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Abstract

Background

HIV/AIDS is a major public health issue in Cameroon. Traditional paper-based monitoring systems often fail to provide timely and accurate data, essential for effective program management. The increasing availability of mobile phones and internet services presents an opportunity for mHealth technologies to improve data collection and monitoring. However, the successful integration of mHealth into existing systems requires an understanding of the attitudes and challenges faced by end-users. This study explores the attitudes of mHealth end-users and the challenges they face in integrating mobile technology into HIV program monitoring in Cameroon.

Methods

A mixed-methods approach was employed, involving both qualitative and quantitative data collection. The study included 251 respondents from 4 regions (East, Adamawa, Centre, and Littoral) and 4 FGDs. Data were collected using questionnaires and focus group discussion guides. The quantitative data were analyzed using SPSS, while qualitative data were transcribed and analyzed using Nvivo9 with an inductive thematic approach.

Results

The quantitative analysis revealed that 78% of respondents had a good attitude toward mHealth, with a mean score of 4.48 ± 0.956. A significant difference was found in the mean attitude scores between those categorized as having good (M = 4.94, SD = 0.244) and poor attitudes (M = 2.71, SD = 0.540; t = -42.50, df = 237, p < 0.001). Qualitative findings supported these results, with most FGD participants expressing a preference for mobile applications over traditional methods due to ease of use and efficiency. Factors such as region of work (F = 5.259, p = 0.002) and educational level (F = 13.45, p < 0.001) significantly influenced attitudes toward mHealth, while age and work experience did not show significant associations. Key challenges identified include the need for better training and support for mHealth technologies, issues with the reliability of mobile devices, and the need for integration with existing systems.

Conclusion

There is strong support for the use of mobile technology in improving data collection and patient care. There is a generally positive attitude toward mHealth among end-users in Cameroon, with significant support for its continued integration into HIV program monitoring. However, challenges such as device usability and internet access need to be addressed to enhance the effectiveness of mHealth interventions. Future initiatives should consider these factors to improve data collection and program monitoring in Cameroon.

Keywords

mHealth mobile applications attitudes HIV program monitoring

Introduction and background

HIV/AIDS is one of the major public health concerns in Cameroon. Despite significant progress in combating the virus, challenges remain, particularly in maintaining accurate and up-to-date information on HIV cases, treatment adherence, and program effectiveness. Traditional paper-based monitoring systems often struggle to provide real-time data and comprehensive insights required for informed decision making in a dynamic landscape of HIV care.^1,2 As access to mobile phones and internet services continues to increase in the region, mHealth-related services will also increase.³ However, the introduction of mobile technology for health programs monitoring and evaluation (M&E) doesn’t connote abandoning the pre-existing systems of data collection, management, and storage. In a report of the 71st World Health Assembly held in March 2018, the WHO adds that, “The spread of digital technologies and global interconnectedness has a significant potential to accelerate Member States’ progress towards achieving Universal Health Coverage (UHC), including ensuring access to quality health services.”⁴

The use of mobile technology for M&E of health programs and surveillance by mainly International nongovernmental organizations (INGOs) is a relatively new but promising phenomenon in the African context. The success or failure of mHealth initiatives heavily relies on the attitudes and perceptions of end-users. Slater et al.⁵ highlight the importance of consideration of user perspectives and attitudes in the adoption of mHealth technologies. To effectively implement mHealth, in HIV program monitoring it is essential to have a contextualized understanding of these attitudes and challenges. Therefore, this study aims to fill this knowledge gap by investigating the attitudes and challenges encountered by mHealth end-users in the context of HIV program monitoring in Cameroon. Studies have demonstrated the relationship between several sociodemographic characteristics (including age, academic qualification, and years of work experience) and attitudes.^6,7 Although mHealth technologies have supported a broad scope of service delivery, our study focused on its use in monitoring, given the current drive by stakeholders to use this innovation to improve the timeliness and quality of data. Findings will provide recommendations on adopting end-user tailored approaches to implementing mHealth interventions for data services in resource-limited settings.

Context

In Cameroon, through technical support of the United States Government (USG) funded projects to the Ministry of Public Health, mHealth have supported the HIV/AIDS program through; SMS-based appointment and medication reminders that improve retention and adherence; smartphone and tablet applications for real-time adherence tracking and counseling; mobile data reporting tools for routine surveillance of HIV indicators; and community health worker data collection platform to collect Prevention of Mother-To-Child Transmission/Early Infant Diagnosis (PMTCT/EID) data and guide referrals. Since health data collection and reporting at the facility level is currently paper-based, some implementing partners of the USG-funded projects adopted the use of mHealth technology to enhance data collection and reporting in HIV service delivery in the regions of the country where they operate.

Selected facilities and NGOs working at the community level in four regions have been supported to implement mHealth technologies. The front line of service delivery in hospitals, health facilities, and community-based organizations that used mHealth technologies to collect and report data were Psychosocial workers, Community Health Workers, and Nurses; all of these Health cadres are herein referred to as “end-user.” These tools did not replace the traditional paper-based registers, as they have not yet been approved by the Ministry of Public Health.

It is important to note that the Ministry of Public Health officially mandates the use of standardized paper-based registers for data collection and reporting at hospital and health facility levels. These registers remain the primary source of national health information and are the official tools for aggregating and transmitting data through the health information system.

Methodology

Research design

We employed a mixed methods design to collect qualitative data through focus group discussions (FGDs), while quantitative data were collected through an online questionnaire from 251 respondents from different work organizations.

Selection of the study site

The study was conducted within four selected regions across the national territory: East, Adamawa, Center, and the Littoral region. These regions were selected for the study because mHealth projects that focus on HIV have been implemented in these areas for 5 to 8 years. These regions also represent a diverse cross-section of the country, encompassing different geographical and socio-economic contexts, rural, urban, and semi-urban settings.

Target population

This study involved all mHealth end-users in the selected regions working either in a hospital-based setting or in NGOs providing HIV services at the community level.

Sample size calculation

Sample size calculation was done using Yamane's formula. This formula is a simplified method to calculate sample sizes for finite populations, particularly useful when dealing with large populations and limited resources for data collection. The formula is expressed as:

n = \frac{N}{1 + N (e^{2})}

n = the sample size

N = the population size

E = the margin of error (which is ± 5%)

n = Minimum Sample size

N = 774

e = acceptance margin of error = 0.05 (5%)

Thus, n = 774

1 + 774 (0.05)² = 288 participants

In this study, given an estimated population of 774 and a 5% margin of error, the calculated minimum sample size was 288. This helped ensure that the views of mHealth end-users were captured with sufficient statistical validity.

Sampling technique

All health facilities and NGOs in the four targeted regions were targeted. Health facilities and NGOs that have implemented mHealth interventions for at least 5 years were selected. The 5-year cut-off was chosen to ensure inclusion of health facilities and NGOs that are at various levels of adoption (routine use of mHealth in M&E). Within the study regions, the selection targeted individuals who were actively involved in the use of mHealth technologies within HIV program monitoring in Cameroon. This included psychosocial agents, case managers, and HIV testing agents, who had direct experience with integrating mobile technology into their work. Respondents were stratified by cadre, that is, Case managers, HIV testing agents (who were community health workers), and psychosocial agents. Respondents were randomly selected proportionate to size to reach the required sample size.

Data collection

Qualitative data

Participants were chosen based on their roles, level of experience, and involvement with mHealth tools, ensuring that the discussions would capture a broad range of perspectives and insights. A FGD guide was used to collect qualitative data to explore the attitudes and challenges mHealth end-users face in integrating mobile technology into HIV program monitoring in Cameroon. The FGD guide was pretested with case managers in a district hospital, which was not in the selected region, and refined accordingly. Participants for the FGDs were purposively selected to ensure gender balance and representation of all cadres engaging in the use of mHealth technologies. In each Region, one FGD was conducted, comprising 7 to 10 participants, with a total of 4 FGDs conducted. FGDs on average lasted 1 hour. True saturation was achieved after the third focus group session, as no new themes emerged. To confirm this, an additional focus group discussion was conducted, which also yielded no new insights.

Quantitative data were collected using a semistructured guide that was piloted with 10 university students and refined accordingly. The questionnaire was organized into three sections: participant demographics, attitudes of mHealth end-users, and the challenges encountered in integrating mHealth technology for HIV program follow up. The questionnaire was distributed via Google Forms. Data were collected between January 2022 and February 2023.

Written informed consent was obtained from all participants prior to their enrollment in the study; end-users who did not provide consent were excluded.

Ethical approval for the study was obtained from the Faculty of Health Sciences Institutional Review Board (FHS IRB) of the University of Buea, Cameroon (Approval No: 2020/1241–06/UB/SG/IRB/FHS).

Data management

Questionnaire entries from participants were properly checked for completion. Daily, questionnaire links were sent to respondents through WhatsApp and email. Responses were stored in an Excel sheet with a copy saved on Google Cloud to ensure security and privacy. The data were exported into Statistical Package for the Social Sciences (SPSS) Version 26 (SPSS Inc., Chicago, USA) for analysis. Consistency and validation checks were done to check for invalid codes and missing data. All missing data were coded as 999 in SPSS.

For qualitative data, extensive notes were taken by one of the researchers during the sessions in addition to audio recordings. Informed consent was sought from the FGD participants prior to the sessions and were informed that they would be recorded (for data analyses purposes) had the right to withdraw from the session anytime. They were assured of anonymity in reporting.

Data analysis

Quantitative data were analyzed using SPSS Statistics version 26. Descriptive statistics were used to summarize the data, with continuous variables presented as means and standard deviations, and categorical variables displayed in frequency tables, bar charts, and pie charts. The attitude of mHealth end-users was assessed by scoring their responses and categorizing them into “good” or “poor” attitude,⁸ groups based on a threshold of 65% answers (which is an average of the Likert scale responses) considered to demonstrate “good” attitude.

n other to assess the attitude of mHealth end-users, end-users would be considered to either have good or poor attitudes toward mHealth based on their responses. The questions were measured on a two-point Likert scale (Yes and No) and each positive (Yes) response to a question was scored 1 point, otherwise 0.⁸ In total, the maximum obtainable score was 6 points corresponding to the 6 questions that were asked. The 6 questions showed good internal consistency in our data (α = 0.92). End-users with scores ≥ 5 were categorized as having a good attitude toward mHealth; otherwise, they were categorized as having a poor attitude.

An F test and chi-square test were employed to examine associations. Logistic regression was then utilized to identify the factors influencing mHealth end-users’ attitudes toward integrating mobile technology in HIV program follow up.

For the qualitative data, audio recordings from the FGDs were transcribed verbatim and processed using NVivo9 software. An inductive content thematic approach was employed to identify and categorize recurring themes. In the first stage, two researchers read the transcribed interviews carefully to identify repeated patterns that were coded and grouped into subcategories and main categories Related issues were them grouped into themes. Where discrepancies arose, these were resolved through discussion and inclusion of two other researchers to reach an agreement. Main messages were identified as quotes to support identified themes.

Results

Level of intervention in the HIV/AIDS program implementation

Because responsibilities overlap, a respondent can be involved at more than one level of intervention in service delivery within the HIV program. According to feedback from participants, majority (73%) indicated they intervene as psychosocial agents where they are responsible for counseling clients, followed by 67% of respondents who indicated they intervene at the level of screening clients (screening here refers to the determination of eligibility for testing) and 61% of respondents intervened at the level of care and treatment. An almost equal percentage of participants intervene in Sensitization and Prevention (55% and 53%, respectively). While 20% of the participants indicated that they facilitate supply chain activities, 4% would intervene in data collection and analysis (Figure 1).

Figure 1.

Level of intervention in the HIV AIDS program.

A follow up to get details on the key activities of the respondents revealed the following activities; counseling clients on prevention and treatment, recording testing and initiation, recording and tracking distribution of prevention materials, reminding clients about their appointments, reminding clients to take medication, creating awareness, and record client activities (Figure 1).

Attitude of mHealth end-users

Seventy-eight percent of end-users have a good attitude toward mHealth, while 22.9% of the respondents had a poor attitude toward mHealth. The overall mean score was 4.48 ± 0.956 with a minimum score of 1pt and a maximum score of 6pts.

Five vital questions were asked to assess participants’ attitudes toward mHealth. More than three-quarters (89%) of the participants affirmed that they use the mobile application for data collection.

This result was supported by feedback from FGDs where almost all participants expressed their preference for mobile applications in terms of data collection compared to using traditional papers or registers. Comparing tablets with smartphones, participants cited numerous reasons for choosing tablets, the most common reason being the larger screen size, making it easier to view work-related materials. Additionally, tablets were deemed to be more conducive to enhanced productivity and a richer media experience. The touchscreen interface was also mentioned as a factor in the preference for tablets/smartphones.

“We're digital, so we're moving forward. We can't go backward and forward at the same time, as we all know, the tablets are a very indispensable tool because we're in WhatsApp groups, we're in forums and these forums allow us to share information concerning work. The information enables us to communicate better with each other. At the moment, if we're chatting, it's because we have these gadgets. So in my opinion, the tablet is way more preferable at the job site as compared to the button phones”. (FGD Male participant)

When asked how often they use mobile applications to collect data, the majority of the participants, equivalent to 85% noted they use it always. This indicates that the few who have not accepted the technology are those not adequately using the platform as recommended. More than three-quarters of the participants, equivalent to 82% of the participants, indicated that the use of mobile applications to deliver their work is “always recommended” to them at their job site.

FGD respondents supported these results, with participants citing how these gadgets help them deliver at work.

One participant, in particular, explained:

“I use my phone to send monthly and activity reports. Without it, it would be difficult for me to do my job effectively”. (FGD Male participant)

All participants agreed with this sentiment, emphasizing that mobile phones are not just convenient but an essential tool for their daily work operations.

Whether or not mobile technology facilitated their work, almost all of the respondents (97%) agreed that mobile devices are of great help to them at their site of work.

FGD respondents supported these statistics as they highlighted various advantages of mobile devices facilitating their work, including easy accessibility and manipulation of data, and seamless triangulation of data using digital platforms. One of the participants specifically mentioned “that the main advantage of using digital platforms is that it provides them with quick and easy access to patient information, no matter where they are.” They can access all the relevant information about a patient from anywhere, without the need to visit a specific site. This is in contrast to paper-based systems, where they would have to physically visit the site to access the information. The participant further added that in facilitating their work, digital platforms eliminate the need for intermediaries to get access to patient information and enable them to make informed decisions quickly and efficiently.

“The digital platform is best in facilitating my work because there, it's easier to manipulate and store data. And of course, from time to time, we can access our data at any time, even if we're at home, if, for example, we've left a document in the office and the management calls us at home saying that he needs a piece of data from that document. If we have the tablet with us, we can easily share the data with our superiors who ask for it, rather than thinking of how we will go to the office to get data from the document. So I think the digital platform is much better”. (FGD Female Participant)

Similarly, a majority of the respondents knew the name of the application/tool they used for work (Table 1).

Table 1.

Attitude of mHealth end-users.

Variables	Responses	Frequency (n)	Percentage
Mobile applications are used for data collection	Yes	233	88.8
Mobile applications are used for data collection	No	28	11.2
Mobile devices are frequently recommended for job-related purposes	Always	205	81.7
	Not all the time	46	18.3
Likes to use the mobile application to carry out my job-related tasks	Always	213	84.9
	Not all the time	38	15.1
The mobile device I use helps me in my work	Yes	243	96.8
The mobile device I use helps me in my work	No	08	3.2
Using mobile devices to collect data is a sustainable method that can last a long time	Yes	201	80.1
	No	50	19.5
Know the name of the mobile application I use for reporting/data collection	Yes No	213 38	84.9 15.1

For respondents who knew the name of the mobile application, a follow up on the name of platforms being used noted that Commcare and Scorecard were the most used platforms (Figure 2).

Figure 2.

Platforms used for data collection.

Association of mHealth end-user attitude with sociodemographic factors

The quantitative study involved 251 participants from 4 regions: East (39.4%), Centre (24.3%), Adamawa (21.1%), and Littoral (15.1%). Twelve end-users declined to participate, and 25 end-users who had initially agreed to participate did not submit their questionnaires, taking the total number of participants down to 251. Participants were mostly aged 30 to 39 years (49%), with 40% aged 20 to 29 years. The majority had a university education (54%), while 71% had 1 to 5 years of work experience. End-users predominantly used smartphones for data collection and reporting (66.1%) (Table 2).

Table 2.

Sociodemographic characteristics of the study population (n = 251).

Characteristic	Category	Frequency, N (%)
Age group (years)	20–29 30 – 39 40 – 49 Above 50 Total	100 (39.8) 122 (48.6) 22 (8.5) 07 (2.8) 251 (100.0)
Region of work	East Adamawa Center Littoral	99 (39.4) 53 (21.1) 61 (24.3) 38 (15.1)
	Totals 251 (100.0)
Academic qualification	PrimarySecondaryUpper SecondaryUniversityTotals	06 (2.4)68 (27.1)43 (17.1)134 (53.4)251 (100.0)
Years of work experience (years)	< 11–5Above 5Total	22 (8.8)178 (70.9)51 (20.3)251 (100.0)
Type of mobile device use for data collection	Android tabletSmart phonesOthersTotals	62 (24.7)166 (66.1)23 (9.3)251 (100)
Which do you prefer for data capture?	Mobile deviceForms and registersBothTotal	19 (7.6)64 (25.5)168 (66.9)251 (100.0)

Age groups exhibited comparable mean attitude scores (F = 1.026, p = 0.382), suggesting age might not strongly influence mHealth attitudes. However, regional disparities were evident (F = 5.259, p = 0.002), with the Adamawa and Littoral regions showing higher mean attitude scores compared to the East and Centre regions.

Educational attainment significantly impacted attitudes (F = 13.45, p < 0.001). Participants with secondary, upper secondary level (General Certificate of Education, GCE advanced level), and university education levels displayed higher mean attitude scores compared to those with primary education. Conversely, work experience showed no significant association with attitudes (F = 1.648, p = 0.194), implying its limited impact (Table 3).

Table 3.

Association of mHealth end-user attitude (scale) with sociodemographic factors.

	Attitude (scale variable)
Variables	M	SD	F	p-Value
Age group (years)
20–29	4.49	0.99
30–39	4.39	1.01	1.026	0.382
40–49	4.50	0.80
Above 50	3.86	1.06
Region of work
East	4.22	1.08
Adamawa	4.68	0.78	5.259	0.002
Centre	4.28	1.08
Littoral	4.82	0.56
Educational level
Primary	2.50	0.84
Secondary	4.75	0.65	13.45	< 0.001
Upper secondary	4.73	0.72
University	4.28	1.08
Work experience (years)
< 1	4.23	1.02
1–5	4.49	0.98	1.648	0.194
Above 5	4.25	0.98

F: F-statistics; M: mean; SD: standard deviation.

The attitude scale variable was categorized into two categories (Good Attitude and Poor Attitude). The chi-square association test was used to test these categories for association with the demographic factors. End-users who work in the Littoral region were significantly more associated with a good attitude toward mHealth (p = 0.004) compared with those in the remaining 3 regions, whose attitude did not show any statistical significance. mHealth end-users with secondary academic qualifications were significantly more associated with a good attitude toward mHealth (p < 0.001) compared with end-users with other educational qualifications (Table 4).

Table 4.

Attitude of mHealth end-users by sociodemographic factors.

Variable	Total number			χ²	p-Value
Variable	PresentNo (%)	Good attitudeNo (%)	Poor attitudeNo (%)	χ²	p-Value
Age group (years)
20–29	100 (39.8)	80 (80.0)	20 (20.0)
30–39	122 (48.6)	98 (76.2)	29 (23.8)
40–49	22 (8.7)	18 (81.8)	04 (18.2)	0.820	0.845
Above 50	07 (2.7)	05 (71.4)	02 (28.6)
Total	251 (100.0)	196 (78.1)	55 (21.9)
Region of work
East	99 (39.4)	68 (68.7)	31 (31.1)
Adamawa	53 (21.1)	47 (88.7)	06 (11.3)
Centre	61 (24.3)	46 (75.4)	15 (24.6)	13.207	0.004*
Littoral	38 (15.1)	35 (92.1)	03 (7.9)
Total	251 (100.0)	196 (78.1)	55 (21.9)
Educational level
Primary	06 (2.4)	01 (16.7)	05 (83.3)	24.883	< 0.001*
Secondary	68 (27.1)	62 (91.2)	06 (8.8)
Upper secondary	43 (17.1)	37 (36.0)	06 (14.0)
University	134 (53.3)	96 (71.6)	33 (28.4)
Total	251 (100.0)	196 (78.1)	55 (21.9)
Work experience (years)
< 1	22 (8.8)	15 (68.2)	07 (31.8)
1–5	178 (70.9)	145 (81.5)	33 (18.5)	4.121	0.127
Above 5	51 (20.3)	36 (70.6)	15 (29.4)
Total	251 (100.0)	196 (78.1)	55 (21.9)

To determine if constant reminders to use mobile devices would encourage their actual use, the results showed that the more frequently their organization recommended the app be used, the more often the mobile applications were utilized, and this association was found to be significant (Table 5).

Table 5.

Association of mobile app use recommendation to frequency of usage.

Variable	Total number	How often do you actually use the mobile device		χ²	p-Value
Variable	Present, No (%)	Always use mobile app, No (%)	Rarely use the mobile app, No (%)	χ²	p-Value
How often is mobile device recommended for use?
Always	205 (81.7)	203 (99.0)	02 (1.0)
Not all the time	46 (18.3)	10 (21.7)	36 (78.3)	174.669	<0.001
Total	251 (100.0)	213 (84.9)	38 (15.1)

A significant association was found between educational level and the preferred data capture method (χ² = 20.349, p = 0.002). Participants with primary and secondary education levels tended to prefer paper-based documentation (forms and registers), while those with university education levels showed a stronger preference for mobile devices. Academic qualification emerged as a strong predictor of attitudes toward mHealth. There was a strong association between higher academic qualifications, ranging from Secondary to University level, and positive attitudes toward mHealth. Participants with Upper secondary educational backgrounds exhibited significantly greater propensities toward embracing mHealth compared to those with only primary education. This association persisted even after controlling for other variables, underscoring the independent influence of academic qualification on attitudes toward mHealth (Table 6).

Table 6.

Preferred methods of data capture by sociodemographic factors.

Variable	Total number	Which do you prefer for data capture			χ²	p-Value
Variable	Present No (%)	Mobile deviceNo (%)	Forms and registersNo (%)	BothNo (%)	χ²	p-Value
Age group (years)
20–29	100 (40.0)	08 (8.0)	29 (29.0)	63 (63.0)
30–39	122 (48.6)	06 (4.9)	28 (23.0)	88 (72.1)
40–49	22 (8.7)	03 (13.6)	06 (27.3)	13 (59.1)	8.496	0.204
Above 50	07 (2.8)	02 (28.6)	01 (4.3)	04 (57.1)
Total	251 (100.0)	19 (7.5)	64 (25.5)	168 (66.9)
Educational level
Primary	06 (2.4)	01 (16.7)	00 (0.0)	05 (83.3)	20.349	0.002
Secondary	68 (27.1)	03 (4.4)	25 (36.8)	04 (58.8)
Upper secondary	43 (17.1)	03 (7.0)	18 (41.9)	22 (51.2)
University	134 (53.3)	12 (9.0)	21 (15.7)	101 (75.4)
Total	251 (100.0)	19 (7.6)	64 (25.5)	168 (66.9)
Work experience (years)
< 1	22 (8.8)	02 (9.1)	10 (45.5)	10 (45.5)
1 – 5	178 (70.9)	11 (6.2)	44 (24.7)	123 (69.1)	20.349	0.002
Above 5	51 (20.3)	06 (11.8)	10 (19.6)	35 (68.6)
Total	251 (100.0)	19 (7.6)	64 (25.5)	168 (66.9)

Additionally, there was a significant association between work experience and the preferred method of data capture (χ² = 20.349, p = 0.002). Individuals with less than 1 year or above 5 years of work experience tended to favor paper-based documentation (forms and registers) while those with 1 to 5 years of work experience showed a higher preference for mobile devices (Table 6).

Participants from the Adamawa and Littoral regions had a significantly higher likelihood of possessing positive attitudes toward mHealth compared to their counterparts in the East region. Even after accounting for other variables, such as academic qualification and work experience, these regional differences remained significant, indicating that the region of work independently influenced attitudes toward mHealth (Table 7).

Table 7.

Determinants of end-users’ attitude towards mHealth.

Variables	Good attitude		Bivariate analysis		Multivariate analysis
Variables	N	% Good attitude	COR (95%CI)	p-Value	AOR (95% CI)	p-value
Region of work
East	68	68.7	1		1
Adamawa	47	88.7	3.571 (1.38–9.28)	0.009*	3.33 (1.18–9.34)	0.022*
Centre	46	75.4	1.398 (0.68–2.87)	0.362	2.05 (0.93–4.51)	0.074
Littoral	35	92.1	5.319 (1.52–18.6)	0.009*	4.50 (1.24–16.3)	0.022*
Total	196	78.1
Academic qualification
Primary	01	16.7	1		1
Secondary	62	91.2	51.67 (5.15–9.28)	0.001*	54.7 (5.07–590)	0.001*
Upper secondary	37	36.0	30.83(3.05–311.8)	0.004*	27.9 (2.61–300)	0.006*
University	96	71.6	12.63(1.43–111.7)	0.023*	12.6 (1.37–117)	0.025*
Total	196	78.1
Work experience (years)
<1	15	68.2	1		1	-
1–5	145	81.5	2.05 (0.78–5.43)	0.148	2.04 (0.68–6.13)	0.201
Above 5	36	70.6	1.12 (0.38–3.30)	0.837	1.26 (0.37–4.28)	0.706
Total	196	78.1

AOR: adjusted odds ratio; COR: crude odds ratio.

Factors favoring mHealth use for M&E in Cameroon

Regarding mentorship, more than three-quarters (84.5%) of the respondents affirmed they get mentored on how to perform their respective tasks using a mobile device. A majority of FGD participants responded affirmed to this, with almost all of them stating that they do receive mentorship and assistance from experts within their organization. Participants cited specific individuals who offered them mentorship assistance, such as M&E officers, data managers, and project managers.

One participant in the FGD shared a personal experience, stating that:

“The M&E officers and project managers attend to us when we face challenges and, on top of that, they're always reassured that the gadget is working normally and that it's giving us the information we need.” (FGD Female Participant)

Some participants also mentioned that distance is not a barrier when it comes to receiving mentorship. They stated that they can always contact their mentors through phone calls for directives when they get stuck in the course of using the device.

Overall, it was evident from the discussion that the respondents receive adequate mentorship and assistance from experts within their organization when it comes to using digital devices for their activities.

More so, over two-thirds (88%) of participants said they have standard operating procedures (SOPs) at their disposal to help them in their work. Region of work also played a crucial role; workers in the Centre region were less likely to have access to SOPs and formal training compared to those in the East, Ngaoundere, and Littoral regions (χ² = 58.127, p < 0.001) (Table 8). Feedback from FGDs noted only a few participants who confirmed that they had received SOPs from experts in their organization on how to use digital devices.

“As far as procedures posted somewhere are concerned, no, there aren't any SOPs. What we do is make use of the user manuals that come out of the equipment boxes, that's all”. (FGD Female participant)

Table 8.

Challenges faced by mHealth end-users.

Demographic characteristics	N (%)	Facing difficulties using your mobile device to do your job, N (%)		Get mentored on how to perform your task using a mobile device, N (%)		Do you have the SOPs at your disposal to help you in your work, N (%)		Ever received formal training from your employer on how to use your device to capture data, N (%)
Total	251 (100.0)	Yes	No	Yes	No	Yes	No	Yes	No
Total	251 (100.0)	41 (16.3)	210 (83.6)	212 (84.5)	39 (15.5)	221 (88.0)	30 (11.9)	198 (78.9)	53 (21.1)
Level of education		χ² = 2.034, p-value = 0.565		χ² = 18.939, p-value <0.001		χ² = 9.443, p-value = 0.024		χ² = 16.972, p-value = 0.001
Primary	06 (2.4)	02 (4.9)	04 (1.9)	02 (0.9)	04 (10.3)	03 (1.4)	03 (10.0)	02 (1.0)	04 (7.5)
Secondary	68 (27.1)	11 (26.8)	57 (27.1)	59 (27.8)	09 (23.1)	59 (26.7)	09 (30.0)	58 (29.3)	10 (18.9)
Upper secondary	43 (17.1)	05 (12.2)	38 (18.1)	42 (19.8)	01 (2.6)	40 (18.0)	03 (10.0)	40 (20.2)	03 (5.7)
University	134 (53.4)	23 (56.1)	111 (52.8)	109 (51.4)	25 (64.1)	119 (53.8)	15 (50.0)	98 (49.5)	36 (67.9)
Job title/position		χ² = 12.046, p-value = 0.017		χ² = 9.833, p-value = 0.043		χ² = 3.412, p-value = 0.491		χ² = 11.883, p-value = 0.018
Psychosocial agent	177 (70.5)	34 (19.2)	143 (80.8)	144 (81.4)	33 (18.6)	154 (87.0)	23 (13.0)	132 (74.6)	45 (25.4)
Community health workers	21 (8.4)	00 (100.0)	21 (100.0)	20 (95.2)	1 (4.8)	19 (90.5)	02 (9.5)	20 (95.2)	01 (4.8)
Data clerk	31 (12.4)	02 (6.5)	29 (93.5)	31 (100.0)	00 (0.0)	30 (96.8)	01 (3.2)	31 (96.8)	01 (3.2)
Nurse	10 (4.0)	04 (40.0)	06 (60.0)	08 (80.0)	2 (20.0)	08 (80.0)	02 (20.0)	07 (70.0)	03 (30.0)
Others	12 (4.8)	01 (8.3)	11 (91.7)	09 (75.0)	03 (25.0)	10 (83.3)	02 (16.7)	09 (75.0)	03 (25.0)
Region of work		χ² = 5.127, p-value = 0.163		χ² = 24.281, p-value <0.001		χ² = 21.513, p-value <0.001		χ² = 58.127, p-value <0.001
East	99 (39.4)	21 (21.2)	78 (78.8)	83 (83.8)	16 (16,2)	89 (89.9)	10 (10.1)	82(82.8)	17 (17.2)
Ngoundere	53 (21.1)	04 (7.5)	49 (92.5)	51 (96.2)	02 (3.8)	51 (96.2)	03 (3.8)	51 (96.2)	02 (3.8)
Centre	61 (24.3)	11 (18.0)	50 (82.0)	41 (67.2)	20 (32.8)	44 (72.1)	17 (29.7)	28 (45.9)	33 (54.1)
Littoral	38 (15.1)	05 (13.2)	33 (86.8)	37 (97.4)	01 (2.6)	37 (97.4)	01 (2.6)	31 (97.4)	01 (2.6)

When asked whether or not they face difficulties using their mobile device to do their job, 16.3% of respondents indicated they face difficulties. However, some FGD, participants cited challenges they faced while using mobile devices at work. They raised concerns about network issues and the tendency of devices to malfunction easily. While acknowledging these challenges, some participants suggested a mitigation strategy adding that paying keen attention to the quality of the tablets before purchasing them.

Job title significantly impacted the likelihood of facing difficulties, with nurses and psychosocial agents reporting more challenges compared to community health workers and data clerks (χ² = 12.046, p = 0.017) (Table 8).

Seventy-nine percent of the respondents indicated they received formal training from their employer on how to use mobile devices to capture data. A strong correlation was found between the level of education and the receipt of formal training on using mobile devices, with those holding a university degree more likely to have received training (χ² = 11.883, p = 0.018) (Table 8).

During FGD, participants highlighted the absence of secure storage space provided by their office for their devices. This lack of secure storage options led to various challenges, including instances where servers were lost due to limited means of equipment preservation. Participants expressed that the provision of secure storage facilities would help alleviate these issues.

Despite the absence of secure storage options, some participants mentioned that their organization has implemented measures to maintain device security, such as requiring individuals to sign documents acknowledging their responsibility for the devices. However, participants noted that these measures may not be sufficient to ensure device safety.

Participants also discussed reasons why their organization has not created secure storage facilities, citing security concerns and the lack of necessary security measures in their department.

Regarding the sustainability of mobile devices for data collection, participants overwhelmingly agreed that it is an efficient and practical approach that organizations should adopt. They emphasized the advantages of using digital platforms, such as preventing duplicate data and facilitating communication and information exchange among colleagues and collaborators.

“Yes, and I in particular think that using a mobile device to collect data is a sustainable method that can last for a long time, and to me, this method should be embraced and congratulated because we're in a digital age. We have to keep up with the times. So, everything is digital and if we want to move forward, we have to adapt to technological change. So, I think it's the best thing ever”. (FGD Female participant)

Some participants emphasized the importance of ensuring the durability of mobile devices to improve sustainability. They suggested avoiding downloading certain content that could potentially damage the device, leading to malfunctioning and data loss due to formatting. Overall, there was a consensus among participants that utilizing mobile devices for data collection is a sustainable option for organizations aiming to streamline their data collection processes.

Discussion

Key findings from this study reveal that 78% of end-users had positive attitudes toward mHealth, preferring mobile applications over traditional methods due to ease of use and efficiency. Attitudes were significantly influenced by region of work and educational level, while age and work experience showed no significant associations. Key challenges included the need for better training, device reliability issues, and integration with existing systems.

These findings offer valuable insights into the current state of mobile health technology implementation in Cameroon. The outcomes are consistent with findings from numerous studies within the African region.^9,10

Eighty-two percent of participants aged between 30 to 39 and 20 to 29 years displayed a positive attitude toward mHealth, with over 80% of them acknowledging its potential benefits. However, this association was not statistically significant, indicating that the observed differences could have occurred by chance. This finding contradicts the results of a study by Faqih, which found that demographic variables such as age and gender significantly influence the adoption of mobile technologies in healthcare systems in Jordan.¹⁰ Similarly, this result contrasts with the study conducted by Babarinde in Lagos, where the younger 20- to 29-year-old end-user population was significantly associated with the use of mobile health.¹¹ Faqih's work focused on healthcare professionals and administrators, groups whose digital skills and roles systematically differ by age, while Babarinde's study targeted a younger end-user cohort within a specific program, naturally accentuating age-related adoption differences; by contrast, our study sample is broader and more uniformly trained, and because the mHealth platform has progressed beyond early pilot stages into scaled-up implementation with repeated mentorship and guidance, older participants have “caught up,” smoothing out any generational adoption gap.

Individuals with higher academic qualifications, ranging from secondary to university level, were more likely to have positive attitudes toward mHealth interventions. This association remained significant even after controlling for other variables, suggesting that academic qualification is an important independent predictor of attitudes toward mHealth. This finding aligns with the results of Osei's study in Ashanti, Ghana, where health workers with a secondary level of education were significantly associated with the use of mobile health.¹² Higher academic qualifications likely equip individuals with stronger eHealth literacy, the composite of traditional, health, information, media, scientific, and computer literacies, which in turn enhances both their perceived ease of use and perceived usefulness of mHealth tools.¹³ Educated users are more accustomed to learning in technology-mediated environments, more confident in troubleshooting technical challenges.

We observed that end-users in the Littoral and Adamawa regions exhibited significantly higher mean attitude scores toward mHealth than those in the East and Centre regions. This pattern mirrors broader findings that regions with superior digital infrastructure and strong facilitating conditions report more favorable technology acceptance.¹⁴

End-users in the Centre region were less likely to have access to SOPs compared with those in other regions. The Centre Region hosts the political capital city of the country (Yaoundé) and, as a result, hosts several main offices for health development partners. Most mHealth projects have been piloted and implemented in this region by various development partners. This places them in a position to adopt and use mHealth relatively faster, with minimal need for SOPs, compared to the other regions that participated in our study

We observed a high adoption rate of mobile applications for data collection, with 89% of participants confirming their usage. This underscores the acknowledgment and acceptance of the benefits that mobile technology brings to data gathering within the context of HIV programs. Most respondents reported frequent use of mobile devices for data collection, indicating that these devices have become integral to their data collection workflow. This preference for mobile technology is attributed to its convenience and efficiency in capturing and managing data in real-time.^15,16 Eighty-two percent of participants indicated that the use of mobile applications for their work is “always recommended” at their job site. This suggests strong endorsement and support for integrating mobile technology into their work processes within HIV program implementation in Cameroon. The recommendation to use mobile applications highlights the recognition of potential advantages such as improved accuracy, timeliness, and ease of data collection and management.

Additionally, an overwhelming majority of respondents (97%) agreed that mobile devices are of great help to them at their work site, indicating the perceived value and usefulness of mobile technology in supporting their daily activities. This underscores that mobile devices significantly contribute to enhancing efficiency and effectiveness in delivering HIV services, aligning with findings from other studies.¹⁷ Most participants highly recommend using mobile applications to perform work-related tasks, consistent with results reported by Rahman and colleagues in 2017.¹⁸ Findings from FGDs further supported these results, with all participants expressing positive sentiments about the benefits of using mobile phones to enhance productivity. They emphasized that mobile phones are not merely convenient tools but essential components of their daily work operations.

There was a significant association between the frequency of organizational reminders for app usage and the actual usage of mobile applications. Specifically, the findings indicate that when organizations actively encouraged and recommended the use of the app, it positively influenced individuals’ behavior, leading to increased usage of the mobile applications. In other words, the level of organizational endorsement and promotion had a positive impact on the adoption and utilization of mobile applications. The significance of this association highlights the importance of organizational support and endorsement in driving the adoption and usage of mobile applications.^19,20

Over three-quarters of participants reported that they do not encounter difficulties when using mobile devices to perform their job responsibilities. This suggests that a large portion of the participants feel comfortable and proficient in utilizing mobile devices as tools for their work-related tasks. Ease in using mobile devices implies that they have the necessary skills, knowledge, and confidence to effectively leverage these devices in their job roles. This positive sentiment toward mobile device usage for work suggests a favorable integration of technology into the workplace and highlights the potential benefits and efficiency offered by mobile devices in supporting job performance. This is also backed by the fact that a significant majority of respondents (84.5%) affirmed that they receive mentoring on how to perform their respective tasks using a mobile device. This indicates the recognition of the importance of guidance and support in effectively utilizing mobile technology in their work. The provision of mentorship suggests a commitment to enhancing the participants’ skills and knowledge in leveraging mobile devices for improved job performance within the context of the HIV program. This finding is consistent with Meeks’ research, which identified that supervision (mentorship) interventions had a significant positive effect on the use of mobile health applications.²¹ Even though over two-thirds of participants (88%) reported having SOPs at their disposal to assist them in their work, FGDs revealed that the participants had a poor understanding of what they termed SOPs, as these SOPs were mostly absent. Participants, however, received guidance and mentorship on how to use these devices. This contradicts work by Jembai in Malaysia, where more than 90% of respondents acknowledged the availability of SOPs, especially during the COVID-19 era.²² SOPs are organizational documents that provide standardized guidelines and procedures and help ensure consistency, quality, and efficiency in their tasks.

Although over two-thirds of participants reported having SOPs, FGDs revealed poor understanding and limited actual availability, highlighting a critical disconnect between policy and practice in digital health implementation.

A handful of participants indicated that they encountered challenges. These difficulties encompass a range of issues from technical problems, usability concerns, and connectivity issues, to insufficient training or support. Identifying these challenges is essential for addressing barriers and improving mobile devices’ overall user experience and effectiveness in their work processes.

One noteworthy finding was that psychosocial agents faced a greater array of challenges compared to other respondents. This suggests that the nature of their work, which likely involves providing emotional support and counseling, may present unique obstacles when utilizing mobile devices. Understanding these challenges can help organizations develop targeted strategies to address the specific needs of psychosocial agents. Integrating digital health courses into the training curricula for health professionals can empower them to utilize mobile devices more effectively in data management and communication tasks.

Conclusion

This study reveals a predominantly positive attitude among end-users toward integrating mHealth technologies into HIV program monitoring in Cameroon, consistent with trends observed across sub-Saharan Africa. While age was not a significant factor, academic qualification emerged as a key predictor of mHealth receptiveness, highlighting the need for capacity-building efforts tailored to varying educational backgrounds. The strong organizational support for mHealth, coupled with high uptake for data collection and reporting, highlights institutional readiness and the potential scalability of mobile technologies in program monitoring.

However, persistent challenges, particularly among psychosocial workers, point to gaps in training, infrastructure, and contextual adaptation of digital tools. These findings call for differentiated implementation strategies that consider cadre-specific needs and foster inclusive digital literacy.

At a broader level, the results affirm that successful integration of mHealth into HIV monitoring systems requires more than technological availability; it demands sustained institutional support, equitable workforce capacity development, and secure data management systems. As countries move toward digitizing health information systems, especially in the context of HIV, these insights contribute to shaping scalable, human-centered, and sustainable digital health strategies. Further longitudinal and implementation research is recommended to assess long-term adoption patterns, impact on health outcomes, and system-level integration across diverse contexts.

Study limitations

The findings were not validated by the respondents, which could have potentially enhanced the work. However, the authors possess substantial knowledge of digital health, which supports the credibility of the results.

Nonresponse of over 25 participants. This reduction in sample size could have lowered the statistical power of our analysis.

The study did not disaggregate the quantitative sample by gender, which limits the ability to assess potential gender-related differences in attitudes toward mHealth.

Footnotes

List of abbreviations

Acknowledgments

The authors would like to express their sincere gratitude to all the participants who contributed to this study. Your willingness to share your experiences, insights, and time was invaluable to the success of this research.

ORCID iDs

Frankline Sanyuy Nsai

Anna Longdoh Njunda

Nicholas Tendongfor

Cho Sabastine Anye

Juliet Nabyonga-Orem

Omer Njajou

Ethical approval

This study was obtained from the Institutional Review Board of the Faculty of Health Sciences, University of Buea, Cameroon, Ref: 2020/1241-06/UB/SG/IRB/FHS. All participants provided informed consent before their inclusion in the study. Participation was voluntary, and confidentiality was assured throughout the research process.

Informed consent

Written informed consent was obtained from all participants before their enrollment in the study; end users who did not provide consent were excluded.

Contributorship

FSN, ON, ALN, and PJN were involved in conception and design; FSN, ON, and NT in administrative support; FSN in provision of study materials or patients; and FSN, ON, CSA, and JNO in collection and assembly of data, data analysis and interpretation. All authors contributed to manuscript writing and final approval of manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Gurantor

FSN is guarantor of this work, who has full access to all the data in this study and take responsibility for the integrity and accuracy of the data.

Peer Review

This article was not commissioned and was submitted to the journal as an original research manuscript. It has undergone external peer review by independent experts. All reviewers were selected by the journal, and the authors have addressed the reviewers' comments and revised the manuscript accordingly. The final version has been approved by all authors and is submitted for publication consideration.

References

Braun

Catalani

Wimbush

, et al. Community health workers and mobile technology: a systematic review of the literature. PLoS ONE 2013; 8: e65772.

World Health Organization . Consolidated guidelines on person-centred HIV patient monitoring and case surveillance. Geneva: World Health Organization, 2017, Available from: https://www.who.int/publications/i/item/9789241512633.

GeoPoll . Smartphone and mobile internet penetration in Africa and globally. 2019. Available: https://www.geopoll. com/blog/ smartphone-mobile-internet- penetration-africa/ [Accessed 05 Oct 2020].

World Health Organization . Digital health: Seventy-First World Health Assembly, WHA71.7. Geneva: World Health Organization, 2018, Available from: https://apps.who.int/gb/ebwha/pdf_files/WHA71/A71_R7-en.pdf.

Slater

Campbell

Stinson

, et al. End user and implementer experiences of mHealth technologies for noncommunicable chronic disease management in young adults: systematic review . J Med Internet Res 2017; 19: e406.

Brinkel

Dako-Gyeke

Krämer

, et al. An investigation of users’ attitudes, requirements and willingness to use mobile phone-based interactive voice response systems for seeking healthcare in Ghana: a qualitative study. Public Health 2017; 144: 125–133.

Leaf

Bruce

Tischler

, et al. The relationship between demographic factors and attitudes toward mental health services. J Community Psychol 1987; 15: 275–284.

Brown

Isbel

, et al. Measuring attitudes: current practices in health professional education. In: Nestel

Reedy

McKenna

, etal (eds) Clinical education for the health professions 2020; Singapore: Springer, pp. 1–28.

Kenny

O’Connor

Eze

, et al. A ground-up approach to mHealth in Nigeria: a study of primary healthcare workers’ attitude to mHealth adoption. Procedia Comput Sci 2017; 121: 809–816.

10.

Faqih

Jaradat

. Mobile healthcare adoption among patients in a developing country environment: exploring the influence of age and gender differencesInt Business Res 2015; 8: 142.

11.

Babarinde

Balogun

Odugbemi

. Knowledge, attitude and use of mobile phones to acquire health-related information among students of Yaba College of Technology, Lagos. Niger Q J Hosp Med 2018;28(1):62–82.

12.

Osei

Agyei

Tlou

, et al. Availability and use of mobile health technology for disease diagnosis and treatment support by health workers in the Ashanti regionof Ghana: a cross-sectional survey. Diagnostics. 2021 Jul 9; 11: 1233. doi:10.3390/diagnostics11071233

13.

Stellefson

Hanik

Chaney

, et al. Ehealth literacy among college students: a systematic review with implications for eHealth education. J Med Internet Res 2011; 13: e102.

14.

Yao

Yan

. Research on digital infrastructure construction empowering new quality productivity. Sci Rep 2025; 15: 6645.

15.

Forrest

Wiens

Kanters

, et al. Mobile health applications for HIV prevention and care in Africa. Curr Opin HIV AIDS 2015; 10: 464–471.

16.

Krah

de Kruijf

. Exploring the ambivalent evidence base of mobile health (mHealth): a systematic literature review on the use of mobile phones for the improvement of community health in Africa. Digital Health 2016; 2: 2055207616679264.

17.

Mengesha

Steege

Kea

, et al. Can mHealth improve timeliness and quality of health data collected and used by health extension workers in rural Southern Ethiopia?. J Public Health 2018; 40: ii74–ii86.

18.

Rahman

Hanifi

Khatun

, et al. Knowledge, attitudes and intention regarding mHealth in generation Y: evidence from a population based cross sectional study in Chakaria, Bangladesh. BMJ Open 2017; 7: e016217. doi: 10.1136/bmjopen-2017-016217

19.

Jacob

Sanchez-Vazquez

Ivory

. Organizational, and technological factors impacting clinicians’ adoption of mobile health tools: systematic literature review. JMIR mHealth Uhealth 2020; 8: e15935.

20.

Currie

. Health organizations’ adoption and use of mobile technology in France, the USA and UK. Procedia Comput Sci 2015; 98: 413–418. 10.1016/j.procs.2016.09.063

21.

Meeks

. The leader's role in motivating public sector workers. Public Pers Manage 2023; 52: 113–128.

22.

Jembai

Wong

Bakhtiar

, et al. Mobile health applications: awareness, attitudes, and practices among medical students in Malaysia. BMC Med Educ 2022; 22: 544.

Attitudes of mHealth end-users toward integrating mobile technology in HIV program monitoring in Cameroon

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction and background

Context

Methodology

Research design

Selection of the study site

Target population

Sample size calculation

Sampling technique

Data collection

Qualitative data

Data management

Data analysis

Results

Level of intervention in the HIV/AIDS program implementation

Attitude of mHealth end-users

Association of mHealth end-user attitude with sociodemographic factors

Factors favoring mHealth use for M&E in Cameroon

Discussion

Conclusion

Study limitations

Footnotes

List of abbreviations

Acknowledgments

ORCID iDs

Ethical approval

Informed consent

Contributorship

Funding

Declaration of conflicting interests

Gurantor

Peer Review

References