Longitudinal declines in life-space mobility in community-dwelling older Brazilian people: REMOBILIZE study

doi:10.21203/rs.3.rs-4724783/v1

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Research Article

Longitudinal declines in life-space mobility in community-dwelling older Brazilian people: REMOBILIZE study

https://doi.org/10.21203/rs.3.rs-4724783/v1

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Objective: To investigate changes in life-space mobility and its relationship to health and functioning determinants on older Brazilian adults.

Methods: We conducted an online and phone survey using an adapted Life-Space Assessment version. Linear regression models were used to investigate patterns of LS mobility during the pandemic decline according to cultural (gender and age groups), health conditions (multimorbidity and pain), functional capacity (activities of daily living limitation), and physical activity (walking) determinants among community-dwelling older Brazilian adults.

Results: Regardless of gender, functioning, and clinical determinants of health, participants experienced significantly declines in life-space mobility since the COVID-19 pandemic. Life-space mobility reduction was higher among older people who had lower levels of walking, aged 80 and more years, women, two or more multimorbidities and limited functioning.

Conclusion: Social restriction measures significantly decrease older adults’ life-space mobility in Brazil.

mobility

health status disparities

aging

disability

Significant declines in life-space mobility persisted over 3, 6, 12, and 18 months during and after restriction mobility.
Declines in mobility were worst for women, those aged 70-79, and 80 and over, with comorbidity, pain, functioning limitations, and poor walking levels.
Long-term consequences of the restriction of the mobility should be urgently monitored and addressed among vulnerable groups.

Mobility is an important aspect for people continue living independently and leading active lifestyle in their spaces. According to a conical conceptual framework [1], it corresponds to how people engage in, maintain social relationships and roles, and participate in meaningful community activities. Life-space assessment (LSA) has been defined as mobility in the area where an individual travels, extending from the room where they sleep outward to the community [2], and recognized as a practical measure to capture older people’s functional ability for moving around in their environments in a specific period [2]. Previous studies have demonstrated that declining life-space mobility is associated with an accelerated decline in the ability to care for and meet basic needs, as well as frequent hospitalizations, falls and fractures, poorer quality of life, and higher mortality [3]. Restriction of life-space (LS) mobility occurs due to a combination of losses in individuals’ intrinsic capacity, disability, limited personal resources, and difficulty dealing with environmental challenges (Xue et al., 2008), but also as a social restriction measure [4].

The social restriction measures during the COVID-19 pandemic, such as social distancing and stay-at-home orders [5, 6], were adopted worldwide and have reduced opportunities for several out-of-home activities [5]. Brazil remained for a long time as one of the epicenters of the pandemic in the number of cases and deaths [7]. The consequences of constriction in LS mobility observed in recent studies are decreased levels of utilitarian walking and walking as exercise [8], higher risk of sarcopenia [9, 10], and discontinuation of health interventions [11]. Other previous evidence showed the association between mobility restriction and increased prevalence of depressive symptoms [12], cognitive decline [13, 14], poor physical capacity [2], and increased risk for developing frailty [6]. Particularly, inactivity related to deconditioning [15] increases the risk of health deterioration associated with multimorbidity [16] and disability [17].

The abrupt changes in mobility population during pandemic period represented an entirely drastic decline, and their impact on the older adult’s recovery life space is still challenge. Repeated infection waves in several countries and the context of already weak economic activity [17], e.g., Brazil, obligated authorities to consider more heterogeneous effects on different groups. This perspective is critical because the mobility risks posed by COVID-19 are very heterogeneous, being much more severe for people aged 60 and above [18]. Massive immunization has allowed individuals to gradually return to their activities outside the home environment [18]. Even with the decree no longer fit public health emergency [WHO, 2023 – https://www.who.int/europe/emergencies/situations/covid-19], we still do not know if the older adults have returned to their previous levels of LS mobility. The long-term effects and how slowly and and gradually resumption of social interaction will happen for the trajectory of mobility in the living space of older persons are still unsure.

We hypothesized that there would be a sustained reduction in LS mobility during the pandemic and that decline among older people with varying cultural characteristics (gender and age groups), health status (morbidities and pain), functional capacity (daily living activities), and determinantes of physical activity (walking) would differ during the pandemic. Therefore, this longitudinal study examined (i) how community-dwelling older adults Brazilian’s LS mobility change throughout the COVID-19 pandemic and (ii) the potential risks to their health and ability to function that came with the adaptation in living space mobility.

2.1 Study Design, Setting, and Participants

We conducted a longitudinal study with data from the REMOBILIZE Study Network, which involved a cohort survey in investigating life-space mobility throughout the COVID-19 pandemic. This study used a task-force research network for a 16-month follow-up period. We surveyed a convenience snowball sample of older adults aged 60 and older (n = 1,482 at baseline) living in 22 (82%) states in Brazil, using the online platform SurveyMonkey®. We used social media (Facebook and Instagram®) and WhatsApp to recruit participants. A website was set up to reinforce the study’s legitimacy and provide a central address for respondents to contact the research team. We got community leaders and allied health professionals working in vulnerable regions to include participants with different educational and income levels, ethnicities, and genders. We excluded bedridden participants and older adults living in long-term care facilities. Older adults with cognitive decline or inability to answer interview questions due to visual or other difficulties, such as digital illiteracy, were helped by a proxy – either a family, friend, or formal caregiver. We collected data between May 18th, 2020, and November 30th, 2021; participants took approximately 30 minutes to complete the survey. More details can be found on the other publications [4, 8, 9, 10, 11].

2.2 Dependent variable – Life-Space Mobility

Life-space mobility was assessed using a Brazilian Portuguese version of the Life-Space Assessment [19], among life-space levels: bedrooms, home, outside house, neighborhood, town, and unlimited. For each level, participants were asked how often and needed any help to move to that level within the week.

At the baseline (2020), we asked participants about the places they reached both before the COVID-19 pandemic and a week before evaluation (since the pandemic period). As most participants in our study answered the questionnaire online without the assistance of an interviewer, we chose to ask about the last week to avoid misinterpretation. Life-space mobility questionnaires have been applied in different timeframes according to specific populations and circumstances [20, 21].

A composite score is calculated by multiplying each life-space level reached by the degree of independence and frequency [22]. Scores range from 0 to 120 points; higher scores represent greater mobility in space [2, 19]. The original instrument demonstrated a reproducibility of 0.97 (95% CI 0.95; 0.98), and a moderate negative correlation between LSA scores and accelerometry was observed (− 0.63, 95% CI − 0.74; −0.40) [19].

2.3 Explanatory variables – demographic, health and functioning

Characteristics variables were gender, age group (60–69, 70–79, and ≥ 80 years), self-report of skin color/race/ethnicity categorized according to official Brazilian classification (white, black, pardo, amarelo, or indigenous), marital status (single, married, divorced, widowed), education level (illiterate, 1–4 years, 5–8 years, and ≥ 9 years of schooling), living alone (yes/no), income level presented as the minimum wage per month guaranteed by law in Brazil (< 1, 2–3, 4–7, 8–10, and > 10 minimum wage salaries), employment (active, inactive, or unemployed) and receiving pension (yes/no).

Exploratory variables include gender, age group, multimorbidity, pain (yes/no), activities of daily limitation, and walking.

We used the Functional Comorbidity Index (FCI) questionnaire [23] to investigate comorbidities. The FCI is composed of 18 comorbidities related mainly to physical function [24]. Comorbidities were summed up, and older adults with two or more diseases were considered multimorbidity [24].

The Brazilian Multidimensional Functional Assessment Questionnaire (BOMFAQ) assessed function limitation [25]. BOMFAQ is an instrument that evaluates the difficulty level in performing the 15 instrumental activities of daily living activities. We categorized each activity as zero (without difficulty) and one (with difficulty). The number of activities performed with difficulty was summed (0–15), and the participants were categorized into two groups: those with mild (0–3 activities) and those with moderate to severe functional limitation (≥ 4 activities) [25, 26].

The walking level was assessed using the Incidental and Planned Exercise Questionnaire (IPEQ), a tool consisting of 10 questions on physical activity about an average week [27]. We used the questions related to walking to estimate the time spent on total walking, walking as exercise, and utilitarian walking [8]. The participants were classified according to their walking level as being more physically active if they reported walking > 150 min per week, as insufficiently physically active if they reported walking between 1 and 150 min per week, and less physically active if they did not report walking (0 min/week), according to the recommendations of the World Health Organization Guidelines on Physical Activity and Sedentary Behavior [28]. More details can be found on the other publication [8].

2.4 Statistical Analysis

Descriptive data of the characteristics of the participants are presented as mean ± standard deviation or absolute and relative frequencies. Waves 1, 2, 3, and 4 are considered T1, T2, T3, and T4, respectively. The analyses of the interaction effects (factor vs. waves) and the main effects referring to each factor and wave for the LSA were calculated using Generalized Estimating Equations (GEE), both for the bivariate and multivariate analyses. Based on the LSA measurement scale and on the QIC (Quasi-likelihood Information Criterion) values, a non-structured work correlation matrix was used, respectively, as well as a Tweedie distribution (1.5) with logarithmic linkage, which resulted in a better fit of the GHG model. To adequacy for residuals’ distribution and for each model, Q-Q graphs were plotted, as well multiple comparisons of the estimated marginal means (EMM) were made using the Bonferroni test.

Comparisons between pairs of estimated marginal means (MME) were made based on the original LSA scale, and effect sizes between each pair of comparisons were presented by mean difference (MD) values when comparing factor levels (groups) and mean change values (MC, mean change) when comparing the four waves of each group.

Were expressed as estimated marginal means (EMM), coefficient or contrast estimates (β), and 95% Wald confidence interval (CI), adopting a 5% significance level. All statistical analyses were performed using the Statistical Package for the Social Science (SPSS, IBM; v.25.0).

3.1 Population characteristics

The study included 1,482 older adults at baseline (73.9% women, mean age 70 years and presence of comorbidity in 56.7%). Table 1 describes the estimated marginal mean mobility and associated factors in the four analyzed waves. Female gender, increasing age, the existence of comorbidities, functional limitations, pain, and reduced levels of physical activity showed lower mobility in the four waves. Female gender had an estimated marginal mean mobility of 35.04 points (95% CI 33.88 to 36.25) at T1 and 51.38 points (95% CI 49.52 to 53.30) at T4 in relation to men, 45.98 (95% CI 43.49 to 48.62) and 59.35 points (95% CI 56.09 to 62.80) respectively. Among inactive older adults, a mean mobility of 30.62 points (95% CI 29.40 to 31.89) was observed at T1 and 43.65 points (95% CI 41.22 to 46.23) at T4. Conversely, active older adults showed mean mobility levels of 50.88 points (95% CI 48.22 to 53.68) at T1 and 61.36 points (95% CI 58.43 to 64.44) at T4.

Table 1

Associated factors, estimated marginal means, and confidence interval regarding life-space mobility in the four waves of 1,482 Brazilian older adults during the COVID-19 pandemic. Brazilian Remobilize Study, 2023.
Factors	Baseline (Jan-Mar, 2020) N (%)	Life-space mobility, EMM (95%CI)
	Baseline (Jan-Mar, 2020) N (%)	T1 (May-Jun, 2020) n = 1,482	T2 (Ago-Sept, 2020) n = 1,087	T3 (Feb-Mar, 2021) n = 933	T4 (Sept-Dec, 2021) n = 811
Sex
Women	1,096 (73.9)	35.04 (33.88 to 36.25)	43.43 (41.94 to 44.96)	42.26 (40.67 to 43.90)	51.38 (49.52 to 53.30)
Men	386 (26.1)	45.98 (43.49 to 48.62)	52.29 (49.39 to 55.35)	51.84 (48.92 to 54.93)	59.35 (56.09 to 62.80)
Age group (years)
80 and over	229 (15.5)	23.47 (21.44 to 25.69)	28.10 (25.50 to 30.97)	27.49 (24.75 to 30.53)	31.50 (28.00 to 35.43)
70–79	420 (28.4)	36.17 (34.28 to 38.16)	42.68 (40.28 to 45.21)	41.27 (38.81 to 43.89)	50.26 (47.41 to 53.29)
60–69	831 (56.1)	42.73 (41.22 to 44.29)	52.25 (50.49 to 54.07)	51.21 (49.33 to 53.16)	60.92 (58.87 to 63.05)
Multimorbidity*
Yes	841 (56.9)	33.66 (32.33 to 35.05)	41.39 (39.66 to 43.20)	40.36 (38.53 to 42.27)	48.03 (45.97 to 50.18)
No	641 (43.1)	43.44 (41.66 to 45.30)	51.43 (49.39 to 53.55)	50.43 (48.29 to 52.67)	60.35 (57.85 to 62.94)
Pain
Yes	--	35.40 (33.71 to 37.17)	43.90 (41.99 to 45.89)	43.96 (41.68 to 46.37)	49.93 (47.24 to 52.77)
No	--	38.78 (37.50 to 40.10)	46.43 (44.88 to 48.04)	45.06 (43.48 to 46.69)	54.59 (52.74 to 56.50)
Functional impairment**
Yes	325 (21.9)	26.98 (25.23 to 28.85)	33.84 (31.50 to 36.36)	30.35 (28.12 to 32.75)	35.01 (32.20 to 38.06)
No	1,157 (79.1)	40.55 (39.31 to 41.83)	48.72 (47.23 to 50.25)	48.17 (46.64 to 49.76)	57.81 (56.08 to 59.59)
Activity level***
Inactive (0min/w)	311 (21.0)	30.62 (29.40 to 31.89)	36.33 (34.59 to 38.17)	35.30 (33.30 to 37.42)	43.65 (41.22 to 46.23)
Insufficiently Active (1-149min/w)	284 (19.2)	41.84 (39.99 to 43.78)	48.31 (46.33 to 50.38)	47.38 (45.53 to 49.31)	55.14 (52.90 to 57.47)
Active (≥ 150min/w)	887 (59.9)	50.88 (48.22 to 53.68)	54.81 (52.59 to 57.13)	53.61 (51.13 to 56.22)	61.36 (58.43 to 64.44)
T1, T2, T3, and T4 are the waves 1, 2, 3, and 4, respectively. EMM: estimated marginal means; 95%CI: confidence 95% intervals Morbidity included stroke, Parkinson’s disease, arthritis, osteoporosis, urinary and fecal incontinence, acute myocardial infarction, intestinal and depressive disease, anxiety, visual and hearing impairment, spine, overweight, hypertension and dizziness. Functional limitation was assessed by disability in lying down and getting out of bed, eating, combing your hair, walking on the plane, showering, getting dressed, going to the bathroom on time, and climbing a flight of stairs, among others. -- Not evaluated at baseline. ** Activity level refers to walking as utilitarian and as exercise in minutes per week

3.2 Life-space mobility and demographic, health and functioning

Table 2 presents the mean mobility differences for each associated factor across the four waves. In T1, individuals aged 80 years or older exhibited mobility 19.26 points lower compared to the 60 to 69 years age group (95% CI -23.76 to -14.76). This disparity progressively increased across the four waves, culminating in a mean difference of 29.43 points (95% CI -36.74 to -22.12) at T4. Older adults with comorbidities exhibited a mean mobility reduction of 9.78 points at T1 and 12.31 points at T4 compared to those without comorbidities (95% CI -13.39 to 6.17 and 95% CI -17.58 to -7.05, respectively). A significant mean mobility difference was observed between inactive and active older adults in the four waves; however, this disparity decreased over time (T1 -15.41 points, 95% CI -20.11 to -10.72; T4 -10.88 points, 95% CI -16.83 to -4.93). Table 3 illustrates the mean changes corresponding to each associated factor in mobility for the time effect.

Table 2

Mean differences for each associated factor with life-space mobility in the four waves of 1,482 Brazilian older adults during the COVID-19 pandemic. Brazilian Remobilize Study, 2023.
Factors	Life-space mobility, MD (95%CI)
	T1 (May-Jun, 2020) n = 1,482	T2 (Ago-Sept, 2020) n = 1,087	T3 (Feb-Mar, 2021) n = 933	T4 (Sept-Dec, 2021) n = 811
Sex, women (ref.: men)	-10.94 (-15.44 to -6.44)	-8.86 (-14.18 to -3.54)	-9.58 (-15.02 to -4.14)	-7.98 (-14.11 to -1.84)
Age group (years) (ref.: 60 to 69)
≥ 80	-19.26 (-23.76 to -14.76)	-24.15 (-29.76 to -18.53)	-23.72 (-29.67 to -17.77)	-29.43 (-36.74 to -22.12)
70–79	-6.56 (-10.81 to -2.32)	-9.57 (-14.81 to -4.33)	-9.94 (-15.40 to -4.47)	-10.66 (-16.86 to -4.46)
Multimorbidity*, two or more (ref.: 0–1)	-9.78 (-13.39 to -6.17)	-10.04 (-14.39 to -5.68)	-10.07 (-14.66 to -5.49)	-12.31 (-17.58 to -7.05)
Pain, yes (ref.: no)	-3.38 (-6.56 to -0.19)	-2.53 (-6.06 to 1.00)	-1.09 (-5.15 to 2.96)	-4.66 (-9.64 to 0.32)
Functional impairment**, yes (ref.: no)	-7.29 (-11.55 to -3.03)	-6.73 (-11.75 to -1.72)	-12.65 (-17.76 to -7.54)	-13.64 (-20.59 to -6.69)
Activity level*** (ref.: active; ≥150min/w)
Inactive (0min/w)	-15.41 (-20.11 to -10.72)	-14.46 (-19.06 to -9.85)	-13.44 (-18.27 to -8.61)	-10.88 (-16.83 to -4.93)
Insufficiently Active (1-149min/w)	-6.49 (-11.18 to -1.79)	-4.32 (-8.49 to -0.15)	-4.04 (-8.14 to 0.06)	-3.43 (-8.42 to 1.57)
Significant differences are in bold compared to the reference category (ref.). MD: mean difference; 95%CI: confidence 95% intervals. Morbidity included stroke, Parkinson’s disease, arthritis, osteoporosis, urinary and fecal incontinence, acute myocardial infarction, intestinal and depressive disease, anxiety, visual and hearing impairment, spine, overweight, hypertension and dizziness. *Functional limitation was assessed by disability in lying down and getting out of bed, eating, combing your hair, walking on the plane, showering, getting dressed, going to the bathroom on time, and climbing a flight of stairs, among others.

Table 3

Mean change for each associated factor with life-space mobility in the four waves of 1,482 Brazilian older adults during the COVID-19 pandemic. Brazilian Remobilize Study, 2023.
Factors	Life-space mobility, MC (95%CI)
	T2 to T1	T3 to T1	T4 to T1
Sex
Women	8.38 (6.12 to 10.64)	7.21 (4.75 to 9.68)	16.33 (13.41 to 19.25)
Men	6.30 (2.28 to 10.33)	5.85 (1.34 to 10.36)	13.37 (8.17 to 18.56)
Age group (years)
≥ 80	4.63 (-0.06 to 9.32)	4.02 (-0.71 to 8.75)	8.03 (1.74 to 14.32)
70–79	6.51 (2.73 to 10.29)	5.11 (0.89 to 9.32)	14.10 (9.16 to 19.03)
60–69	9.52 (6.54 to 12.50)	8.48 (5.20 to 11.76)	18.20 (14.48 to 21.91)
Multimorbidity*
Yes	7.73 (5.27 to 10.19)	6.69 (3.99 to 9.40)	14.37 (11.08 to 17.65)
No	7.99 (4.78 to 11.19)	6.99 (3.46 to 10.52)	16.90 (12.92 to 20.88)
Pain
Yes	8.50 (5.05 to 11.95)	8.56 (4.46 to 12.67)	14.53 (9.79 to 19.26)
No	7.65 (5.21 to 10.10)	6.28 (3.68 to 8.88)	15.81 (12.77to 18.85)
Functional impairment**
Yes	4.99 (0.06 to 9.92)	0.53 (-4.63 to 5.68)	6.00 (-.04 to 12.04)
No	4.44 (1.00 to 7.87)	5.89 (2.00 to 9.77)	12.35 (7.61 to 17.10)
Activity level***
Inactive (0min/w)	4.58 (1.40 to 7.75)	3.24 (-0.42 to 6.89)	10.52 (6.21 to 14.83)
Insufficiently Active (1-149min/w)	5.79 (0.75 to 10.83)	3.71 (-1.43 to 8.85)	9.04 (3.17 to 14.92)
Active (≥ 150min/w)	3.62 (-2.46 to 9.70)	1.26 (-4.92 to 7.44)	5.98 (-1.18 to 13.14)
Significant differences are in bold. MC: mean change; 95%CI: confidence 95% intervals. Morbidity included stroke, Parkinson’s disease, arthritis, osteoporosis, urinary and fecal incontinence, acute myocardial infarction, intestinal and depressive disease, anxiety, visual and hearing impairment, spine, overweight, hypertension and dizziness. *Functional limitation was assessed by disability in lying down and getting out of bed, eating, combing your hair, walking on the plane, showering, getting dressed, going to the bathroom on time, and climbing a flight of stairs, among others.

An interaction effect on mobility was discerned among individuals aged 70 to 79 years and those aged 80 years or older compared to the reference age group of 60 to 69 years, revealing a reduction of -0.37 points and − 0.09 points, respectively (Wald = 54.87, p < 0.001; Wald = 9.49, p = 0.002). The presence of comorbidity demonstrated an independent association with mobility throughout the four waves, exhibiting a coefficient of -0.08 points (Wald = 7.45, p = 0.006). A temporal effect on mobility was identified in females during T4 (B = 0.009 points, Wald = 5.96, p = 0.0015), while the presence of functional limitation at T3 exhibited a significant impact (B = -1.12 points, Wald = 4.61, p = 0.032). Additionally, being inactive during T4 demonstrated a significant association with mobility (B = 0.19 pts, Wald = 14.03, p < 0.001). Other covariates did not significantly impact mobility patterns over time (Table 4).

Table 4. Associated factors with life-space mobility of 1,482 Brazilian older adults during the COVID-19 pandemic. Brazilian Remobilize Study, 2023.

Parameter	Full Model
	ß	Wald’s 95% CI		Wald’s chi-square	p-value
	ß	Lower	Upper	Wald’s chi-square	p-value
(Intercepto)	4.196	4.130	4.261	15808.208	<.001
Wave (ref.: T1)
T4	.146	.067	.225	13.181	<.001
T3	.045	-.029	.119	1.422	.233
T2	.074	.002	.146	4.089	.043
Sex (ref.: men and T1)
Women	-.213	-.274	-.153	47.386	<.001
T4 * Women	.092	.018	.165	5.958	.015
T3 * Women	.044	-.026	.114	1.492	.222
T2 * Women	.039	-.027	.105	1.326	.249
Age groups (ref.: 60 to 69 and T1)
>= 80	-.373	-.471	-.274	54.874	<.001
70 to 79	-.093	-.151	-.034	9.487	.002
T4 * >= 80	-.020	-.164	.123	.077	.781
T4 * 70 to 79	-.036	-.113	.041	.826	.364
T3 * >= 80	.038	-.087	.163	.361	.548
T3 * 70 to 79	-.053	-.127	.021	1.952	.162
T2 * >= 80	-.062	-.182	.059	.998	.318
T2 * 70 to 79	-.054	-.122	.014	2.399	.121
Multimorbidity^a (ref.: no and T1)
Yes	-.080	-.138	-.023	7.447	.006
T4 * yes	-.002	-.074	.070	.002	.964
T3 * yes	-.002	-.075	.070	.003	.953
T2 * yes	.016	-.048	.080	.240	.624
Pain (ref.: no and T1)
Yes	-.029	-.085	.026	1.079	.299
T4 * yes	.012	-.069	.094	.085	.771
T3 * yes	.057	-.021	.136	2.041	.153
T2 * yes	-.004	-.075	.066	.015	.903
Functional impairment^b (ref.: no and T1)
Yes	-.203	-.281	-.125	26.195	<.001
T4 * yes	-.101	-.223	.021	2.620	.105
T3 * yes	-.122	-.234	-.011	4.608	.032
T2 * yes	.037	-.063	.138	.528	.467
Activity level (ref.: active, ≥150min/w, and T1)
Inactive (0min/w)	-.435	-.502	-.368	160.467	<.001
Insufficiently active (1-149min/w)	-.161	-.227	-.095	22.707	<.001
T4 * Inactive	.188	.090	.286	14.034	<.001
T4 * Insufficiently active	.089	.002	.177	4.003	.045
T3 * Inactive	.080	-.011	.171	2.943	.086
T3 * Insufficiently active	.066	-.014	.147	2.606	.106
T2 * Inactive	.070	-.017	.158	2.492	.114
T2 * Insufficiently active	.065	-.015	.145	2.530	.112
(Scale)	1.559

Significant differences are in bold. T1, T2, T3, and T4 are the waves 1, 2, 3, and 4, respectively. 95%CI: confidence 95% intervals. ^aMorbidity included stroke, Parkinson’s disease, arthritis, osteoporosis, urinary and fecal incontinence, acute myocardial infarction, intestinal and depressive disease, anxiety, visual and hearing impairment, spine, overweight, hypertension and dizziness. ^bFunctional limitation was assessed by disability in lying down and getting out of bed, eating, combing your hair, walking on the plane, showering, getting dressed, going to the bathroom on time, and climbing a flight of stairs, among others.

In older Brazilian adults, reduction in LS mobility during the pandemic was more frequent among older people of gender female, 70 or more age, with multimorbidities, pain, limitation in daily living activities and decreased levels of walking, even after adjustment for confounding factors. The estimated marginal means, and mean differences and mean change for each associated factor in the four waves analysis shows decreased life-space mobility according to these caractheristics, reinforcing hypothesis that guided this study. This study demonstrated the LS mobility of older people, during pandemic period, was dramatically reduced and was not recovered even after the relaxation of social restriction measures. It should be noted that such life-space mobility is measures of actual real-world mobility and are gaining increased attention as an important indicator of healthy ageing [29].

The results of this study, align with our hypothesis since, even after widespread vaccination campaigns and a relaxation of social restriction laws in Brazil, the decrease in living space mobility that harmed COVID-19 during pandemic did not recover to pre-pandemic levels. We observed a gradual but slight increase over time among all groups, except among women, those aged ≥ 80 and 70–79 years, with multimorbidity, pain, functioning limitations, and poor levels of walking. Life-space conceptual framework can be used to analyze the mobility transitions, particularly its relationship with environmental conditions. Furthermore, tracking life-space mobility can help the understanding of how older people relate to changes in their health status and social roles over challenging periods of their lifespan [2, 22].

Although mobility has affected the entire worldwide population, there are few studies about mobility restriction patterns during the pandemic. To comprise the effect of restriction measures on mobility was a challenging task globally because of different lockdown policies. Governments were more likely to impose social restriction measures when mortality rates were higher and lessen policies when the situation seemed in control. However, these policies were less likely to affect older people living in different parts of the world. Compared to the period before the COVID-19 lockdown, there was a consistent and moderate decline in the life-space mobility in Finland [5] and a drop of 50 to 79% in Brazil [6], with or without association SARS-COV-2 diagnosis [30]. Only Browne and cols. compared mobility scores over one year during the pandemic and found a significant increase in LSA score (at apartment/ row house, but not detached house) among older adults with hypertension living in Natal (Brazil). The reason for this result is supposed to be the effect of COVID-19 vaccination [31].

Despite the similarities in the change mobility score found, a possible difference between these studies and ours includes the analysis period, for which all studies has been analyzed up to the first year of the pandemic. Other difference refers to a small sample [31] and the disparity in coping with the pandemic in each country, according to summarize of the World Health Organization (WHO, 2023 – https://www.who.int/europe/emergencies/situations/covid-19).

Notwitstanding the results of specific LS mobility observed in this study have not been reported in others studies, hypotheses for explaining the results are based on the multicausal conception of mobility among older adults. Specially during the pandemic period, this conception involve direct and indirect pathways: i) the acute triggering of worldwide social restriction measures, as a social distancing and stay-at-home orders and reduced opportunities for several out-of-home activities, and ii) the changes in musculoskeletal architecture related to inflammatory COVID-19 profile.

First, public health recommendations for quarantine and self-isolation restricted physical activity and exacerbated the decline in mobility and physical function [4, 32]. We explore if older adults of different age groups respond differently to life-space mobility. Because COVID-19 poses much more significant risks for older people, it could be expected greater respect for isolation measures by these people due to the greater likelihood of risk when infections waves rise. On the way, the analysis shows that the oldest reduces mobility even more strongly. This scenario is consistent with survey-level evidence presented by Perracini et al. [4], indicating that the oldest old might be overwhelmed by the fear of contracting and dying from COVID-19. The recent work by Tison et al. [33] analyzed lasting global changes in physical activity patterns assessed through step counts. They compared one-year tracks and observed that the average daily steps globally fell below 4,000. According to the authors, the most significant recovery in steps per day occurred in 2021 (4997 steps per day) but was still 10% lower than pre-pandemic levels. The mean step count for the end of data collection (2022) remained significantly lower, although there was significant pattern variation. Two years after COVID-19 was declared a pandemic, the world continues to take fewer steps per day, even though lockdowns and social distancing have not been a part of daily life for some time [33].

The second hypothesis, the possible pathway in other research suggests coronaviruses can cause systematic inflammation [34]. Supported by this hypothesis, a study showed that SARS-CoV-2 may act as an adjunct in clinical conditions of muscle weakness and explain the subsequent mobility impacts [35, 36, 37], which corroborates the understanding of traditional risk factors concerning mobility restriction among older adults after COVID-19. Some evidence suggests that SARS-CoV-2 can cause neuroinflammation and inflammation in other parts of the body, resulting in neuronal degeneration and release of proinflammatory cytokines, which may explain the subsequent chronic fatigue and functional mobility impacts experienced by many individuals after COVID-19 [35, 36, 37]. Unfortunately, this hypothesis could not be tested in our study, first because we do not know who actually had COVID-19. Furthermore, even those who had it, we do not know if the disease affected the musculoskeletal architecture, although there is a biological plausibility indicated in the literature.

The longitudinal analyses LS mobility, especially during pandemic period, in older population is still incipient. The results of this study contribute to a more detailed understanding of the impact of mobility restriction and multifactors determinants, supporting the development of future studies, which should monitor a greater diversity of groups.

The results of the present analysis have some limitations, such as information bias, due to the difficulty of older people to remember mobility in the last month, which does not seem to have influenced the estimates, considering the frequence found in this study was similar to that of other studies. The same, there was a 44.4% sample loss rate during follow-up, but with no statistical difference between the groups, which demonstrates homogeneous loss, common among cohort studies. This analysis was conducted during period with high Fake News wave in older population and in a sample with high difficult technology (smartphone use), which may explain the reduction in their participation. On the other hand, the information were collected through standardized instruments, besides having included a wide range of cities Brazilians.

In older Brazilian population, social restriction caused substantial decrease in life-space mobility. The perspectives presented in this study can be extrapolated to new contexts of social restriction, but also to highlight the difficulties of older people in recovering their mobility in conditions of abrupt restriction. Worldwide, failure to minimize health inequalities may jeopardize the desired achievements of the Decade of Healthy Aging, specially about mobility in older people.

Ackonowledgements

We woud like to acknowledge the hard work and effort of all those contribute Remobilize Networks: Professor Aurelio Dias Santos, Professor Hércules Campos, Professor Renata Oliveira Dantas, Professor Cristina Cristovão Ribeiro da Silva, Samira Dorta, Daniele Sirineu Pereira, Paulo Henrique Silva Pelicioni, Etiene Duim, Camila Astolphi Lima, Maria Lima, and Renato Barbosa dos Santos.

Corresponding author at Juleimar Amorim. Email address: [email protected]. Rio de Janeiro Federal Institute of Education, Science and Technology Campus Realengo. Rua Professor Carlos Wenceslau, 343. Rio de Janeiro, RJ, Brazil. CEP: 21.710-240.

Competing interest: The authors declare that they have no competing interests.

Funding: none

Authors’ Contributions:

JSCA and MRP worked on the design, planning, analysis, data interpretation, and article writing. FTS provided advice, statistical analysis, and critical review and worked on the final report of the research. PPB, MM, EOSF, and MCCL worked on the study’s planning, development, and reporting.

All authors approved the article’s final version and assume public responsibility for its content.

Data availability

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

Ethics declarations

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations. The Ethical Research Committee of Universidade Cidade de São Paulo approved all research procedures (protocol number 4.032.523). A consent form was included in the online survey questionnaire, and interviews were conducted by telephone. Participants consented or declined to participate in the study by selecting an on-screen button. All analysis was conducted on anonymised data.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Longitudinal declines in life-space mobility in community-dwelling older Brazilian people: REMOBILIZE study

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Version 1

Abstract

Highlights

Introduction

Methods

2.1 Study Design, Setting, and Participants

2.2 Dependent variable – Life-Space Mobility

2.3 Explanatory variables – demographic, health and functioning

2.4 Statistical Analysis

Results

3.1 Population characteristics

3.2 Life-space mobility and demographic, health and functioning

Discussion

Conclusion

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

References

Additional Declarations

Status:

Version 1