Depression and loneliness may predict work inefficiency among professionally active adults

Loneliness in the workplace: a mixed-method systematic review and meta-analysis

BACKGROUND

Loneliness is a risk factor for a range of mental and physical health problems and has gained increasing interest from policy-makers and researchers in recent years. However, little attention has been paid to loneliness at work and its implications for workers and employers.

AIMS

Identify workplace, health and personal factors associated with workplace loneliness.

METHODS

We searched five databases (PubMed, MEDLINE, EMBASE, PsycINFO and EBSCO Business Source Complete) for relevant articles published from 1 January 2000 to 23 February 2023. Quantitative data were synthesized using narrative synthesis and random-effects meta-analysis of correlation coefficients. Qualitative data were synthesized using thematic synthesis. Evidence quality was appraised using the Mixed-Methods Appraisal Tool.

RESULTS

We identified 49 articles meeting the inclusion criteria. Pooled results indicate that workplace loneliness was associated with lower job performance (r = -0.35, 95% CI -0.49, -0.21), reduced job satisfaction (r = -0.34, 95% CI -0.44, -0.24), worse worker-manager relationship (r = -0.31, 95% CI -0.38, -0.24) and elevated burnout (r = 0.39, 95% CI 0.25, 0.51). Qualitative results suggest links between loneliness and inadequate workplace social interactions and mental health problems. As most studies used cross-sectional data and few adjusted for potential confounders, the direction and robustness of the associations remain untested.

CONCLUSIONS

Our results indicate that loneliness is associated with poor occupational functioning and well-being among workers. Results also show that loneliness is associated with modifiable aspects of the work environment, suggesting that the workplace may offer a fruitful avenue for interventions targeting loneliness.

Utilizing variable auto encoder-based TDO optimization algorithm for predicting loneliness from electrocardiogram signals

Several seniors and a substantial part of the general population are living in social isolation. This frequently occurs in vulnerability, isolation, and depression, which then have a poor impact on other health-related factors. A number of health problems, including a higher risk of cardio problems, are brought on by social isolation and loneliness. Electrocardiogram (ECG) usage for mental condition recognition enables accurate determination of a person's internal representation. The electrocardiogram (ECG) signals can be thoroughly analyzed to uncover hidden data that may be helpful for the precise identification of cardiac problems. ECG time-series information typically have great dimensions and complicated componentry. Using relevant information to guide training is among the main achievements of this type of learning. An ECG signal plays a significant part in the individual body's ability to manage behavior. Furthermore, loneliness identification is crucial since it has the worse effect on the circumstances that afflict persons. This study suggested an approach for detecting loneliness from an ECG signal to use a variable auto encoder-based optimization algorithm for ESN technique. The suggested approach consists of three phases for identifying a person's loneliness. Firstly, undecimated discrete wavelet transform is used to preprocess the acquired ECG data. Next, further characteristics are extracted from the precompiled signals using a variable auto encoder. For the precise categorization of loneliness in the ECG signal, a metaheuristic optimized ESN is, therefore, presented. The outcomes of the tests demonstrate that the suggested system with suitable ECG representations produces improved accuracy as well as performance.