Understanding the relationships between mental disorders, self-reported health outcomes and positive mental health: findings from a national survey

Vaingankar, Janhavi Ajit; Chong, Siow Ann; Abdin, Edimansyah; Kumar, Fiona Devi Siva; Chua, Boon Yiang; Sambasivam, Rajeswari; Jeyagurunathan, Anitha; Seow, Esmond; Subramaniam, Mythily

doi:10.1186/s12955-020-01308-0

Cited by 27 publications

(18 citation statements)

References 57 publications

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“…Mental health has been defined as both the absence of psychological distress, including symptoms of anxiety, depression, stress, and associated mental disorders, and the presence of positive psychological well-being, which includes factors like life satisfaction, optimism, and positive emotion [ 31 - 33 ]. Research has demonstrated that psychological distress and positive psychological well-being are related, but distinct constructs that uniquely contribute to the prediction of various health outcomes [ 34 , 35 ], including cardiovascular health [ 36 , 37 ], and therefore a complete description of mental health and its relationship with CVD risk should include both.…”

Section: Introductionmentioning

confidence: 99%

The Effects of a Digital Mental Health Intervention in Adults With Cardiovascular Disease Risk Factors: Analysis of Real-World User Data

Montgomery¹,

Boucher²,

Honomichl³

et al. 2021

JMIR Cardio

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Background The American Heart Association has identified poor mental health as a key barrier to healthy behavior change for those with cardiovascular disease (CVD) risk factors such as high blood pressure, high cholesterol, and diabetes. Digital mental health interventions, like those delivered via the internet to computers or smartphones, may provide a scalable solution to improving the mental and physical health of this population. Happify is one such intervention and has demonstrated evidence of efficacy for improving aspects of mental health in both the general population and in users with chronic conditions. Objective The objectives of this analysis of real-world data from Happify users with self-reported CVD risk factors, including high blood pressure and cholesterol, diabetes, and heart disease, were to examine whether these users would report improvements in subjective well-being and anxiety over time (H1) and use of Happify as recommended would be associated with significantly greater improvement in subjective well-being and anxiety over time compared to less-than-recommended usage (H2). Methods Data were obtained from existing Happify users who reported the aforementioned CVD risk factors. The sample included 1803 users receiving at least 6 weeks’ exposure to Happify (ranging from 42 days to 182 days) who completed at least one activity and two assessments within the app during that time. Subjective well-being was assessed with the Happify Scale, a 9-item measure of positive emotionality and life satisfaction, and anxiety was assessed with the Generalized Anxiety Disorder 2 (GAD-2). To evaluate H1, changes over time in both outcomes were assessed using mixed effects linear regression models, controlling for demographics and usage. For H2, an interaction term was added to the models to assess whether usage as recommended was associated with greater improvement over time. Results Both hypotheses were supported. For both the Happify scale and GAD-2, the initial multivariable model without an interaction demonstrated an effect for time from baseline, and the addition of the interaction term between time and recommended use was significant as well. Conclusions This analysis of real-world data provides preliminary evidence that Happify users with self-reported CVD risk factors including high blood pressure or cholesterol, diabetes, and heart disease experienced improved well-being and anxiety over time and that those who used Happify as recommended experienced greater improvements in these aspects of mental health than those who completed fewer activities. These findings extend previous research, which demonstrated that engagement with Happify as recommended was associated with improved well-being among physically healthy users and in those with chronic conditions, to a new population for whom mental health is especially critical: those at risk of developing CVD.

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Section: Introductionmentioning

confidence: 99%

The Effects of a Digital Mental Health Intervention in Adults With Cardiovascular Disease Risk Factors: Analysis of Real-World User Data

Montgomery¹,

Boucher²,

Honomichl³

et al. 2021

JMIR Cardio

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show abstract

“…Singapore is a multi-ethnic Southeast Asian country of approximately 4 million residents (citizens and permanent residents) comprising 74.4% Chinese, 13.4% Malays, and 9.0% Indians, and other ethnic groups (3.2%) [ 5 ]. The SF-12v2 has been utilized for research within Singapore amongst different populations and samples: patients with peritoneal dialysis [ 14 , 58 ], arthritis [ 17 ], mental illness [ 51 ], substance and alcohol use disorders [ 26 , 59 ], older adults [ 57 ], and the general population [ 28 , 47 ]. It appears that the usage of the SF-12v2 within Singapore will continue to increase, and hence there is a need to examine its factor structure within the general population.…”

Section: Introductionmentioning

confidence: 99%

Confirmatory factor analysis and measurement invariance of the English, Mandarin, and Malay versions of the SF-12v2 within a representative sample of the multi-ethnic Singapore population

Lau

Abdin

Vaingankar

et al. 2021

Health Qual Life Outcomes

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Background The Short Form Health Survey (SF-12v2) is an increasingly popular measure of health-related quality of life (HRQoL) in Singapore. In order to examine whether the SF-12v2 was appropriate for use in the population, the factor structure and validity of the English, Mandarin, and Malay versions were assessed in a representative sample of the general population of Singapore. Methods 6126 respondents were recruited for the Singapore Mental Health Study 2016 (SMHS 2016), a cross-sectional and population-based survey. Confirmatory factor analyses (CFA) were conducted to examine the fit of a two-factor model for the SF-12v2 within a representative sample and amongst the different language (English, Mandarin, Malay) subgroups. Multiple-group CFAs (MGCFA) were conducted to test measurement invariance across the different languages, ethnicities, and chronic illnesses subgroups. CFA-generated latent factor scores (FSCORE command in MPlus) were also compared with the composite scores derived from the developer’s scoring method via correlations. Sociodemographic correlates of the latent physical and mental health scores were explored. Results CFA results within the full sample supported a two-factor model (RMSEA = 0.044; CFI = 0.991; TLI = 0.988; SRMR = 0.044) in which physical functioning, role physical, bodily pain and general health items loaded onto a latent physical health factor, while role emotional, mental health, social functioning, and vitality items loaded onto a latent mental health factor. Physical and mental health factors were allowed to correlate, unlike the developer’s orthogonal scoring method. All standardized loadings were high and statistically significant. Both factors had high internal consistency. CFA within subsamples of English, Mandarin, and Malay languages indicated similar findings. MGCFA results indicate that measurement invariance held across the different languages, ethnicities, and those with and without chronic illnesses. Conclusion The present study identified a two-factor (physical and mental health) structure within the general population and amongst the three different languages and demonstrated the measurement invariance of SF-12v2 across different subgroups. Findings indicate that algorithm-derived PCS and MCS should be interpreted with caution as they may result in inaccurate conclusions regarding the relationships between HRQoL and its correlates. Future studies using the SF-12v2 within the general population of Singapore should consider utilizing the factor structure put forth in the present study to obtain more appropriate estimates of HRQoL.

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“…It was measured using the standard question: "Generally, how do you evaluate your health?" which participants rated using a 6-point scale ranging from 1 ("very bad") to 6 ("excellent") [62].…”

Section: Instruments and Measuresmentioning

confidence: 99%

Social Capital as a Mediator in the Link between Women’s Participation in Team Sports and Health-Related Outcomes

Paldi

Moran

Baron‐Epel

et al. 2021

IJERPH

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The role of social capital in the association between team sports and health-related outcomes has not been well established in the literature. The purpose of this study was to explore whether social capital components (social support, trust, and social involvement) mediate the association between team sports and health-related outcomes (self-reported health, psychosomatic symptoms, and depressive symptoms). In a cross-sectional research design, we obtained data from 759 participants in the Mamanet Cachibol League, a community team sports model for women in Israel, as well as a comparison group of 308 women who did not participate in any team sports. Team captains were sent a link with an online questionnaire, which were then delivered to team members via text message. Using three parallel mediation models, we found that social support mediated the association between team sports and self-reported health, psychosomatic symptoms, and depressive symptoms. Trust mediated the association between team sports and both psychosomatic symptoms and depressive symptoms. Social involvement was not found to be a mediator in the association between team sports and any of the health-related outcomes. Our findings reveal the important role of social capital, specifically social support and trust, in promoting the health of women who participate in team sports.

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Understanding the relationships between mental disorders, self-reported health outcomes and positive mental health: findings from a national survey

Cited by 27 publications

References 57 publications

The Effects of a Digital Mental Health Intervention in Adults With Cardiovascular Disease Risk Factors: Analysis of Real-World User Data

The Effects of a Digital Mental Health Intervention in Adults With Cardiovascular Disease Risk Factors: Analysis of Real-World User Data

Confirmatory factor analysis and measurement invariance of the English, Mandarin, and Malay versions of the SF-12v2 within a representative sample of the multi-ethnic Singapore population

Social Capital as a Mediator in the Link between Women’s Participation in Team Sports and Health-Related Outcomes

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