Happiness, sleep quality, and self-care ability among community-dwelling older adults in Tehran, 2023

This study is a cross-sectional study focused on community-dwelling older adults aged 60 and above. We compiled a comprehensive list of all health centers affiliated with Tehran University of Medical Sciences.

Table of Contents

Participants

The sampling method employed was a multistage cluster design. Initially, we randomly selected three healthcare centers from the list provided. Subsequently, we recruited 306 older adults from each center, which was 20% more than the sample size allocated to each center. We explained the study’s objectives to the participants and invited them to cooperate via phone, ensuring that we obtained informed consent. Finally, we asked them to complete the informed consent forms and self-report questionnaires.

We calculated a sample size of 306 older adults (α = 0.05, β = 0.20, R² = 0.1). Initially, we estimated a sample of 150 older individuals, accounting for a 20% incompleteness in the data. Consequently, the final sample size was adjusted to 188 participants. However, to enhance the validity of the study, we ultimately determined a sample size of 306

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The inclusion criteria for this study encompass vision and hearing health (assessed through health records and self-reports), cognitive and psychological health (based on health records), and the absence of known cancer, severe heart failure, or a history of stroke. Consequently, individuals with blindness, deafness, cognitive impairments, or psychiatric disorders were excluded from participation. The exclusion criteria also included a lack of willingness to cooperate and incomplete questionnaires (note that no incomplete questionnaires were received).

Measurements

After obtaining informed consent, data were collected from 306 older individuals across three health centers using four questionnaires from May to October 2023. It is important to note that the sampler assisted illiterate participants in reading the questionnaire questions. The demographic profiles included age, gender, marital status, education, and employment status.

Oxford Happiness Inventory (OHI)

The Oxford Happiness Questionnaire (OHI) was utilized to assess the level of happiness. This questionnaire consists of 29 questions and is grounded in Argyle’s definition of happiness. It encompasses five components: life satisfaction, self-esteem, active well-being, self-satisfaction, and positive mood. The questions are scored on a four-point scale (zero to 3) to measure individual happiness levels. The maximum score a respondent can achieve on this questionnaire is 87, indicating the highest level of happiness, while the minimum score is zero, signifying that the individual is unhappy and potentially depressed. Argyle et al. reported a Cronbach’s alpha coefficient of 0.90, while Farnham and Bruin obtained a coefficient of 0.87 based on a sample of 101 participants [25]. The reliability of the Persian version of the inventory was found to be between 0.92 and 0.93 [26, 27]. Previous studies have confirmed the face and content validity of the Oral Health Impact (OHI) in older adults. The reliability coefficients for the subscales were calculated using Cronbach’s alpha, yielding values ranging from 73 to 78% [28].

Pittsburgh Sleep Quality Questionnaire (PSQI)

The Pittsburgh Sleep Quality Questionnaire (PSQI) was utilized to assess sleep status. This questionnaire was developed by Buysse et al. at the Pittsburgh Institute of Psychiatry. Their research demonstrated that a score greater than five has a diagnostic sensitivity of 89.6% and a specificity of 86.5%. The PSQI consists of nine questions divided into seven components, which include subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. This questionnaire evaluates sleep quality over the past four weeks. Each component is scored on a scale from zero to 3, and the total score is the sum of the component scores. The overall score ranges from 0 to 21, with a score above 6 indicating poor sleep quality. The reliability coefficient (Cronbach’s alpha) for the seven components of the Pittsburgh index is 0.83; Each component effectively measures a specific aspect of overall sleep quality [29, 30]. The Persian version of the questionnaire was prepared by Farahi Moghadam et al. and validated by Gholi Mazerji et al., with a Cronbach’s alpha of 0.65 [31, 32].

The Self-care Ability Scale for the Elderly (SASE)

The Self-Care Ability Scale for the Elderly (SASE) is a questionnaire designed to assess the self-care abilities of older adults [33]. Developed by Soderham and his colleagues, the SASE consists of 17 questions that address various aspects of daily life, including activities, well-being, mastery, determination, loneliness, and dressing. Responses to each question are measured on a Likert scale, ranging from 1 (strongly agree) to 5 (strongly disagree). The total possible scores range from a minimum of 17 to a maximum of 85, with a cutoff point of 69; scores below 69 indicate low self-care ability, while scores above 69 indicate high self-care ability. The dimensions assessed by the SASE include the ability to manage personal responsibilities, pursue goals, and maintain health. The Cronbach’s alpha coefficient for the scale ranges from 0.68 to 0.88 [34]. Specifically, Cronbach’s alpha for the Chinese version is 0.89, for the Italian version it ranges from 0.72 to 0.90, for the Norwegian version it is 0.85, for the Turkish version it ranges from 0.90 to 0.91, and for the Persian version, it is 0.73 [35,36,37,38]. Additionally, Tamizkar et al. (2019) reported a Cronbach’s alpha coefficient of 0.80 among older adults [39].

Statistical analysis

We examined the collected data to identify and remove unusual and outlier values. Statistical indices were used to describe quantitative data, including frequency, frequency percentage, mean, standard deviation, median, and interquartile range. Additionally, demographic variables were presented in the form of numbers and percentages. We use Spearman’s correlation test to investigate the relationships between quantitative variables, given the non-normal distribution of the tested variables. Furthermore, we used the Kruskal-Wallis test to assess differences in median sleep scores, self-care ability, and happiness scores across categorical variables.

We analyzed data to evaluate the relationship between the independent variables self-care and sleep quality and the dependent variable (see Fig. 1). A regression model was used to assess individuals’ happiness. First, a univariable linear regression model was implemented to investigate the relationship between the independent variables and the dependent variable. Next, a multivariable linear regression model was utilized to account for the effects of confounding variables, including age, sex, education, and marital status, on the impact of each independent variable. The results are presented as effect coefficient values along with 95% confidence intervals (α = 0.05). Data analysis was performed using Armonk, NY: IBM Corp SPSS v.27.