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Abstract

Background

Access to health information on the internet has increased significantly, influencing self-care decisions and the use of medications without a prescription.

Objective

This study aimed to identify the factors associated with the use of online health information and self-medication in a Peruvian sample.

Method

A cross-sectional study was conducted with 493 Peruvian adults selected through nonprobabilistic convenience sampling. An online questionnaire collected data on sociodemographic characteristics, subjective health status, use of online health information, internet competence, and self-medication. Analyses included correlations, Student's t-tests, one-way analysis of variance, and multiple linear regression. A p-value < 0.05 was considered statistically significant.

Results

Among participants, 62.5% reported self-medication and 74.2% reported using the internet to search for health information. Use of online health information was significantly associated with self-medication. Predictors of self-medication included being a woman, living in the jungle region, rural residence, current illness, poor perceived health, and higher internet competence (F = 13.536, p < 0.001; R² = 0.189). Significant predictors were internet competence (β = 0.23, p < 0.001), female sex (β = 0.14, p = 0.002), and poor perceived health (β = 0.13, p = 0.003). In a separate model, internet use for health information was associated with younger age, living in the jungle region, and higher internet competence (F = 5.734, p < 0.001; adjusted R² = 0.071), with internet competence (β = 0.18, p < 0.001) and age (β = –0.15, p = 0.002) being the most relevant factors.

Conclusion

Online health information use is associated with self-medication among Peruvian adults. Internet competence emerged as a key factor for both behaviors.

Keywords

Self-medication online health information health information-seeking behavior self-care health knowledge attitudes practice Peru

Introduction

The advancement and widespread accessibility of information technologies have transformed the way individuals interact with health information. Regardless of their social or geographic background, people now have the ability to rapidly access health-related content online, often turning to the internet as a primary or secondary source of information.¹ This shift has significantly influenced health behaviors, including self-diagnosis and self-medication, as patients increasingly seek to manage their health independently.²

Online health information-seeking behavior plays a dual role. On the one hand, it empowers individuals to make more informed health decisions, reduce healthcare costs, and promote self-care.³ On the other hand, it may lead to potential risks, such as reliance on nonevidence-based content, self-diagnosis, and unsupervised medication use.⁴ In particular, some patients use online sources to supplement professional guidance,⁵ while others resort to self-care out of dissatisfaction or lack of trust in the healthcare system.⁶ These behaviors have contributed to a global rise in self-medication, which is defined as the use of drugs, herbs, or home remedies by individuals to treat self-recognized symptoms or illnesses without professional supervision.^4,7

Although self-medication is considered a component of self-care and is endorsed by various health institutions when practiced responsibly, its misuse can lead to adverse consequences.^8,9 These include incorrect self-diagnosis, inappropriate drug use, delayed medical consultation, drug interactions, and even antimicrobial resistance.^10,11 Evidence also shows that specific sociodemographic factors—such as gender, age, education, among others—influence both the likelihood of seeking health information online^12,13 and engaging in self-medication practices.^14,15 For example, women and individuals with higher education levels are more likely to seek online health information, while people living in urban areas report greater access to such resources.¹⁶

In recent years, Peru has witnessed a marked increase in internet access. According to the National Institute of Statistics and Informatics (INEI), 73% of Peruvians aged 6 and above had internet access during the first quarter of 2022, with mobile phones being the primary means of connection.¹⁷ This rise in connectivity has created a fertile ground for online health information-seeking behavior and self-medication. However, empirical research analyzing the intersection between these two behaviors within the Peruvian context remains scarce. While previous studies have explored self-medication or internet use in isolation, few have investigated how sociodemographic and health-related variables—such as age, sex, region, health perception, and internet competence—jointly influence these behaviors.

This study aims to fill this gap by identifying key factors associated with online health information use and self-medication among Peruvian adults. By doing so, it offers insight into how individual characteristics and digital skills may contribute to health decision making, particularly in resource-constrained settings. Understanding these dynamics is essential for informing future public health strategies, promoting responsible self-care practices, and developing targeted interventions that consider digital inclusion, health literacy, and regional disparities in healthcare access.

Materials and methods

Design and setting

This study employed a cross-sectional design and was conducted in Peru between October and November 2022. Data collection was carried out online through digital platforms.

Participants

The sample size was calculated using the online software proposed by Soper,¹⁸ considering an anticipated effect size of 0.1, a test power of 0.95, a significance level of 0.05, and eight predictor variables. The minimum recommended sample size was 236 participants. However, a total of 493 Peruvian citizens participated in this study. They were selected using a nonprobabilistic sampling method, either purposive or convenience sampling.¹⁹ Inclusion criteria were: (a) being a Peruvian resident, (b) aged 18 years or older, and (c) having access to the internet and digital platforms to complete the questionnaire. Exclusion criteria included: (a) incomplete responses and (b) failure to provide informed consent. Participants ranged in age from 18 to 82 years (mean = 35.79, standard deviation = 13.39), and data were analyzed anonymously.

Procedure

This study was reviewed and approved by the Ethics Committee of the Universidad Peruana Unión (registration number: 2022-CEUPeU-016). Participation was voluntary, and only those who signed the informed consent form were granted access to the questionnaire. To ensure authenticity and prevent duplicate entries, each respondent was required to log in using a unique email account, and responses were limited to one submission per device through Microsoft Forms configuration. The questionnaire explicitly requested that participants confirm their Peruvian residency and age eligibility (18 years or older) prior to proceeding. The data collection instrument was developed using Microsoft Forms and disseminated via social media platforms such as Facebook and WhatsApp. The questionnaire consisted of two sections: (a) sociodemographic information and (b) validated instruments measuring the study variables.

Instruments

Sociodemographic Characteristics Questionnaire: This is an ad hoc questionnaire that includes questions about participants’ characteristics, such as sex, age, educational level, monthly income, place of residence, area of residence, and self-assessed health status.

Measurement of Subjective Health Status: This variable was assessed using a single-item global health measure, frequently used in public health and epidemiological research due to its strong predictive validity. Participants were asked: “In general, how would you rate your current health status?” with five Likert-type response options ranging from 1 = very bad, 2 = bad, 3 = average, 4 = good, to 5 = very good. Higher scores indicated a more positive perception of one's health. For analytical purposes, responses were either treated as continuous scores or grouped into two categories: (a) poor to average (1–3) and (b) good to very good (4–5) to compare outcomes across sociodemographic and behavioral variables.²⁰

Scale for the Use of Online Health Information: This is an ad hoc questionnaire in which participants indicate whether they have used the internet to access health information in the past 12 months and specify the purpose of their search. The questionnaire covers four aspects: general health advice, specific disease-related information, purchasing health products, and selecting hospitals or other healthcare services.²⁰

Health Literacy Scale for the Internet: This instrument, proposed by Norman and Skinner,²¹ assesses eHealth literacy across diverse populations and settings. It consists of eight items that evaluate individuals’ perceived knowledge, comfort, and skills in locating, appraising, and applying electronic health information to health-related decisions. Each item is rated on a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total score reflects the respondent's level of internet-based health literacy. The total score reflects the respondent's level of internet-based health literacy. The scale has demonstrated solid psychometric properties in the original validation study, with item-total correlations ranging from r = 0.51 to 0.76 and test–retest reliability coefficients between r = 0.40 and 0.68. Exploratory factor analysis supported its unidimensionality, with factor loadings between 0.60 and 0.84 and a total variance explained of 56%.

Self-Medication Scale: This instrument, developed by James and French,²² assesses individuals’ behavior regarding their decision to self-medicate when experiencing acute pain. The scale comprises three factors or dimensions, each consisting of three items rated on a five-point Likert scale (1 = never to 5 = always). The scale demonstrates adequate psychometric properties, with reliability coefficients above 0.70 for all dimensions and factor loadings ranging from 0.48 to 0.88, providing evidence of its validity and reliability.

Statistical analysis

A descriptive analysis was conducted using frequencies, percentages, means, and standard deviations to summarize the sociodemographic variables and the main constructs of the study. Comparative analyses were performed to examine mean differences in the use of online health information according to sex and other sociodemographic variables, including age, marital status, educational level, region of origin, area of residence, presence of illness, and self-perceived health. The Kolmogorov–Smirnov test was used to assess the normality of the variable distributions. Nevertheless, parametric tests such as Student's t-test were applied to compare mean scores for self-medication and online health information use across sociodemographic groups. To assess bivariate associations between self-medication, internet competence, and the use of online health information, Spearman's rho correlation coefficients with their respective 95% confidence intervals were calculated using the bootstrapping method. Finally, multiple linear regression analyses were conducted to identify sociodemographic predictors of self-medication and online health information use. Based on prior bivariate analyses, all categorical variables were transformed into continuous variables (0,1), and continuous predictors were standardized prior to inclusion. All analyses were performed using IBM SPSS Statistics software (version 25.0) and R (version 4.5.0), and a significance level of p < 0.05 was considered for all tests.

Results

The descriptive results of the sociodemographic characteristics of the participants are presented in Table 1. A higher participation was observed among individuals aged 21 to 40 years (62.5%), single (54.4%), with a university education (64.1%), from the coastal region (47.7%), and residing in urban areas (91.5%). Similarly, a lower proportion of participants reported having an illness (11.6%), having a sick relative (14.2%), or perceiving their health as poor or very poor (17.2% and 0.4%, respectively).

Table 1.

Sociodemographic characteristics of study participants in Peru (N = 493).

Variables	Categories	Female, n (%)	Male, n (%)	Total, n (%)
Age	≤20	29 (12.1)	15 (5.9)	44 (8.9)
	21–30	104 (43.3)	71 (28.1)	175 (35.5)
	31–40	42 (17.5)	91 (36.0)	133 (27.0)
	41–50	33 (13.8)	26 (10.3)	59 (12.0)
	51+	32 (13.3)	50 (19.8)	82 (16.6)
Marital status	Single	137 (57.1)	131 (51.8)	268 (54.4)
	Married	48 (20.0)	76 (30.0)	124 (25.2)
	Live-in partner	37 (15.4)	31 (12.3)	68 (13.8)
	Divorced	15 (6.3)	11 (4.3)	26 (5.3)
	Widower	3 (1.3)	4 (1.6)	7 (1.4)
Level of education	Secondary	19 (7.9)	15 (5.9)	34 (6.9)
	Technical superior	76 (31.7)	67 (26.5)	143 (29.0)
	University professor	145 (60.4)	171 (67.6)	316 (64.1)
Region of origin	Coast	118 (49.2)	117 (46.2)	235 (47.7)
	Sierra	88 (36.7)	91 (36.0)	179 (36.3)
	Jungle	34 (14.2)	45 (17.8)	79 (16.0)
Residence	Urban	214 (89.2)	237 (93.7)	451 (91.5)
Residence	Rural	26 (10.8)	16 (6.3)	42 (8.5)
Presence of disease	Yes	29 (12.1)	28 (11.1)	57 (11.6)
Presence of disease	No	211 (87.9)	225 (88.9)	436 (88.4)
Sick relative	Yes	30 (12.5)	40 (15.8)	70 (14.2)
Sick relative	No	210 (87.5)	213 (84.2)	423 (85.8)
Perception of health	Very bad	1 (0.4)	1 (0.4)	2 (0.4)
	Bad	46 (19.2)	39 (15.4)	85 (17.2)
	Average	59 (24.6)	42 (16.6)	101 (20.5)
	Good	99 (41.3)	124 (49.0)	223 (45.2)
	Very good	35 (14.6)	47 (18.6)	82 (16.6)

The analysis of online health information use by sex is presented in Table 2. There are differences in proportions between men and women who use the internet to consult and select hospitals or other healthcare services (χ² = 3.998, p < 0.05) and to obtain general health advice (χ² = 13.011, p < 0.001), with women using the internet more frequently for these purposes. However, no significant differences were found between men and women regarding the use of the internet to purchase healthcare products (χ² = 1.932, p > 0.05) or to obtain specific information about a disease (χ² = 0.113, p > 0.05).

Table 2.

Use of online health information by sex among Peruvian adults (N = 493).

Use of health information on the internet	Female n (%)	Male n (%)	χ²	p-value
Purchase healthcare products
Yes	62 (25.8)	52 (20.6)	1.93	0.165
No	178 (74.2)	201 (79.4)
Consult and select hospitals or other
Yes	55 (22.9)	40 (15.8)	3.99	0.046
No	185 (77.1)	213 (84.2)
Obtain general health advice
Yes	189 (78.8)	162 (64.0)	13.01	<0.001
No	51 (21.3)	91 (36.0)
Obtain specific information about a disease
Yes	107 (44.6)	109 (43.1)	0.11	0.737
No	133 (55.4)	144 (56.9)

n = frequencies; χ² = Chi-square test; p = probability.

The correlation analyses are presented in Table 3. Age was negatively correlated with internet competence (ρ = −0.10, p < 0.05) and the use of online health information (ρ = −0.13, p < 0.01). Self-medication was positively correlated with internet competence (ρ = 0.40, p < 0.01) and the use of online health information (ρ = 0.220, p < 0.01). Additionally, internet competence was positively correlated with the use of online health information (ρ = 0.211, p < 0.01).

Table 3.

Correlations between self-medication, internet competence, and use of online health information (N = 493).

Variables	Coefficient	1	2	3	4
1. Age	ρ	-
	95% CI	-
	p-value	-
2. Income	ρ	0.481	-
	95% CI	(0.403, 0.556)	-
	p-value	<0.01	-
3. Self-medication	ρ	−0.044	−0.047	-
3. Self-medication	95% CI	(−0.132, 0.046)	(−0.129, 0.039)	-
	p-value	0.329	0.307	-
4. Internet competence	ρ	−0.103	−0.062	0.408	-
4. Internet competence	95% CI	(−0.200, −0.013)	(−0.153, 0.031)	(0.330, 0.484)	-
	p-value	<0.05	0.173	<0.01	-
5. Use of online health information	ρ	−0.136	−0.11	0.219	0.211
5. Use of online health information	95% CI	(−0.218, −0.047)	(−0.195, −0.020)	(0.131, 0.299)	(0.119, 0.299)
	p-value	<0.01	<0.05	<0.01	<0.01

Note: Values in parentheses indicate the 95% confidence interval for each correlation.

95% CI = 95% confidence interval.

Table 4 shows the differences in self-medication scores according to sociodemographic variables.

Table 4.

Differences in mean scores for self-medication and use of online health information according to sociodemographic variables (N = 493).

Variables	SM				UOHI
Variables	M (SD)	MD (95% IC)	t	p-value	M (SD)	MD (95% IC)	t	p-value
Age
≤30	30.6 (5.5)	0.14 (−0.84, 1.14)	0.29	0.770	1.67 (0.8)	0.18 (0.04, 0.032)	2.57	<0.05
>30	30.5 (5.6)				1.49 (0.7)
Sex
Female	31.6(5.4)	1.95 (0.98, 2.93)	3.96	<0.001	1.72 (0.7)	0.28 (0.14, 0.42)	4.08	<0.001
Male	29.6 (5.5)				1.43 (0.7)
Marital status
Single/without partner	30.55 (5.4)	−0.12 (−1.13, 0.89)	−0.23	0.815	1.60 (0.8)	0.07 (−0.06, 0.22)	1.07	0.281
Married/with partner	30.67 (5.8)				1.53 (0.7)
Level of education
Other	29.90 (5.3)	- 2.38 (−3.45, −1,32)	−4.4	<0.001	1.56 (0.8)	−0.01 (−0.17, 0.13)	−0.24	0.810
Technical superior	32.29 (5.8)				1.58 (0.6)
Region of origin
Coast—Sierra	30.12 (5.5)	−2.93 (−4.25, −1.61)	−4.37	<0.001	1.53 (0.7)	−0.22 (−0.41, −0.31)	−2.28	<0.05
Jungle	33.06 (4.9)				1.75 (0.7)
Residence
Urban	30.28 (5.3)	−3.74 (−5.48, −2.00)	−4.23	<0.001	1.58 (0.8)	0.02 (−0.22, 0.27)	0.22	0.821
Rural	34.02 (6.8)				1.55 (0.5)
Presence of disease
Yes	32.35 (5.7)	1.98 (0.44, 3.51)	2.54	<0.05	1.63 (0.7)	0.06 (−0.15, 0.28)	0.58	0.559
No	30.37 (5.5)				1.57 (0.8)
Sick relative
yes	29.37 (4.9)	−1.42 (−2,83, −0.02)	−1.99	<0.05	1.64 (0.8)	0.08 (−0.11, 0.28)	0.78	0.432
No	30.80 (5.6)	−1.42 (−2,83, −0.02)			1.56 (0.7)
Perception of health
Very bad—average	31.44 (5.8)	1.36 (0.35, 2.37)	2.66	<0.01	1.63 (0.7)	0.08 (−0.05, 0.23)	1.19	0.237
Good—very good	30.08 (5.3)				1.54 (0.8)

F: Fisher’s test; M: mean; MD: mean difference; SD: standard deviation; SM: self-medication; t: test of differences between means; UOHI: use of online health information.

Women showed significantly higher self-medication scores than men (t = 3.96, p < 0.001), as did participants with higher technical education compared to those with other educational levels (t = −4.4, p < 0.001). Similarly, individuals from the jungle region scored higher than those from the coast and highlands (t = −4.37, p < 0.001), and rural residents obtained higher scores than urban residents (t = −4.23, p < 0.001). In the health domain, those who reported illness achieved higher scores compared to healthy individuals (t = 2.54, p < 0.05), and those with a negative perception of their health also outscored those who reported good health (t = 2.66, p < 0.01). In contrast, participants with a sick family member showed significantly lower scores compared to those without (t = −1.99, p < 0.05). On the other hand, no significant differences in mean self-medication scores were observed among individuals according to age (t = 0.29, p = 0.770) and marital status (t = −0.23, p = 0.815). Regarding the use of health information on the internet, individuals aged 30 years or younger showed higher scores than those older than 30 years (t = 2.57, p < 0.05), and women scored higher than men (t = 4.08, p < 0.001). Additionally, participants from the jungle region obtained higher scores compared to those from the coast and highlands (t = −2.28, p < 0.05). No significant differences were found in the other variables analyzed.

Table 5 presents the regression results for self-medication and the use of online health information. Model 1 presents the linear regression analysis with variables predicting self-medication (F = 14.91, p < 0.001; R² = 0.203). It was found that residing in the jungle region (β = 0.15, t = 3.72, p < 0.001), being female (β = 0.14, t = 3.25, p < 0.01), and having technical education (β = 0.13, t = 3.17, p < 0.01) were significant predictors of self-medication. Additionally, internet competence emerged as the strongest predictor (β = 0.29, t = 6.91, p < 0.001). Other significant but weaker predictors included living in a rural area (β = 0.09, t = 2.13, p < 0.05) and the presence of a health condition (β = 0.08, t = 1.99, p < 0.05). On the other hand, variables such as perceived health status (β = 0.07, p = 0.115), age (β = 0.01, p = 0.830), and the use of health information from the internet (β = 0.05, p = 0.254) were not significantly associated with self-medication. Model 2 displays the variables predicting the use of online health information (F = 5.60, p < 0.001; adjusted R² = 0.070). Being female (β = 0.17, t = 3.76, p < 0.001), internet competence (β = 0.15, t = 3.38, p < 0.01), and residing in the jungle region (β = 0.11, t = 2.57, p < 0.05) were significant predictors of increased use of online health information. However, age showed a negative trend toward the use of such information (β = −0.09, t = −1.94, p = 0.053), although this association did not reach statistical significance. Similarly, variables such as technical education (β = −0.02, p = 0.591), rural residence (β = −0.07, p = 0.108), presence of a health condition (β = 0.05, p = 0.271), and perceived health status (β = 0.05, p = 0.260) did not show significant associations with the use of online health information.

Table 5.

Multiple linear regression models of factors associated with self-medication and online health information use among Peruvian adults.

	Model 1: SM					Model 1: UOHI
Variables (categories)	B (SD)	SE	Beta	t	p-value	B (SD)	SE	Beta	t	p-value
(Constant)	15.98	1.9	-	8.32	<0.001	0.72	−0.3		2.45	<0.05
Age	0.01	0.0	0.01	0.21	0.830	−0.01	0.0	−0.09	−1.94	0.053
Sex (0 = male, 1 = female)	1.51	−0.5	0.14	3.25	<0.01	0.26	−0.1	0.17	3.76	<0.001
Education (0 = other, 1 = technical)	1.59	−0.5	0.13	3.17	<0.01	−0.04	−0.1	−0.02	−0.54	0.591
Region of origin (0 = Other, 1 = Jungle)	2.34	−0.6	0.15	3.72	<0.001	0.25	−0.1	0.11	2.57	<0.05
Residence (0 = Urban, 1 = Rural)	1.79	−0.8	0.09	2.13	<0.05	−0.21	−0.1	−0.07	−1.61	0.108
Presence of disease (0 = No, 1 = Yes)	1.48	−0.7	0.08	1.99	<0.05	0.12	−0.1	0.05	1.10	0.271
Perception of health	0.97	−0.6	0.07	1.58	0.115	0.11	−0.1	0.05	1.13	0.260
CPI	0.38	−0.1	0.29	6.91	<0.001	0.03	−0.01	0.15	3.38	<0.01
UOHI	0.34	0.3	0.05	1.14	0.254	-		-	-	-

Note: Model 1 (adjusted R-squared = 0.203, F = 14.91, p < 0.001); model 2 (adjusted R-squared = 0.070, F = 5.60, p < 0.001); B: unstandardized coefficient; beta: standardized coefficient; CPI: internet competence; SD: standard deviation of the error, SM: self-medication; UOHI: use of online health information.

Discussion

The internet is an easily accessible communication tool that allows users to obtain health information, facilitating the understanding of diseases and their potential implications. The objective of this study was to identify the factors associated with the use of online health information and its relationship with self-medication in a Peruvian sample, providing evidence on the determinants of these practices.

In this study, the results showed that there were no significant differences between men and women in terms of self-medication; however, women were found to use online health information more frequently than men. These findings are partially consistent with the existing literature. A previous study found that women tend to self-medicate more than men,²³ while another study indicated that women search for health information on the internet more frequently than men.²⁴ Women's greater online search for health information could be explained by their traditional role in family healthcare, greater concern for wellbeing, and higher propensity to use preventive medical services.²⁵ However, the fact that our study did not observe differences in self-medication may be attributed to contextual factors, such as equitable access to medication or greater awareness of the risks of self-medication in both sexes.²⁶

Regarding educational level, it was found that individuals with higher technical education have higher self-medication scores than those with secondary or university education. Traditionally, a higher educational level would be expected to be associated with a lower propensity to self-medicate,^27,28 due to greater knowledge of the associated risks and better adherence to medical recommendations.²⁷ However, the findings of this study suggest the opposite, which could be explained by several factors. One possible explanation is that, in the Peruvian context, technical education often includes health-related programs such as nursing, pharmacy, or clinical laboratory technology. Students in these fields may have easier access to medications and possess basic pharmacological knowledge, which could encourage self-medication. This pattern has been observed in previous studies involving pharmacy and medical students. However, further research is needed to confirm whether this tendency applies across the broader technical education population. Previous studies have indicated that pharmacy and medical students are particularly prone to self-medication, as they are more familiar with medications and feel they have greater control over their effects.²⁹ Furthermore, the academic environment and work demands can increase the tendency toward self-medication in these groups, as they seek quick solutions to manage symptoms without interrupting their activities.

On the other hand, regarding the use of online health information, several studies have reported that a higher level of education is associated with a greater search for and use of medical information online.^30–33 However, no such relationship was observed in the current study. This discrepancy could be attributed to differences in internet access, digital health literacy, or trust in traditional sources of medical information. It is possible that, in certain contexts, factors such as age, type of academic training, and exposure to awareness campaigns influence the way people search for and use health information online.

This study also found that people living in rural areas are more likely to self-medicate than those living in urban areas. Additionally, those who have an illness and perceive their health as poor are more likely to self-medicate compared to those who are not ill and those who perceive their health as good. Although relatively few studies have explored this specific relationship, some research suggests that individuals experiencing chronic or acute symptoms may resort to self-medication as a coping strategy to manage persistent conditions or avoid healthcare costs.^34,35 These findings underscore the need for targeted public health interventions, especially in populations with limited access to formal healthcare services. Contrary to the results of this study, where no differences in the use of health information were observed between those living in urban and rural areas, one study reported that the area of residence was a factor associated with the use of online health information, with the majority of people seeking health information online living in urban areas.^31,32

Through regression analysis, this study identified several factors significantly associated with self-medication among Peruvian adults. Specifically, being female, residing in the jungle region, living in a rural area, having a current illness, perceiving one's health as poor, and having higher internet competence were all positively associated with self-medication behavior. These findings underscore the influence of both structural determinants (such as geographic and residential context) and individual characteristics (like health status and digital literacy) on self-care practices. Moreover, a study conducted among women in Latin America found that age, place of residence, education level, and health insurance coverage were significant predictors of self-medication, especially among women with limited access to formal healthcare services.³⁶ In Spain, self-medication has also been linked to sociodemographic characteristics, including sex, educational attainment, and age, as well as health-related conditions, such as chronic disease or engagement in physical activity.³⁷ In line with our findings, a study on medical students reported that when discomfort or pain is perceived as mild, individuals are more likely to self-medicate instead of consulting a physician.³⁸ These patterns highlight the complex interplay between health perception, occupational context, and self-management behavior, especially in populations with some level of health-related knowledge or experience.

In parallel, the regression analysis also revealed that younger age, origin from the jungle region, and higher levels of internet competence were significant predictors of the use of online health information. These results are consistent with international evidence indicating that younger individuals tend to be more digitally connected and more likely to seek health information online, particularly in low- and middle-income countries where formal health services may be less accessible. A study by Parija et al.³⁹ found that, among men aged 20 to 45, higher educational level, urban residence, and perceived good health were associated with increased online health information-seeking behavior. In women, education level alone was a significant determinant of this behavior.⁴⁰ This reinforces the idea that digital health literacy is not only a function of access but also of educational background, regional infrastructure, and health awareness. The association observed in our study between internet competence and both self-medication and information seeking suggests a double-edged dynamic: while digital skills can empower individuals to manage their health more independently, they may also lead to greater self-reliance without professional guidance, especially in underserved areas.

Public health implications

The findings of this study highlight the need to strengthen digital literacy strategies in healthcare to promote the responsible use of online health information and reduce the risks associated with self-medication among the Peruvian population. The relationship between greater internet competence and self-medication suggests that easy access to medical information does not necessarily imply its appropriate use, underscoring the importance of educational interventions that help the population differentiate reliable sources from nonvalidated content.

Similarly, the identified sociodemographic factors, such as gender, region of origin, and health perception, can inform public policies aimed at groups with a greater predisposition to self-medication. In particular, the design of preventive campaigns targeting women and rural populations, who showed higher levels of self-medication, is recommended. Furthermore, it is crucial to implement stricter regulations on the sale of over-the-counter medicines and to promote professional guidance in the self-management of health, thereby reducing the risks of misdiagnosis and adverse effects resulting from the inappropriate use of medicines.

Limitations and future considerations

This study has some limitations that should be considered when interpreting the findings. First, the cross-sectional design used prevents the establishment of causal relationships between the use of online health information, digital health competence, and self-medication. Future research could employ longitudinal studies to analyze the evolution of these behaviors over time and determine potential causal relationships.

Secondly, the sample was selected using nonprobabilistic convenience sampling, which limits the generalizability of the results to the entire Peruvian population. Although the study covered a wide range of ages and regions, the inclusion of stratified random sampling in future research would allow for more representative and robust findings.

Furthermore, the information was self-reported, which can introduce memory biases and social desirability effects in the reporting of self-medication practices and the use of online information. Future studies could complement these data with qualitative methods or medical records, which would allow for an objective validation of the information provided by the participants.

Additionally, one of the main limitations of this study is that no formal psychometric validation (e.g. confirmatory factor analysis or internal consistency assessment) was conducted for the instruments in the current sample. Although the measures used—including the eHealth Literacy Scale and the Self-Medication Scale—have shown acceptable psychometric properties in previous studies, their validity and reliability were not re-examined in the Peruvian context for this analysis. Similarly, the ad hoc questionnaires were not subjected to pilot testing or expert validation. Therefore, caution is advised when generalizing the findings, and future studies should aim to validate these instruments within local populations.

Finally, it would be important to explore in greater depth the role of other factors not considered in this study, such as the level of access to healthcare services, the quality of information consulted online, and the impact of health education campaigns on decision making regarding self-medication. Future research could focus on interventions aimed at improving digital health literacy and assessing their effectiveness in reducing inappropriate self-medication.

Conclusion

This study has shown that the use of online health information is related to self-medication, with various associated factors that complement the existing literature. Further research is needed to provide a broader understanding of how the use of health information predicts the consumption of over-the-counter medicines in specific populations, as the tendency for self-medication may vary across different demographic groups.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251355195 - Supplemental material for Factors associated with the use of health information on the internet and self-medication: A cross-sectional study of Peruvian adults

Supplemental material, sj-docx-1-dhj-10.1177_20552076251355195 for Factors associated with the use of health information on the internet and self-medication: A cross-sectional study of Peruvian adults by Verónica Franceska Prado-Aranzábal, Adali S Lozano-García, Percy G Ruiz-Mamani and Jacksaint Saintila in DIGITAL HEALTH

Supplemental Material

sj-pdf-2-dhj-10.1177_20552076251355195 - Supplemental material for Factors associated with the use of health information on the internet and self-medication: A cross-sectional study of Peruvian adults

Supplemental material, sj-pdf-2-dhj-10.1177_20552076251355195 for Factors associated with the use of health information on the internet and self-medication: A cross-sectional study of Peruvian adults by Verónica Franceska Prado-Aranzábal, Adali S Lozano-García, Percy G Ruiz-Mamani and Jacksaint Saintila in DIGITAL HEALTH

Footnotes

ORCID iD

Jacksaint Saintila

Ethical considerations

The research was reviewed and approved by the Institutional Research Ethics Committee of Universidad Peruana Unión (registration number: 2022-CEUPeU-016). Additionally, written informed consent was obtained from each participant, in full compliance with the principles outlined in the Declaration of Helsinki.

Author contributions

VFPA and ASLG designed the study. VFPA and ASLG performed literature searches and provided summaries of previous research studies. PCRM performed the statistical analysis and interpretation of the data. VFPA, ASLG, and JS secured financial support for the publication fees and contributed to the drafting and critical revision of multiple manuscript versions. All read and approved the final manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received financial support for the article processing charges (APC) from the Universidad Señor de Sipán.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data supporting the conclusions of this research will be made available in coordination with the corresponding author.

Guarantor

Supplemental material

Supplemental material for this article is available online.

References

Asif

Gouqing

. Innovative application of artificial intelligence in a multi-dimensional communication research analysis: a critical review. Discov Artif Intell 2024; 4: 37.

Sheehan

Laver

Bhopti

, et al. Methods and effectiveness of communication between hospital allied health and primary care practitioners: a systematic narrative review. J Multidiscip Healthc 2021; 14: 493–511.

Di Novi

Kovacic

Orso

. Online health information seeking behavior, healthcare access, and health status during exceptional times. J Econ Behav Organ 2024; 220: 675–690.

Raja

Bin Amin

Azeem

, et al. Self-diagnosis and self-medication based on internet search among non-medical university students of Karachi. Ann Med Surg 2024; 86: 6507–6513.

Korshakova

Marsh

Kleinberg

. Health information sourcing and health knowledge quality: repeated cross-sectional survey. JMIR Form Res 2022; 6: e39274.

Liu

Ding

Jiang

. Exploring sources of patient dissatisfaction in mobile health communication: a text analysis based on structural topic model. Digit Health 2024; 10: 1–11.

Baracaldo-Santamaría

Trujillo-Moreno

Pérez-Acosta

, et al. Definition of self-medication: a scoping review. Ther Adv Drug Saf 2022; 13: 1–14.

Oviedo Cordoba

Cortina Navarro

Osorio Coronel

, et al. Realidades de la práctica de la automedicación en estudiantes de la Universidad del Magdalena. Enfermería Global 2021; 20: 531–556.

Rathod

Sharma

Ukey

, et al. Prevalence, pattern, and reasons for self-medication: a community-based cross-sectional study from central India. Cureus 2023; 1: 1–9.

10.

Horumpende

Said

Mazuguni

, et al. Prevalence, determinants and knowledge of antibacterial self-medication: a cross sectional study in North-eastern Tanzania. PLoS ONE 2018; 13: e0206623.

11.

Al-Omrani

Marwah

Al-Whaib

, et al. Patterns of drug utilization and self-medication practices: a cross sectional study. Pharmacy 2023; 11: 183.

12.

Goldner

Hale

Cotten

, et al. The intersection of gender and place in online health activities. J Health Commun 2013; 18: 1235–1255.

13.

Bidmon

Terlutter

. Gender differences in searching for health information on the internet and the virtual patient-physician relationship in Germany: exploratory results on how men and women differ and why. J Med Internet Res 2015; 17: e156.

14.

Tripković

Šantrić Milićević

Odalović

. Gender differences in predictors of self-medication with tranquillizers and sleeping pills: results of the population-based study in Serbia. Slovenian J Public Health 2020; 59: 47–56.

15.

Niclós

Olivar

Rodilla

. Factors associated with self-medication in Spain: a cross-sectional study in different age groups. Int J Pharmacy Pract 2018; 26: 258–266.

16.

Cao

Liu

, et al. Factors associated with internet use and health information technology use among older people with multi-morbidity in the United States: findings from the National Health Interview Survey 2018. BMC Geriatr 2022; 22: 733.

17.

INEI. El 72,5% de la población de 6 y más años de edad del país accedió a Internet en el primer trimestre de 2022. https://m.inei.gob.pe/prensa/noticias/el-724-de-la-poblacion-de-6-a-17-anos-de-edad-accedio-a-internet-13723/#:∼:text=En%20el%20primer%20trimestre%20de,de%20Estad%C3%ADstica%20e%20Inform%C3%A1tica%20(INEI) (2022, accessed 4 November 2024).

18.

Soper

. A-priori sample size calculator for structural equation models. Software https://www.danielsoper.com/statcalc2020, Internet. Accessed on November 4, 2024)

19.

Ardila

Rodríguez

Gil

. Población y muestreo. In: Epidemiología clínica: investigación clínica. Colombia, Bogota: Pontificia Universidad Javeriana, 2004, pp.1–202.

20.

Sug Jo

Hwang

M-S

Lee

. Market segmentation of health information use on the Internet in Korea. Int J Med Inform 2010; 79: 707–715.

21.

Norman

Skinner

. eHEALS: The eHealth Literacy Scale. J Med Internet Res 2006; 8: 1–12.

22.

James

French

. The development of the self-medicating scale (SMS): a scale to measure people’s beliefs about self-medication. Pharm World Sci 2008; 30: 794–800.

23.

Fajardo-Zapata

ÁL

Méndez-Casallas

Hernández-Niño

, et al. La automedicación de antibióticos: un problema de salud pública Automedication with antibiotics: public health problem. Barranquilla (Col) 2013; 29: 226–235.

24.

Escoffery

Miner

Adame

, et al. Internet use for health information among college students. Journal of American College Health 2010; 53: 183–188.

25.

Bidmon

Terlutter

26.

Ruiz

. Risks of self-medication practices. Curr Drug Saf 2010; 5: 315–323.

27.

Tohan

Ahmed

Juie

, et al. Knowledge attitude and convenience on self-medication practices among university students in Bangladesh exploration using structural equation modeling approach. Sci Rep 2024; 14: 10837.

28.

Azami-Aghdash

Mohseni

Etemadi

, et al. Prevalence and cause of self-medication in Iran: a systematic review and meta-analysis article. Iran J Public Health 2015; 44: 1580–1593.

29.

Alam

Saffoon

Uddin

. Self-medication among medical and pharmacy students in Bangladesh. BMC Res Notes 2015; 8: 1–6.

30.

Dickerson

Reinhart

Feeley

, et al. Patient internet use for health information at three urban primary care clinics. J Am Med Inform Assoc 2004; 11: 499–504.

31.

Bujnowska-Fedak

Waligóra

Mastalerz-Migas

. The internet as a source of health information and services. Adv Exp Med Biol 2019; 1211: 1–16.

32.

Koo

Lin

. Predictors of internet use for health information among male and female internet users: findings from the 2009 Taiwan National Health Interview Survey. Int J Med Inform 2016; 94: 155–163.

33.

Valero-Aguilera

Bermúdez-Tamayo

García-Gutiérrez

, et al. Factors related to use of the internet as a source of health information by urological cancer patients. Support Care Cancer 2012; 20: 3087–3094. 2012 20:12.

34.

Abay

Amelo

. Assessment of self-medication practices among medical, pharmacy, and health science students in Gondar University, Ethiopia. J Young Pharm 2010; 2: 306–310.

35.

Das

Gnanavel

Smanla

, et al. Polypharmacy and self-medication among older adults in Indian urban communities—a cross-sectional study. Sci Rep 2025; 15: 1–13. 2025 15:1.

36.

Karimy

Rezaee-Momtaz

Tavousi

, et al. Risk factors associated with self-medication among women in Iran. BMC Public Health 2019; 19: 1–7.

37.

Niclós

Olivar

Rodilla

. Factors associated with self-medication in Spain: a cross-sectional study in different age groups. Int J Pharmacy Pract 2018; 26: 258–266.

38.

Sotomayor-Alvarado

Cruz-Vargas

. Prevalencia y factores asociados a la automedicación en estudiantes de medicina. Rev Fac Med Hum 2017; 17: 1–13. Epub ahead of print 2017. DOI: 10.25176/RFMH.V17.N4.1215

39.

Parija

Tiwari

Sharma

, et al. Determinants of online health information-seeking behavior: A cross-sectional survey among residents of an urban settlement in Delhi. J Educ Health Promot 2020; 9: 344–349.

40.

Koo

Lin

. Predictors of internet use for health information among male and female internet users: findings from the 2009 Taiwan National Health Interview Survey. Int J Med Inform 2016; 94: 155–163.

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