Trajectories of Symptoms in Digital Interventions for Depression and Anxiety Using Routine Outcome Monitoring Data: Secondary Analysis Study.

BACKGROUND: Research suggests there is heterogeneity in treatment response for internet-delivered cognitive behavioral therapy (iCBT) users, but few studies have investigated the trajectory of individual symptom change across iCBT treatment. Large patient data sets using routine outcome measures allows the investigation of treatment effects over time as well as the relationship between outcomes and platform use. Understanding trajectories of symptom change, as well as associated characteristics, may prove important for tailoring interventions or identifying patients who may not benefit from the intervention. OBJECTIVE: We aimed to identify latent trajectories of symptom change during the iCBT treatment course for depression and anxiety and to investigate the patients' characteristics and platform use for each of these classes. METHODS: This is a secondary analysis of data from a randomized controlled trial designed to examine the effectiveness of guided iCBT for anxiety and depression in the UK Improving Access to Psychological Therapies (IAPT) program. This study included patients from the intervention group (N=256) and followed a longitudinal retrospective design. As part of the IAPT's routine outcome monitoring system, patients were prompted to complete the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) after each supporter review during the treatment period. Latent class growth analysis was used to identify the underlying trajectories of symptom change across the treatment period for both depression and anxiety. Differences in patient characteristics were then evaluated between these trajectory classes, and the presence of a time-varying relationship between platform use and trajectory classes was investigated. RESULTS: Five-class models were identified as optimal for both PHQ-9 and GAD-7. Around two-thirds (PHQ-9: 155/221, 70.1%; GAD-7: 156/221, 70.6%) of the sample formed various trajectories of improvement classes that differed in baseline score, the pace of symptom change, and final clinical outcome score. The remaining patients were in 2 smaller groups: one that saw minimal to no gains and another with consistently high scores across the treatment journey. Baseline severity, medication status, and program assigned were significantly associated (P<.001) with different trajectories. Although we did not find a time-varying relationship between use and trajectory classes, we found an overall effect of time on platform use, suggesting that all participants used the intervention significantly more in the first 4 weeks (P<.001). CONCLUSIONS: Most patients benefit from treatment, and the various patterns of improvement have implications for how the iCBT intervention is delivered. Identifying predictors of nonresponse or early response might inform the level of support and monitoring required for different types of patients. Further work is necessary to explore the differences between these trajectories to understand what works best for whom and to identify early on those patients who are less likely to benefit from treatment.

Durability of treatment effects following internet-delivered cognitive behavioural therapy for depression and anxiety delivered within a routine care setting.

OBJECTIVE: To investigate post-treatment relapse and remission rates 3, 6 and 9 months after completion of an acute phase of a clinician-supported internet-delivered cognitive-behavioural therapy (iCBT) for anxiety and depressive symptoms, within a routine care setting. METHOD: Secondary analysis from a 12-month pragmatic randomized-controlled trial delivered within the Improving Access to Psychological Therapies (IAPT) programme in England. Participants in the intervention arm were included if they met criteria for reliable recovery from depression (PHQ-9) and anxiety (GAD-7) at post-treatment assessment. Survival analysis was used to assess durability of treatment effects and determine predictors to relapse at 3-, 6- and 9-month follow-up. Hazard ratios predicting time-to-relapse were estimated with semi-parametric Cox proportional hazards model. RESULTS: Of the 241 participants in the intervention arm, 89 participants met the criteria for reliable recovery from depression and anxiety at the post-treatment assessment. Of these 89 eligible cases, 29.2% relapsed within the 9-month period, with 70.8% remaining in remission at 9 months post-treatment. Of those who relapsed, 53.8% experienced a relapse of depression and anxiety; 7.7% experienced a relapse of depression only; and 38.4% experienced a relapse of anxiety only. Younger age, having a long-term condition, and residual symptoms of anxiety at end-of-treatment were all significant predictors of relapse. CONCLUSIONS: This study is the first to explore the remission and relapse rates after an acute phase of iCBT treatment, within a routine, stepped-care setting. The results add to the scarce literature on the durability of the effects of iCBT treatment in routine care settings, where patients are not typically followed up after receiving a completed course of treatment.

Distinct Distress Symptom Trajectories Over 3 Years Are Associated With Baseline Illness Perceptions in Individuals With Coronary Heart Disease.

BACKGROUND: Depression and anxiety symptoms (termed distress) are common among coronary heart disease (CHD) patients and associated with poor outcomes. Illness perceptions predict distinct outcome trajectories in other long-term conditions, yet it is not known how they relate to distress trajectories in CHD. PURPOSE: This study aimed to examine whether baseline illness perceptions are associated with distress symptom trajectories among primary care CHD patients. METHODS: This is a secondary analysis of 803 CHD patients from the UPBEAT-UK study, who completed the Hospital Anxiety and Depression Scale every 6 months for 3 years. Baseline assessments included the Brief Illness Perception Questionnaire. Using latent class growth analysis, Palacios et al. (2018) identified five distinct distress symptom trajectories ("stable low," "chronic high," "improving," "worsening," and "fluctuating") in this cohort. Adjusted multinomial logistic regression analyses were used to test the association between baseline illness perceptions and distress symptom trajectories. RESULTS: Compared with the stable low distress trajectory, stronger illness identity (odds ratio [OR] = 1.31, p < .01), higher perceived consequences (OR = 1.47, p < .01), illness-related emotion (OR = 1.66, p < .01), and illness concerns (OR = 1.36, p < .01) increased the odds of having chronic high distress. Stronger illness coherence (OR = 0.89, p < .05) and personal (OR = 0.77, p < .01) and treatment control (OR = 0.75, p < .01) reduced the odds of chronic high distress. Worsening distress symptoms were associated with weaker perceptions of treatment control, higher perceived consequences, and greater illness-related concerns and emotions. CONCLUSIONS: Illness perceptions of CHD are associated with distress symptom trajectories. Therapeutically modifying unhelpful illness perceptions in CHD patients who experience high levels of distress could potentially improve mental health outcomes.

Chest pain, depression and anxiety in coronary heart disease: Consequence or cause? A prospective clinical study in primary care.

OBJECTIVE: To examine if chest pain increases the risk of depression and anxiety, or, on the other hand, depression and anxiety increase the risk of chest pain onset in patients with coronary heart disease (CHD). DESIGN: Prospective clinical study. SETTING: 16 general practices in the Greater London Primary Care Research Network. PARTICIPANTS: 803 participants with a confirmed diagnosis of CHD at baseline on the Quality and Outcomes Framework (QOF) CHD registers. MAIN OUTCOME MEASURES: Rose Angina Questionnaire, HADS depression and anxiety subscales and PHQ-9 were assessed at seven time points, each 6 months apart. Multi-Level Analysis (MLA) and Structural Equation Modelling (SEM) were applied. RESULTS: Chest pain predicts both more severe anxiety and depression symptoms at all time points until 30 months after baseline. However, although anxiety predicted chest pain in the short term with a strong association, this association did not last after 18 months. Depression had only a small, negative association with chest pain. CONCLUSIONS: In persons with CHD, chest pain increases the risk of both anxiety and depression to a great extent. However, anxiety and depression have only limited effects on the risk for chest pain. This evidence suggests that anxiety and depression tend to be consequences rather than causes of cardiac chest pain. Intervention studies that support persons with CHD by providing this information should be devised and evaluated, thus deconstructing potentially catastrophic cognitions and strengthening emotional coping.

Exploring the Relationship Between Usage and Outcomes of an Internet-Based Intervention for Individuals With Depressive Symptoms: Secondary Analysis of Data From a Randomized Controlled Trial.

BACKGROUND: Internet interventions can easily generate objective data about program usage. Increasingly, more studies explore the relationship between usage and outcomes, but they often report different metrics of use, and the findings are mixed. Thus, current evaluations fail to demonstrate which metrics should be considered and how these metrics are related to clinically meaningful change. OBJECTIVE: This study aimed to explore the relationship between several usage metrics and outcomes of an internet-based intervention for depression. METHODS: This is a secondary analysis of data from a randomized controlled trial that examined the efficacy of an internet-based cognitive behavioral therapy for depression (Space from Depression) in an adult community sample. All participants who enrolled in the intervention, regardless of meeting the inclusion criteria, were included in this study. Space from Depression is a 7-module supported intervention, delivered over a period of 8 weeks. Different usage metrics (ie, time spent, modules and activities completed, and percentage of program completion) were automatically collected by the platform, and composite variables from these (eg, activities per session) were computed. A breakdown of the usage metrics was obtained by weeks. For the analysis, the sample was divided into those who obtained a reliable change (RC)-and those who did not. RESULTS: Data from 216 users who completed pre- and posttreatment outcomes were included in the analyses. A total of 89 participants obtained an RC, and 127 participants did not obtain an RC. Those in the RC group significantly spent more time, had more log-ins, used more tools, viewed a higher percentage of the program, and got more reviews from their supporter compared with those who did not obtain an RC. Differences between groups in usage were observed from the first week in advance across the different metrics, although they vanished over time. In the RC group, the usage was higher during the first 4 weeks, and then a significant decrease was observed. Our results showed that specific levels of platform usage, 7 hours total time spent, 15 sessions, 30 tools used, and 50% of program completion, were associated with RC. CONCLUSIONS: Overall, the results showed that those individuals who obtained an RC after the intervention had higher levels of exposure to the platform. The usage during the first half of the intervention was higher, and differences between groups were observed from the first week. This study also showed specific usage levels associated with outcomes that could be tested in controlled studies to inform the minimal usage to establish adherence. These results will help to better understand how to use internet-based interventions and what optimal level of engagement can most affect outcomes. TRIAL REGISTRATION: ISRCTN Registry ISRCTN03704676; http://www.isrctn.com/ISRCTN03704676. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/1471-244X-14-147.

Inflammation associated with coronary heart disease predicts onset of depression in a three-year prospective follow-up: A preliminary study.

Depression frequently co-occurs with coronary heart disease (CHD), worsening clinical outcomes of both, and inflammation has been proposed as a biological link between these two disorders. The aim of the present study was to investigate the role of inflammation in the development of depression in CHD patients during a 3-year follow-up. We examined the inflammatory biomarker, high-sensitivity C-reactive protein (hsCRP), measured at baseline, as a potential predictor of later onset of depression. We recruited 89 CHD patients, who were assessed at baseline and then every 6 months, for three years. The sample included, at baseline, 25 depressed and 64 non-depressed CHD patients, as confirmed by Clinical Interview Schedule Revised (CIS-R). Depressive symptoms were assessed at baseline and all follow-up points by the Patient Health Questionnaire-9 (PHQ-9). In all CHD patients (n = 89), we found a significant positive correlation between hsCRP levels and the severity of depressive symptoms at baseline (PHQ-9, r = 0.23, p = 0.032). During follow-up, n = 21 patients (of the 64 non-depressed at baseline) developed depression, defined as being PHQ-9 positive (a score ≥ 10) in at least one follow-up assessment. Of these, n = 9 subjects were defined as developing clinically-significant depression, that is, having a positive PHQ-9 in at least 3 of the 6 follow-up assessments, implying a duration of symptoms of at least one year. We found that increased hsCRP values at baseline predicted future onset of depression. Specifically, baseline hsCRP values were higher in patients who later developed clinically-significant depression (mean ±â€¯SD; 6.76 ±â€¯6.52 mg/L) compared with never-depressed (2.77 ±â€¯3.13 mg/L; F(1,48) = 7.29, p = 0.010), even after controlling for baseline PHQ-9 scores. In conclusion, inflammation in CHD patients is associated with future development of clinically-significant depression. HsCRP, a reliable and ready-to-use biological marker of inflammation, may help to identify depression high-risk phenotypes even among CHD patients, who already have high baseline inflammation. Our study conveys important preliminary findings that will require further replication but that have the potential to affect the mental and physical health of a vulnerable group of individuals.

Supported Internet-Delivered Cognitive Behavioral Therapy Programs for Depression, Anxiety, and Stress in University Students: Open, Non-Randomised Trial of Acceptability, Effectiveness, and Satisfaction.

BACKGROUND: Many university campuses have limited mental health services that cannot cope with the high demand. One alternative is to use internet-delivered cognitive behavioral therapy (iCBT) as a way of tackling barriers such as lack of availability and scheduling issues. OBJECTIVE: This study aimed to assess feasibility, acceptability, effectiveness, and satisfaction of a supported iCBT intervention offering 3 programs on depression, anxiety, and stress to university students. The design was an open or nonrandomized feasibility trial. METHODS: Participants were recruited from 3 counseling centers at a large midwestern University in the United States. Those agreeing to take part chose 1 of 3 iCBT programs-Space from Depression, Space from Anxiety, or Space from Stress -all comprised 8 modules of media-rich interactive content. Participants were supported throughout the trial by a trained professional. The Patient Health Questionnaire 9 (PHQ-9), Generalized Anxiety Disorder 7 (GAD-7) questionnaire, and stress subscale of the Depression Anxiety and Stress Scale (DASS-21) were completed at baseline, 8 weeks, and 3-month follow-up. A Satisfaction With Treatment (SAT) questionnaire was completed at 8 weeks, and qualitative interviews were completed by a subsample of participants at 3 months. RESULTS: A total of 102 participants were recruited, with 52 choosing Space from Anxiety, 31 choosing Space from Depression, and 19 choosing Space from Stress. Mixed-effects models showed a significant decrease in symptoms of depression (F4=6.36, P<.001), anxiety (F4=7.97, P<.001), and stress (F4=8.50, P<.001) over time across all 3 programs. The largest decreases in PHQ-9 scores at 8 weeks were among participants who chose the Space from Depression program (d=0.84); at 3 months, the largest decreases in PHQ-9 scores were among those who chose the Space from Stress program (d=0.74). The largest decreases in GAD-7 scores were among those who chose the Space from Anxiety program (d=0.74 at 8 weeks and d=0.94 at 3 months). The largest decrease in DASS-21 stress subscale scores was among those who chose the Space from Stress program (d=0.49 at 8 weeks and d=1.16 at 3 months). The mean time spent using the platform per session was 27.4 min (SD 33.8), and participants completed 53% (SD 37.6) of the total program content on average. Most (37/53, 69%) participants found the programs helpful or very helpful and liked the convenience and flexibility of the intervention. Qualitative interviews (n=14) indicated the intervention met students' expectations, and they saw it as a valuable complement to face-to-face treatment. CONCLUSIONS: The iCBT programs tested in our study appear to be feasible, acceptable, and effective in a university environment. Participants described the benefits of having a flexible, supported Web-based intervention available on campus. Larger trials should be conducted to further test the effectiveness of supported Web-based interventions that give students a choice of program depending on their symptom profile.

A Single, One-Off Measure of Depression and Anxiety Predicts Future Symptoms, Higher Healthcare Costs, and Lower Quality of Life in Coronary Heart Disease Patients: Analysis from a Multi-Wave, Primary Care Cohort Study.

OBJECTIVE: To determine whether a one-off, baseline measure of depression and anxiety in a primary care, coronary heart disease (CHD) population predicts ongoing symptoms, costs, and quality of life across a 3-year follow-up. DESIGN: Longitudinal cohort study. SETTING: 16 General Practice surgeries across South-East London. PARTICIPANTS: 803 adults (70% male, mean age 71 years) contributing up to 7 follow-up points. MAIN OUTCOME MEASURES: Ongoing reporting of symptoms, health care costs, and quality of life. RESULTS: At baseline, 27% of the sample screened positive for symptoms of depression and anxiety, as measured by the Hospital Anxiety and Depression Scale (HADS). The probability of scoring above the cut-off throughout the follow-up was 71.5% (p<0.001) for those screening positive at baseline, and for those screening negative, the probability of scoring below the cut-off throughout the follow-up was 97.6% (p<0.001). Total health care costs were 39% higher during follow-up for those screening positive (p<0.05). Quality of life as measured by the SF-12 was lower on the mental component during follow-up for those screening positive (-0.75, CI -1.53 to 0.03, p = 0.059), and significantly lower on the physical component (-4.99, CI -6.23 to -.376, p<0.001). CONCLUSIONS: A one-off measure for depression and anxiety symptoms in CHD predicts future symptoms, costs, and quality of life over the subsequent three-years. These findings suggest symptoms of depression and anxiety in CHD persist throughout long periods and are detrimental to a patient's quality of life, whilst incurring higher health care costs for primary and secondary care services. Screening for these symptoms at the primary care level is important to identify and manage patients at risk of the negative effects of this comorbidity. Implementation of screening, and possible collaborative care strategies and interventions that help mitigate this risk should be the ongoing focus of researchers and policy-makers.