Barriers to and Facilitators of Engagement With mHealth Technology for Remote Measurement and Management of Depression: Qualitative Analysis

BACKGROUND: Mobile technology has the potential to provide accurate, impactful data on the symptoms of depression, which could improve health management or assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. Although many barriers to and facilitators of the use of this technology are common across therapeutic areas and technology types, many may be specific to cultural and health contexts. OBJECTIVE: This study aimed to determine the potential barriers to and facilitators of engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. METHODS: Participants (N=25; 4:1 ratio of women to men; age range, 25-73 years) who experienced depression participated in five focus groups held in three countries (two in the United Kingdom, two in Spain, and one in Italy). The focus groups investigated the potential barriers to and facilitators of the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. RESULTS: Facilitators and barriers were categorized as health-related factors, user-related factors, and technology-related factors. A total of 58 subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience, and ease of use was noted across all countries. CONCLUSIONS: Similarities in the barriers to and facilitators of the use of mHealth technology have been observed across Spain, Italy, and the United Kingdom. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools. ©Sara Simblett, Faith Matcham, Sara Siddi, Viola Bulgari, Chiara Barattieri di San Pietro, Jorge Hortas López, José Ferrão, Ashley Polhemus, Josep Maria Haro, Giovanni de Girolamo, Peter Gamble, Hans Eriksson, Matthew Hotopf, Til Wykes, RADAR-CNS Consortium. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 30.01.2019

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. KEYWORDS: M-health; Major depressive disorder; Observational cohort; Outcome measurement; Passive sensing; Prospective study; Remote measurement technolog

Processing Argument Structure and Syntactic Complexity in People with Schizophrenia Spectrum Disorders

Introduction: Deficits in language comprehension and production have been repeatedly observed in Schizophrenia Spectrum Disorders (SSD). However, the characterization of the language profile of this population is far from complete, and the relationship between language deficits, impaired thinking and cognitive functions is widely debated. Objective: The aims of the present study were to assess production and comprehension of verbs with different argument structures, as well as production and comprehension of sentences with canonical and non-canonical word order in people with SSD. In addition, the study investigated the relationship between language deficits and cognitive functions. Methods: Thirty-four participants with a diagnosis of SSD and a group of healthy control participants (HC) were recruited and evaluated using the Italian version of the Northwestern Assessment of Verbs and Sentences (NAVS, Cho-Reyes & Thompson, 2012; Barbieri et al., 2019). Results: Results showed that participants with SSD were impaired – compared to HC – on both verb and sentence production, as well as on comprehension of syntactically complex (but not simple) sentences. While verb production was equally affected by verb-argument structure complexity in both SSD and HC, sentence comprehension was disproportionately more affected by syntactic complexity in SSD than in HC. In addition, in the SSD group, verb production deficits were predicted by performance on a measure of visual attention, while sentence production and comprehension deficits were explained by performance on measures of executive functions and working memory, respectively. Discussion: Our findings support the hypothesis that language deficits in SSD may be one aspect of a more generalized, multi-domain, cognitive impairment, and are consistent with previous findings pointing to reduced inter- and intra-hemispheric connectivity as a possible substrate for such deficits. The study provides a systematic characterization of lexical and syntactic deficits in SSD and demonstrates that psycholinguistically-based assessment tools may be able to capture language deficits in this population

Syntax-semantic interface phenomena in people with schizophrenia: preliminary results of an eye-tracking study

We report the preliminary results of a study conducted to investigate eye–movement signatures in a sample of people with Schizophrenia Spectrum Disorders (SSDs) when reading full sentences carrying a semantic anomaly. Patients data were compared to those obtained from a group of healthy control participants. Despite the results do not allow drawing definitive conclusions, data suggest a specific deficit in the processing of semantic violations on the Thematic Role of Agent

Grid infrastructures for computational neuroscience: The neuGRID example

Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid- and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer's and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer's disease imaging community, which will allow the collection and archiving of large amounts of imaging data coupled with Grid-based algorithms and sufficiently powered computational resources. The major benefit will be the faster discovery of new disease markers that will be valuable for earlier diagnosis and development of innovative drugs. The initial setup of neuGRID will feature three nodes equipped with supercomputer capabilities and resources of more than 300 processor cores, 300 GB of RAM memory and approximately 20 TB of physical space. The scope of this article is highlights the new perspectives and potential for the study of the neurodegenerative disorders using the emerging Grid technology. © 2009 Future Medicine Ltd

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

Barriers to and facilitators of engagement with mHealth technology for remote measurement and management of depression: Qualitative analysis

Background: Mobile technology has the potential to provide accurate, impactful data on the symptoms of depression, which could improve health management or assist in early detection of relapse. However, for this potential to be achieved, it is essential that patients engage with the technology. Although many barriers to and facilitators of the use of this technology are common across therapeutic areas and technology types, many may be specific to cultural and health contexts. Objective: This study aimed to determine the potential barriers to and facilitators of engagement with mobile health (mHealth) technology for remote measurement and management of depression across three Western European countries. Methods: Participants (N=25; 4:1 ratio of women to men; age range, 25-73 years) who experienced depression participated in five focus groups held in three countries (two in the United Kingdom, two in Spain, and one in Italy). The focus groups investigated the potential barriers to and facilitators of the use of mHealth technology. A systematic thematic analysis was used to extract themes and subthemes. Results: Facilitators and barriers were categorized as health-related factors, user-related factors, and technology-related factors. A total of 58 subthemes of specific barriers and facilitators or moderators emerged. A core group of themes including motivation, potential impact on mood and anxiety, aspects of inconvenience, and ease of use was noted across all countries. Conclusions: Similarities in the barriers to and facilitators of the use of mHealth technology have been observed across Spain, Italy, and the United Kingdom. These themes provide guidance on ways to promote the design of feasible and acceptable cross-cultural mHealth tools

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.status: publishe