Impact of health information seeking behavior and digital health literacy on self-perceived health and depression symptoms among older adults in the United States

Abstract

Background: Understanding the impact of digital health literacy and health information-seeking behavior on the self-perceived health and depression symptoms of older adults is crucial, particularly as the number of older internet users is increasing. Methods: This study utilized data from the Health Information National Trends Survey to examine the relationship between these factors and the health outcomes of adults aged 50 and above. Results: The study found that digital health literacy has a positive but non-significant relationship with self-perceived health when other factors are considered. However, education level and body mass index consistently predicted self-perceived health. Moreover, higher digital health literacy was associated with a reduced likelihood of perceived depression symptoms, even after adjusting for demographic and health-related factors. Conclusions: These findings highlight the importance of digital health literacy in the mental well-being of older adults and provide insights for shaping future health policies and interventions.

Introduction

Studying how older adults manage their health in today’s digital world is essential¹. As the global population ages, researchers are paying more attention to how older adults’ ability to understand digital health information and their behavior in seeking health information can affect their health outcomes. Health information seeking behavior, which refers to the different ways individuals gather health-related information, has become a crucial aspect of taking an active role in managing one’s health². In older adults, this behavior is often influenced by factors such as cognitive abilities, familiarity with technology, and the complexity of medical information^3,4. Research indicates that actively seeking health information can improve health outcomes by enhancing health autonomy and enabling more informed health decision-making^5,6. Additionally, the increasing prevalence of digital technology has made digital health literacy—an individual’s ability to locate, comprehend, and utilize online health information—an essential skill for effectively navigating health resources⁷.

Research indicates that older adults often encounter distinct challenges when accessing digital health information due to lower levels of digital literacy⁸. Older adults with inadequate digital health literacy may be more vulnerable to negative health outcomes. Therefore, it is crucial to enhance digital health literacy among older adults through targeted education programs to improve health outcomes in this demographic. Additionally, self-perceived health is a well-established predictor of health outcomes. It represents an individual’s overall perception of their physical and mental well-being^9–11. Depression, which is also a significant concern among older adults, notably impacts their quality of life and ability to function independently¹². Considering the interplay between seeking health information, digital health literacy, and these crucial health markers, it is imperative to explore how these factors collectively impact the well-being of older adults¹³.

Despite the significance of digital health literacy and health information-seeking behavior, there is a lack of research specifically examining their relationship to self-perceived health and depression symptoms in older adults^14,15. Existing literature either focuses on younger populations or lacks comprehensive data on how these variables interact within the older adult population, who often face unique challenges related to technology use, access, and health literacy^8,16. Limited sample sizes and specific population groups have hindered the generalizability of existing models^17–19. Furthermore, health-related behaviors like physical activity, smoking, and chronic conditions such as hypertension and diabetes, which are known to impact health outcomes, are often overlooked in studies on digital health literacy.

The current study aims to address this gap by examining the connection between seeking health information, digital health literacy, self-perceived health, and perceived symptoms of depression among older adults. By taking into account various factors such as lifestyle habits and chronic health conditions, this study aims to provide a more comprehensive understanding of how digital health behaviors affect ageing populations’ physical and mental well-being. In doing so, this research offers valuable insights that could guide the creation of focused health interventions and policies to enhance the health and well-being of older adults in the digital age²⁰.

Methods

Participants

This study used data from the Health Information National Trends Survey (HINTS)²¹, a nationally representative survey conducted by the National Cancer Institute (NCI) since 2003. HINTS aims to provide valuable insights into the American public’s knowledge, attitudes, and usage of cancer and health-related information, enhancing health communication strategies across diverse populations. This study used data from the HINTS 5 survey, which targeted civilians aged 18 or above. Ethical approval for the study was approved through expedited review by the Westat Institutional Review Board, and subsequently deemed exempt by the U.S. National Institutes of Health Office of Human Subjects Research Protections. Verbal informed consent was documented for participants who agreed to participate. However, those who declined participation were marked as ‘refusal’ and were not contacted further. To conduct the study, a self-administered questionnaire was sent to a sample of addresses in the United States. The addresses were chosen at random from a database maintained by Marketing Systems Group (MSG). This database contains all non-vacant residential addresses in the United States, including P.O. boxes and seasonal addresses. A modified Dillman approach was used to obtain responses, including an initial questionnaire dispatch, a reminder postcard, and up to two more questionnaire mailings for households that did not respond. Respondents were provided with toll-free telephone numbers for any inquiries or concerns they may have had.

For the HINTS 5 survey, a two-stage sampling strategy was used. The first stage involved selecting a stratified sample of residential addresses that considered both rural and urban areas and areas with high and low concentrations of minority adult populations. The sampling frame was divided into four explicit sampling strata, allowing for oversampling of high-minority and rural strata to improve estimates for these subpopulations. In the second stage, an equal-probability sample of addresses was chosen within each stratum. A total of 29,600 addresses were selected for the HINTS 5 survey. The data for HINTS 5 was collected from April 6 to May 11, 2021. This research includes 1,113 individuals aged 50 years and above who are considered older adults.

Measures

This study examines two key outcome variables: self-perceived health and perceived depression symptoms. Self-perceived health was measured by asking participants, “In general, would you say your health is…?” with responses ranging from excellent, very good, good, fair, to poor. Perceived depression symptoms were assessed by asking, “Over the past two weeks, how often have you been bothered by feeling down, depressed, or hopeless?” with responses being nearly every day, more than half the days, several days, or not at all.

Two main predictor variables were investigated: health information seeking behavior and digital health literacy. Health information seeking behavior was determined by asking participants if they had ever looked for information about health or medical topics from any source. This question produced binary outcomes (yes/no). Respondents who answered affirmatively were asked to identify the sources they typically used, which were grouped into the following categories: written materials, interpersonal sources, healthcare providers, internet, and other sources (e.g., television, radio, telephone, cancer organizations). Digital health literacy was measured using seven dichotomous items focused on digital tools for health-related tasks. Three of these items assessed whether the respondent had used electronic means to communicate with a doctor, track healthcare charges and costs, and view medical test results in the past 12 months. The remaining four items measured whether respondents used digital tools to search for health information, purchase medicine or vitamins online, or seek assistance for caregiving. A combined digital health literacy score was created from these seven responses and was used as an independent variable in the logistic regression models.

The study also considered several sociodemographic variables carefully classified for analysis. Age was divided into four categories: 50–59, 60–69, 70–79, and 80 or above, with individuals aged 50–59 as the reference group. Age was categorized into 10-year bins (50–59, 60–69, etc.) to facilitate subgroup comparisons and enhance interpretability for public health applications. Gender was categorized as male or female, with males being the reference category. Race/ethnicity was classified as Non-Hispanic White (the reference group), Non-Hispanic Black or African American, Hispanic, Non-Hispanic Asian, and Non-Hispanic Other. Marital status was divided into three categories: Married/Living as Married (reference), Divorced/Widowed/Separated, and Never Married. Education was classified into five levels: Less than High School (reference), High School Graduate, Some College, Bachelor’s Degree, and Post-Baccalaureate Degree. Income was categorized into five groups: Less than $20,000 (reference), $20,000 to less than $35,000, $35,000 to less than $50,000, $50,000 to less than $75,000, and $75,000 or More. Body mass index (BMI) was classified as Underweight (< 18.5Kg/m² - reference), Normal weight (18.5Kg/m² − 24.9Kg/m²), Overweight (25.0Kg/m² − 29.9Kg/m²), and Obese (≥ 30Kg/m²). Smoking status was categorized into three groups: Current smoker (reference), Former smoker, and Never smoked. Physical activity was classified as inactive (≤ 150 min of moderate-intensity exercise per week) or active (> 150 min of moderate-intensity exercise per week), with inactive individuals serving as the reference group. In addition to these sociodemographic variables, the study included five chronic health conditions: diabetes, hypertension, heart condition, lung disease, and arthritis.

Statistical analysis

The study used STATA SE version 14.2 (Stata Corp, College Station, TX) to analyze the data. The study presented summary statistics. The association between health information seeking behavior, digital health literacy, self-perceived health, and perceived depression symptoms was estimated using logistic regression analyses. The crude model included health information seeking behavior, and digital health literacy. Model 1 included age, gender, race, marital status, education, and income level. Model 2 included the variables from model 1 plus body mass index, physical health, and smoking status. Model 3 included the variables from model 2 plus diabetes, hypertension, heart condition, lung disease, and arthritis. Digital health literacy and health information seeking behavior were included as predictor variables in all three models (Model 1, Model 2, and Model 3).

Here are the logistic regression equations for Crude model, Model 1, Model 2, and Model 3:

Crude model:

Model 1:

Model 2:

Model 3:

Here:

• p is the probability of the outcome (e.g., perceived depression symptoms).

• β0,β1,…,β14 are the regression coefficients.

• The subscripts 1, 2, and 3 indicate the respective models.

• ϵ is the error term.

Results

The study investigated how older adults perceive their own health and how often they experience symptoms of depression, taking into account various demographic and lifestyle factors. The participants were 64.38 years old on average (SD = 9.47). Also, the self-perceived excellent health was highest among the 50–59 years age bracket (14.07%) and the lowest among the 80 + years age bracket (2.56%). Gender differences were also noted. Males had lower excellent health (10.67%) compared to females, who had a higher percentage of depression symptoms nearly every day (3.15%).

Race played a significant role, with Non-Hispanic Other individuals having the highest proportion of excellent self-perceived health at 16.22%, followed by Non-Hispanic Asians (13.04%), Non-Hispanic Whites (12.78), Hispanics (11.34%), and Non-Hispanic Blacks or African Americans with a proportion of 1.41%. Non-Hispanic Asians had the lowest percentage of reporting depression symptoms for more than half the days, 0.00%, but the second-highest percentage of reporting depression symptoms nearly every day, 4.35%. Marital status also influences health outcomes. Married or those who reported being married had a higher rate of excellent self-perceived health (13.63%) and fewer depression symptoms nearly every day (1.64%) than those who were divorced, widowed, or separated.

Education level was a significant determinant of health perceptions. Individuals with Post-Baccalaureate Degree self-reported the best level of health, 21.86%, while those with Less than High School education self-reported the worst level of health, 1.89%. Likewise, the income level was strongly related to self-perceived health; the respondents earning $75,000 or more reported the highest percentage of excellent health (18.28%), while those earning less than $20,000 reported the lowest rate of excellent health (4.91%).

BMI also had a significant influence on perceived health. The prevalence of excellent self-perceived health was highest among the Underweight (18.18%), and the lowest prevalence of depression symptoms nearly every day was among overweight individuals (1.2%). Active participants had a higher proportion of excellent health (18.72%) and fewer instances of depression symptoms nearly every day (1.28%) than the inactive participants (7.33% excellent health, 4.01% depression nearly every day).

The results indicate that digital health literacy and health information-seeking behavior are linked to better self-perceived health and fewer depression symptoms. The majority of participants (67.65%) used the internet for health information, with 13.41% reporting excellent health. Those using healthcare providers (17.61%) and written materials (11.14%) also showed varying levels of health outcomes, with healthcare users reporting 6.12% excellent health. Participants who engaged in digital health activities, such as looking up health information for themselves (75.11%) or buying medicine online (29.02%), generally reported better health (Table 1).

Table 1.

Descriptive statistics of study participants.

												Perceived
						Self - perceived health						Depression symptoms
		n (%)	M	SD	Excellent %	Very Good %	Good %	Fair %	Poor %		Nearly everyday %	More than half the days %	Several days %	Not at all %
Age			64.38	9.47
50–59		391 (35.13)			14.07	42.20	28.13	12.53	3.07		4.09	6.39	15.86	73.66
60–69		415 (37.29)			10.60	38.07	32.29	15.90	3.13		2.65	5.54	18.8	73.01
70–79		229 (20.58)			10.92	31.88	41.92	13.97	1.31		2.18	2.62	14.41	80.79
80 +		78 (7.01)			2.56	35.90	42.31	16.67	2.56		2.56	2.56	24.36	70.51
Gender
Male		478 (42.95)			10.67	36.82	37.24	12.97	2.3		2.93	3.97	16.32	76.78
Female		635 (57.05)			11.81	39.06	30.71	15.43	2.99		3.15	5.83	17.95	73.07
Race
Non-Hispanic White		814 (73.14)			12.78	40.54	31.57	12	2.7		2.83	4.79	17.57	74.82
Non-Hispanic Black or African American		142 (12.76)			1.41	30.99	42.96	21.83	2.82		2.82	6.34	17.61	73.24
Hispanic		97 (8.72)			11.34	31.96	35.05	19.59	2.06		4.12	7.22	12.37	76.29
Non-Hispanic Asian		23 (2.07)			13.04	30.43	39.13	17.39	0.00		4.35	0	4.35	91.3
Non-Hispanic Other		37 (3.32)			16.22	32.43	32.43	13.51	5.41		5.41	2.7	29.73	62.16
Marital Status
Married/Living as Married		609 (54.72)			13.63	41.38	31.69	11.82	1.48		1.64	3.78	14.12	80.46
Divorced/Widowed/Separated		391 (35.13)			8.95	34.53	36.57	16.88	3.07		3.84	5.63	21.23	69.31
Never Married		113 (10.15)			7.08	32.74	32.74	19.47	7.96		7.96	9.73	20.35	61.95
Education
Less than High School		53 (4.76)			1.89	20.75	47.17	24.53	5.66		5.66	9.43	18.87	66.04
High School Graduate		192 (17.25)			3.65	29.17	41.67	19.79	5.73		4.17	6.77	14.58	74.48
Some College		338 (30.37)			6.8	36.09	35.21	18.64	3.25		3.85	6.21	20.12	69.82
Bachelor’s Degree		283 (25.43)			14.49	41.7	31.8	11.66	0.35		1.06	4.59	15	79.51
Post-Baccalaureate Degree		247 (22.19)			21.86	47.37	23.89	5.26	1.62		2.83	1.62	17.81	77.73
Income level
Less than $20,000		163 (14.65)			4.91	19.02	42.33	26.38	7.36		8.59	12.88	23	55.21
$20,000 to < $35,000		150 (13.48)			8.67	28	36	22.67	4.67		4	7.33	20.67	68
$35,000 to < $50,000		140 (12.58)			6.43	37.86	37.14	16.43	2.14		0.71	2.86	17.86	78.57
$50,000 to < $75,000		217 (19.50)			6.91	42.4	38.25	10.14	2.3		2.76	4.61	14.29	78
$75,000 or More		443 (39.80)			18.28	46.5	25.96	8.58	0.68		1.58	2.26	15.12	81.04
Body Mass Index			28.71	6.34
Underweight		11 (0.99)			18.18	27.27	0	36.36	18.18		9.09	9.09	18.18	63.64
Normal weight		302 (27.13)			18.21	41.39	26.82	10.6	2.98		2.98	3.31	12.91	80.79
Overweight		415 (37.29)			12.29	43.86	31.08	11.08	1.69		1.2	4.34	17.83	76.63
Obese		385 (34.59)			4.68	29.61	42.34	20.26	3.12		4.94	7.01	20	68.05
Smoking Status
Current		132 (11.86)			3.03	26.52	46.21	17.42	6.82		7.58	9.85	22.73	59.85
Former		341 (30.64)			9.68	34.31	35.78	17.6	2.64		3.81	5.87	17.3	73.02
Never		640 (57.50)			13.91	42.5	29.69	12.03	1.88		1.72	3.59	16.09	78.59
Physical Activity			159.57	298.71
Inactive		723 (64.96)			7.33	34.02	37.76	17.15	3.73		4.01	6.36	19.64	70
Active		390 (35.04)			18.72	45.64	25.64	9.23	0.77		1.28	2.56	12.82	83.33
Health Information Seeking Behavior - Sources of information
Written materials		124 (11.14)			4.84	33.06	40.32	20.16	1.61		5.65	6.45	12.10	75.81
Interpersonal sources		36 (3.23)			19.44	33.33	30.56	13.89	2.78		0.00	8.33	16.67	75
Healthcare provider		196 (17.61)			6.12	28.57	40.82	18.88	5.61		3.06	5.61	18.88	72.45
Internet		753 (67.65)			13.41	41.43	30.68	12.35	2.12		2.79	4.52	17.80	74.9
Other		4 (0.36)			0.00	75.00	25.00	0.00	0.00		0.00	0.00	0.00	100
Digital Health Literacy
In the past 12 months have you used a computer, smart phone, or other electronic means to look for health or medical information for yourself?	Yes	836 (75.11)	1.24	0.43	12.08	38.52	32.89	14.11	2.39		2.75	5.26	17.82	74.16
	No	277 (24.89)			9.03	36.82	35.38	15.16	3.61		3.97	4.33	15.52	76.17
In the past 12 months have you used a computer, smart phone, or other electronic means to look for health or medical information for someone else?	Yes	652 (58.58)	1.41	0.49	12.73	40.80	30.98	12.88	2.61		2.91	5.67	17.33	74.08
	No	461 (41.42)			9.33	34.27	37.09	16.49	2.82		3.25	4.12	17.14	75.49
In the past 12 months have you used a computer, smart phone, or other electronic means to buy medicine or vitamins online?	Yes	323 (29.02)	1.71	0.45	14.55	41.80	31.89	10.53	1.24		2.17	4.33	17.96	75.54
	No	790 (70.98)			10.00	36.58	34.18	15.95	3.29		3.42	5.32	16.96	74.30
In the past 12 months have you used a computer, smart phone, or other electronic means to look for assistance for the care that you provide for someone else?	Yes	177 (15.90)	1.84	0.36	16.38	40.68	30.51	10.73	1.69		0.56	4.52	18.64	76.27
	No	936 (84.10)			10.36	37.61	34.08	15.06	2.88		3.53	5.13	16.99	74.36
In the past 12 months have you used a computer, smart phone, or other electronic means to use e-mail or the internet to communicate with a doctor or a doctor’s office?	Yes	452 (40.61)	1.59	0.49	15.04	40.93	30.75	11.50	1.77		1.11	5.09	16.37	77.43
	No	661 (59.39)			8.77	36.16	35.40	16.34	3.33		4.39	4.99	17.85	72.77
In the past 12 months have you used a computer, smart phone, or other electronic means to track health care charges and costs?	Yes	378 (33.96)	1.66	0.47	14.29	43.12	30.16	11.11	1.32		1.06	4.23	19.31	75.40
	No	735 (66.04)			9.80	35.51	35.24	16.05	3.40		4.08	5.44	16.19	74.29
In the past 12 months have you used a computer, smart phone, or other electronic means to look up medical test results?	Yes	443 (39.80)	1.6	0.48	14.90	41.53	31.15	10.38	2.03		1.13	5.19	17.61	76.07
	No	670 (60.20)			11.32	38.10	33.51	14.38	2.70		4.33	4.93	17.01	73.73

M = Mean; SD = Standard Deviation; n = sample size; % = proportion.

In Table 2, the study explored the relationship between health information-seeking behavior, digital health literacy, and self-perceived health. Logistic regression models were used to examine this relationship. The analysis found that digital health literacy had a significant positive association with self-perceived health in the crude model (OR 2.81, 95% CI 1.40–5.63). However, this association became non-significant after adjusting for relevant covariates in Model 1, Model 2, and Model 3. The study found that education level was a reliable predictor of self-perceived health, with a substantial inverse association observed. Individuals with lower education levels were significantly less likely to report poor self-perceived health (OR 0.54, 95% CI 0.44–0.67 in model 1). This association remained highly significant even after adjusting for potential confounders in Model 2 (OR 0.61, 95% CI 0.48–0.76) and Model 3 (OR 0.62, 95% CI 0.49–0.78). Body Mass Index (BMI) was also identified as another influential factor in self-perceived health. Individuals with higher BMI values were found to have lower odds of reporting good self-perceived health (OR 1.74, 95% CI 1.34–2.27 in model 2), and this association remained even after adjusting for various factors in Model 3 (OR 1.45, 95% CI 1.09–1.92). Physical activity significantly impacted self-perceived health, with active individuals having significantly lower odds of reporting poor health (OR 0.44, 95% CI 0.29–0.66 in model 2). This association remained statistically significant after adjustments in Model 3 (OR 0.48, 95% CI 0.31–0.72). Smoking status was significantly associated with self-perceived health in model 2 (OR 0.69, 95% CI 0.48–0.98), indicating that non-smokers were less likely to report poor health. However, this association became non-significant after adjusting for covariates in Model 3. Among health conditions, diabetes was found to be a significant predictor of lower odds of reporting poor health (OR 0.46, 95% CI 0.23–0.91). Similarly, individuals with arthritis had significantly lower odds of reporting poor health (OR 0.48, 95% CI 0.28–0.80).

Table 2.

The association between health information seeking behavior, digital health literacy and self-perceived health.

	Crude	Model 1	Model 2	Model 3
	OR (95% CI)	Adj. OR (95% CI)	Adj. OR (95% CI)	Adj. OR (95% CI)
Health Information Seeking Behavior	0.81 (0.6–1.04)	0.89 (0.69–1.14)	0.88 (0.69–1.14)	0.86 (0.67–1.11)
Digital Health Literacy	2.81 (1.40–5.63) *	1.03 (0.47–2.23)	1.31 (0.59–2.90)	1.34 (0.60–3.00)
Age		1.21 (0.95–1.55)	1.22 (0.96–1.57)	1.04 (0.80–1.34)
Gender		0.80 (0.54–1.19)	0.80 (0.53–1.22)	0.81 (0.52–1.24)
Race		1.00 (0.82–1.22)	1.03 (0.84–1.27)	1.00 (0.81–1.23)
Marital Status		1.30 (0.92–1.84)	1.26 (0.88–1.79)	1.29 (0.90–1.84)
Education level		0.54 (0.44–0.67) ***	0.61 (0.48–0.76) ***	0.62 (0.49–0.78) ***
Income		0.91 (0.75–1.11)	0.96 (0.79–1.17)	1.02 (0.84–1.25)
Body Mass Index			1.74 (1.34–2.27) ***	1.45 (1.09–1.92) **
Physical Activity			0.44 (0.29–0.66) ***	0.48 (0.31–0.72) **
Smoking status			0.69 (0.48–0.98) *	0.75 (0.52–1.07)
Diabetes				0.46 (0.23–0.91) *
Hypertension				0.70 (0.44–1.09)
Heart condition				0.76 (0.28–2.03)
Lung Disease				0.56 (0.24–1.30)
Arthritis				0.48 (0.28–0.80) **

Adj. OR: Adjusted Odds Ratio, CI: Confidence Interval, p-value: *p < 0.05, **p < 0.01, ***p < 0.001.

The logistic regression analysis investigating the link between health information-seeking behavior, digital health literacy, and perceived depression symptoms has produced some significant findings in Table 3. The study reveals that digital health literacy is strongly connected to lower odds of experiencing perceived depression symptoms across all models. In the crude model, older adults with higher digital health literacy have significantly lower odds (OR = 0.12, 95% CI: 0.02–0.53) of reporting depression symptoms. Even after adjusting for various demographic and health-related factors in Model 1, Model 2, and Model 3, the inverse association between digital health literacy and depression symptoms remains statistically significant, with adjusted odds ratios ranging from 0.15 to 0.17. Furthermore, age emerges as a significant predictor of perceived depression symptoms in Model 1, with older individuals having higher odds of reporting depression symptoms (OR = 1.56, 95% CI: 1.00–2.42). However, this association persists only in Model 3. Physical activity and smoking status also show significant associations with perceived depression symptoms. In Model 2, individuals with higher physical activity levels have substantially higher odds of reporting depression symptoms (OR = 2.79, 95% CI: 1.03–7.52), while smoking status is associated with increased odds of depression symptoms in Model 2 (OR = 1.77, 95% CI: 1.12–2.79), and this relationship remains significant in Model 3.

Table 3.

The association between health information seeking behavior, digital health literacy and perceived depression symptoms.

	Crude	Model 1	Model 2	Model 3
	OR (95% CI)	Adj. OR (95% CI)	Adj. OR (95% CI)	Adj. OR (95% CI)
Health Information Seeking Behavior	1.00 (0.71–1.40)	0.98 (0.70–1.38)	1.00 (0.71–1.42)	1.01 (0.71–1.43)
Digital Health Literacy	0.12 (0.02–0.53) **	0.17 (0.03–0.90) *	0.15 (0.02–0.84) *	0.15 (0.02–0.84) *
Age		1.56 (1.00–2.42) *	1.47 (0.93–2.33)	1.61 (0.99–2.62) *
Gender		1.03 (0.50–2.10)	0.94 (0.45–1.96)	0.87 (0.40–1.88)
Race		0.94 (0.67–1.31)	0.89 (0.64–1.26)	0.86 (0.62–1.21)
Marital Status		0.61 (0.36–1.05)	0.65 (0.37–1.12)	0.66 (0.38–1.15)
Education level		0.95 (0.67–1.36)	0.85 (0.59–1.22)	0.85 (0.58–1.23)
Income		1.30 (0.97–1.75)	1.24 (0.93–1.66)	1.28 (0.94–1.72)
Body Mass Index			0.81 (0.51–1.28)	0.73 (0.45–1.18)
Physical Activity			2.79 (1.03–7.52) *	2.56 (0.94–6.95)
Smoking status			1.77 (1.12–2.79) *	1.71 (1.07–2.74) *
Diabetes				0.50 (0.19–1.29)
Hypertension				0.72 (0.32–1.60)
Heart condition				2.41 (0.90–6.44)
Lung Disease				2.24 (0.94–5.29)
Arthritis				1.06 (0.49–2.31)

Adj. OR: Adjusted Odds Ratio, CI: Confidence Interval, p-value: *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

This study is based on the data collected from the Health Information National Trends Survey (HINTS). It provides important insights into the behavior of older adults seeking health information, their digital health literacy, and the impact of these factors on their overall health and symptoms of depression.

A significant finding from this study is that digital health literacy, while identified as a predictor of self-perceived health status, did not contribute additional variance, nor did it provide unique predictive value in the presence of other predicting variables in the model. This significant variation is similar to the observations made by Kayser et al. (2018), which noted a multifaceted interaction between eHealth literacy and socioeconomic status⁷. On the other hand, it contradicts the more straightforward association outlined by Ma and Atkin (2017), highlighting the evolving understanding of the impact of digital health literacy in diverse contexts²².

It was found that there is a significant inverse relationship between education and self-perceived health among the respondents. Our findings align with those of Lynch et al. (2016), who also observed similar trends in their study²³, suggesting a strong relationship between education and health outcomes. According to their research, each additional year of education corresponds to improving health status. This implies that educational attainment is essential to an individual’s health perception. However, the findings challenge the conclusions drawn by Ross and Wu (1995), which reported a more nuanced relationship between education and health perception²⁴. In their study, the relationship was less pronounced in certain demographic groups, suggesting that other factors, such as socioeconomic status and access to healthcare, might also play a significant role. These contrasting viewpoints indicate that while education is essential in health perception, its influence is complex and possibly moderated by other variables.

The study highlights the importance of Body Mass Index (BMI) as a crucial factor in determining individuals’ health perceptions. It reveals that individuals with a higher BMI are less likely to report positive self-perceived health. These findings align with the conclusions of Flegal et al. (2013), which demonstrated the negative impact of elevated BMI on health perceptions²⁵. Their research emphasizes the health risks associated with higher BMI levels. On the other hand, this perspective contrasts with the insights provided by Brewis et al. (2011). They argued that health perceptions related to BMI are often subjective and heavily influenced by cultural factors²⁶. This suggests a more nuanced understanding of how BMI is perceived in relation to health across different societies.

Physical activity was found to be a significant determinant of self-perceived health. Individuals who engage in physical activities regularly report better health conditions. This relationship highlights the critical role that physical activity plays in shaping an individual’s perception of their health. This finding is supported by substantial research in the field. For example, researchers in the field conducted a notable study providing comprehensive evidence of the health benefits of physical activity²⁷. Similarly, a systematic review highlights the positive impact of physical activity on self-perceived health²⁸. These studies collectively confirm the beneficial role of physical activity, aligning with the broader consensus in the health literature. However, the findings of another study contrast with this conclusion. Their study did not establish a significant relationship between physical activity and self-perceived health in their sample population²⁹. This discrepancy suggests the need for a nuanced understanding of the relationship, considering various demographic and lifestyle factors that might influence individual health perceptions.

The study found a significant connection between digital health literacy and a reduced likelihood of experiencing symptoms of depression. This connection was observed across various analytical models and underscored the importance of digital health literacy on mental well-being. Previous studies have also shown a positive relationship between digital health literacy and mental well-being⁷. Other research have identified improved health outcomes associated with higher levels of internet literacy³⁰. Conversely, Cotten. 2001 found no significant relationship between digital health literacy and mental health outcomes, highlighting the variability in research findings and the need to explore further contextual factors influencing these results³¹.

The study’s policy implications are significant and wide-ranging. Enhancing digital health literacy is crucial in public health initiatives to improve self-perceived health and overall mental well-being. This approach emphasizes the vital role of digital health literacy in health outcomes⁷. In addition, addressing underlying socioeconomic determinants such as education and income levels is paramount to fully realizing the potential benefits of enhanced digital health literacy. This perspective aligns with the work of a study which highlighted the significant influence of socioeconomic factors on health outcomes³². A holistic approach encompassing digital health literacy and socioeconomic determinants is essential for maximizing the effectiveness of public health strategies.

Using a nationally representative dataset in this study is a unique strength, which allows for broader generalizability of our findings compared to previous studies that used smaller, localized samples³³. However, it’s essential to acknowledge the limitations of cross-sectional designs, which restrict the ability to infer causality³⁴. Future research should use longitudinal designs to investigate the temporal relationships between digital health literacy and mental health outcomes further. It is also important to note that the potential biases inherent in self-reported data, as highlighted by Lofters et al. (2017), are a critical consideration that must be addressed to contextualize the study’s conclusions fully³⁵.

Conclusion

This study significantly advances our understanding of health perception and mental well-being in the digital age. It confirms previous research findings and offers new perspectives and insights. The study highlights the complex and multifaceted relationship between digital health literacy, education, lifestyle choices and mental health. It emphasizes the need for tailored and nuanced public health initiatives that cater to diverse populations rather than a one-size-fits-all approach. The study calls for a more sophisticated and personalized strategy in public health policies and programs to address the intricate dynamics and enhance physical and mental health in an increasingly digital world.

Abbreviations

BMI

Body mass index

CI

Confidence interval

HINTS

Health Information National Trends Survey

HBM

Health belief model

IMB

Information-motivation-behavioral skills model

MSG

Marketing Systems Group

NCI

National Cancer Institute

OR

Odds ratio

SCT

Social cognitive theory

STATA SE

Stata statistical software: special edition

U.S.

United States

Author contributions

J.A.N. and L.Z. conceived the study. J.A.N. and E.L. provided the software and conducted statistical analyses. S.A. and Y.C. assisted with statistical analyses and interpretation of results. Y.C. prepared the literature review. J.A.N. and S.A. wrote original draft. E.L. made critical revisions of the final manuscript. L.Z. supervised the research. All the authors read and approved the final submission of the study.

Funding

This study was funded by Research on the Construction and Supporting Strategy of Value-oriented Payment Model for Outpatient Care of Chronic Diseases (National Natural Science Foundation of China), grant number 71974064.

Data availability

The datasets generated and analyzed during the current study article are available from the National Cancer Institute website (https://hints.cancer.gov/data/default.aspx).

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

The study was conducted in accordance with the ethical standards seorth in the Helsinki Declaration (1983). The procedure to obtain verbal informed consent was approved through expedited review by the Westat Institutional Review Board, and subsequently deemed exempt by the U.S. National Institutes of Health Office of Human Subjects Research Protections.