Building Digital Literacy in Older Adults of Low Socioeconomic Status in Singapore (Project Wire Up): Nonrandomized Controlled Trial.

BACKGROUND: In a rapidly digitalizing world, the inability of older adults to leverage digital technology has been associated with weaker social connections and poorer health outcomes. Despite the widespread digital adoption in Singapore, older adults, especially those of lower socioeconomic status (SES), still face difficulties in adopting information and communications technology and are typically digitally excluded. OBJECTIVE: We aimed to examine the impact of the volunteer-led, one-on-one, and home-based digital literacy program on digital literacy and health-related outcomes such as self-reported loneliness, social connectedness, quality of life, and well-being for older adults of low SES. METHODS: A nonrandomized controlled study was carried out in Singapore between July 2020 and November 2021 involving 138 digitally excluded community-dwelling older adults aged ≥55 years and of lower SES. Older adults awaiting participation in the program served as controls. Older adults under the intervention were equipped with a smartphone and cellular data, underwent fortnightly to monthly digital literacy training with volunteers to learn digital skills, and digitally connected to their existing social networks. Primary outcome was the improvement in self-reported digital literacy. Secondary outcomes included improvements in University of California, Los Angeles 3-item loneliness scale, Lubben Social Network Scale-6, EQ-5D-3L and EQ visual analogue scale scores, and Personal Wellbeing Score. RESULTS: There were significant improvements in digital literacy scores in the intervention group as compared to controls (mean difference 2.28, 95% CI 1.37-3.20; P<.001). Through multiple linear regression analyses, this difference in digital literacy scores remained independently associated with group membership after adjusting for differences in baseline scores, age, gender, education, living arrangement, housing type, and baseline social connectivity and loneliness status. There was no statistically significant difference in University of California, Los Angeles 3-item loneliness scale, Lubben Social Network Scale-6, Personal Wellbeing Score, or EQ-5D Utility and visual analogue scale score. CONCLUSIONS: This study adds to the growing research on digital inclusion by showing that a volunteer-led, one-on-one, and home-based digital literacy program contributed to increase digital literacy in older adults of low SES. Future studies should look into developing more older adult-friendly digital spaces and technology design to encourage continued digital adoption in older adults and, eventually, impact health-related outcomes.

Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load.

BACKGROUND: Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist. PURPOSE: To evaluate 1) the utility of a fully automated volume-based morphometry (Vol-BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol-BM model construct in classifying the PIGD subtype. STUDY TYPE: Prospective. SUBJECTS: In all, 23 PIGD, 21 PD, and 20 age-matched healthy controls (HC) underwent MRI brain scans and clinical assessments. FIELD STRENGTH/SEQUENCE: 3.0T, sagittal 3D-magnetization-prepared rapid gradient echo (MPRAGE), and fluid-attenuated inversion recovery imaging (FLAIR) sequences. ASSESSMENT: Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol-BM algorithm (MorphoBox) and reviewed by two authors independently. STATISTICAL TESTING: Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis. RESULTS: Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84). DATA CONCLUSION: Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol-BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:748-756.

Smartphone ownership, digital literacy, and the mediating role of social connectedness and loneliness in improving the wellbeing of community-dwelling older adults of low socio-economic status in Singapore.

INTRODUCTION: During the COVID-19 pandemic, safe-distancing measures resulted in many community-dwelling older adults being socially isolated and lonely, with its attending negative impact on wellbeing and quality of life. While digital technology may have mitigated this, older adults of low socioeconomic status (SES) are more likely to be digitally excluded and hence susceptible to the adverse effects of social isolation and loneliness. This study aims to understand the factors that affect digital literacy, smartphone ownership, and willingness to participate in a digital literacy program (DLP), and to test the hypothesized relations between digital literacy, social connectedness, loneliness, wellbeing, and quality of life amongst community dwelling older adults of low SES. MATERIALS AND METHODS: A questionnaire assessing digital literacy, social connectedness, wellbeing and quality of life was administered. Socio-demographic variables, pre-existing internet-enabled, and willingness to participate in a home-based DLP was also collected. Logistic regression was used to identify demographic factors associated with digital literacy, smartphone ownership, and willingness to enroll in a DLP. Serial mediation analysis was also performed using a structural equation model framework. RESULTS: A total of 302 participants were recruited. Female gender, older age, lower education levels were associated with lower digital literacy. Those who owned a smartphone tended to be younger and better educated. Older adults who were better educated, of Chinese descent (the ethnic majority in Singapore), and who had lower digital literacy, were most willing to enroll in the digital literacy education program. Social-use digital literacy had a positive indirect effect on well-being ([Formula: see text]) and Quality of life ([Formula: see text]), mediated by social connectedness and loneliness. In contrast, instrumental-use digital literacy had a negative indirect effect on well-being ([Formula: see text]) and Quality of life ([Formula: see text]), mediated by social connectedness and loneliness. DISCUSSION: The results suggest there are demographic barriers to participation in DLPs and highlight the benefit of focusing on enhancing social-use digital literacy. Further study is needed to evaluate how well specific interventions to improve social-use digital literacy help to reduce social isolation and loneliness, and ultimately improve wellbeing and quality of life.

Clinical correlates of white matter lesions in Parkinson's disease using automated multi-modal segmentation measures.

BACKGROUND: Age-related white matter lesions (WML) are common, impact neuronal connectivity, and affect motor function and cognition. In addition to pathological nigrostriatal losses, WML are also common co-morbidities in Parkinson's disease (PD) that affect postural stability and gait. Automated brain volume measures are increasingly incorporated into the clinical reporting workflow to facilitate precision in medicine. Recently, multi-modal segmentation algorithms have been developed to overcome challenges with WML quantification based on single-modality input. OBJECTIVE: We evaluated WML volumes and their distribution in a case-control cohort of PD patients to predict the domain-specific clinical severity using a fully automated multi-modal segmentation algorithm. METHODS: Fifty-five subjects comprising of twenty PD patients and thirty-five age- and gender-matched control subjects underwent standardized motor/gait and cognitive assessments and brain MRI. Spatially differentiated WML obtained using automated segmentation algorithms on multi-modal MPRAGE and FLAIR images were used to predict domain-specific clinical severity. Preliminary statistical analysis focused on describing the relationship between WML and clinical scores, and the distribution of WML by brain regions. Subsequent stepwise regressions were performed to predict each clinical score using WML volumes in different brain regions, while controlling for age. RESULTS: WML volume strongly correlates with both motor and cognitive dysfunctions in PD patients (p < 0.05), with differential impact in the frontal lobe and periventricular regions on cognitive domains (p < 0.01) and severity of motor deficits (p < 0.01), respectively. CONCLUSION: Automated multi-modal segmentation algorithms may facilitate precision medicine through regional WML load quantification, which show potential as imaging biomarkers for predicting domain-specific disease severity in PD.

Factors influencing protective behaviours during haze episodes in Singapore: A population-based study.

INTRODUCTION: Haze is a recurrent problem in Southeast Asia. Exposure to haze is linked to ophthalmic, respiratory and cardiovascular diseases, and mortality. In this study, we investigated the role of demographic factors, knowledge and perceived risk in influencing protective behaviours during the 2013 haze in Singapore. METHODS: We evaluated 696 adults in a cross-sectional study. Participants were sampled via a 2-stage simple random sampling without replacement from a large residential district in Singapore in 2015. The questionnaire measured the participant's knowledge, perceived risk and behaviours during the Southeast Asian haze crisis in 2013. Reliability and validity of the questionnaire were assessed using comparative fit index (≥0.96) and root mean square error of approximation (≤0.05). We performed structural equation modelling to examine the relationship between the hypothesised factors and protective behaviours. RESULTS: More than 95% of the individuals engaged in at least 1 form of protective behaviour. Knowledge was strongly associated with protective behaviours via direct effect (ß=0.45, 95% CI 0.19-0.69, P<0.001) and indirect effect through perceived risk (ß=0.18, 95% CI 0.07-0.31, P=0.002). Perceived risk was associated with protective behaviours (ß=0.28, 95% CI:0.11-0.44, P=0.002). A lower household income and ethnic minority were associated with protective behaviours. A lower education level and smokers were associated with lower knowledge of haze. A higher education and ethnic minority were associated with a lower perceived risk. Wearing of N95 masks was associated with other haze-related protective behaviours (ß=0.24, 95% CI 0.08-0.37, P=0.001). CONCLUSION: Knowledge was associated with protective behaviours, suggesting the importance of public education. Efforts should target those of lower education level and smokers. The wearing of N95 masks correlates with uptake of other protective behaviours.

Incidence, recurrence and comorbidity of anxiety disorders in four major developmental stages.

BACKGROUND: Anxiety disorders are common in childhood, adolescence, and adulthood, and frequently comorbid with other mental disorders. OBJECTIVE: The main aim of the present study was to examine the incidence, recurrence and comorbidity rates of anxiety disorders across four developmental periods, namely, during childhood (5 - 12.9 years), adolescence (13 - 17.9 years), emerging adulthood (18 - 23.9 years), and adulthood (24 - 30 years). METHOD: Eight hundred and sixteen participants from a large community sample were interviewed twice during adolescence, at age 24, and at age 30. They completed self-report measures of psychosocial functioning and semi-structured diagnostic interviews during adolescence and adulthood. RESULTS: The result showed first incidence of anxiety disorders to be significantly higher in childhood and adulthood than in adolescence and emerging adulthood. Female gender was associated with first incidence, but not with recurrence. Significant comorbidity was found between anxiety disorders and major depressive disorder (MDD) across the four developmental periods. The comorbidity between anxiety and substance use disorders (SUD) was significant in childhood, emerging adulthood and adulthood, but not in adolescence. The presence of anxiety disorders during childhood and adolescence significantly increased the probability of having an anxiety disorder during emerging adulthood. LIMITATIONS: The participants are ethically and geographically homogenous. CONCLUSION: Incidence and recurrence rates of anxiety disorders differed across four diverse developmental periods. The magnitude of comorbidity between anxiety disorders and MDD was comparable across periods.