Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol

Spinazze, Pier; Rykov, Yuri Germanovich; Bottle, Alex; Car, Josip

doi:10.1136/bmjopen-2019-032255

Cited by 32 publications

(16 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The IoT has benefits in terms of monitoring, welfare interventions, and providing alerts and information services [32]. Digital phenotyping with the assessment of data from smartphones, consumer wearables, and social media has been scoped [33] but is yet to provide evaluated evidence regarding the detection and predictive capabilities of mental health interventions. Should the advantages of digital phenotyping be manifested in the clinical setting (eg, observing presented phenomena, opportunities for self-monitoring, and relapse prevention as well as potential interventions), then so will its disadvantages (eg, privacy in the management of personal data, and potentially not improving causal explanations or psychological understanding) [34].…”

Section: Other Emerging Aspects Of Digital Mental Healthmentioning

confidence: 99%

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Balcombe¹,

Leo²

2021

JMIR Ment Health

View full text Add to dashboard Cite

The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in the process, demonstrate capabilities to increase their effectiveness and efficiency. However, technology-enabled services have faced challenges in being sustainably implemented despite showing promising outcomes in efficacy trials since the early 2000s. The ongoing failure of these implementations has been addressed in reconceptualized models and frameworks, along with various efforts to branch out among disparate developers and clinical researchers to provide them with a key for furthering evaluative research. However, the limitations of traditional research methods in dealing with the complexities of mental health care warrant a diversified approach. The crux of the challenges of digital mental health implementation is the efficacy and evaluation of existing studies. Web-based interventions are increasingly used during the pandemic, allowing for affordable access to psychological therapies. However, a lagging infrastructure and skill base has limited the application of digital solutions in mental health care. Methodologies need to be converged owing to the rapid development of digital technologies that have outpaced the evaluation of rigorous digital mental health interventions and strategies to prevent mental illness. The functions and implications of human-computer interaction require a better understanding to overcome engagement barriers, especially with predictive technologies. Explainable artificial intelligence is being incorporated into digital mental health implementation to obtain positive and responsible outcomes. Investment in digital platforms and associated apps for real-time screening, tracking, and treatment offer the promise of cost-effectiveness in vulnerable populations. Although machine learning has been limited by study conduct and reporting methods, the increasing use of unstructured data has strengthened its potential. Early evidence suggests that the advantages outweigh the disadvantages of incrementing such technology. The limitations of an evidence-based approach require better integration of decision support tools to guide policymakers with digital mental health implementation. There is a complex range of issues with effectiveness, equity, access, and ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, and accountability), which warrant resolution. Evidence-informed policies, development of eminent digital products and services, and skills to use and maintain these solutions are required. Studies need to focus on developing digital platforms with explainable artificial intelligence–based apps to enhance resilience and guide the treatment decisions of mental health practitioners. Investments in digital mental health should ensure their safety and workability. End users should encourage the use of innovative methods to encourage developers to effectively evaluate their products and services and to render them a worthwhile investment. Technology-enabled services in a hybrid model of care are most likely to be effective (eg, specialists using these services among vulnerable, at-risk populations but not severe cases of mental ill health).

show abstract

Section: Other Emerging Aspects Of Digital Mental Healthmentioning

confidence: 99%

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Balcombe¹,

Leo²

2021

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

“…These systems may require alterations and advancements for ideal applications to livestock, but they can maintain the same structure of functioning as they do when applied to humans. Figure 2 below illustrates how phenotype sensors can be specified and how their data are analyzed to identify different clinical states [30].…”

Section: Physiological Measurements By Wearable Sensors For Phenotypingmentioning

confidence: 99%

Digital Phenotyping in Livestock Farming

Neethirajan

Kemp

2021

Animals

View full text Add to dashboard Cite

Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals’ individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

show abstract

“…In addition to the aforementioned approaches, digital phenotyping can also inform automated and immediate adjustments to direct clinical intervention. For example, accelerometer readings on smartphones can reflect and predict response to intervention (Spinazze et al, 2019), providing immediate feedback that is clinically useful. In another application, ML has been used to implement adaptive deep brain stimulation, which provides for automatic, real-time adjustments to electrical brain stimulation in response to psychopathology-specific neural patterns (Goodman et al, 2020).…”

Section: Personalized Interventionsmentioning

confidence: 99%

Low Estradiol May Not Impede Cognitive Restructuring in Women With Anxiety Disorders

et al. 2021

View full text Add to dashboard Cite

Nearly all patients interact with critical gatekeepers—insurance companies or centralized healthcare systems. For mental health dissemination efforts to be successful, these gatekeepers must refer patients to evidence-based care. To make these referral decisions, they require evidence about the amount of resources expended to achieve therapeutic gains. Without this information, a bottleneck to widespread dissemination of evidence-based care will remain. To address this need for information, we introduce a new perspective, clinical efficiency. This approach directly ties resource usage to clinical outcomes. We highlight how cost-effectiveness approaches and other strategies can address clinical efficiency, and we also introduce a related new metric, the incremental time efficiency ratio (ITER). The ITER is particularly useful for quantifying the benefits of low-intensity and concentrated interventions, as well as stepped-care approaches. Given that stakeholders are increasingly requiring information on resource utilization, the ITER is a metric that can be estimated for past and future clinical trials. As a result, the ITER can allow researchers to better communicate desirable aspects of treatment, and an increased focus on clinical efficiency can improve our ability to deliver high-quality treatment to more patients in need.

show abstract

Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol

Cited by 32 publications

References 12 publications

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Digital Phenotyping in Livestock Farming

Low Estradiol May Not Impede Cognitive Restructuring in Women With Anxiety Disorders

Contact Info

Product

Resources

About