Health-Related Quality of Life in Stroke Survivors

Survivors of stroke frequently contend with enduring physical, cognitive and emotional challenges that extend well beyond the acute phase of illness. Health-related quality of life (HRQoL) encompasses multiple domains—including mobility, self-care, usual activities, pain and discomfort, and anxiety or depression—and provides a patient-centred gauge of overall wellbeing. Motor impairments such as hemiparesis and balance deficits often dominate initial recovery trajectories, yet non-motor sequelae—language disturbance, visual field loss and mood disorders—can exert equally profound effects on daily functioning. Sociodemographic factors such as age, living arrangements and socioeconomic status intersect with clinical severity to shape long-term outcomes. As stroke survival rates improve globally, there is growing recognition that measures of HRQoL are indispensable for both evaluating the effectiveness of rehabilitation programmes and guiding allocation of resources towards interventions that enhance participation and social reintegration.

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Health-Related Quality of Life in Stroke Survivors publication trend

The graph below shows the total number of articles in health-related quality of life in stroke survivors across all publications each year (not limited to Nature Index journals).

Technical terms

EQ-5D: A standardised questionnaire evaluating five dimensions of health (mobility, self-care, usual activities, pain/discomfort, anxiety/depression) to generate a single utility index.

NIHSS (National Institutes of Health Stroke Scale): A clinician-administered scale that quantifies stroke severity across consciousness, motor function, language and sensory domains.

SF-36 (Short Form-36 Health Survey): A 36-item patient-reported survey measuring eight domains of physical and mental health to assess overall health status.

Non-negative matrix factorisation: An unsupervised machine learning algorithm that decomposes high-dimensional data into interpretable components for clustering individuals by similar response patterns.

References

1. An machine learning model to predict quality of life subtypes of disabled stroke survivors. Annals of Clinical and Translational Neurology (2023).
2. Health-related quality of life in stroke survivors: a 5-year follow-up of The Fall Study of Gothenburg (FallsGOT). BMC Geriatrics (2023).
3. Quality of life after stroke: impact of clinical and sociodemographic factors. Clinics (2018).
4. Potential predictors for health-related quality of life in stroke patients undergoing inpatient rehabilitation. Health and Quality of Life Outcomes (2015).