Ageing accounts for much of the association between decreasing grip strength and subsequent loneliness: the English Longitudinal Study of Ageing

Aerobic Fitness as an Important Moderator Risk Factor for Loneliness in Physically Trained Older People: An Explanatory Case Study Using Machine Learning

BACKGROUND

Loneliness in older people seems to have emerged as an increasingly prevalent social problem.

OBJECTIVE

To apply a machine learning (ML) algorithm to the task of understanding the influence of sociodemographic variables, physical fitness, physical activity levels (PAL), and sedentary behavior (SB) on the loneliness feelings of physically trained older people.

MATERIALS AND METHODS

The UCLA loneliness scale was used to evaluate loneliness, the Functional Fitness Test Battery was used to evaluate the correlation of sociodemographic variables, physical fitness, PAL, and SB in the loneliness feelings scores of 23 trained older people (19 women and 4 men). For this purpose, a naive Bayes ML algorithm was applied.

RESULTS

After analysis, we inferred that aerobic fitness (AF), hand grip strength (HG), and upper limb strength (ULS) comprised the most relevant variables panel to cause high participant loneliness with 100% accuracy and F-1 score.

CONCLUSIONS

The naive Bayes algorithm with leave-one-out cross-validation (LOOCV) predicted loneliness in trained older with a high precision. In addition, AF was the most potent variable in reducing loneliness risk.

Eight-Year Trajectories of Late-Life Loneliness and Incident Dementia: A Nationally Representative Cohort Study

OBJECTIVE

Some older adults with loneliness might have transient loneliness, followed by full remission, while others might have persistent loneliness. Such different courses might differ in predicting the risk of dementia, but most previous studies assessed short-term loneliness at a single time point, ignoring the long-term changes of loneliness. This study aimed to explore the association between 8-year trajectories of late-life loneliness and incident dementia.

METHODS

Data were from the English Longitudinal Study of Ageing (waves 2-9). This study included 6,722 older adults measured for loneliness greater than or equal to three times from wave 2 (2004-2005) to wave 6 (2012-2013) and free from dementia in wave 6. Loneliness was assessed using the short 1980 version of the University of California, Los Angeles Loneliness Scale. Group-based trajectory modeling was used to explore loneliness trajectories during waves 2-6. Cox proportional hazard models were used to investigate the association of loneliness trajectories with incident dementia during waves 6-9.

RESULTS

Five distinct loneliness trajectories were identified: long-term low, persistently decreasing, persistently increasing, long-term moderate, and long-term high. Compared with long-term low loneliness, the HRs (95% confidence intervals) for persistently decreasing, persistently increasing, long-term moderate, and long-term high loneliness were 1.29 (0.83-2.00), 1.55 (1.04-2.31), 1.56 (1.03-2.38), and 3.35 (1.89-5.91), respectively.

CONCLUSION

The elderly show distinct patterns of loneliness over time, which cannot be captured by a single assessment of loneliness. The elderly with long-term loneliness and persistently increasing loneliness might be the high-risk group for dementia. Further studies are needed to determine whether reducing loneliness can prevent dementia.