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e-Mental Health and Cyberpsychology (1386) Innovations in Mental Health Systems (370) Users' and Patients' Needs for Mental Health Services (378) Depression and Mood Disorders; Suicide Prevention (1271) Methods and New Tools in Mental Health Research (424) Anxiety and Stress Disorders (999) Mobile Health in Psychiatry (399) mHealth for Screening (190)

Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28369, first published .
Mental Health Screening in General Practices as a Means for Enhancing Uptake of Digital Mental Health Interventions: Observational Cohort Study

Mental Health Screening in General Practices as a Means for Enhancing Uptake of Digital Mental Health Interventions: Observational Cohort Study

Mental Health Screening in General Practices as a Means for Enhancing Uptake of Digital Mental Health Interventions: Observational Cohort Study

Authors of this article:

Alexis E Whitton1, 2 Author Orcid Image ;   Rebecca Hardy1 Author Orcid Image ;   Kate Cope1 Author Orcid Image ;   Chilin Gieng1 Author Orcid Image ;   Leanne Gow1 Author Orcid Image ;   Andrew MacKinnon1 Author Orcid Image ;   Nyree Gale1 Author Orcid Image ;   Kathleen O'Moore1 Author Orcid Image ;   Josephine Anderson1, 2 Author Orcid Image ;   Judith Proudfoot1, 2 Author Orcid Image ;   Nicole Cockayne1 Author Orcid Image ;   Bridianne O'Dea1, 2 Author Orcid Image ;   Helen Christensen1, 2 Author Orcid Image ;   Jill Maree Newby1, 2 Author Orcid Image
Article Authors Cited by (14) Tweetations (28) Metrics

    Original Paper

    1Black Dog Institute, Randwick, Australia

    2University of New South Wales, Randwick, Australia

    Corresponding Author:

    Alexis E Whitton, PhD

    Black Dog Institute

    Hospital Road

    Randwick, 2031

    Australia

    Phone: 61 293828507

    Fax:61 293828507

    Email: a.whitton@unsw.edu.au


    Background: Digital mental health interventions stand to play a critical role in managing the mental health impact of the COVID-19 pandemic. Thus, enhancing their uptake is a key priority. General practitioners (GPs) are well positioned to facilitate access to digital interventions, but tools that assist GPs in identifying suitable patients are lacking.

    Objective: This study aims to evaluate the suitability of a web-based mental health screening and treatment recommendation tool (StepCare) for improving the identification of anxiety and depression in general practice and, subsequently, uptake of digital mental health interventions.

    Methods: StepCare screens patients for symptoms of depression (9-item Patient Health Questionnaire) and anxiety (7-item Generalized Anxiety Disorder scale) in the GP waiting room. It provides GPs with stepped treatment recommendations that include digital mental health interventions for patients with mild to moderate symptoms. Patients (N=5138) from 85 general practices across Australia were invited to participate in screening.

    Results: Screening identified depressive or anxious symptoms in 43.09% (1428/3314) of patients (one-quarter were previously unidentified or untreated). The majority (300/335, 89.6%) of previously unidentified or untreated patients had mild to moderate symptoms and were candidates for digital mental health interventions. Although less than half were prescribed a digital intervention by their GP, when a digital intervention was prescribed, more than two-thirds of patients reported using it.

    Conclusions: Implementing web-based mental health screening in general practices can provide important opportunities for GPs to improve the identification of symptoms of mental illness and increase patient access to digital mental health interventions. Although GPs prescribed digital interventions less frequently than in-person psychotherapy or medication, the promising rates of uptake by GP-referred patients suggest that GPs can play a critical role in championing digital interventions and maximizing the associated benefits.

    J Med Internet Res 2021;23(9):e28369

    doi:10.2196/28369

    Keywords

    depression; anxiety; general practice; screening; digital mental health 


    Background

    Anxiety and depression are leading causes of disability worldwide, and in the wake of the COVID-19 pandemic, the personal, social, and economic costs of these conditions are expected to rise [1]. Although effective interventions exist, these conditions often go untreated [2]. This poses a significant problem for individuals and health care systems, as anxiety and depression are associated with reduced quality of life [3], increased suicide risk [4], and increased health service use [5]. Although increasing access to evidence-based mental health care seems to be an obvious solution, achieving this in the context of an already overstretched mental health system represents a major challenge.

    Digital mental health interventions (eg, web-based cognitive behavioral therapy), used alone or in blended models of care, have been highlighted as crucial tools for addressing unmet mental health treatment needs [1]. Indeed, health policies in the United Kingdom [6] and Australia [7] recommend digital mental health interventions as the first-line treatment for individuals with mild or subsyndromal depression. These interventions have been shown to be as effective as in-person psychotherapy for a range of disorders [8] and can be delivered with minimal or no therapist input [9]. Furthermore, these interventions can promote improvements in symptoms that are sustained over several years [10], provide immediate support (ie, no waiting time), and are free from many barriers that prevent individuals from accessing face-to-face treatment (eg, cost, inconvenience, and stigmatization). However, the integration of digital mental health interventions into routine mental health care has been slow [11]. New strategies are needed to promote a greater uptake of digital mental health tools.

    One way to increase the integration of digital mental health interventions into routine mental health care is to better support general practitioners (GPs) in prescribing these interventions to their patients. Digital mental health interventions can provide significant benefits in the general practice setting because GPs encounter high rates of common mental health conditions, with 1 in 4 patients experiencing depression [12] and 1 in 5 experiencing anxiety [13]. Furthermore, GPs are often an individual’s first point of contact with mental health services and are a trusted source of information for patients, so they are well placed to facilitate greater access to, and confidence in, digital tools for mental health. However, two major challenges have prevented the adoption of digital mental health tools in general practice. First, although mental health conditions are common among general practice attendees, only half of all patients with a mental health condition are recognized by their GP as having one [14]. Accordingly, low rates of disorder detection may prevent GPs from identifying patients who are suitable candidates for digital mental health interventions. Second, given the broad array of digital apps and tools marketed for mental health, GPs report a lack of confidence in prescribing digital interventions and uncertainty about their evidence base inhibits their routine use [15].

    To address these challenges, the Black Dog Institute developed StepCare, a web-based tool for use in general practice that offers digital mental health screening and treatment recommendations [16,17]. StepCare aims to facilitate the delivery of a stepped care approach to mental health care in the general practice setting. It does so by screening patients in the GP waiting area using a tablet device and then stratifying patients into groups according to symptom severity. StepCare then provides a screening report and set of treatment recommendations to GPs, which are matched in intensity to a patient’s specific level of need. Importantly, evidence-based digital mental health interventions are incorporated into the treatment recommendations for patients who screen positive for mild or moderate depressive or anxious symptoms, and GPs are supported in referring their patients to these interventions via referral links embedded in the program.

    Initial studies of feasibility [16] and implementation [17] demonstrated that StepCare was acceptable to GPs and feasible for delivery in general practice settings. Moreover, two recent randomized controlled trials (RCTs) of similar digital patient stratification tools further support the utility of using digital solutions to deliver stepped care in the general practice setting. Specifically, an RCT evaluating an eHealth platform (Target-D) that aimed to support patients in managing depression found that digitally stratifying patients and providing matched interventions resulted in greater improvements in depressive symptoms over 3 months compared with usual care [18]. Similarly, an RCT of a similar digital decision support tool (Link-me) demonstrated that digitally facilitated stepped care resulted in greater improvements in psychological distress in general practice patients compared with usual care [19].

    Objectives

    A key goal of the StepCare tool was to increase the use of digital mental health interventions in general practice by (1) helping GPs to identify patients who would be suitable candidates for digital interventions (ie, via screening for patients with mild to moderate symptoms), (2) assisting GPs in determining when a digital mental health intervention should be used alone or in combination with a higher-intensity intervention (eg, a psychologist), and (3) reducing GPs’ uncertainty surrounding which digital interventions to prescribe and how to introduce them to the patient in the limited time available during a consultation. StepCare has been implemented in 85 general practices across Australia, and more than 5000 patients were screened from July 2017 to March 2020. The aim of this study is to examine whether StepCare is a suitable means for identifying patients who may be candidates for digital mental health interventions and to promote the uptake of these interventions in the general practice patient population.


    Sample and Recruitment

    Information about the StepCare tool was disseminated to primary health networks (PHNs) via conferences, workshops, and networks. A total of 8 PHNs from New South Wales, Australian Capital Territory, and Victoria signed up to use StepCare during the study period (July 24, 2017, and March 31, 2020), and each PHN invited expressions of interest from general practices in their region. In total, 85 general practices expressed interest, and all GPs within these practices were invited to participate. The GPs who provided informed consent to participate were then provided with information and training from either their PHN or staff from the Black Dog Institute on how to implement StepCare in their practice. Both PHNs and the Black Dog Institute staff provided ongoing support to practices throughout the implementation period.

    Adult patients who were attending a GP appointment at one of the participating practices, regardless of the reason for their visit, were invited to participate in the screening. When patients presented to the reception staff, they were handed a mobile tablet that displayed an information and consent page, and patients who agreed to participate in screening indicated their consent via button click on the tablet. To assess their eligibility for screening, an initial set of questions was administered that confirmed that the patient was aged ≥18 years, had not undergone screening in the past 6 months, and could provide either a mobile phone number or email address (required for patients to receive follow-up monitoring assessments to track symptom change over time).

    Ethics Statement

    This study was approved by the University of New South Wales Human Research Ethics Committee (HC15827).

    Design

    This study used an uncontrolled, observational, prospective cohort design.

    Screening Measures

    Depressive symptoms were screened for using the 9-item Patient Health Questionnaire (PHQ-9) [20]. Total scores range from 0 to 27. StepCare defines severity levels as nil-minimal (0-4), mild (5-9), moderate (10-19), and severe (20-27). Item 9 was used to assess thoughts about suicide and self-harm. The presence of suicidal thoughts was flagged to the GP in a separate column on the patients’ screening report, where a score of 1 on item 9 of the PHQ-9 indicated thoughts of mild severity, a score of 2 as moderate, and a score of 3 as severe. Anxiety symptoms were screened for using the 7-item Generalized Anxiety Disorder (GAD-7) scale [21]. Total scores range from 0 to 21, with severity levels defined as nil-minimal (0-4), mild (5-9), moderate (10-14), and severe (15-21).

    StepCare also included 2 items that assessed whether the patient had previously discussed mental health issues with their GP and whether their current appointment was about mental health. This served as a proxy indicator of whether a patient was likely to be known to their GP as having a mental health condition. Patients were also asked sociodemographic questions, including whether they were Aboriginal or Torres Strait Islander; a carer for children, someone with a disability, someone with a chronic illness, or someone who was frail-aged; spoke a language other than English; had concerns about their accommodation or housing; and what their alcohol use habits were (Alcohol Use Disorders Identification Test) [22]. The PHQ-9 and GAD-7 scores informed the StepCare treatment recommendations; however, all responses were transmitted to GPs for consideration during the consultation.

    Screening Procedure

    On presentation to the practice, the reception staff provided adult patients with an internet-enabled mobile tablet featuring the StepCare information and consent page. Patients could either consent or decline to participate by clicking a button. Those who consented were asked to provide their contact details and complete the sociodemographic questions, the PHQ-9, the GAD-7, and the Alcohol Use Disorders Identification Test. The patient’s screening results were sent directly to their GPs’ medical inbox using a secure messaging service for review during the consultation.

    Treatment Recommendations

    StepCare treatment recommendations were stratified according to the patient’s symptom severity. Digital mental health interventions were incorporated into Steps 1 and 2 as follows.

    Step 1: Web-Based Self-Help

    The self-guided digital mental health intervention myCompass [23] was recommended for patients with mild symptoms. MyCompass has been shown to be effective in improving symptoms of depression and anxiety over 7 weeks relative to waitlist and attention control conditions [23] and has more than 50,000 registered users to date.

    Step 2: Guided Web-Based Therapy or Face-to-Face Psychotherapy (Consider Medication)

    Guided web-based therapy via the MindSpot Clinic or face-to-face therapy with a clinical psychologist was recommended for patients with moderate symptoms. MindSpot offers therapist-guided web-based courses and has been found to produce significant improvements in depression and anxiety [24,25]. The Step 2 recommendation also suggested that the GP consider pharmacotherapy.

    Step 3: Face-to-Face Psychotherapy, Antidepressant Medication (Consider a Psychiatrist)

    Referral to a clinical psychologist and pharmacotherapy were recommended for patients with severe symptoms. The GP was encouraged to consider referral to a psychiatrist for pharmacotherapy management.

    Managing Suicidality

    For patients who scored 1 or higher on item 9 of the PHQ-9, StepCare also provided GPs with several options to assist them in supporting patients who reported suicidal thoughts, including providing prompts to help initiate a discussion with the patient regarding their responses on the screener, steps for further assessing risk, information describing how to develop a safety plan for patients who are actively suicidal, and links to relevant local and national crisis services.

    GP Prescribing Patterns

    During the consultation, GPs were asked to record the treatment they prescribed by filling in checkboxes accessible via the patient’s screening report. Although GPs were strongly encouraged to record prescribed treatments, to minimize the burden on GP workflow, this was not mandatory.

    Patient Outcomes and Treatment Use

    Individuals who screened positive for depression or anxiety were invited to complete follow-up assessments every 2 weeks for 18 weeks to monitor symptoms (those with nil-minimal symptoms were not followed up). These follow-up assessments captured key outcome measures, including depressive symptom severity (PHQ-9) and anxiety symptom severity (GAD-7). In addition to these outcome measures, patients were asked to indicate, via checkboxes, which of a series of mental health treatments they had used in the past 2 weeks. Follow-up assessments were not mandatory, and patients could opt out of receiving assessment reminders at any time.

    Each time a patient completed a follow-up assessment, a report was sent to their GP’s medical inbox. This report displayed the patient’s PHQ-9 and GAD-7 scores, their score on item 9 of the PHQ-9 (indicating suicidal thoughts), and a line graph showing symptom improvement from baseline. Accompanying this report was also a series of alerts that notified GPs of patients who showed signs of improvement, as well as those who showed evidence of deterioration, severe symptoms that did not improve, and nonadherence to monitoring assessments. Improvement was defined as a patient whose PHQ-9 or GAD-7 scores had improved by at least one severity category compared with the previous fortnight; deterioration was flagged when a patient’s PHQ-9 or GAD-7 score increased by at least one severity category relative to the previous fortnightly period; severe and unchanging symptoms were flagged where a patient with PHQ-9 or GAD-7 scores in the severe range at baseline had not improved by at least one severity category by week 4; and nonadherence to assessments was flagged when a patient who screened positive for depressive or anxious symptoms at baseline had not completed two consecutive follow-up assessments. For patients who did not improve, or who deteriorated, the GP was encouraged to consider scheduling a follow-up appointment with the patient to review their treatment plan.

    For the purposes of our statistical analyses, we defined remitters as individuals who were nil-minimal (0-4) on the PHQ-9 and GAD-7 at their last follow-up assessment, and responders as individuals who showed a decrease of at least one severity category on the PHQ-9 or GAD-7 at their last assessment.

    Statistical Analyses

    Descriptive statistics were used to quantify patient symptoms, treatment use, and GP prescription patterns. The number needed to screen (NNS) was calculated using methods aligned with those adopted in multiarm clinical trials, as described by Rembold [26]. NNS is a statistic that is derived from the number needed to treat (NNT) statistic that is commonly reported in clinical trials assessing the effectiveness of an intervention and reflects the number of people who need to be screened to prevent one adverse event. However, it differs from NNT in that it also incorporates information about the prevalence of undetected diseases that can be potentially identified via screening. In our study, it was calculated by first computing the absolute risk (AR) of identifying undiagnosed symptoms of anxiety or depression through screening and the AR of identifying undiagnosed symptoms of anxiety or depression under a hypothetical no screening condition. For the purposes of our calculation, we assumed that in the hypothetical no screening condition, no individuals with undiagnosed symptoms of depression or anxiety would have been identified that would not have already been identified through care as usual without screening. Next, we computed the difference in AR under these two conditions (ie, the AR deduction [ARD]). Finally, we computed the NNS in the same manner as that used to compute NNT, which is the inverse of the ARD (ie, 1/ARD) [26].

    This was supplemented with an intention-to-treat analysis examining symptom improvement over the follow-up period using data from all assessment points (mixed model for repeated measures analysis with random intercepts and slopes; implemented in Stata [version 13.1; StataCorp LP]). We also performed a completer analysis to examine the rates of response and remission.

    Role of the Funding Source

    The funder had no role in the study design, data collection, analysis, manuscript writing, or in the decision to publish the manuscript.


    Sample Characteristics

    Of the 5138 patients who were offered the tool, 3777 (73.51%) completed screening and 3314 (64.50%) met the eligibility criteria (sample characteristics are shown in Table 1).

    Table 1. Characteristics of baseline sample (N=3314).
    CharacteristicParticipants
    Age (years), mean (SD)43.4 (17.0)
    Female, n (%)2316 (69.89)
    Aboriginal or Torres Strait Islander, n (%)165 (4.98)
    English as second language, n (%)423 (12.76)
    Cares for children, n (%)1079 (32.56)
    Cares for frail or disabled individuals, n (%)305 (9.20)
    Accommodation issues, n (%)94 (2.84)
    Seeing general practitioner for mental health reasons, n (%)745 (22.48)
    Seen general practitioner for mental health reasons previously, n (%)1519 (45.84)

    Suitability of StepCare as a Screening Tool

    Prevalence and Severity of Symptoms Identified

    In total, 1428 individuals with symptoms of anxiety or depression were identified through screening (detection rate: 1428/3314, 43.09%). Of the baseline sample, 13.82% (458/3314) had mild symptoms, 17.68% (586/3314) had moderate symptoms, and 11.59% (384/3314) had severe symptoms. Furthermore, just under one third (454/1428, 31.79%) of symptomatic individuals reported suicidal ideation or thoughts of self-harm. Overall, depressive symptoms (1333/3314, 40.22%) were more common than anxiety symptoms (855/3314, 25.80%). There were no differences in the proportion of males and females who screened positive for either depressive or anxious symptoms (both values of P>.18).

    For patients who screened in the mild symptom severity range, most screened positive for depressive symptoms only (346/458, 75.5%) with fewer screening positive for anxiety symptoms only (54/458, 11.8%) or both depressive and anxious symptoms (49/458, 10.7%). In contrast, as symptom severity increased, patients were more likely to screen positive for both depressive and anxious symptoms. Specifically, in the moderate range, 59.6% (349/586) screened positive for both depressive and anxious symptoms [depressive symptoms only (202/586, 34.5%); anxious symptoms only (28/586, 4.8%)], and in the severe range, 94.3% (362/384) screened positive for both depressive and anxious symptoms [depressive symptoms only (7/384, 1.8%); anxious symptoms only (13/384, 3.4%)].

    Previously Unidentified or Untreated Mental Health Symptoms

    Of the 1428 patients who screened positive for depressive or anxious symptoms, 335 (23.46% of symptomatic sample or 10.11% of patients overall) had never seen their GP for mental health reasons, including at the time of screening. These previously unidentified or untreated patients were older than symptomatic patients who had previously seen their GP for mental health reasons (44.73 vs 38.58; t1,426=6.36; P<.001). Most unidentified or untreated patients had mild (184/335, 54.9%) or moderate symptoms (116/335, 34.6%); however, 1 in 10 had severe symptoms (35/335, 10.4%) or reported suicidal ideation or thoughts of self-harm (49/335, 14.6%), indicating that StepCare also identified patients with significant unmet mental health treatment needs.

    Results for NNS

    Of the 5138 patients who underwent screening, 335 (6.52%) had anxious or depressive symptoms that were previously unidentified or untreated. Accordingly, the AR for recognizing previously unidentified symptoms via screening was ARscreening=335/5138=0.0652. Although this was not an RCT, under a hypothetical no screening condition, we can assume that none of these individuals would have been detected, yielding an AR of:

    ARno screening=0/5138=0

    Therefore, the ARD can be calculated as ARscreening–ARno screening:

    ARD=335/5138–0/5138=0.0652

    An NNS can be calculated as the inverse of the ARD:

    NNS=1/0.0652=15.3 (95% CI 13.9-17.1)

    This indicates that for every 16 patients who are offered mental health screening, 1 individual with previously unidentified or untreated depressive or anxious symptoms will be identified.

    Ability of StepCare to Facilitate Uptake of Digital Mental Health Interventions

    GP Prescribing Patterns

    GP prescription data were available for 23.39% (334/1428) of patients. These included 30.2% (101/334) patients with mild symptoms, 41.9% (140/334) with moderate symptoms, and 27.8% (93/334) with severe symptoms. Separate independent samples 2-tailed t tests showed that baseline PHQ-9 and GAD-7 scores did not differ between patients whose GP did and did not provide prescribing data (both values of P>.54).

    Although digital and high-intensity (ie, psychologists, psychiatrists, and pharmacotherapy) interventions were recommended by the StepCare tool at roughly equal rates (digital interventions were recommended for 241/334, 72.2% of patients; high-intensity interventions were recommended for 233/334, 69.8% of patients; categories not mutually exclusive), GPs prescribed high-intensity interventions nearly twice as often as low-intensity digital interventions. Specifically, a high-intensity intervention was prescribed by the GP for 56.3% (188/334) of patients, whereas a digital mental health intervention was prescribed by the GP for 30.8% (103/334) of patients. A side-by-side comparison of the treatments recommended by StepCare, relative to the treatments prescribed by GPs, is shown in Table 2. Furthermore, GP prescription patterns among patients with different levels of symptom severity are shown in Table 3.

    Table 2. Comparison of StepCare treatment recommendations for all symptomatic patients (n=1428) versus GPa prescribed treatments in symptomatic patients with GP prescribing data available (n=334).
    TreatmentbProportion of all symptomatic patients (n=1428) who were recommended a specific treatment by StepCare, n (%)Proportion of symptomatic patients with GP prescribing data (n=334) who were prescribed a specific treatment by their GP, n (%)
    Web-based self-help458 (32.1)60 (18)
    Guided web-based therapy586 (41)54 (16.2)
    Psychologist970 (67.9)135 (40.4)
    Medication970 (67.9)109 (32.6)
    Psychiatrist384 (26.9)31 (9.3)

    aGP: general practitioner.

    bColumn percentage totals do not sum to 100% as patients could be prescribed multiple treatments or no treatment at all.

    Table 3. General practitioner prescribing patterns as a function of patient baseline symptom severity (N=334).
    Treatment prescribed by general practitioneraPatients with mild symptoms (n=101), n (%)Patients with moderate symptoms (n=140), n (%)Patients with severe symptoms (n=93), n (%)
    Prescribed web-based self-help33 (32.7)15 (10.7)12 (12.9)
    Prescribed guided web-based therapy11 (10.9)28 (20)15 (16.1)
    Prescribed psychologist22 (21.8)59 (42.1)54 (58.1)
    Prescribed medication8 (7.9)50 (35.7)51 (54.8)
    Prescribed psychiatrist2 (2)8 (5.7)21 (22.6)

    aPercentages reflect the percentage of patients in each symptom severity category; column percentage totals do not sum to 100% as patients in the same symptom severity category could be prescribed multiple treatments or no treatment at all.

    Patient Treatment Use Patterns

    Treatment use data were available for 42.44% (606/1428) of patients. Most patients (525/606, 86.6%) reported the use of at least one form of mental health treatment during the follow-up period. Patients with treatment use data had higher baseline PHQ-9 (t1426=3.98; P<.001; mean 12.81, SD 6.16) and GAD-7 scores (t1408=3.77; P<.001; mean 9.43, SD 6.59) than patients who did not provide treatment use data (PHQ-9 mean 11.50, SD 6.18; GAD-7 mean 8.10, SD 6.59), indicating that the following patterns may be more applicable to patients with more severe symptoms.

    Of the 606 patients who provided treatment use data, 165 (27.2%) also had GP prescribing data available, which allowed us to determine whether a patient used the treatment prescribed by their GP. More than two-thirds of patients (35/52, 67%) who were prescribed a digital mental health intervention (either alone or in combination with a high-intensity intervention) reported using one of these digital interventions over the follow-up period. The rates of treatment uptake were also high for high-intensity interventions; nearly all patients (96/102, 94.1%) who were prescribed a high-intensity intervention by their GP reported that they had used a high-intensity intervention over the follow-up period. The patient use of GP-prescribed interventions is shown in detail in Figure 1.

    ‎
    Figure 1. Data show the rate at which patients used the treatment that was prescribed to them by their general practitioner. Values are expressed as a proportion of symptomatic patients for whom both patient adherence and general practitioner prescribing data were available (n=165).
    View this figure

    Patient Outcomes

    Symptom Improvement Over Time

    The intention to treat (mixed model for repeated measures) analysis revealed a significant main effect of time for the PHQ-9 (P<.001), where scores decreased by an average of 3.62 points from baseline to week 18 (moderate to mild severity range; Cohen d=0.58 computed using baseline SD of 6.20). Comparisons between baseline and follow-up PHQ-9 scores (Table 4) showed that symptom severity at each follow-up point was significantly lower than the score at baseline (all Ps<.001). Similar effects were observed for the GAD-7, where the main effect of time was significant (P<.001), with scores decreasing by an average of 1.45 points from baseline to week 18 (upper end of mild to lower end of mild severity range; Cohen d=0.22, baseline SD 6.62). Comparisons between baseline and follow-up GAD-7 scores (Table 5) showed that symptom severity at each follow-up point except weeks 2 and 4 was significantly lower than at baseline (all values of P<.001).

    Table 4. Changes in depressive symptom severity (9-item Patient Health Questionnaire) over time.
    Time pointEstimated marginal, mean (SE)Modeled change from baseline, mean (95% CIa)P valueb
    Baseline12.05 (0.16)N/AcN/A
    Week 210.22 (0.22)­−1.84 (−2.23 to −1.44)<.001
    Week 49.75 (0.24)−2.31 (−2.75 to −1.86)<.001
    Week 69.15 (0.25)−2.90 (−3.37 to −2.43)<.001
    Week 88.86 (0.26)−3.19 (−3.70 to −2.69)<.001
    Week 108.81 (0.28)−3.24 (−3.78 to −2.69)<.001
    Week 128.76 (0.29)−3.30 (−3.87 to −2.73)<.001
    Week 148.33 (0.31)−3.72 (−4.31 to −3.13)<.001
    Week 168.42 (0.32)−3.63 (−4.25 to −3.01)<.001
    Week 188.43 (0.33)−3.62 (−4.27 to −2.97)<.001

    aThe 95% CI refers to the change from baseline.

    bP values indicate the significance level for comparisons between baseline scores and scores at each of the follow-up assessments.

    cN/A: not applicable.

    Table 5. Change in anxious symptom severity (7-item Generalized Anxiety Disorder scale) over time.
    Time pointEstimated marginal, mean (SE)Modeled change from baseline, mean (95% CIa)P valueb
    Baseline8.67 (0.18)N/AcN/A
    Week 28.56 (0.20)−0.11 (−0.51 to 0.29).58
    Week 48.26 (0.21)−0.41 (−0.83 to 0.01).06
    Week 67.86 (0.22)−0.81 (−1.27 to −0.36)<.001
    Week 87.63 (0.24)−1.04 (−1.54 to −0.55)<.001
    Week 107.33 (0.25)−1.34 (−1.85 to −0.83)<.001
    Week 127.27 (0.25)−1.40 (−1.92 to −0.88)<.001
    Week 147.18 (0.27)−1.49 (−2.05 to −0.93)<.001
    Week 167.48 (0.30)−1.20 (−1.81 to −0.58)<.001
    Week 187.22 (0.30)−1.45 (−2.07 to −0.83)<.001

    aThe 95% CI refers to the change from baseline.

    bP values indicate the significance level for comparisons between baseline scores and scores at each of the follow-up assessments.

    cN/A: not applicable.

    Rates of Remission and Response

    Completer analyses focused on symptomatic patients who completed at least one follow-up assessment (708/1428, 49.58%). Of this sample, 26.1% (185/708) scored in the asymptomatic range on the PHQ-9 and GAD-7 at follow-up and were classified as remitters. Furthermore, 55.4% (392/708) of cases dropped down at least one severity category on the PHQ-9 or GAD-7 and were considered responders. In contrast, 26.1% (185/708) increased by one or more severity categories during the follow-up period.


    Principal Findings

    This study examined the performance of a digital mental health screening and treatment recommendation tool (StepCare) for improving the identification of depression and anxiety in general practice and for promoting the uptake of digital mental health interventions. Four key findings emerged. First, screening revealed a high prevalence of common mental health conditions among general practice patients, with 40.2% screening positive for depression and 25.8% screening positive for anxiety (cf. Australian population 12-month prevalence estimates of 6.2% for depression and 14.4% for anxiety) [27]. Nearly 1 in 4 of these patients had never consulted their GP about their mental health previously, indicating that screening identified individuals who may have otherwise been undetected. Second, the majority (89.5%) of untreated patients were in the mild to moderate range, demonstrating that screening, when followed by appropriate assessment, could assist GPs in identifying suitable candidates for digital mental health interventions. Third, although digital mental health interventions and high-intensity interventions were recommended by the StepCare tool at approximately equal rates, GPs favored high-intensity interventions over digital interventions. Fourth, although digital interventions were prescribed less often, more than two-thirds of patients who were prescribed a digital intervention by their GP reported using it. This confirms that GPs are well placed to facilitate greater uptake of digital mental health interventions.

    Our findings align with prior studies showing high rates of depressive and anxious symptoms in general practice populations and the ability for mental health screening to identify patients with untreated symptoms [14]. These results extend prior research in two significant ways: (1) by estimating the NNS to detect previously untreated patients with depressive or anxious symptoms in an Australian general practice setting (NNS=16) and (2) by showing that a significant proportion of untreated patients have symptoms in the mild to moderate severity range, where digital mental health interventions may be especially useful. Although the impact of mental health screening on patient outcomes can only be determined via RCTs, our findings suggest that screening could identify important opportunities to implement low or no cost digital interventions, thereby reducing the burden on higher-intensity services.

    Our finding that 1 in every 16 patients invited for screening had unidentified or untreated depressive or anxious symptoms positions mental health screening favorably when compared with other diseases routinely screened for in primary care. For example, type 2 diabetes screening for adults aged ≥40 years yields a detection rate of 1 in 32 [28], breast cancer screening for women aged 50 to 69 years yields a detection rate of approximately 1 in 94 [29], and cervical cancer screening in women aged >20 years yields a detection rate of approximately 1 in 143 [30]. Given that suicide outranks cancer as the leading cause of death for Australians aged 25 to 44 years [31] (the mean age of our sample was 43 years), mental health screening of the general practice population could significantly reduce suicide-related mortality.

    We note that the rates of depressive and anxious symptoms in our study were higher than the population prevalence estimates of depression and anxiety, and there are several possible reasons for this. First, population prevalence estimates are based on individuals who meet the diagnostic criteria for depression or anxiety rather than individuals who demonstrate a positive result on a brief screening measure of depressive or anxious symptoms. In our study, it is likely that a subset of patients who screened positive for depression or anxiety would not have met the diagnostic criteria for depression or anxiety in further follow-up assessments with their GP. Second, it is likely that some participants who screened positive were experiencing transient symptoms rather than a persistent condition, with prior studies indicating that approximately one-fifth of patients who screened positive for depression in primary care no longer met the criteria after 2 weeks [32]. Third, the PHQ-9 and GAD-7 cutoff values we used were designed to capture milder subsyndromal symptoms in addition to more severe symptomatology; hence, the symptomatic sample in our study likely encompasses patients with milder symptoms compared with individuals who are included in epidemiological studies.

    A fourth factor that likely underpinned our higher prevalence rates was the fact that several practices used StepCare to selectively screen high-risk patients (as recommended by the Royal Australian College of General Practitioners) [33] as opposed to implementing universal screening. In addition to biasing screening toward more symptomatic patients, this observation raises an important question regarding the best implementation of screening programs in general practice. Although selectively screening high-risk patients (eg, postpartum women) may increase the cost-effectiveness of screening, this approach may have the unintended effect of exacerbating existing disparities in access to mental health care [34]. For example, the Royal Australian College of General Practitioners guidelines indicate that patients at increased risk for depression are those who have a family history of psychiatric illness, have chronic medical conditions, are unemployed or of low socioeconomic status, have experienced significant life events, family violence or child abuse, or are part of the lesbian, gay, bisexual, transgender, and intersex community [33]. Although these factors may be known to the GP when there is a well-established relationship with the patient, this is less likely when a patient does not have a regular GP (up to 20% of patients) [35] or visits their GP infrequently (eg, indigenous patients or patients from rural areas) [36]. Accordingly, although risk-based screening may have lower opportunity costs than universal screening, it may fail to identify patients who may warrant further assessment by their GP and who may benefit from digital mental health interventions.

    Regarding GP prescribing patterns, our findings suggest that although digital mental health interventions may be well suited to a substantial portion of the symptomatic general practice population, GPs show a preference for prescribing high-intensity treatments. Why might this be? Common barriers to use reported by GPs include uncertainty about a digital intervention’s evidence base and insufficient knowledge of how to refer a patient to a digital intervention [15]. However, StepCare was designed to overcome these barriers, as it only recommends empirically supported digital interventions and includes a referral pipeline that supports GPs in connecting their patients with the intervention. Additional barriers clearly exist and must be addressed. One by-product of the COVID-19 pandemic has been the rapid integration of telehealth services into general practices. This may help to break down attitudinal barriers to digital mental health tools; however, other practical changes, such as allowing GPs to formally itemize and charge for the time taken to discuss digital interventions with their patients, may also be needed to enhance their use in general practice.

    Limitations and Future Directions

    This study has some limitations. First, this was an uncontrolled observational study of a tool used in a clinical setting. RCTs are needed to determine whether the opportunity costs of mental health screening in general practices are offset by superior patient outcomes. Second, GP prescribing data and patient treatment use data were available for only a portion of patients because completion of these surveys was optional. Greater tracking of prescription and treatment use patterns via electronic health records will be critical for providing a comprehensive perspective on digital intervention uptake. Finally, an important question that must be addressed in future research is whether patients who are prescribed digital mental health interventions show sufficient levels of engagement with these interventions to yield therapeutic benefits. If the rates of initial uptake are high when referred by a GP, but ongoing engagement remains low, then this may warrant consideration of alternative modes of delivery. Delivery methods that combine digital mental health interventions with face-to-face services, such as blended care [37-39], may be especially useful in this regard. Blended care has been found to promote greater adherence to digital programs, and preliminary evidence has demonstrated its efficacy in the general practice setting (eg, when guidance is provided by a nurse practitioner [40]).

    Conclusion

    In conclusion, our findings indicate that a digital mental health screening and treatment recommendation tool may increase the opportunities to use digital mental health interventions in general practice. Leveraging these opportunities will be critical in addressing increased mental health treatment needs arising from the COVID-19 pandemic and in reducing existing disparities in access to affordable, evidence-based mental health care.

    Acknowledgments

    The authors wish to thank Mariam Faraj and other staff of the Central and Eastern Sydney PHN for their involvement in the co-design of StepCare, as well as all the GPs, staff, and patients who participated in the research. This research was funded by the Paul Ramsay Foundation. Associate Professor JMN was supported by an Australian Medical Research Future Fund Fellowship.

    Conflicts of Interest

    StepCare was designed and developed by Proudfoot, Anderson, Cockayne, O’Moore, Gale, and Christensen. PHNs in Australia pay a small fee to the Black Dog Institute to use StepCare in their general practices, which contributes to the operational, information technology infrastructure, and personnel costs associated with implementing StepCare, hosting the data, and providing technical support. The authors of this study derive no personal financial benefits from StepCare.

    1. Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 2020 Jun;7(6):547-560 [FREE Full text] [CrossRef] [Medline]
    2. Thornicroft G, Chatterji S, Evans-Lacko S, Gruber M, Sampson N, Aguilar-Gaxiola S, et al. Undertreatment of people with major depressive disorder in 21 countries. Br J Psychiatry 2017 Feb;210(2):119-124 [FREE Full text] [CrossRef] [Medline]
    3. Voros V, Fekete S, Tenyi T, Rihmer Z, Szili I, Osvath P. Untreated depressive symptoms significantly worsen quality of life in old age and may lead to the misdiagnosis of dementia: a cross-sectional study. Ann Gen Psychiatry 2020 Sep 15;19:52 [FREE Full text] [CrossRef] [Medline]
    4. Ribeiro JD, Huang X, Fox KR, Franklin JC. Depression and hopelessness as risk factors for suicide ideation, attempts and death: meta-analysis of longitudinal studies. Br J Psychiatry 2018 May;212(5):279-286. [CrossRef] [Medline]
    5. Pálinkás A, Sándor J, Papp M, Kőrösi L, Falusi Z, Pál L, et al. Associations between untreated depression and secondary health care utilization in patients with hypertension and/or diabetes. Soc Psychiatry Psychiatr Epidemiol 2019 Feb;54(2):255-276. [CrossRef] [Medline]
    6. Depression in adults: recognition and management. Clinical guideline CG90. National institute for Health and Care Excellence.   URL: https://www.nice.org.uk/guidance/cg90 [accessed 2021-08-26]
    7. Australian government response to contributing lives, thriving communities - Review of mental health programmes and services. Commonwealth of Australia Canberra, Australia.   URL: https:/​/www.​health.gov.au/​sites/​default/​files/​documents/​2020/​11/​australian-government-response-to-contributing-lives-thriving-communities-review-of-mental-health- programmes-and-services.​pdf [accessed 2021-08-26]
    8. Carlbring P, Andersson G, Cuijpers P, Riper H, Hedman-Lagerlöf E. Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis. Cogn Behav Ther 2018 Jan;47(1):1-18. [CrossRef] [Medline]
    9. Karyotaki E, Riper H, Twisk J, Hoogendoorn A, Kleiboer A, Mira A, et al. Efficacy of self-guided internet-based cognitive behavioral therapy in the treatment of depressive symptoms: a meta-analysis of individual participant data. JAMA Psychiatry 2017 Apr 01;74(4):351-359. [CrossRef] [Medline]
    10. Hedman E, Furmark T, Carlbring P, Ljótsson B, Rück C, Lindefors N, et al. A 5-Year follow-up of internet-based cognitive behavior therapy for social anxiety disorder. J Med Internet Res 2011 Jun 15;13(2):e39 [FREE Full text] [CrossRef] [Medline]
    11. Ebert DD, Berking M, Cuijpers P, Lehr D, Pörtner M, Baumeister H. Increasing the acceptance of internet-based mental health interventions in primary care patients with depressive symptoms. A randomized controlled trial. J Affect Disord 2015 May 01;176:9-17. [CrossRef] [Medline]
    12. Tamburrino MB, Lynch DJ, Nagel RW, Smith MK. Primary care evaluation of mental disorders (PRIME-MD) screening for minor depressive disorder in primary care. Prim Care Companion J Clin Psychiatry 2009;11(6):339-343 [FREE Full text] [CrossRef] [Medline]
    13. Kroenke K, Spitzer RL, Williams JB, Monahan PO, Löwe B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Ann Intern Med 2007 Mar 06;146(5):317-325. [CrossRef] [Medline]
    14. Carey M, Jones K, Meadows G, Sanson-Fisher R, D'Este C, Inder K, et al. Accuracy of general practitioner unassisted detection of depression. Aust N Z J Psychiatry 2014 Jun;48(6):571-578 [FREE Full text] [CrossRef] [Medline]
    15. Breedvelt JJ, Zamperoni V, Kessler D, Riper H, Kleiboer AM, Elliott I, et al. GPs' attitudes towards digital technologies for depression: an online survey in primary care. Br J Gen Pract 2019 Mar;69(680):e164-e170 [FREE Full text] [CrossRef] [Medline]
    16. Anderson J, O Moore K, Faraj M, Proudfoot J. Stepped care mental health service in Australian primary care: codesign and feasibility study. Aust Health Rev 2020 Dec;44(6):873-879. [CrossRef] [Medline]
    17. Proudfoot J, Gale N, O’Moore K, Faraj M, Gieng C, Anderson J. Implementation of an augmented stepped mental health care service in Australian primary care: a mixed method study. Adv Ment Health 2019 Sep 12;19(1):75-93. [CrossRef]
    18. Fletcher S, Chondros P, Densley K, Murray E, Dowrick C, Coe A, et al. Matching depression management to severity prognosis in primary care: results of the Target-D randomised controlled trial. Br J Gen Pract 2021 Jan 28;71(703):e85-e94 [FREE Full text] [CrossRef] [Medline]
    19. Fletcher S, Spittal MJ, Chondros P, Palmer VJ, Chatterton ML, Densley K, et al. Clinical efficacy of a Decision Support Tool (Link-me) to guide intensity of mental health care in primary practice: a pragmatic stratified randomised controlled trial. Lancet Psychiatry 2021 Mar;8(3):202-214. [CrossRef] [Medline]
    20. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [FREE Full text] [CrossRef] [Medline]
    21. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006 May 22;166(10):1092-1097. [CrossRef] [Medline]
    22. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med 1998 Sep 14;158(16):1789-1795. [CrossRef] [Medline]
    23. Proudfoot J, Clarke J, Birch M, Whitton AE, Parker G, Manicavasagar V, et al. Impact of a mobile phone and web program on symptom and functional outcomes for people with mild-to-moderate depression, anxiety and stress: a randomised controlled trial. BMC Psychiatry 2013 Nov 18;13:312 [FREE Full text] [CrossRef] [Medline]
    24. Titov N, Dear BF, Staples LG, Bennett-Levy J, Klein B, Rapee RM, et al. The first 30 months of the MindSpot Clinic: evaluation of a national e-mental health service against project objectives. Aust N Z J Psychiatry 2017 Dec;51(12):1227-1239. [CrossRef] [Medline]
    25. Titov N, Dear BF, Staples LG, Bennett-Levy J, Klein B, Rapee RM, et al. MindSpot clinic: an accessible, efficient, and effective online treatment service for anxiety and depression. Psychiatr Serv 2015 Oct;66(10):1043-1050. [CrossRef] [Medline]
    26. Rembold CM. Number needed to screen: development of a statistic for disease screening. BMJ 1998 Aug 01;317(7154):307-312 [FREE Full text] [CrossRef] [Medline]
    27. National survey of mental health and wellbeing: summary of results. Australian Bureau of Statistics.   URL: https:/​/www.​abs.gov.au/​statistics/​health/​mental-health/​national-survey-mental-health-and-wellbeing-summary-results/​latest-release#summary-of-findings [accessed 2021-08-26]
    28. Lee CM, Versace VL, Malo JA, Shaw JE, Dunbar JA, Colagiuri S. Screening for diabetes prevention with diabetes risk scores - a balancing act. Diabetes Res Clin Pract 2018 Jan;135:120-127. [CrossRef] [Medline]
    29. BreastScreen Australia monitoring report 2020. Australian Institute of Health and Welfare.   URL: https:/​/www.​aihw.gov.au/​reports/​cancer-screening/​breastscreen-australia-monitoring-report-2020/​contents/​summary [accessed 2021-08-26]
    30. Cervical screening in Australia. Australian Institute of Health and Welfare.   URL: https:/​/www.​aihw.gov.au/​reports/​cancer-screening/​cervical-screening-in-australia-2018/​contents/​table-of-contents [accessed 2021-08-26]
    31. Deaths in Australia. Australian Institute of Health and Welfare. 2021.   URL: https:/​/www.​aihw.gov.au/​reports/​life-expectancy-death/​deaths-in-australia/​contents/​leading-causes-of-death [accessed 2021-08-26]
    32. Gunn JM, Gilchrist GP, Chondros P, Ramp M, Hegarty KL, Blashki GA, et al. Who is identified when screening for depression is undertaken in general practice? Baseline findings from the Diagnosis, Management and Outcomes of Depression in Primary Care (diamond) longitudinal study. Med J Aust 2008 Jun 16;188(S12):S119-S125. [CrossRef] [Medline]
    33. Guidelines for Preventive Activities in General Practice 9th edition. Australia: The Royal Australian College of General Practitioners; 2016.
    34. Akincigil A, Matthews EB. National rates and patterns of depression screening in primary care: results from 2012 and 2013. Psychiatr Serv 2017 Jul 01;68(7):660-666. [CrossRef] [Medline]
    35. Wright M, Hall J, van Gool K, Haas M. How common is multiple general practice attendance in Australia? Aust J Gen Pract 2018 May;47(5):289-296 [FREE Full text] [CrossRef] [Medline]
    36. Harrison C, Henderson J, Miller G, Britt H. Predicting patient use of general practice services in Australia: models developed using national cross-sectional survey data. BMC Fam Pract 2019 Feb 14;20(1):28 [FREE Full text] [CrossRef] [Medline]
    37. Erbe D, Eichert H, Riper H, Ebert DD. Blending face-to-face and internet-based interventions for the treatment of mental disorders in adults: systematic review. J Med Internet Res 2017 Sep 15;19(9):e306 [FREE Full text] [CrossRef] [Medline]
    38. Massoudi B, Blanker MH, van Valen E, Wouters H, Bockting CL, Burger H. Blended care vs. usual care in the treatment of depressive symptoms and disorders in general practice [BLENDING]: study protocol of a non-inferiority randomized trial. BMC Psychiatry 2017 Jun 13;17(1):218 [FREE Full text] [CrossRef] [Medline]
    39. Mathiasen K, Andersen TE, Riper H, Kleiboer AA, Roessler KK. Blended CBT versus face-to-face CBT: a randomised non-inferiority trial. BMC Psychiatry 2016 Dec 05;16(1):432 [FREE Full text] [CrossRef] [Medline]
    40. Van der Zweerde T, Lancee J, Slottje P, Bosmans JE, Van Someren EJ, van Straten A. Nurse-guided internet-delivered cognitive behavioral therapy for insomnia in general practice: results from a pragmatic randomized clinical trial. Psychother Psychosom 2020;89(3):174-184 [FREE Full text] [CrossRef] [Medline]

    ‎
    AR: absolute risk
    ARD: absolute risk detection
    GAD-7: 7-item Generalized Anxiety Disorder scale
    GP: general practitioner
    NNS: number needed to screen
    NNT: number needed to treat
    PHN: primary health network
    PHQ-9: 9-item Patient Health Questionnaire
    RCT: randomized controlled trial

    Edited by R Kukafka; submitted 03.03.21; peer-reviewed by L Martinengo, C Kaylor-Hughes; comments to author 19.06.21; revised version received 07.07.21; accepted 27.07.21; published 16.09.21

    Copyright

    ©Alexis E Whitton, Rebecca Hardy, Kate Cope, Chilin Gieng, Leanne Gow, Andrew MacKinnon, Nyree Gale, Kathleen O'Moore, Josephine Anderson, Judith Proudfoot, Nicole Cockayne, Bridianne O'Dea, Helen Christensen, Jill Maree Newby. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.09.2021.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.