Creating a Resilient Research Program-Lessons Learned From a Palliative Care Research Laboratory

Adapting the serious illness conversation guide for unhoused older adults: a rapid qualitative study

BACKGROUND

Older adults experiencing homelessness (OAEH) age quickly and die earlier than their housed counterparts. Illness-related decisions are best guided by patients' values, but healthcare and homelessness service providers need support in facilitating these discussions. The Serious Illness Conversation Guide (SICG) is a communication tool to guide discussions but has not yet been adapted for OAEH.

METHODS

We aimed to adapt the SICG for use with OAEH by nurses, social workers, and other homelessness service providers. We conducted semi-structured interviews with homelessness service providers and cognitive interviews with OAEH using the SICG. Service providers included nurses, social workers, or others working in homeless settings. OAEH were at least 50 years old and diagnosed with a serious illness. Interviews were conducted and audio recorded in shelters, transitional housing, a hospital, public spaces, and over Zoom. The research team reviewed transcripts, identifying common themes across transcripts and applying analytic notetaking. We summarized transcripts from each participant group, applying rapid qualitative analysis. For OAEH, data that referenced proposed adaptations or feedback about the SICG tool were grouped into two domains: "SICG interpretation" and "SICG feedback". For providers, we used domains from the Toolkit of Adaptation Approaches: "collaborative working", "team", "endorsement", "materials", "messages", and "delivery". Summaries were grouped into matrices to help visualize themes to inform adaptations. The adapted guide was then reviewed by expert palliative care clinicians for further refinement.

RESULTS

The final sample included 11 OAEH (45% Black, 61 ± 7 years old) and 10 providers (80% White, 8.9 ± years practice). Adaptation themes included changing words and phrases to (1) increase transparency about the purpose of the conversation, (2) promote OAEH autonomy and empowerment, (3) align with nurses' and social workers' scope of practice regarding facilitating diagnostic and prognostic awareness, and (4) be sensitive to the realities of fragmented healthcare. Responses also revealed training and implementation considerations.

CONCLUSIONS

The adapted SICG is a promising clinical tool to aid in the delivery of serious illness conversations with OAEH. Future research should use this updated guide for implementation planning. Additional adaptations may be dependent on specific settings where the SICG will be delivered.

Fostering resilience in adolescence and young adulthood: Considerations for evidence-based, patient-centered oncology care

Adolescence and young adulthood are times of growth and change. For adolescents and young adults (AYAs) who are diagnosed with cancer, the demands of illness may compound normal developmental challenges and adversely affect physical, emotional, and social health. Nevertheless, AYAs have a tremendous capacity for psychosocial adaptation and resilience. Informed by the Transactional Model of Stress and Coping, observational studies in AYA oncology suggest consistent individual, social, and existential resources that may promote resilience. To date, few interventions have been designed to examine whether resilience can be taught and whether doing so affects patient-centered outcomes. Findings point to the potential value of multicomponent programs that include various skills-building strategies, such as stress management, mindfulness, gratitude, and positive reappraisal coping, among others. New research directions include the need to evaluate delivery strategies to enhance participant adherence and retention (e.g., eHealth modalities, optimization studies) and to examine program effectiveness in community-based oncology practices (e.g., less resource-rich settings in which most AYAs receive care). Ultimately, this scholarship may inform, refine, and strengthen intervention science in resilience more broadly.

Piloting HealthScore: Feasibility and acceptability of a clinically integrated health coaching program for people living with cancer

BACKGROUND

Cancer supportive care interventions often have limited generalizability, goal misalignment, and high costs. We developed and piloted a health coaching intervention, UNC HealthScore, in patients undergoing cancer treatment (ClinicalTrials.gov identifier NCT04923997). We present feasibility, acceptability, and preliminary outcome data.

METHODS

HealthScore is a six-month, theory-based, multicomponent intervention delivered through participant-driven coaching sessions. For the pilot study, participants were provided a Fitbit, responded to weekly symptom and physical function digital surveys, and met with a health coach weekly to develop and monitor goals. Coaching notes were discussed in weekly interdisciplinary team meetings and provided back to the treating oncology team. Symptom alerts were monitored and triaged through a study resource nurse to relevant supportive care services. Feasibility was determined based on intervention enrollment and completion. Acceptability was based on satisfaction with coaching and Fitbit-wearing and was informed by semistructured exit interviews. Outcomes evaluated for signs of improvement included several PROMIS (Patient-Reported Outcomes Measurement Information System) measures, including the primary intervention target, physical function.

RESULTS

From May 2020 to March 2022, 50 participants completed the single-arm pilot. Feasibility was high: 66% enrolled and 71% completed the full intervention. Participants reported an average of 4.8 and 4.7 (out of 5) on the acceptability of coaching calls and using the Fitbit, respectively. Physical function scores rose 3.1 points (SE = 1.1) from baseline to 3 months, and 4.3 (SE = 1.0) from baseline to 6 months, above established minimal clinically important difference (MCID). Improvements above MCID were also evident in anxiety and depression, and smaller improvements were demonstrated for emotional support, social isolation, cognitive function, symptom burden, and self-efficacy.

DISCUSSION

HealthScore shows feasibility, acceptability, and promising preliminary outcomes. Randomized studies are underway to determine the efficacy of preserving physical function in patients with advanced cancer.

Performance status and trial site-level factors are associated with missing data in palliative care trials: An individual participant-level data analysis of 10 phase 3 trials

BACKGROUND

Missing data compromise the internal and external validity of trial findings, however there is limited evidence on how best to reduce missing data in palliative care trials.

AIM

To assess the association between participant and site level factors and missing data in palliative care trials.

DESIGN AND SETTING

Individual participant-level data analysis of 10 phase 3 palliative care trials using multi-level cross-classified models.

RESULTS

Participants with missing data at the previous time-point and poorer performance status were more likely to have missing data for the primary outcome and quality of life outcomes, at the primary follow-up point and end of follow-up. At the end of follow-up, the number of site randomisations and number of study site personnel were significantly associated with missing data. Trial duration and the number of research personnel explained most of the variance at the trial and site-level respectively, except for the primary outcome where the amount of data requested was most important at the trial-level. Variance at the trial level was more substantial than at the site level across models and considerable variance remained unexplained for all models except quality of life at the end of follow-up.

CONCLUSION

Participants with a poorer performance status are at higher risk of missing data in palliative care trials and require additional support to provide complete data. Performance status is a potential auxiliary variable for missing data imputation models. Reducing trial variability should be prioritised and further factors need to be identified and explored to explain the residual variance.