ArticlePsycho-oncology2026
Identifying Longitudinal Trajectories of Quality of Life and Associated Risk and Protective Factors Among Cancer Patients.
Article in Psycho-oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
objectiveThe 7-item Functional Assessment of Cancer Therapy-General (FACT-G7) is a validated, brief measure of health-related quality of life (QoL) used in oncology settings. While many conceptualize QoL as a static trial endpoint, growing evidence underscores its dynamic nature over time to inform point-of-care interventions. Grounded in the Wilson and Cleary model, this study aims to (1) identify distinct trajectories of QoL over a 12-month period using FACT-G7, and (2) incorporate clinical, mental health, sociodemographic, and healthcare system related factors to uncover risk and protective factors that shape patients' QoL trajectories.
methodsUsing FACT-G7 scores over a 12-month period from a sample of 4104 cancer patients (aged 19-92, M = 60.50, SD = 12.95) receiving cancer treatment, growth mixture modeling was fitted to identify subgroups of QoL trajectories. Multinomial logistic regression was conducted to examine the predictors of class membership.
resultsResults revealed a three-class model provided optimal fit: high QoL (40.1%), average QoL (40.3%), and low QoL (19.6%). Psychosocial factors - particularly loneliness (OR = 1.70, 95% CI:1.35-2.13) and financial difficulties (OR = 1.47, 95% CI:1.27-1.71) - strongly predicted membership in the low QoL trajectory. Clinical factors, including comorbidities (Charlson Comorbidity Index; CCI) and symptom burden severity (PRO-CTCAE), were associated with poorer QoL trajectories, with insomnia and nausea demonstrating the strongest negative effects. Conversely, higher satisfaction with cancer care and cancer-related self-efficacy were protective factors associated with higher QoL trajectories.
conclusionsThe findings identify modifiable risk and protective factors that can inform targeted early interventions to improve long-term QoL outcomes across the cancer care continuum.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.