Combined Impacts of Physical Activity, Dietary Variety, and Social Interaction on Incident Functional Disability in Older Japanese Adults

Satoshi Seino; Yu Nofuji; Yuri Yokoyama; Takumi Abe; Mariko Nishi; Mari Yamashita; Miki Narita; Toshiki Hata; Shoji Shinkai; Akihiko Kitamura; Yoshinori Fujiwara

doi:10.2188/jea.JE20210392

. 2023 Jul 5;33(7):350–359. doi: 10.2188/jea.JE20210392

Combined Impacts of Physical Activity, Dietary Variety, and Social Interaction on Incident Functional Disability in Older Japanese Adults

Satoshi Seino ¹, Yu Nofuji ¹, Yuri Yokoyama ¹, Takumi Abe ^1,², Mariko Nishi ¹, Mari Yamashita ^1,², Miki Narita ¹, Toshiki Hata ¹, Shoji Shinkai ^1,³, Akihiko Kitamura ^1,⁴, Yoshinori Fujiwara ^1,²

PMCID: PMC10257989 PMID: 34924454

Abstract

Background

This 3.6-year prospective study examined combined impacts of physical activity, dietary variety, and social interaction on incident disability and estimated population-attributable fraction for disability reduction in older adults.

Methods

Participants were 7,822 initially non-disabled residents (3,966 men and 3,856 women) aged 65–84 years of Ota City, Tokyo, Japan. Sufficiency of moderate-to-vigorous-intensity physical activity (MVPA) ≥150 min/week, dietary variety score (DVS) ≥3 (median), and social interaction (face-to-face and/or non-face-to-face) ≥1 time/week was assessed using self-administered questionnaires. Disability incidence was prospectively identified using the long-term care insurance system’s nationally unified database.

Results

During a follow-up of 3.6 years, 1,046 (13.4%) individuals had disabilities. Independent multivariate-hazard ratios (HRs) and 95% confidence intervals (CIs) of MVPA, DVS, and social interaction sufficiency for incident disability were 0.68 (95% CI, 0.59–0.78), 0.87 (95% CI, 0.77–0.99), and 0.90 (95% CI, 0.79–1.03), respectively. Incident disability HRs gradually reduced with increased frequency of satisfying these behaviors (any one: HR 0.82; 95% CI, 0.65–1.03; any two: HR 0.65; 95% CI, 0.52–0.82; and all three behaviors: HR 0.54; 95% CI, 0.43–0.69), in an inverse dose-response manner (P < 0.001 for trend). Population-attributable fraction for disability reduction in satisfying any one, any two, and all three behaviors were 4.0% (95% CI, −0.2 to 7.9%), 9.6% (95% CI, 4.8–14.1%), and 16.0% (95% CI, 8.7–22.8%), respectively.

Conclusion

Combining physical activity, dietary variety, and social interaction substantially enhances the impacts on preventing disability among older adults, with evidence of an inverse dose-response manner. Improving insufficient behavior elements through individual habits and preexisting social group activities may be effective in preventing disability in the community.

Key words: physical activity, dietary variety, social interaction, disability, population-attributable fraction

INTRODUCTION

Maintaining functional ability and preventing disability are important challenges in an aging society. Functional ability is determined by the person’s intrinsic capacity (ie, combination of all physical and mental capacities), relevant environmental factors, and the interaction between the two.¹ This suggests that only providing preventive services that enhance intrinsic ability, such as physical activity (PA) and/or nutritional programs, is insufficient to promote effective disability prevention. It is also essential to create a social environment in which older adults can improve their physical, mental, and social health through social participation.

There is increasing evidence that combining healthy lifestyle behaviors effectively reduces disability risk.²^–⁴ However, the health behaviors examined previously were limited to behaviors related to the prevention of non-communicable diseases, such as PA (eg, walking), diet (eg, fruit and vegetable consumption), smoking, and alcohol consumption.²^,³ In order to prevent frailty and sarcopenia, which are major causes of disability, it is important to also include moderate-to-vigorous-intensity PA (MVPA; eg, muscle strengthening activities) and the consumption of a varied diet containing protein and vitamin D.⁵ There is also growing recognition of the importance of the social aspect of frailty,⁶^,⁷ with studies reporting that social relationships greatly impact health outcomes, such as disability⁸ and mortality.⁹ Thus, it is necessary to focus on MVPA, dietary variety, and social relationships from the perspective of disability prevention in the community.

To our knowledge, only two observational studies have examined the combined impacts of these behaviors on adverse health outcomes. Although Haider et al¹⁰ examined the synergistic impacts of the combinations of any two of PA, protein intake, and social network on the development of frailty, they did not investigate the combined impact of all three variables. Pérez-Tasigchana et al⁴ examined the association of six healthy behaviors (not smoking, MVPA, healthy diet, adequate sleeping duration, not being sedentary, and daily social interaction) with incident frailty and disability. However, they failed to include daily social interaction in their analyses of the association between the number of healthy behaviors an individual performs and the incidence of frailty and disability.⁴ Moreover, they did not focus on the combination patterns of the health behaviors. Thus, the combined and public health impacts of MVPA, dietary variety, and social relationships on incident disability are still unclear.

We examined the combined impacts of MVPA, dietary variety, and social interaction on incident disability and estimated the population-attributable fraction (PAF) for disability reduction in community-dwelling older adults.

METHODS

Study population

We used data from a community-wide intervention study, launched in 2016, on preventing frailty in Ota City, Tokyo, Japan.¹¹^,¹² On August 1, 2016, Ota City had a population of 716,645, among whom 162,443 (22.7%) were aged 65 years or older. Of the population aged 65 and older, 15,500 residents (range: 500–4,000 people per district) aged 65–84 years, stratified by sex and age group (65–74 and 75–84 years), were selected using random sampling strategies in all 18 districts of the city.¹¹ All participants were physically and cognitively independent, which was defined as the absence of long-term care insurance (LTCI) certification.¹³

Of the 15,500 self-administered questionnaires distributed in July 2016, 11,925 were returned (response rate: 76.9%). The mean district response rate was 77.1% (range: 71.8–80.8%). As Figure 1 shows, 7,822 questionnaires (from 3,966 men and 3,856 women) were included in our analysis. The number of people included in the analysis per district ranged from 214 to 1,920 people (mean: 435).

The study protocol was developed in accordance with the guidelines proposed in the Declaration of Helsinki and was approved by the Ethical Committee of the Tokyo Metropolitan Institute of Gerontology. All participants gave their informed consent before participation.

Measurements

MVPA

We evaluated individuals’ levels of MVPA using the Japanese version of the International Physical Activity Questionnaire-Short Form (IPAQ-SF).¹⁴^,¹⁵ Its external reliability and validity have been previously reported.¹⁴^,¹⁵ Participants were assessed based on their time spent performing vigorous and moderate PA, as well as their walking time during a typical week. Using these values, we defined total MVPA as 7 days × (8.0 metabolic equivalents [METs] × vigorous PA hours/day + 4.0 METs × moderate PA hours/day + 3.3 METs × walking time hours/day).¹⁶ A total MVPA time ≥150 minutes/week was defined as sufficient PA.¹⁷

Dietary variety

Participants were assessed based on their weekly frequency of consumption of 10 main Japanese food groups (meat, fish/shellfish, eggs, milk, soybean products, green/yellow vegetables, potatoes, fruit, seaweed, and fats/oils). Participants were able to choose from four responses in relation to their consumption of each food group (almost daily, 3 or 4 days a week, 1 or 2 days a week, or hardly ever). A Dietary Variety Score (DVS) was calculated for each participant, using the sum of the number of times each participant answered “almost daily” for each food group (range: 0–10).¹⁸^,¹⁹ Although various DVS cut-off scores (eg, DVS ≥4,¹⁸^,¹⁹ DVS ≥7,¹⁹ or DVS ≥9¹⁸) were used in previous studies, these DVS scores came from health check-up data from which DVS scores are typically reported as higher (mean scores ranging from 5 to 7)¹⁸^,¹⁹ than those from self-administered mail survey data (mean scores ranging from 3 to 4).¹¹^,²⁰ Therefore, based on the distribution of the study population, the median DVS (score of 3) was used as the cut-off value, and DVS ≥3 was defined as sufficient dietary variety in this study.

Social interaction

The level of social interaction was assessed based on the frequency of face-to-face and non-face-to-face (eg, telephone, e-mail, letters) interactions they had with relatives, friends, neighbors, and family who did not live with them. Frequency of contact with others (apart from cohabiting family members) of less than 1 time/week was defined as social isolation.²¹^,²² Thus, sufficient social interaction was defined as contact with others ≥1 time/week.

Functional disability

Disability was identified in the participants using the nationally unified database of the LTCI system, enrolment in which is mandatory for all Japanese adults aged 40 years and older. The system provides formal care and support to Japanese adults aged 65 and older with physical and mental disabilities.¹³ LTCI certification is based on a nationally standardized multistep assessment.¹³ Ultimately, the Municipal Certification Committee of Needed Long-Term Care (comprised of physicians, nurses, and health and social service experts) decides whether an older adult should be certified as requiring long-term care and classifies care needs under one of seven levels (support level: 1–2; care level: 1–5).¹³

Disability was defined as the onset of long-term care needs at the support level 1 or above,²³ using the date of the LTCI application as the date of disability incident. Because new LTCI applications were affected by the novel coronavirus disease pandemic,²⁴ we set January 31, 2020 as the endpoint.

Covariates

The covariates included age, sex, living situation (living with others or alone), marital status (married, widowed, divorced, or never married), educational attainment (junior high school, high school, or junior college/vocational college/college/graduate school graduate), equivalent income (<2.0, 2.0–3.99, ≥4.0 million yen, or unknown), body mass index (BMI; <18.5, 18.5–24.9, or ≥25 kg/m²), alcohol drinking and tobacco smoking statuses (current, never, or former), hypertension, hyperlipidemia, heart disease, stroke, diabetes mellitus, cancer, depressive mood, lower back pain, knee pain, sitting time (<180, 180–299, 300–479, ≥480 minutes/day), and instrumental activities of daily living (IADL) dependence (presence or absence). Equivalent income was calculated by dividing household income by the square root of the number of household members.²⁵ BMI was defined as self-rated body weight (kg) divided by self-rated height squared (m²). Depressive mood was defined as a score of ≥2 on the 5-item Geriatric Depression Scale.²⁶^,²⁷ Sitting time was evaluated using the Japanese version of the IPAQ-SF.¹⁴^,¹⁵ IADL was assessed using the instrumental self-maintenance subscale of the Tokyo Metropolitan Institute of Gerontology Index of Competence,²⁸ the total score of which ranges from 0 to 5. Participants whose total score was 5 were classified as independent; those whose total score was ≤4 were classified as dependent.²⁹

Statistical analyses

Data were analyzed using Stata 16.0 (StataCorp, College Station, TX, USA). An α of 0.05 indicated statistical significance. To examine whether minutes/week of MVPA, DVS, and other continuous variables were systematically associated with the number of satisfying health behaviors, we used a trend test (“nptrend” command). Chi-square test compared social interaction and other categorical variables related to the number of satisfying health behaviors.

First, we examined the impact of individual health behaviors on incident disability risk. Because our data had a multilevel structure comprised of individuals (at level 1) nested within 18 districts (at level 2), we used multilevel survival analyses with fixed slopes and random intercept models, calculating adjusted hazard ratios (HR) and 95% confidence intervals (CIs) for incident disability, the dependent variable. Individual health behaviors were included simultaneously as fixed factors. The district was a random factor. We constructed four analytic models. Model 1 was adjusted for age and sex. Model 2 additionally adjusted for living situation, marital status, educational attainment, equivalent income, BMI, hypertension, hyperlipidemia, heart disease, stroke, diabetes mellitus, cancer, alcohol drinking status, and smoking status. Model 3 additionally adjusted for depressive mood, lower back pain, knee pain, and sitting time. Model 4 additionally adjusted for IADL dependency.

Second, the Kaplan-Meier method was used to produce the cumulative probability of remaining disability-free according to the number of satisfying health behaviors. To examine whether the number of satisfying health behaviors was systematically positively associated with the cumulative probability, we used a trend test (“sts test, trend” command).

Third, we performed two analyses: with the number of satisfying health behaviors as the fixed factor and with the combination pattern of health behaviors as the fixed factor. They were included in a dummy form (not satisfying any behaviors was the reference) as fixed factors in the same statistical approach. We tested the statistical interaction between the number of satisfying health behaviors or combination patterns of health behaviors and sex or age group (65–74 or 75–84 years).

Finally, PAFs and their 95% CIs were estimated for individual health behaviors and number of satisfying health behaviors in model 4, using the “punafcc” command.³⁰ In the estimation of the PAF of individual health behaviors, we calculated how much the disability rate might have decreased, if participants with insufficient levels of MVPA (<150 minutes/week), DVS (<3), and social interactions (<1 time/week) would have reached the previously defined sufficient level for each behavior. In the estimation of the PAF for the number of satisfying health behaviors, we calculated how much the disability rate might have decreased, if participants with 0 to 2 satisfying health behaviors would have reached sufficient levels for all three health behaviors. The estimations of the PAF when any one or two health behaviors were satisfied were performed in the same manner.

In secondary analyses of the possible influence of reverse causation, we performed sensitivity analyses using the same statistical approach, after excluding individuals who developed disability during the first year of follow-up.

We excluded participants with missing information regarding their disability, IPAQ-SF, DVS, and social interaction from the analysis. The missing covariates were assigned to the “missing” category and included in the analysis to reduce the selection bias.

RESULTS

During a median follow-up of 3.6 (interquartile range, 3.6–3.6) years, 1,046 participants (13.4%; 553 men and 493 women) presented with disabilities, with rates of 46.3 per 1,000 person-years, and 291 participants (27.8%; 163 men and 128 women) presented with disability during the first year.

Table 1 shows the baseline characteristics of the study population according to the number of satisfying MVPA, dietary variety, and social interaction. Minutes/week of MVPA, DVS, and prevalence of sufficiency of MVPA, DVS, and social interaction were systematically higher with increasing number of satisfying health behaviors (all P < 0.001). There were significant differences between groups in sex, marital status, education, equivalent income, BMI, sitting time, and prevalences of hypertension, hyperlipidemia, heart disease, stroke, diabetes mellitus, smoking, depressive mood, lower back pain, and IADL dependency.

Table 1. Characteristics of study participants, according to number of satisfying MVPA, dietary variety, and social interaction.

Variables		Number of satisfying MVPA, dietary variety, and social interaction^a

	All	Not satisfying any behavior	Satisfying any one behavior	Satisfying any two behaviors	Satisfying all three behaviors	P-value
	(n = 7,822)	(n = 451, 5.8%)	(n = 1,620, 20.7%)	(n = 2,975, 38.0%)	(n = 2,776, 35.5%)
Minutes/week of MVPA, median (interquartile range)	420 (180–840)	0 (0–80)	160 (30–490)	430 (210–900)	570 (350–1,020)	<0.001^b
MVPA ≥150 minutes/week, n (%)	6,156 (78.7)	0 (0.0)	861 (53.2)	2,519 (84.7)	2,776 (100.0)	<0.001
DVS, mean (SD)	3.2 (2.2)	0.9 (0.8)	1.7 (1.6)	2.8 (2.1)	4.8 (1.7)	<0.001^b
DVS ≥3, n (%)	4,457 (57.0)	0 (0.0)	292 (18.0)	1,389 (46.7)	2,776 (100.0)	<0.001
Social interaction of 1 time/week, n (%)	5,285 (67.6)	0 (0.0)	467 (28.8)	2,042 (68.6)	2,776 (100.0)	<0.001
Age (years), mean (SD)	73.6 (5.5)	74.0 (5.8)	73.3 (5.6)	73.5 (5.4)	73.8 (5.4)	0.15^b
Sex (women), n (%)	3,856 (49.3)	133 (29.5)	570 (35.2)	1,416 (47.6)	1,737 (62.6)	<0.001
Living alone, n (%)	1,535 (19.6)	98 (21.7)	306 (18.9)	569 (19.1)	562 (20.2)	0.22
Marital status, n (%)						<0.001
Married	5,327 (68.1)	288 (63.9)	1,104 (68.2)	2,025 (68.1)	1,910 (68.8)
Widowed or divorced	1,811 (23.2)	103 (22.8)	345 (21.3)	682 (22.9)	681 (24.5)
Never married	595 (7.6)	53 (11.8)	150 (9.3)	236 (7.3)	156 (5.6)
Education, n (%)						<0.001
Junior high school graduation	1,641 (21.0)	139 (30.8)	460 (28.4)	639 (21.5)	403 (14.5)
High school graduation	2,946 (37.7)	186 (41.2)	587 (36.2)	1,182 (39.7)	991 (35.7)
Junior college/vocational college/ college/graduate school graduation	3,034 (38.8)	109 (24.2)	511 (31.5)	1,091 (36.7)	1,323 (47.7)
Other/missing	201 (2.6)	17 (3.8)	62 (3.8)	63 (2.1)	59 (2.1)
Equivalent income, n (%)						<0.001
<2.0 million yen	1,298 (16.6)	84 (18.6)	291 (18.0)	501 (16.8)	422 (15.2)
2.0–3.99 million yen	2,739 (35.0)	195 (43.2)	678 (41.9)	1,071 (36.0)	795 (28.6)
≥4.0 million yen	2,372 (30.3)	127 (28.2)	435 (26.9)	903 (30.4)	907 (32.7)
Unknown/missing	1,413 (18.1)	45 (10.0)	216 (13.3)	500 (16.8)	652 (23.5)
Body mass index (kg/m²), mean (SD)	22.7 (3.1)	23.2 (3.4)	22.9 (3.3)	22.8 (3.1)	22.4 (2.9)	<0.001^b
<18.5	609 (7.8)	39 (8.7)	121 (7.5)	208 (7.0)	241 (8.7)
18.5–24.9	5,563 (71.1)	277 (61.4)	1,113 (68.7)	2,120 (71.3)	2,053 (74.0)	<0.001
≥25	1,606 (20.5)	130 (28.8)	374 (23.1)	631 (21.2)	471 (17.0)
Hypertension (presence), n (%)	4,079 (52.2)	266 (59.0)	898 (55.4)	1,582 (53.2)	1,333 (48.0)	<0.001
Hyperlipidemia (presence), n (%)	3,241 (41.4)	197 (43.7)	664 (41.0)	1,227 (41.2)	1,153 (41.5)	0.043
Heart disease (presence), n (%)	1,704 (21.8)	123 (27.3)	391 (24.1)	666 (22.4)	524 (18.9)	<0.001
Stroke (presence), n (%)	522 (6.7)	50 (11.1)	130 (8.0)	194 (6.5)	148 (5.3)	<0.001
Diabetes mellitus (presence), n (%)	1,375 (17.6)	95 (21.1)	314 (19.4)	524 (17.6)	442 (15.9)	0.001
Cancer (presence), n (%)	1,266 (16.2)	79 (17.5)	254 (15.7)	510 (17.1)	423 (15.2)	0.08
Alcohol drinking status (current), n (%)	4,478 (57.3)	246 (54.6)	949 (58.6)	1,713 (57.6)	1,570 (56.6)	0.73
Smoking status (current), n (%)	991 (12.7)	100 (22.2)	310 (19.1)	381 (12.8)	200 (7.2)	<0.001
Depressive mood (presence), n (%)	2,564 (32.8)	247 (54.8)	709 (43.8)	978 (32.9)	630 (22.7)	<0.001
Lower back pain (presence), n (%)	2,918 (37.3)	190 (42.1)	671 (41.4)	1,131 (38.0)	926 (33.4)	<0.001
Knee pain (presence), n (%)	2,352 (30.1)	135 (29.9)	497 (30.7)	933 (31.4)	787 (28.4)	0.20
Sitting time, median (interquartile range)	300 (180–480)	360 (240–600)	300 (180–480)	300 (180–480)	300 (180–480)	<0.001
<180 minutes/day, n (%)	1,296 (16.6)	45 (10.0)	258 (15.9)	506 (17.0)	487 (17.5)	<0.001
180–299 minutes/day, n (%)	1,751 (22.4)	91 (20.2)	327 (20.2)	682 (22.9)	651 (23.5)
300–479 minutes/day, n (%)	2,031 (26.0)	100 (22.2)	408 (25.2)	800 (26.9)	723 (26.0)
≥480 minutes/day, n (%)	2,276 (29.1)	179 (39.7)	511 (31.5)	811 (27.3)	775 (27.9)
IADL dependency (presence), n (%)	893 (11.4)	130 (28.8)	271 (16.7)	328 (11.0)	164 (5.9)	<0.001

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DVS, Dietary Variety Score; IADL, instrumental activities of daily living; MVPA, moderate-to-vigorous physical activity; SD, standard deviation.

^aTotal MVPA time ≥150 minutes/week, DVS ≥3, and social interaction (apart from cohabiting family members) ≥1 time/week were defined as sufficiency, respectively.

^bP for trend test.

The sum of the percentage of respondents and the percentage of missing values (not shown in the table) for each category is 100%.

Table 2 shows independent associations of MVPA, DVS, and social interaction with incident disability. Sufficiency of MVPA (HR 0.68; 95% CI, 0.59–0.78) and DVS (HR 0.87; 95% CI, 0.77–0.99) were independently and significantly associated with lower HRs for incident disability even in the fully adjusted model 4. Although sufficiency of social interaction was significantly associated with lower HR for incident disability up to model 3, the significant association disappeared after adjusting for IADL dependency (model 4: HR 0.90; 95% CI, 0.79–1.03). PAF for disability reduction in satisfying MVPA, DVS, and social interaction were 6.8% (95% CI, 4.8–8.8%), 5.6% (95% CI, 0.7–10.3%), and 3.1% (95% CI, −0.9 to 7.0%), respectively.

Table 2. Multilevel survival analyses of independent associations of each MVPA, dietary variety, and social interaction with incident disability (n = 7,822).

Variables included simultaneously in the same model	Number of events/ participants	Incidence rate per 1,000 person-years	Model 1			Model 2			Model 3			Model 4			PAF, % (95% CI)
															PAF, % (95% CI)
			HR	(95% CI)	P	HR	(95% CI)	P	HR	(95% CI)	P	HR	(95% CI)	P	Insufficiency →sufficiency
MVPA
Insufficiency	355/1,666	70.1	1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		6.8	(4.8–8.8)
Sufficiency	691/6,156	35.8	0.58	(0.51–0.66)	<0.001	0.60	(0.53–0.69)	<0.001	0.63	(0.55–0.72)	<0.001	0.68	(0.59–0.78)	<0.001
Dietary variety
Insufficiency	479/3,365	37.8	1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		5.6	(0.7–10.3)
Sufficiency	567/4,457	33.4	0.83	(0.73–0.94)	0.004	0.87	(0.76–0.98)	0.030	0.87	(0.77–0.99)	0.037	0.87	(0.77–0.99)	0.034
Social interaction
Insufficiency	423/2,537	41.3	1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		3.1	(−0.9 to 7.0)
Sufficiency	623/5,285	28.4	0.77	(0.68–0.87)	<0.001	0.81	(0.71–0.93)	0.002	0.86	(0.76–0.99)	0.032	0.90	(0.79–1.03)	0.14

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CI, confidence interval; HR, hazard ratio; IADL, instrumental activities of daily living; MVPA, moderate-to-vigorous intensity physical activity; PAF, population-attributable fraction.

Total MVPA time ≥150 minutes/week, dietary variety (Dietary Variety Score) ≥3, and social interaction (apart from cohabiting family members) ≥1 time/week were defined as sufficiency, respectively.

Model 1: Adjusted for age and sex.

Model 2: Adjusted for variables in model 1, plus living situation, marital status, education, equivalent income, body mass index, hypertension, hyperlipidemia, heart disease, stroke, diabetes mellitus, cancer, alcohol drinking status, and smoking status.

Model 3: Adjusted for variables in model 2, plus depressive mood, lower back pain, knee pain, and sitting time.

Model 4: Adjusted for variables in model 3, plus IADL dependency.

Figure 2 shows crude cumulative probability of remaining disability-free according to number of satisfying health behaviors. Compared with people who did not satisfy any behaviors, disability risk gradually decreased with increasing number of satisfying behaviors (any one: HR 0.67; 95% CI, 0.53–0.83; any two: HR 0.49; 95% CI, 0.40–0.61; all three: HR 0.38; 95% CI, 0.30–0.47; P < 0.001 for trend).

Table 3 shows associations of combination of MVPA, DVS, and social interaction with incident disability. Compared with people who did not satisfy any behaviors, disability risk gradually decreased with increasing number of satisfying behaviors even in model 4 (any one: HR 0.82; 95% CI, 0.65–1.03; any two: HR 0.65; 95% CI, 0.52–0.82; all three: HR 0.54; 95% CI, 0.43–0.69; P < 0.001 for trend). PAF for disability reduction in satisfying any one, any two, and all three behaviors were 4.0% (95% CI, −0.2 to 7.9%), 9.6% (95% CI, 4.8–14.1%), and 16.0% (95% CI, 8.7–22.8%), respectively. In the combination pattern, disability risk was consistently and significantly lower in the satisfying only MVPA (HR 0.69; 95% CI, 0.53–0.90), MVPA + dietary variety (HR 0.64; 95% CI, 0.49–0.83), MVPA + social interaction (HR 0.62; 95% CI, 0.48–79), and all three behaviors, (HR 0.54; 95% CI, 0.43–69) even in model 4, as compared with people who did not satisfy any behaviors. The significance of dietary variety + social interaction disappeared in model 4 (HR 0.78; 95% CI, 0.58–1.05; P = 0.10). These results did not vary by age group (P > 0.24 for all interactions) or sex (P > 0.16 for all interactions).

Table 3. Multilevel survival analyses of associations of combination of MVPA, dietary variety, and social interactions with incident disability (n = 7,822).

Combination categories	Number of events/ participants	Incidence rate per 1,000 person-years	Model 1			Model 2			Model 3			Model 4			PAF, % (95% CI)
															PAF, % (95% CI)
			HR	(95% CI)	P	HR	(95% CI)	P	HR	(95% CI)	P	HR	(95% CI)	P	Insufficiency →sufficiency
Number of satisfying behaviors
Not satisfying any behaviors	109/451	88.2	1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)
Satisfying any one behavior	274/1,620	59.9	0.72	(0.58–0.90)	0.004	0.73	(0.59–0.92)	0.007	0.75	(0.60–0.94)	0.012	0.82	(0.65–1.03)	0.08	4.0 (−0.2 to 7.9)
Satisfying any two behaviors	382/2,975	44.4	0.51	(0.41–0.63)	<0.001	0.55	(0.44–0.68)	<0.001	0.58	(0.47–0.73)	<0.001	0.65	(0.52–0.82)	<0.001	9.6 (4.8–14.1)
Satisfying all three behaviors	281/2,776	34.4	0.38	(0.30–0.47)	<0.001	0.42	(0.34–0.53)	<0.001	0.47	(0.37–0.60)	<0.001	0.54	(0.43–0.69)	<0.001	16.0 (8.7–22.8)
	1,046/7,822	46.3		Trend	<0.001		Trend	<0.001		Trend	<0.001		Trend	<0.001
Combination pattern of satisfying behaviors
Not satisfying any behaviors	109/451	88.2	1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)		1.00	(Ref.)
Only MVPA	113/861	45.6	0.60	(0.46–0.78)	<0.001	0.61	(0.46–0.79)	<0.001	0.62	(0.47–0.81)	<0.001	0.69	(0.53–0.90)	0.007
Only dietary variety	72/292	89.7	0.86	(0.64–1.16)	0.32	0.87	(0.64–1.17)	0.36	0.85	(0.63–1.15)	0.29	0.86	(0.63–1.16)	0.32
Only social interaction	89/467	68.7	0.83	(0.63–1.10)	0.20	0.87	(0.65–1.15)	0.33	0.91	(0.69–1.22)	0.54	1.02	(0.77–1.36)	0.88
MVPA + dietary variety	129/933	47.6	0.51	(0.40–0.66)	<0.001	0.55	(0.42–0.71)	<0.001	0.58	(0.44–0.75)	<0.001	0.64	(0.49–0.83)	0.001
Dietary variety + social interaction	85/456	66.4	0.66	(0.49–0.87)	0.004	0.71	(0.53–0.95)	0.020	0.73	(0.54–0.98)	0.034	0.78	(0.58–1.05)	0.10
MVPA + social interaction	168/1,586	36.4	0.46	(0.36–0.59)	<0.001	0.49	(0.39–0.63)	<0.001	0.54	(0.42–0.69)	<0.001	0.62	(0.48–0.79)	<0.001
MVPA + dietary variety + social interaction	281/2,776	34.4	0.38	(0.30–0.47)	<0.001	0.43	(0.34–0.54)	<0.001	0.48	(0.38–0.61)	<0.001	0.54	(0.43–0.69)	<0.001
	1,046/7,822	46.3

Open in a new tab

CI, confidence interval; HR, hazard ratio; IADL, instrumental activities of daily living; MVPA, moderate-to-vigorous intensity physical activity; PAF, population-attributable fraction.

Model 1: Adjusted for age and sex.

Model 3: Adjusted for variables in model 2, plus depressive mood, lower back pain, knee pain, and sitting time.

Model 4: Adjusted for variables in model 3, plus IADL dependency.

The sensitivity analyses results reveal that the individual association of DVS and incident disability was not robust (see eTable 1). Although the individual association of social interaction and incident disability remained statistically insignificant, it was statistically marginal (HR 0.86; 95% CI, 0.73–1.00; P = 0.052). Other results were not substantially different from those of the primary analyses (see eTable 2).

DISCUSSION

Our study yielded the following findings. First, MVPA was significantly and consistently associated with lower risk of disability in both primary and sensitivity analyses. Second, disability risk gradually decreased with increasing number of satisfying MVPA, DVS, and social interaction in an inverse dose-response manner. Third, if all participants adhered to all three behaviors (except those who satisfied all three behaviors at baseline), 3.6-year disability would be reduced by 16.0%. Finally, these results were not substantially different in the sensitivity analysis, suggesting the importance of combining MVPA, dietary variety, and social interaction in disability prevention efforts in the community.

Our results for each health behavior were consistent with previous studies. Recent studies showed that PA was most protective against frailty⁴ and disability³¹ among traditional and nontraditional health behaviors. There is growing evidence that the Japanese dietary pattern is associated with lower risk of disability,³² and dietary variety especially reduces all-cause and cancer mortality.³³ Although our results on the association of social interaction alone with incident disability were not robust, they were consistent with a recent study.⁴

Interestingly, despite the non-robust associations of DVS and social interaction with incident disability, additional risk reduction was observed when combined with MVPA. Previous studies consistently reported that exercising,³⁴^–³⁷ eating,³⁸^–⁴⁰ and doing social activities³⁵ with others brought various health benefits compared to doing these activities alone. Evidence of the relationship between this kind of interactive behavior and positive psychological outcomes³⁹ is especially robust, and the mechanism can be explained by the psychosocial factors (eg, reducing stress and anxiety and enhancing social connection and support) that mediate the positive effects on mental health.⁴¹ Moreover, interaction with others not only promotes PA⁴² and dietary variety⁴³ but also affects the risk reduction of health outcomes.⁹ By adding a social interaction element, the preventive impact on disability was considered to be reinforced from a psychosocial aspect.

On the contrary, MVPA and dietary variety would enhance the preventive impact on disability from medical and physiological aspects. Previous systematic reviews and meta-analyses of randomized controlled trials showed some positive effects of exercise and nutritional interventions for reducing sarcopenia⁴⁴ and frailty.⁴⁵ In particular, for reducing frailty, a combination of exercise and nutritional supplementation/education—not nutrition only—was found to be effective.⁴⁵ The additive effects on muscle mass, strength, and physical performance may play an important role in preventing frailty and disability. Our results provided further evidence that combining PA and dietary variety with social interaction reduced disability risk and enhanced the public health impact. The clear inverse dose-response association of the number of satisfying MVPA, DVS, and social interaction with disability risk is a noteworthy and important finding of this study.

From our findings and from a public health perspective, we suggest effective countermeasures against disability in the community setting. To combine MVPA, dietary variety, and social interaction, promoting the establishment and continuation of social group activities in the community is a main strategy. Several quasi-experimental studies reported that participation in social activities, such as community salons and self-managed exercise groups, reduced the risk of disability.⁴⁶^,⁴⁷ Increasing population coverage rates of participation in such groups is an important challenge for achieving population-level disability prevention.

Another important point is adding the insufficient element among MVPA and dietary variety to preexisting social group activities to make it a habit.¹² This could include adding a 10-minute dietary habit check and/or exchange program to a self-managed exercise group, or a simple muscle strengthening activity and/or the dietary habit check to a cultural group. This may lead to increased diversity of activities and strengthen the preventive functions against disability. Promoting such efforts in collaboration with multiple occupations, institutions, and organizations may be also an effective strategy for preventing disability in the community.

Our study has several limitations. First, the self-administered questionnaire used for measurement might involve recall bias. Although the IPAQ-SF is widely used for the assessment of PA, it tends to overestimate PA, as compared with objective measurement tools.¹⁵ Moreover, DVS from self-administered questionnaires may be underestimated compared to face-to-face interview surveys.¹⁸ Although our results were based on the median DVS (of 3) of the study population, desirable DVS is considered to be a higher value.¹⁸^,¹⁹ Thus, our results should be generalized with caution. Second, selection bias is a concern, as people who did not satisfy any behaviors constituted only 5.8% in this study. The exclusion of 3,649 (30.6%) respondents who had missing IPAQ-SF, DVS, or social interaction data may have led to the biased inclusion of a health-conscious population in the analysis. Third, the follow-up period was relatively short. Although our results have been confirmed by a sensitivity analysis, the possibility that undiagnosed medical conditions may have affected health behaviors and/or incident disability cannot be completely excluded. Fourth, an older adult must contact the municipal government to officially certify his or her care needs.¹³ Therefore, some disabled individuals might have failed to report themselves, which may have resulted in the underestimation of disability incidence (detection bias). Finally, it should be noted that this study used data sources from a community-wide intervention study. Although the intervention did not affect population-level frailty at 2 years, the walking time and DVS in the intervention subgroup improved at the population-level.¹² The present study only assessed baseline health behaviors and was not able to take into account the improvements at 2 years and subsequent changes in these health behaviors during the follow-up. If health behaviors improved further during the subsequent follow-up period after 2 years, the relationships between health behaviors and disability risk shown in this study may have been underestimated.

Despite these limitations, to our knowledge, this study is the first to report the combined impact of MVPA, DVS, and social interaction and its PAF for disability risk. These results are strengthened by the large sample of randomly recruited participants and the high response of 76.9% and follow-up rate of 99.84% (ie, all respondents except people whose survey ID label was removed and some unaddressed people).

Conclusion

The combination of regular PA, dietary variety, and social interaction substantially enhances the impacts on preventing disability of older adults in an inverse dose-response manner. An approach that adds the insufficient behavior element to individual habits and preexisting social group activities may be effective in preventing disability in a community. A further study will be necessary to confirm the hypothesis in real-world settings.

ACKNOWLEDGEMENTS

We are very grateful to the residents and staff members of Ota City and to our collaborator, Izumi Tanaka. This study was supported by grants from Ota City (no available number), the Japan Foundation for Aging and Health (no available number), and JSPS KAKENHI Grant Number 19H03914.

Data availability: Data available on request from authors.

Conflicts of interest: None declared.

SUPPLEMENTARY MATERIAL

The following is the supplementary data related to this article:

eTable 1. Multilevel survival analyses of independent associations of each MVPA, dietary variety, and social interaction with incident disability, excluding individuals who developed disability that occurred during the first year of follow-up (n = 7,531)

eTable 2. Multilevel survival analyses of associations of combinations of MVPA, dietary variety, and social interactions with incident disability, excluding individuals who developed disability that occurred during the first year of follow-up (n = 7,531)

je-33-350-s001.pdf^{(124.2KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

je-33-350-s001.pdf^{(124.2KB, pdf)}

[r01] 1.World Health Organization. Global Strategy and Action Plan on Ageing and Health. https://www.who.int/ageing/GSAP-Summary-EN.pdf; 2017 Accessed on August 1, 2021.

[r02] 2.Artaud F, Dugravot A, Sabia S, et al. Unhealthy behaviours and disability in older adults: three-City Dijon cohort study. BMJ. 2013;347:f4240. 10.1136/bmj.f4240 [DOI] [PubMed] [Google Scholar]

[r03] 3.Zhang S, Tomata Y, Newson RB, et al. Combined healthy lifestyle behaviours and incident disability in an elderly population: the Ohsaki Cohort 2006 Study. J Epidemiol Community Health. 2018;72:679–684. 10.1136/jech-2018-210464 [DOI] [PubMed] [Google Scholar]

[r04] 4.Pérez-Tasigchana RF, Sandoval-Insausti H, Donat-Vargas C, et al. Combined impact of traditional and nontraditional healthy behaviors on frailty and disability: a prospective cohort study of older adults. J Am Med Dir Assoc. 2020;21:710.e1–710.e9. 10.1016/j.jamda.2019.08.025 [DOI] [PubMed] [Google Scholar]

[r05] 5.Morley JE, Vellas B, van Kan GA, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14:392–397. 10.1016/j.jamda.2013.03.022 [DOI] [PMC free article] [PubMed] [Google Scholar]

[r06] 6.Lee Y, Chon D, Kim J, et al. The predictive value of social frailty on adverse outcomes in older adults living in the community. J Am Med Dir Assoc. 2020;21:1464–1469.e2. 10.1016/j.jamda.2020.03.010 [DOI] [PubMed] [Google Scholar]

[r07] 7.Yamada M, Arai H. Social frailty predicts incident disability and mortality among community-dwelling Japanese older adults. J Am Med Dir Assoc. 2018;19:1099–1103. 10.1016/j.jamda.2018.09.013 [DOI] [PubMed] [Google Scholar]

[r08] 8.Otsuka T, Tomata Y, Zhang S, et al. Association between social participation and incident risk of functional disability in elderly Japanese: the Ohsaki Cohort 2006. J Psychosom Res. 2018;111:36–41. 10.1016/j.jpsychores.2018.05.004 [DOI] [PubMed] [Google Scholar]

[r09] 9.Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Med. 2010;7:e1000316. 10.1371/journal.pmed.1000316 [DOI] [PMC free article] [PubMed] [Google Scholar]

[r10] 10.Haider S, Grabovac I, Drgac D, et al. Impact of physical activity, protein intake and social network and their combination on the development of frailty. Eur J Public Health. 2020;30:340–346. 10.1093/eurpub/ckz191 [DOI] [PubMed] [Google Scholar]

[r11] 11.Seino S, Kitamura A, Tomine Y, et al. A Community-wide intervention trial for preventing and reducing frailty among older adults living in metropolitan areas: design and baseline survey for a study integrating participatory action research with a cluster trial. J Epidemiol. 2019;29(2):73–81. 10.2188/jea.JE20170109 [DOI] [PMC free article] [PubMed] [Google Scholar]

[r12] 12.Seino S, Tomine Y, Nishi M, et al. Effectiveness of a community-wide intervention for population-level frailty and functional health in older adults: a 2-year cluster nonrandomized controlled trial. Prev Med. 2021;149:106620. 10.1016/j.ypmed.2021.106620 [DOI] [PubMed] [Google Scholar]

[r13] 13.Tsutsui T, Muramatsu N. Japan’s universal long-term care system reform of 2005: containing costs and realizing a vision. J Am Geriatr Soc. 2007;55:1458–1463. 10.1111/j.1532-5415.2007.01281.x [DOI] [PubMed] [Google Scholar]

[r14] 14.Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–1395. 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]

[r15] 15.Lee PH, Macfarlane DJ, Lam TH, et al. Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act. 2011;8:115. 10.1186/1479-5868-8-115 [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Combined Impacts of Physical Activity, Dietary Variety, and Social Interaction on Incident Functional Disability in Older Japanese Adults

Satoshi Seino

Yu Nofuji

Yuri Yokoyama

Takumi Abe

Mariko Nishi

Mari Yamashita

Miki Narita

Toshiki Hata

Shoji Shinkai

Akihiko Kitamura

Yoshinori Fujiwara

Abstract

Background

Methods

Results

Conclusion

INTRODUCTION

METHODS

Study population

Figure 1. Flow diagram of study participants. IPAQ-SF: International Physical Activity Questionnaire-Short Form; LTCI, long-term care insurance.

Measurements

MVPA

Dietary variety

Social interaction

Functional disability

Covariates

Statistical analyses

RESULTS

Table 1. Characteristics of study participants, according to number of satisfying MVPA, dietary variety, and social interaction.

Table 2. Multilevel survival analyses of independent associations of each MVPA, dietary variety, and social interaction with incident disability (n = 7,822).

Table 3. Multilevel survival analyses of associations of combination of MVPA, dietary variety, and social interactions with incident disability (n = 7,822).

DISCUSSION

Conclusion

ACKNOWLEDGEMENTS

SUPPLEMENTARY MATERIAL

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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