Response to Thanchonnang et al – From Efficacy to Implementation – Critical Insights for Clinical Practice

Dear Editor,

We thank Thanchonnang et al for their thoughtful commentary. Beyond addressing their points, we would like to share observations that may guide implementation and highlight critical research gaps.

Sleep Quality as Gateway Symptom

The authors correctly identify baseline sleep quality as our strongest predictor of treatment response (β = −.432, P < .001). ¹ This has practical implications: patients with baseline sleep disturbances showed significantly greater improvements not only in sleep itself, but across all quality-of-life domains – depression, anxiety, fatigue, and functional well-being (figure 1C). ¹ This suggests sleep disturbances may function as a “gateway symptom” where targeted interventions catalyze broader psychological benefits. Similar patterns have been reported: Leonhardt et al ² observed that more than half of participants reported clinically relevant sleep difficulties, with improvement in sleep quality closely linked to reductions in distress and fatigue. These converging data support the notion that addressing sleep may unlock broader psychophysiological benefits in cancer survivorship.

Therapeutic Timing: Evidence Versus Selection Bias

Our regression analysis revealed that longer time between diagnosis and program start was associated with better functional outcomes (β = .088, P = .005), although this did not survive Bonferroni correction. ¹ The median time from diagnosis to enrollment was 8 months. ¹ This aligns with Thanchonnang et al’s hypothesis of cognitive overload early in the cancer trajectory. However, we must acknowledge survivor bias: patients enrolling months post-diagnosis may represent a self-selected group with better prognosis, higher health literacy, or greater resilience. ³ Prospective studies randomizing patients to immediate versus delayed intervention are needed. Thanchonnang et al’s proposed staggered model requires validation before implementation.

The Gender Gap: A Field-Wide Challenge

In our cohort, 94.7% of participants were female, and breast cancer was the most common diagnosis (58.9%). ¹ Additionally, 42.9% were professionally active before starting the program. ¹ This striking imbalance illustrates a broader gender gap extending beyond disease prevalence. Women tend to engage more frequently in mind-body approaches, whereas men are less likely to enroll despite similar needs. ² We hypothesize factors may include gender differences in help-seeking behavior, program design emphasizing emotional sharing, and scheduling. ⁴ However, without comparative data or qualitative interviews, these remain speculations. Programs might consider gender-sensitive adaptations: separate tracks, alternative terminology, or other formats to improve male engagement.

Biomarkers and Mechanisms: Promise and Pragmatism

We endorse the authors’ call for integrating diurnal cortisol slopes, heart rate variability, and inflammatory markers (IL-6, CRP). However, methodological challenges warrant consideration: Which markers should be included? Cytokines show high temporal variability. ⁵ Do biomarker changes mediate improvements in health-related quality of life, or merely correlate? Our retrospective design and anonymized data preclude such analyses. Future RCTs should embed biomarker substudies from the outset, ideally with mechanistic mediation analyses.

The Non-Responder Question

While our manuscript emphasized positive outcomes, 30% to 50% of participants did not achieve clinically meaningful improvements across various domains. ¹ Unfortunately, our anonymized dataset prevents further characterization of non-responders. This knowledge gap has resource implications: our 11-week program requires substantial multidisciplinary staff time. The authors’ vision of “precision mind-body medicine” could optimize resource allocation, but requires prospective studies with comprehensive baseline phenotyping.

Practical Recommendations

Based on our findings, oncology centers considering MBM programs might:

1. Screen for sleep disturbances: Baseline sleep quality was our strongest response predictor ¹

2. Consider timing carefully: Our cohort enrolled a median of 8 months post-diagnosis ¹ ; whether earlier or later timing is optimal requires prospective study

3. Address gender disparities: Actively recruit diverse patient populations through tailored messaging

Research Agenda for the Field

Key questions remain unanswered: What baseline characteristics predict non-response? Do neuroimmune biomarkers mediate clinical benefits? How do we optimize engagement across gender, age, and disease stage? What is the optimal timing relative to cancer treatment phases?

Thanchonnang et al articulate a compelling vision for next-generation MBM research. Our data demonstrate that sleep quality may be a critical leverage point, that substantial minorities do not achieve clinical benefit, and that gender representation remains problematic. However, the path from research to routine care requires implementation science addressing real-world barriers. Our dataset’s limitations underscore the need for prospective, biomarker-enriched trials with comprehensive baseline characterization. We look forward to collaborative efforts advancing mind-body medicine.

Severin Schricker, Dr. med.Felix Breßmer, Dr. med.Mark Dominik Alscher, Prof. Dr. med.Robert-Bosch-Hospital, Stuttgart, Germany Claudia Löffler, Dr. med.University Hospital of Wuerzburg, Germany Holger Cramer, PhDUniversity Hospital Tübingen, Germany Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany Marcela Winkler, Dr. med.Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany Robert-Bosch-Hospital, Stuttgart, Germany