Perceived social support in the daily life of people with Parkinson’s disease: a distinct role and potential classifier – Nature

 

Report on the Role of Perceived Social Support in Parkinson’s Disease and its Alignment with Sustainable Development Goals

Abstract

This report details a study investigating the role of Perceived Social Support (PSS) in the well-being of individuals with Parkinson’s disease (PD), aligning with Sustainable Development Goal 3 (Good Health and Well-being). The research focused on non-motor symptoms, which are critical to daily well-being but often overlooked in standard treatment protocols. Utilizing remote monitoring technologies to enhance accessibility, in line with SDG 10 (Reduced Inequalities), the study examined 92 participants (45 with PD, 47 controls). Key findings indicate a significant correlation between PSS and non-motor outcomes (cognition, anxiety, depression) specifically in the PD cohort. Conversely, no link was found with motor-related measures. Furthermore, leveraging innovation as highlighted in SDG 9 (Industry, Innovation, and Infrastructure), machine learning models demonstrated significantly higher accuracy (13% improvement) in classifying PD status in individuals with low PSS. These results underscore the importance of PSS in managing the non-motor aspects of PD and suggest its potential as an accessible, non-motor marker for identifying at-risk subgroups, thereby contributing to the promotion of mental health and well-being as targeted by SDG 3.4.

1. Introduction: Addressing Non-Communicable Diseases and Well-being (SDG 3)

The management of Parkinson’s Disease (PD), a prevalent neurodegenerative disorder, has traditionally centered on motor symptoms. However, this focus neglects the significant impact of non-motor symptoms on patient quality of life. This study addresses this gap by exploring the role of Perceived Social Support (PSS), a subjective measure of available social resources. Understanding PSS is crucial for promoting mental health and well-being, a key target (3.4) of SDG 3.

People with PD face a unique combination of challenges that can create social barriers:

• Mobility and Motor Impairments: Physical difficulties and fear of falling can lead to social withdrawal.
• Cognitive Alterations: Cognitive deficits can interfere with social interactions and daily functioning.
• Communication Difficulties: Symptoms like hypomimia (reduced facial expression) and dysarthria (speech impairment) hinder effective communication and can lead to social stigma.

This research posits that PSS is a critical buffer against the mental health consequences of these challenges. By comparing individuals with PD to a matched control group and examining both motor and non-motor outcomes, this study aims to provide a comprehensive understanding of the distinct role PSS plays in the daily lives of patients.

2. Methodology: Fostering Innovation and Accessibility (SDG 9 & 10)

In alignment with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 10 (Reduced Inequalities), this prospective, observational study employed remote online technologies to ensure broad and accessible participation, overcoming mobility challenges often faced by the PD community.

2.1. Participants and Data Collection

1. Participants: The study included 92 individuals: 45 people with PD (PwP) and 47 neurotypical healthy (NH) controls, matched for age and education.
2. Remote Assessment: Data was collected via online interviews and questionnaires, allowing participants to report from their natural environment. This approach enhances access to healthcare research, a principle of SDG 3.8 (Universal Health Coverage).
3. Measures:
   • Non-Motor Outcomes: Montreal Cognitive Assessment (MoCA) for cognition, PROMIS scales for anxiety and depression, and the Interpersonal Support Evaluation List (ISEL-12) for PSS.
   • Motor-Related Outcomes (PD Group): MDS-UPDRS III for disease severity, disease duration, and Hoehn and Yahr (H&Y) scale for disease stage.

2.2. Analytical Approach

The analysis was twofold:

1. Correlational Analysis: Statistical tests were used to examine the associations between PSS and both non-motor and motor outcomes within and between the PD and control groups.
2. Machine Learning (ML) Classification: In a direct application of SDG 9‘s focus on technological advancement, ML models were developed to classify participants (PD vs. control) using only non-motor features. The performance of these models was compared between high-PSS and low-PSS cohorts to determine if PSS level modulates the detectability of the disease.

3. Results: Evidence for the Role of PSS in Health Outcomes

3.1. Association Between PSS and Non-Motor Outcomes

The findings reveal a distinct and significant relationship between PSS and non-motor symptoms in individuals with PD, supporting the holistic health approach of SDG 3.

• In the PD group, higher PSS was significantly correlated with better cognitive status (r = .330), lower anxiety (r = −.389), and lower depression (r = −.574).
• These associations were significantly stronger in the PD group compared to the control group, where no significant correlations were found.
• This highlights the selective importance of PSS for the well-being of people with PD.

3.2. Lack of Association with Motor-Related Measures

To test the specificity of PSS, its relationship with motor outcomes was examined.

• No significant correlations were found between PSS and motor severity, disease duration, or disease stage in the PD group.
• This result indicates that the benefits of PSS are primarily linked to the non-motor, psychosocial aspects of the disease, which are central to well-being under SDG 3.4.

3.3. Machine Learning Classification Performance

The use of ML models provided innovative insights into the diagnostic potential of non-motor features, demonstrating a tangible outcome of applying SDG 9 principles to healthcare challenges.

• Low PSS Cohort: The model performed with moderate-high discriminatory power (AUC = 0.80), effectively distinguishing between individuals with and without PD.
• High PSS Cohort: The model’s performance was significantly lower (AUC = 0.67), indicating that high PSS may mask the non-motor symptoms used for classification.
• Performance Gap: The 13% improvement in AUC in the low-PSS cohort was statistically significant (p < .0001), suggesting that PSS level is a critical factor and a potential classifier itself.

4. Discussion and Implications for Sustainable Development

This study’s findings have significant implications for achieving key targets within the Sustainable Development Goals framework.

4.1. Enhancing Well-being and Mental Health (SDG 3)

The strong, selective association between PSS and non-motor outcomes in PwP confirms that psychosocial factors are integral to health. By identifying PSS as a key component of well-being, this research provides a clear path for interventions aimed at improving mental health and quality of life for those with non-communicable diseases, directly supporting SDG Target 3.4.

4.2. Leveraging Innovation for Accessible Healthcare (SDG 9 & 10)

The successful use of remote assessment and ML models demonstrates the power of innovation (SDG 9) to create more equitable healthcare systems (SDG 10).

• Remote Tools: Online platforms can reduce barriers to diagnosis and care for individuals with mobility impairments, promoting universal access as envisioned in SDG 3.8.
• ML as a Classifier: The finding that ML models can effectively use non-motor data for classification, and that this is modulated by PSS, opens new avenues for accessible, scalable, and low-cost screening tools.

5. Conclusion and Recommendations

This report concludes that Perceived Social Support is uniquely and significantly linked to the non-motor well-being of people with Parkinson’s disease. PSS does not correlate with motor severity but acts as a powerful factor in the psychosocial experience of the disease. The level of PSS can even modulate the effectiveness of diagnostic classification models based on non-motor symptoms.

In pursuit of the Sustainable Development Goals, the following recommendations are made:

1. Integrate PSS into Clinical Practice: Healthcare providers should incorporate accessible PSS measures into routine assessments for PwP to identify at-risk individuals and guide targeted, modifiable interventions that support mental health (SDG 3.4).
2. Develop PSS-Focused Interventions: Design and implement programs that focus on enhancing patients’ subjective perception of social support, such as facilitating support groups and family counseling.
3. Advance Remote and Digital Health Platforms: Continue to invest in and validate remote technologies and ML models for the assessment and screening of neurological conditions, ensuring they are accessible and reduce inequalities in care (SDG 9, 10, and 3.8).

By focusing on the crucial role of PSS, the healthcare community can better address the holistic needs of individuals with Parkinson’s disease, advancing global goals for health, well-being, and equality.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on Parkinson’s Disease (PD) and Perceived Social Support (PSS) connects to several Sustainable Development Goals (SDGs) by addressing health, technological innovation, and social inclusion for a vulnerable population.

• SDG 3: Good Health and Well-being

  This is the most central SDG to the article. The research focuses on understanding and improving the “daily well-being” of individuals with Parkinson’s disease, a non-communicable neurodegenerative disorder. It specifically investigates non-motor symptoms like “cognition, anxiety, and depression,” which are critical components of mental health and overall well-being.

• SDG 9: Industry, Innovation, and Infrastructure

  The study explicitly utilizes and evaluates modern technological solutions to address health challenges. It employs “remote monitoring technologies,” “online platforms,” and develops “machine-learning classifiers (ML)” for disease classification. This aligns with SDG 9’s emphasis on enhancing scientific research and upgrading technological capabilities to foster innovation.

• SDG 10: Reduced Inequalities

  The article focuses on people with Parkinson’s disease (PwP), a group that often faces significant “social barriers,” “social withdrawal,” and challenges due to disability. By examining the role of social support in mitigating negative outcomes and improving well-being, the research addresses the need for social inclusion for persons with disabilities, a key aspect of reducing inequalities.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s focus, the following specific targets are relevant:

1. Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.

   The article directly contributes to the second part of this target. It investigates factors (PSS) that influence “anxiety, and depression” in people with PD, a non-communicable disease. The conclusion suggests that interventions aimed at “enhancing the perception of social support” are a “promising method to improve non-motor outcomes,” thereby promoting mental health and well-being in this patient group.

2. Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries…

   The research is a clear example of enhancing scientific research to solve a health problem. The methodology section details the development of “ML classification models using Generalized Linear Models” and the use of “remote online technologies” for data collection. The study’s aim to create a “potential classifier for PD” using non-motor features is a direct effort to upgrade technological capabilities in diagnostics and patient screening.

3. Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status.

   The article addresses this target by focusing on the social inclusion of people with disabilities (PwP). It highlights how “mobility limitations, cognitive alterations, and communication difficulties” can lead to “social withdrawal.” The core of the study is to understand how PSS can act as a “buffer against the mental and physical health consequences of illness” and improve daily life, thereby promoting the social inclusion and well-being of this group.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions and uses several specific measures that can serve as indicators for the identified targets.

• Indicators for Target 3.4 (Promote mental health and well-being):

  • PROMIS-anxiety and PROMIS-depression scores: The article explicitly uses the “Patient-Reported Outcomes Measurement Information System (PROMIS) scales for anxiety and depression” to quantify levels of mental distress. The results show a significant negative correlation between PSS and these scores in PwP, making them direct indicators of mental well-being.
  • Cognitive status (MoCA scores): The “Montreal Cognitive Assessment (MoCA) test” is used to evaluate cognitive status. The study finds that higher PSS is associated with better cognitive scores in PwP, linking social factors to cognitive well-being.

• Indicators for Target 9.5 (Enhance scientific research and technology):

  • Application of Machine Learning (ML) Models: The development and use of “ML classification models” is a direct indicator of technological application in research.
  • ML Model Performance Metrics (AUC, Accuracy): The article reports quantitative metrics for its ML model, such as the “Area Under the Curve (AUC) of 0.8” and an “accuracy of 0.72” in the low PSS cohort. These metrics serve as specific indicators of the effectiveness and success of the developed technology.
  • Use of Remote/Online Assessment Platforms: The study’s reliance on “remote online technologies” and “online platforms” for data collection is a qualitative indicator of innovation in research methodology, making assessment more accessible.

• Indicators for Target 10.2 (Promote social inclusion):

  • Perceived Social Support (PSS) Scores: The “Interpersonal Support Evaluation List (ISEL-12)” is used to measure PSS. The scores from this questionnaire and its subscales (Appraisal, Tangible, Belonging Support) serve as a direct indicator of the perceived level of social inclusion and support available to individuals.
  • Prevalence of Social Withdrawal: While not measured with a specific scale, the article frequently discusses “social withdrawal” as a key negative outcome for PwP resulting from motor and non-motor symptoms. The reduction of factors leading to social withdrawal would be an implied indicator of increased social inclusion.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article. In this table, list the Sustainable Development Goals (SDGs), their corresponding targets, and the specific indicators identified in the article.

SDGs	Targets	Indicators
SDG 3: Good Health and Well-being	Target 3.4: Reduce mortality from non-communicable diseases and promote mental health and well-being.	PROMIS scores for anxiety and depression. Cognitive status as measured by MoCA scores. Well-being of people with a non-communicable disease (Parkinson’s).
SDG 9: Industry, Innovation, and Infrastructure	Target 9.5: Enhance scientific research and upgrade technological capabilities.	Application of remote monitoring and online assessment technologies. Development of machine-learning (ML) classification models. Performance metrics of ML models (e.g., AUC, accuracy, F1-score).
SDG 10: Reduced Inequalities	Target 10.2: Empower and promote the social inclusion of all, including persons with disabilities.	Perceived Social Support (PSS) scores from the ISEL-12 questionnaire. Measurement of PSS sub-domains: Appraisal, Tangible, and Belonging support. Analysis of social withdrawal and social barriers faced by PwP.

Source: nature.com