Grant Proposal Example - Long

Specific Aims

Cognitive-behavioral therapy (CBT) has been proven to be an effective treatment for a variety of disorders including anxiety disorders, substance use disorders and depression (Hofmann et al., 2012). While there is an increasing body of work showcasing the effectiveness of treating anxiety disorders with CBT, researchers are continually trying to find more innovative modes of delivery of the treatment. As technology rapidly advances and these technological advances (e.g.., smartphones, tablets and computers) are becoming more accessible (Smith, 2017), researchers have been investigating how to adapt evidence-based treatments, including CBT, to be delivered using technology.

The primary aim of this research proposal is to investigate whether CBT delivered as an app-based mental health intervention is more effective in treating generalized anxiety disorder (GAD) than computer-based CBT (cCBT) interventions. MoodMission and MoodGYM are two CBT treatments that differ in the modality of treatment delivery that will be investigated in this study, with MoodMission being delivered through a smartphone app, and MoodGYM being delivered via the internet on a computer. MoodGYM has been shown to significantly decrease symptoms of anxiety compared to controls (Powell et al., 2013) however MoodMission has not been shown to have significant effects on symptoms of GAD (Bakker, Kazantzis, Rickwood, & Rickard, 2018); based on these findings I expect MoodGYM (cCBT) to significantly decrease symptoms of anxiety but I expect that there will be no significant effect of MoodMission treatment on symptoms compared to a control condition. Mental well-being will also be measured as another measure of the efficacy of the treatment. As both MoodGYM and MoodMission have been shown to significantly improve mental-wellbeing (Bakker & Rickard, 2019; Twomey et al., 2014), I expect both to significantly improve psychological well-being compared to the control condition. However, I expect MoodGYM to have greater degree of improvement in mental well-being compared to MoodMission due to the expected reductions in anxiety symptoms.

This study also will also address whether ratings of adherence to CBT treatments differ by the delivery modality. Apps have been shown to increase adherence to treatment programs as a result of notification reminders, as well as having the ability to have access to treatment immediately regardless of location (Price et al., 2013). Therefore I hypothesize that the app-based CBT treatment will have greater adherence than the computer based CBT treatment.

The third question that this study will address is whether ratings of acceptability differ for app-based CBT compared to cCBT. I hypothesize that app-based and computer-based treatments will generally high acceptability in accordance with past research (Smith et al., 2019; Carter, Bell, & Colhoun, 2019), although I expect the app-based treatment to have higher ratings of acceptability compared to the computer-based treatment due to the higher adherence rating.

Although there is a body of work showing that CBT is effective in treating generalized anxiety disorder (Cuijpers et al., 2014), there is currently no research that compares the delivery modality of the CBT treatment to see which modality is the most effective. This study will provide evidence for which treatment modality is more effective in decreasing symptoms of GAD which has great potential clinical implications including overcoming geographic boundaries and reducing costs of treatment.

Background and Significance

This research study specifically focuses on the population of individuals with a diagnosis of generalized anxiety disorder (GAD). A diagnosis of GAD is characterized by chronic worry that is challenging to control and results in significant impairment in functioning (American Psychiatric Association, 2013). This worry must also be associated with three or more physical symptoms of the following symptoms: restlessness, fatigue, issues with concentration, irritability, muscle tension and sleep disturbances (American Psychiatric Association, 2013). GAD has a lifetime prevalence of around 5% in the United States, with a prevalence rate of between 2% and 3% at any current moment (Weisberg, 2009). The disorder tends to a be a long term disorder, persisting for longer than five years in 40% of the individuals who have received a diagnosis (Robins, 1991), therefore it is extremely important to develop treatments that aim to improve the well-being of individuals who will likely experience psychological distress for a prolonged period of time.

In-person CBT has been shown to be particularly effective in treating GAD (Covin, Ouimet, Seeds, & Dozois, 2008). When treating GAD, CBT typically includes the following key features: psychoeducation, relaxation training, cognitive restructuring, systematic exposure and problem-solving training (Dugas et al., 2010). The psychoeducation aspect of CBT for treating GAD allows the client to understand more about the disorder and what therapeutic tools will be used in treatment, thereby facilitating the motivation to adhere to treatment (Borza, 2017). Clients are taught relaxation techniques (e.g., slowed diaphragmatic breathing, relaxing imagery, and meditation relaxation), cognitive restructuring techniques (e.g., the client identifies and challenges anxious thoughts and worries and reevaluates the usefulness of these worries) and are guided through systematic exposure by a therapist (e.g.., use imaginal exposure of their hypothetical worst worries), as a method of reducing feelings of anxiety (Borkovec, Newman, Pincus, & Lytle 2002; Dugas et al., 2010). Finally the client is taught problem-solving techniques including problem orientation (i.e., immediate reactions to problems including expectations of the outcome and beliefs regarding the ability to solve the problem), problem definition and formulation (i.e., understanding the problem and identify realistic goals), generation of alternatives (i.e., identifying multiple solutions to a problem), decision making (i.e., calculating the costs and benefits of each of these solutions and identifying the most cost effective solution), and solution implementation and verification (i.e., carrying out the chosen solution), to reduce anxiety related to objective stressors (D’Zurilla, 1986).

A potential issue that clinicians face when treating GAD is that the disorder has been shown to be one of the most comorbid mental disorders with other psychological disorders; one study showed that only 25% of clients seeking treatment present symptoms of GAD without any other comorbid diagnoses (Maier et al., 2000). Individuals with a comorbid diagnosis of GAD have been shown to have greater impairment and worse longer-term outcomes than those who only have a pure diagnosis of GAD (Noyes, 2001). With the high comorbidity rates of the disorder this poses potential confounds as it increases the variance within the sample population, potential impacting the efficacy of treatments. Although GAD is a highly prevalent mental disorder, many individuals who receive a diagnosis of the disorder are unable to receive treatment. Increasing access to treatments for individuals with a diagnosis of generalized anxiety disorder is crucial as only 43.2% of individuals with a diagnosis of GAD are receiving treatment for the disorder (Wang et al., 2005) with multiple factors making treatment challenging (e.g., georgraphic barriers, cost of treatment). There is an increasing body of research investigating whether novel methods of delivery including over the phone (Farrand & Williams, 2010), app-based (Bakker, Kazantzis, Rickwood, & Rickard, 2018) and computer-based (Twomey et al., 2014) interventions are effective in delivering CBT as a treatment and how they can improve the accessibility of treatments to overcome these barriers.

So and colleagues (2013) however found that the effectiveness of cCBT treatments was attenuated in the long-term, which may be a major limitation of using the treatment as an intervention for individuals with GAD due to the long-term nature of the disorder. While a potential issue that many researchers discuss in studies involving cCBT is higher dropout rates than traditional treatments, computer-based treatments for anxiety have been found to have similar dropout rates to standard therapies (Buglione et al., 1990; Ghosh et al., 1988). A computer-based CBT program includes the program MoodGYM aims to treat anxiety and depressive disorders. MoodGYM aims to improve psychological well-being and manage the symptoms of these disorders by having users complete five core modules focused on different aspects of evidence-based CBT techniques (i.e., cognitive restructuring, relaxation; Farrer, Christensen, Griffiths, & Mackinnon, 2011). MoodGYM has been shown to significantly decrease symptoms of psychological distress and stress (Twomey et al., 2014) and significantly improved anxiety symptoms compared to a control condition (Ellis, Campbell, Sethi & O’Dea, 2011). A meta-analysis showed that MoodGYM had medium effect sizes in reducing anxiety symptoms (Twomey & O’Reilly, 2017). High adherence to MoodGYM has also been shown to significantly increase the effect of the intervention when compared to control participants and participants who had low adherence to the treatment (Calear, Christensen, Mackinnon & Griffiths, 2013). One study also showed that participants were more likely to see online therapy as just as acceptable a traditional, in-person therapy if they used MoodGYM compared to those who just used informational websites about mental health (Schneider, Foroushani, Grime & Thornicroft, 2014).

CBT has also successfully been delivered as an intervention and treatment when delivered by an app (Depp et al., 2015). Smart-phone applications in particular may increase the engagement of clients when participating in treatment as they can provide education about a treatment prior to the treatment, overcome geographical barriers and make treatments cheaper (Price et al., 2014). Price, Anderson, Henrich, and Rothbaum (2008) found that the gains that individuals made by using apps for treatment were maintained even after treatment had terminated, making this method of delivery particularly suitable for treating GAD. One app-based method of delivering CBT is through the app MoodMission, an app designed to treat depressive disorders and anxiety disorders similarly to MoodGYM. The app has short missions tailored to the information on mood provided by users, with these missions aiming to improve mood using evidence-based CBT techniques (e.g.., behavioral activation activities, gratitude thought experiments; Bakker & Rickard, 2019). Previous research has shown that use of MoodMission results in improvements of mental well-being when compared to a control condition (Bakker & Rickard, 2019). Although fewer studies have investigated the efficacy of MoodMission as a treatment for anxiety due to it being a relatively new app, previous studies have shown that MoodMission does not significantly decrease scores of anxiety (Bakker, Kazantzis, Rickwood, & Rickard, 2018). Instead the app has only been shown to be beneficial in improving psychological well-being, which has indirectly been linked to benefits of improving anxiety in individuals in the long-term. There is little research, however, that investigates how clients perceive the app (e.g., whether or not clients continue to use the app and adhere to the treatment, and how acceptable clients believe the treatment is).

Novel methods of delivery allow more people feeling psychological distress the opportunity to receive treatment to improve psychological well-being and to allow clinicians the opportunity to see more clients due to reducing the amount of time spent with each client (Kazdin & Rabbitt, 2013). One of the key issues with both cCBT and app-based CBT is that currently neither of the programs mentioned (i.e., MoodMission and MoodGYM) are evidence based-treatments for GAD as they have not been shown to significantly decrease symptoms in randomized controlled trials (RCTs). This makes it hard for clinicians to recommend these programs and clients seeking help with their mental health to choose a suitable program without these RCTs. As the programs are updated and improved over time this could possibly change, therefore it is imperative that more research investigating the efficacy of both of these programs in conducted in clinical populations. While research has shown that both computer-based (Andrews et al., 2018) and app-based (Pramana et al., 2019; Christoforou, Fonseca, & Tsakanikos, 2019) CBT treatments have been significantly effective in treating anxiety disorders, there is a lack of research directly comparing the methods of delivery.

Innovation

The innovation of this project primarily lies in (1) the comparison of novel models of CBT treatment for individuals with a diagnosis of GAD and (2) investigating client perceptions of these novel models. To date, no studies have compared delivering CBT by computer to delivering CBT by app as a treatment for GAD. These delivery methods of the treatment utilise CBT, a treatment which has been proven to effectively improve symptoms of GAD, and deliver these techniques in novel ways.

While we have some research has been conducted comparing app-based CBT and cCBT in a population of individuals with major depressive disorder (Watts et al., 2013), to date a study has not been conducted comparing these interventions (i.e., app-based CBT and cCBT) in a population of individuals with a diagnosis of GAD. Both app-based interventions and computer-based interventions allow for the expansion of the dissemination of evidence-based treatments, thereby reaching more individuals in need of treatment. This study therefore will be able to address whether the modality of the treatment delivery significantly affects its effectiveness in delivering CBT for individuals with GAD, which could help identify key features of the treatment delivery that are particularly effective. This will also provide indications of whether the interventions may be better to be used as prevention and early intervention techniques for subclinical symptoms (i.e., if the treatment only improves mental well-being) or as treatments after symptoms reach diagnostic level (i.e., if the treatment reduces symptoms of GAD).

There is also little research investigating how clients participating in these particular treatments (i.e., MoodGYM and MoodMission) feel about these program as a treatment, in terms of adherence and acceptability. This research study aims to fill this gap in the literature by not only addressing adherence and acceptability in terms of each individual program, but also to compare the programs to each other in order to see which treatments clients perceive as “better”. Research has shown that perceptions of the efficacy of a treatment effect both the seeking of the treatment and the effectiveness of the treatment (ten Have et al., 2010)., therefore better perceptions of these programs may lead to an increase in individuals seeking mental-health improvement by using these programs. This is particularly important as only 43.2 % of individuals with a diagnosis of GAD are receiving treatment, and of those receiving treatment 18.9% are receiving minimally adequate treatment (Wang et al., 2005). By increasing perceptions of novel models of treatment delivery this could increase the number of people seeking and receiving treatment that they need.

This study also is investigating how effective these delivery methods of CBT are with little contact with a therapist, therefore investigating whether they have the potential to act as a stand-alone intervention in the clinical population of individuals with a diagnosis of GAD. Stand-alone app-based and cCBT treatments are crucial interventions as they allow psychologists to overcome some of the major barriers that clients face when seeking treatment (e.g. geographic location, expensive specialized treatments). This may increase the number of individuals with a diagnosis of GAD who are actually receiving treatment for their diagnosis. These novel methods also provide the opportunity to potential reduce the amount of time clients have to spend with therapists, allowing therapists to potentially treat more clients and use their resources more efficiently.

Approach

Participants

90 participants will be recruited using newspaper advertisements and referrals from outpatient mental health clinics in the Cleveland area. Newspaper advertisements will consist of print and online advertisement that will be posted in local Cleveland newspapers. Individuals referred from local clinical practices will not have just completed treatment for generalized anxiety disorder from the clinic before participating in the study.

In order to take part in the study, participants must meet the following criteria: (1) be above the age of 18-years old (2) meet criteria for a DSM-5 diagnosis of generalized anxiety disorder (GAD); (3) have access to an internet-enabled mobile phone; and (4) have access to a desktop computer or laptop computer with internet capability. Individual will be excluded from participating in the study if they are participating in another treatment program throughout the duration of the study. Participants will not be if they have excluded for having any current comorbid psychiatric diagnoses with GAD,

A diagnosis of GAD will be diagnosed first through telephone interview conducted by a licensed clinician using GAD-7 with a score of greater than 10 moving individuals to the next stage, where they receive a diagnosis by a clinician in-person using of the structured diagnostic interview for DSM-5 Axis I disorders (SCID-5; First, Williams, Karg, & Spitzer, 2015). The SCID-5 will also be used as a method of measuring comorbid diagnoses, therefore participants who meet criteria for other disorders as judged by a clinician using the SCID-5 will be excluded from participating in the study.

Treatment

Participant will be randomly allocated using a true randomization process (random.org) into one of three conditions: (1) a waitlist control group, (2) CBT via the MoodMission app, or (3) computerized-CBT via MoodGYM. This randomization process will occur after screening for GAD when eligibility requirements have been met, and individuals who do not meet inclusion criteria to take part in the study will be directed towards more appropriate treatments.

The app MoodMission will be used as a method of administering cognitive behavioral therapy via app to participants. The MoodMission app was developed to utilise validated CBT principles as an intervention method for individuals with low mood and anxiety (Bakker et al., 2018). The program MoodGYM will be used to administer computerized-CBT to individuals allocated to the condition of cCBT. MoodGYM is a internet-based cCBT program that is specifically designed to treat to reduce the symptoms of depression, anxiety and psychological distress (Twomey & O’Reilly, 2017).

Each treatment condition (MoodMission and MoodGYM) will last five-weeks and will include a brief (10-15 minute) weekly clinical monitoring session provided in-person at a clinic by a psychologist. These monitoring sessions will give participants the opportunity to address any concerns or questions about the use of the app or the internet-based program, and will also act as a component to monitor the safety of participants by ensuring that symptoms are not worsening. To ensure the safety of participants and to improve treatment engagement, participants in these treatment conditions will receive weekly automated emails (personalized only to their names and their treatment condition) to remind them to progress through MoodGYM’s five sessions and complete missions on MoodMission. High dropout rates have been previously noted from treatment with both MoodMission MoodGYM (Andersson & Cuijpers, 2008; Donker et al., 2013), and therefore the weekly clinical monitoring sessions also aim to reduce dropout rates.

MoodMission prompts the user to describe their mood and displays five missions tailored to the individuals current mood. These missions in MoodMission may be physical activities (e.g., running on the spot), behavior-based activities (e.g., learn how to knit, crochet, or sew) or thought and emotion-based (e.g., decatastrophize). The missions displayed change as the mood of the individual changes, and therefore the app contains a wealth of different missions within these categories. Participants allocated to this treatment group will be encouraged to use to report anxious moods and complete missions until their anxious mood is alleviated.

MoodGYM consists of five core modules that teach the principles of CBT (e.g., relaxation techniques, relationship between thoughts and emotion) and 29 online exercises that are aimed to increase mental wellness (Todkill & Powell, 2013). Participants in this treatment condition will be encouraged to complete one module per week, therefore participants should have all 5 modules completed by the end of the study.

While MoodGYM requires participants to complete five longer modules focused on particular CBT techniques, MoodMission contains many different shorter missions that are tailored to the current mood of the individual, allowing participants more control of which CBT techniques they will complete. This means that participants in the MoodGYM condition may experience more in depth use of CBT techniques in a similar manner to that of in-person CBT, MoodMission participants may complete more shorter and easier CBT-based tasks.

Participants allocated to the waitlist control will be placed on a waitlist for other psychological services and will not receive treatment for the five-weeks of the of the trial. After the five-weeks, participants in this group will be assigned to receive CBT via MoodMission as I predict this treatment will have the best outcome in terms of efficacy, adherence and acceptability.

Assessments

To initially screen for GAD, individuals will complete the GAD-7 to measure the symptoms of generalized anxiety disorder (Spitzer et al., 2006) through a telephone interview with a clinician. The GAD-7 scale has been used in many studies investigating GAD as both a diagnosis measure for inclusion criteria (Jones, Hadjistavropoulos & Soucy, 2016) and as a method of measuring change of symptoms across time (Christensen et al., 2014). Individuals with a score of between 10 and 14 will be invited to have a structured diagnostic interview (SCID-5) with a clinician.

After conducting the structured diagnostic interview, clinicians will also assess participants on their level of functioning using the Global Assessment of Functioning measure (GAF; Pedersen, Hagtvet, & Karterud, 2007), with this being remeasured after treatment cessation. This scale that consists of a 0-100 rating, with a higher rating indicating a greater level of functioning. Both the GAD-7 scale and the ratings by clinicians using GAF during the structured diagnostic interview will enable us to see how symptoms of anxiety change over the course of treatment.

To measure mental well-being, The Warwick-Edinburgh Mental Well-being Scale will be administered (WEMWBS; Stewart-Brown & Janmohamed, 2008). The WEMWBS is a 14-item assessment of subjective well-being and psychological functioning with a higher score being indicative of better mental well-being. This measure allows for a more broad view of an efficacious treatment rather than just investigating whether symptoms themselves improve.

While the computerized-CBT program, MoodGYM, is divided into five longer modules, the app-based CBT program, MoodMission, is divided into many smaller modules that change dependent on the users mood. As these modules do not directly map onto each other, I will not be able to use completion of modules as a method of assessing adherence to the treatment. Instead, I will be collecting data into how often individuals logged onto MoodGYM or MoodMission. Each time a participant logs into MoodMission or MoodGYM is recorded, therefore the number of times an individual logged into the program throughout the course of treatment (5 weeks) will act as a measure of treatment adherence. This data in particular will be collected at the end of the 5 weeks of treatment.

The Treatment Acceptability and Preferences (TAP) measure will be given to patients at the end of each treatment (after five weeks) measuring the acceptability of each treatment (Sidani et al., 2009). This measure in particular aims to address four particular attributes: (a) the appropriateness of the treatment option; (b) the suitability of the treatment to the lifestyle of the participant; (c) the effectiveness in managing GAD and (d) the convenience of the treatment. Ratings of acceptability for each treatment will be compared to each other to give a measure of which treatment participants perceived as being more acceptable.

Data Analytic Plan

To analyse the efficacy of the treatments, I plan to run a repeated measures ANOVA with treatment group as the independent variable and change in GAD-7 scores as the dependent variable. I will also conduct a repeated-measures ANOVA with treatment group as the independent variable and change in clinician’s rating using the GAF measure over time as thee dependent variable. A repeated measures ANOVA with treatment group as the independent variable and change in WEMWBS as the dependent variable will also be conducted. These analyses will show whether the treatments were significant in improving scores. This will provide evidence of the effectiveness of the treatments compared to the control condition. I will also run post-hoc t-tests comparing the treatment groups to each other for both GAD-7 score changes and changes in ratings using SCID-5 to show which treatment, MoodMission or MoodGYM was more effective in treating generalized anxiety disorder.

In order to analyse adherence to the treatment, I will conduct an independent samples t-test comparing the scores of the number times logging on the app or computer program. I will also use an independent samples t-test comparing the ratings of the TAP measure for each the treatment groups, which will provide evidence of whether treatment acceptability differed by treatment.

References

Adelman, C.B., Panza, K.E., Bartley, C.A., Bontempo, A., & Bloch, M.H. (2014). A meta‐analysis of computerized cognitive‐behavioral therapy for the treatment of DSM‐5 anxiety disorders. Journal of Clinical Psychiatry, 75, E695– E704.

American Psychiatric Association (APA). (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.

Andersson, G., Carlbring, P., Ljotsson, B., & Hedman, E. ( 2013). Guided internet‐based CBT for common mental disorders. Journal of Contemporary Psychotherapy, 43, 223– 233.

Andrews, G., Basu, A., Cuijpers, P., Craske, M., McEvoy, P., English, C., & Newby, J. (2018). Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: An updated meta-analysis. Journal of Anxiety Disorders, 55, 70-78.

Bakker, D., Kazantzis, N., Rickwood, D., & Rickard, N. (2018). Development and pilot evaluation of smartphone-delivered cognitive behavior therapy strategies for mood- and anxiety-related problems: Moodmission. Cognitive and Behavioral Practice, 25(4), 496-514.

Bakker, D., & Rickard, N. (2019). Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: Moodmission. Australian Psychologist. Advance online publication.

Borkovec, T. D., Newman, M. G., Pincus, A. L., & Lytle, R. (2002). A component analysis of cognitive-behavioral therapy for generalized anxiety disorder and the role of interpersonal problems. Journal of Consulting and Clinical Psychology, 70(2), 288.

Borza, L. (2017). Cognitive-behavioral therapy for generalized anxiety. Dialogues in Clinical Neuroscience, 19(2), 203.

Buglione, S. A., DeVito, A. J., & Mulloy, J. M. (1990). Traditional group therapy and computer administered treatment for test anxiety. Anxiety Research, 3, 33-39.

Calear, A. L., Christensen, H., Mackinnon, A., & Griffiths, K. M. (2013). Adherence to the MoodGYM program: Outcomes and predictors for an adolescent school-based population. Journal of Affective Disorders, 147(1-3), 338-344.

Carter, F. A., Bell, C. J., & Colhoun, H. C. (2013). Suitability and acceptability of computerised cognitive behaviour therapy for anxiety disorders in secondary care. Australian & New Zealand Journal of Psychiatry, 47(2), 142-152.

Christensen, H., Mackinnon, A. J., Batterham, P. J., O'Dea, B., Guastella, A. J., Griffiths, K. M., ... & Hickie, I. (2014). The effectiveness of an online e-health application compared to attention placebo or sertraline in the treatment of generalised anxiety disorder. Internet Interventions, 1(4), 169-174.

Christoforou, M., Fonseca, J. A. S., & Tsakanikos, E. (2017). Two novel cognitive behavioral therapy–based mobile apps for agoraphobia: Randomized controlled trial. Journal of Medical Internet Research, 19(11), e398.

Covin, R., Ouimet, A. J., Seeds, P. M., & Dozois, D. J. (2008). A meta-analysis of CBT for pathological worry among clients with GAD. Journal of Anxiety Disorders, 22(1), 108-116.

Cuijpers, P., Sijbrandij, M., Koole, S., Huibers, M., Berking, M., & Andersson, G. (2014). Psychological treatment of generalized anxiety disorder: A meta-analysis. Clinical Psychology Review, 34(2), 130-140.

Depp, C. A., Ceglowski, J., Wang, V. C., Yaghouti, F., Mausbach, B. T., Thompson, W. K., & Granholm, E. L. (2015). Augmenting psychoeducation with a mobile intervention for bipolar disorder: A randomized controlled trial. Journal of Affective Disorders, 174, 23-30.

Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M. R., & Christensen, H. (2013). Smartphones for smarter delivery of mental health programs: A systematic review. Journal of Medical Internet Research, 15(11), e247.

Dugas, M. J., Brillon, P., Savard, P., Turcotte, J., Gaudet, A., Ladouceur, R., Leblanc, R., & Gervais, N. J. (2010). A randomized clinical trial of cognitive-behavioral therapy and applied relaxation for adults with generalized anxiety disorder. Behavior Therapy, 41(1), 46-58.

D'Zurilla, T. J. (1986). Problem-solving therapy. A Social Competence Approach to Clinical Intervention.

Ellis, L. A., Campbell, A. J., Sethi, S., & O'Dea, B. M. (2011). Comparative randomized trial of an online cognitive-behavioral therapy program and an online support group for depression and anxiety. Journal of CyberTherapy and Rehabilitation, 4(4), 461-467.

Farrand, P., & Williams, C. (2010). Low intensity CBT assessment: In person or by phone. In J. Bennett‐Levy, D. A. Richards, P. Farrand, H. Christensen, K. Griffiths, D. Kavanagh, … J. White (Eds.), Oxford guide to low intensity CBT interventions (pp. 89– 96). Oxford: Oxford University Press.

Farrer, L., Christensen, H., Griffiths, K. M., & Mackinnon, A. (2011). Internet-based CBT for depression with and without telephone tracking in a national helpline: Randomised controlled trial. PLOS One, 6(11), e28099.

First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured Clinical Interview for DSM-5 Disorders—Clinician version (SCID-5-CV). Arlington, VA: American Psychiatric Association.

Ghosh, A., Marks, I. M., & Carr, A. C. (1988). Therapist contact and outcome of self exposure treatment for phobias: A controlled study. British Journal of Psychiatry, 152, 234-238.

Hofmann, S. G., Asnaani, A., Vonk, I. J., Sawyer, A. T., & Fang, A. (2012). The efficacy of cognitive behavioral therapy: A review of meta-analyses. Cognitive therapy and research, 36(5), 427-440.

Jones, S., Hadjistavropoulos, H., & Soucy, J. (2016). A randomized controlled trial of guided internet-delivered cognitive behaviour therapy for older adults with generalized anxiety. Journal of Anxiety Disorders , 37, 1–9.

Kazdin, A. E., & Rabbitt, S. M. (2013). Novel models for delivering mental health services and reducing the burdens of mental illness. Clinical Psychological Science, 1, 170-191.

Maier, W., Gänsicke, M., Freyberger, H. J., Linz, M., Heun, R., & Lecrubier, Y. (2000). Generalized anxiety disorder (ICD‐10) in primary care from a cross‐cultural perspective: A valid diagnostic entity?. Acta Psychiatrica Scandinavica, 101(1), 29-36.

Noyes Jr, R. (2001). Comorbidity in generalized anxiety disorder. Psychiatric Clinics of North America, 24(1), 41-55.

Pedersen, G., Hagtvet, K. A., & Karterud, S. (2007). Generalizability studies of the Global Assessment of Functioning–Split version. Comprehensive Psychiatry, 48, 88–94.

Pramana, G., Parmanto, B., Kendall, P. C., & Silk, J. S. (2014). The SmartCAT: An m-health platform for ecological momentary intervention in child anxiety treatment. Telemedicine and e-Health, 20(5), 419-427.

Powell, J., Hamborg, T., Stallard, N., Burls, A., McSorley, J., Bennett, K., ... & Christensen, H. (2013). Effectiveness of a web-based cognitive-behavioral tool to improve mental well-being in the general population: Randomized controlled trial. Journal of Medical Internet Research, 15(1), e2.

Price, M., Anderson, P., Henrich, C.C., & Rothbaum, B.O. (2008). Greater expectations: Using hierarchical linear modeling to examine expectancy for treatment outcome as a predictor of treatment response. Behavior Therapy, 39(4), 398–405.

Price, M., Yuen, E. K., Goetter, E. M., Herbert, J. D., Forman, E. M., Acierno, R., & Ruggiero, K. J. (2014). mHealth: a mechanism to deliver more accessible, more effective mental health care. Clinical psychology & psychotherapy, 21(5), 427-436.

Robins, L. N., & Regier, D. A. (1991). Psychiatric disorders in America:The Epidemiologic Catchment Area Study. New York: Free Press.

Schneider, J., Foroushani, P. S., Grime, P., & Thornicroft, G. (2014). Acceptability of online self-help to people with depression: Users’ views of MoodGYM versus informational websites. Journal of Medical Internet Research, 16(3), e90.

Sidani, S., Epstein, D. R., Bootzin, R. R., Moritz, P., & Miranda, J. (2009). Assessment of preferences for treatment: Validation of a measure. Research in Nursing & Health, 32(4), 419-431.

Smith, A. (2017). Record shares of Americans now own smartphones, have home broadband. Retrieved from http://www.pewresearch.org/fact-tank/2017/01/12/evolution-of-technology/

Smith, S. K., Kuhn, E., O'donnell, J., Koontz, B. F., Nelson, N., Molloy, K., ... & Hoffman, J. (2018). Cancer distress coach: Pilot study of a mobile app for managing posttraumatic stress. Psychooncology, 27(1), 350-353.

So, M., Yamaguchi, S., Hashimoto, S., Sado, M., Furukawa, T. A., & McCrone, P. (2013). Is computerised CBT really helpful for adult depression?-A meta-analytic re-evaluation of CCBT for adult depression in terms of clinical implementation and methodological validity. BMC Psychiatry, 13(1), 113.

ten Have, M., de Graaf, R., Ormel, J., Vilagut, G., Kovess, V., Alonso, J., & ESEMeD/MHEDEA 2000 Investigators. (2010). Are attitudes towards mental health help seeking associated with service use? Results from the European Study of Epidemiology of Mental Disorders. Social Psychiatry and Psychiatric Epidemiology, 45(2), 153-163.

Todkill, D., & Powell, J. (2013). Participant experiences of an internet based intervention and randomised control trial: Interview study. BMC Public Health, 13(1), 1017.

Twomey, C., O'reilly, G., Byrne, M., Bury, M., White, A., Kissane, S., ... & Clancy, N. (2014). A randomized controlled trial of the computerized CBT programme, MoodGYM, for public mental health service users waiting for interventions. British Journal of Clinical Psychology, 53, 433-450.

Twomey, C., & O’Reilly, G. (2017). Effectiveness of a freely available computerised cognitive behavioural therapy programme (MoodGYM) for depression: Meta-analysis. Australian & New Zealand Journal of Psychiatry, 51(3), 260-269.

Vincent, N., & Walsh, K. (2013). Hyperarousal, sleep scheduling, and time awake in bed as mediators of outcome in computerized cognitive-behavioral therapy (cCBT) for insomnia. Behaviour Research and Therapy, 51(3), 161-166.

Wang, P. S., Lane, M., Olfson, M., Pincus, H. A., Wells, K. B., & Kessler, R. C. (2005). Twelve-month use of mental health services in the United States: Results from the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 629-640.

Watts, S., Mackenzie, A., Thomas, C., Griskaitis, A., Mewton, L., Williams, A. and Andrews, G. (2013). CBT for depression: A pilot RCT comparing mobile phone vs. computer. BMC Psychiatry, 13(1).

Weisberg, R. B. (2009). Overview of generalized anxiety disorder: Epidemiology, presentation, and course. Journal of Clinical Psychiatry, 70, 4-9. doi:10.4088/JCP.s.7002.01

#typography #lessons #expert