Sedentary Patterns and Health Outcomes in the Oldest-Old: a Latent Profile Analysis

doi:10.21203/rs.3.rs-3352579/v1

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Sedentary Patterns and Health Outcomes in the Oldest-Old: a Latent Profile Analysis

https://doi.org/10.21203/rs.3.rs-3352579/v1

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Background. Sedentary behavior is most prevalent among those aged 80 and above. Current literature emphasizes the significance of SB patterns, but further evidence is required to understand how these patterns relate to specific health outcomes and to identify at-risk profiles for tailored interventions in the oldest old. Therefore, the aim of this study was to identify profiles of older adults based on their sedentary patterns and health outcomes, and to examine associations between profiles and socio-demographics.

Methods. A cross-sectional study was performed between February 2021 and December 2022 in Flanders, Belgium. Distinct profiles of device-based sedentary patterns and physical and cognitive functioning, mental health-related quality of life (QoL) and social isolation were identified using a latent profile analysis on data of 90 older adults (80+). Associations with socio-demographics were analyzed using one-way ANOVAs and chi²-tests.

Results. Three distinct profiles were identified: (1) the ‘cognitively and physically frail’ profile, (2) the ‘healthy’ profile and (3) the ‘lower mental health-related QoL’ profile. Those in the ‘cognitively and physically frail’ profile exhibited the least favorable sedentary pattern, and had a higher likelihood of residing in a nursing home. No significant differences were found for the other socio-demographic variables, being age, sex, educational degree and family situation.

Conclusions. Individuals with lower physical and cognitive functioning have the most unhealthy sedentary patterns, often involving prolonged bouts lasting at least one hour. Therefore, it is crucial to prioritize interventions that address and interrupt extended sedentary behavior in this subgroup.

Elderly

sitting

typologies

mental health

physical health

Sedentary behavior (SB) is defined as “any waking behavior characterized by an energy expenditure of ≤ 1.5 metabolic equivalents (METs), while in a sitting, reclining or lying posture” (Sedentary Behaviour Research, 2012). Growing evidence identifies SB as an important risk factor for detrimental health-related conditions, including type 2 diabetes, metabolic syndrome, cardiovascular disease, cancer, and increased risk of mortality in both adult and older adult populations (de Rezende et al., 2014; Ekelund et al., 2019; Katzmarzyk et al., 2019; Wilmot et al., 2012). Older adults (65+) are the most sedentary age group, spending on average 9.4 hours/day sedentary (Harvey et al., 2013, 2015). Furthermore, a recent meta-analysis showed that the oldest old (80+) spend on average one or two hours/day more in sedentary activities, compared to participants aged 60 years or older (Webster et al., 2021), which underlines the importance of studying SB specifically in the oldest old.

Recent studies point out the importance of accumulation patterns of sedentary time (ST) beyond overall time spent sedentary in relation to different health-related outcomes. The pattern of accumulation of SB refers to “a regular and intelligible form or sequence discernible in the way in which SB happens” (Boerema et al., 2020). A sedentary bout can be defined as “a period of uninterrupted ST”, whereas a sedentary interruption or break is defined as “a non-sedentary bout in between two sedentary bouts” (Tremblay et al., 2017). Next to the association of overall ST with negative health outcomes, longer sedentary bouts and fewer breaks in SB seem to be negatively associated with physical function in older adults (Gennuso et al., 2016; Han et al., 2022). Similarly, older women with a higher mean sedentary bout duration have increased risk of falling (Rosenberg et al., 2021) and show higher odds of diabetes (Bellettiere et al., 2019). Another study suggests that for every additional hourly break in ST in older women, the odds for abdominal obesity decreased with 7% (Júdice et al., 2015). Furthermore, a study in community-dwelling older men found that men who spent significantly more time in prolonged sedentary bouts were more likely to be depressed, obese and to suffer from chronic disease(s) (Jefferis et al., 2015).

Previous studies examining patterns of SB and their associations with health outcomes mainly focused on the general population of older adults being over 65 years, whereas very few studies focused specifically on those ≥ 80 years. However, SB patterns tend to change with age as older adults aged 65–69 tend to have more sit-to-stand transitions (i.e. sedentary breaks) and shorter sedentary bout durations compared to older adults aged 81–87 and ≥ 90 (I. M. Dohrn et al., 2020). Therefore, more research is required on how SB patterns of accumulation relate to health outcomes in the oldest-old.

Moreover, existing literature studying the association of SB with health outcomes in older adults primarily centers on physical health aspects, notably chronic diseases. It is imperative, however, to also address mental, cognitive and social dimensions of health given their inherent impact on independence and quality-of-life (Dogra et al., 2022). Consequently, it is recommended to adopt a broader, multidisciplinary approach in order to obtain a holistic perspective. By taking this perspective, it becomes possible to identify profiles of older adults that are at risk and in need of tailored interventions aimed at improving sedentary patterns. This is highly needed, as previous studies have indicated that tailored interventions tend to yield greater success compared to non-tailored ones. Considering that studies have underscored the significance of socio-demographic factors in influencing the interplay between sedentary patterns and health outcomes (S. F. M. Chastin et al., 2015; J. L. Copeland et al., 2017), determining the link between socio-demographics and these at-risk profiles can provide valuable insights.

In conclusion, ST is highest in the oldest old (80+) and current literature points towards the importance of patterns in SB. Still, more evidence is needed to unravel how certain SB accumulation patterns cluster with specific health outcomes to identify at-risk profiles for future tailored SB interventions in the oldest old. Therefore, the aim of this study is to identify person-centered profiles, using a Latent Profile Analysis (LPA), based on accumulation patterns of SB and health outcomes, being physical, cognitive and psychosocial functioning in the oldest old (≥ 80 years). After identifying the different profiles, associations with different socio-demographic variables will be investigated to gain further insight into the characteristics of older adults in the most vulnerable profiles.

Study Design

A cross-sectional study, called ‘Healthy 80+’, was performed between February 2021 and December 2022 in Flanders, Belgium. The study was approved by the ethical committee of the [blinded], and all participants provided informed consent. This paper follows the STROBE guidelines for reporting observational studies (von Elm et al., 2014).

Participants and Procedure

Older adults aged 80 years or older were recruited for this study through convenience sampling. Recruitment was done through word of mouth, social media and local service centers. Local service centers offer informative and recreational activities within neighborhoods to facilitate self-reliance among people in a novice care situation. Participants had to be Dutch speaking, able to stand independently with or without an assistive device and could not have been diagnosed with cognitive disorders to be eligible for the study. Participants who were willing to engage in the study were contacted by phone to arrange the dates for the two home visits. The first home visit consisted of explaining the purpose and procedures of the study, signing the informed consent, completing a questionnaire during a structured interview and executing the Short Physical Performance Battery. At the end of the first home visit, an activPAL accelerometer was attached to the participants thigh to measure SB for seven consecutive days. Participants were also asked to keep a sleep diary. On day eight, the second home visit was planned to recollect the activPAL accelerometer and the sleep diary, and to perform a cognitive functioning test.

Measures

Sedentary Behavior

Accelerometer Data Processing

An activPAL accelerometer (Physical Activity Technologies, Glasgow, Scotland) was attached on the midline of participants’ right anterior thigh for seven consecutive days to objectively measure ST. The activPAL is an accelerometer that is known as gold standard for objectively measuring ST since it can distinguish between sitting/lying and standing (Hart et al., 2011). Data were summarized in epochs of 15 seconds and were downloaded and processed using manufacturer proprietary software (activPAL 173 Professional v8.11.6.70). A day was considered invalid if there was limited postural variation (i.e., ≥ 95% of wear time in one activity) or fewer than 10 hours of valid waking wear time (Morris et al., 2019; Winkler et al., 2016). Only participants with four days of valid activPAL data were included in the analysis (Heesch et al., 2018). The ‘Processing PAL’ (Winkler et al., 2016) application was used to manually double-check the algorithms to exclude sleep for the analysis. Therefore, participants were asked to report the times they went to bed and when they got up in the morning in their sleep diary. If there was an incongruence between self-reported and algorithm-derived sleep time, adaptations were made using the ‘Processing PAL’ application based on the self-reported data (Winkler et al., 2016). Likewise, the created Heatmaps from the ‘Processing PAL’ application were used to manually check the algorithm for potentially misclassified invalid days, since the algorithm seems to be less reliable in older adult populations (Winkler et al., 2016).

Total Sedentary Time and Sedentary Accumulation Patterns: Total ST (minutes/day) was extracted from the accelerometer data to maximize comparability with other studies. For identifying SB patterns many measures exist, but consensus about the best indicators is still lacking (Boerema et al., 2020). Alpha (unitless) is one of the most robust measures and is very sensitive to change (Sebastien F. M. Chastin et al., 2015). Alpha is defined as “the cumulative distribution of bout lengths” and represents the frequency distribution of SB bout duration, which follows the power-law probability distribution (Chastin & Granat, 2010; Sebastien F. M. Chastin et al., 2015). Therefore it acts as a measure that captures the diversity of bout lengths during a day (Boerema et al., 2020). Lower values indicate a SB accumulation pattern with more prolonged bouts, whereas higher values represent a more fragmented SB pattern. One disadvantage of using alpha, is that it can be difficult to interpret. Therefore, a second sedentary accumulation pattern measure, usual bout duration (UBD), was calculated. UBD is the median value of the cumulative sedentary bout duration distribution, and is a universal measure to report bout lengths in relation to total ST (Boerema et al., 2020). All three measures: total ST, alpha and UBD, were calculated using Python. The script can be found at OSF [blinded for peer review].

Physical Functioning

During the first home visit, the Short Physical Performance Battery (SPPB) was performed by trained researchers. This test battery consists of three physical performance tests (balance, walking, sit-to-stand) and is a well-established tool to evaluate functional capability (Guralnik et al., 1994). For each test a subscore ranging from 0 to 4 was created. A summary score for physical functioning was obtained by summing the three subscores, resulting in a score ranging from 0 to 12, with higher scores indicating better physical functioning. A detailed summary of the scoring system can be found elsewhere (De Fátima Ribeiro Silva et al., 2021).

Cognitive Functioning

The Cambridge Neuropsychological Test Automated Battery (CANTAB) was performed during the second home visit. This battery focuses on three cognitive domains: (1) working memory and planning, (2) attention and (3) visuospatial memory.

The CANTAB is the most widely published battery for cognitive function, is largely independent of verbal instruction and is a relatively cheap and accessible method to assess cognitive functioning compared to face-to-face assessments (Lenehan et al., 2016; Smith et al., 2013). The CANTAB was performed using an iPad. To make sure participants were familiar with the device and understood all instructions, a familiarization exercise was performed first (motor screening task, MOT). After that, a subset of five tests was performed: reaction time (RTI, attention and psychomotor speed), paired associates learning (PAL, memory), spatial working memory (SWM, executive functions), delayed matching to sampling (DMS, memory) and rapid visual information processing (RVP, attention and psychomotor speed).

First, Z-scores for all outcomes (e.g. reaction time, error percentage) were calculated (one for RTI, two for PAL, SWM and DMS, one for RVP; n = 8). Second, negatively scored outcomes were reversed so that all were positive scores with a higher score indicating better cognitive functioning. Third, for the cognitive tests with multiple outcomes, composite scores were calculated by averaging the Z-scores of all respective outcomes. To calculate overall cognitive functioning of the participants, an average score of all composite scores was calculated (Gheysen et al., 2019).

Mental Health-Related Quality of Life

Mental health-related quality of life (QoL) was derived from the RAND-36 questionnaire, assessed during the first home visit. First, scores for the following subscales for mental health were calculated: vitality, social functioning, emotional role functioning and mental health (Ware, 1994). Second, the sum of these scores was divided by 4, which resulted in the unweighted RAND-36 Mental Composite Summary (MCS). This RAND-36 MCS is a simple, validated way of interpreting the mental health aspect of the RAND-36 (Andersen et al., 2022).

Feelings of Social Isolation

The Patient-Reported Outcome Measurement Information System (PROMIS) short form for social isolation was assessed. Four items concerning feelings of social isolation were asked, with five answer possibilities ranging from ‘never’ to ‘always’. PROMIS item banks and validated short-forms are created to enable researchers to get insight into participants’ symptoms, functioning and health-related quality of life in an efficient, flexible and precise way (Cella et al., 2010). Scoring was done conform the PROMIS scoring system. A sum-score of all items was calculated to represent ‘feelings of social isolation’, which could only be calculated with complete data. From this raw sum-score, a T-score with mean of 50 and standard deviation of 10 was calculated using the score conversion table, which can be found here.

Socio-Demographic, BMI and co-morbidities

Following variables were obtained via a questionnaire during the first home visit: age (years); sex (male or female); weight (kg) and height (cm), of which BMI was calculated; marital status (single, in a relationship, cohabiting/married); living situation (home, service flat, nursing home); and education (no degree, primary education, vocational secondary education, technical secondary education, general secondary education, higher education and university; which was recoded into ‘no higher education’ or ‘higher education’). Also comorbidities (NSHAP Comorbidity Index, measuring burden of chronic diseases and conditions) were assessed by asking “has a medical doctor told you that you have (had) [condition]?” for a list of 15 conditions, with a range from 0 to 21; scoring details can be found in Vasilopoulos et al., 2014.

Statistical Methods

All analysis were executed using RStudio (R version 3.4.1), the script and anonymized data can be found at the OSF page [blinded for peer review]. Descriptive statistics were performed on socio-demographic data, BMI and comorbidities, SB measures and health outcomes.

Latent profile analysis (LPA), performed using the R package ‘tidyLPA’ (Rosenberg et al., 2018), was conducted to identify latent subgroups of older adults. LPA assumes that subjects can be subtyped with varying degrees of probabilities into subgroup categories that have different configural profiles (Collins & Lanza, 2009). This profile membership is considered as an unobserved categorical variable (cfr. latent) and is estimated based on the optimal fitted model. This is a person-centered approach, which focuses on identifying subpopulations of individuals who show various patterns of values for certain variables and outcomes (Muthen & Muthen, 2000). The following steps were executed for the LPA (Bauer J, 2021):

First, possible theoretically-based indicators for the LPA model were selected, being mean ST/day, alpha and UBD (SB accumulation patterns), global cognitive functioning (CANTAB), mental health-related quality-of-life (unweighted RAND-36 MCS), feelings of isolation (PROMIS) and physical functioning (SPPB), and inter-correlation analyses were performed. For the SB measures, UBD seemed to be more correlated to the other indicators compared to mean ST/day and alpha. Since they are all indicators for SB accumulation patterns, mean ST/day and alpha were removed from the model. Furthermore, to uncomplicate interpretation of the LPA results, all negatively scored indicators (i.e., PROMIS and USB) were scored positively. Second, different models with different numbers of latent classes were estimated and compared based on the following fit indices: Bayesian Information Criterium (BIC), sample-size adjusted BIC (SABIC) and Akaike Information Criterion (AIC) (lower values indicate a better model fit to the data), the Bootstrap Likelihood Ratio Test (BLRT) and entropy values (indicates the overall ability of a model to return well-separated profiles, higher value means better fit with one being perfect classification). Hereafter, the target solution was interpreted substantively. Therefore standardized scores were generated for the included variables, and the class-specific means, probability profiles across indicators and class sizes were consulted. After the LPA, one-way ANOVA or non-parametric Kruskal-Wallis analysis were performed to identify whether different profile groups significantly differed from each other in terms of SB and health outcomes.

One-way ANOVA and Chi² analysis were used to compare socio-demographic characteristics with memberships of different profile groups. When observations for certain subgroups were too small to perform a Chi-square (χ²) test, the Fisher’s exact test was performed. Following socio-demographic variables were used: age (continuous), sex (1 = male, 2 = female), education (1 = no higher education; 2 = higher education), living situation (dichotomous: 1 = living at home or service flat; 2 = nursing home), family situation (1 = living alone; 2 = living together with partner). P-values of 0.05 or less were considered statistically significant.

Characteristics of Study Population

A total number of 112 participants were included in the study. However, after exclusion due to incomplete CANTAB (n = 8) and activPAL data (n = 14), analyses were performed on 90 participants. Socio-demographic, SB and health-related characteristics can be found in Table 1. The study sample had a mean age of 84.9 ± 4.0 years, with a maximum of 95.0 years. About half of the participants were women (53.3%) and about one third of the population had a high education level (30.0%). The majority of participants lived independently at home (83.3%) with their partner (55.1). About one quarter were underweight (24.7%), 43.8% had a healthy weight, and 31.5% was overweight or obese. The mean sitting time of the participants was 9.6 ± 1.9 hours per day, with a mean UBD of 44.4 ± 21.2 minutes.

Table 1 Sample Characteristics
Demographics			Total Sample (N = 90)	Missing (N)
	Age, mean (SD)		84.9 (4.0)
	%Women		53.3
	Marital status			1
		%Single	42.7
		%In a relationship, not living together	2.2
		%Cohabiting/married	55.1
	%Participants without children		10.0
	%Highly educated		30.0
	Living situation
		%Living at home	83.3
		%Living at assisted-living apartment	7.8
		%Living at nursing home	8.9
	BMI, mean (SD)		26.1 (3.9)	1
		%Underweight	24.7
		%Healthy weight	43.8
		%Overweight	28.1
		%Obese	3.4
	Comorbidity index¹, mean (SD); median (IQR)		2.5 (1.6); 2.0 (2.0)
Accelerometer-based SB levels, mean (SD); median (IQR)
	Sitting time (hours/day)		9.6 (1.9); 9.3 (2.8)
	Alpha²		1.3 (0.0); 1.3 (0.0)
	Usual bout duration (min)		44.4 (21.2), 40.1 (21.0)
Physical functioning³, mean (SD); median (IQR)			8.4 (2.5); 9.0 (2.0)
Cognitive functioning, mean (SD); median (IQR)
	Reaction time (RTI)
		Median duration of the reaction time (ms; negative*)	443.1 (84.1); 428.0 (88.0)
	Paired associates learning (PAL)
		Number of correct responses at first attempt	6.1 (3.4); 5.0 (4.8)
		Number of errors (negative*)	42.2 (15.5); 45.0 (27.0)
	Spatial working memory (SWM)
		Number of errors (negative*)	21.8 (7.0); 22.0 (8.0)
		Strategy use (number, negative*)	9.4 (1.9); 10.0 (2.0)
	Delayed matching to sampling (DMS)
		Latency of responses to correct trials (ms; negative*)	4666.3 (1852.4); 4326.0 (2026.4)
		Percentage of correct trials	66.6 (14.9); 67.0 (20.0)
	Rapid visual information processing (RVP)
		Target sensitivity	0.8 (0.1); 0.8 (0.1)
	Overall cognitive functioning⁴		0.0 (0.6); 0.0 (0.8)
	Mental health-related QoL⁵, mean (SD); median (IQR)		73.3 (15.4); 77.5 (16.9)
	Social isolation⁶, mean (SD); median (IQR) (negative*)		42.0 (7.8); 40.4 (10.9)
BMI = Body Mass Index (kg/m²), range 18.5 to 24.9 = healthy, 25.0 to 29.9 = overweight, ≥ 30 = obese; ¹Comorbidity score calculated with NSHAP Comorbidity Index, range 0 to 21; SB = Sedentary Behavior; ²alpha = cumulative distribution of bout lengths (unitless); ³ Short Physical Performance Battery for physical functioning score; ⁴Average score of all composite scores, calculated from the average Z-scores of each cognitive test; ⁵RAND-36 Mental Health-Related QoL score; ⁶PROMIS Social Isolation score; QoL = Quality of Life ; *negative = low score represents better performance/functioning

Identified Profiles in the Oldest Old

The results of the LPA indicated that a three profile solution provided both an interpretable number of distinct profiles and optimal fit, based on BIC, AIC, the BLRT and entropy values. See Table 2 for details.

Table 2

Model Fit Parameters for Different Profile Solutions
	Fit Indices
Solution	AIC²	BIC²	SABIC²	Entropy	BLRT p-value	Probability Range*
2-profile	2705.856	2745.853	2695.355	0.890	0.010	0.91–0.98
3-profile	2668.792	2723.788	2654.354	0.953	0.010	0.94–0.99
4-profile	2670.098	2740.093	2651.723	0.905	0.297	0.80–0.97
AIC = Akaike Information Criterion; BIC = Bayesian Information Criterium; SABIC = sample-size adjusted BIC; BLRT = Bootstrap Likelihood Ratio Test; ²lower scores represent better fit; *probability range of profile membership (min-max)

Figure 1 shows group profiles based on the five indicators: UBD, physical and cognitive functioning, mental health-related QoL and feelings of social isolation. Profiles one, two and three included 12.2% (N = 11), 75.6% (N = 68) and 12.2% (N = 11) of the sample, respectively. Given the differences in ST and health outcomes, profile one was labeled as ‘cognitively and physically frail’, profile two as ‘healthy’ and profile three as ‘lower mental health-related QoL’. A detailed overview of the descriptive statistics per profile can be found in Table 3.

Table 3 Descriptive Statistics by Profile
	Profile 1 (n = 11)	Profile 2 (n = 68)	Profile 3 (n = 11)	Significance of difference	Post-Hoc
	Cognitively and physically frail	Healthy	Lower mental health-related QoL	Significance of difference	Post-Hoc
Usual Bout Duration (min), median (IQR)	47.6 (30.6)	37.2 (21.1)	40.2 (13.6)	Kruskal-Wallis = 6.3; p = 0.04	P1 > P2*
Physical Functioning, median (IQR)	3.0 (1.5)	9.0 (2.0)	8.0 (2.0)	Kruskal-Wallis = 32.2; p < 0.001	P1 < P2* P1 < P3 P2 > P3**
Cognitive Functioning, mean (SD)	-0.6 (0.4);	0.2 (0.5)	-0.2 (0.5)	F = 11.7; p < 0.001	P1 < P2***
Mental Health-Related QoL, mean (SD)	64.1 (6.0);	80.2 (7.2)	40.1 (9.5)	F = 150.2; p < 0.001	P1 < P2* P1 > P3* P2 > P3***
Feelings of Social Isolation, median (IQR)	45.7 (10.9)	40.4 (8.5)	45.7 (9.0)	Kruskal-Wallis = 5.8; p = 0.05	P2 < P3*
Age, mean (SD)	87.1 (3.9)	84. (4.0)	84.4 (3.8)	χ²=1.6; p = 0.20
Female, N (% within profile)	6 (54.5)	33 (48.5)	9 (81.8)	χ²=4.26; p = 0.12
Tertiary education (college/university), N (% within profile)	2 (18.2)	22 (32.4)	3 (27.2)	χ²=0.9; p = 0.62
Living in nursing homes, N (% within profile)	3 (27.2)	3 (0.04)	2 (18.2)	χ²=7.4; p = 0.02416
Single, N (% within profile)	8 (72.7)	35 (51.5)	6 (54.5)	χ²=2.9; p = 0.24
* p < 0.05; p < 0.01; * p < 0.0001
Sedentary Behavior

Median UBD differed between profiles one and two, where profile one had a statistically significant longer UBD, with 47.6 (± 30.6) minutes compared to profile two, with a mean duration of 37.2 (± 21.1) minutes (p = 0.42). No statistically significant differences were found with profile three.

Physical Functioning

The median physical functioning scores of all three profile groups were significantly different from each other. Profile one had a median score of 3.0 (± 1.5), which was lower than profile two (p < 0.001) and three (p = 0.002). Median scores of profiles two and three were 9.0 (± 2.0) and 8.0 (± 2.0) respectively (p = 0.015).

Cognitive Functioning

The overall cognitive functioning scores were significantly lower in profile one than in profile two, with mean scores of -0.6 (± 0.4) and 0.2 (± 0.5) respectively (p < 0.001). Borderline significant results were found between the first and third profile, where the third profile had a mean cognitive functioning score of -0.2 (± 0.5) (p = 0.064). No significant differences were found between profiles two and three.

Mental Health-Related Quality of Life

The first profile had a mean score of 64.1 (± 6.0), which was significantly lower than profile two with a mean mental health-related QoL of 80.2 (± 7.2) (p < 0.001) and higher than profile three with 40.1 (± 9.5) (p < 0.001). Mental health-related QoL differed also significantly between profile two and three.

Feelings of Social Isolation

Borderline significant differences were found for feelings of social isolation between profile two and three, with median scores of 40.4 (± 8.5) and 45.7 (± 9.0) respectively (p = 0.051). No significant differences were found with profile one.

Differences in Socio‑Demographics Across Profiles

The Chi-square test showed significant differences between profiles for living in nursing homes (χ²=7.446, p = 0.026). Within profile one, 27.2% (N = 3) of the subgroup lived in a nursing home. For profiles two and three, this was 4.4% (N = 3) and 18.2% (N = 2) respectively.

No significant differences were found for the other socio-demographic variables, being age, sex, educational degree and family situation.

This study examined person-centered profiles in the oldest old (80+) using latent profile analysis (LPA), based on sedentary accumulation patterns and health outcomes, including physical and cognitive functioning, mental health-related QoL and feelings of social isolation. After the identification of those distinct profiles, associations with socio-demographic variables were investigated. To the best of our knowledge, no previous studies examined such person-centered profiles in the oldest old. Nevertheless, gaining insight into these person-centered profiles and their associations with socio-demographic characteristics allows us to better tailor future SB interventions to the needs of specific subgroups. Growing evidence supports the importance of tailoring (i.e. personalizing) in health behavior change interventions (Baker et al., 2010). A recent systematic review of Johansson and colleagues (2022) argues that SB interventions tailored to older adults’ health outcomes like physical function or mental wellbeing could be more beneficial than providing more general interventions (Johansson et al., 2022).

The results of the LPA identified three distinct person-centered profiles: ‘cognitively and physically frail’ older adults, ‘healthy’ older adults and older adults with ‘lower mental health-related QoL’. The ‘cognitively and physically frail’ profile was characterized by lower scores on both physical and cognitive functioning. The co-occurrence of cognitive and physical impairment is not surprising, as previous research also showed that deterioration in these domains frequently goes hand in hand in older individuals (Bruce-Keller et al., 2012). Next to the diminished physical and cognitive functioning, those in the ‘cognitively and physically frail’ profile also exhibited a significantly less favorable sedentary accumulation pattern compared to those of the ‘healthy’ profile. It is concerning to observe that the median UBD in this profile was 48 minutes, as prolonged sedentary bouts are associated with reduced blood flow, diminished shear stress, and disrupted postprandial metabolism (Dempsey et al., 2018; Dempsey & Thyfault, 2018; Peddie et al., 2021). Our findings suggest that interventions aimed at interrupting prolonged bouts of SB with short bouts of movement would be particularly relevant for older adults classified under the ‘cognitively and physically frail’ profile. Fortunately, frequently interrupting sedentary time has been identified as a reasonable starting point for initiating changes in movement behaviors among vulnerable older adults (Shilpa Dogra et al., 2022). Addressing this behavior in this cognitively and physically frail group of older adults might have the potential to improve overall functioning and mitigate the negative consequences associated with excessive SB.

The ‘healthy profile’, which was the most prevalent among the oldest old, scored rather good on all health outcomes, and had the lowest UBD. Although the profile is labelled ‘healthy’, it should be noted that this is based on relative values and that older adults in this profile still spent a significant portion of their sedentary time in prolonged bouts, as evidenced by the median UBD of 37 minutes. This finding aligns with previous research of Dohrn et al, who found a UBD of 32 minutes in those aged 80 years and over (I.-M. Dohrn et al., 2020). So, despite the relatively high scores on cognitive functioning, physical functioning, mental health-related QoL and low scores on social isolation, the high UBD also identified in this subgroup underscores the need for increased efforts to reduce prolonged SB in the oldest olds (Jennifer L Copeland et al., 2017).

Finally, the ‘lower mental health related QoL’ profile was characterized by low mental health-related QoL and high feelings of social isolation. The clustering of these concepts aligns with the findings form population-based cohort studies conducted by Boehlen et al., and Tan et al., indicating that lonely older adults often experience a lower mental health-related QoL (Boehlen et al., 2022; Tan et al., 2020). Surprisingly, older people belonging to this profile did not exhibit a more unhealthy sedentary accumulation pattern than those in the ‘healthy’ profile. It should be noted that the association between mental health and sedentary accumulation patterns has not yet been investigated in the oldest-old, and thus, the current results cannot be directly compared with earlier findings. However, a UK study conducted among a general cohort of older adults (mean age 78.4 years) did show that depressed older adults spent more time in longer sedentary bouts compared to their non-depressed counterparts (Jefferis et al., 2015).

In an attempt to identify at-risk target groups for future tailored SB interventions in the oldest-old, we also examined how specific socio-demographic variables (i.e., age, sex, educational degree, living situation and family situation) related to these three profiles. The results revealed that older adults living in nursing homes were mainly present in the ‘cognitively and physically frail’ profile. This profile comprised older adults with lower physical and cognitive functioning, and longer average UBD. Although these results should be interpreted with caution as the number of individuals in profile one was low (n = 11 of which three were living in a nursing home), it is interesting to have a closer look at this result. The observed association can be logically explained in the light of other evidence. Another study, conducted in nursing homes in the USA, also found that levels of physical frailty were high, with more than 90% of residents suffering from moderate to severe physical frailty. These high levels of physical frailty were also associated with high levels of cognitive impairment (Yuan et al., 2021). Furthermore, a systematic review and meta-analysis concluded that prefrailty and frailty are significant predictors of nursing home placement among older adults (Kojima, 2018). Previous studies also reported high levels of sedentary time in care facilities (up to 85% of the time) (Parry et al., 2019), with differences in the amount of daily sedentary time depending on the levels of care provided (i.e., more sedentary time in high level care facilities like nursing homes than in intermediate level care facilities like assisted living) (Leung et al., 2021). Consequently, nursing homes (and other long-term care facilities) seem to be important settings to target in order to reduce prolonged sedentary bouts, and improve health outcomes in the oldest-old.

The fact that age, sex, educational degree and family situation were not associated with belonging to a specific profile, might be partly due to the small sample size, particularly in profiles one and three. Therefore, these results should be interpreted with caution. Nonetheless, our findings imply that when designing interventions to combat prolonged sedentary behavior, a more individualized approach based on health outcomes relevant to older adults and the duration of sedentary bouts would be more beneficial than targeting specific socio-demographic subgroups within the oldest-old population. This approach ensures a focus on the individuals who are most likely to benefit from the intervention, regardless of their demographic characteristics.

This study has some important strengths. First, the study focused on a unique target population (older adults aged 80+), which is currently underrepresented in scientific research. As life expectancy has been increasing steadily over the last decades, and our ageing society is imposed with major challenges, it is of utmost importance to examine how we should target and tailor our interventions to keep the oldest-old adults as healthy as possible for as long as possible. Second, ST cognitive functioning and physical functioning were measured with valid devices and tests, and mental health-related QoL and social isolation were assessed using valid self-report scales. Next to these strengths, some limitations should be acknowledged. A first limitation is the convenience sampling to recruit the participants, as this limits the generalizability of the results to ‘all’ older adults aged 80+. Nonetheless, the sample showed a good distribution of sex, age, educational attainment, marital status and BMI. However, only a limited number of participants lived in a nursing home, making it difficult to draw firm conclusions. Finally, the sample size was rather small, which was reflected in the small numbers of participants included in profiles one and three. These limitations emphasize the need for further research with larger and representative samples to enhance the generalizability of the findings across diverse populations of the oldest-old.

In conclusion, this study classified the oldest-old population into three distinct typologies based on sedentary accumulation patterns and geriatric-relevant health outcomes. The findings highlight that individuals with lower levels of physical and cognitive functioning exhibit the unhealthiest sedentary patterns, with a significant portion of their sedentary bouts lasting at least one hour. As a result, interventions targeting the interruption of prolonged sedentary behavior are particularly important for this subgroup. While this subgroup is not associated with specific socio-demographic factors, it is noteworthy that older adults in this typology are more likely to reside in nursing homes. Therefore, additional efforts to address sedentary behavior seem warranted within this specific setting.

Ethics approval and consent to participate

We conducted the studies in accordance with the Helsinki Declaration and the underlying data protection regulation. The studies were approved by the Ethics Committee of the Ghent University Hospital [blinded for peer review]. All participants provided written informed consent.

Consent for publication

Not applicable.

Availability of data and materials

Data and scripts are publicly available on the Open Science Framework (https://osf.io/g2cn7/).

Competing interests

The authors declare that they have no competing interests.

Funding

[Blinded for peer review] are supported by [blinded for peer review]. The funding bodies had no input in study design and the collection, analysis and interpretation of data and the writing of the article and the decision to submit it for publication.

Authors’ Contributors

Elien Lebuf: Conceptualization, Data collection, Data Analysis, Writing – original draft. Delfien Van Dyck: Conceptualization, Writing – original draft, Reviewing and Editing. Laura Van de Velde: Data collection and Reviewing. Melanie Beeckman: Conceptualization and Reviewing. Jelle Van Cauwenberg: Conceptualization, Data analysis, and Reviewing. Sofie Compernolle: Conceptualization, Data collection, Writing – original draft, Reviewing and Editing.

Acknowledgements

The authors would like to acknowledge all researchers and students involved in the data collection, and all participants.

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No competing interests reported.

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Sedentary Patterns and Health Outcomes in the Oldest-Old: a Latent Profile Analysis

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