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Record W4417321519 · doi:10.1186/s12877-025-06674-2

The impact of loneliness on balance learning ability in aging patients with Parkinson’s disease

2025· article· en· W4417321519 on OpenAlexaff
Seyede Zohreh Jazaeri, Mohammad Taghi Joghataei, Akram Jamali, Ghorban Taghizadeh

Bibliographic record

VenueBMC Geriatrics · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Ottawa
FundersIran University of Medical Sciences
KeywordsLonelinessFeelingBalance (ability)Psychological interventionRehabilitationDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: Loneliness is highly prevalent among patients with Parkinson's disease (PwPD) and, through complex body-brain interactions, increases their vulnerability to health issues, including balance impairments. Despite extensive evidence linking loneliness to cognitive-motor dysfunctions, the direct impact of loneliness on motor learning, particularly dynamic balance learning, has not been comprehensively examined. As the first study in this field, our research investigates the effect of loneliness on the ability to learn dynamic balance in PwPD, addressing a critical research gap in this domain. METHODS: This study was conducted over six months with the participation of sixty volunteers divided into three distinct groups: 20 PD patients who reported loneliness (loneliness-positive group), 20 PD patients who did not report loneliness (loneliness-negative group), and 20 healthy individuals as the control group. Participants were matched for sex, age, and body mass index (BMI) and were recruited from hospitals and specialized movement disorder clinics. We utilized standardized instruments, including the Mini Balance Evaluation Systems Test (MiniBESTest) to assess balance performance, the de Jong-Gierveld Loneliness Scale (DjG)/ the Three-Item Loneliness Scale (TILS/ the Social and Emotional Loneliness Scale for Parkinson's Disease (SELSA-PD) to assess loneliness levels, the Rosenberg Self-Esteem Inventory (RSI) to assess self-steem, and the Multidimensional Scale of Perceived Social Support (MSPSS) to assess social support in one day before performing balance learning task. Additionally, balance learning was evaluated using a stabilometer, and parameters such as learning rate, learning curve slope, and short- and long-term memory were analyzed. RESULTS: The results indicated that PwPD in the loneliness-positive group exhibited poorer balance performance (MiniBESTest) and higher scores on various loneliness scales, including DjG, TILS, and SELSA-PD. Additionally, this group did not demonstrate balance learning potential (learning rate and slope) compared to the other groups. In contrast, PD patients in the loneliness-negative group showed improvement in the early stages of balance learning (Block1 vs. Block3: p = 0.001; Block1 vs. Block4: p = 0.001; Block1 vs. Block5: p < 0.001; Block2 vs. Block5: p = 0.014).), while the control group exhibited continuous improvement (p = 0.00). Both the loneliness-negative and control groups retained their balance skills in both short-term and long-term assessments (p > 0.05). CONCLUSION: This study is the first to directly examine the impact of loneliness on dynamic balance learning in PwPD. The findings revealed that loneliness can act as a significant inhibitory factor in balance rehabilitation for these patients. The results underscore the importance of designing targeted interventions to reduce feelings of loneliness to enhance balanced learning. Furthermore, the study paves the way for future research to investigate the underlying neural mechanisms and explore the effects of social and psychological interventions on improving motor learning in these patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.343
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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