Segmentation of Older Adults in the Acceptance of Social Networking Sites Using Machine Learning.
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ABSTRACT:
SUBMITTER: Ramirez-Correa PE
PROVIDER: S-EPMC8385199 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundReminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations.ObjectiveThe aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts.MethodsThe methods in this study comprise (1) collecting and coding of transcripts of older adults' conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies.ResultsClass-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs.ConclusionsThis study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults' everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults' well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health.
| S-EPMC7525396 | biostudies-literature
Project description:This study aims to examine the capacity of the Theory of Planned Behaviour (TPB) to explain the intention to use social networking sites by older people in two time periods, before and after confinement due to the COVID-19 epidemic, as well as the evolution of effects (paths) over time of TPB's determinants. Based on interviews from samples of 384 and 383 elderly Chilean adults collected before and after confinement, the evolution of the effects (paths) was analysed using the TPB model applying the PLS-SEM technique. The intention to use social networks and its association with three factors were evaluated: attitude toward the behaviour, subjective norms, and perceived control over the behaviour. The model explains the intention to use social networks by 27% before confinement, increasing its magnitude to 50% after confinement. After the period of confinement, their attitudes become more significant, their perceptions of control become less important, and social pressures remain permanent in predicting the behaviour. In conclusion, better access and greater use of social networks by older people during the lockdown period increased the predictive strength of the attitude towards these technologies.
| S-EPMC9602972 | biostudies-literature
Project description:Older adults can take advantage of social networking sites (SNS). However, SNS are not without the access gap among elders. Assuming that the data are homogenous within the same population may not be precise in social science research. What is known about the heterogeneous nature of older people? Considering this issue and the lack of research to help reflect the heterogeneity of elderly users of technologies, this study aims to identify segments in the use of SNS by the elderly. Data were collected from older Chilean adults. Cluster analysis suggested different profiles of adult users regarding the Technology Readiness Index. We used a hybrid multigroup partial least squares-structural equation model, including the Pathmox algorithm, to identify segments in the structural model. Based on the technology readiness profiles and the generation, we identified three segments with different determinant effects to explain the intention to use SNS: independent elder, technological-apathetic elder, and technological-eager elder. The contributions from this study are triple. First, this study helps to better understand how the elderly adopt information technology. Second, this study complements the existing corpus of research on using the technology readiness index in the elderly population. Third, we used an innovative method to segment users in the acceptance technology model.
| S-EPMC10112803 | biostudies-literature
Project description:Abstract Background Older people are at greater risk of infection and mortality from COVID‐19 and 52.3% of the deceased in Italy are Lombardy’s residents. Moreover, lockdown measurements may disproportionately affect elderly, since they are less familiar with communication technology used to overcome the lack of in person social contacts. The present study aimed at exploring how older adults residing in Lombardy are experiencing the lockdown period, in relation to loneliness, social isolation and use of communication technology. Method In the first two weeks of May (2020) a telephone interview was conducted with people aged between 81 and 85, residing in the community, who participated in a study aimed at evaluating the impact of Social Networking Sites (SNS) use and older people’s social relationship (clinicaltrials.gov: NCT04242628). We collected information on SNS use, self‐perceived loneliness (UCLA scale 3‐item), social engagement with family and friends (Lubben Social Network Scale 6‐item). Among the eligible individuals (N=144), 1 deceased, 4 refused and 9 were unreachable, resulting in a sample of 130 interviewed. For the aim of the present investigation, participants were stratified as trained (N=60) and untrained (N=70) for SNS use, based on their attendance to group courses held last year as part of the main experimental study. Result Trained and untrained participants were comparable for age, sex, education, percentage of living alone, global cognition, depressive symptoms and comorbidity (Table 1). Participants trained for SNS use reported significantly higher usage of Facebook and WhatsApp and reduced feeling of being left out during the COVID‐19 lockdown (Table 2). Moreover, a trend toward significance was found for an inverse relationship between WhatsApp usage frequency and feeling of lack of companionship (rs = ‐0.257, p = 0.051; Table 3). Conclusion Though SNS are unable to completely replace in person contact, these preliminary results support the utility to train older adults for SNS use in order to improve their social inclusion, even in extreme conditions of self‐isolation and vulnerability due to COVID‐19 pandemic.
| S-EPMC7883182 | biostudies-literature
Project description:Social networking sites (SNS) are used for social and professional interaction with people. SNS popularity has encouraged researchers to analyze the relationship of activities performed on SNS with user behavior. In doing so, the term "user behavior" is rather used ambiguously with different interpretations, which makes it difficult to identify studies on user behavior in relation to SNS. This phenomenon has encouraged this thorough research on the characteristics of user behavior being discussed in the literature. Therefore, in this study, we aim to identify, analyze, and classify the characteristics associated with user behavior to answer the research questions designed to conduct this research. A mapping study (also called scoping study), which is a type of systematic literature review, is employed to identify potential studies from digital databases through a developed protocol. Thematic analysis is carried out for the classification of user behavior. We identified 116 primary studies for full analysis. This study found seven characteristics associated with behavior that have direct influence on SNS use and nine factors that have an indirect effect. All studies were conducted largely under seven areas that set the context of these studies. Findings show that the research on SNS is still in its early stage. The range of topics covered in the analyzed studies is quite expansive, although the depth in terms of number of studies under each topic is quite limited. This study reports that activities performed on SNS are either associated with user behavior or reflect personality characteristics. The findings of this study could be used by practitioners to evaluate their SNS platforms and develop more user-centered applications. These studies can also help organizations to understand better the needs of their employees.
| S-EPMC5289435 | biostudies-literature
Project description:BackgroundThe longitudinal rates of cognitive decline among aging populations are heterogeneous. Few studies have investigated the possibility of implementing prognostic models to predict cognitive changes with the combination of categorical and continuous data from multiple domains.ObjectiveImplement a multivariate robust model to predict longitudinal cognitive changes over 12 years among older adults and to identify the most significant predictors of cognitive changes using machine learning techniques.MethodIn total, data of 2733 participants aged 50-85 years from the English Longitudinal Study of Ageing are included. Two categories of cognitive changes were determined including minor cognitive decliners (2361 participants, 86.4%) and major cognitive decliners (372 participants, 13.6%) over 12 years from wave 2 (2004-2005) to wave 8 (2016-2017). Machine learning methods were used to implement the predictive models and to identify the predictors of cognitive decline using 43 baseline features from seven domains including sociodemographic, social engagement, health, physical functioning, psychological, health-related behaviors, and baseline cognitive tests.ResultsThe model predicted future major cognitive decliners from those with the minor cognitive decline with a relatively high performance. The overall AUC, sensitivity, and specificity of prediction were 72.84%, 78.23%, and 67.41%, respectively. Furthermore, the top 7 ranked features with an important role in predicting major vs minor cognitive decliners included age, employment status, socioeconomic status, self-rated memory changes, immediate word recall, the feeling of loneliness, and vigorous physical activity. In contrast, the five least important baseline features consisted of smoking, instrumental activities of daily living, eye disease, life satisfaction, and cardiovascular disease.ConclusionThe present study indicated the possibility of identifying individuals at high risk of future major cognitive decline as well as potential risk/protective factors of cognitive decline among older adults. The findings could assist in improving the effective interventions to delay cognitive decline among aging populations.
| S-EPMC9983884 | biostudies-literature
Project description:Older adults are less familiar with communication technology, which became essential to maintain social contacts during the COVID-19 lockdown. The present study aimed at exploring how older adults, previously trained for Social Networking Sites (SNSs) use, experienced the lockdown period. In the first two weeks of May 2020, telephone surveys were conducted with individuals aged 81-85 years and resident in Abbiategrasso (Milan), who previously participated in a study aimed at evaluating the impact of SNSs use on loneliness in old age (ClinicalTrials.gov, NCT04242628). We collected information on SNSs use, self-perceived loneliness, and social engagement with family and friends. Interviewed participants were stratified as trained (N = 60) and untrained (N = 70) for SNSs use, based on their attendance to group courses held the previous year as part of the main experimental study. The groups were comparable for sociodemographics and clinical features. Participants trained for SNSs use reported significantly higher usage of SNSs and reduced feeling of being left out. Compared to pre-lockdown levels, individuals trained for SNSs use showed a lighter reduction in social contacts. These findings support the utility of training older adults for SNSs use in order to improve their social inclusion, even in extreme conditions of self-isolation and perceived vulnerability.
| S-EPMC7662584 | biostudies-literature
Project description:Golf swing segmentation with inertial measurement units (IMUs) is an essential process for swing analysis using wearables. However, no attempt has been made to apply machine learning models to estimate and divide golf swing phases. In this study, we proposed and verified two methods using machine learning models to segment the full golf swing into five major phases, including before and after the swing, from every single IMU attached to a body part. Proposed bidirectional long short-term memory-based and convolutional neural network-based methods rely on characteristics that automatically learn time-series features, including sequential body motion during a golf swing. Nine professional and eleven skilled male golfers participated in the experiment to collect swing data for training and verifying the methods. We verified the proposed methods using leave-one-out cross-validation. The results revealed average segmentation errors of 5-92 ms from each IMU attached to the head, wrist, and waist, accurate compared to the heuristic method in this study. In addition, both proposed methods could segment all the swing phases using only the acceleration data, bringing advantage in terms of power consumption. This implies that swing-segmentation methods using machine learning could be applied to various motion-analysis environments by dividing motion phases with less restriction on IMU placement.
| S-EPMC7472298 | biostudies-literature
Project description: Not available
| S-EPMC11339331 | biostudies-literature
Project description:Speech perception in noisy environments depends on complex interactions between sensory and cognitive systems. In older adults, such interactions may be affected, especially in those individuals who have more severe age-related hearing loss. Using a data-driven approach, we assessed the temporal (when in time) and spatial (where in the brain) characteristics of cortical speech-evoked responses that distinguish older adults with or without mild hearing loss. We performed source analyses to estimate cortical surface signals from the EEG recordings during a phoneme discrimination task conducted under clear and noise-degraded conditions. We computed source-level ERPs (i.e., mean activation within each ROI) from each of the 68 ROIs of the Desikan-Killiany (DK) atlas, averaged over a randomly chosen 100 trials without replacement to form feature vectors. We adopted a multivariate feature selection method called stability selection and control to choose features that are consistent over a range of model parameters. We use parameter optimized support vector machine (SVM) as a classifiers to investigate the time course and brain regions that segregate groups and speech clarity. For clear speech perception, whole-brain data revealed a classification accuracy of 81.50% [area under the curve (AUC) 80.73%; F1-score 82.00%], distinguishing groups within ?60 ms after speech onset (i.e., as early as the P1 wave). We observed lower accuracy of 78.12% [AUC 77.64%; F1-score 78.00%] and delayed classification performance when speech was embedded in noise, with group segregation at 80 ms. Separate analysis using left (LH) and right hemisphere (RH) regions showed that LH speech activity was better at distinguishing hearing groups than activity measured in the RH. Moreover, stability selection analysis identified 12 brain regions (among 1428 total spatiotemporal features from 68 regions) where source activity segregated groups with >80% accuracy (clear speech); whereas 16 regions were critical for noise-degraded speech to achieve a comparable level of group segregation (78.7% accuracy). Our results identify critical time-courses and brain regions that distinguish mild hearing loss from normal hearing in older adults and confirm a larger number of active areas, particularly in RH, when processing noise-degraded speech information.
| S-EPMC7378401 | biostudies-literature