Back

Surveying Concerns about the Future in People with Parkinsons Disease

Gandhi, P.; Lin, L.; Coles, T.; Steiger, D.; Rapoport, R.; Chahine, L.; Marras, C.; Mantri, S.

2026-08-06 neurology
10.64898/2026.08.04.26359699 medRxiv
Show abstract

Background: People with Parkinsons disease (PD) experience substantial psychosocial burden, but the extent of their concern about the future is not well characterized. Objective: The objective of the present study is to characterize concerns about the future among people with PD (PwP), with an emphasis on the clinical and demographic correlates of future-oriented concerns. Methods: A survey in the online Fox Insight study platform asked PwP to rate their degree of concern about the future across seven domains: quality of life, disease progression, healthcare needs, social relationships, financial responsibilities, stigma, and specific symptoms. Relationships among uncertainty and demographic and clinical features, such as age of onset, gender, and disease severity, were examined. Latent class analysis was conducted to identify patterns of fear/uncertainty. Results: Among 3372 respondents, concerns about the future were common and spanned cognitive, functional, social, and symptom-related domains. Concerns about future cognitive impairment, independence, mobility, and disease progression were most prominent. Women and individuals with young-onset PD reported higher levels of concern than other groups. Latent class analysis revealed two clear patterns, including a high-concern subgroup with elevated worry across nearly all domains. Fewer than half of respondents had discussed these concerns with a healthcare professional. Conclusion: Future-related concerns are common among people with PD but is not routinely explored in clinical care. Greater attention to these concerns, especially for young-onset individuals and women, may help clinicians offer more timely and supportive guidance.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.