The association between persistent cognitive difficulties and depression and functional outcomes in people with major depressive disorder.

BACKGROUND: Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes. METHODS: This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent major depressive disorder (MDD; N = 623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every 3 months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up. RESULTS: 508 individuals (81.5%) provided data (mean age: 46.6, s.d.: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (<25% of timepoints), those with high persistence (>75% of timepoints) reported significantly higher levels of depression (B = 5.17, s.e. = 2.21, p = 0.019) and functional impairment (B = 4.82, s.e. = 1.79, p = 0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms. CONCLUSIONS: We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity.

BACKGROUND: Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS: Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS: Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS: Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

The role of spirituality and identity formation in personal recovery from traumatic brain injury: A qualitative analysis through the personal experiences of survivors.

Traumatic brain injury (TBI) is a type of acquired brain injury (ABI) that happens when a sudden, external, physical assault damages the brain. TBI can cause long-term cognitive impairments and other lifestyle changes that may affect psychological wellbeing. Among the psychological challenges people recovering from TBI often face is the subjective loss of their pre-injury identity. Quantitative and qualitative research suggests that spirituality can play a positive role in recovery from TBI, increasing the quality of life and overall mental health. However, thus far, the research into this topic has not directly addressed the relationship between identity and spirituality after TBI. The present study sought to do this by thematically analyzing 22 public podcasts featuring interviews of people recovering from TBI telling their stories. The authors review the spiritual themes discussed in the podcasts and then propose a hypothesis about how, through a sense of connection to something self-transcendent, spirituality may enable people to test new meanings and identities, relatively free from the consequences of discrepancy in meaning and identity after TBI.

Investigating a psychological model of mental conditions and coping during the COVID-19 pandemic driven by participatory methods.

BACKGROUND: There is evidence of increased mental health problems during the early stages of the COVID-19 pandemic. We aimed to identify the factors that put certain groups of people at greater risk of mental health problems. METHODS: We took a participatory approach, involving people with lived experience of mental health problems and/or carers, to generate a set of risk factors and potential moderators of the effects of COVID on mental health. An online cross-sectional survey was completed by 1464 United Kingdom residents between 24th April and 27th June 2020. The survey had questions on whether respondents were existing mental health service users and or carers, level of depression (PHQ9) and anxiety (GAD7), demographics, threat and coping appraisals, perceived resilience (BRS), and specific coping behaviours (validated as part of this study). The relationship between responses and coping strategies was measured using tetrachoric correlations. Structural equation modelling was used to test the model. RESULTS: A model significantly fit our data (rel χ2 = 2.05, RMSEA = 0.029 95%, CI (0.016, 0.042), CFI = 0.99, TLI = 0.98, SRMR = 0.014). Age and coping appraisal predicted anxiety and depression. Whereas, threat appraisal and ethnicity only predicted anxiety, and resilience only predicted depression. Additionally, specific coping behaviours predicted anxiety and depression, with overlap on distraction. CONCLUSIONS: Some, but not all, risk factors significantly predict anxiety and depression. While there is a relationship between anxiety and depression, different factors may put people at greater risk of one or the other during the pandemic.

Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol.

BACKGROUND: There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. METHODS: RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants' sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation's self-reported Composite International Diagnostic Interview (CIDI-SF). DISCUSSION: This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed.

The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder.

BACKGROUND: Changes in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity. METHODS: Data were collected as part of a longitudinal observational mobile Health (mHealth) cohort study in people with recurrent MDD. Participants wore a Fitbit device and completed regular outcome assessments via email for a median follow-up of 541 days. We used multivariable regression models to test the effects of sleep features on depression outcomes. We considered respondents with at least one assessment of relapse (n = 218) or at least one assessment of depression severity (n = 393). RESULTS: Increased intra-individual variability in total sleep time, greater sleep fragmentation, lower sleep efficiency, and more variable sleep midpoints were associated with worse depression outcomes. Adjusted Population Attributable Fractions suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression relapse by up to 22 %. LIMITATIONS: Limitations include a potentially underpowered primary outcome due to the smaller number of relapses identified than expected. CONCLUSION: Our study demonstrates a role for consumer-grade activity trackers in estimating relapse risk and depression severity in people with recurrent MDD. Variability in sleep duration and midpoint may be useful targets for stratified interventions.

Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment.

BACKGROUND: In time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and understanding the importance of these factors to users is vital to ensure maximum adoption. METHOD: In a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, established benefit and device accuracy (i.e., ability to detect symptoms), with different levels. Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality. RESULTS: Higher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important. Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits. Preferences were moderated by technology acceptance, age, nationality, and educational background. CONCLUSION: For people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attainment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support. These preferences should help shape design of mHealth tools.

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder.

BACKGROUND: Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics. METHODS: The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables. RESULTS: A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance. LIMITATIONS: Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions. CONCLUSIONS: These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.

Emotional outcomes in clinically isolated syndrome and early phase multiple sclerosis: a systematic review and meta-analysis.

OBJECTIVE: To study depression, anxiety, suicide risk, and emotional health-related quality of life (HRQoL) in people with clinically isolated syndrome (CIS) and in early phase multiple sclerosis (MS). METHODS: A systematic literature review was conducted with inclusion criteria of observational studies on outcomes of depression, anxiety, suicide risk, and emotional HRQoL in CIS and within five years since diagnosis of MS. Studies were screened using the Preferred Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, and study quality was determined for included studies. Meta-analysis and meta-regression were performed if applicable. RESULTS: Fifty-one studies were included in the systematic review. In early phase MS, meta-analyses of the Hospital Anxiety Depression Scale (HADS) indicated prevalence levels of 17% (95% confidence interval (CI): 9 to 25%; p < .001) for depressive and 35% (95% CI: 28 to 41%; p < .001) for anxiety symptoms. Meta-regression analyses revealed an increase in mean HADS-D and HADS-A associated with larger sample size, and higher HADS-D mean with increased study quality. Similar depressive and anxiety symptoms were observed in CIS, and increased suicide risk and low emotional HRQoL was associated with depressive symptoms in early phase MS. The methodological quality of the studies was considered fair. CONCLUSIONS: Findings suggest that mild-to-moderate symptoms of depression and anxiety might be prevalent in CIS and in early phase MS. Future research on both clinical populations are needed, especially longitudinal monitoring of emotional outcomes.