Crystal L. Park, James Eric Pustejovsky, Kelly M. Trevino, Allen C. Sherman, Craig Esposito, Mark A. Berendsen, John M. Salsman
8 min
This study sought to determine whether psychosocial interventions improve the sense of meaning and purpose for adults diagnosed with cancer. As a cancer diagnosis often precipitates existential distress and threatens a patient's core sense of purpose, understanding how interventions can address these needs is vital for patient-centered care. The researchers conducted a systematic review and meta-analysis of 29 randomized controlled trials (RCTs), involving a total of 2,305 participants, to assess the impact of various interventions on meaning and purpose outcomes.
Across the 29 included studies, psychosocial interventions were associated with a statistically significant improvement in meaning and purpose, with an overall effect size of g = 0.37. The meta-analysis revealed that interventions specifically designed to target meaning and purpose were significantly more effective (g = 0.42) than those targeting other primary outcomes (g = 0.18). Furthermore, interventions delivered in-person showed higher effect sizes compared to other delivery formats, such as digital or print media. The results indicate that the benefits of these psychosocial interventions are comparable to existing evidence for interventions targeting depression, pain, and fatigue in oncology settings.
Meaning and purpose are not just auxiliary benefits but are fundamentally valued patient-reported outcomes that contribute to overall mental and physical health. The findings suggest that clinicians can meaningfully impact a patient's existential wellbeing through targeted psychosocial strategies. As this field matures, the study emphasizes the need for more rigorous, transparent reporting—such as better allocation concealment—and the development of interventions tailored to specific phases of the cancer journey, such as active treatment versus survivorship.
Meaning and purpose in life are associated with the mental and physical health of patients with cancer and survivors and also constitute highly valued outcomes in themselves. Because meaning and purpose are often threatened by a cancer diagnosis and treatment, interventions have been developed to promote meaning and purpose. The present meta-analysis of randomized controlled trials (RCTs) evaluated effects of psychosocial interventions on meaning/purpose in adults with cancer and tested potential moderators of intervention effects. Six literature databases were systematically searched to identify RCTs of psychosocial interventions in which meaning or purpose was an outcome. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, rater pairs extracted and evaluated data for quality. Findings were synthesized across studies with standard meta-analytic methods, including meta-regression with robust variance estimation and risk-of-bias sensitivity analysis. Twenty-nine RCTs were identified, and they encompassed 82 treatment effects among 2305 patients/survivors. Psychosocial interventions were associated with significant improvements in meaning/purpose (g = 0.37; 95% CI, 0.22-0.52; P < .0001). Interventions designed to enhance meaning/purpose (g = 0.42; 95% CI, 0.24-0.60) demonstrated significantly higher effect sizes than those targeting other primary outcomes (g = 0.18; 95% CI, 0.09-0.27; P = .009). Few other intervention, clinical, or demographic characteristics tested were significant moderators. In conclusion, the results suggest that psychosocial interventions are associated with small to medium effects in enhancing meaning/purpose among patients with cancer, and the benefits are comparable to those of interventions designed to reduce depression, pain, and fatigue in patients with cancer. Methodological concerns include small samples and ambiguity regarding allocation concealment. Future research should focus on explicitly meaning-centered interventions and identify optimal treatment or survivorship phases for implementation.
Alex: So an intervention could be spiritual in approach, but the outcome still had to capture meaning. That separates what the treatment does from what the review measures.
Sam: Yes, although the retained measures still covered different facets of meaning. The authors searched six databases through September 2018, and also sought unpublished studies and missing information. Pairs of reviewers independently assessed eligibility, extracted data, and evaluated bias.
Alex: Once they had the trials, what was the key statistical problem? A study might measure meaning repeatedly, using several questionnaires.
Sam: That happened here. Results from the same participants are related, so treating each result as independent would misrepresent uncertainty. The authors used robust variance estimation, which accounts for related estimates within studies. They also allowed underlying treatment effects to vary across studies, rather than assuming every intervention had the same effect.
Alex: And the comparison itself was improvement with treatment versus improvement in a control condition, rather than simply asking whether participants improved?
Sam: Generally, yes. They estimated treatment-versus-control differences adjusted for baseline, with exceptions where baseline data were unavailable. The combined sample included about 2,300 adults with cancer or a history of cancer. Control conditions ranged from usual care and waiting lists to active alternatives, such as education or attention.
Alex: Give us the main result in a form we can carry around. How large was the average benefit?
Sam: The average effect was about 0.37 on Hedges’ g, a standardized measure of the treatment-versus-control difference. The authors describe that as small-to-medium. It was statistically significant, but it is not a percentage improvement or a promise about any individual patient.
Alex: The review compares that with effects reported for depression, pain, and fatigue interventions in cancer. Does that mean these treatments work equally well?
Sam: Not from a direct comparison. The authors say the magnitude compares favorably with other meta-analyses of those outcomes. That provides context for the size of the benefit, not evidence that the treatments are interchangeable.
Alex: The opening emphasized explicitly targeting meaning. How much difference did that make?
Sam: Interventions targeting meaning had an average effect of about 0.42. Those without that explicit target averaged about 0.18. The difference was statistically significant, suggesting that making meaning an intentional focus may matter.
Alex: But patients weren’t randomly assigned across the whole literature to explicit versus incidental targeting. Could other differences between those trials explain that pattern?
Sam: They could. This is a comparison between studies, not a randomized test of that design choice. It supports further testing of meaning-focused interventions, rather than proving that the focus itself caused the larger effect.
Alex: Did the review identify who benefits most, or when support should be offered during cancer care?
Sam: It found no strong evidence that benefits differed by average age, proportion of women, or treatment phase. Breast cancer studies did not differ significantly from studies of other cancers. But those findings don’t establish equal benefits across patient groups; the authors call for more work on treatment phase and other patient characteristics.
Alex: Let’s turn to confidence. With varied treatments and populations, how much should we trust that single average?
Sam: Effects varied substantially across studies. Individual trials were often small, and methods were not always clearly reported. In particular, many reports left allocation concealment unclear: whether upcoming treatment assignments stayed hidden during enrollment.
Alex: Does the benefit survive when the authors restrict the analysis to studies with fewer bias concerns?
Sam: It does, but the estimate falls. In the subset at low risk for problems with random sequence generation and outcome reporting, the average was about 0.27. That remains a positive result, while making the overall estimate look less secure.
Alex: So the direction looks more stable than the precise size. Was there also evidence that unusually favorable small studies might be influencing the picture?
Sam: A statistical test indicated possible small-study effects. Removing influential studies also reduced the average, although it remained significant. My reading is that an average benefit is supported, but its exact size—and the best way to achieve it—remain uncertain.
Alex: If someone is designing a trial now, what unresolved choice deserves attention first?
Sam: Specify which aspect of meaning the intervention targets, then choose a matching measure. The authors also suggest studying people who begin with low meaning; none of the included trials screened participants that way. Whether that improves effectiveness is a proposal, not an established result.
Alex: Who should read the full document, and where should they start?
Sam: Researchers developing psychosocial cancer interventions should start with Meaning Outcomes, then Moderator analyses and Risk of Bias. Those sections show what counted, which comparisons favored explicit targeting, and why the effect size needs caution. For everyone else: meaning is not merely a side effect of feeling better; it is a patient-valued outcome that support can address.
Alex: A useful distinction to take back into research and care.