Self-management Elucidates How Practicing Physical Exercises Influences the Health Related Quality of Life of Independently Dwelling Older Adults

Abstract

According to active aging theory, self-management plays a pivotal role for well-being of older adults as they navigate the aging process. The current quasi-experimental study, employing a between-within design, examines the impact of guided group physical training on changes in self-management and its subsequent effect on quality of life among a sample of independently living old adults. We assessed balance, strength, mobility, self-management, and quality of life were among 149 older adults (123 females, 26 males), mean age = 77.21. Half of the participants then began a 6 months of chair exercise training, consisting of one session per week. However, the training program was interrupted after 22 sessions due to the outbreak of the Covid-19 pandemic. Subsequently, participants were re-evaluated following the initial lockdown announcement but before its implementation. Show first, that practicing physical exercises, among the intervention group, led to increases in the three measured physiological abilities—balance, strength and movement—as well self-management and physical and mental quality of life. Second, the physiological abilities, were fully indirectly associated through self-management with physical and mental quality of life. The present findings provide a clear understanding of the role of self-management as a psychological outcome of reflected physical activity, as well as a mediator for health related quality of life. Further, self-management abilities among older adults can be regarded as a protective factor against adverse psychological outcomes at times of trauma.

Public significant statement

• Practicing reflected physical activity improves older adults’ self-management abilities. Older adults who are actively managing their time and activities have a better quality of life. Public interventions should focus on strengthening self-management.

• What do we already know about this topic?

• Physical training is crucial for well-being among older adults coping with aging.

• How does your research contribute to the field?

• Practicing physical exercise increases physiological abilities, self-management, and physical and mental quality of life. However, physiological abilities were found to be fully indirectly associated through self-management with physical and mental quality of life.

• What are your research’s implications toward theory, practice, or policy?

• The relation between practicing physical training and quality of life is mediated by other internal mechanisms. Focusing on developing self-management programs for older adults coping with aging is essential and will contribute to a better quality of life.

Introduction

The number of older adults (65 and above) within the general worldwide population is increasing and expected to rise by 1.5% until 2030 and 7.5% until 2060 reaching 17% of the general worldwide population, 25% among Americans ¹ and 30% among Europeans. ² Old age is associated with increasing prevalence of frailty, ³ manifested by a slow and steady decline in physical, mental, and social functioning as well as reduced ability to recover from health problems. ⁴ Old adults experiencing frailty often require complicated combination of health and social care services over long terms. ⁵ Old age is also associated with decreased Quality of life (QoL) and Health Related Quality of life (HRQoL) that refers to the health aspects of the physical, psychological, and social functioning as reflecting well-being. ⁶ Among old adults physical activity is one aspect that is strongly associated to QoL and HRQoL.^7-9Decreasing QoL among old adults is associated with decreasing in physical activity^10,11 while practicing physical activities contribute to physical functioning, mental health, and social welfare.^12,13 However, it is still unclear whether practicing physical activities influence only directly on HRQoL or as well as indirectly and how? Based on previous findings ¹⁴ we suggest that practicing physical activities also increases self-management that in turn contributes to the increase in HRQoL.

This paper delivers 2 significant contributions. First, we show that practicing physical exercise emphasizing on individual’s self-reflection while practicing can contribute to the improvement of self-management. Second, while the influence of practicing physical exercise on HRQoL is strongly known its pass is not yet fully comprehended. We explore and show that beyond the improvement in physical functioning the improvement in self-management due to practicing physical exercise has a substantial unique contribution to HRQoL (see Figure 1).

Figure 1.

Hypothesized models of the physical abilities indirect association with physicial HRQoL and mental HRQoL through self-management.

Note. Direct associations = c′; Indirect association = ab; Total association = c′ + ab.

In what follows, we first briefly review background information about physical activities, HRQoL and successful aging as well as on self-management and the influence of the Covid-19 Pandemic on older adults. We then introduce the present study and present our hypotheses. Following this, we describe our method and present the results. We conclude with a general discussion, limitations, and suggestions for future research.

Physical Activity, HRQoL and Successful Aging

Physical activity has been shown to be associated with better HRQoL among all age groups: children and adolescence, ¹⁵ adults, ¹⁶ and old adults.^17-19 Findings support the assumption that frequent visits of old adults to fitness centers are associated with higher levels of physical and mental HRQoL. ⁷ Also, studying old adults’ self-reports show associations between physical activity and the subjective feeling and estimation of successful aging ²⁰ as well as objective criterions like absence of disability, mental health problems, good cognition, and functioning. ²¹ Other studies support the findings that increasing daily steps as a measure of physical activity contribute to HRQoL.^22,23 Overall, among older adults the relation between physical activity, even as basic as daily steps, and HRQoL is quite substantial.

Self-management

Self-management is an aspect of aging management ²⁴ —a relatively novel term that emerged from the theory of active aging.^25,26 According to this theory, older adults individuals can maintain positive well-being through a combination of physical, social, educational, and cultural activities.^27,28 Previous work has shown that among older adults, physical activity improves autonomy and reduces dependence. ²⁹ Being active helps keep the individual emotionally and mentally fit, ³⁰ whereas passivity leads to loneliness and social isolation, ³¹ and to negative emotions such as unhappiness and depression. ³²

From a resource perspective, successful aging requires the proactive management of resources. ³³ Self-management enables the individual to use internal resources, such as initiative, self-efficacy, or a positive frame of mind, ³⁴ to manage external resources (eg, food, friends, family) in such a way that physical and social well-being are maintained or restored. ³⁵ Even when resources are declining, for instance in the wake of illness or other major life events, successful management ensures the availability of reserve capacities to realize and sustain physical and social well-being. ³⁶ Findings have shown associations between self-management among older adults and reduced loneliness,^37-39 increased well-being,^40-42 and the prevention of falls. ⁴³

Effects of the Covid-19 Pandemic on Older Adults

Since the start of the outbreak, most mortality from Covid-19 has occurred among older people. ⁴⁴ For older adults the fear of death, and awareness to their vulnerability, have led to chronic psychological pressure. ⁴⁵ Beyond the fear of death, among this age group loneliness is strongly related to depression. ⁴⁶ The social disconnection and isolation imposed by government restrictions have put older adults at greater risk of depression, anxiety, loneliness, and grief.^47,48 For many older adults the problem was compounded since the senior centers and other social spaces that have traditionally been central in promoting active aging ⁴⁹ were closed by law precisely when these places were most needed. Given these exceptional circumstances, self-management could be imperative for preserving older adults’ mental health.

Several studies have addressed problems of aid and social care to the older adults due to their unique vulnerability during the Covid-19 pandemic.^50,51 For example, Flores Tena ⁵² argues that programs to promote active aging by encouraging active participation and healthy habits can reduce older people’s dependence during the pandemic.

The Present Study

As far as we are aware, there has been no study that has explored the association between practicing physical activity, self-management and HRQoL among older adults. The current study compares physiological improvement, self-management improvement and HRQoL improvement among old adults that practice physical activities against a control group that did engage in the exercises. Also, the direct contribution of physical improvement to old adults’ HRQoL and the indirect association via influencing self-management is studied. For this research, we initially collected data on basic physiological abilities (strength, balance, and mobility), self-management and HRQoL. Subjects then underwent about 6 months of weekly training—physical exercises performed while seated on a chair, designed to engage all parts of the body and to improve strength, balance, and mobility. The initial training phase was planned to last 24 weeks, with 1 training session per week. Due to the onset of the Covid-19 pandemic and Israel’s first lockdown, the training was halted after 22 weeks, and residents were instructed to stay isolated in their apartments.

The initial lockdown was confusing and emotionally demanding for all the country’s residents regardless of their age. Accurate information regarding the virus—its transmissibility, mortality rates, effective means of prevention, etc.—was lacking. Likewise, nobody knew how long the pandemic might last, and when restrictions on residents’ movements and social interactions would be lifted.

Final data of both groups, post intervention—weekly training, chair seated, physical exercises—and control, was collected after the pandemic outbreak and the first lockdown announcement and before the lockdown actually started. However, its psychological impact was already present.

Drawing from theory and prior findings, we anticipated the following:

• Hypothesis 1: The intervention group practicing physical activity will increase basic physiological abilities—balance, strength, and mobility, whereas among the control group basic physiological abilities will stay the same or decrease.

• Hypothesis 2: Self-management will be significantly increased among the group practicing physical activity whereas among the control group Self-management will stay the same or decrease.

• Hypothesis 3: HRQoL both physical and mental will be significantly higher among the group practicing physical activity relative to the group who did not practice.

The next 4 hypothesis are related only to the experiment group post practicing -

• Hypothesis 4: The physiological abilities—balance, strength, and mobility—will be associated with HRQoL physical and mental aspects. Because of the way the tasks are measured balance will be positively associated with HRQoL physical and mental aspects and strength and mobility negatively associated.

• Hypothesis 5: The physiological abilities—balance, strength, and mobility—will be associated with self-management. Because of the way the tasks are measured balance will be positively associated with self-management, and strength and mobility negatively associated.

• Hypothesis 6: Self-management will be positively associated with HRQoL physical and mental aspects. However, based on the physical nature of HRQoL the relation will be stronger for HRQoL physical aspect than for the HRQoL mental aspect.

• Hypothesis 7: The physiological abilities will be indirectly associated with HRQoL. We expect that based on the physical nature of the HRQoL, the association will be stronger for HRQoL physical aspect than for the HRQoL mental aspect.

Method

Transparency and Openness

The Study design, hypotheses, analytic plan, and analyses were not preregistered. The de-identified data on which the study results and conclusions are based are available in https://www.dropbox.com/s/6l7ih77arpcjx8w/negevpluscorona.spv?dl=0.

Participants and Procedure

In the current study, we use a mixed-design between-within subject quasi experimental study. Self-management was a within variable, while practicing physical exercises a between variable and the physiological abilities—balance, strength and movement and physical and mental quality of life as dependents variables.

Exclusion criteria applied to all participants were as follows: (1) under the age of 65; (2) those with severe and diagnosed dementia; (3) diagnosed with significant heart disease or recognized cardiovascular or respiratory system difficulties; (4) experiencing vision loss; (5) diagnosed with vertigo symptoms or another condition affecting the balance system such as epilepsy; (6) suffering from lower limb weakness hindering standing on both legs; (7) experiencing severe bodily pain impeding joint movement.

One hundred forty-nine old adults participated in the study. A satisfactory number of sample size according to Gpower software analysis relating to the analysis performed, effect size of .40, power analysis of 0.95, number of degree of freedom (independent variables) and error of 0.05. The experiment group was comprised of 82 participants and the control group of 67 participants. None of the participants were diagnosed during the study with Covid-19. The majority of participants hailed from average to upper middle-class background. Participants were recruited through local senior centers. The demographic makeup of the sample across the different groups is detailed in Table 1. IRB approval under the name—Managing the Elderly Stataus—was obtained at the beginning of the process.

Table 1.

Demographic Characteristics of Participants by Group.

	Control	Experimental	Statistics
N	67	82	χ2 (1149) = 4.6, P = .00, n.s.
Male	11	15
Female	56	67
Age (mean in years, range, and sd in parentheses)	75.98	78.44	t(148) = 1.94
	(65-92)	(62-89)
	(8.57)	(6.77)
Marital status (frequencies)			χ2 (3149) = 4.6, P = .76, n.s.
Single	0	1
Married	36	37
Divorced	3	4
Widow	28	40
Income (frequencies)			χ2 (2149) = 0.47, P = .78, n.s.
Lower than average	11	16
Average	20	21
Higher than average	36	45

^*P < .05. **P < .005. ***P < .001.

The physical training program was introduced to older adults who attended local senior centers. Participants who wanted to take part in the program were initially interviewed in a closed room at the local senior centers. After obtaining written consent, participants were asked to complete a demographic form and physiological measurement for strength, balance, and mobility were evaluated by one of the research team. Also, self-management in various areas of life and their quality of life was measured. Then participants were randomly divided into the experiment group or a postponed group, the control group.

The participants in the experiment group engaged in 22 group sessions—one session per week—of physical exercises training before the onset of the pandemic. The sessions consisted of a series of physical exercises done while seated on a chair. The exercises were suitable for this age group and tailored to engage the entire body. During the exercises routines, participants were instructed to focus their attention on their body movements, remain mindful of their balance and mobility compared to previous classes, and be vigilant for any signs of discomfort or pains. Participants were advised to stop any exercises causing pain even if their range of movement had previously been greater, thereby keeping their physical movements within a safe range. However, participants were strongly encouraged to exert themselves to the fullest extent possible within this established safe range.

Measurements were taken at 2 points. As mentioned above, before the beginning of the training at the initial recruitment. Then, after 22 weeks final data of both groups was collected. Data was collected from all participants for 3 consecutive days after the first lockdown was announced and its actual starting time.

Measures

Physiological abilities were measured by the following three tests: Unipedal stance test (UST), Sit to stand test (STS), and timed up and go (TUG).

Unipedal stance test ⁵³ —is a static balance test. Participants were asked to stand on one leg as long as possible until the other leg touches the ground. Also, they were directed to avoid leg contact and strive to sustain a unipedal stance for as long as feasible. Normative functioning score is over 9.3 s.

Sit to Stand Test ⁵⁴ —measures the strength of the neuromuscular function of lower limbs. Participants were instructed to sit on a standard (45 cm) chair without armrests. They were then prompted to scoot forward on the chair until their feet were flat on the floor. In the next step they were guided to stand up completely until their knees and hips were fully extended and then sit back down with their upper limbs folded across the chest. Participants were tasked with repeating this sequence 5 times as quickly as they could. Normative functioning score is below 19.4 s.

Timed Up and Go test ⁵⁵ —assess mobility. Participants were directed to rise from a standard (45 cm) chair, walk a distance of 3 m, perform a 180° turn, return to the chair, and sit down while completing another 180° turn. Throughout the test, participants were advised to wear their usual footwear and utilize any mobility aids they typically use. Normative functioning score is below 14 s.

Health Related Quality of Life was measured by the Short Form Health Survey ⁵⁶ (SF-12) consisting of 12 items that comprising 1 physical component summary score (PCS; eg, “During the past four weeks, how much of your time you were limited in your regular daily activities as a result of your physical health?”) and 1 mental component summary score (MCS; eg, “How much of the time during the past 4 week have you felt calm and peaceful?”). The scores in each area (PCS/MCS) are standardized to range between 0 (lowest QoL) and 100 (highest QoL). The MCS score under 42 can be used as a screening method indicating a depressed mood and is hence, useful to discover depressed patients (also including those not expressing this complaint). The F-12, the shortest version of the SF-36 has been found to be suitable for older people because the questionnaire is short and easy to administer and does not contain questions that emphasize workplace. ⁵⁷ Internal consistencies for the pre-training and post-pandemic onset measures, respectively, were as follows: PCS—α = .84, α = .77; MCS—α = .81, α = .80

Self-management was assessed through the Self-Management Ability Scale (SMAS). ³⁴ This self-report measure, consisting of 30 items, encompasses diverse aspects of self-management, including initiative (“How often do you take the initiative to keep yourself busy?”), self-efficacy (“Are you capable of taking good care of yourself?”), investment in the self (“Do you ensure that you have enough interests on a regular basis?”), positive frame of mind (“How often are you able to see the positive side of the situation when something disagreeable happens?”), resources (“Do you have different ways to relax when necessary?”), and multifunctionality (“The activities I enjoy, I do together with others”). Most statements are scoured on a 5-point scale (1 = never to 5 = very often), with some adjustment for the multifunctionality, self-efficacy, and positive frame of mind dimensions. Internal consistencies for the pre-training and post-pandemic onset measures, respectively, were as follows: initiative—α = .60, α = .88; self-efficacy—α = .86, α = .92; investment—α = .75, α = .89; positive frame of mind—α = .82, α = .76; resources—α = .73, α = .87; and multifunctionality—α = .82, α = .85. Cronbach’s alphas for the total scale were .93 and α = .95 for the pre-training and post-training, respectively.

Data Analysis

For testing the hypotheses, we used three different analyses. H1 to H3 were analyzed using ANCOVA analysis. H4 to H6 by correlational analysis and H7 by SPSS macro developed by. ⁵⁸ Each analysis is addressed by a thorough pre-explanation including pre-analysis tests.

Results

The analysis is structured into 3 sections. First, we present the descriptive analyses and outcomes, addressing hypotheses H1 to H3. Subsequently, we offer the descriptive analysis and results pertaining to hypotheses H4 to H6. Finally, the findings regarding the indirect association hypothesis (H7) are presented.

Physiological Abilities—Hypotheses (H1–H3)

For analyzing whether the intervention group that practiced the physical activity protocol increased its basic physiological abilities—balance, mobility, and strength—whereas the control group basic physiological abilities stayed the same or decreased (H1) we used a mixed-design 3 × 2 × 2 ANCOVA (Physiological abilities [balance, mobility, and strength] × group [intervention vs control]) × time [before (time1) vs after (time2)]. We considered the physical abilities and the time as within-participant variables while the group was treated as a between participants variable. Age, gender, income and marital status were controlled and treated as covariates in the analysis. None of the covariates were related. Before conducting the analyses, we screened the data for outliers, following Ratcliff ⁵⁹ criteria of ±2 SD above or below the mean. Additionally, preliminary analysis indicated that Skewness was below 1 for all variables, and all kurtosis criteria were within acceptable limits. These pre analysis tests were done for all furtherer analysis. Since as the time of the UST scale is higher the balance is better but as the time of the STS and TUG is lower the strength and mobility, correspondingly, are better, calculating the mean of all 3 physical abilities will not reflect the overall physical condition. Therefor looking at the main effects is not informative, however, analyzing the triple interaction—physical abilities × Group × Time—is revealing. As expected, there was a significant triple interaction F(2,292) = 34.41, P < .0001, ηp² = 0.22, observed power = 1. Figure 2 depicts the follow-up t-test analysis with the alpha adjusted to 0.05/6 = 0.008 (Bonferroni correction for multiple comparisons). As anticipated, there was no significant difference between the groups for any of the physical abilities at time1, prior to the commencement of the intervention—balance t(146) = 0.27, P = .78; mobility t(146) = 0.53, P = .59; and strength t(146) = 0.62, P = .53. However, significant differences were found for all of the physical abilities at time2 after the intervention took place—balance t(146) = 5.74, P < .0001; mobility t(146) = 5.37, P < .0001; and strength t(146) = 2.95, P < .005.

Figure 2.

Interaction between the physical abilities, group and time.

Note. Each physical ability was analyzed separately by conducting two-tailed independent sample t-tests. Bonferroni adjustment of the alpha was conducted (a = 0.05/6 = 0.008).

*P < .05. **P < .005. **P < .001.

Moving forward, to test whether self-management will be significantly increased among the group practicing the physical activity protocol whereas among the control group Self-management will stay the same or decreased (H2) a mixed-design 2 × 2 ANCOVA (group [intervention vs control]) × time [before (time1) vs after (time2)] was conducted. We treated the time as a within-participant variable and the group as a between participants variable. Age, gender, income, and marital status were controlled and treated as covariates. Overall, there was an effect of the physical intervention on the groups. The dual two-way disordinal interaction between group and time reveals that due to the intervention the self-management was different among the groups F(1,142) = 110.63, P < .0001, ηp² = 0.43, observed power = 1. Follow-up t-test analysis (with Bonferroni adjustment of the alpha to 0.05/2 = 0.025) show that as expected, there was not any significant difference among the self-management ability between the groups before the intervention took place t(148) = 1.64, P = .10 (M = 3.90, SE = 0.07; M = 3.73, SE = 0.07 for the intervention and control groups at time1 correspondingly). But at time2 self-management was increased among the intervention group and decreased among the control group t(148) = 14.04, P < .0001 (M = 4.77, SE = 0.07; M = 3.2, SE = 0.08 for the intervention and control groups correspondingly). We believe that the decrease in self-management among the control group at time2 was related also to the outbreak of the covid19. This will be further elaborated in the discussion below.

At the end of the first part of the results our third hypothesis—HRQoL both physical and mental will be significantly higher among the group who practiced the physical activity protocol relative to the group who did not practice—was explored by a mixed-design 2 × 2 × 2 ANCOVA (HRQoL [physical and mental] × group [intervention vs control]) × time [before (time1) vs after (time2)]. We treated the physiological abilities and the time as within-participant variables and the group as a between participants variable. Age, gender, income, and marital status were controlled and treated as covariates.

As expected, there was a significant triple interaction F(1,137) = 12.27, P < .001, ηp² = 0.08, observed power = 0.93. Figure 3 presents the follow-up t-test analysis (with Bonferroni adjustment of the alpha to 0.05/4 = 0.0125). There was not a significant difference between the groups for physical and mental quality of life in time1, preintervention. Though the difference in physical quality of life was close to being significant. Physical, t(148) = 1.93, P = .54, P = .054; mental, t(148) = 1.61, P = .10; Nonetheless robust significant differences were found between the groups for physical and mental quality of life at time2. Physical, t(148) = 4.64, P < .0001; mental, t(148) = 5.37, P < .0001. The impact of the intervention was also studied within the groups by conducting follow-up t-test analysis (with Bonferroni adjustment of the alpha to 0.05/4 = 0.0125). The physical quality of life was increased for the intervention group at time2 compering to time1 t(78) = 2.59, P < .05 (M = 65.49, SE = 2.73; M = 72.54, SE = 2.19 for time1 and time2 correspondingly), but not for the control group t(63) = 0.12, P = .90. (M = 56.21, SE = 3.03; M = 57.60, SE = 2.43 for time1 and time2 correspondingly). Interestingly, the mental quality of life at time2 compared to time1was decreased, however, not significantly for the intervention group but significantly, and extremely, for the control group. Intervention, t(78) = 1.62, P = .10 (M = 71.76, SE = 2.78; M = 65.11, SE = 2.35 for time1 and time2 correspondingly); control, t(63) = 8.18, P < .0001 (M = 64.29, SE = 3.09; M = 35.03, SE = 2.61 for time1 and time2 correspondingly). As with the self-management variable we believe that this outcome, the dramatic decrease in mental quality of life among the control group was influenced by the Covid19 outbreak. This idea will be further elaborated in the discussion below. The moderate, and insignificant, observed decrease in mental quality of life among the intervention group at time2 can be attributed to the indirect influence of the improvement in the physiological abilities on mental quality of life through the increase in self-management—Our final hypothesis (H7) which is examined below after the exploration of the direct associations.

Figure 3.

Interaction between physical and mental qulity of life and time.

Note. Physical and mental qulity of life were analyzed separately by conducting two-tailed independent sample t-tests. Bonferroni adjustment of the alpha was conducted (a = 0.05/4 = 0.0125).

*P < .05. **P < .005. **P < .001.

Direct Associations, Control Group—Hypotheses (H4–H6)

Wishing to explore the role of self-management in the relation between the physical abilities and HRQoL physical and mental aspects due to physical training, the following results will be presented only for the experiment group post practicing. Table 2 presents the direct associations for the 2 hypothesized models (see Figure 1). Including first, the direct associations between the physical abilities and HRQoL physical and mental aspects (H4), then the direct association between the physical abilities and self-management (H5) and finally the direct association between self-management and HRQoL physical and mental aspects (H6).

Table 2.

Means, Standard Deviations, and Intercorrelations Post Practicing.

Variables	M	SD	1	2	3	4	5
Balance	22.53	13.57	—
Strength	11.65	2.55	−0.34**	—
Mobility	14.65	3.63	−0.32**	0.50***	—
Self management	4.76	0.45	0.11	−0.37***	−0.46***	—
Physical HRQoL	72.54	16.24	0.15	−0.23*	−0.34**	0.52***
Mental HRQoL	65.11	25.11	0.10	−0.25*	−0.24**	0.65***	.41***

^*P < .05. **P < .005. ***P < .001.

As can be seen in Table 2 there was a significant correlation between all 3 physical abilities, though the correlation was negative between balance and strength and balance and mobility due to the different nature of the tasks. Further, as posited by H4, there was a significant negative correlation between strength and mobility and self-management as well as significant negative correlation between strength and mobility and physical and mental quality of life (H5). Relating to these correlations it is worth keeping in mind that for strength and mobility as the time performing the tasks is shorter the strength and mobility are better. Also, balance was not correlated with self-management or HRQoL physical and mental aspects. Moving ahead, Table 2, further presents the positive correlation between self-management and physical quality of life and mental quality of life (H6). The latter was, as expected, indeed stronger.

Indirect Association Between Physical Abilities and HRQoL Physical and Mental Through Self-management

As shown in Table 2, there was no correlation between balance and self-management and balance and HRQoL physical and mental aspects. Therefore, we conducted the analysis only for strength and mobility. Also, since strength and mobility behaved alike, revealing the same pattern of correlations (see Table 2) we used an aggregated value reflecting the sum of both abilities, presenting the physiological aspect of both abilities. Hence, we present 2 set of indirect association regression results, one for model 1 and the other for model 2, rather than 6 sets (corresponding to 3 types of physiological abilities multiplied by 2 types of quality of life).

In our analyses we followed the recommendations of Preacher and Hayes ⁶⁰ and Hayes, ⁶¹ and we used the SPSS macro developed by Hayes and Preacher. In the first indirect association analysis, we examine the relation between the physiological abilities and physical quality of life through self-management (Model 1). In the second indirect association analysis, the relation between physiological abilities and mental quality of life through self-management was studied (Model 2). For both analyses, we examined the impact of physiological abilities on HRQoL (total association, path c′ + ab) and the effect of physiological abilities on HRQoL (direct association, path c′). Bootstrapping with 5000 samples was used to formally test whether the strength of the difference between the total association (path c′ + ab) and the direct association (path c′)—the indirect association—was significantly different from zero. ⁶⁰ When the confidence intervals (CIs) of bootstrapping point estimates do not contain zero, the indirect association is considered statistically significant. We also computed the ratio of the indirect association to the total association as recommended by Preacher and Kelley. ⁶² Before conducting the analysis, all variables were standardized.

As presented in Table 3 for both indirect association analyses, physiological abilities exhibited significant associations with the predicted variable (HRQoL physical and mental aspects) as well as with self-management, and self-management demonstrating significant association with the outcome variable. Further, the direct association between physiological abilities and the 2 forms of HRQoL ceased to be significant once self-management was included. The results are displayed in Table 3. The indirect association was −0.76, 95% CI (−1.28, −0.41) for physical quality of life and −1.50, 95% CI (−2.42, −0.94) for mental quality of life, accounting for 93% and 69% of the total association, respectively. These findings suggest that regardless of whether the HRQoL pertains to physical or mental aspects, the association between physical abilities and HRQoL is indirect through self-management. Hence, hypothesis 7 is supported for models 1 and 2.

Table 3.

Summary of Indirect Association Results (Beta Values, Standard Errors in Parentheses, 5000 Bootstrap Samples).

Independent variable (IV)	Dependent variable (DV)	Effect of IV on SM (a)	Effect of SM on DV (b)	Total association (c′ + ab)	Direct association (c′)	Indirect association (ab)
Physical abilities	Physical HRQol	–0.045 (0.008)***	16.77 (4.1)***	–1.10 (33)***	–0.33 (0.35)	–0.76 (0.22)***
physical abilities	Mental HRQol	–0.045 (0.008)***	33.01 (5.4)***	–1.61 (0.49)***	–0.11 (.47)	–1.50 (.37)***

Note. N = 82. SM = self management.

^*P < .05. **P < .01. ***P < .001.

Discussion

Physical activity has been found to be strongly associated, among older adults, to QoL and HRQoL.^7-9 In this study we propose a proceeding step, namely, a model proposing that—practicing physical exercises, emphasized on individual’s self-reflection while doing the exercise, is associated indirectly to physical and mental HRQoL by contributing to the improvement of self-management. Our results showed first, that practicing physical exercises, among the intervention group, increased the 3 measured physiological abilities—balance, strength, and movement—as well as self-management and physical and mental HRQoL. Second, the physiological abilities, were fully indirectly associated through self-management with physical and mental HRQoL.

Physical Activity and Self-management

The current findings regarding the improvement in self-management due to practicing physical activity among older adults support our previous finding. ¹⁴ Practicing physical exercises emphasized on individual’s self-reflection while doing the exercises, contribute to the individual self-management. These findings are related to the importance of establishing a solid self-management ability within this age group. Being active helps keeping the individual emotionally and mentally fit, ³⁰ whereas passivity leads to loneliness and social isolation, ³¹ and to negative emotions such as unhappiness and depression. ³² In a compatible manner Schoon et al ⁴³ have shown robust associations between self-management among older adults and prevention of falls. Mishra et al ³⁸ and Nieboer et al ³⁹ have studied the effect on reduced loneliness and increased well-being. ⁴²

Physical Activity and HRQoL

The current findings relating to the association between physical activity and HRQoL among older adults verify previous findings. Name only a few—Lee et al ²² and Vallance et al ²³ found that conducting basic steps is associated with QoL. Kell and Rula ⁷ revealed that frequent visits of old adults to fitness centers are associated with higher levels of physical and mental HRQoL. Choi et al ²⁰ showed associations between physical activity and the subjective feeling of successful aging. Menai et al ²¹ extended these results revealing the associations between physical activity, not only to HRQoL, but also to the absence of disability, mental health problems, deteriorating cognition and functioning.

In the current study the physical and mental HRQoL, seemingly, behaved differently. Whereas the physical HRQoL has increased among the intervention group at time2 relative to time1 the mental HRQoL did not increase but instead decreased. One could interpret these results as justifying the argument that practicing physical activity improve the body health condition and in turn contribute to the physical HRQoL while the mental HRQoL is less related to the body condition but to other factors in the older adult’s life. However, interestingly, among the control group the mental HRQoL was decreased significantly in time2 relative to time1 while this measurement was only modestly and non-significantly decreased among the intervention group. Still, we expected to see the mental HRQoL rising at time2 for the intervention group. However, toward the end of the intervention sessions the Covid-19 pandemic outbreaked followed by the bewildering and frightening announcement of a general lockdown. We propose that the decrease in mental HRQoL at time2 was related to the emotional reaction of the Covid-19 outbreak on the quality of life. ⁴⁵ Nonetheless, judging by the amount of decrease in the mental HRQoL among the 2 groups, the intervention group reacted modestly compared to the control group. This, we argue, is related not only to the impact of exercising physical activity on mental HRQoL but to the impact of exercising physical activity on the increase of self-management as a mediator between physical activity and mental HRQoL, the indirect pass.

Indirect Association Between Physical Abilities and HRQoL Through Self-management

The idea regarding mediating effect of psychological outcomes to the relation between physical abilities and QoL among older adults was suggested by Elavsky et al. ⁶³ They found that physical activity mediated by intermediate psychological outcomes such as positive emotions can have long-term effects on well-being. Nevertheless, among older adults, psychological outcomes as mediators were not extensively studied. However, abounding studies did explore, among older adults, the relation between physical activity and QoL. Among younger groups such as students, though, self-efficacy, a psychological outcome was studied as a mediator. ⁶⁴

In the present study we explored self-management as a psychological outcome and a mediator in its wider spectrum including aspects of initiative as well as self-efficacy, investment in the self, positive frame of mind, resources and multifunctionality. Our findings clearly indicate that regardless of whether the HRQoL is physical or mental, the association between physical abilities and HRQoL is indirect through self-management.

Going back to reflecting on the modest and non-significant decrease at time2 in mental HRQoL among the intervention group relative to the control group who had a tremendous decrease, we deduce that self-management in its role as a psychological outcome mediator, behaved as a mental protector to the psychological pressure felt due to the Covid-19 outbreak and the announcement of the first lockdown.

Final Remarks

The present study focused on the following main question—Is the impact of physical activity on HRQoL direct or indirect? Our findings provide support for the proposed model, suggesting that self-management serves as a psychological outcome that mediates the relation. Based on the current findings, several promising avenues for further research emerge. Firstly, further studies could explore alternative ways and methods to develop and strengthen self-management abilities as part of healthy aging management preparation. Secondly, additional research could expand our understanding regarding other psychological outcomes that due to practicing physical activity serve also as mediating or moderating variable to QoL. Specific training programs can be developed to reinforce these abilities.

Limitations

in closing, the generalizability of the current findings is constrained by several factors. First, toward the end of the training intervention period the Covid-19 pandemic outbreak stopped training 2 weeks before the planned date, at session 22 instead of session 24. Though we suggested some reflection regarding self-management as protecting against Covid-19 mental impact, the current results should be replicated to verify that QoL does improve as expected due to physical activity. Second, the data set exhibits a gender imbalance, with a higher proportion of female participants compared to male participants. Third, the current study solely investigated the impact of physical training on self-management, overlooking other activities which are highly important in active aging, particularly social gatherings. Future research should encompass more diverse populations over extended durations, exploring various facets of active aging.

Conclusion

Nonetheless, the findings from the present study offer a coherent understanding of the role of self-management as a psychological outcome of reflected physical activity as well as a mediator for QoL and HRQoL.