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Article

Comparison of Physical Activity Patterns Between Individuals with Early-Stage Alzheimer’s Disease and Cognitively Healthy Adults

by
Léonie Moll
1,2,
Michèle Häner
1,2,*,
Roland Rössler
2,3 and
Sabine Krumm
2,4
1
Department of Psychology, University of Basel, 4055 Basel, Switzerland
2
University Department of Geriatric Medicine FELIX PLATTER Hospital, 4055 Basel, Switzerland
3
Department of Sport Exercise and Health, University of Basel, 4052 Basel, Switzerland
4
Faculty of Medicine, University of Basel, 4056 Basel, Switzerland
*
Author to whom correspondence should be addressed.
J. Dement. Alzheimer's Dis. 2025, 2(3), 23; https://doi.org/10.3390/jdad2030023
Submission received: 29 January 2025 / Revised: 22 April 2025 / Accepted: 6 June 2025 / Published: 1 July 2025
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Abstract

Background: Physical activity (PA) has been shown to prevent Alzheimer’s disease (AD) by reducing amyloid accumulation, lowering inflammatory factors, and increasing hippocampal grey matter. While high lifetime PA engagement is associated with a reduced risk of AD, the relationship between specific types of PA and early-stage AD remains unclear. As AD primarily affects cognitive function before physical capabilities, PA engagement—an important factor in PA—needs further investigation. Objectives: This study explores the potential association between current participation in open-skill sports (OSSs) versus closed-skill sports (CSSs) and early-stage AD. Methods: The sample (N = 128) included a cognitively healthy (HC, n = 78) group and an Alzheimer’s disease (AD) group, combining amnestic mild cognitive impairment due to AD patients (n = 22) and early-stage Alzheimer’s dementia patients (n = 28), reflecting the continuum of progression from aMCI to dAD (n = 50). PA was assessed with the Physical Activity Scale for the Elderly questionnaire, specifically focusing on PA within the last seven days. The statistical analyses included Mann–Whitney U tests and backwards stepwise logistic regression models. Results: Key predictors of group classification (AD vs. NC) included sex, high frequency of PA, and high duration of PA, each for the last seven days. Participation in OSS was significantly associated with medium PA frequency, high PA duration, both within the last seven days, and age, but not with diagnostic status. No statistically significant differences in PA levels (OSSs or CSSs) executed within the last seven days were observed between the AD and HC groups. Conclusions: Participation in OSSs or CSSs within the last seven days was only a marginally significant predictor of AD vs. HC status, and a diagnosis of AD was not predictive of OSS participation within the last seven days. Given the protective role of PA in AD, future research should aim to identify specific PA types that effectively support cognitive health in older adults with early cognitive decline.

1. Introduction

Engaging in physical activity (PA) is widely recognized as beneficial for preserving cognitive health during aging, particularly in maintaining memory performance [1,2]. However, several factors pose challenges to cognitively healthy aging, with Alzheimer’s disease (AD) being the most common cause of dementia [2,3]. In its early stages, AD predominantly manifests as cognitive impairments, with physical functional decline occurring later in the disease progression [4].
Recent studies explored the extent to which PA exerts a preventive influence on the development of AD. Findings suggest that PA is associated with decreased amyloid accumulations, a reduction in inflammatory factors, and augmented grey matter volumes in the hippocampal region [5,6]. Furthermore, individuals with a high lifetime engagement in PA demonstrate a lower risk of developing AD in later life [7,8]. Despite these findings, early symptoms of mild cognitive impairment (MCI), which typically manifest at the cognitive level, do not appear to correlate with differences in PA between MCI patients and cognitively healthy controls [7].
While PA encompasses all bodily movements, physical exercise is more accurately defined as a specific and intentional form of training [9,10,11]. However, the literature often uses these terms interchangeably without clear distinction [12,13]. To ensure clarity and inclusivity of the outcome described in the literature, this paper uses the term “PA” to refer collectively to both concepts. Key factors in operationalizing PA include frequency in combination with duration, physical intensity, and type of sport [5,8,9,14,15]. According to Knapp (1967) [16], PA can be divided into open-skill and closed-skill sports. Open-skill sports (OSSs) are defined as externally paced sports and require higher levels of cognitive engagement and response [17,18]. Examples of OSSs include tennis, football, and basketball [18,19]. In contrast, closed-skill sports (CSS) are self-paced activities performed in predictable and consistent settings, which are less cognitively demanding. Examples of CSSs include jogging, yoga, and walking [19,20,21]. Individuals engaged in OSS demonstrate enhanced cognitive reaction times and better executive network interactions compared to those involved in CSSs or individuals who do not perform any sport [22]. This finding is attributed to the optimization of attentional network control facilitated by OSSs [19,21]. Additionally, OSS participants engage in fewer weekly sessions but spend more time per session, potentially offering an advantage over CSSs [23].
Previous studies have primarily focused on cognitively healthy individuals of all ages, examining the relationship between cognition and participation in OSSs versus CSSs. This study explores the potential association between current participation in OSSs versus CSSs executed within the last seven days based on a self-reported PA questionnaire and early-stage AD status. To our knowledge, no publication exists to date that investigates this potential correlation.

2. Materials and Methods

2.1. Participants

The study sample was drawn from the Ambizione study at the Memory Clinic FELIX PLATTER, University Department of Geriatric Medicine, Basel, Switzerland. The original cohort consisted of 181 native Swiss–German- or German-speaking adults. Written informed consent was obtained from all individuals prior to participation, and the study was approved by the local ethics committee (EKNZ: Ethikkommission Nordwest- und Zentralschweiz). At the time of enrollment, each individual was at an early stage of cognitive impairment and was legally entitled and cognitively capable of giving their own consent, in accordance with the Federal Constitution of the Swiss Confederation (1999) [24] and Article 21 of the Swiss Human Research Act (HRA; SR 810.30, 2011) [25]. None of the participants had a legal guardian.
For this analysis, a subsample of 128 participants was used, given that not all participants responded to the Physical Activity Scale for the Elderly (PASE), which was the relevant questionnaire in this context [26]. Our sample included a cognitively healthy control (HC) group (n = 78) and an Alzheimer’s disease (AD) group (n = 50). Since AD progresses continuously over time [27], the AD group combined amnestic mild cognitive impairment due to AD (aMCI) patients (n = 22) as well as early-stage Alzheimer’s dementia patients (dAD, n = 28) into one AD group.
The aMCI diagnosis followed Winblad et al. (2004)’s criteria [28] and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; [29]). The diagnosis of dAD is based on the criteria from the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA [30]) and the DSM-IV criteria [29]. The diagnostic process was conducted through an interdisciplinary consensus conference at the Memory Clinic of the University Department for Geriatric Medicine FELIX PLATTER, Basel, Switzerland. All included patients underwent a neuropsychological assessment, medical examination, structural magnetic resonance imaging, and evaluation of informant questionnaires. Potential participants with evidence or history suggestive of non-Alzheimer’s MCI etiologies, including significant cerebrovascular disease, Parkinsonian features, or behavioral changes incompatible with amnestic presentation, were excluded. The remaining cohort consisted exclusively of clinically probable aMCI due to Alzheimer’s disease.
The HC group was recruited from the Memory Clinic’s “Registry of Healthy Individuals Interested to Participate in Research”, matching the AD patients regarding age and education. To select persons that were cognitively healthy, we used comprehensive neurological and psychiatric screenings as well as cognitive assessments, as listed in Appendix A.

2.2. Materials and Procedure

At the beginning of the study, the subjects were instructed to answer the PASE questionnaire as accurately as possible. The participants filled out the questionnaire at home and brought it to the study visit. The study coordinator checked it together with the participant, and, in the event of discrepancies or ambiguities, the participants were directly consulted to ensure clarity and completeness of the data. The subsection assessing the last seven days from the self-reported PASE questionnaire was used to quantify the self-reported PA [26]. Each item of the questionnaire assesses the participant’s PA over the previous seven days. Although the PASE also includes questions on lifelong PA, the present analysis focuses solely on the last seven days. This decision was made because the participants provided more precise information about their recent activities, whereas responses regarding long-term PA were often vague. The risk of recall bias typically increases with longer recall periods. Therefore, we decided to rely exclusively on the reports of recent PA. PA was evaluated using the following criteria from the PASE questionnaire: (1) frequency (days per week); (2) duration (hours per day); (3) PA type categorized as nil (i.e., individuals never engaging in PA), low (e.g., yoga), medium (e.g., volleyball), or high (e.g., jogging); and (4) level of cognitive challenge of the PA, which was manually categorized into OSSs and CSSs (see Appendix B for details). For the PA type (nil, low, medium, and high), the highest intraindividual value was selected for the participants who were active in different types of PA. PA frequency and PA duration were categorized as low, medium, and high for each participant separately. In addition to the neuropsychological test battery, as described in Appendix A, we administered the 16-item Informant Questionnaire on Cognitive Decline in the Elderly [31] to a close relative or friend.

2.3. Statistical Methods

The data were analyzed using R (version 4.4.2, R Foundation for Statistical Computing, Vienna, Austria, 2020) [32], alongside R Studio (version 2024.04.2, Posit (Formerly RStudio), Posit, PBC, Boston, Massachusetts, USA, 2024) [33]. To preserve the original sample size, missing values in this categorical dataset were replaced by the value of zero. The first hypothesis proposed that the AD group would not differ from the HC group in terms of PA frequency (low, medium, or high) or PA duration (low, medium, or high). Since the dataset was not normally distributed, a Mann–Whitney U test was conducted to quantitatively evaluate the differences between the two groups [34].
We further hypothesized that differences in PA participation between the AD and HC groups would only be observed if PA itself required specific (and presumably higher) cognitive engagement, as is the case in OSSs. Therefore, it was expected that MCI patients would be more likely to engage in CSSs, while the healthy controls would more frequently participate in OSSs. To test this, Model 1 was constructed with diagnosis as the dependent variable, predicted by PA frequency (low, medium, or high), PA duration (low, medium, or high), PA type (nil, low, medium, or high), and cognitive challenge of the PA (OSS or CSS). Individuals who participated in both OSSs and CSSs were included in the OSS group.
Furthermore, the reverse relationship was tested to determine whether cognitive impairment affects decision-making regarding the choice of practicing cognitively more challenging PA (i.e., OSSs). For this purpose, Model 2 was constructed, in which the likelihood of engaging in OSSs or CSSs was predicted by diagnosis, PA duration (low, medium, or high), PA frequency (low, medium, or high), and PA type (nil, low, medium, or high).
To evaluate the predictive capacity of both models, two backwards stepwise logistic regression analyses were performed. Variables with the highest p-values were removed stepwise to derive a simplified model with fewer predictors to explain the diagnosis variable [35]. Given the established links between Alzheimer’s disease and demographic factors, age, sex, and education were included as control variables in both models. This decision was based on evidence that Alzheimer’s prevalence increases with age, is higher in females, and is modulated by educational attainment [36,37,38].

3. Results

Significant between-group differences were observed for education and MMSE scores. Since the MMSE was one of the diagnostic tools for AD, a significant difference in MMSE scores between the groups was expected. The results of the analyses of the demographic variables are provided in Table 1.
Mann–Whitney U tests did not reveal any significant differences between the AD and HC groups regarding PA frequency or PA duration (see Table 2).
The backward stepwise logistic regression analysis identified high PA frequency, high level of PA type, and sex (female) as significant predictors for diagnosis. These results indicate that participating in a high PA type within the last seven days as well as being a male participant are associated with being in the HC group. In contrast, participating in PA with high frequency for the last seven days is associated with being in the AD group. Participation in OSSs was only marginally significant but was retained in the model due to its theoretical relevance (see Table 3 and Figure 1).
To elaborate on Model 2 (i.e., OSSs vs. CSSs as the outcome), we first examined how many people participated in OSSs or CSSs. Those who did not participate in any PA within the last seven days (n = 31) were excluded from the analysis in Model 2. Among all physically active participants (n = 97), only 16 reported engaging in OSSs during the past seven days, while 81 participated in CSSs. Of the 16 OSS participants, 12 were in the HC group, while only 4 were in the AD group (results are shown in Table 4 and Figure 2).

4. Discussion

This study explored the potential association between current participation in OSSs versus CSSs and early-stage AD. The overall analysis regarding the PA frequency and duration within the last seven days revealed no statistically significant difference between the AD and HC groups at any level of PA, which aligns with the initial hypothesis. The prediction model of AD or HC (Model 1) status demonstrated a strong correlation with high frequency and high PA type. In line with our hypothesis, being a female participant and participating in a CSS instead of an OSS is correlated with being in the AD group. Model 2 highlighted the medium frequency of PA, high duration of PA (each for the last seven days), and age as significant predictors of OSS participation, while diagnosis was excluded as a relevant predictor in this context.
Interestingly, most AD patients exhibited high levels of PA, which contrasts with the findings of Watts et al. (2013) [39], who reported lower levels of PA among AD patients without distinguishing between OSSs and CSSs in their analyses. This discrepancy to the study of Watts et al. (2013) [39] may be attributed to the relatively good physical condition of participants in our study. Another possible explanation for the observed phenomenon is that the patients were in an early-stage AD, which suggests that the manifestation of inactivity might become more apparent in later stages. Furthermore, PA data were collected through the self-reported PASE questionnaire, and it is important to consider the potential for subjective distortion in the participant’s responses. The findings of Scheyer et al. (2018) [37] regarding the role of sex as a significant risk factor for developing AD were replicated, highlighting the importance of sex as a predictor. Although OSSs were not a primary predictor of AD or HC status, it is noteworthy that in the backward stepwise logistic regression model, the OSS vs. CSS identifier was the last variable to be removed before the final model was established.
In the prediction model of OSS participation (Model 2), the observed significance of medium frequency of PA was unexpected. However, previous studies [14,40] have demonstrated a positive association between cognitive health and moderate exercise intensity, a factor that is intimately associated with the frequency of PA when determining the efficacy of PA [9]. Lastly, the observed significance of high-duration PA as a predictor of OSS participation could be attributed to the nature of the OSS activities. Team sports, such as football, which are classified as OSSs, typically have longer durations (e.g., 90 min football matches) compared to individual CSS activities, like running, where extended durations are uncommon [23]. This distinction in activity structure may explain why high-duration PA was found to be significant.
A key limitation of this study is the reliance on the PASE questionnaire to measure PA, as it is a subjective, self-reported instrument prone to recall bias and misreporting. Especially the recall aspect is probably more relevant in the AD group, as these patients have problems with their memory. Objective measuring devices, such as wearable fitness trackers, could provide more accurate, reliable, and comprehensive data on PA duration and frequency and could also assess PA intensity, an important variable in measuring the physical load induced by PA [9]. Additionally, the PASE questionnaire captures PA over a limited timeframe, rather than long-term PA patterns. This limitation restricts the study’s ability to assess the sustained impact of PA on cognitive health.
Another methodological constraint is the division of PA into OSS and CSS categories, which was not part of the original study design. This post hoc categorization, coupled with the imbalance in the number of participants engaged in OSSs and CSSs, limits the generalizability of the findings.
Additionally, the absence of cerebrospinal fluid or positron emission tomography biomarker data limits the pathological certainty of our “AD group”. Future investigations incorporating amyloid and tau biomarkers are warranted to validate and extend our clinical findings.
In contrast to the current literature and to what we were expecting, our analysis showed a positive correlation between higher PA frequency and being in the AD group. This finding could be due to the imbalanced group and the small group size.
To strengthen the analysis, future research should longitudinally collect data on the number of years that individuals have engaged in OSS or CSS activities, as longer-term engagement may have a greater impact on cognitive outcomes. Retrospective longitudinal studies could provide valuable insight into the relationship between long-term OSS/CSS engagement and cognitive health trajectories. Furthermore, expanding the participant pool and balancing the distribution of OSS and CSS participants would improve the robustness of future analyses. Should OSS participation prove to be a significant factor in early AD detection, it could serve as an additional criterion for early prevention strategies.

5. Conclusions

The study identified high PA frequency, high PA type, and sex as the significant predictors of AD versus HC status, while medium PA frequency, high PA duration, and age were significant predictors of OSS participation. The overall analysis regarding the PA frequency and duration revealed no statistically significant differences between AD and HC groups at any level of PA. The results indicate that OSS vs. CSS participation is a marginally significant predictor of AD or HC status within this sample. Diagnosis was excluded as a predictor for OSS participation during the backwards stepwise selection process.
The protective role of PA in AD has been well documented. However, this study’s findings suggest that more precise distinctions within PA types and levels are needed to better understand their role in maintaining cognitive health. While OSS participation did not emerge as a significant predictor in the current sample, future studies with larger and more balanced samples may nevertheless reveal its potential relevance. Ultimately, identifying specific types and levels of PA that protect cognitive resources could inform tailored preventions for individuals at risk of cognitive decline.

Author Contributions

Conceptualization, L.M. and S.K.; methodology, L.M.; formal analysis, L.M.; resources, S.K.; data curation, L.M. and S.K.; writing—original draft preparation, L.M.; writing—review and editing, M.H., S.K. and R.R.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.

Funding

The dataset used in this study was supported by a grant from the Swiss National Science Foundation (Ambizione fellowship PZ00P1\_126493) awarded to Kirsten I. Taylor.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Ethik Kommission Nordwestschweiz (EKNZ; Switzerland) (protocol code: 257/10; date of approval: 2. November 2010).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that general data sharing was not planned at the start of the study, and the participants did, thus, not provide consent for general data sharing. To share the data, a separate agreement is required. The authors will make every effort to accommodate relevant requests.

Acknowledgments

The authors sincerely thank Kirsten I. Taylor, principal investigator of the study whose data were used, for kindly granting permission to utilize the dataset in their analyses.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ADAlzheimer’s disease
AICAkaike Information Criteria
aMCIAmnestic mild cognitive impairment due to Alzheimer’s disease
BBeta value
CIConfidence interval
CSSsClosed-skill sports
dADEarly-stage Alzheimer’s dementia patients
DSM-IVDiagnostic and Statistical Manual of Mental Disorders, Fourth Edition
HCCognitively healthy control
MCIMild cognitive impairment
MMSEMini-Mental State Examination
NSample size
NINCDS-ADRDANational Institute of Neurological and Communicative Diseases and Stroke/Alzheimer’s Disease and Related Disorders Association
OROdds ratio
OSSsOpen-skill sports
PAPhysical activity
PASEPhysical Activity Scale for the Elderly
SDStandard deviation
SEStandard error

Appendix A

Table A1. Neuropsychological testing and scales (German versions).
Table A1. Neuropsychological testing and scales (German versions).
NameSource
CERAD Mini-Mental StatusFolstein et al. (1975) [41]
CERAD CalculationsThalmann et al. (2002) [42]
CERAD Clock Drawing TestThalmann et al. (2002) [42]
Basel Verbal Learning TestThe German equivalent to the California Verbal Learning Test (CVLT, Delis et al., 1987) [43]
Semantic Fluency (60 sec animals, 60 sec fruits, 60 sec vehicles, 60 sec tools)Straus et al. (2006) [44]
CERAD Boston Naming TestMorris et al. (1988) [45]
16-item Informant Questionnaire on Cognitive Decline in the ElderlyJorm (1994) [31]

Appendix B

Table A2. Differentiation between OSSs and CSSs.
Table A2. Differentiation between OSSs and CSSs.
OSSsCSSs
Light physical activity Easy bike training, playing boules, ninepins, water gymnastics, golf with a cart, yoga, tai chi, fishing, and others
Medium physical activityTennis doubles, pair dancing, volleyball, and othersGolf without a cart, and others
High physical activityTennis singles, aerobic dance, and othersJogging, swimming, biking, skiing, and others

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Jdad 02 00023 g001
Figure 1. The ROC curve illustrates the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) for Model 1 across different threshold settings. ROC = receiver operating characteristic. Area under the curve = 0.7313.
Figure 1. The ROC curve illustrates the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) for Model 1 across different threshold settings. ROC = receiver operating characteristic. Area under the curve = 0.7313.
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Jdad 02 00023 g002
Figure 2. The ROC curve illustrates the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) for Model 1 across different threshold settings. ROC = receiver operating characteristic. Area under the curve = 0.5144.
Figure 2. The ROC curve illustrates the trade-off between the true positive rate (sensitivity) and the false positive rate (1-specificity) for Model 1 across different threshold settings. ROC = receiver operating characteristic. Area under the curve = 0.5144.
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Table 1. Demographic information for the overall sample, the AD group, and the HC group.
Table 1. Demographic information for the overall sample, the AD group, and the HC group.
Overall Sample
N = 128		AD Group
n = 50		NC Group
n = 78		p-Value
Sex 0.05
Malen (%)70 (55)22 (44)48 (62)
Femalen (%)58 (45)28 (56)30 (38)
Age (years)Mean (SD)74.84 (7.10)75.78 (7.83)74.23 (6.57)0.27
Education (years)Mean (SD)12.77 (3.22)12.12 (2.83)13.18 (3.40)0.04
MMSEMean (SD)28.19 (3.13)27.30 (2.34)28.76 (3.45)<0.01
AD = Alzheimer’s disease; HC = cognitively healthy control; SD = standard deviation; MMSE = Mini-Mental State Examination.
Table 2. Mann–Whitney U tests comparing the AD group and the HC group in each level of PA.
Table 2. Mann–Whitney U tests comparing the AD group and the HC group in each level of PA.
Group Size (n)Mann–Whitney U Tests (U)z-Valuep-Value
Low-level PA frequency AD = 12
HC = 15		1677.0−0.790.43
Medium-level PA frequency AD = 6
HC = 8		1698.5−0.680.49
High-level PA frequency AD = 17
HC = 39		1651.0−1.000.32
Low-level PA durationAD = 12
HC = 15		653.0−0.820.41
Medium-level PA durationAD = 6
HC = 8		128.5−0.320.75
High-level PA durationAD = 17
HC = 39		348.5−1.480.14
AD = Alzheimer’s disease; HC = cognitively healthy control; PA = physical activity executed for the last seven days.
Table 3. Model 1: backward stepwise logistic regression with diagnosis (AD vs. HC) as the dependent variable.
Table 3. Model 1: backward stepwise logistic regression with diagnosis (AD vs. HC) as the dependent variable.
VariablesB (SE)OR95% CIp-Value
OSS−1.47 (0.75)0.230.05, 0.930.05
PA frequency (high)1.03 (0.43)2.791.26, 6.890.02
PA type (high)−3.11 (1.11)0.040.00, 0.330.01
Fixed variables
Sex (female)0.87 (0.44)2.391.02, 5.780.05
Age0.04 (0.03)1.040.98, 1.110.23
Education−0.10 (0.07)0.900.78, 1.030.14
The outcome variable diagnosis is categorized in the HC and AD groups. A positive Beta indicates an increased likelihood of being in the AD group, while a negative Beta suggests a decreased likelihood. AD = Alzheimer’s disease; HC = cognitively healthy control; PA = physical activity executed for the last seven days; B = Beta value; SE = standard error; OR = odds ratio; CI = confidence interval. The final model stopped eliminating variables at an Akaike Information Criterion (AIC) = 168.29.
Table 4. Model 2: backward stepwise logistic regression with OSSs (vs. CSSs) as the dependent variable.
Table 4. Model 2: backward stepwise logistic regression with OSSs (vs. CSSs) as the dependent variable.
VariablesB (SE)OR95% CIp-Value
PA frequency (medium)0.98 (0.34)2.661.40, 5.41<0.01
PA duration (high)0.80 (0.25)2.231.37, 3.78<0.01
Fixed variables
Sex (female)0.07 (0.74)1.070.24, 4.70>0.90
Age0.11 (0.05)1.121.01, 1.240.03
Education−0.04 (0.12)0.960.75, 1.200.80
The outcome variable OSSs (vs. CSSs) is categorized in the OSS and CSS groups. A positive Beta indicates an increased likelihood of being in the OSS group, while a negative Beta suggests a decreased likelihood. OSSs = open-skill sports; CSSs = closed-skill sports; PA = physical activity executed for the last seven days; B = Beta value; SE = standard error; OR = odds ratio; CI = confidence interval. The final model stopped eliminating variables at an Akaike Information Criterion (AIC) = 83.53.
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Moll, L.; Häner, M.; Rössler, R.; Krumm, S. Comparison of Physical Activity Patterns Between Individuals with Early-Stage Alzheimer’s Disease and Cognitively Healthy Adults. J. Dement. Alzheimer's Dis. 2025, 2, 23. https://doi.org/10.3390/jdad2030023

AMA Style

Moll L, Häner M, Rössler R, Krumm S. Comparison of Physical Activity Patterns Between Individuals with Early-Stage Alzheimer’s Disease and Cognitively Healthy Adults. Journal of Dementia and Alzheimer's Disease. 2025; 2(3):23. https://doi.org/10.3390/jdad2030023

Chicago/Turabian Style

Moll, Léonie, Michèle Häner, Roland Rössler, and Sabine Krumm. 2025. "Comparison of Physical Activity Patterns Between Individuals with Early-Stage Alzheimer’s Disease and Cognitively Healthy Adults" Journal of Dementia and Alzheimer's Disease 2, no. 3: 23. https://doi.org/10.3390/jdad2030023

APA Style

Moll, L., Häner, M., Rössler, R., & Krumm, S. (2025). Comparison of Physical Activity Patterns Between Individuals with Early-Stage Alzheimer’s Disease and Cognitively Healthy Adults. Journal of Dementia and Alzheimer's Disease, 2(3), 23. https://doi.org/10.3390/jdad2030023

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