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Sanmartin Berglund, Johan, ProfessorORCID iD iconorcid.org/0000-0003-4312-2246
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Publications (10 of 150) Show all publications
Javeed, A., Anderberg, P., Ghazi, A. N., Noor, A., Elmståhl, S. & Sanmartin Berglund, J. (2024). Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia. Frontiers in Bioengineering and Biotechnology, 11, Article ID 1336255.
Open this publication in new window or tab >>Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia
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2024 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1336255Article in journal (Refereed) Published
Abstract [en]

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia.

Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency.

Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535.

Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2024
Keywords
dementia, voting classifier, F-score, machine learning, feature selection
National Category
Computer Sciences Geriatrics
Research subject
Software Engineering; Computer Science; Applied Health Technology
Identifiers
urn:nbn:se:bth-25877 (URN)10.3389/fbioe.2023.1336255 (DOI)2-s2.0-85182656352 (Scopus ID)
Projects
National E-Infrastructure for Aging Research (NEAR)
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-02-02Bibliographically approved
Nyholm, J., Ghazi, A. N., Ghazi, S. N. & Sanmartin Berglund, J. (2024). Prediction of dementia based on older adults’ sleep disturbances using machine learning. Computers in Biology and Medicine, 171, Article ID 108126.
Open this publication in new window or tab >>Prediction of dementia based on older adults’ sleep disturbances using machine learning
2024 (English)In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 171, article id 108126Article in journal (Refereed) Published
Abstract [en]

Background: The most common degenerative condition in older adults is dementia, which can be predicted using a number of indicators and whose progression can be slowed down. One of the indicators of an increased risk of dementia is sleep disturbances. This study aims to examine if machine learning can predict dementia and which sleep disturbance factors impact dementia.

Methods: This study uses five machine learning algorithms (gradient boosting, logistic regression, gaussian naive Bayes, random forest and support vector machine) and data on the older population (60+) in Sweden from the Swedish National Study on Ageing and Care — Blekinge (). Each algorithm uses 10-fold stratified cross-validation to obtain the results, which consist of the Brier score for checking accuracy and the feature importance for examining the factors which impact dementia. The algorithms use 16 features which are on personal and sleep disturbance factors.

Results: Logistic regression found an association between dementia and sleep disturbances. However, it is slight for the features in the study. Gradient boosting was the most accurate algorithm with 92.9% accuracy, 0.926 f1-score, 0.974 ROC AUC and 0.056 Brier score. The significant factors were different in each machine learning algorithm. If the person sleeps more than two hours during the day, their sex, education level, age, waking up during the night and if the person snores are the variables that most consistently have the highest feature importance in all algorithms.

Conclusion: There is an association between sleep disturbances and dementia, which machine learning algorithms can predict. Furthermore, the risk factors for dementia are different across the algorithms, but sleep disturbances can predict dementia.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Dementia; Sleep; Risk factors; Machine learning
National Category
Geriatrics Computer Sciences
Research subject
Applied Health Technology; Software Engineering; Computer Science
Identifiers
urn:nbn:se:bth-25960 (URN)10.1016/j.compbiomed.2024.108126 (DOI)
Available from: 2024-02-10 Created: 2024-02-10 Last updated: 2024-02-14Bibliographically approved
Flyborg, J., Renvert, S., Anderberg, P., Larsson, T. & Sanmartin Berglund, J. (2024). Results of objective brushing data recorded from a powered toothbrush used by elderly individuals with mild cognitive impairment related to values for oral health. Clinical Oral Investigations, 28(1), Article ID 8.
Open this publication in new window or tab >>Results of objective brushing data recorded from a powered toothbrush used by elderly individuals with mild cognitive impairment related to values for oral health
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2024 (English)In: Clinical Oral Investigations, ISSN 1432-6981, E-ISSN 1436-3771, Vol. 28, no 1, article id 8Article in journal (Refereed) Published
Abstract [en]

Objectives: The study aimed to investigate how the objective use of a powered toothbrush in frequency and duration affects plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm in elderly individuals with MCI. A second aim was to compare the objective results with the participants’ self-estimated brush use. Materials and methods: Objective brush usage data was extracted from the participants’ powered toothbrushes and related to the oral health variables plaque index, bleeding on probing, and periodontal pocket depth ≥ 4 mm. Furthermore, the objective usage data was compared with the participants’ self-reported brush usage reported in a questionnaire at baseline and 6- and 12-month examination. Results: Out of a screened sample of 213 individuals, 170 fulfilled the 12-month visit. The principal findings are that despite the objective values registered for frequency and duration being lower than the recommended and less than the instructed, using powered toothbrushes after instruction and information led to improved values for PI, BOP, and PPD ≥ 4 mm in the group of elderly with MIC. Conclusions: Despite lower brush frequency and duration than the generally recommended, using a powered toothbrush improved oral health. The objective brush data recorded from the powered toothbrush correlates poorly with the self-estimated brush use. Clinical relevance: Using objective brush data can become one of the factors in the collaboration to preserve and improve oral health in older people with mild cognitive impairment. Trial registration: ClinicalTrials.gov Identifier: NCT05941611, retrospectively registered 11/07/2023. © 2023, The Author(s).

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2024
Keywords
Elderly individuals, Mild cognitive impairment, Oral health, Powered toothbrush
National Category
Dentistry
Identifiers
urn:nbn:se:bth-25835 (URN)10.1007/s00784-023-05407-2 (DOI)2-s2.0-85180240432 (Scopus ID)
Available from: 2023-12-30 Created: 2023-12-30 Last updated: 2023-12-30Bibliographically approved
Svärd, A., Kastbom, A., Roos Ljungberg, K., Potempa, B., Potempa, J., Persson, G. R., . . . Söderlin, M. K. (2023). Antibodies against Porphyromonas gingivalis in serum and saliva and their association with rheumatoid arthritis and periodontitis: Data from two rheumatoid arthritis cohorts in Sweden. Frontiers in Immunology, 14, Article ID 1183194.
Open this publication in new window or tab >>Antibodies against Porphyromonas gingivalis in serum and saliva and their association with rheumatoid arthritis and periodontitis: Data from two rheumatoid arthritis cohorts in Sweden
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2023 (English)In: Frontiers in Immunology, E-ISSN 1664-3224, Vol. 14, article id 1183194Article in journal (Refereed) Published
Abstract [en]

Background: Periodontitis and oral pathogenic bacteria can contribute to the development of rheumatoid arthritis (RA). A connection between serum antibodies to Porphyromonas gingivalis (P. gingivalis) and RA has been established, but data on saliva antibodies to P. gingivalis in RA are lacking. We evaluated antibodies to P. gingivalis in serum and saliva in two Swedish RA studies as well as their association with RA, periodontitis, antibodies to citrullinated proteins (ACPA), and RA disease activity. Methods: The SARA (secretory antibodies in RA) study includes 196 patients with RA and 101 healthy controls. The Karlskrona RA study includes 132 patients with RA ≥ 61 years of age, who underwent dental examination. Serum Immunoglobulin G (IgG) and Immunoglobulin A (IgA) antibodies and saliva IgA antibodies to the P. gingivalis–specific Arg-specific gingipain B (RgpB) were measured in patients with RA and controls. Results: The level of saliva IgA anti-RgpB antibodies was significantly higher among patients with RA than among healthy controls in multivariate analysis adjusted for age, gender, smoking, and IgG ACPA (p = 0.022). Saliva IgA anti-RgpB antibodies were associated with RA disease activity in multivariate analysis (p = 0.036). Anti-RgpB antibodies were not associated with periodontitis or serum IgG ACPA. Conclusion: Patients with RA had higher levels of saliva IgA anti-RgpB antibodies than healthy controls. Saliva IgA anti-RgpB antibodies may be associated with RA disease activity but were not associated with periodontitis or serum IgG ACPA. Our results indicate a local production of IgA anti-RgpB in the salivary glands that is not accompanied by systemic antibody production. Copyright © 2023 Svärd, Kastbom, Ljungberg, Potempa, Potempa, Persson, Renvert, Berglund and Söderlin.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
anti-citrullinated antibodies (ACPAs), gingipain and periodontitis, periodontitis, porphyromonas gingivalis, rheumatoid arthritis, saliva
National Category
Rheumatology and Autoimmunity Dentistry
Identifiers
urn:nbn:se:bth-25062 (URN)10.3389/fimmu.2023.1183194 (DOI)001006380100001 ()2-s2.0-85162055082 (Scopus ID)
Funder
Region BlekingeThe Crafoord FoundationSwedish Rheumatism Association
Available from: 2023-06-30 Created: 2023-06-30 Last updated: 2024-01-17Bibliographically approved
Idrisoglu, A., Moraes, A. L., Anderberg, P. & Sanmartin Berglund, J. (2023). Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review. Journal of Medical Internet Research, 25, Article ID e46105.
Open this publication in new window or tab >>Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review
2023 (English)In: Journal of Medical Internet Research, E-ISSN 1438-8871, Vol. 25, article id e46105Article, review/survey (Refereed) Published
Abstract [en]

Background: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research. © 2023 Journal of Medical Internet Research. All rights reserved.

Place, publisher, year, edition, pages
JMIR Publications, 2023
Keywords
diagnosis, digital biomarkers, machine learning, monitoring, voice features, voice-affecting disorder, Humans, Monitoring, Physiologic, human, physiologic monitoring
National Category
Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:bth-25253 (URN)10.2196/46105 (DOI)001048954300007 ()2-s2.0-85165520794 (Scopus ID)
Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2024-02-20Bibliographically approved
Javeed, A., Saleem, M. A., Moraes, A. L., Ali, L., Sanmartin Berglund, J. & Anderberg, P. (2023). Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning. Applied Sciences, 13(8), Article ID 5188.
Open this publication in new window or tab >>Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 8, article id 5188Article in journal (Refereed) Published
Abstract [en]

Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a ?(2) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model (?(2)_RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model ?(2)_RF achieved the highest accuracy of 94.59%. The proposed model ?(2)_RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model ?(2)_RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module (?(2)).

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
heart morality, feature ranking, random forest, imbalance classes
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:bth-24631 (URN)10.3390/app13085188 (DOI)000980955700001 ()
Available from: 2023-05-26 Created: 2023-05-26 Last updated: 2023-05-26Bibliographically approved
Kvist, O., Dorniok, T., Sanmartin Berglund, J., Nilsson, O., Flodmark, C.-E. & Diaz, S. (2023). DTI assessment of the maturing growth plate of the knee in adolescents and young adults. European Journal of Radiology, 162, Article ID 110759.
Open this publication in new window or tab >>DTI assessment of the maturing growth plate of the knee in adolescents and young adults
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2023 (English)In: European Journal of Radiology, ISSN 0720-048X, E-ISSN 1872-7727, Vol. 162, article id 110759Article in journal (Refereed) Published
Abstract [en]

Purpose: To assess the growth plates of the knee in a healthy population of young adults and adolescents using DTI, and to correlate the findings with chronological age and skeletal maturation.Methods: A prospective, cross-sectional study to assess the tibial and femoral growth plates with DTI in 155 healthy volunteers aged between 14.0 and 21 years old. Echo-planar DTI with 15 directions and b value of 0 and 600 s/mm2 was performed on a 3 T whole-body scanner.Results: A relationship was observed between chronological age and most DTI metrics (fractional anisotropy, mean diffusivity, and radial diffusivity), tract length and volume. (No significant relationship could be seen for axonal diffusivity and tract length.) Subdivision according to skeletal maturation showed the greatest tract lengths and volumes seen in stage 4b and not 4a. The intra-observer agreement was significant (P = 0.01) for all the measured variables, but agreement varied (femur 0.53 - 0.98; tibia 0.58 - 0.98). Spearman's correlation showed a significant correlation for age (P = 0.05; P = 0.01) as well as for the fractional anisotropy value within all variables in both femur and tibia. Tract number and volume had a similar correlation with most variables, especially the DTI metrics, and would seem to be interchangeable.Conclusion: The current study indicates that DTI metrics could be a tool to assess the skeletal maturation process of the growth plate and its activity. Tractography seems promising to assess the activity of the growth plate in a younger population but must be used with caution in the more mature growth plate.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Magnetic resonance imaging, Diffusion tensor imaging, Growth plate, Maturation process, Puberty
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:bth-24443 (URN)10.1016/j.ejrad.2023.110759 (DOI)000954219700001 ()36931119 (PubMedID)
Funder
Swedish National Board of Health and Welfare
Available from: 2023-04-14 Created: 2023-04-14 Last updated: 2023-04-14Bibliographically approved
Javeed, A., Moraes, A. L., Sanmartin Berglund, J., Idrisoglu, A., Ali, L., Rauf, H. T. & Anderberg, P. (2023). Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification. Biomedicines, 11(2), Article ID 439.
Open this publication in new window or tab >>Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
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2023 (English)In: Biomedicines, E-ISSN 2227-9059, Vol. 11, no 2, article id 439Article in journal (Refereed) Published
Abstract [en]

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
dementia, feature fusion, machine learning, imbalance classes
National Category
Computer Sciences Geriatrics
Research subject
Applied Health Technology; Computer Science
Identifiers
urn:nbn:se:bth-24292 (URN)10.3390/biomedicines11020439 (DOI)000938259800001 ()2-s2.0-85148904251 (Scopus ID)
Projects
National E-Infrastructure for Aging Research (NEAR)
Available from: 2023-02-16 Created: 2023-02-16 Last updated: 2023-03-16Bibliographically approved
Berner, J., Moraes, A. L., Palm, B., Sanmartin Berglund, J. & Anderberg, P. (2023). Five-factor model, technology enthusiasm and technology anxiety. Digital Health, 9
Open this publication in new window or tab >>Five-factor model, technology enthusiasm and technology anxiety
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2023 (English)In: Digital Health, E-ISSN 2055-2076, Vol. 9Article in journal (Refereed) Published
Abstract [en]

Older adults need to participate in the digital society, as societal and personal changes and what they do with the remaining time that they have in their older years has an undeniable effect on motivation, cognition and emotion. Changes in personality traits were investigated in older adults over the period 2019–2021. Technology enthusiasm and technology anxiety are attitudes that affect the relationship to the technology used. The changes in the score of technology enthusiasm and technology anxiety were the dependent variables. They were investigated with personality traits, age, gender, education, whether someone lives alone, cognitive function, digital social participation (DSP) and health literacy as predictors of the outcome. The Edwards-Nunnally index and logistic regression were used. The results indicated that DSP, lower age, lower neuroticism and higher education were indicative of less technology anxiety. High DSP and high extraversion are indicative of technology enthusiasm. DSP and attitude towards technology seem to be key in getting older adults to stay active online. © The Author(s) 2023.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
digital social participation, five-factor model, older adults, personality, Technology anxiety, technology enthusiasm
National Category
Gerontology, specialising in Medical and Health Sciences
Identifiers
urn:nbn:se:bth-25426 (URN)10.1177/20552076231203602 (DOI)001069602300001 ()2-s2.0-85171753514 (Scopus ID)
Available from: 2023-10-02 Created: 2023-10-02 Last updated: 2023-10-30Bibliographically approved
Axén, A., Taube, E., Sanmartin Berglund, J. & Skär, L. (2023). Loneliness in Relation to Social Factors and Self-Reported Health Among Older Adults: A Cross-Sectional Study. Journal of Primary Care & Community Health, 14
Open this publication in new window or tab >>Loneliness in Relation to Social Factors and Self-Reported Health Among Older Adults: A Cross-Sectional Study
2023 (English)In: Journal of Primary Care & Community Health, ISSN 2150-1319, E-ISSN 2150-1327, Vol. 14Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Loneliness is described as a public health problem and can be both a consequence of aging and a cause of ill health. Lonely older adults tend to have difficulties making new social connections, essential in reducing loneliness. Loneliness often varies over time, but established loneliness tends to persist. Maintaining good health is fundamental throughout the life course. Social connections change with aging, which can contribute to loneliness. AIM: This study aimed to investigate loneliness in relation to social factors and self-reported health among older adults. METHOD: A cross-sectional research design was used based on data from the Swedish National Study on Aging and Care, Blekinge (SNAC-B), from February 2019 to April 2021. Statistical analysis consisted of descriptive and inferential analysis. RESULTS: Of n = 394 participants, 31.7% (n = 125) stated loneliness. Close emotional connections were necessary for less loneliness. Loneliness was more common among those who did not live with their spouse or partner and met more rarely. Furthermore, seeing grandchildren and neighbors less often increased loneliness, and a more extensive social network decreased loneliness. CONCLUSION: This study underlined the importance of social connections and having someone to share a close, emotional connection with to reduce loneliness.

Place, publisher, year, edition, pages
Sage Publications, 2023
Keywords
community health, health promotion, lifestyle change, prevention, primary care, aged, aging, article, cross-sectional study, female, grandchild, human, human experiment, lifespan, lifestyle modification, loneliness, major clinical study, male, primary medical care, public health, social aspect, social network, spouse
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
urn:nbn:se:bth-25411 (URN)10.1177/21501319231198644 (DOI)001064610600001 ()2-s2.0-85170625592 (Scopus ID)
Projects
SNAC
Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-10-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4312-2246

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