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Effectiveness of online and mobile telephone applications (‘apps’) for the self-management of suicidal ideation and self-harm: a systematic review and meta-analysis

BMC Psychiatry volume 17, Article number: 297 (2017) Cite this article

Abstract

Background

Online and mobile telephone applications (‘apps’) have the potential to improve the scalability of effective interventions for suicidal ideation and self-harm. The aim of this review was therefore to investigate the effectiveness of digital interventions for the self-management of suicidal ideation or self-harm.

Methods

Seven databases (Applied Science & Technology; CENTRAL; CRESP; Embase; Global Health; PsycARTICLES; PsycINFO; Medline) were searched to 31 March, 2017. Studies that examined the effectiveness of digital interventions for suicidal ideation and/or self-harm, or which reported outcome data for suicidal ideation and/or self-harm, within a randomised controlled trial (RCT), pseudo-RCT, or observational pre-test/post-test design were included in the review.

Results

Fourteen non-overlapping studies were included, reporting data from a total of 3,356 participants. Overall, digital interventions were associated with reductions for suicidal ideation scores at post-intervention. There was no evidence of a treatment effect for self-harm or attempted suicide.

Conclusions

Most studies were biased in relation to at least one aspect of study design, and particularly the domains of participant, clinical personnel, and outcome assessor blinding. Performance and detection bias therefore cannot be ruled out. Digital interventions for suicidal ideation and self-harm may be more effective than waitlist control. It is unclear whether these reductions would be clinically meaningful at present. Further evidence, particularly with regards to the potential mechanisms of action of these interventions, as well as safety, is required before these interventions could recommended.

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Background

Self-harm, which includes intentional self-injury or self-poisoning irrespective of type of motivation and/or degree of suicidal intent [1], and attempted suicide, which refers to any intentionally self-inflicted self-injurious and/or self-poisoning behaviour with clear suicidal intent [2, 3], are associated with suicidal ideation [4], non-fatal repetition of self-harm, and completed suicide [5]. Although in the United States of America (USA) distinction is frequently made between non-suicidal self-injury and suicidal behavior [6], outside of the USA these terms are yet to receive widespread acceptance [7]. Instead, this review includes all forms of self-inflected self-injury or self-poisoning, irrespective of type of motivation or degree of suicidal intent, which we refer to collectively as ‘self-harm’ [8].

Effective face-to-face treatments for self-harm and suicidal ideation are available [9,10,11]. Many effective psychotherapeutic options for the treatment of suicidal ideation and self-harm are resource intensive and require specialist clinician training, however. Resource limitations in some low-to-middle income countries therefore limits access to these professionals. Negative associations have, for example, been found between per capita availability of mental health services and suicide rates in a number of countries [12,13,14].

Even in countries where access to psychotherapy for suicidal ideation and self-harm are available, less than one-half of those who self-harm receive treatment [15]. There are a number of barriers to treatment for those who experience self-harm or suicidal ideation, including: beliefs that treatment is not warranted and/or is likely to be ineffective, stigma, shame, negative prior experiences with mental health care providers, and financial difficulties [15]. Young people in particular also cite a preference for self-management as a major obstacle to help-seeking for self-harm from clinical services [16].

Given that an estimated 85% of the global population is covered by a commercially-available wireless signal, and further, over five billion persons have a mobile phone subscription [17], digital interventions, including both online programs and mobile telephone applications (‘apps’) (collectively referred to here as ‘digital interventions’), have been proposed as one mechanism by which the scalability of effective treatments for self-harm and suicidal ideation may be improved [18]. Such interventions may also help to overcome some of the attitudinal and structural barriers which prevent those who engage in self-harm from accessing clinical services [19], and may therefore represent a valuable addition to a stepped care treatment model in which access is improved through the provision of lower intensity ‘self-help’ interventions in addition to high intensity psychotherapeutic treatments for those whose symptoms do not resolve.

To date, the effectiveness of these digital interventions has not been routinely evaluated. This is problematic given that the widespread implementation of these interventions without appropriate evaluation of their useability and effectiveness could lead to the development and promulgation of ineffective, or even harmful, interventions [20]. We therefore present a systematic review and meta-analysis of the characteristics and effectiveness of digital interventions, including both online resources and mobile telephone apps for suicidal ideation and self-harm.

Methods

The reporting of this systematic review and meta-analysis conforms to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [21].

Search strategy and selection criteria

We searched for literature indexed in seven electronic databases covering a wide range of disciplines, including: computing and information technology (Applied Science & Technology) clinical trials (Cochrane Central Register of Controlled Trials [CENTRAL]), medicine (Embase; Medline), psychology (PsycARTICLES; PsycINFO), and public health (Global Health) as well as a database that specifically indexes literature on interventions for the prevention of suicide (Centre for Research Excellence in Suicide Prevention [CRESP]). Clinical trial registries were also searched using these same keywords to identify ongoing studies. All databases were searched from their respective start dates until 31 March, 2017.

A two stage process was used to locate relevant studies. At the first stage, keywords inclusive of digital interventions and platforms were combined. Next, using standard Boolean operators, these were combined with keywords related to suicidal ideation and self-harm. There were no restrictions either on publication language or status. For further information on this electronic search strategy, please see the Appendix.

Ancestry searches were also conducted by manually screening the reference lists of included studies and previous reviews [22,23,24,25,26]. Where information on either study design, methodology, or results was either unclear or missing from the published study, we sought clarification from corresponding authors.

Selection criteria

Studies were eligible for inclusion if: (1) the effectiveness of a standalone digital intervention (i.e., any online or mobile telephone app) was evaluated; (2) the intervention was designed for the self-management of suicidal ideation or self-harm; (3) data on the effectiveness of the intervention with respect to any suicidal outcome (i.e., suicidal ideation, repetition of self-harm, attempted suicide, or completed suicide) were reported; and (4) either a randomized, pseudo-randomized, or observational pre-test/post-test design was used.

Studies were excluded if: (1) the program was not a standalone digital intervention. Thus multimodal interventions, in which the digital intervention was intended to serve either as a complement or adjunct to traditional face-to-face psychosocial therapy or required significant input or involvement from face-to-face treating clinicians, were not eligible for inclusion in this review. Brief contact-based programs delivered via text messaging or e-mail services were also excluded as no form of active psychosocial therapy is typically provided in the context of these programs. Studies were also excluded if: (2) the intervention targeted gatekeepers (i.e., carers, other health care professionals, or bystanders who may come into contact with suicidal persons); or (3) no data on suicidal ideation, self-harm, attempted and/or completed suicide were reported. Descriptions of programs without data on effectiveness were also excluded.

Two authors (KW and AM) independently screened studies for inclusion. Firstly, titles of all retrieved studies were screened. Next, only studies meeting inclusion criteria following a full text screen were retained. Any disagreements regarding study eligibility were resolved following consensus discussions with the broader group of review authors. Once again, corresponding authors were contacted to request further information on program design, study design, data analysis, or methodology as required.

Data analysis

Methodological details were extracted from included studies using a standardized extraction form by two authors (KW and AM) working independently of one another. Disagreements were resolved through consensus discussions. Methodological details included: research design, treatment setting, and outcome ascertainment. We also assessed whether those evaluating the intervention were independent from those who developed the intervention.

Data on the primary outcome, suicidal ideation, were also extracted by KW and AM working independently of one another. To make maximal use of the available data, information was extracted irrespective of whether these outcomes were measured continuously, for example as scores on a psychometrically validated measure of suicidal ideation or as numbers of repeat episodes of self-harm, or categorically, as the proportion of participants reporting thoughts of suicide or number of self-harm events. Care was taken, however, to ensure the items used to determine these outcomes were comparable between studies.

Secondary outcomes included: episodes of self-harm, attempted suicide, and completed suicide measured according to self-report and/or hospital or medical records. Where a study reported outcomes at multiple time points, for example at six and 12 months’ follow-up, only data for the longest follow-up period were extracted, in line with recommendations [27].

Statistical analysis

Data on the primary outcome (i.e., suicidal ideation) were synthesized using one of two approaches. Where these were reported categorically (e.g., as a difference in proportions reporting thoughts of suicide in the intervention group as compared to the control group), odds ratios (OR) and their accompanying 95% confidence intervals were calculated.

Where outcomes were reported as scores on a continuous scale, such as total scores on the Beck Scale of Suicidal Ideation (BSSI; [28]), the mean difference (MD) or the standard mean difference (SMD), with an accompanying 95% confidence interval, was calculated as appropriate. Specifically, where the same scale was used to investigate the outcome of interest in all studies included in a meta-analysis, the MD was used. The SMD, on the other hand, was used where outcomes were measured using a variety of different scales as recommended [27].

Pooled ORs, MDs, or SMDs were calculated by using the DerSimonian and Laird random effects model [29], as implemented by RevMan for Windows, version 3.5. The impact of between-study heterogeneity was quantified by the I 2 statistic [30]. We interpreted an I 2 statistic of ≥75% as indicating substantial levels of between-study heterogeneity. Where we found evidence of this, we undertook sensitivity analyses to explore potential causes of this between-study heterogeneity, as outlined below.

Sensitivity analyses

Given recent findings suggesting that psychological interventions that directly address suicidal ideation and self-harm are more effective in reducing attempted and completed suicide than interventions which indirectly target the symptoms associated with suicidality (e.g., anxiety, depression, hopelessness) [31], we conducted sensitivity analyses to investigate whether digital interventions developed specifically for the self-management of suicidal ideation or self-harm would be more effective in preventing suicidal ideation and repetition of self-harm than interventions developed for the self-management of depression symptomatology more generally.

Sub-group analyses

The inclusion of RCTs and non-randomized observational studies within a single meta-analysis is becoming increasingly common as reliance on RCT evidence alone can lead to knowledge translation bias [32]. RCTs, for example, typically recruit highly selected patient populations with lower risk profiles as compared to “real world” populations [33]. The inclusion of results from pre-test/post-test observational studies together with those from RCTs, however, can also lead to over-estimation of the treatment effect size [34]. To balance these two concerns, all studies were eligible for inclusion in our review, irrespective of study design; however, we did not pool data from RCTs together with data from observational studies. Instead, we calculated separate sub-group analyses by study design to investigate what impact, if any, study design had on the magnitude of the effect size observed for these interventions.

Risk of bias

Risk of bias was assessed using the Cochrane Collaboration tool for randomised and pseudo-randomised controlled trials [35] and, for controlled before/after designed studies, the Risk of Bias In Non-Randomized Studies of Interventions (ROBINS–I; [36]). The Cochrane Collaboration tool assesses bias in seven domains including: adequacy of the random sequence generation, allocation concealment, blinding of participants, clinical personnel, and outcome assessors, as well as incomplete data, selective outcome reporting, and other bias. The ROBINS–I assesses bias in eight domains including: confounding, participant selection, classification of the intervention, departures from the intended intervention, missing data, measurement of outcomes, selection of the reported results, and overall bias.

Role of the funding source

The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. KW and AM had full access to all the data in this study, and all authors had final responsibility for the decision to submit for publication.

Results

The electronic search strategy outlined in the Appendix initially identified a total of 9033 potentially relevant records. Four additional records were retrieved following ancestry searching. After excluding duplicates, this figure reduced to 6382 records. Of these, 6063 records were screened out after a review of their titles, whilst 305 records were omitted following full text screening. A total of 14 independent, non-overlapping studies were therefore included in the review, reporting data on a total of 3356 participants (Fig. 1; Table 1).

Fig. 1
figure 1

PRISMA flow diagram of included and excluded studies

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Table 1 Study characteristics, methodological details, and risk of bias assessment for the 14 studies included in this review
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Study characteristics

The majority of these studies had been conducted either in Australia (four studies: [37,38,39,40]) or the USA (four studies: [41,42,43,44]). Two studies were conducted in Germany [45, 46], one in the Netherlands [47], one in Sweden [48], and one in Switzerland [49]. One further study recruited participants through online forums and therefore included participants from a number of different countries, including countries in North America, Europe, and Australasia [50].

Most studies evaluated the effectiveness of online programs [37,38,39, 41,42,43,44,45,46,47,48,49]. Only two evaluated the effectiveness of mobile telephone apps [40, 50]. Most programs were developed for the self-management of depression [37, 38, 42, 43, 45, 46, 48, 49]. However, as these studies assessed the effectiveness of these programs on to at least one suicide-related primary or secondary outcome (i.e., suicidal ideation, self-harm, attempted and/or completed suicide) they were nonetheless eligible for inclusion in this review. Only five programs were developed specifically for the self-management of suicidal ideation [39,40,41, 44, 47], and only one was developed for the self-management of self-harm [50].

In terms of study design, eight were randomised controlled trials [37, 40, 41, 44, 46, 47, 49, 50], four were observational pre-test/post-test studies [38, 39, 43, 48], and one was a pseudo-randomised controlled trial in which sequential participants were alternately allocated to the intervention and control conditions [45]. For one RCT, although participants were randomised to the intervention and control conditions, all participants received access to the digital intervention [42]. For the purposes of this review, data from this RCT was therefore treated as pre-test/post-test comparisons instead. For two additional RCTs, outcomes at pre-test/post-test were also reported for the intervention group, enabling their inclusion in pre-test/post-test comparisons as well as in RCT comparisons [45, 49]. However, to ensure results from these studies were not double-counted, analyses were pooled separately by study design for all outcomes reported in this review. In one further RCT, participants assigned to the wait-list control condition crossed over to receive the intervention after six weeks [40]. To avoid contamination from any ‘carry-over’ effects, we only extracted data for the first six week period prior to cross-over as recommended [51].

Types of digital interventions

Most programs were developed by clinical psychologists and/or psychiatrists with experience treating suicidal ideation and/or self-harm [37, 39,40,41,42,43,44,45, 47,48,49,50], and were evaluated by those who developed the intervention [37,38,39,40, 42, 44, 46,47,48,49,50]. In terms of therapeutic approach, most programs were based on the principles of cognitive behavioural therapy (CBT). Some also included elements of ‘third wave’ CBT, such as mindfulness [45], dialectical behaviour therapy [47], or mentalisation-based cognitive therapy [47]; all of which hold the rationale that challenging thoughts, a principle feature of CBT, is less important than understanding and accepting thoughts in a non-judgemental manner. Other programs included a variety of treatment approaches, including: acceptance-based therapy [40], problem-solving therapy [47], interpersonal therapy [42], mood monitoring [46], and crisis planning [46]. Only one program utilised gamification in which participants were presented with a series of visual stimuli pairs designed to condition aversive reactions to self-harming thoughts or behaviours [50].

Types of control conditions

For the eight RCTs and one pseudo-RCT designed trials, the interventions were compared against a number of types of control conditions, including: wait-list control [37, 40, 45], attentional control [41, 47, 50], psychoeducation [44], treatment as usual [46], or face-to-face psychotherapy [49].

Ongoing studies

An additional 10 ongoing studies were identified at the time of the systematic search [52,53,54,55,56,57,58,59]. Further details of these ongoing trials are reported in Table 2.

Table 2 Study characteristics, methodological information, and trial registration information for the 10 ongoing studies identified by this review
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Suicidal ideation

Four studies reported data on the number of participants self-reporting suicidal ideation. At post-intervention, there was some suggestion of a reduction in the proportion of participants self-reporting suicidal ideation in three observational pre-test/post-test studies (OR 0.36, 95% CI 0.27 to 0.49, 3 studies, I 2 = 0%, p < 0.0001; Fig. 2). However, by the final follow-up assessment, data from one RCT suggested no evidence of a treatment effect for these interventions (Fig. 2). There was no evidence of a significant difference in the magnitude of the treatment effect by study design for this outcome (χ2 = 0.61, df = 1, p = 0.44). As all four studies included in this analysis investigated the effectiveness of digital interventions specifically developed for the self-management of depression symptoms (rather than suicidal ideation or self-harm specifically), sensitivity analyses could not be undertaken.

Fig. 2
figure 2

Random effects odds ratio (OR) and accompanying 95% confidence interval (CI) for digital interventions on the proportion of participants reaching defined clinical thresholds for suicidal ideation

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One study covering three related RCTs reported information on the frequency of self-reported episodes of suicidal ideation at post-intervention (for all three studies) and at the conclusion of the final follow-up assessment (for one of these three studies) [50]. However, no evidence of a treatment effect was found for these interventions at either time point (Fig. 3). As only one RCT was included in this analysis, neither tests for subgroup differences nor sensitivity analyses could be undertaken.

Fig. 3
figure 3

Random effects mean difference (MD) and accompanying 95% confidence interval (CI) for digital interventions on frequency of self-reported suicidal ideation

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Eight studies reported data on suicidal ideation scores using a number of different psychometric instruments, including: the BSSI [44, 47, 49], the Depressive Symptom Inventory–Suicidality Subscale (DSI-SS; [60]) [40], the Suicide Behaviors Questionnaire–Revised (SBQ–R; [61]) [45], the Suicidal Ideation Questionnaire–Junior (SIQ–J; [62]) [39], the four item suicidal ideation sub-scale of the General Health Questionnaire (GHQ–28; [63]) [37], and the suicidal ideation item of the Hopkins Symptom Checklist (HSCL–20; [64]) [43]. As raw means and standard deviations were not reported for suicidal ideation scores in one RCT, data were instead estimated from graphics in the original report for this study [37].

At post-intervention, there was evidence these interventions were associated with a significant reduction in suicidal ideation scores in five RCTs (SMD -0.26, 95% CI -0.44 to −0.08, 5 studies, I 2 = 0.0%, p = 0.005; Fig. 4). There was no evidence of a significant treatment effect for these interventions in either one pseudo-randomised controlled trial or four observational studies, however (Fig. 4). The test for subgroup differences was non-significant suggesting there was no difference in magnitude of the effect size by study design for this outcome (χ2 = 0.25, df = 2, p = 0.88). Sensitivity analyses including only those studies in which the intervention was specifically developed for the self-management of suicidal ideation or self-harm also did not materially affect these results (results not shown).

Fig. 4
figure 4

Random effects standard mean difference (SMD) and accompanying 95% confidence interval (CI) for digital interventions of suicidal ideation scores

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Two RCTs reported data on suicidal ideation at the final follow-up assessment [37, 44]. For one of these trials, however, data on suicidal ideation scores at final follow-up had to be estimated by review authors from graphics presented in the original trial report [37]. Data from these trials suggested these digital interventions were not associated with a significant treatment effect for suicidal ideation by this time point (SMD -0.34, 95% CI –0.70 to 0.01, 2 studies, I 2 = 0.0%, p = 0.06; Fig. 4). Given that both studies included in this analysis were RCTs, neither tests for subgroup differences nor sensitivity analyses could be undertaken. One further RCT presented data on outcomes at final follow-up for the intervention group only [65]. Analyzing these data as pre-test/post-test comparisons suggested a significant reduction in suicidal ideation scores at the final follow-up assessment in this trial (MD -4.90, 95% CI -7.07 to −2.73, 1 study, I 2 = not applicable, p < 0.001). As only one RCT reported data at the final follow-up assessment, neither tests for subgroup differences nor sensitivity analyses could be undertaken.

Self-harm

One study, covering three related RCTs, evaluated the effectiveness of digital interventions on frequency of self-harm episodes [50]. At post-intervention, there was no indication of a treatment effect for these interventions on either self-reported frequency of self-cutting or non-suicidal self-injury in these three RCTs (Fig. 5). There was also no indication of a treatment effect for this intervention on frequency of self-reported self-cutting or non-suicidal self-injury at the final follow-up assessment (at one month) in one of these RCTs (Fig. 5). Sensitivity analyses could not be undertaken for this outcome as only one study investigated outcomes relating to repetition of self-harm. As all three studies were RCTs, subgroup analyses to investigate the impact of study design on the magnitude of the effect size could also not be undertaken.

Fig. 5
figure 5

Random effects mean difference (MD) and accompanying 95% confidence interval (CI) for digital interventions on frequency of self-reported self-cutting and non-suicidal self-injury (NSSI)

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Combined self-harm and attempted suicide

One RCT evaluated the effectiveness of a digital intervention on attempted suicide and self-harm [46]. As results for self-harm could not be disaggregated from that for attempted suicide, they are instead analysed here as a combined outcome. There was no evidence of a reduction in the proportion of participants who attempted suicide and/or engaged in self-harm over a 24 month follow-up period in this study (OR 2.11, 95% CI 0.19 to 23.81, 1 study, I 2 = not applicable, p = 0.55). Once again, as only one study investigated outcomes relating to combined attempted suicide and/or self-harm, sub-group and sensitivity analyses could not be undertaken.

Attempted suicide

One RCT evaluated the effectiveness of a digital intervention on attempted suicide [47]; however, no evidence of a reduction in the proportion of participants self-reporting a suicide attempt was noted by the post-intervention assessment in this study (OR 0.58, 95% CI 0.16 to 2.02, 1 study, I 2 = not applicable, p = 0.39). As only one study investigated outcomes relating to combined attempted suicide, sub-group and sensitivity analyses could not be undertaken.

Discussion

This systematic review and meta-analysis evaluated the effectiveness of digital interventions, including both online and mobile telephone applications (‘apps’) for the self-management and/or treatment of suicidal ideation or behaviours. A total of 14 studies were included in the present review, reporting data for a total of 3356 participants.

Overall, this review found some evidence that digital interventions may be associated with reductions in suicidal ideation, particularly at the post-intervention assessment. It is notable, however, that where these interventions were associated with significant treatment benefits for suicidal ideation, these effects tended to be stronger in observational pre-test/post-test-designed studies as compared with RCTs. Most of the included studies conducted to date, however, have utilised a pre-test/post-test observational design. There was no evidence to suggest these interventions are associated with reductions in self-harm or attempted suicide, although only three studies investigated these outcomes [46, 47, 50]. Few of these studies would have been adequately powered to evaluate rare outcomes, including repetition of self-harm and suicide reattempts.

Adherence was poor in majority of these studies; although this was not clearly related to the number of treatment modules. Of those studies that reported information on adherence [38,39,40,41,42,43,44,45,46,47,48,49,50], for example, up to one-half of participants allocated to the intervention group did not complete all treatment modules. Adherence during the long-term follow-up period, which was reported in one RCT [50], was also poor; over one-half (64%) of participants allocated to the intervention group in this trial did not access the intervention at all during the one month follow-up period in this RCT [50]. This suggests that the use of digital innovations, including gamification, may be insufficient to keep these interventions engaging over the longer term.

Limitations of the included studies

Most studies were of low to moderate quality, as assessed using the Cochrane Collaboration’s tool for randomised and pseudorandomised controlled trials [35], or the ROBINS–I tool for controlled pre-test/post-test studies [36], with biases most apparent for the domains of participant, clinical personnel, and outcome assessor blinding. Performance and detection bias therefore cannot be ruled out.

Of those studies utilising an RCT design, most compared the intervention to either waitlist control [37, 45] or attentional control [41, 47, 50] conditions. Variability in the control condition has been found to be associated with the magnitude of the treatment effect in office-based psychosocial interventions for depression and anxiety [66, 67]. Yet, despite this, variability in the control condition is a rarely investigated source of heterogeneity in meta-analyses of digital interventions.

The included studies report data on a variety of different outcomes. Although all studies included at least one suicidal ideation and/or behaviour relevant outcome, these were measured in a variety of different ways, including from psychometric scales, self-report, or according to hospital or medical records. Given that these outcomes were measured using the same methodology within individual studies, the effect of outcome measure definition on case identification at the post-intervention and follow-up assessments could be expected to affect the intervention and control groups equally. Between studies, however, outcome measure definition may have affected our results in light of research findings suggesting that self-reported self-harm underestimates hospital-treated self-harm [68]. We were unable to assess whether variability in the magnitude of the treatment effect size was related to the way in which the outcome measure was assessed in this review, however, owing to the small number of studies included in any one meta-analysis. Further work will be necessary to pick apart the influence of outcome definition on the apparent effectiveness of interventions, including digital interventions, for the prevention of self-harm.

Most studies (71.4%) also reported information on depression [38,39,40, 42,43,44,45, 47,48,49]. Fewer studies reported data on other clinically relevant outcomes, such as hopelessness [40], and none reported information on problem-solving following the completion of the intervention. The National Institute for Health and Clinical Excellence (NICE) guidelines recommend that all interventions for self-harm should, at a minimum, investigate the effect of psychological interventions for self-harm on potential mechanisms of action, including depression, hopelessness, and problem-solving [69], echoing more recent calls to this effect in the international scholarship [70, 71]. Online resources may normalize self-harm and may provide vulnerable individuals with access to self-harm content and imagery, including information on methods of self-harm [72]. Additionally, digital interventions that provide some form of active psychosocial therapy, and particularly those that include a focus on mood monitoring, may lead to increased negative affectivity and rumination [73]. Outcomes relating to negative affect and rumination should therefore also be reported for all evaluations of digital interventions for this patient group in future.

Most programs (57.1%) were developed for the self-management of depression [37, 38, 42, 43, 45, 46, 48, 49]. Recent findings for psychosocial interventions, however, suggest that those developed specifically for the management of suicidal thinking are associated with greater impacts for the reduction of attempted and completed suicide as compared to those targeting indirect symptoms associated with suicidal behaviour, such as anxiety, depression, or hopelessness [31]. Future studies in this area should evaluate the degree to which these digital interventions lead to meaningful change in the proposed mechanism(s) of action in order to identify the treatment module(s) associated with the greatest impacts in reducing suicidal ideation and self-harm in this population.

The included studies also examined interventions of varying intensity. Whilst it could be expected that treatments of greater intensity will have greater impacts on reducing suicidal ideation and self-harm, a recent meta-regression review found no evidence to suggest that treatment intensity, measured as the total number of available treatment sessions, was associated with greater effectiveness for office-based psychosocial therapy for self-harm repetition [74].

Finally, it is also likely that these populations will have a very low risk for suicidal behaviour as all studies recruited participants from the community. Despite this, almost all (78.6%) studies included indicated samples, such as callers to telephone or online counselling services [37, 45, 50], or those already in contact with primary care [38, 42, 43], counselling [39, 40, 49] or psychiatric services [46, 48].

Strengths and limitations of the present review

The majority of these interventions were based on the principles of standard cognitive behavioural therapy (CBT), which has been found to have efficacy in reducing repetition of self-harm in clinical populations in a recent systematic review of psychosocial interventions for the treatment of self-harm [10]. The findings of the present review significantly extend this by suggesting that digital interventions which incorporate the principles of standard CBT may have promising effects in reducing suicidal ideation in non-clinical populations, at least in the short-term.

Whilst we utilized a comprehensive search to locate all relevant trials of digital interventions for the self-management of self-harm, we identified only two mobile telephone apps [40, 50]. Given that a recent Australian study identified a total of 24 apps for the prevention of suicidal behaviour are currently available for download from the Australian Google Play and iOS store [75], this would suggest that a large number of these apps have no evidence to support their effectiveness. It is likely that a similar proportion of online interventions would have little evidence to support their effectiveness.

Conclusions

Although a growing number of both online and mobile telephone applications (‘apps’) for the self-management and treatment of suicidal thinking and behaviours are now available, few of these have been evaluated for their effectiveness in reducing these outcomes. We identified just 14 in this review. Overall, while there is some promise of these interventions to reduce suicidal ideation, how this translates into reductions in self-harm and/or attempted suicide is unclear at present. Given the prevalence of suicidal ideation in clinical populations, additionally, it is unclear whether these reductions would be clinically meaningful at present.

Abbreviations

App:

Application

BDI–II:

Beck Depression Inventory, version two

BSSI:

Beck Scale of Suicidal Ideation

CBT:

Cognitive behavioural therapy

CDRS–R:

Children’s Depression Rating Scale–Revised

CENTRAL:

Cochrane Central Register of Controlled Trials

CES–D:

Center for Epidemiologic Studies Depression scale

CI:

Confidence Interval

CRCT:

Cluster randomised controlled trial

CRESP:

Centre for Research Excellence in Suicide Prevention

DBT:

Dialectical behaviour therapy

DSI–SS:

Depressive Symptom Inventory-Suicidality Subscale

DSM-5:

Diagnostic and Statistical Manual of Mental Disorders, fifth revision

GHQ–28:

General Health Questionnaire, 28 item

HSCL–20:

Hopkins Symptom Checklist, 20 item

MADRS:

Montgomery-Åsberg Depression Rating Scale

ME:

Mean difference

OR:

Odds ratio

PHQ–9:

Patient Health Questionnaire, 9 item

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

RADS–2:

Reynolds Adolescent Depression Scale-Version 2

RCT:

Randomised controlled trial

ROBINS–I:

Risk of Bias in Non-Randomized Studies of Interventions

SBQ–R:

Suicide Behaviors Questionnaire-Revised

SIQ–J:

Suicidal Ideation Questionnaire-Junior

SMD:

Standard mean difference

TAU:

Treatment as usual

UK:

United Kingdom

USA:

United States of America

WHO:

World Health Organization

References

  1. Hawton K, Zhal D, Weatherall R. Suicide following deliberate self-harm: long term follow-up of patients who presented to a general hospital. Br J Psychiatry. 2003;182:537–42.

    Article  PubMed  Google Scholar 

  2. Logan C. Managing Clinical Risk: A Guide to Effective Practice. In: Logan C, Johnstone L, editors. Suicide and self-harm: Clinical risk assessment and management using a structured professional judgement approach. Abingdon: United Kingdom: Routledge; 2013. p. 115–25.

    Google Scholar 

  3. McLean J, Maxwell M, Platt S, Harris F, Jepson R. Risk and protective factors for suicide and suicidal behaviour: a literature review. Edinbrugh, United Kingdom: Scottish Government; 2008.

    Google Scholar 

  4. Paul E, Tsypes A, Eidlitz L, Ernhout C, Whitlock J. Frequency and functions of non-suicidal self-injury: associations with suicidal thoughts and behaviors. Psychiatry Res. 2015;255:276–82.

    Article  Google Scholar 

  5. Carroll R, Metcalfe C, Gunnell D. Hospital presenting self-harm and risk of fatal and non-fatal repetition: systematic review and meta-analysis. PLoS One. 2014;9(2):e89944.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Franklin J, Nock M. The Oxford Handbook of Behavioral Emergencies and Crises. In: Keleespies P, editor. Nonsuicidal self-injury and its relation to suicidal behavior. New York, NY: Oxford University Press; 2017.

    Google Scholar 

  7. Kapur N, Cooper J, O'Connor R, Hawton K. Non-suicidal self-injury v. Attempted suicide: new diagnosis or false dichotomy? Br J Psychiatry. 2013;202(5):326–8.

    Article  PubMed  Google Scholar 

  8. Hawton K, Harriss L, Hall S, Simkin S, Bale E, Bond A. Deliberate self-harm in Oxford, 1990-2000: A time of change in patient characteristics. Psychol Med. 2003;33:987–95.

    Article  CAS  PubMed  Google Scholar 

  9. Hawton K, Witt K, Taylor Sailsbury T, Arensman E, Gunnell D, Townsend E, van Heeringen K, Hazell P. Interventions for self-harm in children and adolescents. Cochrane Database Syst Rev. 2015;9:CD012013.

    Google Scholar 

  10. Hawton K, Witt K, Taylor Salisbury T, Arensman E, Gunnell D, Hazell P, van Heeringen K. Psychosocial interventions for self-harm in adults. Cochrane Database Sys Rev. 2016;14:CD012189.

    Google Scholar 

  11. Hawton K, Witt KG, Salisbury TLT, Arensman E, Gunnell D, Hazell P, Townsend E, van Heeringen K. Psychosocial interventions following self-harm in adults: a systematic review and meta-analysis. Lancet Psychiatry. 2016;3(8):740–50.

    Article  PubMed  Google Scholar 

  12. Tondo L, Albert M, Baldessarini R. Suicide in relation to health care access in the United States: an ecological study. J Clin Psychiatry. 2006;67(4):517–23.

    Article  PubMed  Google Scholar 

  13. Kapusta N, Poschm N, Niederkrotenthaler T, Fischer-Kern M, Etzersdorfer M, Sonneck G. Availability of mental health service providers and suicide rates in Austria: a nationwide survey. Psychiar Serv. 2010;61(12):1198–203.

    Article  Google Scholar 

  14. Kawaguchi H, Koike S. Association between the density of physicians and suicide rates in Japan: Nationwide ecological study using a spatial Bayesian model. PLoS One. 2016;11(2):e0148288.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bruffaerts R, Demytenaere K, Hwang I, Chiu W, Sampson N, Kessler R, Alonso J, Borges G, de Girolamo G, de Graaf R, et al. Treatment of suicidal people around the world. Br J Psychiatry. 2011;199(1):64–70.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Czyz E, Horwitz A, Eisenberg D, Kramer A, King C. Self-reported barriers to professional help seeking among college students at elevated risk for suicide. J Am College Health. 2013;61(7):398–406.

    Article  Google Scholar 

  17. Aggarwal N. Applying mobile technologies to mental health service delivery in South Asia. Asian J Psychiatr. 2012;5(3):225–30.

    Article  PubMed  Google Scholar 

  18. de Beurs D, Kirtley O, Kerkhof A, Portzky G, O'Connor R. The role of mobile phone technology in understanding and preventing suicidal behavior. Crisis. 2015;36(2):79–82.

    Article  Google Scholar 

  19. Kerkhof A, van Spijker B, Mokkenstorm J. Suicide Prevention and New Technologies: Evidence-Based Practice. In: Mishara B, Kerkhof A, editors. Reducing the burden of suicidal thoughts through online cognitive behavioural therapy self-help. New York, NY: Palgrave Macmillian; 2013.

    Chapter  Google Scholar 

  20. Hayes J, Maughan D, Grant-Peterkin H. Interconnected or disconnected? Promotion of mental health and prevention of mental disorder in the digital age. Br J Psychiatry. 2016;208(3):205–7.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Moher D, Liberati A, Tetzlaff J, Altman D. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339(7):b2535.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Christensen H, Batterham P, O'Dea B. E-health interventions for suicide prevention. Int J Environ Res Public Health. 2014;11(8):8193–212.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Donker T, Petrie K, Proudfoot J, Clarke J, Birch M-R, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15(11):e247.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Lai M, Maniam T, Chan L, Ravindran A. Caught in the web: a review of web-based suicide prevention. J Med Internet Res. 2014;16(1):e30.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jacob N, Scourfield J, Evans R. Suicide prevention via the internet: a descriptive review. Crisis. 2014;35(4):261–7.

    Article  PubMed  Google Scholar 

  26. Perry Y, Werner-Seidler A, Calear A, Christensen H. Web-based and mobile suicide prevention interventions for young people: a systematic review. J Can Acad Child Adolesc Psychiatry. 2016;25(2):73–9.

    PubMed  PubMed Central  Google Scholar 

  27. Deeks J, Higgins J, Altman D. Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S, (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration; 2011. Available from http://handbook.cochrane.org.

  28. Beck A, Kovacs M, Weissman A. Assessment of suicidal intention: the scale for suicidal ideation. J Consult Clin Psychol. 1979;47(2):343–52.

    Article  CAS  PubMed  Google Scholar 

  29. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.

    Article  CAS  PubMed  Google Scholar 

  30. Higgins J, Thompson S, Deeks J, Altman D. Measuring inconsistency in meta-analysis. BMJ. 2003;327(7414):557–60.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Meerwijk E, Parekh A, Oquendo M, Allen I, Franck L, Lee K. Direct versus indirect psychosocial and behavioural interventions to prevent suicide and suicide attempts: a systematic review and meta-analysis. Lancet Psychiatry. 2016;3:544–54.

    Article  PubMed  Google Scholar 

  32. Faber T, Ravaud P, Riveros C, Perrodeau E, Dechartres A. Meta-analyses including non-randomized studies of therapeutic interventions: a methodological review. BMC Med Res Methol. 2016;16:35.

    Article  Google Scholar 

  33. Kennedy-Martin T, Curtis S, Faries D, Robinson S, Johnston J. A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results. Trials. 2015;16:495.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Parker L, Saez N, Porta M, Hernández-Aguado I, Lumbreras B. The impact of including different study designs in meta-analyses of diagnostic accuracy studies. Eur J Epidemiol. 2013;28:713–20.

    Article  PubMed  Google Scholar 

  35. Higgins J, Altman D, Sterne J. Assessing risk of bias in included studies. In: Higgins JPT, Green S, (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration; 2011. Available from http://handbook.cochrane.org.

  36. A tool for assessing Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I), version 7.0. http://www.riskofbias.info. Accessed 7 Mar 2016.

  37. Christensen H, Farrer L, Batterham P, Mackinnon A, Griffiths K, Donker T. The effect of a web-based depression intervention on suicidal ideation: secondary outcome from a randomised controlled trial in a helpline. BMJ Open. 2013;3(6):e002886.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Mewton L, Andrews G. Cognitive behaviour therapy via the internet for depression: a useful strategy to reduce suicidal ideation. J Affect Disord. 2015;170(1):78–84.

    Article  PubMed  Google Scholar 

  39. Robinson J, Hetrick S, Cox G, Bendall S, Yuen H, Yung A, Pirkis J. Can an internet-based intervention reduce suicidal ideation, depression and hopelessness among secondary school students: results from a pilot study. Early Interv Psychiatry. 2016;10(1):28–35.

    Article  PubMed  Google Scholar 

  40. Tighe J, Shand F, Ridani R, Mackinnon A, de la Mata N, Christensen H. iBobbly mobile health intervention for suicide prevention in Australian indigenous youth: a pilot randomised controlled trial. BMJ Open. 2017;7:e013518.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Guille C, Zhao Z, Krystal J, Nichols B, Brady K, Sen S. Web-based cognitive behavioral therapy intervention for the prevention of suicidal behaviour in medical interns: a randomized clinical trial. JAMA Psychiatry. 2015;72(12):1192–8.

    Article  PubMed  PubMed Central  Google Scholar 

  42. van Voorhees B, Fogel J, Reinecke M, Gladstone T, Stuart S, Gollan J, Bradford N, Domanico R, Fagan B, Ross R, et al. Randomized clinical trial of an internet-based depression prevention program for adolescents (project CATCH-IT) in primary care: 12-week outcomes. J Dev Behav Pediatr. 2009;30(1):23–37.

    Article  PubMed  Google Scholar 

  43. Whiteside U, Richards J, Steinfeld B, Simon G, Caka S, Tachibana C, Stuckey S, Ludman E. Online cognitive behavioral therapy for depressed primary care patients: a pilot feasibility project. Perm J. 2014;18(2):21–7.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Hill RM, Pettit JW. Pilot Randomized Controlled Trial of LEAP: A Selective Preventive Intervention to Reduce Adolescents’ Perceived Burdensomeness. J Clin Child Adolesc Psychol. 2016. ePub ahead of print. doi:10.1080/15374416.15372016.11188705.

  45. Moritz S, Schilling L, Hauschildt M, Schröder J, Treszel A. A randomized controlled trial of internet-based therapy in depression. Behav Res Ther. 2012;50(7–8):513–21.

    Article  PubMed  Google Scholar 

  46. Kordy H, Wolf M, Aulich K, Bürgy M, Hegerl U, Hüsing J, Puschner B, Rummel-Kluge C, Vegger H, Backenstrass M. Internet-delivered disease management for recurrent depression: a multicentre randomized controlled trial. Psychother Psychosom. 2016;85(2):91–8.

    Article  PubMed  Google Scholar 

  47. van Spijker B, van Straten A, Kerkhof A. Effectiveness of online self-help for suicidal thoughts: results of a randomised controlled trial. PLoS One. 2014;9(2):e90118.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Hedman E, Ljótsson B, Kaldo V, Hesser H, Alaoui S, Kraepelien M, Andersson E, Rück C, Svanborg C, Andersson G, et al. Effectiveness of internet-based cognitive behaviour therapy for depression in routine psychiatric care. J Affective Disord. 2014;155(2):49–58.

    Article  Google Scholar 

  49. Wagner B, Horn A, Maercker A. Internet-based versus face-to-face cognitive behavioral intervention for depression: a randomized controlled non-inferority trial. J Affective Disord. 2014;152(7):113–21.

    Article  Google Scholar 

  50. Franklin J, Fox K, Franklin C, Kleiman E, Ribeiro J, Jaroszewski A, Hooley J, Nock M. A brief mobile app reduces nonsuicidal and suicidal self-injury: evidence from three randomized controlled trials. J Consult Clin Psychol. 2016;84(6):544–57.

    Article  PubMed  Google Scholar 

  51. Elbourne D, Altman D, Higgins J, Curtin F, Worthington H, Vail A. Meta-analyses involving cross-over trials: methodological issues. Int J Epidemiol. 2002;31:140–9.

    Article  PubMed  Google Scholar 

  52. Boele F, Verdonck-de-Leeuw I, Cuijpers P, Reijneveld J, Heimans J, Klein M. Internet-based guided self-help for glioma patients with depressive symptoms: design of a randomized controlled trial. BMC Neurol. 2014;14:81.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Eylem O, van Straten A, Bhui K, Kerkhof AJ. Reducing suicidal ideation among Turkish migrants in the Netherlands and in the UK: effectiveness of an online intervention. Int Rev Psychiatry. 2015;27(1):72–81.

    Article  PubMed  Google Scholar 

  54. Mühlmann C, Madsen T, Hjorthøj C, Kerkhof A, Nordentoft M, Erlangsen A. The self-help online against suicidal thoguhts (SOS) trial: study protocol for a randomized controlled trial. Trials. 2017;18:45.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Perry Y, Calear A, Mackinnon A, Batterham P, Licinio J, King C, Thomsen N, Scott J, Donker T, Merry S, et al. Trial for the prevention of depression (TriPoD) in final-year secondary students: study protocol for a cluster randomised controlled trial. Trials. 2015;16(6):451.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Robinson J, Hetrick S, Cox G, Bendall S, Yung A, Yuen H, Templer K, Pirkis J. The development of a randomised controlled trial testing the effects of an online intervention among school students at risk of suicide. BMC Psychiatry. 2014;14(5):155.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Simon G, Beck A, Rossom R, Richards J, Kirlin B, King D, Shulman L, Ludman E, Penfold R, Shortreed S, et al. Population-based outreach versus care as usual to prevent suicide attempt: study protocol for a randomized controlled trial. Trials. 2016;17:452.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Stallard P, Porter J, Grist R. Safety, acceptability, and use of a smartphone app, BlueIce, for young people who self-harm: protocol for an open phase I trial. JMIR Res Protoc. 2016;5:e217.

    Article  PubMed  PubMed Central  Google Scholar 

  59. van Spijker B, Calear A, Batterham P, Mackinnon A, Gosling J, Kerkhof A, Solomon D, Christensen H. Reducing suicidal thoughts in the Australian general population through web-based self-help: study protocol for a randomized controlled trial. Trials. 2015;16(8):62.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Joiner T, Pfaff J, Acres J. A brief screening tool for suicidal symptoms in adolescents and young adults in general health settings: reliability and validity data from the Australian national general practice youth suicide prevention project. Behav Res Ther. 2002;40:471–81.

    Article  PubMed  Google Scholar 

  61. Osman A, Bagge C, Gutierrez P, Konick L, Kopper B, Barrios F. The suicidal behaviors Questionniare-revised (SBQ-R): validation with clinical and nonclinical samples. Assessment. 2001;8(4):443–54.

    Article  CAS  PubMed  Google Scholar 

  62. Reynolds W. Suicidal Ideation Questionnaire Junior. Odessa, FL: Psychological Assessment Resources; 1987.

    Google Scholar 

  63. Goldberg D, Hillier V. A scaled version of the general health questionnaire. Psychol Med. 1979;9(1):139–45.

    Article  CAS  PubMed  Google Scholar 

  64. Derogatis L, Lipman R, Rickels K, Uhlenhuth E, Cori L. The Hopkins symptom checklist (HSCL): a self-report symptom inventory. Behav Schi. 1974;19(1):1–15.

    Article  CAS  Google Scholar 

  65. van Spijker B, van Straten A, Kerkhof A. Online self-help for suicidal thoughts: 3-month follow-up results and participant evaluation. Internet Interventions. 2015;2:283–8.

    Article  Google Scholar 

  66. Watts S, Turnell A, Kladnitski N, Newby J, Andrews G. Treatment-as-usual (TAU) is anything but usual: a meta-analysis of CBT versus TAU for anxiety and depression. J Affect Disord. 2015;175:152–67.

    Article  PubMed  Google Scholar 

  67. Wampold B, Budge S, Laska K, Del Re A, Baardseth T, Flükiger C, Minami T, Kivlighanm D, Gunn W. Evidence-based treatments for depression and anxiety versus treatment-as-usual: a meta-analysis of direct comparisons. Clin Psychol Rev. 2011;31:1304–12.

    Article  PubMed  Google Scholar 

  68. Mars B, Cornish R, Heron J, Boyd A, Crane C, Hawton K, Lewis G, Tilling K, Macleod J, Gunnell D. Using data linkage to investigate inconsistent reporting of self-harm and questionnaire non-response. Arch Suicide Res. 2016;20:113–41.

    Article  PubMed  PubMed Central  Google Scholar 

  69. NICE. Self-harm: the short-term physical and psychological management and secondary prevention of self-harm in primary and secondary care. Leicester, UK: British Psychological Society; 2004.

    Google Scholar 

  70. Witt K. The use of emergency department-based psychological interventions to reduce repetition of self-harm behaviour. Lancet Psychiatry. 2017;4:428–9.

    Article  PubMed  Google Scholar 

  71. Milner A, Spittal M, Kapur N, Witt K, Pirkis J, Carter G. Mechanisms of brief contact interventions in clinical populations: a systematic review. BMC Psychiatry. 2016;16:194.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Daine K, Hawton K, Singaravelu V, Stewart A, Simkin S, Montgomery P. The power of the web: a systematic review of studies of the influence of the internet on self-harm and suicide in young people. PLoS One. 2013;8:e77555.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Swinkles A, Giuliano T. The measurement and conceptualization of mood awareness: attention directed towards one's mood states. Personal Soc Psychol Bull. 1995;21:934–49.

    Article  Google Scholar 

  74. Hetrick S, Robinson J, Spittal M, Carter G. Effective psychological and psychosocial approaches to reduce repetition of self-harm: a systematic review, meta-analysis, and meta-regression. BMJ Open. 2016;6:e011024.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Larsen M, Nicholas J, Christensen H. A systematic assessment of smartphone tools for suicide prevention. PLoS One. 2016;11:e0152285.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors wish to thank Constance Guille, Joe Franklin, Bregje van Spijker, and Ben van Voorhees for providing raw data and/or methodological clarification relating to their studies.

Funding

This work was supported by grants awarded to AM by beyondblue and the Movember Foundation. No other specific funding was received for the remaining authors. Funders had no role in either the design, interpretation of the findings, or writing of this manuscript.

Availability of data and materials

This review involves analysis of previously published data available in the studies included in this review. The final dataset is available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Population Health, Turning Point, Eastern Health Clinical School, Monash University, 54-62 Gertrude Street, Fitzroy, Victoria, 3065, Australia

    Katrina Witt

  2. Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia

    Matthew J. Spittal, Jane Pirkis & Dianne Currier

  3. Centre for Translational Neuroscience and Mental Health, Faculty of Health and Medicine, University of Newcastle, Callaghan, New South Wales, Australia

    Gregory Carter

  4. Orygen, the National Centre of Excellence in Youth Mental Health and the Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia

    Sarah Hetrick & Jo Robinson

  5. Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia

    Allison Milner

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Contributions

KW had the idea for the paper, conducted the systematic review, extracted and analysed the data. AM and MS assisted with data extraction and analysis. All authors contributed to the writing of the manuscript and approved the final version of the manuscript for publication.

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Correspondence to Katrina Witt.

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Two of the authors of this review (SH, JR) were authors of one of the included studies. The remaining authors have no other competing interest to declare.

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Appendix

Appendix

Electronic Search Strategy for the Medline and the Cochrane Library

Provides keywords used and electronic search strategy for the Medline and the Cochrane Library. Current to 31 March, 2017.

Electronic search strategy

Table 3 Cochrane Library
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Table 4 MEDLINE (OVID interface)
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Witt, K., Spittal, M.J., Carter, G. et al. Effectiveness of online and mobile telephone applications (‘apps’) for the self-management of suicidal ideation and self-harm: a systematic review and meta-analysis. BMC Psychiatry 17, 297 (2017). https://doi.org/10.1186/s12888-017-1458-0

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BMC Psychiatry

ISSN: 1471-244X

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