The Association Between Healthy Lifestyle Behaviors and Polypharmacy in Older Adults: The Loma Linda Longevity Study

Abstract

Introduction

Polypharmacy—defined as taking numerous medications that may not be clinically necessary—is becoming a significant concern among the older adult population. This study examined the associations between lifestyle behaviors and the number of prescribed medications in older adults (75 years and older) living in the counties of San Bernardino and Riverside.

Methods

This study employed a cross-sectional survey to assess lifestyle behaviors and medication use in 611 older adults aged 75 or over. Bivariate correlation and linear regression analyses were used to assess potential relationships between lifestyle behaviors and the number of prescribed medications.

Results

Fruit consumption (P = 0.005), mild physical activity per week (P < .001), and lifestyle index (P = 0.003) had a highly significant inverse association with the number of prescribed medications. Fat consumption had a direct positive relationship with the number of prescribed medications (P = 0.02).

Conclusion

Higher fat intake was directly linked to an increased need for medications, while regular physical activity, a higher fruit intake, and a healthy overall lifestyle were all associated with a lower likelihood of polypharmacy. Future research can explore the mechanisms linking these behaviors with medication usage.

“Our study revealed a potential link between high-fat dietary patterns and polypharmacy in older adults.”

Introduction

A chronic disease is a medical condition that lasts a year or longer, necessitating continuing medical care, restricting daily activities, or both. ¹ Cancer, heart disease, stroke, diabetes, and chronic obstructive pulmonary disease are the most prevalent chronic conditions that account for over two-thirds of all mortalities in the United States (U.S.). Moreover, seven out of ten deaths in the U.S. are caused by chronic diseases, which claim the lives of over 1.7 million persons annually. ¹ Additionally, a substantial portion of the nation’s $4.1 trillion annual healthcare expenses can be attributed to these diseases. ²

Polypharmacy involves taking an excessive number of medications, often more than five, that are clinically unnecessary, ineffective, or duplicated. Polypharmacy is becoming a significant concern among the older adult population. ³ Studies have shown a strong association between polypharmacy and adverse outcomes including increasing healthcare expenses, higher risk of drug-related side effects, a significant interaction between medications, non-compliance to taking medications, decreased capacity for carrying out essential everyday tasks, and a variety of geriatric disorders. ³

According to the National Council on Aging, there are 56 million people aged 65 and over in the U.S., and predictions indicate that number will increase to 94.7 million by 2060. A 65-year-old can anticipate an additional 17 years of life on average. However, chronic diseases are more prevalent among older individuals. Almost 95% of the older adult population has a chronic illness, and 80% are affected by at least two chronic diseases. ⁴ According to medical practices, the best clinical outcome frequently requires the use of several drugs to treat each chronic condition. As a result, older people who have two disease states (e.g., cardiac failure and chronic obstructive pulmonary disease) will typically take more than five medications. ³

The main causes of chronic diseases are unhealthy changes in lifestyle, including an increase in the consumption of processed foods and sugary beverages, smoking, stress, substance misuse, a decline in physical activity, and an increase in sedentary behavior.^5,6 As a result, altering lifestyle choices including a healthy diet, adequate exercise, sleep, quitting smoking, and other similar actions can reduce the occurrence of chronic diseases. ⁶ The Polpharma study investigated if lifestyle choices, such as eating habits, were associated with increased or decreased polypharmacy. The study indicated that individuals following a vegan diet may require fewer medications than those who consume omnivorous diets. ⁷

The effectiveness of a healthy lifestyle in treating and alleviating chronic diseases is well-established.^8,9 In a cohort study, the authors explored the relationship between a healthy lifestyle and all-cause mortality in polypharmacy patients. The authors concluded that, independent of the amount of prescription drugs taken, better commitment to a healthy lifestyle is linked to reduced all-cause mortality. ¹⁰ A Spanish population-based cohort found that maintaining a healthy lifestyle can lower the mortality risk that comes with polypharmacy in older adults. ¹¹ Particularly, people with polypharmacy who led a healthy lifestyle had a 60% decreased risk of death from cardiovascular disease and a 54% decreased risk of death from all causes compared to those who led an unhealthy lifestyle. ¹¹

In summary, a healthy lifestyle is associated with less morbidity and mortality. This paper is interested in determining if a healthy lifestyle in older adults is associated with less multiple medication usage.

Diet and Polypharmacy

The Polypharma Study found that people who follow a vegan diet take fewer medications than those who do not. This is likely because vegan diets are generally lower in overall calories. ⁷ The study also suggested that eating a vegan diet may help to prevent or control cardiovascular disease and other risk factors, which could further reduce the number of medications needed. ⁷ A case report suggested a plant-based diet and exercise may improve cardiovascular disease and reduce medication use in older adults. ¹² Another study revealed that people who followed a Mediterranean diet more closely took fewer medications overall. They also found that Mediterranean diet staples such as olive oil, fish, legumes, vegetables, and nuts were linked to taking fewer medications. ¹³ Moreover, another study examined the effects of an eight-week group program centered on a whole-food plant-based diet. The findings revealed more than 25% of individuals were able to reduce or stop using a minimum of one medication. ¹⁴

Smoking, Alcohol, and Polypharmacy

Tobacco smoking remains a significant contributor to chronic disease and the foremost preventable cause of death globally. ¹⁵ Smoking is a leading risk factor for cardiovascular disease (CVD), accounting for one in four CVD-related deaths. ¹⁶ Smoking substantially increases the risk of type-2 diabetes by 30-40% and complicates diabetes management. Consequently, smokers may require higher insulin doses to control blood glucose, increasing the need for medication. ¹⁶

Alcohol is the fourth leading cause of death after smoking, poor diet, and lack of physical activity. ¹⁷ Globally, alcohol consumption causes around 3 million deaths annually, according to the World Health Organization (WHO). ¹⁸ Of these alcohol-related deaths, 28% resulted from injuries, 21% from digestive disorders, and 19% from cardiovascular diseases, all of which can increase the need for multiple medications. ¹⁸ Few studies have examined the relationship between alcohol consumption and polypharmacy. The English Longitudinal Study of Ageing (ELSA) found no correlation between elevated amounts of self-reported alcohol intake and low self-rated health.^19,20

Exercise and Polypharmacy

Physical exercise is a significant lifestyle intervention that has the potential to partially replicate the impacts of drugs on cardiometabolic diseases. ²¹ Moreover, muscle contractions during physical activity led to the synthesis of various drug-like compounds that have positive impacts on people of all ages. ²² Another study demonstrated that patients with metabolic syndrome (MetS) did not require more medication to regulate their blood glucose levels after participating in a twice-yearly, 4-month exercise program compared to their non-exercising counterparts. ²³ A follow-up randomized controlled trial investigated the impact of extending the exercise program for five years. The results showed that physical activity can significantly lower the requirement for medication to control MetS. ²⁴

The lack of physical activity has recently been identified as a risk factor for several chronic diseases.^25,26 Studies demonstrated that both exercise and medication can decrease the risk of premature death among those with several chronic diseases such as diabetes, cancer, and heart disease. At the same time, consistent physical activity has been shown to decrease the number of medications taken by heart disease and diabetic patients.^27,28 A systematic review found that polypharmacy and physical function are strongly associated bidirectionally. ²⁹ In particular, polypharmacy has a negative impact on most physical function outcomes, such as grip strength and walking speed. Conversely, older adults who have lower levels of physical activity are more likely to use multiple medications. ²⁹

A systematic review and meta-analysis were conducted to compare the efficacy of exercise, antidepressants, and their combined use in treating adults with depression. Their findings revealed that exercise was just as effective as antidepressants in decreasing depressive symptoms. These results support the use of exercise as a supplemental or alternate treatment for individuals with mild to moderate depression. Additionally, combining exercise with medication may help to alleviate the side effects of antidepressants and accelerate recovery. ³⁰

In conclusion, limited research has explored how individual lifestyle factors like diet (including specific components like fruits, vegetables, sugar, and fat), sleep, exercise, smoking, and alcohol influence polypharmacy among older adults (aged 75+) in California, U.S. This study aimed to bridge this gap by examining these associations in a population of older adults residing in San Bernardino and Riverside Counties, California. We assessed the individual effects of these lifestyle behaviors and created a combined “lifestyle index” to evaluate their overall impact on polypharmacy.

Methods

Study Design

This cross-sectional study leverages data from The Loma Linda Longevity Study while introducing new data to the analysis. The first Loma Linda Study included 380 participants. ³¹ To further enhance this study’s statistical power, an additional 231 individuals were recruited from churches, senior centers, community centers, retirement homes, and assisted living facilities in San Bernardino and Riverside counties, California, resulting in a final sample size of 611 participants. The inclusion criteria included 75 years of age or older, English speaking, and residing in either San Bernardino or Riverside County in California, U.S.; the exclusion criteria included incomplete surveys (1.1% of the sample were missing the dependent variable) and participants younger than 75.

Variables, Instrumentation, and Measurement

The Loma Linda Longevity Study has developed a comprehensive questionnaire to gather data on participants' demographics, dietary habits, smoking status, alcohol consumption, physical activity, sleep quality, number of prescribed medications, and other variables unrelated to this study. ³¹

Independent Variables

To measure the independent variables (i.e., lifestyle behaviors), a lifestyle index was created using the following variables: fruit, vegetable, sugar, and fat consumption; smoking status; alcohol consumption; mild physical activity per week; and sleep duration. A lifestyle index score was created by summing the scores of these eight lifestyle risk factors, each rated according to a point system detailed below. The resulting lifestyle index ranged from 0 to 18, with higher scores corresponding to healthier lifestyle behaviors. ³²

To measure fruit and vegetable consumption, the following survey questions were used: “How many servings of fruits do you eat daily?” and “How many servings of vegetables do you eat daily?” with the following answer choices and scores: 5 or more servings a day = 4; 3-4 servings a day = 3; 1-2 servings a day = 2; less than 1 serving a day = 1; and none = 0. To measure sugar and fat consumption, participants were asked, “How often do you consume sugar-sweetened beverages and products (soda, cookies, cakes, etc.)?” and “How often do you consume foods that are high in fat (cheese, butter, red meats, French-fries, processed meats, etc.)?” with the following answer choices and scores: never = 3; once a month = 2; weekly = 1; and daily = 0. To measure smoking and alcohol status, the questions “Have you ever smoked?” and “How often do you consume alcohol (beer, wine, liquor, spirits)?” were asked, with the answer choices grouped into the following categories and scores: no consumption = 1 and any consumption = 0. For mild and vigorous physical activity, the following questions were used: “How often do you engage in mild physical activities, such as gardening, house chores, walking, etc.?” and “How often do you engage in regular vigorous activity, such as jogging, bicycling, or other activities that make you sweat?” with the following answer choices and scores: 5 or more times a week = 4; 3-4 times a week = 3; 1-2 times a week = 2; less than once a week = 1; and no exercise = 0. However, due to missing data, vigorous activity was not included in the lifestyle index. For sleep duration, the following question was used: “On average, how many hours do you sleep each night?” and answers were grouped into the following categories and scores: 7-9 hours of sleep = 1 and more than 9 or less than 7 hours of sleep = 0.

The dependent variable includes the number of prescribed medications a person took, which was a categorical variable with four levels. The following survey question was used: “How many prescribed medications are you taking daily?” with the following answer choices: none, 1-3 meds, 4-6 meds, and more than 6 meds Table 1. Covariates included in the analysis were demographic variables, including body mass index (BMI), age, gender, race/ethnicity, marital status, education, and individual annual income.

Table 1.

Independent and Dependent Variable Scoring.

Variable	Scoring
Fruit consumption	5 or more servings/day = 4, 3-4 servings/day = 3, 1-2 servings/day = 2, Less than 1 serving/day = 1, None = 0
Vegetable consumption	5 or more servings/day = 4, 3-4 servings/day = 3, 1-2 servings/day = 2, Less than 1 serving/day = 1, None = 0
Sugar consumption	Never = 3, Once a month = 2, Weekly = 1, Daily = 0
Fat consumption	Never = 3, Once a month = 2, Weekly = 1, Daily = 0
Smoking status	No consumption = 1, Any consumption = 0
Alcohol consumption	No consumption = 1, Any consumption = 0
Mild physical activity	5 or more times/week = 4, 3-4 times/week = 3, 1-2 times/week = 2, Less than once/week = 1, No exercise = 0
Sleep duration	7-9 hours/night = 1, More than 9 or less than 7 hours/night = 0
Number of prescribed medications	None, 1-3 meds, 4-6 meds, More than 6 meds

Data Analysis

The SPSS statistics software, version 29.0 (IBM SPSS, Inc, Armonk, NY), was used to analyze the data. A bivariate analysis (Chi-square test) was performed to test the association between the dependent variable (i.e., the number of prescribed medications a person is taking) and all the independent variables, one by one, including demographics (age, gender, income, marital status, education, and race/ethnicity), lifestyle index, as well as each of the individual lifestyle index components. Then, a linear regression analysis was performed to build different models for the lifestyle index and the number of prescribed medications, along with the individual lifestyle index components and the number of prescribed medications, adjusting for selected covariates. Moreover, a bivariate Spearman correlation was calculated between the eight lifestyle index components and three covariates (BMI, age, and education). To ensure a statistical power of 80% and maintain a type I error rate of 5%, it was determined using G*Power software that a minimum of 254 participants were required.

Ethical Considerations

Participation in the study was entirely voluntary, without any form of coercion or undue influence. A written informed consent was presented to all participants upon receiving the study questionnaire. Participants who verbally consented continued to fill out the questionnaire but retained the right to withdraw from the study at any time. Data confidentiality was ensured as the survey was completely anonymous. Equal opportunities for all eligible individuals to take part in the study were ensured. The survey collected was approved by the University Institutional Review Board (IRB#5210280) ³¹ and the study was conducted with all the ethics and safety concerns.

Results

This study examined the association between several lifestyle factors and the number of medications taken daily. A total of 611 participants were included in the final analysis. The P-values depicted in Tables 2 and 3 were derived from chi-square analyses. Table 2 shows a significant association was found between the number of prescribed medications taken daily and gender, marital status, race/ethnicity, BMI, and education. While the majority (63%) of those who took no medications were female, a significantly higher proportion of males than females used more than 6 medications daily (53.5% vs 46.5%, P = 0.02), respectively. Unmarried participants were significantly more likely to take more than 6 medications daily compared to married individuals (63.3% vs 36.6%, P = 0.03), respectively. A significantly higher proportion of white participants took more than 6 medications daily compared to non-white participants (71.8% vs 28.2%, P = 0.03), respectively. Additionally, 59.2% of the participants who did not take medication fell into the healthy weight range (BMI of 18.5 to 24.9), whereas 73.8% of the participants who were overweight or obese took more than 6 medication prescriptions (P < .001). Also, 76.3% of individuals with a bachelor’s degree or higher education did not use any medications, while only 23.7% of those with a high school diploma or less education managed without medication. A significantly higher proportion (46.4%) of the less-educated group relied on multiple medications (more than 6 per day).

Table 2.

Sociodemographic Characteristics by the Number of Prescribed Medications Taken.

	Number of prescribed medications/day
	None (n = 76) (%)	1-3 (n = 280) (%)	4-6 (n = 178) (%)	>6 (n = 71) (%)	P-value
Gender					.02*
Male	36.8	43.5	54.5	53.5
Female	63.2	56.5	45.5	46.5
Marital status					.03*
Married	56.6	57.2	52.8	36.6
Divorced	18.4	9.0	10.7	19.7
Single	5.3	5.8	4.5	7.0
Widowed	19.7	28.1	32.0	36.6
Age					.46
75-79	50.0	49.5	49.4	50.7
80-84	24.3	22.9	26.4	23.9
85-89	18.9	14.7	15.2	9.9
90-94	4.1	9.3	3.9	12.7
95-102	2.7	3.6	5.1	2.8
Race/Ethnicity					.03*
American Indian or Alaska Native	6.6	1.8	0.6	2.8
Native Hawaiian or other Pacific Islander	1.3	0.7	0.6	1.4
Asian	18.4	21.8	15.2	7.0
Black or African American	1.3	3.9	7.9	5.6
Caucasian or White	63.2	61.4	61.2	71.8
Hispanic or Latino	2.6	6.8	7.3	7.0
Not Hispanic or Latino	6.6	3.6	7.3	4.2
BMI 1 (kg/m 2 )					<.001*
<18.5	2.6	4.0	1.1	0.0
18.5-24.9	59.2	48.6	31.0	26.1
25-29.9	27.6	28.4	40.2	30.4
30-34.9	5.3	12.9	17.2	21.7
35-39.9	3.9	4.3	6.3	13.0
40+	1.3	1.8	4.0	8.7
Individual income					.09
$29,000 or less	28.0	28.4	27.7	33.8
$30,000 to $59,999	41.3	37.8	33.3	25.4
$60,000 to $89,999	16.0	18.3	12.4	19.7
$90,000 or more	9.3	12.2	18.1	11.3
Unknown	5.3	3.2	8.5	9.9
Education					.01*
Some high school or less	5.3	4.3	2.8	5.6
High school graduate	18.4	21.5	28.7	40.8
Bachelor’s degree	39.5	34.1	38.8	31.0
Master’s degree or more	36.8	40.1	29.8	22.5

¹Body Mass Index.

*Boldface indicates statistical significance (P < 0.05).

Table 3.

Independent Variables by the Number of Prescribed Medications Taken.

	Number of prescribed medications/day
	None (n = 76) (%)	1-3 (n = 280) (%)	4-6 (n = 178) (%)	>6 (n = 71) (%)	P-value
Fruits (servings/day)					<.001*
None	5.3	3.2	4.5	10.0
less than 1	5.3	5.0	1.7	4.3
1-2	47.4	58.2	67.4	77.1
3-4	34.2	27.9	25.3	7.1
5 or more	7.9	5.7	1.1	1.4
Vegetables (servings/day)					.01*
None	2.6	1.4	1.7	7.1
less than 1	3.9	5.0	2.8	4.3
1-2	53.9	54.3	69.7	60.0
3 or more	39.5	39.3	25.8	28.6
Sugar consumption					.07
Never	23.7	20.0	13.5	11.4
Once a month	18.4	28.9	28.1	20.0
Weekly	30.3	31.1	33.1	45.7
Daily	27.6	20.0	25.3	22.9
Fat consumption					<.001*
Never	26.3	23.9	12.4	14.3
Once a month	28.9	28.9	28.1	12.9
Weekly	34.2	32.9	39.3	55.7
Daily	10.5	14.3	20.2	17.1
Smoking status					.02*
No	78.9	75.4	62.4	64.3
Yes, in the past	15.8	22.1	32.0	30.0
Yes, currently	5.3	2.5	5.6	5.7
Alcohol consumption					.31
Never	63.2	67.5	57.3	62.9
A few times a year	14.5	17.1	19.1	18.6
Monthly	11.8	4.3	6.2	4.3
Weekly	1.3	5.0	9.0	5.7
Several times a week	6.6	3.9	5.1	4.3
Daily	2.6	2.1	3.4	4.3
Vigorous PA 1 per week					<.001*
None	25.0	30.2	43.3	66.2
Less than once	14.5	14.4	19.7	14.1
1-2	23.7	23.4	14.6	12.7
3-4	17.1	15.1	11.8	5.6
5 or more	19.7	16.9	10.7	1.4
Mild PA 1 per week					<.001*
None	1.3	1.8	8.4	19.7
Less than once	7.9	7.9	16.3	18.3
1-2	18.4	17.6	21.9	28.2
3-4	27.6	32.3	26.4	12.7
5 or more	44.7	40.5	27.0	21.1
Sleep (hours/day)					.01*
3-6.5	19.7	24.7	23.7	33.8
7-9	78.9	74.6	73.4	59.2
More than 9	1.3	0.7	2.8	7.0

¹Physical Activity.

*Boldface indicates statistical significance (P < 0.05).

Table 3 shows a statistically significant association between several lifestyle factors and the number of medications taken daily. Individuals who met the recommended daily intake of fruits (three or more servings) were significantly less likely (P < .001) to be using prescription medications compared to those who consumed less fruit (two or less servings per day). Moreover, 91.4% of those consuming less than three servings of fruit relied on six or more prescriptions. Vegetable intake followed a similar trend to fruit consumption. Individuals who consumed 3 or more servings of vegetables daily were significantly less likely to rely on multiple medications (more than 6 prescriptions) than those who consumed less than 3 servings (28.6% vs 71.4%, P = .01). Conversely, the majority (71%) of those who consumed 2 or less servings of vegetables daily relied on a high number of medications (more than 6 prescriptions).

Fat consumption showed a direct positive relationship with medication use. Over half of those who did not use medications (55.2%) and those who used only 1-3 medications (52.8%) consumed fat at most once a month. Conversely, a significantly higher proportion (72.8%) of those who took more than 6 medications consumed fat at least weekly (P < .001).

Nearly 79% of the participants who have never smoked avoided medications entirely. This contrasts with current or past smokers, where over a third (37.6%) of those taking 4-6 medications and 35.7% of those taking more than 6 medications were current or past smokers (P = 0.02).

Individuals who engaged in less physical activity per week were more likely to take multiple medications daily. For instance, 80% of those who used more than 6 medications reported engaging in vigorous activity less than once per week. On the other hand, 72.3% of those not using medications engaged in mild physical activity at least 3 or more times per week (P < .001). Also, almost 79% of participants who did not require any medications slept the recommended 7-9 hours per night.

Sensitivity Analysis

In Table 3, when we combined categories of sleep to reduce the number of low frequency cells for the variable sleep, the P-value changed moderately from .01 to .03 but remained statistically significant.

Table 4 shows the results of the linear regression analysis of nine separate models including the eight lifestyle index components and the combined lifestyle index, where the number of prescribed medications taken was the dependent variable, and all models were adjusted for gender, BMI, age, education, and marital status. Three models showed a highly significant inverse relationship with the number of prescribed medications, including fruit consumption (P = 0.005), mild physical activity per week (P < .001), and lifestyle index (P = 0.003). Fat consumption had a direct positive relationship with the number of prescribed medications (P = 0.02).

Table 4.

Linear Regression Analyses of Lifestyle Index Components and Lifestyle index in Nine Separate Models Where the Number of Prescribed Medications Taken is the Dependent Variable ¹ (n = 611).

	Beta	95% Confidence Interval	P-value
Fruit consumption	−.114	[-.210, −.037]	0.005*
Vegetable consumption	−.055	[-.179, .033]	.17
Sugar consumption	.013	[-.056, .077]	.75
Fat consumption	.098	[.012, .159]	.02*
Smoking status	.068	[-.021, .275]	.09
Alcohol consumption	.008	[-.127, .156]	.83
Mild PA 2 per week 3	−.253	[-.233, −.124]	<.001 *
Sleep duration	−.073	[-.289, .010]	.06
Lifestyle Index 4	−.121	[-.223, −.047]	.003 *

¹Covariates for each of the above nine models: gender, body mass index, age, education, and marital status.

²Physical Activity.

³Vigorous PA was excluded from the lifestyle index due to a moderate correlation with mild PA and to avoid undue influence of both types of PA on the lifestyle index.

⁴To avoid issues with multicollinearity, the above table consists of nine separate models.

*Boldface indicates statistical significance (P < 0.05).

Table 5 shows a linear regression analysis of the eight lifestyle index components fitted into one model, where the number of prescribed medications taken was the dependent variable, adjusted for gender, BMI, age, education, and marital status. In this model, fat consumption had a significant direct relationship with the number of prescribed medications (P = 0.02). Furthermore, mild physical activity per week had a significant inverse relationship with the number of prescribed medications (P < .001).

Table 5.

Single Linear Regression Analysis of Lifestyle Index Components Where the Number of Prescribed Medications Taken is the Dependent Variable ¹ (n = 611).

	Beta	95% Confidence Interval	P-value
Fruit consumption	−.038	[-.139, .055]	.39
Vegetable consumption	.013	[-.097, .132]	.76
Sugar consumption	−.051	[-.114, .030]	.24
Fat consumption	.107	[.011, .175]	.02*
Smoking status	.052	[-.058, .253]	.21
Alcohol consumption	−.013	[-.169, .125]	.76
Mild PA 2 per week 3	−.248	[-.230, −.118]	<.001*
Sleep duration	−.016	[-.178, .118]	.69

¹Covariates: gender, body mass index, age, education, and marital status.

²Physical Activity.

³Vigorous PA was excluded from the lifestyle index due to a moderate correlation with mild PA and to avoid undue influence of both types of PA on the lifestyle index.

*Boldface indicates statistical significance (P < 0.05).

The lack of significance for fruit in Table 5, where it competed with the other lifestyle components, suggests other factors in this model (not included in the analysis of fruit in Table 4) may explain its prior association with polypharmacy. These changes in the component regression coefficients led us to calculate a bivariate Spearman’s correlation between the eight lifestyle index components and three covariates (BMI, age, and education) as shown in Table 6. Fruit consumption (Q1) was significantly correlated with every other variable in the model. Specifically, fruit consumption was significantly inversely correlated with fat consumption (Q4), with a correlation coefficient of −0.318 (P < 0.001).

Table 6.

Spearman’s correlations Between the eight Lifestyle Index Components and Three Covariates (n = 611).

	Q1	Q2	Q3	Q4	Q5	Q6	Q7	Q8	Q9	Q10	Q11
Q1	1.00
Q2	.468 <.001	1.00
Q3	−.170 <.001	−.164 <.001	1.00
Q4	−.318 <.001	−.217<.001	.474 <.001	1.00
Q5	−.172 <.001	−.165 <.001	.181 <.001	.230 <.001	1.00
Q6	−.105 .010	−.163 <.001	.097 .017	.214 <.001	.383 <.001	1.00
Q7	.228 <.001	.148 <.001	−.040 .330	.013 .757	−.072 .077	.010 .812	1.00
Q8	.135 <.001	.094 .021	.030 .464	−.060 .143	−.006 .891	.049 .227	.174 <.001	1.00
Q9	.135 <.001	.146 <.001	−.161 <.001	−.290 <.001	−.142 <.001	−.113 .005	.091 .026	−.001 .984	1.00
Q10	.086 .034	.142 <.001	−.050 .220	−.264 <.001	−.073 .074	−.235 <.001	−.059 .147	−.034 .410	.124 .002	1.00
Q11	.157 <.001	.188 <.001	−.206 <.001	−.226 <.001	−.140 <.001	−.093 .022	.000 1.000	.063 .123	.111 .007	.097 .017	1.00

The P-values are below the correlation values for each correlation.

Caption: Q1) Fruit consumption; Q2) Vegetable consumption; Q3) Sugar consumption; Q4) Fat consumption; Q5) Smoking status; Q6) Alcohol consumption; Q7) Mild physical activity per week; Q8) Sleep duration; Q9) Body mass index; Q10) Age; and Q11) Education.

In summary, higher fat intake was directly linked to an increased need for medications, while regular physical activity, a higher fruit intake, and a healthier lifestyle (reflected by the lifestyle index) were all associated with a lower likelihood of polypharmacy.

Discussion

By exploring the link between healthy habits and medication use in older adults, this study identified statistically significant associations between various lifestyle factors and the number of daily medications.

Fruit Intake and Polypharmacy

Our study showed that 91.4% of those consuming less than 3 servings/day relied on six or more medications daily (P < .001). The linear regression analysis confirmed an inverse relationship (P = 0.005) between fruit consumption and medication use when fruit was examined in isolation (without considering other lifestyle factors). These findings align with previous research on patients with chronic kidney disease, where the authors observed that increased fruit and vegetable intake was associated with lower medication dosages for specific conditions. ³³ Our study extends this knowledge by focusing solely on fruit consumption in older adults.

When including the other lifestyle components in the linear regression model, the effect of fruit diminished. This can be explained by the strong negative correlation between fruit consumption and fat consumption, meaning that as fat increased, fruit consumption decreased in general. Fruit was also negatively correlated with sugar consumption, fat consumption, smoking status, and alcohol consumption. This correlation indicates that as fruit consumption increased, fat consumption, sugar consumption, smoking, and alcohol consumption decreased. Our study suggests that older adults who regularly choose fruits are more likely to follow a healthy lifestyle overall, eat less fat and sugar, and avoid smoking and alcohol. Health behaviors tend to co-occur, as a study showed that individuals with higher activity levels tended to consume less fat and more fruits and vegetables. ³⁴ Another study showed that smokers displayed the worst combination of health behaviors, reporting the highest rates of poor sleep, low activity, heavy alcohol use, and low fruit and vegetable intake. ³⁵

Vegetable Intake and Polypharmacy

Our study observed a significant association between vegetable intake and medication use. Older adults who consumed 3 or more daily servings of vegetables were considerably less likely to rely on over 6 prescription medications compared to those with lower vegetable intake (28.6% vs 71.4%, P = .01). Conversely, the majority (71%) of participants consuming 2 or fewer servings of vegetables daily used more than 6 prescription medications (as shown in Table 2). This aligns with previous research in older adults with type-II diabetes, where higher consumption of cooked vegetables, salads, leafy greens, and cruciferous vegetables was linked to a lower need for multiple medications to manage numerous conditions. ³⁶

Fat Intake and Polypharmacy

Our study revealed a potential link between high-fat dietary patterns and polypharmacy in older adults. Over half of the participants who used no medications (55.2%) and those who used only 1-3 medications (52.8%) reported consuming fat infrequently (at most once a month). Conversely, a significantly higher proportion (72.8%) of those who used more than 6 medications consumed fat at least weekly (P < .001). This suggests a trend where individuals with higher medication use tend to also have higher fat intake. Statistical analyses further supported this observation. The linear regression model examining fat consumption alone (without considering other lifestyle factors) revealed a direct, positive relationship between fat intake and the number of medications taken (P = 0.02). This association remained statistically significant even when other lifestyle components were incorporated into the model, suggesting that the link between fat intake and medication use was independent of these other factors. This finding aligns with a previous cross-sectional study investigating older adults. ³⁷ Their work also identified a positive association between dietary components—specifically cholesterol, glucose, and sodium intake—and the use of multiple medications. Our study extends these findings by focusing on saturated fat consumption and demonstrating a similar link, even after considering the influence of other lifestyle behaviors. ³⁷

Smoking, Alcohol, and Polypharmacy

Our study found a clear association between smoking and medication use in older adults. Nearly 79% of participants who never smoked reported using no medications at all. In stark contrast, over a third (37.6%) of those taking 4-6 medications and 35.7% of those taking more than 6 medications had a history of current or past smoking (P = 0.02). These findings suggest a significant link between smoking and polypharmacy. Our observations were consistent with previous studies on smoking and polypharmacy.^38-40 A cross-sectional study from Switzerland identified a similar relationship, highlighting a higher prevalence of polypharmacy among smokers. There are a few reasons why this could be the case, such as smokers' increased risk of lung disease, cancer, and mental illness. ³⁸ Further strengthening this link, another study found that smoking was strongly associated with polypharmacy, with smokers having a nearly threefold higher likelihood of taking four or more medications. ⁴⁰ Additionally, a prospective study showed that current smokers are more likely to start and maintain polypharmacy than former smokers and nonsmokers. ³⁹

Our study did not identify any significant association between alcohol consumption and the number of medications prescribed. The existing literature suggests that polypharmacy is inversely related to alcohol intake. Research showed that polypharmacy rates were negatively correlated with heavy alcohol use in the previous year. ⁴¹ Likewise, a Spanish cross-sectional study found that alcohol consumption may be associated with a lower risk of polypharmacy in women, even at high alcohol levels. ⁴²

Physical Activity and Polypharmacy

This study examined the link between physical activity and medication use in older adults. Our results revealed a compelling trend: individuals who engaged in less physical activity per week were more likely to take multiple medications daily. A high proportion (80%) of participants taking more than 6 medications engaged in vigorous activity less than once per week. In contrast, a significantly larger group (72.3%) of those who did not use any medications participated in mild physical activity at least three times a week (P < .001). Statistical analysis using linear regression models further strengthened these findings. Even when considering other lifestyle factors, a clear inverse relationship remained (P < .001), meaning that higher levels of mild physical activity were consistently associated with a lower number of medications used daily.

Our findings align with a growing body of research highlighting the complex interplay between polypharmacy and physical activity in older adults. This relationship appears to be bidirectional. A study observed that individuals using five or more medications daily exhibited significantly lower levels of light and moderate-to-vigorous physical activity. ⁴³ Similarly, another research revealed that low physical activity levels were associated with increased medication use and multimorbidity, particularly among women experiencing anxiety and depression. ⁴⁴ A cross-sectional study suggested that adults with knee osteoarthritis who take multiple medications engage in less moderate-to-vigorous physical activity each week. ⁴⁵ The same study also found that individuals taking five or more medications averaged nearly 13 minutes less of this activity per week. ⁴⁵

Sleep and Polypharmacy

Our study explored the association between sleep and medication use in older adults. We found a noteworthy connection: nearly 79% of participants who reported sleeping the recommended 7-9 hours nightly did not require any medications. This aligns with previous research suggesting a link between healthy sleep patterns and lower medication use. A cross-sectional study identified a strong association between sleep disruption and polypharmacy in older hospitalized patients. ⁴⁶ Similarly, another research found that older adults with polypharmacy were more likely to experience delayed sleep onset and shortened sleep duration. ⁴⁷ According to a retrospective study assessing sleep cycle in relation to polypharmacy, a greater number of prescribed drugs was associated with fewer hours of deep sleep, a greater rate of light sleep, a lower rate of rapid eye movement (REM) sleep, and a later onset for deep sleep. However, the study found no significant effects of polypharmacy on enhancing overall sleep duration. ⁴⁸ Moreover, polypharmacy may set off a series of events that worsen insomnia, since numerous drugs have the potential to interfere with sleep, necessitating the need for additional prescriptions. ⁴⁹

Lifestyle and Polypharmacy

Our study found a strong inverse relationship between the number of medications older adults take and their overall lifestyle score (P = 0.003). This means individuals with healthier lifestyles, as measured by our index, were significantly less likely to rely on multiple medications. These findings align with other studies highlighting the benefits of a healthy lifestyle for managing medication use. A recent study developed a “Healthy Behavior Score” that assessed diet, exercise, smoking habits, and sedentary time. Their results showed those with healthy lifestyles have a lower risk of developing polypharmacy. ⁵⁰ In another research study, the authors conducted a program focused on healthy lifestyle changes—including diet, exercise, and stress management; they evaluated Cleveland Clinic’s Lifestyle 180 program combining diet, exercise, and stress management. ⁵¹ At 30 weeks, the program showed promise in reducing medication use. Many participants stopped medications, lowered dosages, or avoided them altogether. While some required new medications, the study found a positive trend: for every new/increased medication, there were 3.3 stopped/reduced/avoided medications, suggesting the program’s effectiveness. ⁵¹ This highlights the potential of lifestyle interventions to promote medication reduction in older adults.

Strengths and Limitations

Our study adds to the growing body of evidence highlighting the potential benefits of healthy lifestyles for older adults. This research emphasizes the potential of modifiable lifestyle factors, such as diet and physical activity, in promoting health and potentially reducing medication use in older adults. A key strength of this study is its comprehensive examination of various lifestyle factors and their association with polypharmacy in a sizeable sample of older adults from two large counties in California. This improves the ability to generalize the results to the older adult population. The study also focuses on the oldest of the old, aged 75 years and older. The cross-sectional design, characterized by its efficiency and cost-effectiveness, provides an ideal method for determining the prevalence of our various variables and examining their relationships. Furthermore, future research with a greater level of rigor can be built upon the design and conclusions of this study.

As for limitations, the cross-sectional design limits the study’s ability to determine cause and effect relationships due to the single data collection point. Furthermore, the use of self-reported health metrics through surveys can introduce bias due to potential over or under-reporting of health conditions, which may compromise the validity of the study findings. The non-random sampling method raises concerns about the sample’s representativeness of the whole population. This restricts our ability to draw conclusions about the general population from the study’s results. For instance, around 81% of participants reported minimal to no alcohol consumption. This skewed sample composition limits our ability to draw definitive conclusions about the relationship between alcohol and medication use. Rigorous study designs hold the key to uncovering the causal link between these variables through further investigation. Exploring how lifestyle behaviors like nutrition and exercise influence biological processes and disease risk is crucial for developing interventions that minimize medication reliance and optimize long-term health outcomes in older adults.

Conclusion

This research sheds light on the relationship between lifestyle behaviors and medication use, potentially impacting future treatment approaches. Promoting healthy aging in older adults can be effectively achieved by encouraging regular physical activity, balanced diets, and adequate sleep—potentially reducing reliance on pharmaceuticals. Physicians and healthcare providers are uniquely positioned to advocate for these lifestyle modifications, collaborating with patients to tailor strategies that optimize medication use and improve overall well-being. By giving priority to research that focuses on establishing causal linkages, uncovering underlying mechanisms, and creating effective interventions, we may make a substantial contribution to the promotion of healthy aging, the optimization of medication usage among older adults, and ultimately, the improvement of their general well-being.

Ethical Statement

Ethical approval for this study was obtained from the Loma Linda University Institutional Review Board (IRB#5210280). Verbal informed consent was obtained from all subjects before the study.

ORCID iDs

Abrar Bardesi https://orcid.org/0009-0003-4911-1940

Alaa Alabadi-Bierman https://orcid.org/0000-0002-9311-1425

W. Lawrence Beeson https://orcid.org/0000-0002-7415-6046