Digital Life Data in the Clinical Whitespace

Coppersmith, Glen

doi:10.1177/09637214211068839

Cited by 10 publications

(9 citation statements)

References 31 publications

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“…JITAIs typically guide the sequencing and adaptation of digital-intervention components. These interventions are becoming increasingly popular across various domains of psychological sciences, including health (Conroy et al, 2020; Nahum-Shani, Rabbi, et al, 2021), clinical (Comer et al, 2019; Coppersmith, 2022), educational (Cook et al, 2018), and organizational (Valle et al, 2020) psychology. Leveraging powerful digital technologies, a JITAI is a protocol that specifies how rapidly changing information about the individual’s internal state (e.g., mood, substance use) and context (e.g., geographical location, presence of other people) should be used in practice to decide whether and how to deliver intervention content (e.g., feedback, motivational messages, behavioral or cognitive strategies) in real time in the individual’s natural environment (Nahum-Shani et al, 2015, 2018).…”

Section: Jitaismentioning

confidence: 99%

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Nahum‐Shani

Dziak

Walton

et al. 2022

Advances in Methods and Practices in Psychological Science

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Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)—a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).

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Section: Jitaismentioning

confidence: 99%

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Nahum‐Shani

Dziak

Walton

et al. 2022

Advances in Methods and Practices in Psychological Science

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“…Detecting shifts in mental health from language use could assist in identifying episodes of mental ill health and providing in-time treatment for conditions such as depression or anxiety. The accessibility and abundant usage of social media (Coppersmith, 2022) in comparison to traditional healthcare data (e.g. hospital visits) is enabling first steps toward unprecedented assessment and understanding of mental health, including detection of elevated risks Guntuku et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

2022

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Mental distress like depression and anxiety contribute to the largest proportion of the global burden of diseases. Automated diagnosis system of such disorders, empowered by recent innovations in Artificial Intelligence, can pave the way to reduce the sufferings of the affected individuals. Development of such systems requires information-rich and balanced corpora. In this work, we introduce a novel mental distress analysis audio dataset DEPAC, labelled based on established thresholds on depression and anxiety standard screening tools. This large dataset comprises multiple speech tasks per individual, as well as relevant demographic information. Alongside, we present a feature set consisting of hand-curated acoustic and linguistic features, which were found effective in identifying signs of mental illnesses in human speech. Finally, we justify the quality and effectiveness of our proposed audio corpus and feature set in predicting depression severity by comparing the performance of baseline machine learning models built on this dataset with baseline models trained on other well-known depression corpora.

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Section: Introductionmentioning

confidence: 99%

WWBP-SQT-lite: Multi-level Models and Difference Embeddings for Moments of Change Identification in Mental Health Forums

Ganesan¹,

Varadarajan²,

Mittal³

et al. 2022

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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Psychological states unfold dynamically; to understand and measure mental health at scale we need to detect and measure these changes from sequences of online posts. We evaluate two approaches to capturing psychological changes in text: the first relies on computing the difference between the embedding of a message with the one that precedes it, the second relies on a "human-aware" multi-level recurrent transformer (HaRT). The mood changes of timeline posts of users were annotated into three classes, 'ordinary,' 'switching' (positive to negative or vice versa) and 'escalations' (increasing in intensity). For classifying these mood changes, the difference-between-embeddings technique -applied to RoBERTa embeddings -showed the highest overall F1 score (0.61) across the three different classes on the test set. The technique particularly outperformed the HaRT transformer (and other baselines) in the detection of switches (F1 = .33) and escalations (F1 = .61). Consistent with the literature, the language use patterns associated with mental-health related constructs in prior work (including depression, stress, anger and anxiety) predicted both mood switches and escalations.

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Digital Life Data in the Clinical Whitespace

Cited by 10 publications

References 31 publications

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Hybrid Experimental Designs for Intervention Development: What, Why, and How

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

WWBP-SQT-lite: Multi-level Models and Difference Embeddings for Moments of Change Identification in Mental Health Forums

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