Digital Exclusion Among Mental Health Service Users: Qualitative Investigation

Greer, Ben; Robotham, Daniel; Simblett, Sara; Curtis, Hannah; Griffiths, Helena; Wykes, Til

doi:10.2196/11696

Cited by 96 publications

(101 citation statements)

References 23 publications

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“…Wider concerns around digital exclusion and broader ethical concerns have been emphasised elsewhere, 22 , 53 including in the Ada Lovelace Institute rapid review, 21 but are not currently well quantified. Some populations that are particularly vulnerable to the health impacts of COVID-19 (eg, older adults, people who are homeless, and socioeconomically deprived populations) are also less likely to own a smartphone, 17 , 22 , 54 , 55 potentially amplifying their risks because contact-tracing apps could—for similar reasons—be less likely to reduce transmission within their social circles. 33 Such challenges are more acute in low-income countries than in high-income countries.…”

Section: Discussionmentioning

confidence: 99%

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Braithwaite¹,

Callender²,

Bullock³

et al. 2020

The Lancet Digital Health

292

258

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Summary Evidence for the use of automated or partly automated contact-tracing tools to contain severe acute respiratory syndrome coronavirus 2 is scarce. We did a systematic review of automated or partly automated contact tracing. We searched PubMed, EMBASE, OVID Global Health, EBSCO Medical COVID Information Portal, Cochrane Library, medRxiv, bioRxiv, arXiv, and Google Advanced for articles relevant to COVID-19, severe acute respiratory syndrome, Middle East respiratory syndrome, influenza, or Ebola virus, published from Jan 1, 2000, to April 14, 2020. We also included studies identified through professional networks up to April 30, 2020. We reviewed all full-text manuscripts. Primary outcomes were the number or proportion of contacts (or subsequent cases) identified. Secondary outcomes were indicators of outbreak control, uptake, resource use, cost-effectiveness, and lessons learnt. This study is registered with PROSPERO (CRD42020179822). Of the 4036 studies identified, 110 full-text studies were reviewed and 15 studies were included in the final analysis and quality assessment. No empirical evidence of the effectiveness of automated contact tracing (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies that suggested that controlling COVID-19 requires a high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partly automated contact tracing generally reported more complete contact identification and follow-up compared with manual systems. Automated contact tracing could potentially reduce transmission with sufficient population uptake. However, concerns regarding privacy and equity should be considered. Well designed prospective studies are needed given gaps in evidence of effectiveness, and to investigate the integration and relative effects of manual and automated systems. Large-scale manual contact tracing is therefore still key in most contexts.

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Section: Discussionmentioning

confidence: 99%

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Braithwaite¹,

Callender²,

Bullock³

et al. 2020

The Lancet Digital Health

292

258

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“…First, not every outpatient from the FEPP was eligible for inclusion, as some patients did not have their own smartphone with an internet connection or did not have the ability to use the app or understand it due to language barriers. Developing strategies to prevent digital exclusion should be a priority to ensure that every patient could benefit from these technologies [ 33 ]. Second, as a real-world study, this study was not randomized.…”

Section: Discussionmentioning

confidence: 99%

ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study

Bonet¹,

Torous

Arce

et al. 2020

JMIR Mhealth Uhealth

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Background eHealth interventions are widely used in clinical trials and increasingly in care settings as well; however, their efficacy in real-world contexts remains unknown. ReMindCare is a smartphone app that has been systematically implemented in a first episode of psychosis program (FEPP) for patients with early psychosis since 2018. Objective The objective of this study was to assess the efficacy of ReMindCare after 19 months of use in the clinic and varying use by individual patients. Methods The integration of the ReMindCare app into the FEPP started in October 2018. Patients with early psychosis self-selected to the app (ReMindCare group) or treatment as usual (TAU group). The outcome variables considered were adherence to the intervention and number of relapses, hospital admissions, and visits to urgent care units. Data from 90 patients with early psychosis were analyzed: 59 in the ReMindCare group and 31 in the TAU group. The mean age of the sample was 32.8 (SD 9.4) years, 73% (66/90) were males, 91% (83/90) were White, and 81% (74/90) were single. Results Significant differences between the ReMindCare and TAU groups were found in the number of relapses, hospitalizations, and visits to urgent care units, with each showing benefits for the app. Only 20% (12/59) of patients from the ReMindCare group had a relapse, while 58% (18/31) of the TAU patients had one or more relapses (χ2=13.7, P=.001). Moreover, ReMindCare patients had fewer visits to urgent care units (χ2=7.4, P=.006) and fewer hospitalizations than TAU patients (χ2=4.6, P=.03). The mean of days using the app was 352.2 (SD 191.2; min/max: 18-594), and the mean of engagement was 84.5 (SD 16.04). Conclusions To our knowledge, this is the first eHealth intervention that has preliminarily proven its benefits in the real-world treatment of patients with early psychosis. International Registered Report Identifier (IRRID) RR2-10.1111/eip.12960

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“…What's missing in such arguments, is the acknowledgement that not all people may have access to, or continuously carry, laptops or mobile phones (e.g . , due to the digital divide [ 70 ]).…”

Section: Justicementioning

confidence: 99%

“…Finally, if ML applications are to find widespread adoption and success in real-world mental health contexts, it is also crucial to consider potentially far reaching personal, societal, and economic implications that the introduction of ML interventions can have. This includes ethical questions about responsibility and accountability for ML-directed decision making [ 15 ]; risks of potentially fallible ML outputs and biases; malicious uses of ML (see related works in domains of criminal justice, loan decisions [ 161 ] or automated facial analysis [ 24 ], and adversarial attacks in image processing [ 74 ] and speech recognition [ 37 ]); or digital exclusion due to lack of knowledge, access or other barriers to technology use [ 70 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

255

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High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

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Digital Exclusion Among Mental Health Service Users: Qualitative Investigation

Cited by 96 publications

References 23 publications

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

ReMindCare App for Early Psychosis: Pragmatic Real World Intervention and Usability Study

Machine Learning in Mental Health

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