The association of mental health with positive behaviours,attitudes and virtues in community-dwelling older adults: Results of a population-based study

Abstract

Background:

Despite major advancements in understanding of the factors associated with mental health in older adults, studies assessing positive behaviours, attitudes and virtues are still scarce in the literature.

Aims:

This study aims to investigate whether factors related to positive behaviours, attitudes and virtues are associated with mental health (i.e. depression, anxiety and stress) and satisfaction with life in Brazilian community-dwelling older adults.

Methods:

A population-based, cross-sectional study of Brazilian older adults who were users of the public health system and seen by Family Health teams was conducted in 2017. Instruments were applied to assess cognition, physical activity, sleep, quality of life, social support, religiousness, spirituality, satisfaction with life, resilience, altruism, volunteerism, loneliness, meaning in life and mental health (i.e. depression, anxiety and stress). Analyses were carried out using linear and logistic regression models.

Results:

A total of 534 (93.5%) older adults were included. Positive behavioural factors and values including volunteerism, meaning in life, resilience, peace, loneliness, faith and religiousness were associated with mental health outcomes (i.e. depression, anxiety and stress), as were traditional factors, such as sleep, gender, social support and cognitive state.

Conclusions:

Aspects related to positive behaviour, attitudes and virtues can impact the mental health of the older population. These results can serve to alert health professionals on the importance of addressing these factors and help guide the implementation of preventive measures and interventions for this age group.

Keywords

Positive psychology depression mental health geriatrics spirituality

Introduction

The ageing of the population is global phenomenon. In Brazil, the proportion of older adults is growing rapidly and this age group currently represents 14.6% of the population (IBGE, 2018). Older age is a phase in life that is generally accompanied by a high prevalence of physical limitations, cognitive decline, stress and social isolation (Ramos, 2003).

Studies show that negative life events contribute to negative feelings and promote depressive and anxious symptoms (Guerra et al., 2008; Zunzunegui et al., 2003). A number of factors are traditionally associated with worse mental health in older people, such as female gender, cognitive impairment, low educational level, alcohol abuse and low social support (Beutel et al., 2017; Machado et al., 2016; Robles et al., 2014).

Although these factors have been extensively discussed in the scientific literature, recent lines of research have investigated whether good feelings (i.e. emotional state or emotional reaction) over time have positive health consequences (Mauss & Robinson, 2009). In this context, the field of positive psychology involves the study of positive emotions including subjective experiences (e.g. meaning, wellbeing, satisfaction, hopefulness), individual traits (e.g. perseverance, spirituality, forgiveness) and civic virtues that move individuals towards better citizenship, for the success of the community (e.g. altruism, volunteering, tolerance) (Seligman & Csikszentmihalyi, 2000). Studies have now shown that the positive behavioural factors can have as strong an influence on health outcomes as ‘traditional factors’ (i.e. those factors which are well-established, well-studied and generally consensual among experts).

Pillemer et al. assessed 7000 individuals over a 20-year period and found that altruism was associated with better perceived health and lower depressive symptoms (Pillemer et al., 2010). Similarly, other studies showed loneliness was associated with greater depressive symptoms in older persons with mood disorder (Holvast et al., 2015) and having a little meaning in life was associated with a greater risk of depression(Volkert et al., 2019). Finally, a recent systematic review (Braam & Koenig, 2019) found religious and spiritual beliefs were protective for mental health in most of the 152 longitudinal studies included, while resilience was associated with less depressive symptoms in older adults in another meta-analysis (Wermelinger Avila et al., 2017).

Despite this promising evidence, the prevailing healthcare model remains based on traditional factors, revealing a lack of integrality regarding healthcare of older adults. While life expectancy is rising, the quality of life of older adults appears to be lagging (Miranda et al., 2016). However, besides the desire to live a longer life, there is now greater concern with promoting health and wellbeing of older adults, as opposed to merely preventing the development of disease (Brasil, 2010). The healthcare system should therefore place emphasis on quality of life as a broader concept, in which older adults are construed as multidimensional where these dimensions can influence wellbeing.

Despite major advancements in understanding of the factors associated with mental health in older adults over recent years, there is a scarcity of studies investigating the influence of determinants related to positive behaviours, attitudes and virtues on mental health outcomes, and whether these factors are as important as traditional factors assessed (Schui & Krampen, 2010). With the aim of bridging this gap, the present study investigated whether factors related to positive behaviours, attitudes and virtues are associated with mental health and satisfaction with life in Brazilian community-dwelling older adults.

Methods

Study design

A longitudinal population-based study involving a population of older adults treated by the Family Health teams of the city of São João del-Rei, Brazil and which commenced in 2017 was carried out. The data for the present study were drawn from a cross-sectional sample (collected at baseline of the cited study in 2017). This study was approved by the Research Ethics Committee of the Federal University of Juiz de Fora, Brazil, under permit no. 1.431.964 and all participants signed the consent form.

Site and catchment area of Family Health teams

São João del-Rei is a city situated in Brazil’s interior with a population of 84,469 (Estatística, 2011). The city has a predominantly urban population (79,857: 94.5% of total) and had a Human Development Index (HDI-M) of 0.758 in 2010 (Pnud et al., 2010). In 2017 (study baseline), the city had a public health structure consisting of 13 Family Health Teams catering for around 52.5% of the population, two of which served the rural area.

Population

In 2010, the population of older adults (aged ⩾ 60 years) in São João del-Rei was 11,456 (13.5% of total population). Of this population, 4,698 (41.0%) were men and 6,758 (59.0%) were women. A total of nine urban teams took part in the study, representing all districts covered by the Family Health Teams in the city, and serving a population of 3,293 older adults.

Eligibility criteria

Older adults aged ⩾ 60 years who were users of the public health care system (‘Sistema Único de Saúde’ in Portuguese) and resided within the catchment area of the Family Health teams were included. Individuals with severe hearing or visual deficits, stroke sequela or disorders with impairment of language and/or comprehension, serious illness or undergoing palliative care, temporarily or permanently bedridden, attaining below the minimum cut-off score on the Mini-Mental State Exam (MMSE) according to educational level and living in institutions or nursing homes were not included.

Sample size calculation

Based on the population covered by the Family health teams included in the study (i.e. 3,293 older adults aged ⩾ 60 years), for a 95% confidence level and sample error tolerance of 5%, the sample size was determined using the Slovin/Yamane formula for calculating sample size (Singh & Masuku, 2014): n = N/(1+Ne²), where n = required sample size, N = total population of older adults enrolled on the Family Health Strategy (3,293) and e = error tolerance (level of precision) (α = 0.05). The calculation yielded a sample size of 356 subjects. The addition of a further 30% to the sample size, calculated to offset inevitable losses during interviews conducted over the follow-up (inevitable losses defined as situations where individuals were selected who evolved to death and those who changed address to locations difficult to reach or in other cities), gave a final sample size of at least 508 individuals assessed at baseline, to allow a minimum of 356 for the follow-up.

Selection of sample

The sample was selected using the stratified proportional sampling technique, with randomisation (probabilistic sample). The city was first divided into five major geographic areas (Centre, North, South, East and West). The population enrolled on each Family Health Strategy program was stratified by gender, producing two strata per region (Supplementary Material). The proportion of each stratum was established and the sample calculated taking proportionality into account. Based on lists provided previously by each Community Health Worker (CHW), randomised selection was performed.

Procedures

Data collection was carried out with the aid of 42 CHWs. CHWs are primary health workers who work in association with the Family Health Teams. The role of CHWs entails disease prevention and health promotion activities, through actions that are home or community-based, individual or collective, and carried out in accordance with public healthcare system guidelines. These professionals work with populations from defined geographic areas, making home visits to collect and record data, besides providing regular follow-up of individuals from high-risk or socially vulnerable groups.

The CHWs took part in sensitisation meetings and training sessions on applying the questionnaire, besides the running of a pilot project (one questionnaire per CWS, overseen by researchers) to identify potential difficulties in use. Questionnaires were applied in participants’ homes and at a previously scheduled time. Each CHW received a list of older adults who were randomly selected from within their micro-area. A limit of three attempts at applying the questionnaire was established. In the event of refusal or impossibility of applying the questionnaire to the individual, a list was drawn up containing other names (also stratified and randomised) for substitution, thereby preventing sample losses.

The study objectives were first explained and data collection commenced only after participants had read, consented to and signed the Consent Form.

Instruments

The questionnaire applied took, on average, 80 minutes to apply and complete. Although some scales can be self-completed, the questionnaire in the present study was applied by the researcher owing to the reading difficulties, visual problems and illiteracy commonly found in this group of older adults. The instruments employed collected data on the following aspects:

Traditional factors

Sociodemographic data: age, gender, marital status, education, occupation, monthly income (individual and family), race/skin colour, smoking habits or alcohol use.

Mini-Mental Status Exam (MMSE): developed by Folstein et al. (1975) as a brief simple standardised means of assessing cognitive status in the clinical setting. The 30-item MMSE validated for Portuguese was used in this study (Bertolucci et al., 1994). Cut-off points adopted were a score of 11 for illiterate individuals, 17 for subjects with ⩽9 years of formal education and 25 for those with ⩾9 years (Melo & Barbosa, 2015).

International Physical Activity Questionnaire (IPAQ): classification of physical activity level according to criteria defined in the ‘Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ)’, based on the scale validated for use in Brazil (Matsudo et al., 2012). Physical activity is classified as Low, Moderate or High, based on the calculation of energy expenditure.

Mini-Sleep Questionnaire (MSQ): The Portuguese version contains 10 items scored on a Likert-type scale and is validated for use in Brazil (Falavigna et al., 2011). Higher scores on the scale indicate poorer sleep quality.

World Health Organization Quality of Life-Bref (WHOQOL- Bref): this brief version contains 26 items, subdivided into four domains: physical, psychological, social relationships, and the environment. The WHOQOL-bref has been validated for Brazil (Fleck et al., 2000).

Social Support Questionnaire (SSQ): The short version has been validated for Portuguese (Castelar Perim et al., 2015), and comprises six questions assessing perceived social support, based on number of people available to the individual and satisfaction with the support available. Higher scores on the questionnaire indicate greater social support.

Positive behavioural factors, attitudes and values

Functional Assessment of Chronic Illness Therapy-Spirituality (FACIT-Sp). This instrument addresses spiritual and religious aspects of individuals with chronic diseases. The FACIT-Sp comprises 12 items and three subdomains of spiritual wellbeing (i.e. Faith, Meaning and Peace), where higher values indicate better spiritual wellbeing. The version used has been validated for Portuguese (Lucchetti et al., 2013).

Spirituality Self Rating Scale (SSRS). This SSRS contains six items scored on a 5-point Likert scale. Final score ranges from 6 to 30 and denotes the level of spiritual orientation. The scale has been validated for use in Brazil (Gonçalves & Pillon, 2009).

Duke University Religion Index (DUREL): This instrument measures three dimensions of religious involvement and correlations with health outcomes: organisational religiosity – OR (religious attendance), non-organisational religiosity – NOR (private religious activities, such as prayer, and religious meditation) and intrinsic religiosity – IR (religion as the ultimate significance of life). Scores on the three dimensions (OR, NOR and IR) should be analysed separately. The scale has been validated for use in Brazil (Lucchetti et al., 2012).

Psychological Resilience Scale: This 25-item scale is validated for Brazil and assesses the construct of resilience (Pesce et al., 2005). Higher scores indicate greater resilience levels.

Volunteerism questionnaire: This questionnaire is based on information required to characterise voluntary activity based on a previously published study (Corrêa et al., 2019). The questionnaire has three questions for defining whether the individual is engaged in voluntary activity, how long they have been involved and time dedicated to the activity.

Self-Report Altruism Scale (SRA): This is a 17-item scale validated for use in Brazil (Gouveia et al., 2010). Higher scores on the scale indicate greater perceived altruism.

UCLA Loneliness Scale: This scale, validated for Brazil (Barroso et al., 2016), contains 20 items corresponding to how often respondents experience situations of social interaction and activities performed alone. Higher scores on the scale indicate greater loneliness.

The Meaning in Life Questionnaire: This questionnaire has been validated for Brazil (Aquino et al., 2015) and comprises 10 items whose score measures the extent to which respondents are seeking to understand or increase the purpose of their life (a meaning throughout life). Higher scores denote greater meaning in life.

Mental health outcomes

Satisfaction With Life Scale (SWLS-BP): the scale has been validated for Brazil and is a five-item instrument measuring an individual’s personal judgement of their life satisfaction. Higher scores indicate greater life satisfaction.

Depression, Anxiety and Stress Scale (DASS-21): the 21-item scale was developed by Lovibond and Lovibond (1995) and assesses depression, anxiety and stress symptoms according to seven items each. The DASS-21 is validated for Brazil (Vignola & Tucci, 2014) and higher scores represent greater depressive, anxious and stress symptom severity. In the present study, DASS-21 was selected due to the fact that this scale is able to assess three important mental health outcomes (depression, anxiety and stress), is brief (only 21 questions), reduces participant burden and is free of charge. Likewise, its use has already been validated into older adults with good psychometric properties (Gloster et al., 2008; Wood et al., 2010).

Statistical analysis

An initial descriptive analysis in terms of frequency (relative and absolute), mean and standard deviation was performed for sociodemographic variables and for scores on the different instruments.

Pearson’s correlation was determined between the different dimensions of the DASS-21 and the SWLS for independent (continuous) variables, while Spearman’s correlation was used for the ordinary/nominal variables.

All variables that exhibited a correlation of p < .10 on the correlation matrix were included in the regression model. For the linear regression, independent variables associated with p < .05 were retained, while those with p > .10 were dropped. This approach yielded us to reveal the most important factors in the order of importance to the model, highlighting which factors (traditional or not) were the most associated with the mental health outcomes. The dependent variables were defined as depression, anxiety, stress and satisfaction with life scores. On logistic regression, the dependent variables (depression, anxiety and stress) were dichotomised into ‘normal’ (0) and altered (1) according to the cut-off values for the DASS21 derived from a previous study and a stepwise approach was used (Lovibond, 1995).

The statistics R, R-squared, Adjusted R-squared and multicolinearity values were assessed using the Variance Inflation Factor (VIF) in the linear regression, whereas and Nagelkerke R² values were evaluated for the logistic regression.

All statistical analyses were carried out using the software package SPSS 19 (SPSS Inc.) and the level of significance adopted was 0.05.

Results

Of a total of 571 older adults invited by the researcher, 534 (93.5%) were included: 23 individuals were dropped for meeting the study exclusion criteria (i.e. three due to debilitating disease and 20 for failing to attain the minimum cut-off score on the MMSE) and 14 subjects refused to take part.

The sample subjects were predominantly female (59.0%), married/cohabitating (57.7%), had complete primary level education (36.7%), brown skin (42.7%), mean age of 69.22 years (SD = 7.02 years), mean income of R$2,032.40 (SD = 1,459.62), and reported no alcohol (98.1%) or tobacco use (92.3%) (Table 1).

Table 1.

Characteristics of participants.

n = 534	n	(%)
Gender
Female	315	59.0
Male	219	41.0
Marital status
Single	40	7.5
Married/cohabiting	308	57.7
Widow(er)	126	23.6
Divorced/separated	45	8.4
Others	15	2.8
Skin colour
White	223	41.8
Black	70	13.1
Yellow	9	1.7
Brown	228	42.7
Indigenous	2	0.4
Not declared	2	0.4
Alcohol use
No	524	98.1
Yes	10	1.9
Smoking
No	493	92.3
Yes	41	7.7
	Mean	SD
Age (years)	69.22	7.02
Family income (Brazilian R$)	2,032.40	1,459.62
Education (years)	4.63	3.55

Note. SD = standard deviation.

With regard to results on the DASS-21; 9.4% of the participants displayed symptoms of depression, 4.8% anxiety and 9.4% stress. Around 46.1% of the participants had a low physical activity level and 40.3% reported sleep problems.

The correlation matrix of anxiety, depression, stress and life satisfaction scores and sociodemographic data is shown in Table 2. The correlations between anxiety, depression, stress and life satisfaction with the other independent variables are shown in Table 3.

Table 2.

Correlation of mental health and life satisfaction with demographic data.

	DASS21 depression	DASS21 anxiety	DASS21 stress	Satisfaction with life
DASS21 depression	1
DASS21 anxiety	0.740**	1
DASS21 stress	0.754**	0.751**	1
Satisfaction with life	−0.377**	−0.280**	−0.326 **	1
Mini-mental state examination	−0.090*	–0.060	–0.010	0.091*
Age	0.025	–0.033	–0.014	0.085*
Gender^a	0.141**	0.181**	0.176**	–0.072
Education (years)	–0.009	–0.016	0.027	0.025
Individual income	0.001	–0.066	–0.003	0.087*
Tobacco use^a	0.063	0.073	0.056	−0.131**
Alcohol use^a	–0.015	–0.054	0.013	0.068
Marital status^a	–0.028	–0.016	0.004	0.013
Skin color^a	–0.059	–0.045	–0.012	0.077

Note. *Correlation is significant at the 0.05 level (2-tailed).

Bold values mean statistically significant.

Correlation is significant at the 0.01 level (2-tailed).

Spearman correlation.

Table 3.

Correlation of mental health and life satisfaction score with scales used.

	DASS21 depression	DASS21 anxiety	DASS21 stress	Satisfaction with life
DASS21 depression	1
DASS21 anxiety	0.740**	1
DASS21 stress	0.754**	0.751**	1
Satisfaction with life	−0.377**	−0.280**	−0.326**	1
Physical activity (METs/min/week)	–0.042	0.003	0.041	–0.007
Mini-sleep questionnaire	0.415**	0.480**	0.451**	−0.323**
WHOQOL total	−0.470**	−0.362**	−0.365**	0.631**
Social support questionnaire	−0.099*	–0.077	–0.080	0.225**
Spirituality self rating scale	0.026	0.049	–0.006	0.081
FACIT-Sp: meaning in life	−0.369**	−0.233**	−0.240**	0.611**
FACIT-Sp: peace	−0.460**	−0.384**	−0.395**	0.524**
FACIT-Sp: faith	0.001	0.017	–0.037	0.224**
FACIT-Sp: final	−0.367**	−0.265**	−0.295**	0.596**
Organisational religiosity	−0.117**	−0.108*	−0.093*	0.161**
Non-organisational religiosity	0.015	0.053	0.047	0.092*
Intrinsic religiosity	−0.097*	–0.057	−0.100*	0.135**
Resilience scale	−0.139**	−0.094*	−0.142**	0.262**
Time dedicated to voluntary activities	−0.119**	−0.116**	−0.140**	0.117**
Self-report altruism scale	0.009	0.030	0.097*	0.118**
UCLA loneliness scale	0.505**	0.426**	0.456**	−0.564**
Meaning in life questionnaire	−0.145**	−0.076	−0.118**	0.360**

Note. DASS21 = depression, anxiety and stress scale; WHOQOL = world health organization quality of life; FACIT-Sp = functional assessment of chronic illness therapy-spirituality.

Bold values mean statistically significant.

Correlation is significant at the 0.05 level (2-tailed).

Correlation is significant at the 0.01 level (2-tailed).

The linear regression models are given in Table 4. Statistically significant associations were found for: (a) depression levels: greater loneliness (β = 0.303), worse sleep quality (β = 0.213), less peace (β = −0.246), less faith (β = −0.115) and less intrinsic religiosity (β = −0.072); (b) anxiety levels: greater loneliness (β = 0.211), worse sleep quality (β = 0.337), less peace (β = −0.150) and female gender (β = −0.087); (c) stress levels: greater loneliness (β = 0.262), worse sleep quality (β = 0.286), less peace (β = −0.138), less volunteerism (β = −0.095) and female gender (β = 0.085); and (d) life satisfaction: less loneliness (β = −0,255), better sleep quality (β = −0.093), greater FACIT meaning (β = 0.295), greater resilience (β = 0.236), greater meaning in life (β = 0.121), and better social support (β = 0.070).

Table 4.

Linear regression of factors associated with mental health and satisfaction with life.

	Unstandardised coefficient	Beta	p	Tolerance	VIF	R ²	R² change
Depression
Loneliness	0.255 (0.035)	0.303	<.001	0.681	1.469	0.255	-
Sleep quality	0.177 (0.032)	0.213	<.001	0.820	1.220	0.317	0.062
FACIT peace	–0.891 (0.158)	–0.246	<.001	0.634	1.577	0.349	0.032
FACIT faith	–0.448 (0.147)	–0.115	.002	0.853	1.172	0.357	0.008
DUREL intrinsic	–0.414 (0.208)	–0.072	.047	0.926	1.080	0.362	0.005
Anxiety
Sleep quality	0.233 (0.027)	0.337	<.001	0.819	1.221	0.231	-
Loneliness	0.148 (0.031)	0.211	<.001	0.670	1.492	0.304	0.073
FACIT-peace	–0.451 (0.130)	–0.150	.001	0.682	1.466	0.318	0.014
Gender female	1.304 (0.546)	0.087	.017	0.956	1.046	0.325	0.007
Stress
Loneliness	0.255 (0.042)	0.262	<.001	0.668	1.496	0.208	-
Sleep quality	0.275 (0.038)	0.286	<.001	0.818	1.223	0.302	0.094
FACIT peace	–0.578 (0.182)	–0.138	.002	0.671	1.490	0.316	0.014
Volunteerism	–1.010 (0.383)	–0.095	.009	0.978	1.022	0.324	0.009
Gender female	1.760 (0.754)	0.085	.020	0.956	1.046	0.331	0.007
Satisfaction with life
FACIT meaning	0.579 (0.073)	0.295	<.001	0.619	1.616	0.374	-
Loneliness	–0.127 (0.018)	–0.255	<.001	0.658	1.521	0.469	0.096
Resilience	0.085 (0.013)	0.236	<.001	0.674	1.484	0.521	0.052
Life meaning	0.059 (0.016)	0.121	<.001	0.852	1.173	0.534	0.013
Sleep quality	–0.046 (0.016)	–0.093	.003	0.855	1.169	0.541	0.007
Social support	0.758 (0.327)	0.070	.021	0.933	1.072	0.546	0.005

Note. FACIT = functional assessment of chronic illness therapy-spirituality; Mini-mental = mini-mental state examination.

Results of both the logistic and linear and regressions (Table 5) proved similar. Statistically significant associations were found for: (a) depression: lower MMSE (OR = 0.910), worse sleep quality (OR = 1.046), less peace (OR = 0.856), less volunteerism (OR = 0.757) and greater loneliness (OR = 1.062); (b) anxiety: female gender (OR = 2.119), lower score on MMSE (OR = 0.974), worse sleep quality (OR = 1.077), less peace (OR = 0.899) and greater loneliness (OR = 1.036); (c) stress: female gender (OR = 2.393), worse sleep quality (OR = 1.063), less peace (OR = 0.866), greater loneliness (OR = 1.054), worse social support (OR = 1.736) and less intrinsic religiosity (OR = 0.818).

Table 5.

Logistic regression of factors associated with mental health and satisfaction with life.

	OR	Lower	Upper	p	Nagelkerke R²
Depression
Mini-mental	0.910	0.863	0.959	<.001
Sleep quality	1.046	1.023	1.070	<.001
FACIT peace	0.856	0.771	0.950	.004
Volunteerism	0.757	0.574	1.000	.050
Loneliness	1.062	1.036	1.089	<.001	0.330
Anxiety
Gender female	2.119	1.338	3.357	.001
Mini-mental	0.947	0.900	0.996	.035
Sleep quality	1.077	1.053	1.102	<.001
FACIT peace	0.899	0.813	0.995	.039
Loneliness	1.036	1.012	1.061	.003	0.321
Stress
Gender female	2.393	1.417	4.041	.001
Sleep quality	1.063	1.037	1.089	<.001
Social support	1.736	1.043	2.890	.034
FACIT peace	0.866	0.777	0.965	.009
Loneliness	1.054	1.028	1.081	<.001
Intrinsic religiosity	0.818	0.708	0.946	.007	0.338

Note. FACIT = functional assessment of chronic illness therapy-spirituality; Mini-mental = mini-mental state examination.

Discussion

The results of the present study suggest that positive behavioural factors, attitudes and values such as volunteerism, meaning in life, resilience, peace, loneliness, faith and religiousness may be as strongly associated with mental health markers as the traditional factors sleep, gender, social support and cognitive state. These findings can help health professionals and healthcare managers in addressing these issues and devising preventive strategies.

With regard to factors traditionally explored in the field of mental health of older adults, studies have shown worse mental health in individuals who are female (Zunzunegui et al., 2007), enjoy less social support (Sachs-Ericsson et al., 2019), have worse cognition (Sachs-Ericsson et al., 2019) and worse sleep quality (Cho et al., 2019), results corroborated by the findings of the present study and extensively discussed in the current literature.

However, the present study results also suggest that the non-traditional factors explored may also be associated with mental health of older adults. Perceived loneliness was strongly associated with mental health in the sample assessed, a result consistent with previous studies (Cho et al., 2019; Domènech-Abella et al., 2018). Other feelings closely related to positive psychology, were also associated with mental health, as seen for volunteerism, meaning in life and resilience (Anderson et al., 2014; MacLeod et al., 2016; Steger et al., 2011; Tay & Lim, 2020; Taylor & Carr, 2020; Wermelinger Avila et al., 2017). Religious and spiritual beliefs of the older adults were also important predictors of mental health in the population studied. In our findings, factors related to spiritual wellbeing and intrinsic religiosity proved more strongly associated with mental health than were religious practices and activities. In older adults, owing to physical limitations, the frequency of attendance of religious service by this group declines with age (Hayward & Krause, 2013) which, according to the present findings, did not significantly impact the association between beliefs and health outcomes.

The probable mechanisms explaining the aforementioned findings have not been fully elucidated (Ong et al., 2016; Webb et al., 2013). Positive thinking and religious and spiritual beliefs are generally accompanied by an increase in social interactions (Rico-Uribe et al., 2016); besides a stimulus of positive emotions such as gratitude, faith, compassion, forgiveness and hope (Vaillant, 2013) and better coping with critical life situations (Pargament et al., 1998). Some studies have shown that optimism, positive affectivity and beliefs of the individual can influence physiological parameters such as cortisol, immunity and proinflammatory markers (Lai et al., 2005; Lutgendorf et al., 2004; Tobin & Slatcher, 2016), factors also implicated in mental health outcomes (Young et al., 2014).

In relation to the influence of our results in the population residing in the area of the study, our findings may have important repercussions for public health. As seen in other Brazilian cities; São João del Rei’s public health administrators tend to focus more on the biological aspects of the population, such as the medical treatment of acute and chronic conditions, the promotion of healthy habits and the improvement of screening strategies for diseases. Despite these efforts, other aspects are often overlooked.

In the present study, we have advanced further in this field of knowledge, demonstrating that behavioural factors can influence mental health as consistently as traditionally-studied factors for this older population. This finding is particularly important in low to middle income populations, where the access to health services is limited and low cost interventions are desirable.

Given the high prevalence of late-life depression and its association with mortality, interventions aimed at preventing anxiety and depression among older adults may be important targets and can help reduce the morbidity and the mortality in this population. Studies have shown that interventions based on positive attitudes and behaviours are able to reduce psychiatric symptoms, and improve self-esteem and feelings of wellbeing (Ho et al., 2014; Steptoe et al., 2006). A meta-analysis published in 2013 (Bolier et al., 2013) of 6,139 participants found that interventions in positive psychology increased feelings of wellbeing and reduced depressive symptoms.

The present results can help inform action strategies in Primary Healthcare for improving the quality of life, wellbeing and mental health of older people. Several strategies should be developed to achieve this goal. Training should be available for health professionals aiming to make them aware and encouraged to address these factors in clinical practice. Likewise, public health programs should stimulate mental health interventions using positive attitudes and behaviours. These programs could be incorporated in primary care facilities and delivered by different healthcare professionals such as psychologists, social assistants, and physicians, among others. One of the interventions proposed is the use of the ‘appreciative inquiry’, in which participants are led through a series of systematic and provocative but affirming questions, aiming to identify what is positive in the group and to connect people, promoting significant changes (Kobau et al., 2011). Finally, health professional educational organisations such as the Liaison Committee on Medical Education, the Accreditation Council of Graduate Medical Education (Lianov et al., 2020) and the Brazilian Medical Education Association have already recommended a broader focus on behavioural and socioeconomic aspects in the curricula, increasing the awareness of such topics to healthcare students and educators.

The present study has some limitations, which should be taken into account. First, although this study was representative of the population served by the public health system, it does not represent older Brazilians as a whole. Therefore, any generalisations should be made with caution. Second, several scales were used which, although serving to screen health problems, may not provide a diagnosis. Third, mental health outcomes were assessed using DASS-21. It is important to highlight that this is a self-report screening tool (instead of an instrument to provide diagnosis). Therefore, our study has assessed mental health symptoms and not accurate diagnosis. Forth, some important risk factors that can have an impact the variables of this study (such as pain, vascular factors, chronic illnesses, personality disorders/traits, attitudes towards ageing and frailty) were not investigated. Fifth, it is important to note that the findings of the present study may differ from other age groups (e.g. younger adults). The older population tends to be more resilient, probably due to the higher rates of chronic conditions and the proximity of death (Gooding et al., 2012). Likewise, the levels of religiousness (Bengtson et al., 2015) and loneliness (Lara et al., 2020) in the geriatric population are also higher. These differences may impact our findings since younger adults seem to have other priorities. A previous study (Fegg et al., 2007) has identified that younger adults tend to consider ‘friends’, ‘partnership’ and ‘work’ as important meaning of life areas, as compared to older adults who tend to consider ‘health’, ‘altruism’, ‘spirituality’ and ‘nature’. Finally, the cross-sectional design of the study precludes conclusions on causal relationship, for which future longitudinal investigations are needed. Despite the limitations, strengths of this study include a selected sample, with low losses due to refusal or exclusion, and assessment of a large number of non-traditional aspects in a vulnerable population, justifying efforts by health managers to improve actions for the benefit of these public health system users.

Conclusions

The results of the present study suggest that positive behavioural factors, attitudes and values such as volunteerism, meaning in life, resilience, peace, loneliness, faith and religiousness can impact the mental health of the older population in a similar or greater way as compared to the ‘traditional’ factors. These results can serve to alert health professionals to the importance of addressing these factors in clinical practice since these factors are seldom included in routine medical encounters. Likewise, health managers could use this data to implement preventive measures and interventions for this age group, fostering education to their health professionals and providing a more comprehensive care. Future research should be carried out using diagnostic mental health criteria and replicating our findings in other settings and cultural backgrounds.

Supplemental Material

sj-docx-1-isp-10.1177_0020764021999690 – Supplemental material for The association of mental health with positive behaviours, attitudes and virtues in community-dwelling older adults: Results of a population-based study

Supplemental material, sj-docx-1-isp-10.1177_0020764021999690 for The association of mental health with positive behaviours, attitudes and virtues in community-dwelling older adults: Results of a population-based study by Eduardo Luiz Mendonça Martins, Laís Cunha Salamene, Alessandra Lamas Granero Lucchetti and Giancarlo Lucchetti in International Journal of Social Psychiatry

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Giancarlo Lucchetti

Supplemental material

Supplemental material for this article is available online.

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