Examining the Client Experience of Digital Tools in Blended Care Therapy: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Lyra Health's short-term blended care therapy model, Lyra Care Therapy (LCT), has demonstrated effectiveness at scale. In LCT, clients participate in synchronous telehealth sessions and asynchronous guided practice sessions, in which they are provided with digital tools to reinforce key concepts and skills. These digital tools include animated video lessons that use storytelling to show characters learning and implementing new skills from therapy, written psychoeducational materials, interactive exercises that prompt reflection and skills practice, symptom assessments, and messaging with therapists. Past research on LCT found that time spent in therapy sessions and viewing digital video lessons predicts improvements in depression and anxiety symptoms. OBJECTIVE: This study aims to explore the client experience of LCT digital tools and to understand clients' perceived benefits and challenges of using digital tools while in LCT. METHODS: In total, 12 ethnically and racially diverse adults (5 male, 6 female, and 1 pangender) who had graduated from LCT in the previous 4 months participated in semistructured interviews. Interviews focused on experiences with the digital components of LCT (ie, video lessons, symptom assessments, and written exercises). Transcripts were analyzed using thematic analytic methods to determine the benefits and challenges associated with components of the LCT model. RESULTS: In total, 3 primary themes were generated through thematic analysis. These themes centered around supporting knowledge and skill development, the benefits and challenges associated with the range of digital tools, and the combination of flexibility and accountability promoting positive change. First, we summarize the ways in which guided practice sessions allow clients to develop additional knowledge and learn skills related to their mental health and well-being. Then, we describe how the range of digital tools included in LCT presents different benefits and challenges for clients. Finally, we discuss how flexibility inherent in having both synchronous and asynchronous sessions, along with the accountability from a provider, encourages clients to continue to practice skills related to their mental health and well-being. CONCLUSIONS: Results provide insights into the unique contributions of different components of the LCT model on therapeutic gains. While perceived time constraints and content preferences can impact engagement with digital tools, overall the digital tools were perceived as carrying significant value for participants in the LCT program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".