International Journal of

Environmental Research
and Public Health

Article
The Relationship between Physical Activity and Mental
Depression in Older Adults during the Prevention and Control
of COVID-19: A Mixed Model with Mediating and
Moderating Effects
Guoyan Xiong 1 , Caixia Wang 2 and Xiujie Ma 1,3, *

1 School of Wushu, Chengdu Sport University, Chengdu 610041, China

2 School of Physical Education, Handan University, Handan 056005, China
3 Chinese Guoshu Academy, Chengdu Sport University, Chengdu 610041, China
* Correspondence: ma.xiujie@outlook.com; Tel.: +86-(028)-8501-5753

Abstract: Background: Several studies have found a strong relationship between physical activity
and mental depression in older adults. Despite this, the social isolation, limited physical activity, and
decreased social interactions caused by the 2020 COVID-19 pandemic control measures of “home
isolation and reduction unnecessary travel” had a significant mental impact on older adults. Objective:
the goal of this study was to look into the complex effects of physical activity participation on mental
health in older adults during COVID-19 prevention and control and the relationship between physical
activity and mental depression in older adults through the mediating effect of self-efficacy and the
moderating effect of social support. Methods: The Physical Activity Rating Scale (PARS-3), the
Center for Streaming Depression Scale (CES-D), the Self-Efficacy Scale (GSES), and the Social Support
Rating Scale (SSRS) were used to assess 974 older adults in five urban areas of Chengdu, China.
The SPSS was used to analyze the collected data using mathematical statistics, linear regression
analysis, and the AMOS to construct the research model. Results: The study’s findings revealed
Citation: Xiong, G.; Wang, C.; Ma, X. that self-efficacy mediated the relationship between physical activity and mental depression in older
The Relationship between Physical adults (β = −0.101, 95%CI (−0.149, −0.058)), and social support moderated the relationship between
Activity and Mental Depression in physical activity and mental depression in older adults (t = −9.144, p < 0.01). Conclusions: Physical
Older Adults during the Prevention activity reduces psychological depressive symptoms in older adults and modulates psychological
and Control of COVID-19: A Mixed
depression in older adults via the mediation efficacy of self-efficacy and the moderating effect of
Model with Mediating and
social support.
Moderating Effects. Int. J. Environ.
Res. Public Health 2023, 20, 3225.
Keywords: COVID-19; physical activity; mental depression; social support; self-efficacy; older adults
https://doi.org/10.3390/
ijerph20043225

Academic Editor: Paul B. Tchounwou

Received: 21 November 2022

1. Introduction
Revised: 31 January 2023 The coronavirus disease 2019 (COVID-19) has become a public health emergency
Accepted: 10 February 2023 of international concern since the associated outbreak [1]. Its high contagiousness and
Published: 12 February 2023 rapid spread not only endanger public health but also have a significant impact on pro-
duction, lifestyle, and quality of life. Moreover, in many cases, it has caused unbearable
psychological stress. The public has faced severe physical and mental health challenges,
particularly vulnerable groups such as the elderly. It has been demonstrated that older
Copyright: © 2023 by the authors.
adults are more vulnerable to physical and psychological effects during COVID-19 [2] and
Licensee MDPI, Basel, Switzerland.
that psychological changes are more pronounced in those over 60 years old than in other
This article is an open access article
age groups [3]. The most common mental problems among older adults during COVID-19
distributed under the terms and
outbreak prevention and control are panic disorder, anxiety disorder, and depression [4],
conditions of the Creative Commons
and the negative impact of depression on older adults cannot be overlooked. Depression
Attribution (CC BY) license (https://
is typically characterized by a depressed mood, pessimism, appetite disorders or sleep
creativecommons.org/licenses/by/
4.0/).
deprivation, low self-worth, slowed thinking, and reduced responsiveness. These issues

Int. J. Environ. Res. Public Health 2023, 20, 3225. https://doi.org/10.3390/ijerph20043225 https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2023, 20, 3225 2 of 18

frequently become chronic, resulting in disability or, in the worst-case scenario, suicide
attempts [5].
A growing number of studies has recently discovered that physical activity has been
shown to be as effective as antidepressant medication in alleviating depression [6]. Further-
more, engaging in moderate physical exercise improves older people’s mental health [7],
and physical activity not only boosts good feelings [8], but it also improves cognitive and
emotional functioning, which has a favorable impact on physical functioning, mental health
status, quality of life, and subjective well-being [9,10].
There have been numerous studies on the relationship between physical activity and
mental health in older adults during COVID-19, but few have examined the intricate
mechanisms underlying this relationship. In addition, existing research studies still have
blind spots, such as the part that self-efficacy plays in the association between physical
activity and psychological depression in older adults. Current studies on self-efficacy
and mental depression during the pandemic have included children [11], adolescents [12],
healthcare workers [13], and pregnant women [14], but no studies on the effect of self-
efficacy on mental depression in older adults during COVID-19 pandemic prevention and
control have been found. Moreover, there is still space for advancements in methodology
since earlier studies only employed a single statistical method and had tiny sample sizes.
In the context of a rapidly aging population and the COVID-19 pandemic, older adults’
physical and mental health status as well as physical activity levels are deteriorating year
by year. Exploring the impact of physical activity on depression in older adults during
the COVID-19 pandemic prevention and control could help them achieve healthy aging,
strengthen their immunity, and reduce the national medical burden, among other things.
Therefore, this study chose self-efficacy as a mediating variable and social support as a
moderating variable to investigate the relationship between physical activity and mental
depression among older adults during pandemic prevention and control. This study
focused on the mechanisms and effects of physical activity, self-efficacy, social support, and
mental depression.

1.1. Physical Activity and Mental Depression

Prior to the COVID-19 outbreak, mental health problems in older adults were already
common. According to a survey in 2017, 10–15% of older adults had clinically significant
depression [15]. The COVID-19 outbreak exacerbated the mental health crisis in the elderly
population. During the outbreak prevention and control period, a series of measures
such as “home isolation and reduction of unnecessary travel” reduced not only physical
activity and social interaction among older adults but also sleep quality, which together
may have resulted in a high prevalence of symptoms such as frailty and depression [16].
Many researchers have found a strong relationship between physical activity and mental
depression in older adults [17,18]. Physical activity, for example, has been shown to be a
protective factor against depression [19], and long-term physical activity is an effective and
convenient way to prevent depressive symptoms in older adults [20]. Physical activity has
been suggested to have a therapeutic effect on depression [21,22]. For example, physical
exercise is beneficial in the treatment of depression’s “negative effects”, such as loss of
interest, reduced activity, and mood instability [23], and various types of physical activity
may also be an effective non-pharmacological treatment for depression in older adults [24].
In addition, regular physical activity not only relieves psychological stress and anxiety
disorders in older adults but also helps in the maintenance and development of physical
and mental health [25]. As a result, physical activity among older adults during COVID-19
prevention and control may help to alleviate their psychological depression. The following
hypothesis is proposed in this study based on the above analysis:

H1: Physical activity has a significant negative effect on psychological depression.

Int. J. Environ. Res. Public Health 2023, 20, 3225 3 of 18

1.2. Mediation of Self-Efficacy

As psychology research has progressed, the benefits of physical activity on mental
health have gained widespread recognition [26]. Physical activity has a significant impact
on self-efficacy according to the majority of academics. Self-efficacy and physical activity
were found to have a positive correlation in college students, teenagers, and older adults,
with the relationship becoming stronger the more physical activity they engaged in [27–29].
Furthermore, those who exercised regularly had much higher self-efficacy than those who
were sedentary, and regular exercise was shown to have a positive impact on one’s mental
health in addition to being the best way to raise self-efficacy [30]. The development and
enhancement of self-efficacy have been shown to reduce depressive symptoms [31]. Self-
efficacy is recognized as an important factor in determining health [32] and can be used
to predict depressive symptoms [33]. Depression may result from low self-efficacy [34],
whereas high self-efficacy is associated with a significantly lower risk of depression [35].
Despite previous studies showing the benefits of physical activity to promote self-efficacy,
could self-efficacy indirectly affect psychological depression in older adults when physical
activity acts upon depression? As a result, the following theory was explored in this study:

H2: Self-efficacy acts as a mediator in the connection between physical activity and mental depression.

1.3. Moderation of Social Support

A growing body of research has shown that social support has a general beneficial
effect on the development of psychological well-being in individuals [36] and that higher
levels of social support can reduce the negative effects of stressful events on the individual’s
mind and body, reduce the bonding relationship between stress and depression, and thus
reduce the degree and generation of depression [37]. Studies on social support and older
adults found that social support allows older adults to feel cared for and loved, which
is beneficial for the maintenance and development of physical and mental health, and
that family and friend support reduces psychological stress and improves well-being,
promoting psychological health and preventing depression in later life [38]. Furthermore,
there is mounting evidence showing that social support is an important factor in influencing
behavioral change and that the more social support people receive, the more physically
active they are [39], an important factor in influencing physical activity levels [40]. Physical
activity has a greater impact on individual psychological depression when there is a
high level of social support. In other words, social support can help to strengthen the
link between physical activity and self-efficacy or depression. As a result, the following
hypothesis was proposed:

H3: Social support moderates the relationship between physical activity and mental depression.

2. Materials and Methods

2.1. Hypotheses and Conceptual Model
This study developed a research model of the mediating and moderating mechanisms
of physical activity and mental depression in older adults during pandemic prevention
and control, using mental depression as the dependent variable, physical activity as the
independent variable, self-efficacy as the mediating variable, and social support as the
moderating variable. This model was based on the aforementioned literature review and
the proposed hypotheses (Figure 1). The relationships between mental depression, physical
activity, self-efficacy, and social support were revealed by the model. First, there was a
direct link between physical activity and mental depression; second, physical activity was
indirectly linked to mental sadness through self-efficacy; and third, social support might
step in to strengthen the link between physical activity and mental depression.
Int. J. Environ. Res. Public Health 2023, 20, 3225 4 of 18
Int. J. Environ. Res. Public Health 2022, 19, x 4 of 19

Figure1.1.Hypothetical
Figure Hypotheticalmodel
modelofofthe
therelationship
relationshipbetween
betweenphysical
physicalactivity
activityand
andmental
mental depression.
depression.

2.2.Participants
2.2. Participantsand
andProcedures
Procedures
Thisstudy
This studyexamined
examinedthe thephysical
physicalactivity
activityand andmental
mentaldepression
depressionofofolder
olderindividuals
individuals
ininfive
fiveChengdu
Chengduurban urbanregions:
regions:Qingyang,
Qingyang,Jinniu,Jinniu,Chenghua,
Chenghua,Jinjiang,
Jinjiang,and
andWuhou,
Wuhou,using using
random
randomsampling.
sampling.The Thenumber
numberofofsurveys
surveysinineach eachurban
urbanregion
regionwas wasrestricted
restrictedtoto200 200inin
order
ordertotoensure
ensureproper
propersample
sampledispersion.
dispersion.InInthe theend,
end,1257
1257questionnaires—1012
questionnaires—1012offline offline
and
and245 245online—were
online—wereretrieved,retrieved,yielding
yieldingaatotal totalofof974
974valid
validquestionnaires
questionnairesand anda avalid
valid
recovery
recoveryrate rateofof77.49%.
77.49%.FigureFigure22illustrates
illustratesthe thescreening
screeningprocess
processof ofthe
thestudy
studysample.
sample.
Inclusion
Inclusionand andexclusion
exclusioncriteria
criteriawere
wereestablished
establishedbecause
becausethe thepurpose
purposeofofthis thisstudy
studywaswastoto
investigate the connection between physical activity and mental depression
investigate the connection between physical activity and mental depression in older peo- in older people.
The
ple.inclusion standards
The inclusion standardswerewereas follows:
as follows: (1) people
(1) peopleover thethe
over ageage
of of
60;60;(2)(2)permanent
permanent
residents
residentswith withChengdu
Chengduhousehold
householdregistration;
registration;(3) (3)experience
experiencewith withthe
thenew
newcoronavirus
coronavirus
being
beinginhibited
inhibitedand and controlled;
controlled; (4) informed consent
(4) informed consentand andvoluntary
voluntaryparticipation;
participation;and and(5)
(5) unrestricted communication. The exclusion criteria were as follows:
unrestricted communication. The exclusion criteria were as follows: (1) a questionnaire (1) a questionnaire
completion
completiontime timeofofless
lessthan
than9090s sand
and(2)(2)repeated
repeatedand andinvalid
invalidanswers.
answers.
AAsimultaneous on-site distribution of paper
simultaneous on-site distribution of paper surveys surveys and online
and questionnaires,
online questionnaires, which
took 3 to 5 min to complete, were used in the research process.
which took 3 to 5 min to complete, were used in the research process. Two to three re- Two to three researchers
who lookedwho
searchers for places
lookedwhere olderwhere
for places peopleoldermightpeople
engage in physical
might engageactivity
in physicaldistributed
activity
the questions both locally and online using Questionnaire Star to
distributed the questions both locally and online using Questionnaire Star to the target the target audience. The
Chengdu
audience.Institute of Physical
The Chengdu Education
Institute gave its
of Physical prior ethical
Education gaveapproval.
its prior The
ethical locations in
approval.
the five urban areas that had been subject to closure control management
The locations in the five urban areas that had been subject to closure control management were identified
using the official microblogging platform of Healthy Chengdu before the questionnaires
were identified using the official microblogging platform of Healthy Chengdu before the
were distributed, and the questionnaires were then given out to the designated groups of
questionnaires were distributed, and the questionnaires were then given out to the desig-
people at the designated locations. The study’s initial purpose, the use of the study’s data,
nated groups of people at the designated locations. The study’s initial purpose, the use of
confidentiality of personal data, and its risks were all described to the participants by the
the study’s data, confidentiality of personal data, and its risks were all described to the
researchers before they began the questionnaire. Thereafter, the subjects were presented
participants by the researchers before they began the questionnaire. Thereafter, the sub-
with and asked to sign an informed consent form. When necessary, researchers were
jects were presented with and asked to sign an informed consent form. When necessary,
required to explain questions to participants that they did not understand and to provide
researchers were required to explain questions to participants that they did not under-
oral interpretation in the participants’ dialects. Participants were given a red packet or gift
stand and to provide oral interpretation in the participants’ dialects. Participants were
as a token of appreciation after completing the survey.
given a red packet or gift as a token of appreciation after completing the survey.
Int. J. Environ. Res. Public Health 2023, 20, 3225 5 of 18
Int. J. Environ. Res. Public Health 2022, 19, x 5 of 19

Figure2.2.Illustrates
Figure Illustratesthe
thesteps
steps for
for the
the screening
screening process
process of
of the
the study
studysample.
sample.

2.3.
2.3.Control
ControlVariables
Variables
Since
Since thelevel
the levelof
of psychological depression affects
psychological depression affectsolder
olderadults’
adults’subjective
subjectivewell-being
well-being
and
andisisinfluenced
influenced by
by individual
individual and social factors,
factors, gender,
gender, age,
age,education,
education,income,
income,and
and
health
healthstatus
statuswere
wereused
usedas as control
control variables
variables to reduce the
the risk
risk of
of statistical
statisticalbias.
bias.

2.4.
2.4.Measurements
Measurements
2.4.1.
2.4.1.Physical
PhysicalActivity
Activity
The
ThePhysical
PhysicalActivity
Activity Rating
Rating Scale (PARS-3) revised
Scale (PARS-3) revised by
by Liang,
Liang,D.C.
D.C.etetal.
al.was
wasused
usedtoto
assess
assess physical activity in the elderly. The amount of exercise was examined in terms ofof
physical activity in the elderly. The amount of exercise was examined in terms
three
threeaspects,
aspects,namely
namely intensity,
intensity, time, and frequency
time, and frequencyof ofparticipation
participationininphysical
physical activity,
activity,
using a 5-point scale [41]. The intensity is divided into four categories: light
using a 5-point scale [41]. The intensity is divided into four categories: light exercise (such exercise (such
asaswalking, doing radio gymnastics, etc.), less-intense exercise (such as
walking, doing radio gymnastics, etc.), less-intense exercise (such as recreational ping recreational ping
pong, jogging, tai chi, etc.); more intense and lasting exercise of medium
pong, jogging, tai chi, etc.); more intense and lasting exercise of medium intensity (such intensity (such
asascycling,
cycling, running, etc.);heavy
running, etc.); heavyintensity
intensity with
with shortness
shortness of breath
of breath and and
muchmuch sweating
sweating but
but
notnot lasting
lasting exercise
exercise (such(such as playing
as playing badminton,
badminton, basketball,
basketball, tennis,tennis, etc.);heavy
etc.); and and heavy
and
and lasting
lasting exercise
exercise withwith shortness
shortness of breath
of breath and much
and much sweating
sweating (such as(such as racing,
racing, sets of
sets of aero-
aerobics exercises,
bics exercises, swimming,
swimming, etc.);etc.);
time time
span span is 10–60
is 10–60 min; frequency
min; frequency is calculated
is calculated as the
as the num-
number of physical activities performed per month and per week and
ber of physical activities performed per month and per week and contains less than 1 time contains less than
1 atime
month, 3 to 5 times a week, 2 to 3 times a month, approximately 1 time a day, and 1 to 2 1
a month, 3 to 5 times a week, 2 to 3 times a month, approximately 1 time a day, and
to 2 times a week. Specifically, amount of exercise = intensity × time × frequency. Intensity
Int. J. Environ. Res. Public Health 2023, 20, 3225 6 of 18

and frequency were scored from 1 to 5 levels and recorded as 1 to 5 points; time was scored
from 1 to 5 levels recorded as 0 to 4 points, with a maximum score of 100 points and a
minimum score of 0 points. The activity level assessment criteria are as follows: 19 for low
exercise levels, 20–42 for moderate exercise levels, and 43 for high exercise levels. In this
study, the Cronbach coefficient for this scale was 0.797.

2.4.2. Mental Depression

Jie Zhang et al. revised the Streaming Center Depression Scale (CES-D) to assess
depression. It has 16 negative affective items and 4 positive affective items for a total of
20 items, 4 of which are reverse scoring questions [42]. To complete the form, subjects were
asked to rate the frequency of symptoms in the previous week on a 4-point scale, with 0
indicating rarely or never (less than 1 day), 1 indicating occasionally (1–2 days), 2 indicating
occasionally or half the time (3–4 days), and 3 indicating most of the time or continuously
(5–7 days), with a total score of 0–60 points. The scale was divided into scoring criteria:
16 points for no depression, 16 total points for suspected depression, and 20 points for
some degree of depression. In this study, the Cronbach coefficient of the scale was 0.952.

2.4.3. Self-Efficacy
In this study, the General Self-Efficacy Scale (GSES) developed by Schwarzer et al.
was used, which is a unidimensional scale with 10 items [43]. A 4-point Likert scale was
used, with a score of 1 indicating “not at all correct”, 2 indicating “somewhat correct”,
and 3 indicating “mostly correct.” The higher the score, the greater the self-efficacy. The
overall score ranged from 10 to 40, with 10–20 for low self-efficacy, 21–30 for moderate
self-efficacy, and 31–40 for high self-efficacy. In this study, the Cronbach coefficient for this
scale was 0.934.

2.4.4. Social Support

The Social Support Rating Scale (SSRS) was used in this study. It was developed
by Xiao Shuihui and other mental health workers on the basis of foreign scales and has
10 entries. It includes three dimensions of objective support, subjective support, and
utilization of social support [44]. Articles 1–5 and 8–10 were scored on a 4-point scale, with
a value of 1–4 points; articles 6–7 were scored on a 2-point scale, with a value of 0–1 points,
and the answer “no source” received 0 points. The scale’s total score ranged from 12 to
66, with 0–22 indicating low social support, 23–44 indicating moderate social support,
and 45–66 indicating high social support. Higher total scores and scores on each scoring
scale indicate greater social support. In this study, the Cronbach coefficient for this scale
was 0.947.

2.5. Statistical Analysis

First, SPSS 25.0 software (IBM, Armonk, NY, USA)was used to analyze the valid
survey data. The model and the scale’s structural validity were assessed using the Amos
24.0 software package(SPSS, Chicago, USA). Validated factor analysis (CFA) was used in
this study to test for convergent validity, the factor loading coefficient values showed the
correlations between the factors and the analyzed items, and the combined reliability and
validity of the scales were further examined using the average variance extracted values
(AVE) and the factor loading combined reliability (CR).
Second, Pearson correlation coefficients were used to assess the linear relationships
between physical activity, self-efficacy, and psychological depression in older persons. The
study then used the AMOS 24.0 software program to conduct a goodness-of-fit analysis of
the conceptual construct mediation model to confirm the mediating function of self-efficacy
between physical activity and psychological depression, and the bootstrap method was
used to assess whether there is a mediating effect of self-efficacy between physical activity
and mental depression in older adults. The bootstrap approach is commonly used to test
the mediating impact. Repeated sampling from the original sample is used in this technique
 Int. J. Environ. Res. Public Health 2022, 19, x 7 of 19

method
Int. J. Environ. Res. Public Health 2023, 20, 3225 was used to assess whether there is a mediating effect of self-efficacy between 7 of 18
physical activity and mental depression in older adults. The bootstrap approach is com-
monly used to test the mediating impact. Repeated sampling from the original sample is
used
to in this technique
determine to determine
the coefficient the coefficient
of the mediating effect’s of the mediating
significance usingeffect’s
a 95% significance
confidence
using a 95%
interval [45]. confidence interval [45].
Finally,linear
Finally, linearregression
regression was was used
used toto test
test the
the role
roleof ofsocial
socialsupport
supportasasa amoderator
moderator in
the relationship between physical activity and mental depression
in the relationship between physical activity and mental depression in older adults. The in older adults. The
three-steptest
three-step testof
ofthe
thehierarchical
hierarchicalmoderated
moderatedregression
regression(HMR) (HMR)analysis
analysiswas wasusedusedin inthis
this
studyto
study to test
test the
the moderating
moderating effects,
effects, and
and thetheinteraction
interactionterm termofofthe thevariables
variableswas wasusedused to
test the moderating effects. To elaborate, the empirical test was
to test the moderating effects. To elaborate, the empirical test was carried out using the carried out using the fol-
lowing procedures,
following procedures, and andSPSSSPSS25.025.0
waswas
utilized to conduct
utilized to conduct the statistical analysis
the statistical of phys-
analysis of
ical activity. Correlation analysis was used to perform the initial
physical activity. Correlation analysis was used to perform the initial test of hypothesis test of hypothesis testing
based on
testing basedthe test of common
on the method method
test of common bias, andbias, then,andlinear
then, regression analysis was
linear regression used
analysis
to perform
was used to the moderating
perform model test.
the moderating Step-by-step
model analyses were
test. Step-by-step conducted
analyses using three
were conducted
linearthree
using regression
linearmodels:
regression first,models:
model 1first,(M1)model
was fitted
1 (M1) withwas gender,
fitted age,
witheducation,
gender, age, in-
come, andincome,
education, health condition
and healthascondition
control variables
as controland different
variables and physical
different activity
physicalscores
activity as
independent variables for regression; model 2 then added
scores as independent variables for regression; model 2 then added moderating variablesmoderating variables (social
support)
(social to model
support) 1; and1;finally,
to model modelmodel
and finally, 3 added interaction
3 added termsterms
interaction (product termsterms
(product of inde-of
pendent andand
independent moderating
moderating variables)
variables)to model
to model 2. 2.
Thisstudy
This studydivided
dividedthe themean
mean ofof social
social support
support andand physical
physical activity
activity (M)(M)plusplus or mi-
or minus
one
nusstandard
one standarddeviation (SD) into
deviation (SD)high
intoand
high low
and subgroups
low subgroups and plotted a simple
and plotted slope test
a simple of
slope
social
test ofsupport betweenbetween
social support physicalphysical
activity activity
and mental anddepression in order in
mental depression to order
clearlytopresent
clearly
the moderating
present effect of social
the moderating effectsupport
of social(Figure
support 3) (Figure
based on 3) the method
based on the suggested by Aiken
method suggested
and West [46].
by Aiken and West [46].

56 Low Social
54 Support
High Social
52 Support

50

48

46

44

42
Low Physical Activity Level High Physical Activity Level

Figure3.3.The
Figure Themoderating
moderatingrole
roleofofsocial
socialsupport
supportbetween
betweenphysical
physicalactivity
activityand
andmental
mentaldepression.
depression.

3.3.Results
Results
3.1.
3.1.Analysis
Analysisofofthe
theSample
SampleSituation
Situation
AAtotal
total of 974 valid
of 974 valid questionnaires
questionnaireswerewererecovered
recoveredfromfromthisthis survey,
survey, as as shown
shown in
in Ta-
Table
ble 1.1. Males
Males made
made up up490490 of them
of them (50.3%),
(50.3%), whilewhile females
females mademadeup 484up(49.7%).
484 (49.7%). The
The major-
majority of them, i.e., 303 of them, were aged 60 to 64 (31.1%); a higher percentage
ity of them, i.e., 303 of them, were aged 60 to 64 (31.1%); a higher percentage of them had of them
had completed
completed primary
primary school
school (26.8%);
(26.8%); themajority
the majorityofofthe
theolder
older individuals
individuals had
had an
an annual
annual
income of between USD 2000 and 3000 (30%); and a higher percentage of the elderly were
income of between USD 2000 and 3000 (30%); and a higher percentage of the elderly were
in very good health (45%).
in very good health (45%).
During the new pandemic, high-risk areas implemented control measures such as
During the new pandemic, high-risk areas implemented control measures such as
“foot out of the district, door-to-door service”, and no new infections for 7 consecutive
“foot out of the district, door-to-door service”, and no new infections for 7 consecutive
days result downgrading to a medium-risk area, and no new infections for 3 consecutive
days result downgrading to a medium-risk area, and no new infections for 3 consecutive
days result in downgrading to a low-risk area, according to information released by the
Chengdu Municipal Health and Wellness Commission. A medium-risk area can be lowered
to a low-risk area by implementing “foot out of the district, staggered pick up” and other
control measures and no new infections for seven consecutive days. Low-risk regions take
Int. J. Environ. Res. Public Health 2023, 20, 3225 8 of 18

preventive steps such as “personal protection and avoid gathering” and allow people to
travel freely under the circumstances [47]. As a result, a high-risk area needs at least 10
days to be released from control, while a low-risk area takes at least 7 days. It was found
through the survey that more older adults in the respondent group engaged in low levels
of physical activity (43.3%), 52.8% of the group were found to have signs of depression,
38.4% of respondents had low levels of self-efficacy, and 65.2% of respondents reported
moderate levels of social support. Although the sample data cannot be used to directly infer
a relationship between physical activity and psychological depression in older adults, it
does allow readers and other researchers to fully comprehend the sample’s characteristics.

Table 1. Demographic characteristics of the sample.

Variable Frequency Percentage (%)

Gender
Male 490 50.308
Female 484 49.692
Age
60–64 303 31.109
65–69 274 28.131
70–74 229 23.511
75–79 94 9.651
≥80 74 7.598
Education Level
No schooling 174 17.864
Primary school 261 26.797
Middle school 206 21.15
High school or technical secondary school 167 17.146
College (including higher vocational education) 72 7.392
Bachelor degree 50 5.133
Graduate and higher 44 4.517
Income (USD)
<2000 253 25.975
2000–3000 292 29.979
3000–4000 156 16.016
4000–5000 175 17.967
>5000 98 10.062
Health condition
Very Good 438 44.969
Good 146 14.99
Poor 341 35.01
Bad 49 5.031
Physical Activity
Low exercise levels 422 43.326
Moderate exercise levels 302 31.006
High exercise levels 250 25.667
Mental Depression
No depression 394 40.452
Suspected depression 66 6.776
Some degree of depression 514 52.772
Self-Efficacy
Low self-efficacy 374 38.398
Moderate self-efficacy 346 35.524
High self-efficacy 254 26.078
Social Support
Low social support 152 15.606
Moderate social support 635 65.195
High social support 187 19.199
Int. J. Environ. Res. Public Health 2023, 20, 3225 9 of 18

3.2. Reliability and Validity Tests

First, as shown in Table 2, all question items had factor loadings that ranged from
0.664 to 0.910, all of which were higher than 0.6, demonstrating a significant association
between the factors and the analyzed terms. It was discovered that each factor’s AVE
and CR values were greater than 0.5 and greater than 0.7, respectively, indicating that
the data had good construct validity and consistency. In addition, it is clear to see from
the model fit indicators of the validation factor analysis results in Table 3 that the model
fits well: χ2 /df < 3, GFI > 0.9, AGFI > 0.9, CFI > 0.9, NFI > 0.9, NFI > 0.9, RMSEA < 0.05.
The questionnaire survey in this research, therefore, has a high level of internal validity
and reliability.

Table 2. Confirmatory factor analysis.

Parameters of Significant Test

Dimension Items SMC CR AVE
Estimate S.E. C.R. p-Value
Frequency 0.746 0.019 39.263 *** 0.557
PA Time 0.764 0.019 40.211 *** 0.584 0.798 0.568
Intensity 0.750 0.018 41.667 *** 0.562
Depression1 0.700 0.015 46.667 *** 0.490
Depression2 0.693 0.015 46.200 *** 0.480
Depression3 0.713 0.015 47.533 *** 0.508
Depression4 0.716 0.015 47.733 *** 0.512
Depression5 0.700 0.014 50.000 *** 0.489
Depression6 0.707 0.014 50.500 *** 0.500
Depression7 0.688 0.015 45.867 *** 0.473
Depression8 0.732 0.014 52.286 *** 0.535
Depression9 0.714 0.014 51.000 *** 0.510
Depression10 0.726 0.014 51.857 *** 0.527
Depression 0.952 0.501
Depression 11 0.718 0.014 51.286 *** 0.515
Depression 12 0.720 0.015 48.000 *** 0.518
Depression 13 0.705 0.016 44.063 *** 0.497
Depression 14 0.689 0.016 43.063 *** 0.474
Depression 15 0.715 0.014 51.071 *** 0.511
Depression 16 0.729 0.013 56.077 *** 0.532
Depression 17 0.689 0.014 49.214 *** 0.475
Depression 18 0.695 0.015 46.333 *** 0.483
Depression 19 0.693 0.015 46.200 *** 0.480
Depression 20 0.707 0.015 47.133 *** 0.500
SE1 0.769 0.012 64.083 *** 0.591
SE2 0.761 0.013 58.538 *** 0.579
SE3 0.768 0.012 64.000 *** 0.589
SE4 0.767 0.013 59.000 *** 0.588
SE5 0.770 0.012 64.167 *** 0.593
SE 0.934 0.585
SE6 0.756 0.013 58.154 *** 0.571
SE7 0.750 0.014 53.571 *** 0.562
SE8 0.776 0.012 64.667 *** 0.603
SE9 0.765 0.013 58.846 *** 0.586
SE10 0.765 0.012 63.750 *** 0.585
US 0.859 0.016 53.688 *** 0.738
SS SUS 0.882 0.019 46.421 *** 0.779 0.843 0.646
OS 0.649 0.022 29.500 *** 0.421
Note: *** p < 0.001; SMC, squared multiple correlation coefficient; CR, combination reliability; AVE, average
variance extraction; PA, physical activity; SE, self-efficacy; SS, social support; SUS, subjective support; US,
utilization of support; OS, objective support.
Int. J. Environ. Res. Public Health 2023, 20, 3225 10 of 18

Table 3. Questionnaire model fitting indicators.

χ2 df χ2 /df P GFI AGFI CFI NFI IFI RMSEA

Model 730.005 588 1.242 0.000 0.960 0.955 0.993 0.964 0.993 0.016
Note: GFI, goodness-of-fit index; AGFI, adjusted goodness-of-fit index; CFI, comparative fit index; NFI, normed
fit index; IFI, increased fit index; RMSEA, root mean square error of approximation.

3.3. Descriptive Statistics and Correlation Analysis

Table 4 displays descriptive statistics. According to Table 4, physical activity had a
significant negative correlation with mental depression (r = −0.649, p < 0.01). That is, the
likelihood of experiencing depression symptoms decreases with increasing physical exer-
cise. There was also a significant positive correlation with self-efficacy (r = 0.551, p < 0.01).
That is, physical activity and self-efficacy levels both grew in the same manner. More-
over, self-efficacy had a significant negative correlation with mental depression (r = −0.485,
p < 0.01). In other words, the risk of having a depression mood decreases with a higher
sense of self-efficacy.

Table 4. Descriptive statistics and correlation of variables.

Variable M S.D. PA Depression SE SS

PA 29.24 22.702 0.753
Depression 42.98 13.156 −0.649 *** 0.708
SE 24.57 7.392 0.551 *** −0.485 *** 0.765
SS 34.06 10.376 0.233 *** −0.081 *** 0.199 *** 0.804
Note: *** p < 0.001; M, mean; S.D., standard deviation; PA, physical activity; SE, self-efficacy; SS, social support.

Furthermore, with correlation coefficients of 0.233 and −0.081, respectively, social

support had strong positive and negative correlations with physical activity and mental
depression, both at a significant level of 0.01 or less. That is, there is an inverse or di-
minishing growth between social support and psychological depression, whereas there
is a homogeneous increase between social support and physical activity. In this paper,
hypotheses H1, H2, and H3 were initially supported.

3.4. Mediating Analysis

The standardized path coefficient model of physical activity and self-efficacy affecting
mental depression is shown in Figure 2. According to Table 5’s model fit indices for
the mediating role of physical activity, self-efficacy, and mental depression, the model of
how physical activity affects mental depression fits well: χ2 /df < 3, GFI > 0.9, AGFI > 0.9,
CFI > 0.9, NFI > 0.9, NFI > 0.9, and RMSEA < 0.05. As shown in Figure 4 the path coefficient
of physical activity → mental depression was significant (β = −0.55), indicating a direct
effect of physical activity on mental depression. Thus, hypothesis H1 holds. The path
coefficients for both physical activity → self-efficacy and self-efficacy (β = 0.55) → mental
depression (β = −0.18) were significant, suggesting a mediating effect of self-efficacy
between physical activity and mental depression. Thus, hypothesis H2 holds.

Table 5. Mediated effect model fit indices for physical activity, self-efficacy, and mental depression.

χ2 df χ2 /df P GFI AGFI CFI NFI IFI RMSEA

Model 571.176 492 1.161 0.008 0.966 0.961 0.998 0.969 0.996 0.013
Note: GFI, goodness-of-fit index; AGFI, adjusted goodness-of-fit index; CFI, comparative fit index; NFI, formed fit
index; IFI, increased fit index; RMSEA, root mean square error of approximation.
Int.
Int.J.J.Environ.
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Figure4.4 A
Figure A structural
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modelon
onthe
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between ac-
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tivity and mental depression.
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3.5. A
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minimum Analysis
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bootstrap-mediated
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investigate effects test results
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the bootstrap
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this study
7, theby estimatingvariable
independent the bootstrap
(phys-
95% CI of the mediation effect by sampling 2000 times, and the results are shown
ical activity) showed significance (t = −18.866, p = 0.000 < 0.05), implying that physical ac- in Table 6.
The standard
tivity error (S.E.)effect
has a significant of theonindirect effect of
depression, physical
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action between withofaphysical
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=52.174
self-efficacy
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=60.008). physical activity
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model depression.
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physical of the
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and social of physical
wasactivity → mental
significant depression
(t = −9.144, p = 0.000was<0.046,
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implying −11.913,
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tude of the effect of physical activity on psychological depression varied significantly contain a 0 value.
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moderating variableactivity → mental depression was 0.034,
(social support).
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depicts the bootstrap (slope)
magnitude 95% CIdifference
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activity) on that
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dependent activity had a(psychological
variable significant effect on both
depression)
the direct and total effects of mental depression.
when the moderating variable (social support) is varied. The test results show that the
specific moderating effect is the difference in the magnitude (slope) of the effect of the
Table 6. Mediating effect analysis.
independent variable (physical activity) on the dependent variable (psychological depres-
sion) when the moderating variable (social support) is taken at different levels. The test
Bootstrapping
results revealed that social support had a stronger effect on mental depression in the high-
Path Estimate S.E. Z Bias-Corrected Percentile
social-support condition and a weaker effect in the low-social-support condition. This
Lower Upper Lower Upper
suggests that the predictive effect of physical activity on mental depression in older adults
Indirect effect PA → SE → Depression − 0.101 0.023 − 4.391 − 0.149 − 0.058 − 0.147
gradually increases with increasing social support. Thus, hypothesis H3 holds true. − 0.057
Direct effect PA → Depression −0.548 0.046 −11.913 −0.638 −0.461 −0.638 −0.461
Total effect PA → Depression −0.649 0.034 −19.088 −0.716 −0.583 −0.714 −0.581
Table 7. Moderating effect analysis.
Note: Bootstrap sample size is set to 2000. PA, physical activity; SE, self-efficacy.
Model 1 Model 2 Model 3
β T β T β T
Int. J. Environ. Res. Public Health 2023, 20, 3225 12 of 18

3.5. Moderating Analysis

The goal of model 1 was to investigate the effect of the independent variable (physical
activity) on the dependent variable (depression) when the moderating variable (social
support) was not taken into account. As shown in Table 7, the independent variable
(physical activity) showed significance (t = −18.866, p = 0.000 < 0.05), implying that
physical activity has a significant effect on depression, further supporting hypothesis
H1. The interaction between the effect of physical activity and social support on mental
depression (M2 and M3) revealed a significant change in the value of F from M2 to M3
(F (7966) = 52.174 → F (8965) = 60.008). Additionally, model 3’s interaction term between
physical activity and social support was significant (t = −9.144, p = 0.000 < 0.05), implying
that the magnitude of the effect of physical activity on psychological depression varied
significantly depending on the level of the moderating variable (social support).

Table 7. Moderating effect analysis.

Model 1 Model 2 Model 3

β T β T β T
Constants - 24.306 ** - 24.289 ** - 25.725 **
Gender 0.006 0.23 0.009 0.318 0.015 0.552
Age −0.014 −0.494 −0.015 −0.563 −0.02 −0.775
Education 0.016 0.585 0.017 0.618 0.016 0.604
Income −0.027 −0.965 −0.027 −0.981 −0.04 −1.515
Health condition −0.013 −0.464 −0.011 −0.39 0.001 0.034
PA −0.519 −18.866 ** −0.537 −18.803 ** −0.449 −15.442 **
SS 0.067 2.331 * 0.074 2.618 **
PA×SS −0.258 −9.144 **
R2 0.27 0.274 0.332
Adjusted R2 0.266 0.269 0.327
F (6967) = 56.690, F (7966) = 52.174, F (8965) = 60.008,
F-value
p = 0.000 p = 0.000 p = 0.000
∆R2 0.27 0.004 0.058
F (6967) = 56.690, F (1966) = 5.435, F (1965) = 83.613,
∆F value
p = 0.000 p = 0.020 p = 0.000
Note: Dependent variable mental depression. * p < 0.05 ** p < 0.01; PA, physical activity; SE, self-efficacy; SS,
social support.

The slope plot depicts the magnitude (slope) difference in the effect of the independent
variable (physical activity) on the dependent variable (psychological depression) when
the moderating variable (social support) is varied. The test results show that the specific
moderating effect is the difference in the magnitude (slope) of the effect of the independent
variable (physical activity) on the dependent variable (psychological depression) when the
moderating variable (social support) is taken at different levels. The test results revealed
that social support had a stronger effect on mental depression in the high-social-support
condition and a weaker effect in the low-social-support condition. This suggests that
the predictive effect of physical activity on mental depression in older adults gradually
increases with increasing social support. Thus, hypothesis H3 holds true.

4. Discussion
4.1. Physical Activity and Psychological Depression in Older Adults
This study investigated the relationship between physical activity, self-efficacy, social
support, and mental depression among older adults during pandemic prevention and
control and the mediating effect of self-efficacy and the moderating effect of social support
on the relationship between physical activity and mental depression among older adults.
Based on the analysis of the sample situation, it was discovered that the majority of
older adults had low levels of exercise and a high depression mood during the pandemic
closure period. It was also discovered that there was a negative correlation between
physical activity and psychological depression, as evidenced by the correlation analysis
between physical activity and psychological depression. That is, the higher the level of
Int. J. Environ. Res. Public Health 2023, 20, 3225 13 of 18

physical activity in older adults, the lower the likelihood of them experiencing psychological
depression. This is consistent with previous research findings [50–53]. Physical activity has
been shown to produce antidepressant effects via multiple biological and psycho-social
pathways primarily through exercise and that physical activity causes changes in the brain
to create an environment that prevents depression [54]. Physical activity increases the
body’s metabolism, which is beneficial for the production of positive emotions, and it can
also diminish unpleasant emotions. Increased social prevention and control, particularly
during the COVID-19 pandemic, led to increased rates of depression in older people [55],
increasing the need for interventions such as physical activity to regulate adverse moods
and alleviate and prevent depressive symptoms. A cross-sectional study of 200 older adults
of both sexes found that physical activity increased physical vitality and quality of life,
reducing depressive symptoms in older adults [56]. In an analysis of factors influencing
depression in older adults in Korea, Kim and Park et al. found that cognitive decline was
strongly associated with the development of depression in older adults, and by participating
in physical activity, they were able to maintain a healthy lifestyle and reduce the risk of
cognitive decline, thereby preventing and reducing depression and improving health in
older adults [57]. Zhang and Xiang et al. discovered that physical activity may benefit older
adults with depression through both physiological and psychological pathways in a study
that reviewed the relationship between older adults and depression. Moderate-intensity
exercise can boost metabolism and help people release negative emotions, resulting in
more positive mental energy. It can also boost self-esteem and self-efficacy in older people,
effectively preventing and treating depression on both the physiological and psychological
levels [58]. Thus, physical activity’s positive effects can further influence mental depression
in older adults, confirming the research hypothesis that physical activity has a significant
negative effect on mental depression in older adults.

4.2. Mediating Effect of Self-Efficacy

The study’s findings revealed that during the pandemic prevention and control, the
majority of older persons had higher self-efficacy and that self-efficacy mediates the re-
lationship between physical activity and mental depression in older adults, confirming
research hypothesis 2. That is, physical activity can indirectly affect mental depression in
older adults via self-efficacy, which is consistent with previous findings [59,60], indicating
that self-efficacy is a mediator of the effect of physical activity on mental depression. Pre-
vious research has found that changes in physical activity cause changes in self-efficacy
and that changes in self-efficacy may indirectly mediate the link between physical activity
and depression, acting as a potential indirect mediator between physical activity and de-
pression [61]. By investigating exercise training for the treatment of depression in older
adults, Barbour and Blumenthal et al. discovered that exercise can serve as an effective
treatment for depression in older adults; i.e., it can reduce depression through its effects
on self-esteem and self-efficacy in older adults [62]. Similarly, Singh and Clements et al.
argued that exercise produces positive emotions in older adults and can increase the indi-
rect psychological benefits of self-efficacy on depression production in older adults [63]. As
a result, self-efficacy serves as a mediator in the mechanisms by which physical activity
affects depression in older adults. In the present study, older adults affected by pandemic
prevention and control were more likely to suffer from depression, particularly those with
suspected COVID-19 symptoms [64]. Because of the restrictions on outdoor activities
during this time, the elderly’s physical activity was naturally reduced. Participants who
were physically active during the lockdown had higher levels of resilience (including an
assessment of self-efficacy) and positive attitudes and lower levels of depression, according
to Zach and Fernandez-Rio et al. [65]. This study adds to the argument that physical
activity levels in older adults during COVID-19 prevention and control affect self-efficacy,
which in turn affects psychological depression in older adults, and that self-efficacy plays a
mediating role in the relationship between physical activity and psychological depression.
Int. J. Environ. Res. Public Health 2023, 20, 3225 14 of 18

4.3. Moderating Effect of Social Support

This study also sought to determine the moderating role of social support in the
relationship between physical activity and mental depression in older adults; i.e., when
social support is higher in older adults, the effect of physical activity on mental depression
is strengthened. In other words, social support can strengthen and even reinforce the rela-
tionship between the effects of physical activity and mental depression. This is consistent
with previous research findings [66]. It has been demonstrated that the decreased level of
physical activity and increased sedentary time of older adults during the COVID-19 closure
had a greater negative impact on their mood, resulting in an increased incidence of depres-
sion [67], whereas positive changes in social support helped to mitigate the negative impact
of the pandemic closure on the mental health of older adults [68]. Possible explanations in-
clude the following: On the one hand, social support, as a social determinant of health, can
improve physical activity and enhance life satisfaction and subjective well-being in older
adults, and various sources of social support (such as friends, family, and neighbors) can
have a significant impact on the level of physical activity in older adults [69]; on the other
hand, social support can improve the health and well-being of older adults by enabling
them to handle stressful situations more effectively and lowering their risk of developing
depression [70]. This finding supports the idea that social support has a moderating impact
on depressed symptoms in older adults.

4.4. Contributions
This research makes three major contributions. First, this study investigated the rela-
tionship between physical activity and mental depression in older adults during COVID-19,
which broadens the research and theoretical knowledge concerning the study of physical
activity and mental depression in older adults. Second, this study investigated the mecha-
nisms through which physical activity acts on mental depression in older adults, demon-
strating that self-efficacy mediates between the two and social support moderates between
the two. Finally, this study provides a guiding direction and reference for the improvement
and prevention of depressive symptoms in older adults through physical activity.

4.5. Limitations
This study made various positive contributions to the mental health of older people;
however, there are a number of limitations of which it is important to be aware. Firstly,
the dimensions of physical activity investigated in this study require expansion, different
forms of physical activity were not adequately analyzed in terms of their effects on mental
depression in older adults, and the various dimensions of social support were not suffi-
ciently analyzed in terms of their effects on mental depression in older adults. Secondly, as
in other cross-sectional analytic studies, this one employed cross-sectional research, which
may make it harder to discern the causal association between variables.
Finally, even though the assessments used in the study have been validated, there are
still some limitations, especially when it comes to physical activity, which is subjective,
because of sample recruitment in a specific context. The use of successfully validated
assessments, however, increases the study’s scientific validity and has some implications.
The relationship between physical activity and mental depression in older adults should
be investigated further in the future through subsequent research designs or pilot studies
and increased measures of psychological attributes. In addition to self-mediating efficacy’s
role in the relationship between physical activity and mental depression in older adults,
additional mediating and moderating variables should be investigated in the future.

5. Conclusions
Despite certain limitations, the study found that physical activity in older adults
during the prevention and control of the COVID-19 has a significant negative effect on
mental depression and plays a key role in the prevention of depression in older adults.
After further investigation into the connection between self-efficacy, physical activity, and
Int. J. Environ. Res. Public Health 2023, 20, 3225 15 of 18

mental depression in older people, it was discovered that self-efficacy acted as a mediator
in this association. Finally, the findings concerning the moderating effect demonstrate
that social support could help to moderate the relationship between physical activity and
mental depression in older adults. This study adds to the body of evidence elucidating the
link between physical activity and mental depression in older adults, with a significant
impact on preventing depression in elderly people.
This study contends that self-efficacy’s effect on mental depression in older adults
should be highlighted and that long-term physical activity can help prevent and treat
depression. It is also critical to emphasize the value of social support in assisting older
adults in developing social relationships in order to better cope with negative events and
reduce the likelihood of depression.

Author Contributions: Conceptualization, X.M. and G.X.; methodology, X.M. and G.X.; formal
analysis, X.M. and G.X.; investigation, G.X. and X.M.; resources, X.M. and G.X.; software, X.M.
and G.X.; data curation, G.X. and X.M.; writing—original draft preparation, X.M., C.W. and G.X.;
writing—review and editing, X.M., C.W. and G.X.; project administration, X.M.; funding acquisition,
X.M. All authors have read and agreed to the published version of the manuscript.
Funding: This research were funded by the Sichuan Social Science Fund (grant number SC21ZW003)
and First Class Discipline Construction of Chengdu Sport University (grant number 07).
Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Ethics Committee of Chengdu Sport University (code
2022-85, approved 15/08/22).
Informed Consent Statement: Consent was obtained from all the participants on the condition that
their anonymity was guaranteed.
Data Availability Statement: The data presented in this study are available upon request from the
corresponding author. The data are not publicly available due to an ethical agreement with the
Chengdu Sport University Social Sciences Ethics Panel to keep them under Ma Xiujie’s personal
OneDrive account, which is not accessible to the public.
Conflicts of Interest: The authors declare no conflict of interest.

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