Additional file 1: Table S1. of Usefulness of the SF-36 Health Survey in screening for depressive and anxiety disorders in rheumatoid arthritis

Title of data – SF36 Mental Health (MH) and Mental Component Summary (MCS) scores as predictors of pMDD, pGAD or any psycholgical disorder (pMDD or pGAD) according to PHQ9 or GAD7 criteria. Description of data – summary of all potential thresholds identified for indicating presence of pMDD or pGAD, along with relevant sensitivity and specificity data. (DOCX 17 kb

Antidepressants for the treatment of depression in people with cancer

Background: Major depression and other depressive conditions are common in people with cancer. These conditions are not easily detectable in clinical practice, due to the overlap between medical and psychiatric symptoms, as described by diagnostic manuals such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). Moreover, it is particularly challenging to distinguish between pathological and normal reactions to such a severe illness. Depressive symptoms, even in subthreshold manifestations, have been shown to have a negative impact in terms of quality of life, compliance with anti-cancer treatment, suicide risk and likely even the mortality rate for the cancer itself. Randomised controlled trials (RCTs) on the efficacy, tolerability and acceptability of antidepressants in this population are few and often report conflicting results. Objectives: To assess the efficacy, tolerability and acceptability of antidepressants for treating depressive symptoms in adults (aged 18 years or older) with cancer (any site and stage). Search methods: We searched the following electronic bibliographic databases: the Cochrane Central Register of Controlled Trials (CENTRAL 2017, Issue 6), MEDLINE Ovid (1946 to June week 4 2017), Embase Ovid (1980 to 2017 week 27) and PsycINFO Ovid (1987 to July week 4 2017). We additionally handsearched the trial databases of the most relevant national, international and pharmaceutical company trial registers and drug-approving agencies for published, unpublished and ongoing controlled trials. Selection criteria: We included RCTs comparing antidepressants versus placebo, or antidepressants versus other antidepressants, in adults (aged 18 years or above) with any primary diagnosis of cancer and depression (including major depressive disorder, adjustment disorder, dysthymic disorder or depressive symptoms in the absence of a formal diagnosis). Data collection and analysis: Two review authors independently checked eligibility and extracted data using a form specifically designed for the aims of this review. The two authors compared the data extracted and then entered data into Review Manager 5 using a double-entry procedure. Information extracted included study and participant characteristics, intervention details, outcome measures for each time point of interest, cost analysis and sponsorship by a drug company. We used the standard methodological procedures expected by Cochrane. Main results: We retrieved a total of 10 studies (885 participants), seven of which contributed to the meta-analysis for the primary outcome. Four of these compared antidepressants and placebo, two compared two antidepressants, and one three-armed study compared two antidepressants and placebo. In this update we included one additional unpublished study. These new data contributed to the secondary analysis, while the results of the primary analysis remained unchanged. For acute-phase treatment response (6 to 12 weeks), we found no difference between antidepressants as a class and placebo on symptoms of depression measured both as a continuous outcome (standardised mean difference (SMD) -0.45, 95% confidence interval (CI) -1.01 to 0.11, five RCTs, 266 participants; very low certainty evidence) and as a proportion of people who had depression at the end of the study (risk ratio (RR) 0.82, 95% CI 0.62 to 1.08, five RCTs, 417 participants; very low certainty evidence). No trials reported data on follow-up response (more than 12 weeks). In head-to-head comparisons we only retrieved data for selective serotonin reuptake inhibitors (SSRIs) versus tricyclic antidepressants, showing no difference between these two classes (SMD -0.08, 95% CI -0.34 to 0.18, three RCTs, 237 participants; very low certainty evidence). No clear evidence of a beneficial effect of antidepressants versus either placebo or other antidepressants emerged from our analyses of the secondary efficacy outcomes (dichotomous outcome, response at 6 to 12 weeks, very low certainty evidence). In terms of dropouts due to any cause, we found no difference between antidepressants as a class compared with placebo (RR 0.85, 95% CI 0.52 to 1.38, seven RCTs, 479 participants; very low certainty evidence), and between SSRIs and tricyclic antidepressants (RR 0.83, 95% CI 0.53 to 1.30, three RCTs, 237 participants). We downgraded the certainty (quality) of the evidence because the included studies were at an unclear or high risk of bias due to poor reporting, imprecision arising from small sample sizes and wide confidence intervals, and inconsistency due to statistical or clinical heterogeneity. Authors' conclusions: Despite the impact of depression on people with cancer, the available studies were very few and of low quality. This review found very low certainty evidence for the effects of these drugs compared with placebo. On the basis of these results, clear implications for practice cannot be deduced. The use of antidepressants in people with cancer should be considered on an individual basis and, considering the lack of head-to-head data, the choice of which agent to prescribe may be based on the data on antidepressant efficacy in the general population of individuals with major depression, also taking into account that data on medically ill patients suggest a positive safety profile for the SSRIs. To better inform clinical practice, there is an urgent need for large, simple, randomised, pragmatic trials comparing commonly used antidepressants versus placebo in people with cancer who have depressive symptoms, with or without a formal diagnosis of a depressive disorder

Antidepressants for the treatment of depression in people with cancer

Background Major depression and other depressive conditions are common in people with cancer. These conditions are not easily detectable in clinical practice, due to the overlap between medical and psychiatric symptoms, as described by diagnostic manuals such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). Moreover, it is particularly challenging to distinguish between pathological and normal reactions to such a severe illness. Depressive symptoms, even in subthreshold manifestations, have a negative impact in terms of quality of life, compliance with anticancer treatment, suicide risk and possibly the mortality rate for the cancer itself. Randomised controlled trials (RCTs) on the efficacy, tolerability and acceptability of antidepressants in this population are few and often report conflicting results. Objectives To evaluate the efficacy, tolerability and acceptability of antidepressants for treating depressive symptoms in adults (aged 18 years or older) with cancer (any site and stage). Search methods We used standard, extensive Cochrane search methods. The latest search date was November 2022. Selection criteria We included RCTs comparing antidepressants versus placebo, or antidepressants versus other antidepressants, in adults (aged 18 years or above) with any primary diagnosis of cancer and depression (including major depressive disorder, adjustment disorder, dysthymic disorder or depressive symptoms in the absence of a formal diagnosis). Data collection and analysis We used standard Cochrane methods. Our primary outcome was 1. efficacy as a continuous outcome. Our secondary outcomes were 2. efficacy as a dichotomous outcome, 3. Social adjustment, 4. health-related quality of life and 5. dropouts. We used GRADE to assess certainty of evidence for each outcome. Main results We identified 14 studies (1364 participants), 10 of which contributed to the meta-analysis for the primary outcome. Six of these compared antidepressants and placebo, three compared two antidepressants, and one three-armed study compared two antidepressants and placebo. In this update, we included four additional studies, three of which contributed data for the primary outcome. For acute-phase treatment response (six to 12 weeks), antidepressants may reduce depressive symptoms when compared with placebo, even though the evidence is very uncertain. This was true when depressive symptoms were measured as a continuous outcome (standardised mean difference (SMD) -0.52, 95% confidence interval (CI) -0.92 to -0.12; 7 studies, 511 participants; very low-certainty evidence) and when measured as a proportion of people who had depression at the end of the study (risk ratio (RR) 0.74, 95% CI 0.57 to 0.96; 5 studies, 662 participants; very low-certainty evidence). No studies reported data on follow-up response (more than 12 weeks). In head-to-head comparisons, we retrieved data for selective serotonin reuptake inhibitors (SSRIs) versus tricyclic antidepressants (TCAs) and for mirtazapine versus TCAs. There was no difference between the various classes of antidepressants (continuous outcome: SSRI versus TCA: SMD -0.08, 95% CI -0.34 to 0.18; 3 studies, 237 participants; very low-certainty evidence; mirtazapine versus TCA: SMD -4.80, 95% CI -9.70 to 0.10; 1 study, 25 participants). There was a potential beneficial effect of antidepressants versus placebo for the secondary efficacy outcomes (continuous outcome, response at one to four weeks; very low-certainty evidence). There were no differences for these outcomes when comparing two different classes of antidepressants, even though the evidence was very uncertain. In terms of dropouts due to any cause, we found no difference between antidepressants compared with placebo (RR 0.85, 95% CI 0.52 to 1.38; 9 studies, 889 participants; very low-certainty evidence), and between SSRIs and TCAs (RR 0.83, 95% CI 0.53 to 1.22; 3 studies, 237 participants). We downgraded the certainty of the evidence because of the heterogeneous quality of the studies, imprecision arising from small sample sizes and wide CIs, and inconsistency due to statistical or clinical heterogeneity. Authors' conclusions Despite the impact of depression on people with cancer, the available studies were few and of low quality. This review found a potential beneficial effect of antidepressants against placebo in depressed participants with cancer. However, the certainty of evidence is very low and, on the basis of these results, it is difficult to draw clear implications for practice. The use of antidepressants in people with cancer should be considered on an individual basis and, considering the lack of head-to-head data, the choice of which drug to prescribe may be based on the data on antidepressant efficacy in the general population of people with major depression, also taking into account that data on people with other serious medical conditions suggest a positive safety profile for the SSRIs. Furthermore, this update shows that the usage of the newly US Food and Drug Administration-approved antidepressant esketamine in its intravenous formulation might represent a potential treatment for this specific population of people, since it can be used both as an anaesthetic and an antidepressant. However, data are too inconclusive and further studies are needed. We conclude that to better inform clinical practice, there is an urgent need for large, simple, randomised, pragmatic trials comparing commonly used antidepressants versus placebo in people with cancer who have depressive symptoms, with or without a formal diagnosis of a depressive disorder

What impacts the acceptability of wearable devices that detect opioid overdose in people who use opioids?: Qualitative study

IntroductionDrug-related deaths involving an opioid are at all-time highs across the United Kingdom. Current overdose antidotes (naloxone) require events to be witnessed and recognised for reversal. Wearable technologies have potential for remote overdose detection or response but their acceptability among people who use opioids (PWUO) is not well understood. This study explored facilitators and barriers to wearable technology acceptability to PWUO.MethodsTwenty-four participants (79% male, average age 46 years) with current (n = 15) and past (n = 9) illicit heroin use and 54% (n = 13) who were engaged in opioid substitution therapy participated in semi-structured interviews (n = 7) and three focus groups (n = 17) in London and Nottingham from March to June 2022. Participants evaluated real devices, discussing characteristics, engagement factors, target populations, implementation strategies and preferences. Conversations were recorded, transcribed and thematically analysed.ResultsThree themes emerged: device-, person- and environment-specific factors impacting acceptability. Facilitators included inconspicuousness under the device theme and targeting subpopulations of PWUO at the individual theme. Barriers included affordability of devices and limited technology access within the environment theme. Trust in device accuracy for high and overdose differentiation was a crucial facilitator, while trust between technology and PWUO was a significant environmental barrier.Discussion and ConclusionsDeterminants of acceptability can be categorised into device, person and environmental factors. PWUO, on the whole, require devices that are inconspicuous, comfortable, accessible, easy to use, controlled by trustworthy organisations and highly accurate. Device developers must consider how the type of end-user and their environment moderate acceptability of the device.</p

Towards robust paralinguistic assessment for real-world mobile health (mHealth) monitoring: an initial study of reverberation effects on speech

Speech is promising as an objective, convenient tool to monitor health remotely over time using mobile devices. Numerous paralinguistic features have been demonstrated to contain salient information related to an individual's health. However, mobile device specification and acoustic environments vary widely, risking the reliability of the extracted features. In an initial step towards quantifying these effects, we report the variability of 13 exemplar paralinguistic features commonly reported in the speech-health literature and extracted from the speech of 42 healthy volunteers recorded consecutively in rooms with low and high reverberation with one budget and two higher-end smartphones, and a condenser microphone. Our results show reverberation has a clear effect on several features, in particular voice quality markers. Our findings point to new research directions investigating how best to record and process in-the-wild speech for reliable longitudinal mobile health state assessment.</p

Wearables, sensors and the future of technology to detect and infer loneliness in older adults

Loneliness is a growing concern affecting the health and quality of life of older adults living in the community. Addressing loneliness in ageing populations is an important policy priority. Central to this is the detection of type and severity of loneliness. Advancement in technology provides an opportunity for loneliness to be inferred through physiological and behavioural changes. In this article, we provide an overview of the current evidence on wearable and sensor technologies to detect loneliness in older adults including reviewing physiological measures of loneliness. Two recent reviews have highlighted how loneliness in older adults can be inferred using in-home sensors and smartphones. However, ethical and privacy issues remain an unaddressed issue in the development of technologies to measure loneliness in this population. Ongoing research is working to address this through the development a new multi-functional sensor which can be used in fabrics and textiles in the home to measure loneliness in people age 65 and over. We present an overview of the DEsign for healthy ageing: A smart system to decrease LONELINESS for older people (DELONELINESS) study.</p

Understanding the psychological experiences of loneliness in later life: qualitative protocol to inform technology development

Objectives Loneliness is a public health issue impacting the health and well-being of older adults. This protocol focuses on understanding the psychological experiences of loneliness in later life to inform technology development as part of the 'Design for health ageing: a smart system to detect loneliness in older people' (DELONELINESS) study. Methods and analysis Data will be collected from semi-structured interviews with up to 60 people over the age of 65 on their experiences of loneliness and preferences for sensor-based technologies. The interviews will be audio-recorded, transcribed and analysed using a thematic codebook approach on NVivo software. Ethics and dissemination This study has received ethical approval by Research Ethics Committee's at King's College London (reference number: LRS/DP-21/22-33376) and the University of Sussex (reference number: ER/JH878/1). All participants will be required to provide informed consent. Results will be used to inform technology development within the DELONELINESS study and will be disseminated in peer-reviewed publications and conferences

Stakeholder-led understanding of the implementation of digital technologies within heart disease diagnosis: a qualitative study protocol

Introduction Cardiovascular diseases are highly prevalent among the UK population, and the quality of care is being reduced due to accessibility and resource issues. Increased implementation of digital technologies into the cardiovascular care pathway has enormous potential to lighten the load on the National Health Service (NHS), however, it is not possible to adopt this shift without embedding the perspectives of service users and clinicians. Methods and analysis A series of qualitative studies will be carried out with the aim of developing a stakeholder-led perspective on the implementation of digital technologies to improve holistic diagnosis of heart disease. This will be a decentralised study with all data collection being carried out online with a nationwide cohort. Four focus groups, each with 5-6 participants, will be carried out with people with lived experience of heart disease, and 10 one-to-one interviews will be carried out with clinicians with experience of diagnosing heart diseases. The data will be analysed using an inductive thematic analysis approach. Ethics and dissemination This study received ethical approval from the Sciences and Technology Cross Research Council at the University of Sussex (reference ER/FM409/1). Participants will be required to provide informed consent via a Qualtrics survey before being accepted into the online interview or focus group. The findings will be disseminated through conference presentations, peer-reviewed publications and to the study participants

Predicting depressive symptom severity through individuals' nearby bluetooth device count data collected by mobile phones: preliminary longitudinal study

Background:Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective: This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods: The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results: A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions: Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.</p

Multilingual markers of depression in remotely collected speech samples: a preliminary analysis

BackgroundSpeech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data.MethodsWe collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18 months. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features.ResultsIncreased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses.LimitationsOur findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features.ConclusionsParticipants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD.</p