Effect of a Combined Exercise and Cognitive Activity Intervention on Cognitive Function in Community-dwelling Older Adults: A Pilot Randomized Controlled Trial

Social participation classification and activities in association with health outcomes among older adults: Results from a scoping review

AIMS

The aim of this study is to summarize the characteristics of social participation classification and examine the association between activities and health outcomes among older adults.

DESIGN

Scoping review.

DATA SOURCES

Eight databases (Cumulative Index to Nursing and Allied Health Literature, The Cochrane Library, Embase, ProQuest, Psychological Information Database, PubMed, Scopus and Web of Science) were searched. Reference lists of relevant social participation reviews were also considered.

METHODS

This study applied a five-stage methodological framework. A narrative synthesis summarized social participation classification and activities and their associations with health outcomes among older adults (≥65 years) living at home, in the community or in nursing residences.

RESULTS

Forty-two articles published between 1975 and 2022 were selected. Four classification criteria of social participation were extracted and summarized from these studies. Based on the depth and breadth of social interactions, this review proposed a four-level classification schema. A lower risk of mortality and less visual impairment were associated with participation in level-one, level-three or level-four activities, whereas less depression, less pain and better cognitive function were linked to participation in level-three or level-four activities.

CONCLUSION

Future studies should provide a clear definition, establish classification criteria for participation and properly select activity forms while considering both subjective and objective dimensions.

IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE

The results could provide data for designing targeted social participation interventions to improve specific health outcomes among older adults.

IMPACT

This review could help researchers examine the role of social participation activities in specific health outcomes. Moreover, a proposed classification of social participation activities would benefit researchers and community nurses in discerning the similarities and differences among activities.

REPORTING METHOD

This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Scoping Reviews guideline.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease

BACKGROUND

Causal feature selection is essential for estimating effects from observational data. Identifying confounders is a crucial step in this process. Traditionally, researchers employ content-matter expertise and literature review to identify confounders. Uncontrolled confounding from unidentified confounders threatens validity, conditioning on intermediate variables (mediators) weakens estimates, and conditioning on common effects (colliders) induces bias. Additionally, without special treatment, erroneous conditioning on variables combining roles introduces bias. However, the vast literature is growing exponentially, making it infeasible to assimilate this knowledge. To address these challenges, we introduce a novel knowledge graph (KG) application enabling causal feature selection by combining computable literature-derived knowledge with biomedical ontologies. We present a use case of our approach specifying a causal model for estimating the total causal effect of depression on the risk of developing Alzheimer's disease (AD) from observational data.

METHODS

We extracted computable knowledge from a literature corpus using three machine reading systems and inferred missing knowledge using logical closure operations. Using a KG framework, we mapped the output to target terminologies and combined it with ontology-grounded resources. We translated epidemiological definitions of confounder, collider, and mediator into queries for searching the KG and summarized the roles played by the identified variables. We compared the results with output from a complementary method and published observational studies and examined a selection of confounding and combined role variables in-depth.

RESULTS

Our search identified 128 confounders, including 58 phenotypes, 47 drugs, 35 genes, 23 collider, and 16 mediator phenotypes. However, only 31 of the 58 confounder phenotypes were found to behave exclusively as confounders, while the remaining 27 phenotypes played other roles. Obstructive sleep apnea emerged as a potential novel confounder for depression and AD. Anemia exemplified a variable playing combined roles.

CONCLUSION

Our findings suggest combining machine reading and KG could augment human expertise for causal feature selection. However, the complexity of causal feature selection for depression with AD highlights the need for standardized field-specific databases of causal variables. Further work is needed to optimize KG search and transform the output for human consumption.