Digital technology: coming of age?

Marzano, Lisa; Hollis, Chris; Cipriani, Andrea; Malhi, Gin S

doi:10.1136/eb-2017-102821

Cited by 7 publications

(7 citation statements)

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“…A recent special edition of Evidence-Based Mental Health highlighted promising advances in digital mental health3 including innovative uses of smartphones 4. With meta-analyses suggesting benefits of smartphone apps for conditions like depression5 and anxiety,6 one might expect apps to be commonly used clinical tools.…”

Section: Introductionmentioning

confidence: 99%

Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements

Torous

Nicholas

Larsen

et al. 2018

Evid Based Mental Health

549

447

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The potential of smartphone apps to improve quality and increase access to mental health care is increasingly clear. Yet even in the current global mental health crisis, real-world uptake of smartphone apps by clinics or consumers remains low. To understand this dichotomy, this paper reviews current challenges surrounding user engagement with mental health smartphone apps. While smartphone engagement metrics and reporting remains heterogeneous in the literature, focusing on themes offers a framework to identify underlying trends. These themes suggest that apps are not designed with service users in mind, do not solve problems users care most about, do not respect privacy, are not seen as trustworthy and are unhelpful in emergencies. Respecting these current issues surrounding mental health app engagement, we propose several solutions and highlight successful examples of mental health apps with high engagement. Further research is necessary to better characterise engagement with mental health apps and identify best practices for design, testing and implementation.

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Section: Introductionmentioning

confidence: 99%

Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements

Torous

Nicholas

Larsen

et al. 2018

Evid Based Mental Health

549

447

View full text Add to dashboard Cite

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“…The use and ownership of smartphones and wearable technology has increased exponentially in the last decade. These technologies provide the opportunity to collect data using unobtrusive, inbuilt sensors requiring minimal input from users [ 9 , 10 ]. In additional to unobtrusive passive data collection, there is scope for more frequent self-report information to be collected.…”

Section: Introductionmentioning

confidence: 99%

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

et al. 2022

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Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. A key question for the field is the extent to which participants can adhere to research protocols and the completeness of data collected. We aimed to describe drop out and data completeness in a naturalistic multimodal longitudinal RMT study, in people with a history of recurrent MDD. We further aimed to determine whether those experiencing a depressive relapse at baseline contributed less complete data. Methods Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program. People with a history of MDD were provided with a wrist-worn wearable device, and smartphone apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks, and cognitive assessments. Participants were followed-up for a minimum of 11 months and maximum of 24 months. Results Individuals with a history of MDD (n = 623) were enrolled in the study,. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group.

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“…The use and ownership of smartphones and wearable technology has increased exponentially in the last decade; new technological advances facilitate the collection of digital biomarkers using unobtrusive, inbuilt sensors requiring minimal input from users (9,10). Many features of MDD, including symptoms, risk factors for, and consequences of, depression are amenable to assessment via remote measurement technologies (RMT): for example, heart rate from photoplethysmography (PPG) sensors and activity from accelerometery sensors in wrist-worn wearable devices can give information indicative of sleep patterns and physical activity levels.…”

Section: Introductionmentioning

confidence: 99%

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study.

Matcham

Leightley

Siddi

et al. 2021

Preprint

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Background Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks. Our aim is to describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population. Methods The Remote Assessment of Disease and Relapse – Central Nervous System (RADAR-CNS) program explores the potential to use RMT across a range of central nervous system disorders. Remote Assessment of Disease and Relapse – Major Depressive Disorder (RADAR-MDD) is a multi-centre, prospective observational cohort study conducted as part of the RADAR-CNS program. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments. Participants were followed-up for a maximum of 2 years. Results A total of 623 individuals with a history of MDD were enrolled in the study. We report 80% completion rates for primary outcome assessments across all follow-up timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. In total, 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses. Conclusions RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible. We found comparable levels of data availability in active and passive forms of data collection, demonstrating that both are feasible in this patient group. Our next steps are to illustrate the predictive value of these data, which will be the focus of our future data analysis aims.

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Digital technology: coming of age?

Cited by 7 publications

References 0 publications

Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements

Clinical review of user engagement with mental health smartphone apps: evidence, theory and improvements

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study

Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): Recruitment, retention, and data availability in a longitudinal remote measurement study.

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