- Study protocol
- Open Access
- Open Peer Review
A smartphone application for treating depressive symptoms: study protocol for a randomised controlled trial
© The Author(s). 2018
- Received: 4 December 2017
- Accepted: 17 May 2018
- Published: 1 June 2018
Depression is a commonly occurring disorder linked to diminished role functioning and quality of life. The development of treatments that overcome barriers to accessing treatment remains an important area of clinical research as most people delay or do not receive treatment at an appropriate time. The workplace is an ideal setting to roll-out an intervention, particularly given the substantial psychological benefits associated with remaining in the workforce. Mobile health (mhealth) interventions utilising smartphone applications (apps) offer novel solutions to disseminating evidence based programs, however few apps have undergone rigorous testing. The present study aims to evaluate the effectiveness of a smartphone app designed to treat depressive symptoms in workers.
The present study is a multicentre randomised controlled trial (RCT), comparing the effectiveness of the intervention to that of an attention control. The primary outcome measured will be reduced depressive symptoms at 3 months. Secondary outcomes such as wellbeing and work performance will also be measured. Employees from a range of industries will be recruited via a mixture of targeted social media advertising and Industry partners. Participants will be included if they present with likely current depression at baseline. Following baseline assessment (administered within the app), participants will be randomised to receive one of two versions of the Headgear application: 1) Intervention (a 30-day mental health intervention focusing on behavioural activation and mindfulness), or 2) attention control app (mood monitoring for 30 days). Participants will be blinded to their allocation. Analyses will be conducted within an intention to treat framework using mixed modelling.
The results of this trial will provide valuable information about the effectiveness of mhealth interventions in the treatment of depressive symptoms in a workplace context.
The current trial is registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12617000547347, Registration date: 19/04/2017).
Depression is now recognised as one of the leading causes of disability worldwide . As well as being relatively common , depression can also be very debilitating , with core symptoms inclusive of; an absence of positive affect, persistent low mood, and low activity . Persons with severe depressive symptoms report serious difficulties in all aspects of their life, including work, home, and social activities . In response to the increased identification and recognition of depression , there has been greater emphasis on the development of innovative and effective psychological treatments.
The treatment of depression, both pharmacologically and with non-pharmacological interventions, remains a critical area of ongoing clinical research. There is substantial evidence supporting the efficacy of psychotherapy in the treatment of both sub-clinical  and clinical levels of depression . Whilst cognitive behaviour therapy (CBT) remains the most empirically supported treatment , other therapies such as behavioural activation therapy (BAT) has been shown to be equally effective and also less complex to administer . Service use statistics indicate that despite the effectiveness of such treatments, few people readily access these services . Overcoming identified barriers to accessing psychotherapy such as cost, convenience and accessibility  may enhance people’s ability to access evidence based treatments.
Despite experiencing depression many people still remain as active participants in the workforce . Ongoing employment is associated with numerous individual benefits such as economic, psychosocial, and emotional wellbeing . Given its immediacy and core role in the lives of individuals, the workplace may be an avenue for providing psychological interventions to those who might not otherwise access mental health services in a conventional manner . Interventions implemented in the workplace have been found to be effective in improving both mental health and occupational outcomes . This highlights an added benefit of workplace interventions as medical interventions in isolation have not shown as positive an effect on work-related outcomes when compared to workplace interventions . To date, most workplace interventions face accessibility and scalability issues, thus limiting the opportunity for employees to access these services. This issue appears to be particularly prevalent in workers employed in industries where roles are mobile and/or isolated; work hours are often intermittent or excessive; and tasks may be repetitive and high-risk (e.g. such as construction, transport and mining). These industries tend to be male-dominated (males > 70% of workforce)  and are associated with higher rates of mental health concerns . Technological innovations, namely smartphones, provide an opportunity for individuals in such roles to learn how to manage their wellbeing and seek further support if required .
Mobile health (mhealth) technologies are increasingly being recognised as an effective means through which mental health interventions can be disseminated in the population [20, 21]. Such interventions overcome numerous barriers associated with treatment seeking, including stigma, cost, and accessibility . Encouragingly, there is preliminary support that mhealth interventions can effectively treat symptoms in adults with depression . Moreover, mhealth interventions offer the opportunity for an autonomous, user-directed approach that motivates and offers personalised support by allowing the delivery of content to be tailored to an individual’s interests and needs . This may be particularly advantageous to workplace contexts where, individualised interventions are associated with more consistent outcomes than organisational wide interventions . However, of the many mhealth interventions available on the market, few have been trialled  and even fewer have been specifically tailored to a workplace setting.
The proposed HeadGear trial aims to evaluate the effectiveness of a new smartphone app in treating depressive symptoms in a workplace context.
The aim of the current study will be achieved through a multicentre randomized controlled trial, with two parallel arms. The trial will compare two smartphone app-based interventions: a novel intervention app designed for the treatment of depression (Headgear) and an attention control app (mood monitoring). Assessments will occur at baseline, post-intervention (5-weeks), and at 3-month follow-up. The study is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12617000547347) and has ethical approval from the University of New South Wales (UNSW) Human Research Ethics Committee (HC17021). Consent to participate in the trial will be obtained electronically from all participants. The study will be conducted in accordance with the Helsinki Declaration  and is compliant with the CONSORT guidelines .
Setting and participants
The study will recruit Australians who are currently employed, and will sample more selectively from a range of male-dominated industries . In Australia, these industries include agriculture/forestry/fishing, utility services (electricity, gas, water and waste), wholesale trade, manufacturing, transport/postal/warehousing, mining, and construction . Emergency services and defence also fit this definition, but were not considered unique industries by the ABS, though for this study they will also be considered as male-dominated.
Industry partner organisations will assist with recruitment by promoting the study among specific groups or their entire workforce. We will aim to recruit at least 850 employed adults across Australia. Organisations that elected to participate will promote the study via their respective health and wellbeing officers, along with email and newsletter advertisements. The study will also be promoted via members of the research team presenting at partner worksites. Social media advertising targeted at employed people will also be utilised to recruit individuals employed externally of partner organisations using an evidence-informed advertising approach . Both males and females will be recruited.
Initial eligibility criteria are: having a valid telephone number, ownership of an Apple/Android-operating smartphone, fluent in English and living in Australia. As this trial is focused on the treatment of depression, participants will also be excluded from this trial (although still permitted to use the app) if they do not have substantial levels of depressive symptoms at baseline, as indicated by either a PHQ-9 score below 15, or the PHQ-9’s algorithm for a diagnosis for Major Depressive Disorder (MDD) not being met . Participants were excluded if they were under 18 or not currently employed. No exclusion criteria were in place regarding comorbidities or medication use.
Active intervention: HeadGear
The intervention condition, HeadGear, is a smartphone application-based intervention utilising Behavioural Activation Therapy (BAT) and mindfulness-based therapies. Behavioural Action is a therapy based on learning theory that reconnects people to an environment of positive reinforcement, incorporating elements of value driven action and goal setting [31, 32]. When delivered face-to-face, BAT has been shown to perform as well as CBT for the treatment of depression  and preliminary evidence that this translates in mhealth form . The other component utilised in Headgear, Mindfulness, draws on meditation practices to allow individuals to gain further insight into their emotional, physical, and/or cognitive experience to ultimately shape it . Mindfulness has been identified as a transtherapeutic process that targets transdiagnostic mental processes , as evidenced by its effect in treating a range of psychopathologies including but not limited to mood, anxiety and substance use disorders [37, 38].
The therapeutic component of HeadGear encourages the user to complete one ‘challenge’ each day (5–10 min per day), over 30 days. These ‘challenges’ incorporate a variety of evidence-based BAT and mindfulness techniques and skills, including psychoeducational videos, value-driven activity planning and goal-setting, practice exercises, and techniques for developing coping and resilience (e.g., problem solving, improving sleep, minimising alcohol use, and/or assertiveness training). Other components of the Headgear intervention app include mood monitoring, a skill ‘toolbox’ (progressively built as the skills are completed), and a technical service helpline. Steps have been taken to promote user motivation and engagement by incorporating a ‘daily challenge’ framework where the user is ‘rewarded’ (through skill tokens) upon completing each daily challenge. A challenge framework has been shown to increase the general appeal of an app [39–41]. In addition to these elements, the Headgear application has been designed using participatory approaches and iterative human-computer interaction design strategies [42, 43].
The attention-matched control condition is a smartphone application that will have the same name and a virtually identical look and ‘feel’ as the intervention version of Headgear and is accessed in the same manner. However, there is no skill development and no component of behavioural activation or mindfulness therapy. To control for the attentional component of the HeadGear application, the control condition will encourage users to use the inbuilt mood monitor daily over a 30-day period and users will also have access to the ‘risk calculator.’
Randomisation will occur immediately following completion of the baseline assessment using automated procedures integrated into the trial management software. The algorithm for randomisation will consist of a block design, stratified by industry type, with a block size of 10.
Administration of assessments
Assessments will be completed at baseline, post-intervention (5-weeks), and 3-month follow-up. Baseline assessment includes outcome measures pertaining to depression symptomatology (Patient Health Questionnaire (PHQ-9) ), wellbeing (World Health Organisation-5 (WHO-5) Well-Being Index [45, 46]), anxiety symptomatology (General Anxiety Disorder-2 item (GAD-2) , resilience (Connor-Davidson Resilience Scale 10-item (CD-RISC10) ), work performance and absenteeism (Health and Work Performance Questionnaire (HPQ) ). In addition to these measures, demographic and service use information will be collected. The application monitors usage data including number of log-ins, frequency of use, time spent in-app, and activity completion rates. This data will be used to examine program engagement.
Patient Health Questionnaire-9 item (PHQ-9) 
General Anxiety Disorder-2 item (GAD-2) 
Connor-Davidson Resilience Scale 10-item (CD-RISC10) 
Health and Work Performance Questionnaire (HPQ) 
Service utilisation and management items
Specific measures used in online assessments
The PHQ-9 will be used to measure depression symptomatology . The PHQ-9 is a reliable and valid nine-item measure of depression severity over the past 2 weeks [44, 51]. Each of the nine items of the PHQ-9 is scored as 0 (not at all), 1 (several days), 2 (more than half the days), or 3 (nearly every day). Scores are summed to provide a total score (score range 0–27 with 0 indicating no depressive symptoms and 27 indicating all symptoms occurring nearly daily). The criterion and construct validity of the PHQ-9 has previously been demonstrated, with 73% sensitivity and 98% specificity in detecting major depression compared to clinician-based assessment [28, 50] and, regardless of diagnostic status, typically represents clinically significant depression . The measure has excellent internal consistency (Cronbach α > 0.85 in multiple samples) and test-retest reliability (α = 0.84) .
Anxiety will be measured using the 2-item Generalised Anxiety Disorder scale (GAD-2) . The GAD-2 consists of the two core criteria for generalized anxiety disorder, which have been shown to be effective screening items for panic, social anxiety, and post-traumatic stress disorders . The GAD-2 begins with the stem question: “Over the last 2 weeks, how often have you been bothered by the following problems?” Response options are “not at all”, “several days”, “more than half the days”, and “nearly every day”, scored as 0, 1, 2, and 3, respectively (total ranging from 0 to 6). A total scale score ≥ 3 is suggested as a cut-off point between the normal range and “probable anxiety” .
Resilience will be measured by the 10 item Connor-Davidson Resilience Scale (CD-RISC-10) . The CD-RISC-10 is a self-rated measure, with each question rated on a 5-point scale from 0 (‘not true at all’) to 4 (‘true nearly all the time’). The CD-RISC-10 has been shown to differentiate between individuals who function well after adversity and those who do not and measures the core features of resilience and the ability to tolerate experiences . It is believed that increased resilience may reduce rates of mental ill health .The scale demonstrates high internal consistency (Cronbach’s α = 0.89), construct validity, and test-retest reliability in the general population and in clinical settings . Total scores range from 0 to 40 with higher scores corresponding to greater resilience. The scale has been shown to have good concurrent validity, with higher resilience on the scale associated with lower levels of perceived stress  Validity is high relative to other measures and reflects differentiation in resilience among diverse populations, showing that higher levels of resilience are consistent with lower levels of perceived stress vulnerability .
Wellbeing will be assessed using the 5-item WHO Wellbeing Index (WHO-5) [45, 46]. Participants are asked to self-report on the presence or absence of wellbeing on a 6-point scale ranging from 5 (‘all of the time’) to 0 (‘none of the time.’) Raw scores range from 0 to 25 where 0 indicates the worst possible quality of life while a score of 25 represents the best possible quality of life. A score ≤ 13 or an answer of 0 or 1 on any of the five items shows poor wellbeing. WHO-5 is a psychometrically sound measure of well-being with high internal consistency (Cronbach’s α = 0.84) and convergent associations with other measures of well-being .
Work Performance will be measured using three items (performance items A10, A11, A12) from the Health and Work Performance Questionnaire (HPQ) ) and two additional items pertaining to: i) sickness absence over the past month (days absent more generally, and days absent specifically for mental health reasons), and ii) weeklong sickness absence over the past 6-months (weeks absent more generally, and weeks absent specifically for mental health reasons).
Service use and management items comprised of seven items assessing lifetime and past month service use, along with current medication use. Participants were also asked about their abilities (perceived capability and effectiveness) to manage their mental fitness, and autonomy (choice and freedom) in management. These were scored on a 7-point Likert scale from strongly disagree to strongly agree.
In any trial concerned with mental health, there is the potential for participants to experience psychological distress. Those who meet criteria for the trial will within the app trigger the user to be directed to a “get support” page (at each assessment point) and will suggest the participant seek further help from these support services or their general practitioner (GP). Additionally, an optional call-back service for individuals requiring further support or direction is provided. This call-back will be conducted by a team-member with psychology training, within 4 days, with the purpose to guide participants into necessary care arrangements. If the team member still has concerns for the participant’s safety, an accredited psychiatrist will contact the participants within the next 24 working hours. Participants will also receive an SMS with a range of support service contacts, and another reminder to consult with their GP regarding their mental health.
Study hypotheses and outcomes
We hypothesise that participants receiving the HeadGear intervention will have reduced levels of depression symptomatology at post-intervention and 3-month follow-up, compared to participants in the attention-matched control condition. While the primary analyses will be conducted on the entire sample (to examine the intervention effect). We also predict the intervention effect to vary according to the level of depression symptoms at baseline , with a greater effect amongst those with higher levels of symptomatology.
Secondly, it is hypothesized that—relative to the control group—HeadGear Intervention participants will have lower rates of depressive disorder as detected by the PHQ algorithm at all follow-up time points. We also hypothesise that the intervention group will have reduced levels of anxiety symptomatology, and improved wellbeing, resilience, and work performance, at all follow-up time points, relative to controls.
The primary outcome measure of the study will be the level of depressive symptomatology (as measured by the PHQ-9) at the 3-month follow up period.
A range of secondary outcomes will be considered, including change in anxiety symptomatology (as measured by the PHQ-2) at both 5-week and 3-month follow up. Other secondary outcomes include incident caseness of depression at 5-week and 3-month follow-up (as measured by the PHQ-9 diagnostic algorithm), and the level of depression symptoms (PHQ-9) at 5-week follow up. Finally, change in Wellbeing (as measured by the WHO-5) and change in occupational functioning (as measured by the HPQ and sickness absence questions) at both 5-week and 3-month follow-ups will be outcomes of interest.
Primary analyses will be undertaken on an intent-to-treat basis, including all participants as randomised, regardless of treatment received or withdrawal from the study. Likelihood based methods (mixed-model repeated measures (MMRM)) methods will be favoured to analyse change in the primary outcome measure (PHQ-9). A priori planned comparisons of change from baseline across the 3-month follow up period will be used to test the primary hypothesis. An unstructured variance-covariance matrix will be used to accommodate relationships between observations at different occasions. Variables found to be substantially imbalanced between groups post randomisation will be tentatively included in these models and retained if statistically significant and influential on outcomes. Similar analyses of scaled secondary measures will assess differential change due to intervention arm. Mathematical transformation or categorisation of raw scores may be undertaken to meet distributional assumptions and address any violation of assumptions attributable to outliers.
Baseline characteristics will be used to define subgroups that would be the targeted if the app were offered as treatment. Group membership will be used for models to evaluate moderation of effectiveness by adding appropriate interaction terms and undertaking planned comparisons. The effect on outcome of level of baseline depressive symptom levels, level of functional improvement and recruitment method at baseline will be explored using an analysis of covariance approach using baseline measures as a covariate and including a covariate by intervention arm interaction term in models. The effectiveness of the active intervention at clinically relevant levels of baseline covariates will be assessed using planned comparisons while the lowest values of covariates associated with a significant benefit of the intervention will be established using a Johnson and Neyman  approach.
All tests of treatment effects will be conducted using a two-sided alpha level of 0.05 and 95% confidence intervals.
As a treatment, the size of the effect of the intervention is anticipated to be moderate. Meta-analysis of previous trials of internet and mobile based treatment of depression showed a large effect size of g = − 0.90 ; however unguided interventions typically show smaller effect sizes. Power calculations were carried out using the R package simR . Power was set at 80%, alpha at 0.05, 2-tailed tests, and a correlation of .50 between pre- and post- intervention scores was assumed. Based on these calculations, a sample size of 266 per group was needed (total N = 532). A conservative dropout rate of 40% at follow-up was estimated. An initial sample of 851 will therefore be recruited for the trial.
Results of this study will be disseminated for publication in peer-reviewed journals and key findings presented at national and international conferences.
This study is planned to be the largest randomised controlled trial of a smartphone intervention for depression. By targeting the workers, this trial will provide valuable evidence regarding the effectiveness of mhealth interventions in treating depressive symptoms in a workplace context. Given the substantial impact that depression has on the individual and the employer, if shown to be effective, this program would allow for a simple and economical means by which an organisations or governments could disseminate a tailored intervention for workers. Given the proliferation of untested smartphone applications, the dissemination of evidence based products into the workplace, and indeed the wider community, remains a pressing need.
Employees with significant depressive symptoms have higher rates of absenteeism, presenteeism and job turnover . Remaining in the workforce is important as it offers structure, empowerment, financial security and protection from the psychological impacts of unemployment . Using the workplace as a means to dispense or promote an intervention may not only be protective for the individual concerned, but may also assist in overcoming the issue of individuals delaying or not receiving treatment . From an economic standpoint, it is has been established that, for the workplace, such an approach is more cost-effective for the organisation : utilising smartphone technologies would improve upon this cost-effectiveness. It is also hoped that dissemination via workplaces and social media will help engage individuals who would not usually access help via the health care system.
This study will provide valuable evidence regarding the effectiveness of mhealth tools in the treatment of depressive symptoms. Despite the proliferation of mental health apps, there is scarce research on the effectiveness of such apps. Indeed, in a systematic review of the literature on smartphone interventions, only five mental health apps were empirically tested and only one of these did not require the input from a mental health professional . mHealth interventions offer advantages in that they increase user autonomy and anonymity, which may be important as stigma  and lack of knowledge of services  can impact upon help-seeking behaviour in the workplace. An intervention developed for the workplace also carries the benefit in that it could ameliorate some of the financial burden placed solely on the healthcare system.
The proposed trial does carry with it a number of limitations. The use of a smartphone app as a delivery modality does mean that the intervention is unguided and that the user is responsible for managing their interaction with the program. Thus, trial attrition and disengagement are expected issues [64, 65]. It is worth noting, however, that this has also been an issue for face-to-face psychotherapy trials . The reason for drop out in both mhealth and face-to-face trials is often multi-faceted, and whilst can be related to engagement with the program, it is rarely only due to dissatisfaction .To account for potential drop out, two procedures were put into place. Firstly, all follow-up communication would occur via phone numbers to ensure that if the participant uninstalled the app before the follow-up period, the participant could still be reached. Secondly, conservative drop-out estimation and the use of statistical methods robust to data missing at random, it is believed that this limitation will be minimised.
Another limitation of the present trial is related to the reliance on self-reported depressive symptoms, rather than a diagnosis of depression achieved through a structured diagnostic interview. This is a common issue faced by most similar trials given the constraints around time and resources. To overcome this issue, we will use a well-validated measure (PHQ-9) that contains two methods for classifying depression: 1) threshold total score above 14 (sensitivity = 67%; specificity = 95%)  or 2) meeting the depression algorithm’s criteria (sensitivity = 0.53; specificity = 0.94) . By using both methods, we are confident that participants without significant symptomatology will be excluded and those with significant symptomatology will be included.
The treatment of depression utilising evidence based mhealth interventions remains an important area of clinical research. The Headgear trial will be the largest trial of a smartphone application that seeks to offer an alternate or augmentation to traditional face-to-face therapy through which working adults can manage their mental health and wellbeing. This trial will be unique in that it is advertising a treatment via a workplace setting, and allowing for the assessment of clinical and occupational outcomes. Finally, the Headgear Trial will provide much needed information on the general effectiveness of evidence-based interventions (BAT and mindfulness) delivered through smartphone technology.
This study was developed in partnership with beyond blue with donations from the Movember Foundation. RAC is funded by an Australian Research Council Future Fellowship FT140100824. SBH and MD are supported by funding from iCare and NSW Health.
Availability of data and materials
The anonymized datasets generated and analysed during the current study will not be publicly available due to legal and ethical restrictions.
The current trial has received ethics approval from the University of New South Wales Human Research Ethics Committee (HC17021). Research findings will be disseminated via peer reviewed journals, conferences and internal reports.
All of the other authors contributed to reviews of the manuscript and the original research design and the analysis of data. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The proposed trial has received ethical approval from the University of New South Wales (UNSW) Human Research Ethics Committee (HC17021).
Prior to study participation all participants viewed and consented to the information that was provided in the Participant Information Sheet.
All researchers have remained independent from the funders in the completion and submission of this work. All authors have no conflict of interest or competing interests to declare, except that the intellectual property of the smartphone application is jointly owned.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Organization, W.H., Depression and other common mental disorders: global health estimates. 2017.Google Scholar
- Slade T, et al. 2007 National Survey of mental health and wellbeing: methods and key findings. Aust N Z J Psychiatry. 2009;43(7):594–605.View ArticlePubMedGoogle Scholar
- Harvey SB, et al. Depression and work performance: an ecological study using web-based screening. Occup Med (Lond). 2011;61(3):209–11.View ArticleGoogle Scholar
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5). 5th ed. Washington, DC: APA; 2013.View ArticleGoogle Scholar
- Kessler RC, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Jama. 2003;289(23):3095–105.View ArticlePubMedGoogle Scholar
- Harvey SB, et al. Is the prevalence of mental illness increasing in Australia? Evidence from national health surveys and administrative data, 2001–2014. Med J Aust. 2017;206(11):490–3.View ArticlePubMedGoogle Scholar
- Cuijpers P, et al. Psychotherapy for subclinical depression: meta-analysis. Br J Psychiatry. 2014;205(4):268–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Cuijpers P, et al. Psychotherapy for depression in adults: a meta-analysis of comparative outcome studies. J Consult Clin Psychol. 2008;76(6):909–22.Google Scholar
- Butler AC, et al. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin Psychol Rev. 2006;26(1):17–31.View ArticlePubMedGoogle Scholar
- Richards DA, et al. Cost and outcome of Behavioural activation versus cognitive Behavioural therapy for depression (COBRA): a randomised, controlled, non-inferiority trial. Lancet. 2016;388(10047):871–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Olfson M, Marcus SC. National trends in outpatient psychotherapy. Am J Psychiatr. 2010;167(12):1456–63.View ArticlePubMedGoogle Scholar
- Mohr DC, et al. Barriers to psychotherapy among depressed and nondepressed primary care patients. Ann Behav Med. 2006;32(3):254–8.View ArticlePubMedGoogle Scholar
- Sanderson K, Andrews G. Common mental disorders in the workforce: recent findings from descriptive and social epidemiology. Can J Psychiatry. 2006;51(2):63–75.View ArticlePubMedGoogle Scholar
- Modini M, et al. The mental health benefits of employment: results of a systematic meta-review. Australas Psychiatry. 2016;24(4):331–6.View ArticlePubMedGoogle Scholar
- Tan L, et al. Preventing the development of depression at work: a systematic review and meta-analysis of universal interventions in the workplace. BMC Med. 2014;12(1):74.View ArticlePubMedPubMed CentralGoogle Scholar
- Joyce S, et al. Workplace interventions for common mental disorders: a systematic meta-review. Psychol Med. 2016;46(4):683–97.Google Scholar
- Nieuwenhuijsen K, et al. Interventions to improve occupational health in depressed people. Cochrane Database Syst Rev. 2008;2(2):CD006237. https://doi.org/10.1002/14651858.CD006237.pub2.
- Roche AM, et al. Men, work, and mental health: a systematic review of depression in male-dominated industries and occupations. Safety and health at work. 2016;7(4):268–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Deady M, et al. Designing smartphone mental health applications for emergency service workers. Occup Med. 2017:kqx056.Google Scholar
- Donker T, et al. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013;15(11):e247.Google Scholar
- Calvo RA, et al. Computing in mental health. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. San Jose: ACM; 2016.Google Scholar
- Josephine K, et al. Internet-and mobile-based depression interventions for people with diagnosed depression: a systematic review and meta-analysis. J Affect Disord. 2017;223:28–40.View ArticlePubMedGoogle Scholar
- Calvo R, et al. Positive computing: a new partnership between psychology, social sciences and technologists (editorial introduction), vol. 6; 2016.Google Scholar
- Reynolds S. Psychological well-being at work: is prevention better than cure? J Psychosom Res. 1997;43(1):93–102.View ArticlePubMedGoogle Scholar
- Association, W.M. World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. Jama. 2013;310(20):2191–4.View ArticleGoogle Scholar
- Schulz KF, Altman DG, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMC Med. 2010;8(1):18.View ArticlePubMedPubMed CentralGoogle Scholar
- Australian Bureau of Statistics. Australian and New Zealand standard industrial classification. Canberra: Australian Bureau of Statistics; 2008.Google Scholar
- Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann. 2002;32(9):509–15.View ArticleGoogle Scholar
- Choi I, et al. Using different Facebook advertisements to recruit men for an online mental health study: engagement and selection bias. Internet Interventions. 2017;8:27–34.View ArticleGoogle Scholar
- Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Hopko DR, et al. Contemporary behavioral activation treatments for depression: procedures, principles, and progress. Clin Psychol Rev. 2003;23(5):699–717.View ArticlePubMedGoogle Scholar
- Lejuez CW, et al. Ten year revision of the brief behavioral activation treatment for depression: revised treatment manual. Behav Modif. 2011;35(2):111–61.View ArticlePubMedGoogle Scholar
- Richards DA, et al. Cost and outcome of Behavioural activation versus cognitive Behavioural therapy for depression (COBRA): a randomised, controlled, non-inferiority trial. Lancet. 388(10047):871–80.Google Scholar
- Ly KH, et al. Behavioural activation versus mindfulness-based guided self-help treatment administered through a smartphone application: a randomised controlled trial. BMJ Open. 2014;4(1):e003440.View ArticlePubMedPubMed CentralGoogle Scholar
- Brown KW, Ryan RM, Creswell JD. Mindfulness: theoretical foundations and evidence for its salutary effects. Psychol Inq. 2007;18(4):211–37.View ArticleGoogle Scholar
- Greeson J, Garland EL, Black D. Mindfulness: a transtherapeutic approach for transdiagnostic mental processes. Wiley Blackwell handbook of mindfulness. 2014:531–62.Google Scholar
- Hofmann SG, et al. The effect of mindfulness-based therapy on anxiety and depression: a meta-analytic review. J Consult Clin Psychol. 2010;78(2):169.View ArticlePubMedPubMed CentralGoogle Scholar
- Zgierska A, et al. Mindfulness meditation for substance use disorders: a systematic review. Subst Abus. 2009;30(4):266–94.View ArticlePubMedPubMed CentralGoogle Scholar
- Titov N, et al. Improving adherence and clinical outcomes in self-guided internet treatment for anxiety and depression: randomised controlled trial. PLoS One. 2013;8(7):e62873.View ArticlePubMedPubMed CentralGoogle Scholar
- Matthews M, et al. Designing mobile applications to support mental health interventions. In: Jo L, editor. Handbook of Research on User Interface Design and Evaluation for Mobile Technology. London: IGI, Information Science Reference; 2008. p. 635–56.Google Scholar
- Bakker D, et al. Mental health smartphone apps: review and evidence-based recommendations for future developments. JMIR Mental Health. 2016;3(1):e7.View ArticlePubMedPubMed CentralGoogle Scholar
- Deady M, et al. Designing smartphone mental health applications for emergency service workers. Occup Med. 2017;Google Scholar
- Calvo RA, Peters D. Positive computing: technology for wellbeing and human potential. Cambridge: MIT Press; 2014.Google Scholar
- Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16(9):606–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Bech P. Measuring the dimensions of psychological general well-being by the WHO-5. QoL Newsletter. 2004;32:15–6.Google Scholar
- Bech P, Gudex C, Staehr Johansen K. The WHO (ten) well-being index: validation in diabetes. Psychother Psychosom. 1996;65:183–90.View ArticlePubMedGoogle Scholar
- Kroenke K, et al. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med. 2007;146:317–25.View ArticlePubMedGoogle Scholar
- Connor KM, Davidson JR. Development of a new resilience scale: the Connor-Davidson resilience scale (CD-RISC). Depress Anxiety. 2003;18(2):76–82.View ArticlePubMedGoogle Scholar
- Kessler RC, et al. The world health organization health and work performance questionnaire (HPQ). J Occup Environ Med. 2003;45(2):156–74.View ArticlePubMedGoogle Scholar
- Spitzer RL, et al. Validation and utility of a self-report version of PRIME-MD - the PHQ primary care study. JAMA. 1999;282(18):1737–44.View ArticlePubMedGoogle Scholar
- Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of mental disorders. Patient health questionnaire. JAMA. 1999;282(18):1737–44.View ArticlePubMedGoogle Scholar
- Kroenke K, Spitzer RL, Williams JBW. The PHQ-9 - validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the connor–Davidson resilience scale (CD-RISC): validation of a 10-item measure of resilience. J Trauma Stress. 2007;20(6):1019–28.View ArticlePubMedGoogle Scholar
- Southwick SM, Vythilingam M, Charney DS. The psychobiology of depression and resilience to stress: implications for prevention and treatment. Annu Rev Clin Psychol. 2005;1:255–91.View ArticlePubMedGoogle Scholar
- Krieger T, et al. Measuring depression with a well-being index: further evidence for the validity of the WHO well-being index (WHO-5) as a measure of the severity of depression. J Affect Disord. 2014;156:240–4.View ArticlePubMedGoogle Scholar
- Imamura KKNF, Matsuyama Yutaka TA, Akihito S, Rino U, Sonoko K, Kiyoto K. Effects of an internet-based cognitive behavioral therapy (iCBT) program in manga format on improving subthreshold depressive symptoms among healthy workers: a randomized controlled trial. PLoS One. 2014;9(5):e97167.View ArticlePubMedPubMed CentralGoogle Scholar
- Johnson PO, Neyman J. Tests of certain linear hypotheses and their application to some educational problems. Stat Res Memoir. 1936;1:57–93.Google Scholar
- Green P, MacLeod CJ, SIMR. An R package for power analysis of generalized linear mixed models by simulation. Methods Ecol Evol. 2016;7(4):493–8.View ArticleGoogle Scholar
- Lerner D, Henke RM. What does research tell us about depression, job performance, and work productivity? J Occup Environ Med. 2008;50(4):401–10.View ArticlePubMedGoogle Scholar
- Wang PS, et al. Twelve-month use of mental health services in the United States: results from the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):629–40.View ArticlePubMedGoogle Scholar
- Whiteford HA, et al. The work outcomes research cost-benefit (WORC) project: the return on investment for facilitating help seeking behaviour. Aust N Z J Psychiatry. 2005;39:A37.Google Scholar
- Hanisch SE, et al. The effectiveness of interventions targeting the stigma of mental illness at the workplace: a systematic review. BMC Psychiatry. 2016;16(1):1.View ArticlePubMedPubMed CentralGoogle Scholar
- French MT, et al. Factors that influence the use and perceptions of employee assistance programs at six worksites. J Occup Health Psychol. 1997;2(4):312.View ArticlePubMedGoogle Scholar
- Leykin Y, et al. Results from a trial of an unsupported internet intervention for depressive symptoms. Internet Interventions. 2014;1(4):175–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Donker T, et al. Internet-delivered interpersonal psychotherapy versus internet-delivered cognitive behavioral therapy for adults with depressive symptoms: randomized controlled noninferiority trial. J Med Internet Res. 2013;15:e82.View ArticlePubMedPubMed CentralGoogle Scholar
- De Maat S, et al. Relative efficacy of psychotherapy and pharmacotherapy in the treatment of depression: a meta-analysis. Psychother Res. 2006;16(5):566–78.View ArticleGoogle Scholar
- Christensen H, et al. Free range users and one hit wonders: community users of an internet-based cognitive behaviour therapy program. Aust N Z J Psychiatry. 2006;40(1):59–62.View ArticlePubMedGoogle Scholar
- Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the patient health questionnaire (PHQ-9): a meta-analysis. Can Med Assoc J. 2012;184(3):E191–6.View ArticleGoogle Scholar
- Manea L, Gilbody S, McMillan D. A diagnostic meta-analysis of the patient health Questionnaire-9 (PHQ-9) algorithm scoring method as a screen for depression. Gen Hosp Psychiatry. 2015;37(1):67–75.View ArticlePubMedGoogle Scholar