoms in the mild-to-moderate range [
21]. This paper reports outcomes of a secondary objective of the RCT, namely, to explore the possibility that self-efficacy contributes to symptom improvement and functional gains. Specifically, using data collected at baseline and post-intervention, we tested the hypotheses that: (a) use of the mobile phone and web intervention would increase people’s confidence in their ability to manage their mental health problems, that is their mental health self-efficacy (MHSE), relative to active control (AC) and waitlist (WL) conditions; (b) MHSE would account for all or part of the effect of the intervention on mental health symptom and functional outcomes (i.e., MHSE mediates the treatment effect); and (c) the effect on outcomes of the intervention would differ for those with high and low pre-intervention levels of MHSE (i.e., MHSE moderates the treatment effect).
Self-efficacy is a task-specific construct that varies across distinct groups of behaviours [
22]. In contrast with the plethora of self-efficacy scales for physical health and lifestyle improvement, we were able to locate only one scale measuring people’s confidence in managing mental health issues [
23]. Developed and validated for use in people with severe mental illness, Carpinello et al.’s [
23] Mental Health Confidence Scale relies heavily on recovery-related items, including items referring to mental illness diagnosis and treatment, and may be inappropriate for people with symptoms in the mild-to-moderate range who are unlikely to consider themselves unwell, meet diagnostic criteria or seek treatment [
24]. Accordingly, in order to investigate the effects of self-efficacy on therapeutic gains in online psychological interventions, we developed and psychometrically evaluated a new measure of MHSE for common mental health problems, the Mental Health Self-efficacy Scale (MHSES).
Discussion
In the present study, we provide preliminary data on a new scale measuring people’s confidence in managing issues related to their mental health, the MHSES. We also explored hypotheses derived from Bandura’s SLT: first, that symptom and functional gains in a mobile phone and web psychotherapeutic intervention would be mediated by MHSE; and second, that program outcomes would differ between people with high and low levels of pre-intervention MHSE.
Data from both Studies I and II provide support for the MHSES as a parsimonious and reliable measure of MHSE, with high construct validity. Factor analysis showed that the Scale is best considered unidimensional - the high internal consistency estimate providing further evidence that scale items function well together to consistently measure MHSE. Moderate correlations in the expected direction with measures of depressive symptoms, overall psychological distress, work and social functioning and emotional stability support the construct validity of the MHSES, while at the same time indicating that the scale measures a discrete construct. Harrison et al. [
25] have previously reported sensitivity of MHSES scores to change, a finding consistent with Bandura’s [
9] proposition that self-efficacy is a malleable psychological state, as opposed to a more permanent personality trait. Together, the available data provide preliminary endorsement for the MHSES as a psychometrically sound and easily administered measure of MHSE. Further testing of the measure in other mental health interventions, including face-to-face therapies, is essential, as is comparing the Scale’s results with those derived from measures of other related psychological states, such as generalised self-efficacy, coping skills and perceived control.
In Study II, use of the mobile phone and web-based intervention was associated with increased MHSE, and MHSE was linked with reduced depression, anxiety and stress symptoms, and improved work and social functioning. Importantly, we also found evidence for a potential mediating effect of MHSE on anxiety and stress symptoms, with improvements in MHSE associated with the greatest symptom gains. Together, these findings are in line with studies showing the benefits for health behaviours and physical health outcomes of interventions that enhance self-efficacy [
10]-[
14], and are consistent with findings supporting the role of cognitive factors, including perceived control, as mediators of outcomes of face-to-face [
17],[
18] and web-based therapies [
4].
Data also identified MHSE as a potential moderator of treatment outcomes in the mobile phone and web-based intervention. Interestingly, while Bandura’s SLT posits greater therapeutic gain for people with high pre-treatment MHSE (due to their perception of tasks as being within their control, as well as their greater motivation, and more active task engagement), we found that users of the intervention with low MHSE typically reported the greatest symptom improvement. One possibility is that gains were greatest for low self-efficacy users because their higher symptom scores at baseline left them with greater potential for improvement. Alternatively, given that individuals with low self-efficacy typically lack confidence and require more guidance in managing activities [
44], a self-efficacy enhancing web-based intervention (like myCompass) may provide exactly what they need; the skills, motivation, and self-assurance necessary to better manage their mental health symptoms.
Although unexpected, the finding that MHSE did not mediate or moderate work and social functioning outcomes is most likely reflective of the behaviour-specific nature of SE beliefs [
22]. We speculate that MHSE beliefs may be more predictive of people’s functioning in the mental health domain (for example, treatment attendance, medication adherence, and active self-monitoring). This question needs to be explored in further research.
Implications for program design and clinical practice
The finding that MHSE enhancement mediated symptom improvement suggests that precise targeting of MHSE may have the potential to increase the therapeutic potency and clinical efficiency of online interventions for common mental health problems. Research has shown that self-efficacy can be reinforced via a range of information sources, including performance mastery, verbal persuasion and social influence, vicarious learning, and emotional arousal [
15], and studies show that self-management programs incorporating these strategies produce more favourable physical health outcomes [
45],[
46]. In the case of myCompass, Bandura’s SLT may provide a useful theoretical basis upon which the program’s self-efficacy promoting content and functions can be enhanced.
The analyses also indicated a sub-set of individuals with symptoms in the mild-to-moderate range who may indeed benefit most from web-based psychotherapeutic interventions, namely those with low MHSE. Primary care of people with symptoms in this range is often complicated by the fact that providers, especially general practitioners (GP), face difficulties identifying which of their patients will take-up and benefit from the various treatment options available (e.g., face-to-face or online psychotherapy, supportive counselling, and medication; [
47],[
48]). At minimum, our findings suggest that screening of patients using a short, simple, measure of MHSE (such as the MHSES) might be useful for recognising patients who are most likely to benefit from self-help interventions delivered online.
Study limitations and future research
Some limitations of the study should be noted. First, data were derived from volunteers with mild-to-moderate symptoms who agreed to use a mobile phone and web-based self-help psychotherapeutic intervention. It is possible, therefore, that our findings are not generalisable to non-volunteers, whose decision not to use a self-help intervention may variously reflect people’s low or high confidence that they can self-manage their mental health symptoms. Future studies might shed light on this issue.
Second, although our research design enables us to examine the status of MHSE as a potential mediator of symptom and functional outcomes in web-based interventions [
20], we are prohibited from making firm statements about the causal role of the construct in determining treatment gains. It is not possible, for example, for us to discount the possibility that change in MHSE is an epiphenomenon of improved mental wellbeing. A more statistically robust test of mediation would require demonstration of change in MHSE prior to change in symptom and functional outcomes. Alternatively, if another RCT demonstrated increased effectiveness of a web-based psychotherapeutic intervention after more precise targeting of MHSE, then confidence in the causal role of MHSE would increase [
20].
Finally, MHSE was the only potential mediator considered in this study, thereby precluding us from commenting on its relative utility in predicting symptom and functional outcomes in web-based psychotherapies. For example, there are other variables from Bandura’s SLT, including outcome expectancies and personal goals [
9], that may combine with such cognitive variables as attitudes, thoughts, and attributional styles, to affect outcomes of face-to-face and online therapies. Multiple mediator models in which MHSE is pitted along-side other theoretically relevant mediator variables should be studied. As suggested by our data, it is likely that differences exist in the putative mediators of mental health symptom versus functional outcomes in web-based interventions, with important implications for designing program content and functions.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
JC undertook data collection, and performed the statistical analyses and data interpretation, and drafted the manuscript; JP conceived and designed the study, contributed to questionnaire development, supervised the data collection, participated in the data analysis and interpretation of results, and carried out critical revision for intellectual content; MRB undertook data collection and revised the manuscript critically; AW assisted with data collection, statistical analysis and interpretation of results, and revised the manuscript for intellectual content; GP was involved in the concept and design of the study and revised the manuscript critically; VM participated in the design and concept of the study, questionnaire development and manuscript review; VH was involved in the concept and design of the study, questionnaire development, and revision of the manuscript; HC participated in manuscript preparation and revision for intellectual content; DH-P was involved in the concept and design of the study, questionnaire development, statistical analysis and manuscript revision. All authors read and approved the final manuscript.