Factors Associated with Lower Social Activity in German Older Adults at Increased Risk of Dementia: A Cross-Sectional Analysis

Abstract

Background:

Studies demonstrate associations between low social activity in older adults and cognitive decline. Little has been investigated regarding which factors are associated with low social activity in older adults at increased risk of dementia.

Objective:

We investigate which sociodemographic, psychological, health-related, and environmental factors are associated with low social activity in older adults at increased risk of dementia. Additionally, we describe the stages of health behavior change, the types of social activities, and the duration of the current level of social activity.

Methods:

We used baseline data of 1,015 participants from the AgeWell.de trial. We conducted logistic and Poisson regression analyses to investigate factors associated with low social activity. We report descriptive statistics on the stages of change in the sample, the types of social activities most frequently pursued, and the duration of the current level of social activity.

Results:

Lower income, non-usage of public transport, depressive symptoms, cognitive, mobility, and hearing impairment were negatively associated with social activity. The majority of the sample was in the maintenance stage, followed by the precontemplation stage. The most common social activities were traveling and hobbies with others. Participants have maintained their current level of social activity for several years.

Conclusions:

We identified a lack of resources (income, transport), depressive symptoms and poorer health (cognitive, mobility and hearing impairment) as barriers to social activity. Interventions promoting social activity in older adults at risk of dementia may specifically target individuals with these risk factors. Low-threshold opportunities for social activity may be particularly beneficial.

Keywords

Alzheimer’s disease dementia lifestyle older adults prevention risk factors social engagement social health social participation transtheoretical model

German Clinical Trials Register (DRKS) trial identifier DRKS00013555

INTRODUCTION

The rising number of dementia cases is one of the major public health challenges of our time [1]. In 2021, approximately 1.8 million people in Germany were living with dementia [2, 3]. This number is expected to increase in the coming decades. First projections estimate that in 2033, two million people in Germany will be living with dementia [2, 3]. Currently, there is no disease-modifying treatment for common forms of dementia [4]. To curb the drastically increasing number of dementia cases, prevention of cognitive decline and dementia is paramount [5].

A growing body of literature reports lifestyle factors associated with increased dementia risk [6 –9] among these lifestyle factors is social isolation in later life [4 , 11]. The Lancet commission on dementia prevention, intervention, and care estimates that around 3.5% of dementia cases are associated with social isolation in older age (age > 65 years) [10]. Low social activity, also known as low social participation or low social engagement, is considered an aspect of social isolation [12, 13]. Social activity may be defined as “enactment of potential ties in real life activity”, comprising activities like attending social functions, meeting friends, participating in occupational or social roles or church attendance [14]. A large number of studies demonstrate associations between low social activity in older adults and cognitive decline [12 , 15–17], dementia [18 –21] and a broad range of further mental and physical illnesses [22]. A socially stimulating environment may contribute to brain health through at least two pathways: by promoting neuroprotective mechanisms and by enhancing cognitive reserve [16].

In order to identify risk groups and tailor interventions promoting social activity, it is crucial to gather knowledge about inhibiting and facilitating factors of social activity in older age. Previous research has revealed several individual factors associated with lower social activity in older adults, among them sociodemographic factors like higher age [23, 24], male sex [24, 25], lower education [26, 27], and lower income [23, 27], as well as psychological factors like low self-efficacy [28, 29] and depressive symptoms [23]. Moreover, many studies report the influence of health status [27 , 31] and individual health-related factors like lower cognitive status [32]. Urinary incontinence has also been discussed as a potential risk factor for social inactivity [33 –35]. Besides individual factors, there are also environmental factors associated with lower social activity in older age, for example a lack of access to public transport [23, 25].

Currently, only a limited number of studies explore predictors of low social activity in the specific subgroup of older adults at an increased risk of developing dementia. Dementia prevention programs often specifically target people with a risk profile for dementia [36] because they can particularly benefit from these interventions. If one wants to specifically target and support people at increased risk of dementia with prevention programs, it is particularly important to examine which barriers and facilitators for health behavior—in this case social activity—play a role in this group. People with an increased risk of dementia differ from the general population, for example, by having cardiovascular risk factors; they are more often overweight, are more likely to have high blood pressure, and are less physically active [37].

To our knowledge, we are the first study to comprehensively investigate factors associated with low social activity in a sample of older adults at increased risk of developing dementia. The first aim of the present study was to explore sociodemographic (age, sex, marital status, education, income), psychological (self-efficacy, depressive symptoms), health (cognitive functioning, mobility impairment, visual impairment, hearing impairment, urinary incontinence), and environmental factors (usage of public transport) associated with lower social activity in this sample.

In social cognitive models of behavioral change, not only individual barriers and facilitators (such as sociodemographic and health factors) are important, but also the person’s intention to engage in a health behavior like social activity [38, 39]. Depending on the extent to which this intention is present, prevention measures should be designed differently. To tailor interventions that aim to promote social activity, it is important to gather insights into the population of interests’ stages of behavior change [40]. Health behavior change is a complex process that may be described through a sequence of stages [40, 41]. To promote change, interventions should match an individual’s stage of change. For example, if a person does not intend to change his or her behavior and is not aware that a behavior has negative consequences, a first step may be to raise awareness about the behavior’s negative consequences [40]. The transtheoretical model of health behavior change defines several stages of readiness for change: people in the (a) precontemplation stage do not intend to change their behavior in the foreseeable future, while people in the (b) contemplation stage intend to change their behavior but are also acutely aware of the costs. People in the (c) preparation stage have already taken some action and intend to change their behavior in the near future. Finally, both people in the (d) action and (e) maintenance stage have already changed their behavior, but people in the maintenance stage have a lower temptation to relapse [40, 41]. The transtheoretical model of health behavior change also integrates Bandura’s concept of self-efficacy [42]. An individual’s belief in their capability of performing and maintaining a desired behavior is theorized as an important psychological factor for navigating the stages of change [43].

Although interventions should be matched to an individual’s stage of change, to our knowledge, there is a lack of studies investigating the stages of change with regard to social activity. Therefore, our second aim is to describe the current stage of health behavior change towards social activity in a population of older adults at increased risk of dementia. Moreover, we also describe the types of social activities most commonly pursued and the self-reported duration of the current level of social activity.

METHODS

The AgeWell.de trial and participants

The present analyses used baseline data from the AgeWell.de trial [44]. AgeWell.de is a two-armed multicentric, cluster-randomized controlled trial (registered in the German Clinical Trials Register DRKS; ID: DRKS00013555) conducted at five study sites in Germany (Leipzig, Greifswald, Kiel, Munich, and Halle). The aim of the AgeWell.de trial was to evaluate the effectiveness of a multicomponent intervention to delay cognitive decline in older adults at increased risk of dementia. Participants received either the multicomponent lifestyle intervention (intervention group) or general health advice and General Practitioner (GP) treatment as usual (control group). At baseline and several follow-ups, fully structured interviews were conducted at the participant’s homes [44]. Further study details are published elsewhere [44, 45]. GPs from the five study sites where the trial took place recruited potential study participants between June 2018 and October 2019. Community-dwelling patients between 60 and 77 years and with an increased CAIDE (Cardiovascular Risk Factors, Aging and Incidence of Dementia) dementia risk score (score≥9) [37] were eligible to participate. The CAIDE score has previously been shown to predict dementia risk 20 years later [37]. It is an additive risk score, including information on age, education, sex, blood pressure, body mass index, total cholesterol, and physical activity. With a cut-off value of 9, the test’s sensitivity is 0.77 and its specificity is 0.63 [37]. Conditions affecting safe participation in the trial, like a fatal illness, significant, very severe loss of vision and hearing, or insufficient ability to speak, as well as diagnosed or GP-suspected dementia were exclusion criteria for participation in the AgeWell.de trial [44].

Ethics approval and consent to participate

The AgeWell.de trial was evaluated and approved by the responsible ethical board (Ethics Committee of the Medical Faculty of the University of Leipzig, ethical vote number: 369/17-ek, 12 October 2017) as well as from all local ethics committees of the participating study sites. All participants provided written informed consent. The study was conducted according to the Declaration of Helsinki. We confirm that all methods were carried out in accordance with relevant guidelines and regulations.

Measures

Social activity, stages of behavior change, and duration of current level of social activity

In this study, two measures were employed to assess social activity. Firstly, participants were asked a self-constructed item: “Do you regularly participate in social activities (at least once a week) such as clubs, senior afternoon gatherings, church, classes, trips, cultural events?” They could respond with “yes” or “no,” which allowed us to categorize them as socially active or low in social activity [44]. Additional response options enabled the classification of participants based on their stage of health behavior change [40, 41]. For those who indicated that they are not regularly participating in social activities, they could further specify whether:

They do not intend to become more socially active (precontemplation stage).

They are considering becoming more socially active (contemplation stage).

They have a firm intention to become more socially active (preparation stage).

For those who indicated that they are socially active, they could specify whether:

It is challenging for them to maintain this level of social activity (action stage).

It is easy for them to maintain this level of social activity (maintenance stage).

Additionally, participants were asked about the duration of their current level of social activity using the item “Since when have you been as socially active as you are currently? Since ___Weeks ___ Months ___Years”.

Secondly, in a self-constructed questionnaire consisting of nine items, study participants were asked to indicate the different types of social activities they engage in. This questionnaire was based on a previous study [46] and was updated for the AgeWell.de trial. These activities included hobbies with other people, involvement in clubs or organizations, going to the cinema, restaurant, theatre, or pub and engaging in volunteer work. We calculated the total count of different social activities pursued by each individual by summing their responses, ranging from 0 to 9. These items were also used to examine which activities were most commonly pursued in the sample.

Individual factors

Sociodemographic factors: age, sex, marital status, education level, net equivalence income. We used standard items to assess age, sex, marital status, education level (according to CASMIN [47]) and equivalence income. For the regression analyses, we recoded monthly income into a categorical variable (0–500 €, 501–1000 €, 1001–1500 €, ...) to facilitate interpretation of results.

Psychological factors: self-efficacy. The validated self-efficacy scale assesses beliefs about subjective control and competence expectations in various demanding situations in general life using ten items [48, 49]. The individual test score is calculated by summing all items and ranges between 0 points (low self-efficacy) and 30 points (high self-efficacy).

Psychological factors: Self-reported depressive symptoms were measured with the 15-item Geriatric Depression Scale (GDS) [50, 51]. GDS Scores ranged between 0 and 15, with higher scores indicating more depressive symptoms.

Health-related factors: cognitive functioning –Montreal Cognitive Assessment [52]. The Montreal Cognitive Assessment (MoCA) is a brief screening tool to capture an individual’s cognitive functioning. It covers the domains memory, visuospatial abilities, executive function, attention/working memory, language, reasoning, and orientation. A maximum of 30 points can be achieved, less than 26 points indicates cognitive impairment.

Health-related factors: physical impairments—mobility, vision, hearing, urinary incontinence. Mobility impairment (“Do you have walking problems?”), visual impairment (“Do you have vision problems?”) and hearing impairment (“Do you have hearing problems?”) were assessed using self-constructed items adapted from Luck et al. [53]. Response options ranged from 0 (no impairment) to 3 (severe impairment). For the analyses, responses were dichotomized into 0 (no impairment, for response option 0) and 1 (impairment, for response options 1–3). Visual and hearing impairments compensated by glasses or hearing aids were counted as 0 (no impairment).

Urinary incontinence was measured using an item from the Barthel index [54]. Response options were 10 (no urinary incontinence or successful compensation without wetting clothes or bed), 5 (mostly successful compensation of urinary incontinence with not more than 1 time per day wetting of clothes or bed) or 0 (urinary incontinence more than once per day). Responses were dichotomized into 0 (no urinary incontinence, for response option 10) and 1 (urinary incontinence, for response options 5 and 0).

Environmental factor

Usage of public transport: Amsterdam –Instrumental Activities of Daily Living [55]. To capture the patients’ usage of public transportation, we included the item “Within the past four weeks: Did you use public transport?” from the Amsterdam Instrumental Activities of Daily Living (IADL) questionnaire [55].

Statistical analyses

For our analyses, we divided participants into two groups: Firstly, the socially active group, comprising those who engaged in social activities regularly (at least once a week). Secondly, the group low in social activity, consisting of individuals who participated in social activities less than once a week.

In the first step, we calculated descriptive statistics for the entire sample and the two groups, considering sociodemographic, psychological, health, and environmental factors. Additionally, we provided a descriptive overview of the frequency of the reported social activities.

In the second step, we conducted a multiple logistic and a Poisson regression analysis, wherein the two measures of social activity (at least once a week: logistic regression, count of social activities: Poisson regression) were regressed on sociodemographic factors (sex, age, marital status, education, income), psychological factors (self-efficacy, depressive symptoms), health factors (cognitive functioning, mobility impairment, visual impairment, hearing impairment, urinary incontinence), and an environmental factor (usage of public transport). All variables were entered simultaneously. There was no indication of multicollinearity, as the variance inflation factor (VIF) consistently remained below 2.

In the third step, to provide insights for prevention approaches aimed at promoting social activity, we calculated the proportion of participants in each stage of change according to the transtheoretical model of health behavior change and the proportion of participants who engaged in different types of social activities. To investigate the stability of this health behavior, we also calculated the median duration of the current level of social activity for the entire sample, as well as for the socially active group and the group low in social activity. Additionally, we explored whether there were differences between the socially active group and the group low in social activity regarding the duration of their current level of social activity. Due to non-normal distribution of the duration variable, as indicated by visual inspection and statistical tests, the non-parametric Mann-Whitney U test was employed to test for group differences.

RESULTS

Description of the sample

Out of the 1030 participants who took part in the baseline assessment of the AgeWell.de trial [56], 1015 (98.5%) responded to the item asking whether they were or were not regularly socially active (at least once a week) and were included in our analyses. Out of these, 432 (43%) responded that they were not regularly socially active (less than once a week) and were categorized as low in social activity. In contrast, 583 (57%) individuals reported being socially active. Table 1 presents the baseline characteristics of the total sample, as well as divided by groups. Mean age of the total sample was 68.9 years, 52% of the participants were female, 64.7% were married or living with a partner, 24.3% had a low, 53% had a medium and 22.7% had a high education.

Table 1

Characteristics of the total sample, the socially active group (regularly socially active, at least once a week) and the group low in social activity

	Total sample	Low social	Socially active	p
	(n = 1,015)	activity (n = 432)	(n = 583)
Sociodemographic factors
Age, M±SD	68.9±4.9	68.7±5.0	69.1±4.9	0.206^a
Sex, “female”, n (%)	528 (52.0)	207 (47.9)	321 (55.1)	0.024^b
Marital status, n (%)
Married/cohabitating	657 (64.7)	279 (64.6)	378 (64.8)	0.933^b
Education, n (%)
low	247 (24.3)	109 (25.2)	138 (23.7)	<0.001^b
medium	538 (53.0)	255 (59.0)	283 (48.5)
high	230 (22.7)	68 (15.7)	162 (27.8)
Equivalence income in Euros, M±SD	1564±812	1353±640	1716±886	<0.001^a
Psychological factors
Depressive symptoms (GDS Score), M±SD	1.62±2.0	2.15±2.3	1.23±1.7	<0.001^a
Self-efficacy score, M±SD	22.4±4.9	22.3±5.2	22.5±4.7	0.464^a
Health factors
Cognitive functioning (MoCA score), M±SD	24.5±3.1	23.9±3.3	25.0±2.8	<0.001^a
Mobility impairment, “Yes”, n (%)	201 (19.8)	122 (28.2)	79 (13.6)	<0.001^b
Visual impairment, “Yes”, n (%)	68 (6.7)	37 (8.6)	31 (5.3)	0.041^b
Hearing impairment, “Yes”, n (%)	60 (5.9)	39 (9.0)	21 (3.6)	<0.001^b
Urinary incontinence, “Yes”, n (%)	18 (1.8)	11 (2.5)	7 (1.2)	0.109^b
Environmental factor
Usage of public transport, n (%)	545 (53.7)	189 (43.9)	356 (61.1)	<0.001^b

^at-test; ^b χ²test; m, mean; SD, standard deviation; n, number of observations; MoCA, Montreal Cognitive Assessment; GDS, Geriatric Depression Scale.

Regression analyses

The results of the logistic regression (dependent variable: being socially active at least once a week) are presented in Table 2. The model accounted for 18.8% of the variance in social activity (Nagelkerke’s R²). Concerning sociodemographic variables, income was positively associated with social activity: A higher income of 500 € was associated with 1.2 times (95% CI [1.11, 1.39]) higher odds of being socially active. Regarding psychological factors, a one-point higher score in depressive symptoms was associated with 14% lower odds of being regularly socially active (OR = 0.86, 95% CI [0.79, 0.93]). Further, better cognitive functioning significantly predicted social activity; the odds of being socially active were 1.1 higher (95% CI [1.04, 1.16]) for each one-point higher score in the MoCA. Moreover, participants with mobility impairment (OR = 0.65, 95% CI [0.26, 0.90]) were significantly less likely to be socially active than participants without impairments. Finally, the environmental variable of using public transport was significantly associated with social activity, as participants who used public transport were more likely to be socially active (OR = 1.65, 95% CI [1.22, 2.21]) than those who did not. Neither age, nor sex, marital status, education, self-efficacy, visual impairment nor urinary incontinence were significant predictors of social activity in this model.

Table 2

Results of the logistic regression of social activity (at least once a week) on sociodemographic, psychological, health and environmental factors

	b	SE	p	OR	OR 95% CI
					Lower	Upper
Constant	–3.790	1.43	<0.001
Sociodemographic factors
Age	0.02	0.02	0.194	1.020	0.990	1.052
Sex (reference: male)	0.24	0.16	0.122	1.277	0.937	1.740
Marital status (reference: single)	–0.01	0.16	0.537	0.904	0.655	1.246
Education low (reference category)
Education medium	–0.23	0.19	0.220	0.796	0.553	1.146
Education high	0.16	0.24	0.510	1.174	0.729	1.891
Income	0.22	0.06	<0.001	1.244	1.113	1.389
Psychological factors
Depressive symptoms	–0.15	0.04	<0.001	0.858	0.790	0.932
Self-efficacy	–0.00	0.02	0.876	0.997	0.966	1.030
Health factors
Cognitive functioning	0.09	0.03	<0.001	1.095	1.038	1.155
Mobility impairment	–0.44	0.19	0.021	0.646	0.445	0.938
Visual impairment	–0.35	0.31	0.261	0.706	0.384	1.296
Hearing impairment	–0.72	0.31	0.022	0.487	0.264	0.900
Urinary incontinence	–0.35	0.55	0.523	0.702	0.237	2.077
Environmental factors
Usage of public transport	0.49	0.15	0.001	1.646	1.224	2.212

Table 3 shows the results of the Poisson regression (dependent variable: count of social activities). The model shows an adequate goodness-of-fit with a deviance value of 0.941. Income, depressive symptoms, cognitive status, mobility and hearing impairment, as well as using public transport, were all significant predictors of the number of social activities an individual engaged in.

Table 3

Results of the Poisson regression of social activity count on sociodemographic, psychological, health and environmental factors

	b	SE	p	IRR	IRR 95% CI
					Lower	Upper
Sociodemographic factors
Age	0.004	0.004	0.259	1.004	0.997	1.012
Sex (reference: male)	0.037	0.039	0.340	1.038	0.961	1.121
Marital status (reference: single)	0.010	0.041	0.802	1.010	0.932	1.095
Education low (reference category)
Education level high	0.070	0.056	0.216	1.072	0.960	1.197
Education level medium	–0.083	0.048	0.081	0.920	0.838	1.010
Income	0.046	0.011	<0.001	1.047	1.025	1.070
Psychological factors
Depressive symptoms	–0.091	0.012	<0.001	0.913	0.891	0.935
Self-efficacy	–0.006	0.004	0.151	0.994	0.986	1.002
Health factors
Cognitive functioning	0.020	0.007	<0.001	1.020	1.006	1.034
Mobility impairment	–0.166	0.054	<0.001	0.847	0.762	0.941
Visual impairment	–0.162	0.087	0.062	0.850	0.717	1.008
Hearing impairment	–0.200	0.088	0.023	0.819	0.689	0.973
Urinary incontinence	–0.029	0.151	0.849	0.972	0.724	1.305
Environmental factor
Usage of public transport	0.174	0.038	<0.001	1.191	1.105	1.283

Stages of behavior change

Table 4 displays the distribution of participants in each stage of behavior change regarding social activity. The majority of participants in the sample were in the maintenance stage (56.4%), they reported being socially active and that they found it easy to engage in such activities. The second most common stage was the precontemplation stage (30.8%), where participants reported that they are not regularly socially active and also do not intend to become more active. A smaller percentage of participants were in the contemplation stage (8.7%), indicating that they were not regularly socially active but were considering becoming more active. Even fewer participants were in the preparation stage (2.2%), indicating a firm intention to become more socially active. Only a small number of participants reported being regularly socially active but finding it difficult (action stage, 1.9%).

Table 4

Number of participants in the different stages of change according to the transtheoretical model of health behavior change

	Stages of change	Item	Number (%) of participants
Low social		Not regularly (at least once a week)	N = 414
activity		socially active and ...
	Precontemplation	I do not intend to be.	306 (73.9)
	Contemplation	I think about it.	86 (20.8)
	Preparation	I firmly intend to be.	22 (5.3)
Socially active		Regularly (at least once a week)	N = 578
		socially active and ...
	Action	I find it difficult.	19 (3.3)
	Maintenance	I find it easy.	559 (96.7)

Types of social activities

Figure 1 illustrates the proportion (in %) of participants who participated in the specified social activities, differentiated by whether they were socially active or low in social activity. A substantial proportion of the socially active participants reported that they have a hobby which they share with others (90.1%), followed by traveling with others (83.2%), and going to the cinema, restaurant, theatre, or pub (81.8%). The least frequently mentioned activities were volunteering (34.9%), visits to adult education centers (13.1%), and attending church (11.4%). Participants who were classified as less socially active reported all activities less frequently. The differences between the two groups were statistically significant at p < 0.001.

Fig. 1

Proportion (%) of individuals who participate in the specific social activity, separated for the group low in social activity versus the socially active group.

Duration of current level of social activity

When participants were asked about the duration of their current level of social activity, the overall median response was 13 years. Specifically, the socially active group reported a median duration of 18 years, while the group low in social activity reported a median duration of 10 years. The difference in duration between the two groups was statistically significant, as confirmed by the Mann-Whitney U test (U = 72,573.5, p < 0.001).

DISCUSSION

In the present study, we investigated factors associated with lower social activity in older adults at increased risk of dementia. Low social activity is an aspect of social isolation and has frequently been linked to cognitive decline [12 , 15–17], dementia [18 –21] and a broad range of other physical and mental illnesses [22]. It is therefore a target for prevention in general [57] and dementia prevention in particular [18]. Our study confirms previous findings regarding factors associated with low social activity in older adults and extends these findings to older adults at increased risk of dementia. Consistent with previous research [23, 25], our study shows that lower social activity in older adults at increased risk of dementia is linked to a lack of individual resources (e.g., income) and environmental factors (e.g., using public transport). Moreover, in line with earlier research [27 , 58], we identified psychological and health-related factors, specifically depressive symptoms, lower cognitive functioning, mobility impairment, and hearing impairment, as barriers to a socially active lifestyle. In contrast to our expectations, we did not find statistically significant associations between visual impairment, urinary incontinence, and the level of social activity. However, the latter may be due to a lack of statistical power as the number of people reporting urinary problems was small. Nevertheless, we are also aware that statistically significant findings are not guaranteed by a larger sample size. However, continence problems are a public health concern, and future studies should further investigate the potential role of incontinence for lower social activity.

Our findings have implications for the development of prevention approaches and public policy. Firstly, they can help identify specific risk groups for low social activity within the group of older adults at increased risk of dementia. Our results indicate that individuals with limited financial resources or those not using public transport, as well as those with depressive symptoms and poorer cognitive or physical health, are more at risk of being less socially active. These risk groups may be specifically targeted by prevention approaches aiming to promote social activity. In this context, low income, as a social determinant of health, may play a key role. It is important to recognize that lower income is not just one factor associated with lower social activity, but a factor that is also associated with poorer health (morbidity, mental health, cognitive impairment) [59 –61], which, in turn, is associated with lower social activity. Secondly, knowledge of these factors allows us to identify barriers to social activity, which can inform public policy. For instance, creating accessible and low-threshold options for social engagement, especially in local communities, can enable older adults with limited financial resources or mobility impairments to participate in social activities.

Surprisingly, we did not find a significant association between self-efficacy and social activity, despite self-efficacy being considered a theoretically important factor for health behavior change [43] and being empirically linked to higher social activity in previous studies [28, 29]. One potential explanation could be related to the composition of our sample in terms of stages of behavior change [62]. Self-efficacy may have a stronger impact on individuals who are actively contemplating behavior change or who have already initiated the change and are facing challenges in maintaining it [35]. However, in our sample, the majority of participants with low social activity were in the precontemplation stage, indicating they had no intention of increasing social activity in the near future. Another explanation may be that in our group of older individuals at increased risk of developing dementia, other factors may play a stronger role, such as physical and health limitations, which also emerged as strong predictors in our study. Finally, the general self-efficacy scale [48] which we have used in our study, despite being validated in samples of older adults, may not have been sensitive enough to detect variations in the specific type of self-efficacy relevant to social activities. The recently developed “self-efficacy for social participation” scale by Oe and Tadaka could be an adequate tool to capture self-efficacy with regard to social activity in future studies [63].

Our findings carry implications for the development and evaluation of interventions aimed to address social inactivity. An intervention needs to align with an individual’s stage of behavior change in order to be successful [40]. As our sample is composed of many participants in the precontemplation stage, an intervention focusing particularly on this group—individuals in the earliest stage of behavior change—could be particularly effective. A promising strategy may be to increase knowledge and raise awareness about the benefits of social activity [40]. Providing information on the positive impact of social engagement on brain health or its wide-ranging positive effects on mental and physical well-being could be valuable. However, for these efforts to be fruitful, it is equally essential to offer realistic opportunities to participate in enjoyable social activities. This underlines the significance of providing accessible and low-threshold avenues for social engagement, as mentioned earlier.

Finally, we examined the types of social activities that participants engaged in and the duration of their current level of social activity. As expected, people who were socially active mentioned all social activities more frequently than those who were less active. Having a hobby with others was the most frequently mentioned social activity, followed by travelling with others and going to the cinema, restaurant, theatre, or bars in socially active individuals.

Moreover, participants reported that their level of social activity has been stable for several years. This finding is in line with the continuity theory of aging, which states that individuals often show continuity and consistency in their activities as they age [64, 65]; therefore, low social activity may not be a particularly easy-to-change health behavior. An alternative explanation for the stable levels of social activity might be found in the link between lower income and lower social activity; stability of the level of social activity could also be related to consistently high versus low financial resources.

However, these results should not discourage researchers and practitioners from developing and implementing interventions aimed at addressing low social activity, because research also shows that some older adults start new activities, for example as they retire, and that others give up activities due to poor health or environmental barriers [64].

Limitations

In the literature, there is a lack of consistent definitions and operationalizations for many constructs related to social health, including social activity [12, 15]. While attempts have been made to provide conceptual clarity [66], a consensus has not been reached. In our study, similar to other contributions on this topic [12, 14], we adopted a broad definition of social activity to encompass a wide range of activities. Moreover, in the current study, we defined regular, at least weekly social activity as desirable, recognizing that there are likely individual differences in the preferred and healthy level of social engagement.

Additionally, we acknowledge the limitation of our cross-sectional design, which prevents us from establishing causality. The results of our study should be interpreted with caution and in consideration of its cross-sectional design. However, a strength of our study is that we examine a range of factors that can play a role in a person’s level of social activity.

Another limitation is that our data contains only limited information about socioeconomic factors (e.g., no information about lifetime socioeconomic status) or environmental factors (e.g., no information about access to green spaces, no area deprivation index available). Due to the strict data protection laws in Germany, study data could also not be retrospectively linked with public data. Future research should explore the role of socioeconomic and environmental factors for the level of social activity more comprehensively.

Conclusions

We identified two main barriers for social activity in older adults at increased risk of dementia. The first barrier is a lack of individual resources, such as income, and environmental resources, such as limited usage of public transport. The second barrier is related to poorer health and psychological factors, including the presence of depressive symptoms, cognitive, mobility, and hearing impairments. Notably, a significant number of participants with low social activity reported that they have no intention of becoming more socially active in the future. Furthermore, they reported that they have been as low in social activity as they are now for several years. Strategies to promote social activity in this group may include raising awareness for its health-related benefits and providing accessible, low-threshold opportunities for social engagement. From a public health perspective, it would be desirable to offer opportunities for enjoyable social activities to everyone and to harness the many health-related positive aspects of social activity.

AUTHOR CONTRIBUTIONS

Maresa Buchholz (Conceptualization; Visualization; Writing – original draft; Writing – review & editing); Isabel Zöllinger (Conceptualization; Writing – original draft; Writing – review & editing); Jochen René Thyrian (Funding acquisition; Writing – review & editing); Melanie Luppa (Writing – review & editing); Andrea Zülke (Writing – review & editing); Juliane Döhring (Writing – review & editing); Laura Lunden (Writing – review & editing); Linda Sanftenberg (Writing – review & editing); Christian Brettschneider (Writing – review & editing); David Czock (Writing – review & editing); Thomas Frese (Writing – review & editing); Jochen Gensichen (Writing – review & editing); Wolfgang Hoffmann (Writing – review & editing); Hanna Kaduszkiewicz (Writing – review & editing); Hans-Helmut König (Writing – review & editing); Birgitt Wiese (Writing – review & editing); Steffi G Riedel-Heller (Funding acquisition; Supervision; Writing – review & editing); Iris Blotenberg (Conceptualization; Formal analysis; Methodology; Supervision; Writing – original draft; Writing – review & editing).

Footnotes

ACKNOWLEDGMENTS

Members of the AgeWell.de-study group: Principal and Co-Principal Investigators: Steffi G. Riedel-Heller (PI); Wolfgang Hoffmann, Jochen Gensichen, Walter E. Haefeli, Hanna Kaduszkiewicz, Hans-Helmut König, Thomas Frese, David Czock, Jochen Rene Thyrian; Birgitt Wiese, Franziska Berg, Andrea Bischhoff, Christian Brettschneider; Mandy Claus, Juliane Dohring, Alexander Eser, Corinna Grable, Stephanie Hingst, Caroline Jung-Sievers, Kerstin Klauer-Tiedtke, Kerstin Krebs-Hein, Flora Kuhne, Sebastian Lange, Paula Liegert, Dagmar Lochmann, Tobias Luck, Melanie Luppa, Silke Mamone, Lea Markgraf, Andreas Meid, Michael Metzner, Lydia Neubert, Anke Oey, Susanne Rohr, Franziska-Antonia Zora Samos, Karin Schumacher, Theresa Terstegen, Anne Henrike Wagner, Lars Wamsiedler, Tanja Wehran, Marina Weißenborn, Ines Winkler, Isabel Zollinger, Andrea Zülke, Ina Zwingmann.

FUNDING

This publication is part of the study “AgeWell.de – a multi-centric cluster-randomized controlled prevention trial in primary care” and was funded by the German Federal Ministry for Education and Research (BMBF; grants: 01GL1704A, 01GL1704B, 01GL1704 C, 01GL1704D, 01GL1704E, 01GL1704F).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The datasets used and/or analyzed during the current study is available from the corresponding author on reasonable request.

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