Willingness and Preferences for Mobile Health Versus In‐Person Healthcare Services Among People Living With HIV: A Cross‐Sectional Study

ABSTRACT

Background and Aims

Mobile health technology (mHealth) has made significant progress in the fields of disease prevention, diagnosis, and treatment. This technology is particularly effective for people living with human immunodeficiency virus (HIV) who often face social stigma and make in‐person visits to health centers difficult. Therefore, this study aimed to investigate the willingness and preferences of people living with HIV (PLWH) regarding the use of mHealth services.

Methods

This cross‐sectional descriptive‐analytical survey was conducted in Iran in 2024. Data were collected using a researcher‐made checklist with three sections: demographic variables, willingness to use mHealth services, and preferred treatment methods. The checklist, designed based on previous studies, was administered to PLWH attending health centers in Abadan, Iran. Data were analyzed using descriptive and inferential statistics with Statistical Package for the Social Sciences (SPSS) 22 software.

Results

A total of 144 patients participated in the study; 26.4% were female, and 73.6% were male. The findings showed that 30 (20.8%) patients preferred in‐person clinic visits, 47 (32.6%) desired to use a mobile application, and 67 (46.5%) favored a combination of both methods for disease management. Furthermore, a significant relationship was found between preferred treatment method (face‐to‐face, mobile, or both) and residency (rural or urban), familiarity with mHealth applications, receipt of services through mHealth, and type of mobile phone.

Conclusions

Most PLWH expressed willingness to use mHealth services, with a blended model combining mHealth and face‐to‐face care being the most preferred approach. mHealth may complement conventional HIV care by improving accessibility and continuity of services. These findings can inform the development of patient‐centered mHealth interventions, although further research is needed to evaluate their effectiveness and long‐term outcomes.

1. Introduction

Human immunodeficiency virus (HIV) weakens the immune system by attacking Cluster of Differentiation 4 (CD4) cells, ultimately leading to the development of other infections and diseases [1, 2]. The HIV epidemic has escalated beyond global control efforts, affecting every country in the world [3, 4]. As of 2022–2023, approximately 39.9 million people worldwide are living with HIV [3, 4]. In Iran, about 24,760 individuals have been diagnosed with the virus, leading to an annual mortality rate of around 1900 [5].

The final stage of HIV infection is acquired immunodeficiency syndrome (AIDS), classified as a chronic disease. Recently, there has been a growing emphasis on the central role of patients in managing their chronic conditions [6]. AIDS presents numerous health challenges within societies, and negative social perceptions and attitudes towards the disease expose patients and healthcare providers to various moral, physical, psychological, and social issues [7]. Additionally, inadequate communication and a lack of modern education significantly contribute to the reluctance and unexpected behaviors of patients with chronic diseases, often leading to suboptimal treatment and increased confusion and anxiety [8]. Effective prevention and management of chronic diseases are crucial for maintaining patient health [9]. Chronic disease management typically requires a long‐term plan, and information and communication technologies are becoming an integral part of healthcare systems [10]. Mobile health (mHealth) applications utilize mobile phone technologies to support the delivery of health information, clinical data collection, real‐time monitoring of patients’ vital signs, and sometimes direct therapeutic interventions [11]. mHealth applications are a promising tool for enhancing patient education and self‐care due to their popularity, convenience, accessibility, and personalization [12]. In recent years, mHealth technology has been utilized for various purposes, including improving access to healthcare services, managing chronic diseases, enhancing treatment adherence, providing education, facilitating patient monitoring and communication, and alleviating the burden of poverty‐related diseases [13, 14, 15].

Studies indicate that mHealth interventions can enhance medication adherence, promote self‐management participation, and improve overall health [16, 17, 18, 19]. A mixed‐methods study found that individuals with advanced diseases can significantly benefit from mHealth systems, which facilitate ongoing disease management and allow for patient assessment [20]. Especially for chronic diseases that complicate patient care needs, mHealth leads to increased patient engagement, reduced disease burden, effective treatment monitoring, and improved participation in decision‐making [21, 22, 23]. Furthermore, mHealth technology can offer evidence‐based guidance in an engaging and user‐friendly format, thereby reducing healthcare costs [10]. It can also play a critical role in overcoming geographical barriers to healthcare delivery, ensuring that care is provided to the right person, at the right time, in the right place, and with the proper decision‐making [22, 24, 25]. Given the limited research in this field, particularly among people living with HIV (PLWH), and the sensitivity of the disease and its associated challenges, this study aimed to investigate patients’ willingness and preferences regarding the use of mHealth technology for healthcare delivery.

2. Methods

2.1. Study Design and Setting

This descriptive‐analytical cross‐sectional study was conducted in Abadan, Iran, from February to March 2024. Its primary objective was to evaluate PLWH’ willingness and preferences to adopt mHealth technologies, in‐person healthcare visits, or a combination of both for managing their healthcare needs. The study aimed to comprehensively understand the factors influencing the acceptance of mHealth services in a real‐world setting. A thorough literature review, utilizing scientific databases, was conducted to explore existing knowledge regarding mHealth and AIDS care. Insights from this review informed the development of a tailored checklist to collect data from eligible participants, ensuring all relevant factors were considered. The cross‐sectional design allowed for data collection at a single point in time, providing valuable insights into PLWH’ willingness to use mHealth services and their preferences regarding healthcare delivery methods.

2.2. Study Population and Sample

The study population consisted of PLWH who visited the clinics of the Health Vice‐Chancellor of Abadan University of Medical Sciences between February and March 2024. A convenience sampling method was used, initially identifying 240 eligible patients. The sample size was calculated using the Cochran formula for a finite population: The formula is as follows: $n=\frac{{p.(1-p).N.Z}^2}{(N-1).E2+\mathrm{Z}2.p.(1-p)}$, where: n is the sample size, N is the population size (240 patients), p is estimated proportion, Z is the Z critical value for the selected confidence level (1.96 for a 95% confidence level), and $E$ is the margin of error (0.05). Based on this formula, a sample size of 144 participants was calculated. Statistical power was assessed using the statistical power, G*Power software (version 3.1). The power analysis revealed a statistical power of 0.85 at a significance level of 0.05, which is considered adequate for detecting meaningful effects. From a total population of 240 PLWH, 144 participants were included in the survey after sample size calculations and exclusion of individuals who did not meet the inclusion criteria. This sample size achieved a statistical power of 85%.

2.3. Inclusion and Exclusion Criteria

Eligible participants were at least 18 years old, possessed a mobile phone, and did not suffer from any mental or cognitive disorders. Patients with mental or cognitive disorders, as well as those unable to provide informed consent or who did not meet other inclusion criteria, were excluded from the study.

2.4. Data Collection

2.4.1. Checklist Development

A researcher‐developed checklist was used for data collection. The checklist was specifically designed for this study based on a comprehensive review of the literature regarding HIV/AIDS care management and the use of mHealth technologies. Relevant items were identified from previous studies and adapted according to the objectives of the present research. The initial draft of the checklist was reviewed by experts in Health Information Management (HIM), Infectious Diseases, and Medical Informatics to assess the relevance, clarity, comprehensiveness, and appropriateness of the items. Based on the experts’ feedback, modifications were made to improve the wording, structure, and content of the checklist. The initial checklist was developed in English, translated into Persian, and subsequently back‐translated into English by independent bilingual translators to ensure linguistic consistency and conceptual equivalence. A pilot study was conducted 1 week before the main data collection to evaluate the clarity, feasibility, and comprehensibility of the checklist in the study setting. Based on the pilot findings, minor revisions were made, and the final version of the checklist was prepared. The final data collection checklist consisted of three sections. The first section included participants’ sociodemographic characteristics. The second section included items assessing access to mobile communication technologies and participants’ willingness to use mHealth services for HIV/AIDS care. The third section assessed participants’ preferences regarding healthcare delivery methods, including face‐to‐face visits, mHealth‐based services, or a combination of both. Items in this section were measured using a four‐point Likert scale ranging from strongly agree to strongly disagree.

2.5. Data Collection Procedure

Before the beginning of data collection, four clinical nurses working at the participating HIV clinics received standardized training regarding the study objectives, ethical considerations, interview procedures, confidentiality requirements, and standardized completion of the data collection checklist. The training emphasized maintaining a neutral attitude, avoiding leading questions, and recording participants’ responses accurately without interpretation. Eligible patients attending the participating HIV clinics were approached consecutively during routine clinic visits. The study objectives, the concept of mHealth, and the voluntary nature of participation were explained to potential participants in clear, understandable language. Participants were informed refusing or withdrawing from the study would not affect the healthcare services they received. Written informed consent was obtained before conducting the interviews. Face‐to‐face interviews were conducted individually in a private setting by trained clinical nurses. Participants verbally provided their responses, and interviewers completed the data collection checklist based on the information provided. Interviewers clarified questions when necessary but avoided suggesting responses or influencing participants’ opinions. Upon completion of each interview, the checklist was reviewed for completeness and accuracy.

2.6. Quality Assurance and Bias Reduction

Given the sensitive nature of HIV infection and the potential influence of HIV‐related stigma on participants’ responses, several measures were implemented to promote trust, protect confidentiality, and minimize potential biases. Since the interviewers were clinical nurses from the participating HIV clinics, their familiarity with the clinical environment facilitated communication and may have helped establish rapport with participants. However, to minimize the potential influence of the existing patient–provider relationship on participants’ responses, interviewers received specific training on maintaining neutrality, avoiding judgmental communication, and ensuring that participation and responses would have no impact on patients’ treatment or healthcare services. Before each interview, participants were assured that their information would remain confidential, no identifying information would be included in the research database, and their responses would be used only for research purposes. Conducting interviews in a private setting and emphasizing voluntary participation were intended to reduce concerns related to HIV‐related stigma and encourage honest responses. To reduce response bias and social desirability bias, participants were informed that there were no correct or incorrect answers and that expressing their actual opinions was important for the accuracy of the research findings. Standardized interview procedures were followed for all participants, and interviewers provided clarification only when required, without influencing responses. All completed checklists were reviewed daily by the principal investigator to identify missing information, inconsistencies, or recording errors. Feedback was provided to interviewers whenever necessary to improve data completeness and consistency. Although these measures were implemented to minimize potential sources of bias, residual bias inherent to observational studies cannot be eliminated and should be considered when interpreting the findings.

2.7. Pretest and Refinement of the Data Collection Checklist

A pretest was conducted 1 week before the main data collection among 20 participants who met the inclusion criteria but were not included in the final study sample. These participants were selected from a population with characteristics similar to those of the main study participants. The primary objectives of the pretest were to evaluate the clarity, comprehensibility, applicability, and feasibility of the data collection checklist and the overall data collection process. During the pretest, participants were asked to provide feedback regarding the clarity of the items, ease of understanding, and any difficulties encountered while responding to the checklist items. Items that were unclear, ambiguous, or difficult to interpret were identified and reviewed. Participants’ feedback was also considered to determine whether the items appropriately addressed the intended aspects of access to mobile communication technologies, willingness to use mHealth, and preferences regarding healthcare delivery methods. In addition, the pretest was used to assess the practical aspects of data collection, including the estimated time required to complete the checklist, the sequence of items, and the overall flow of the interview process. Based on participant feedback and pretest observations, minor modifications were made to improve the wording, organization, and clarity of some items. These modifications were incorporated into the final version of the data collection checklist before the main study.

2.8. Data Analysis

Data were analyzed using descriptive and inferential statistical methods. Descriptive statistics, including frequencies, percentages, means, and standard deviations, were used to summarize participants’ sociodemographic characteristics, access to mobile communication technologies, willingness to use mHealth services, and preferences regarding healthcare delivery methods. For the checklist items assessing participants’ willingness to use mHealth services, both item‐level and summary‐level analyses were performed. Individual items were analyzed separately to describe response patterns and examine differences across sociodemographic groups. In addition, mean scores were calculated for the Likert‐scale items assessing participants’ willingness and preference to use mHealth services. All items contributed equally to the mean score calculation, and no weighting procedure was applied. The normality of continuous variables was assessed using the Kolmogorov–Smirnov test. As the data were normally distributed, parametric statistical tests were applied. Associations between categorical variables were examined using the Chi‐square test, while differences in mean scores between two independent groups were assessed using the independent‐samples t‐test. Pearson's correlation coefficient was used to evaluate the relationships between continuous variables. Univariate analyses were performed to examine the association between each independent variable and participants’ willingness to use and preference to use mHealth services. All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) software (version XX; IBM Corp., Armonk, NY, USA). All statistical tests were two‐sided, and a p‐value of < 0.05 was considered statistically significant. For this study, mHealth was defined as the use of mobile communication technologies to support care among PLWH. The concept included services such as medication adherence support, appointment and medication reminders, health education, remote counseling, and communication with healthcare providers through mobile technologies. Before the interview, participants received a brief standardized explanation of these services to ensure a common understanding of the concept of mHealth before responding to the checklist items.

2.9. Ethical Considerations

This study was conducted in accordance with ethical principles, and informed consent was obtained from all participants before their participation (IR.ABADANUMS.REC.1401.036).

3. Results

3.1. Participant Characteristics

Of the 240 patients attending the infectious disease clinics affiliated with Abadan University of Medical Sciences, 144 met the inclusion criteria and agreed to participate in the study. Participants ranged in age from 18 to 59 years, with a mean age of 32.78 (SD = ±9.96) years. The mean distance from participants’ residences to the healthcare center was 7.67 km (SD = ±8.11 km), and the mean number of visits to healthcare centers was 7.10 (SD = ±3.14). The majority of participants were male (73.6%) and resided in urban areas (85.4%). Most participants had access to mobile phones (79.2%), and 69.2% reported using smartphones. Internet access was available among 70.8% of participants. Approximately one‐third of participants (31.9%) were familiar with mHealth applications, whereas only 16.0% had previously received healthcare services through mHealth technologies. Regarding healthcare access and treatment‐related challenges, 57.6% of participants reported difficulty traveling to healthcare centers, 75.7% reported delaying treatment due to limited access to specialists, and 77.1% reported discontinuing treatment because of fatigue, lack of patience, or insufficient time. Detailed sociodemographic characteristics and baseline study variables are presented in Table 1.

Table 1.

Sociodemographic characteristics, healthcare access, and mobile technology access among study participants (N = 144).

Variables	Minimum	Maximum	Mean	SD	n	%
Age (years)	—	—	32.78	±9.95	—	—
Distance to healthcare center (km)	—	—	66.11	±7.80	—	—
Number of visits to healthcare center	—	—	7.30	±1.13	—	—
Gender
Male	—	—	—	—	106	73.6
Female	—	—	—	—	38	26.4
Education level
Illiterate	—	—	—	—	11	7.6
Primary education	—	—	—	—	48	33.3
Middle school	—	—	—	—	32	22.2
High school	—	—	—	—	23	16.0
Diploma	—	—	—	—	26	18.1
Bachelor's degree	—	—	—	—	3	2.1
Master's degree	—	—	—	—	1	0.7
Residence
Urban	—	—	—	—	123	85.4
Rural	—	—	—	—	21	14.6
Occupation
Unemployed	—	—	—	—	44	30.6
Self‐employed	—	—	—	—	76	52.8
Employee	—	—	—	—	7	4.9
Student	—	—	—	—	6	4.2
Housewife	—	—	—	—	11	7.6
Monthly income (million Tomans)
< 2	—	—	—	—	73	50.7
2–3	—	—	—	—	48	33.3
3–5	—	—	—	—	16	11.1
5–7	—	—	—	—	5	3.5
7–9	—	—	—	—	1	0.7
> 9	—	—	—	—	1	0.7
Internet access
Yes	—	—	—	—	102	70.8
No	—	—	—	—	42	29.2
Mobile phone access
Yes	—	—	—	—	114	79.2
No	—	—	—	—	30	20.8
Type of phone
Smartphone	—	—	—	—	99	69.2
Basic phone	—	—	—	—	44	30.8
Familiarity with mHealth applications
Yes	—	—	—	—	46	31.9
No	—	—	—	—	98	68.1
Previous receipt of mHealth services
Yes	—	—	—	—	23	16.0
No	—	—	—	—	121	84.0
Personal transportation to healthcare centers
Yes	—	—	—	—	87	60.4
No	—	—	—	—	57	39.6
Difficulty traveling to healthcare centers
Yes	—	—	—	—	83	57.6
No	—	—	—	—	61	42.4
Delayed treatment due to limited access to specialists
Yes	—	—	—	—	109	75.7
No	—	—	—	—	35	24.3
Ability to pay treatment costs
Yes	—	—	—	—	69	47.9
No	—	—	—	—	75	52.1
Treatment discontinuation due to fatigue, lack of patience, or insufficient time
Yes	—	—	—	—	111	77.1
No	—	—	—	—	33	22.9

3.2. Association Between Participant Characteristics and the Overall Checklist Score

The overall willingness and preference score to use mHealth services ranged from 8 to 81, with a mean score of 62.68 (SD = ±9.02). The overall checklist scores were compared across participants' sociodemographic, technology‐related, and healthcare access characteristics (Table 3). Significant differences were observed according to gender, occupation, Internet access, telephone access, type of mobile phone, familiarity with mHealth applications, and income categories. Male participants had a higher mean checklist score compared with female participants (63.28 vs. 61.02, p = 0.038). Occupational status was significantly associated with the overall checklist score (p = 0.048). Employees had the highest mean checklist score (69.00 ± 3.51), followed by students (63.50 ± 7.94), housewives (62.68 ± 9.02), self‐employed participants (62.42 ± 7.30), and unemployed participants (60.70 ± 11.99). Participants with Internet access showed higher mean scores than those without Internet access (64.09 vs. 59.35, p = 0.005). Similarly, participants with telephone access had higher mean scores compared with those without telephone access (63.42 vs. 59.90, p = 0.010), and smartphone users had higher scores than users of basic phones (64.37 vs. 59.54, p = 0.002). A significant difference was found among occupational groups (p = 0.048), with employees showing the highest mean checklist score (69.00). Participants reporting difficulty traveling to healthcare centers had a statistically significant difference in checklist scores compared with those without such difficulty (62.66 ± 7.53 vs. 62.72 ± 10.80, p = 0.001), although the absolute difference in mean scores was minimal. Furthermore, participants who were not familiar with mHealth applications had higher mean scores compared with those who were familiar with mHealth applications (63.72 vs. 60.45, p = 0.042). Differences were also observed across income categories (p = 0.048). No statistically significant differences were observed according to education level, delayed treatment due to limited access to specialists, ability to pay treatment costs, treatment discontinuation due to fatigue, lack of patience or insufficient time, or previous receipt of mHealth services (Table 2).

Table 3.

Pearson correlation coefficient between average visits to the center, distance to the center, total scores, and age.

	Age	Mean visits to the center	Distance to center	Total
Age	1	r = 0.148	r = 0.32	r = 0.12
		p = 0.077	p = 0.7	p = 0.151
Mean visits to the center	r = 0.148	1	r = 0.198	r = 0.19
	p = 0.077		p = 0.017	p = 0.022
Distance to center	r = 0.32	r = 0.198	1	r = 0.236
	p = 0.7	p = 0.017		p = 0.004
Total	r = 0.236	r = 0.19	r = 0.236	1
	p = 0.004	p = 0.022	p = 0.004

Table 2.

Univariate analysis of the association between participant characteristics and the overall checklist score.

Variables	Categories	Mean (SD)	p‐value
Gender	Female	61.02 (11.52)	0.038
	Male	63.28 (7.92)
Education level	Illiterate	63.36 (7.36)	0.715
	Primary education	62.39 (9.63)
	Middle school	62.18 (7.45)
	High school	61.86 (6.55)
	Diploma	62.59 (13.17)
	Associate	64.50 (2.51)
	Bachelor's degree	71.33 (4.16)
	Master's degree	73.00 (0)
Occupation	Unemployed	60.70 (11.99)	0.048
	Self‐employed	62.42 (7.30)
	Employee	69.00 (3.51)
	Student	63.50 (7.94)
	Housewife	62.68 (9.02)
Internet access	Yes	64.09 (8.52)	0.005
	No	59.35 (9.61)
Telephone access	Yes	63.42 (8.88)	0.010
	No	59.90 (9.16)
Type of phone	Simple phone	59.54 (7.63)	0.002
	Smartphone	64.37 (8.91)
Difficulty traveling to healthcare centers	Yes	62.66 (7.53)	0.001
	No	62.72 (10.80)
Delayed treatment due to limited access to specialists	Yes	62.41 (9.53)	0.521
	No	63.54 (7.81)
Ability to pay treatment costs	Yes	63.26 (9.83)	0.476
	No	62.16 (8.24)
Treatment discontinuation due to fatigue, lack of patience, or insufficient time	Yes	62.37 (9.08)	0.453
	No	63.72 (8.89)
Familiarity with mHealth applications	Yes	60.45 (11.28)	0.042
	No	63.72 (7.58)
Receiving services through mHealth	Yes	62.13 (6.60)	0.748
	No	62.79 (9.43)
Income (million Tomans)	< 2	61.76 (8.49)	0.048
	2–3	61.35 (10.61)
	3–5	68.50 (4.03)
	5–7	68.40 (2.19)
	7–9	68.00 (0.00)
	> 9	67.00 (0.00)

Note: Data are presented as mean (SD = ± standard deviation). p‐values represent comparisons of mean overall checklist scores across participant groups.

3.3. Correlation Between Continuous Variables and the Overall Checklist Score

Pearson's correlation analysis was performed to assess the relationships between the overall checklist score and continuous variables, including participants’ age, distance to healthcare centers, and the number of visits to healthcare centers (Table 3). A significant positive correlation was observed between the overall checklist score and the number of visits to healthcare centers (r = 0.190, p = 0.022). In addition, the overall checklist score showed a significant positive correlation with the distance to healthcare centers (r = 0.236, p = 0.004). No significant correlation was found between participants’ age and the overall checklist score (r = 0.120, p = 0.151). The correlation matrix also showed no significant association between age and the number of visits to healthcare centers or distance to healthcare centers (Table 3).

3.4. Patients’ Willingness to Use mHealth Services

The distribution of participants’ responses and the mean scores for each item assessing willingness to use mHealth services are presented in Table 4. The mean scores of the 21 items ranged from 2.09 to 3.43 (out of 4). The highest mean score was observed for the statement that using mobile applications could reduce treatment costs compared with in‐person visits (mean score = 3.43). Other items with relatively high mean scores included increased self‐confidence (mean score = 3.29), reduced anxiety and stress (mean score = 3.27), improved peace of mind (mean score = 3.22), reduced travel distance to healthcare centers (mean score = 3.20), and reduced social stigma and improved psychological and social support (mean score = 3.15). The lowest mean score was observed for the statement that treatment progress could not be determined through mobile applications and that visiting a healthcare center would still be required to assess disease progress (mean score = 2.09). The remaining items showed mean scores ranging from 2.70 to 3.14. The frequency distribution of responses (strongly agree, agree, disagree, and strongly disagree) and the mean scores for all items are presented in Table 4.

Table 4.

Distribution of responses and mean scores of participants’ willingness to use mHealth services for HIV/AIDS care.

No.	Item	Strongly agree, n (%)	Agree, n (%)	Disagree, n (%)	Strongly disagree, n (%)	Mean score (out of 4)
1	Using mobile applications can reduce my treatment costs compared with in‐person visits to healthcare centers.	101 (70.1)	18 (12.5)	11 (7.6)	14 (9.7)	3.43
2	Using mobile applications can increase my self‐confidence compared with in‐person visits to healthcare centers.	72 (50.0)	49 (34.0)	16 (11.1)	7 (4.9)	3.29
3	Using mobile applications can reduce my anxiety and stress compared with in‐person visits to healthcare centers.	74 (51.4)	42 (29.2)	21 (14.6)	7 (4.9)	3.27
4	Using mobile applications can help me better manage my clinical symptoms compared with in‐person visits.	54 (37.5)	42 (29.2)	36 (25.0)	12 (8.3)	2.95
5	Using mobile applications can improve cooperation with healthcare providers in treatment decisions and disease monitoring.	61 (42.4)	47 (32.6)	32 (22.2)	4 (2.8)	3.14
6	Using mobile applications can improve my medication adherence compared with in‐person visits.	50 (34.7)	57 (39.6)	30 (20.8)	7 (4.9)	3.04
7	Using mobile applications can improve my lifestyle compared with visiting healthcare centers in person.	60 (41.7)	42 (29.2)	30 (20.8)	12 (8.3)	3.04
8	Using mobile applications can help me follow treatment instructions more easily.	66 (45.8)	39 (27.1)	26 (18.1)	13 (9.0)	3.09
9	Using mobile applications can provide me with greater peace of mind compared with in‐person visits.	71 (49.3)	42 (30.6)	20 (13.9)	9 (6.3)	3.22
10	Using mobile applications can reduce the distance I need to travel to healthcare centers.	74 (51.4)	40 (27.8)	16 (11.1)	14 (9.7)	3.20
11	Using mobile applications requires extensive technical knowledge and time for learning.	58 (40.3)	40 (27.8)	34 (23.6)	12 (8.3)	3.00
12	Using mobile applications may negatively affect my relationship with healthcare providers.	48 (33.3)	27 (18.8)	47 (32.6)	22 (15.3)	2.70
13	Using mobile applications cannot determine my treatment progress, and I still need to visit healthcare centers.	19 (13.2)	17 (11.8)	67 (46.5)	41 (28.5)	2.09
14	I would use mobile applications if recommended by my doctor or healthcare providers.	57 (39.6)	40 (27.8)	32 (22.2)	15 (10.4)	2.96
15	Mobile applications cannot replace in‐person treatment and should only be used as a complementary approach.	51 (35.4)	41 (28.5)	37 (25.7)	15 (10.4)	2.88
16	I am not willing to pay for these applications and would use them only if they are free.	56 (38.9)	38 (26.4)	35 (24.3)	15 (10.4)	2.93
17	Mobile applications can provide better education regarding sexual health behaviors and disease prevention.	49 (34.0)	54 (37.5)	32 (22.2)	9 (6.3)	2.99
18	Mobile applications can improve adherence to physical activity and dietary recommendations.	63 (43.8)	25 (17.4)	35 (24.3)	21 (14.6)	2.90
19	Mobile applications can facilitate rapid sharing of test results with healthcare providers.	45 (31.3)	41 (28.5)	33 (22.9)	25 (17.4)	2.73
20	Mobile applications can reduce social stigma and provide psychological and social support.	59 (41.0)	37 (25.7)	38 (26.4)	10 (6.9)	3.15
21	Mobile applications can maintain confidentiality and provide a more private communication environment with healthcare providers.	64 (44.4)	44 (30.6)	28 (19.4)	8 (5.6)	3.13

3.5. Preferred Healthcare Delivery Method and Associated Factors

Among the 144 participants, 30 (20.8%) preferred face‐to‐face visits, 47 (32.6%) preferred mHealth‐based services, and 67 (46.5%) preferred a combination of face‐to‐face visits and mHealth services for AIDS care management. The relationship between preferred healthcare delivery method and the studied variables was assessed using the chi‐square test or Fisher's exact test, as appropriate. Significant associations were found between preferred healthcare delivery method and familiarity with mHealth applications (p = 0.001), receiving services through mHealth (p = 0.007), type of mobile phone (p = 0.007), and place of residence (p = 0.002). No statistically significant associations were observed between preferred healthcare delivery method and other assessed variables, including gender, internet access, difficulty traveling to healthcare centers, delayed treatment due to limited access to specialists, ability to pay treatment costs, and premature discontinuation of treatment (Table 5).

Table 5.

Association between studied variables and preferred healthcare delivery method among participants (n = 144).

Variable	Category	Face‐to‐face visit n (%)	mHealth service n (%)	Combination of face‐to‐face visit and mHealth n (%)	Total n (%)	p‐value
Familiarity with mHealth applications	Yes	12 (8.3)	18 (12.5)	16 (11.1)	46 (31.9)	0.001*
	No	18 (12.5)	29 (20.1)	51 (35.4)	98 (68.1)
Receiving services through mHealth	Yes	5 (3.5)	5 (3.5)	13 (9.0)	23 (16.0)	0.007*
	No	25 (17.4)	42 (29.2)	54 (37.5)	121 (84.0)
Type of mobile phone	Simple phone	10 (6.9)	14 (9.7)	20 (13.9)	44 (30.6)	0.007*
	Smartphone	20 (13.9)	33 (22.9)	47 (32.6)	100 (69.4)
Residence	Rural	5 (3.5)	5 (3.5)	11 (7.6)	21 (14.6)	0.002*
	Urban	25 (17.4)	42 (29.2)	56 (38.9)	123(85.4)
Gender	Female	9 (6.3)	13 (9.0)	16 (11.1)	38 (26.4)	0.796
	Male	21 (14.6)	34 (23.6)	51 (35.4)	106 (73.6)
Internet access	Yes	21 (14.6)	34 (23.6)	49 (34.0)	104 (72.2)	0.963
	No	9 (6.3)	13 (9.0)	18 (12.5)	40 (27.8)
Difficulty traveling to healthcare centers	Yes	14 (9.7)	34 (23.6)	35 (24.3)	83 (57.6)	0.400
	No	16 (11.1)	13 (9.0)	32 (22.2)	61 (42.4)
Delayed treatment due to limited access to specialists	Yes	23 (16.0)	37 (25.7)	49 (34.0)	109 (75.7)	0.783
	No	7 (4.9)	10 (6.9)	18 (12.5)	35 (24.3)
Ability to pay treatment costs	Yes	11 (7.6)	21 (14.6)	37 (25.7)	69 (47.9)	0.207
	No	19 (13.2)	26 (18.1)	30 (20.8)	75 (52.1)
Premature discontinuation of treatment	Yes	21 (14.6)	41 (28.5)	49 (34.0)	111 (77.1)	0.453
	No	9 (6.3)	6 (4.2)	18 (12.5)	33 (22.9)

^*Chi‐square test or Fisher's exact test, as appropriate.

4. Discussion

The results of this study showed that most patients preferred using mHealth technology rather than consulting their doctors directly. All participants reported finding mobile phones easy to use, and over two‐thirds expressed interest in employing mHealth technology to provide services to patients. Similarly, according to the study performed by Abbasi et al. [9], patients not only showed a willingness to use mHealth but also felt that it provided them with peace of mind; more than half of the participants in their study used smartphones. Additionally, a study by Browning et al. involving kidney transplant recipients found that the majority now own smartphones and are eager to utilize mHealth applications to enhance their medication management [26].

In this study, most HIV‐infected patients expressed interest in using technology to communicate with healthcare providers if it were available for free. In contrast to prior studies, our findings revealed a positive attitude towards mHealth technology among patients. Despite this optimistic trend, most patients indicated they would be willing to use the technology if it were free. A study performed by Meffert et al. [27] found that patients with depression and posttraumatic stress disorder (PTSD) preferred mHealth technology due to its affordability and convenience. Additionally, research by Browning et al. [26] indicated that a segment of respondents was less likely to own or use this technology, typically those with lower incomes and those over 55 years of age. This potential barrier to using mHealth applications could be mitigated by ensuring the system is user‐friendly and by providing skilled assistance and training. Hossain et al. [28] found that while the majority of individuals were aware of the mHealth applications, few were accustomed to using them. Only 13% of patients agreed to pay for interventions delivered through mobile applications. Consistent with our study and others, the cost of mHealth has been identified as a significant barrier to its utilization [9, 27, 28]. Therefore, while there are positive attitudes toward mHealth technology that may facilitate its acceptance, the cost remains a concern that must be addressed before widespread adoption can occur.

Our results indicate that the variable “Service mHealth” is significantly associated with patient awareness. This suggests that patients with HIV believe this technology can effectively remind them of their doctor's orders. Numerous studies have shown that reminders can enhance chronic disease management [29, 30]. Therefore, leveraging such capabilities in mHealth technology may increase patients’ willingness to adopt it. In our study, over half of the HIV patients identified two main challenges to using mHealth technology. The first challenge was ineffective communication with their doctor, and the second was concerns about the privacy of their information. These issues have also been noted in other studies [9, 26, 28, 29]. Alhammad et al. stated in a systematic review [31] that patients’ reluctance to adopt this technology is mainly stems from reduced face‐to‐face communication with doctors and privacy concerns. Consequently, it is essential to address various aspects of patient privacy while ensuring effective communication between patients and clinicians. Our findings revealed that the tendency to use mHealth technology is directly related to age. Younger individuals reported feeling more comfortable with this technology than older individuals. This aligns with the previous literature, which indicates that age is a significant determinant in the acceptance of mHealth technology; as age increases, acceptance tends to decrease [32, 33, 34]. Several factors may contribute to this trend. Younger individuals are typically more accustomed to mHealth, have higher levels of digital literacy, and demonstrate greater self‐assurance in using mobile apps. They also tend to find mHealth tools appropriate, affordable, and well‐suited to their daily practices. In contrast, older adults may face barriers such as limited digital skills, vision or cognitive disorders, and privacy or security concerns, all of which can reduce their inclination to adopt mHealth [35, 36, 37]. Additionally, a study by Guo et al. [33] found that perceived personalization among younger users can lead to greater acceptance of mHealth applications. This suggests that not only are younger people more interested in mobile health tools, but that adaptive and user‐friendly designs—such as personalized feedback, gamification, and interactive interfaces—can enhance their motivation and trust, making them more likely to use mobile health apps.

The findings indicated that education level was significantly associated with the use of mHealth technology. In line with our study, Hossain et al. found that participants with higher education levels were more likely to perceive mHealth as useful. An exploratory analysis revealed that those who viewed mHealth as more beneficial also reported higher awareness and usage of mHealth technologies [28]. Additionally, the present study confirms a significant correlation between the tendency to use mHealth technology and gender. Khatun et al. [38] suggested that gender may influence mHealth usage; their results indicated that women use mobile phones and mHealth services less than men. The study by Zhang et al. [39] echoed this sentiment, demonstrating that gender influences the adoption of mHealth from a social psychological perspective. Consequently, both gender and literacy levels appear to be significant factors in the adoption of mHealth technology. Many HIV patients may be inclined to use mHealth technology due to their reduced reliance on physicians. The study revealed that 20.8% preferred in‐person clinic visits, 32.6% favored mobile apps, and 46.5% chose a combination of both mobile apps and in‐person visits for managing their condition. This indicates that a hybrid approach to healthcare delivery might be more appealing to patients than relying solely on in‐person visits or mHealth. It underscores the potential of digital health solutions to align with patient preferences and to integrate digital data with medical records effectively.

Prior studies indicate mHealth apps offer significant benefits, particularly when paired with in‐person visits. While patients appreciate the convenience and efficiency of digital healthcare, they also value the personal attention and comprehensive assessments that come with face‐to‐face visits [40]. A flexible approach that incorporates both remote and in‐person visits is crucial for ensuring continuity of care, real‐time monitoring of vital signs, and direct care delivery through mobile devices, all while addressing specific patient needs [23, 41, 42]. This integration can enhance outcomes for individuals living with HIV. Numerous studies underscore the potential benefits of technology for HIV patients, including improved adherence, convenience, and cost savings. Nevertheless, in‐person interactions remain essential for certain aspects of care, such as facilitating faster progress and delivering higher‐quality services [43, 44].

The study revealed a significant association between the residence location (rural or urban) and preferred treatment modality (face‐to‐face, mobile apps, or both). Among the factors influencing treatment modality choice, only the relationship between receiving services through mHealth and “mobile phone type” showed a statistically significant association with the chosen treatment modality. These findings align with those of Greenberg et al., which indicated that despite comparable rates of smartphone ownership and health app installation, rural residents are less likely to utilize digital health tools for communicating with healthcare providers or managing their personal health information online. This suggests that although rural residents have access to technology, they may encounter barriers to effectively using digital health tools for healthcare purposes [45]. Numerous studies have shown significant disparities in socioeconomic conditions, access to health services, and lifestyle between rural and urban areas. These differences notably affect individual preferences for treatment methods. Additionally, factors such as healthcare cost and waiting times for medical services play a crucial role in shaping these preferences [40, 46, 47].

Patients with HIV in the late or advanced stages of the disease often do not need to doctor visits due to complications and their compromised immunity. Instead, they can utilize mHealth technology to send the relevant information and photos to their health care providers. Doctor can also reach out to patients to discuss routine test results. However, some patients feel that visiting specialized centers or receiving in‐person counseling is more effective than indirect communication, which may contribute to their reluctance to use this technology. Therefore, It is essential to develop and implement mHealth technologies that facilitate better communication between patients and healthcare providers. Various studies have proven that mHealth technology can reduce unnecessary physician referrals and save patients money [48, 49].

5. Conclusions

Most patients with HIV/AIDS in this study expressed willingness to use mHealth services for disease management, although a blended approach combining mHealth with face‐to‐face care was the most preferred option. Preferences for the mode of care were significantly associated with place of residence, familiarity with mHealth applications, previous experience of receiving mHealth services, and type of mobile phone. These findings suggest that mHealth may serve as a complementary approach to conventional HIV care rather than a replacement for in‐person services. Integrating mobile‐based services with existing healthcare delivery may improve accessibility, flexibility, and continuity of care, particularly for patients who may experience barriers to frequent visits to healthcare facilities. Healthcare policymakers, clinicians, and mHealth developers should consider patients’ technological familiarity, access to mobile devices, and contextual needs when designing and implementing HIV‐related mHealth interventions. Future studies should evaluate the feasibility, acceptability, effectiveness, and long‐term outcomes of mHealth models in routine HIV care.

Author Contributions

Hadi Kazemi‐Arpanahi: methodology, writing – review and editing, supervision, validation, visualization, formal analysis. Soheila Saeedi: conceptualization, data curation, formal analysis, writing – original draft, visualization, validation. Alireza Banaye Yazdipour: conceptualization, project administration, formal analysis, methodology, resources, writing – original draft, data curation. Marjan Ghazisaeedi: conceptualization, writing – review and editing, methodology, investigation, data curation, supervision. Hassan Bostan: resources, conceptualization, writing – original draft, data curation, formal analysis, methodology, writing – review and editing, visualization, project administration.

Funding

The authors have nothing to report.

Ethical Statement

The Ethics Committee of Abadan University of Medical Sciences approved the study with ethical approval code (IR.ABADANUMS.REC.1401.036). All patients were assured that their information would be confidential.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The Corresponding author (Hassan Bostan) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. All authors have read and approved the final version of the manuscript. Hassan Bostan had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.