Results of the study selection process
A total of 1289 publications was retrieved for this review, including 1189 from the databases and 100 from Google search. After deleting duplicates, 1013 remained for screening the titles. 301 articles were chosen from the screened abstracts, selecting 82 full-texts eligible for full-text screening and 4 papers were identified from reference lists. As a result, 36 full texts were included for the data extraction. The selection process is shown in the PRISMA flow diagram Fig. 1 (Moher et al., 2009).
Figure 1
Descriptive results of geographic distribution and study designs
The studies included came from twenty different countries (Fig. 2. Geographic distribution of mobile phone-based studies). Eighteen studies were conducted in the American regions (Colombia, United States, Brazil, Guatemala, Peru and Mexico), of which one study was conducted in four countries (El Salvador, Honduras, Dominican Republic and Guatemala). Twelve were conducted in the Asian region (Nepal, Singapore, Sri. Lanka, India, China, Malaysia and Pakistan), four in the Africa region (Kenya and Madagascar) and only two were identified in other regions (Fiji and Spain). Most studies were focussing on urban areas where our target diseases are prevalent, only three were specifically conducted in a rural area. Brazil and United States of America were the countries with the highest number of publications (each one with six), however the studies identified in Unites States were not performed under real-world conditions, but rather under laboratory conditions. Most studies were published in the last three years (n = 22), reflecting a recent increase in the use of mobile phones for the prevention and control of arbovirus diseases.
Figure 2
From 36 identified studies, most of them had a descriptive approach (n = 12), of which some provided preliminary results with small groups of people who "tested" the mobile technology in controlled environments and a few studies described their lessons learned after being conducted at a large scale. Some studies included pilot/feasibility studies (n = 6), diagnostic test studies (n = 6), retrospective studies (n = 4), cross sectional studies (n = 4), randomized controlled trials (n = 3), quasi-experimental studies (n = 2), non-randomized control trial (n = 1), and a qualitative study (n = 1). Regarding our target diseases, the majority of the 36 studies focused on dengue (n = 15), six studies on Zika and no study on chikungunya specifically. However, seven publications covered arboviral diseases in general or Aedes vectors. Seven studies on the mobile health technology targeted more than one infectious disease including arboviruses.
Mobile phone categories
With respect to the mobile phone technology, we classified each service of a cell phone to identify which type of mobile phone category were most frequently used in terms of our outcomes. Five mobile phone categories were identified: mobile applications (mobile apps, smartphone apps, mobile software), SMS (Short Message Services), mobile phone tracking data (call detail records, mobile phone signals), camera phone (camera module/image sensor) and simple communication service (calls). An overview is presented in Table 3. The most widely used mobile phone category was mobile applications (n = 18). Simple mobile communication (e.g. voice communication) were used less often.
Table 3
Mobile phone categories according to the 36 studies
Mobile phone category | Definition and considerations | Number of hits |
Mobile applications | Mobile applications, commonly referred to as mobile apps, are software programs designed to run on a mobile device, such as a smartphone or tablet. Many mobile apps have corresponding programs meant to run on desktop computers. This category comprises mobile apps, iPhone apps, smartphone apps, mobile software and m-learning platforms that were run on mobile phone or smartphone. | 18 |
Short message service | Short message service (SMS) is a service for sending electronic message to and from a mobile phone. Messages are usually no longer than 160 alpha-numeric characters and contain no images or graphics. SMS is also known as text messaging. | 7 |
Camera phone | A camera phone is a mobile phone that can take pictures and record video clips. Most new cellular phones are already equipped with cameras which include an image sensor, the lens and microelectronic mechanical system. Smartphone cameras are used for imagen processing and visual readout. | 6 |
Mobile phone tracking data | mobile phone tracking data are often call detail records (CDR) that log the location of mobile phone users when they make telecommunication transactions, such as a phone call or text message. This category comprises mobile phone signals. | 4 |
Simple mobile communication | Simple mobile phone communication involves the use of mobile phone numbers to allow contact with others including voice communication (e.g. calls). | 1 |
Purpose of mobile phone use in health programmes
To analyse the support that mobile phones are promoting, we noticed that the included studies in this review were focussing on three major purposes: surveillance, disease prevention and disease management which are summarized in Table 4. Three studies were identified for both purposes: surveillance and prevention (Reddy et al., 2015, Lwin et al., 2017; Rodriguez et al., 2018), resulting in 39 studies (including 3 addressing both surveillance and prevention) to be analysed. This review also identified specific aims in each purpose which are presented in Table 4. Some mobile applications were assigned to more than one aim, thus being able to perform a variety of functions such as data collection, health education, geolocation among others (e.g. The App, Mosquito Alert; Palmer et al. 2017). In total the mobile phone-based studies included 25 for surveillance, 7 for disease prevention and 7 for disease-management.
Twenty-five surveillance studies aimed at collecting data and reporting mosquitoes, patients, symptoms, socio-demographic factors and perceptions of the population about the disease (Lozano-Fuentes et al., 2012; Lozano-Fuentes et al., 2013; Tai-Ping et al., 2016; Randrianasolo et al, 2010; Toda et al., 2016; Toda et al., 2017; Kumoji and Khan Sohail, 2019; Ocampo et al., 2019; Eiras and Resende, 2009; Pepin et al. 2013; Sanavria et al. 2017; Palmer et al., 2017; Reddy et al., 2015; Leal-Neto et al., 2017; Olson et al., 2017; Randriamiarana et al., 2018). Others used mobile apps for georeferencing or mapping of users’ locations or breeding sites visualizing high risk areas for arboviral diseases (Tai-Ping et al., 2016; Palmer et al., 2017; Mukundarajan et al., 2017; Leal-Neto et al., 2017; Hewavithana et al., 2018; Ocampo et al., 2019). Other mobile services were used for estimating human movements through mobile phone tracking data to predict outbreaks or possible risk areas on maps (Wesolowki et al, 2015; Mao et al., 2016; Massaro et al., 2017; Rajarethinam et al., 2019). Another group of devices captured sounds or images of Aedes mosquitoes to identify species using the phone camera or running mobile applications (Palmer et al., 2017, Mukundarajan et al., 2017).
The seven studies reporting on disease prevention interventions aimed at providing information on arbovirus diseases and preventative measures as well as at promoting behavioural change through mobile applications and short message services (Dammert et al., 2014; Reddy et al., 2015, Lwin et al., 2017; Rodriguez et al., 2018; Bhattai et al., 2019). Some studies tested a mobile learning approach to teach about arbovirus diseases to specific population groups (students) using mobile apps or platforms integrated in mobile devices (Patil et al., 2016; Abel-Mangueira et al., 2019).
The seven mobile phone-based studies for disease management aimed at facilitating the contact between health workers and patients for timely diagnosis (Barde et al. 2018) and detecting viruses of dengue, Zika and chikungunya in samples of serum, saliva, blood, urine from humans using platforms that were supplemented by a smartphone camera to acquire real time images or display a visual readout of the assays (Thiha and Ibrahim, 2015; Chan et al., 2016; Priye et al., 2017; Ganguli et al., 2017; Rong et al., 2018; Kaarj et al., 2018; Bhadra et al., 2018).
In summary, the mobile phone technology, mainly taking advantage of mobile applications, has been most frequently used for surveillance purposes including data collection, reporting and geolocation and estimation of human movements. Mobile phone technology focussing at communication between health staff and patients was less explored. There was also a number of mobile applications with multiple purposes for surveillance, prevention and management of arbovirus diseases.
Table 4
Mobile phone-based studies by purpose and mobile technology category
Purpose | Specific aims in mobile phone | Mobile phone category | Application or system' names / Mobile phone projects |
surveillance (n = 25 studies) | Data collection and reporting | mobile apps | VECTOS app and mosquito social app; Mobile device with OruxMaps, AutoNavi navigation and Baidu Map; mSOS app; Chaak system; Vigilant-e; Mosquito Alert; Healthy cup; Monitoring app in Fiji, MID system; OlympTRIP; Mo-Buzz,Abuzz. |
SMS | SMS survey in four countries; SMS for sentinel surveillance; SMS for IDSR system in Madagascar |
Geolocation and mapping | mobile apps | Monitoring app in Fiji, VECTOS system; Google maps®app; Mosquito Alert; Healthy cup; Abuzz; OlympTrip; Mo-buzz; mobile device with OruxMaps, AutoNavi navigation and Baidu Map |
Mobile phone tracking data | Two studies using CDR in Singapore; mobile phone signals in China; CDR in Pakistan |
Estimation of human movements | Mobile phone tracking data | Two studies using CDR in Singapore; mobile phone signals in China; CDR in Pakistan |
Capturing vector’ photos or sounds | mobile apps | Mosquito Alert; Abuzz; Mo-Buzz |
camera phone | Smartphone for a visual readout tool in USA |
Prevention (n = 7) | Health education and behaviour change | mobile apps | Monitoring app in Fiji; OlympTRIP; Mo-buzz |
SMS | SMS conducted in Nepal; SMS conducted in Perú |
m-learning approach | mobile apps | m-learning platform in Brazil; mobile social app in India |
Management (n = 7) | Detection of arbovirus and point of care diagnosis | camera phone | Three diagnostic studies using smartphone camera in USA and one in China |
mobile apps | Mobile app for image processing in USA and Malaysia |
Communication between health staff and patients | simple mobile communication | Contact using mobile phone number of patients in India |
Among the included studies, we assessed the different target groups or users of the mobile phone technology. Health workers were the main target group for receiving mobile phone services (n = 12). This group consisted of vector control staff, healthcare workers, physicians, practitioners, health managers and other health specialists. The second most frequent group were researchers (n = 11) who conducted studies that used mobile phone tracking data or designed platforms with smartphone cameras under controlled settings. The third most frequent group was the general public (n = 9), which includes communities and specific population groups (students, athletes, police officers). Three mobile phone interventions targeted both groups, general public and health workers. Only one mobile phone service was designed for patients.
Outcome measurements
This review assessed the following outcome dimensions: performance, acceptance, feasibility, usability, costs and effectiveness (Osorio et al., 2018; Krick et al.,2019). A description is given in Table 5 summarizing the scope of expected outcomes in the 36 studies included.
Table 5
Description of outcome dimensions in 36 studies
Outcome | Description |
Performance | Operational characteristics of the mobile phone technology in terms sensitivity, specificity, predictive values, accuracy, completeness, quality data, timeliness and concordance with other tests. |
Feasibility | The extent to which the mobile health intervention implemented under real conditions can be successfully used in a specific context |
Acceptance | User’ attitudes towards the mobile phone technology perceived to be satisfactory. |
Usability | Users who are testing the mobile phone technology. This comprises users who downloaded the application/service and used it or active users. |
Cost | Monetary effort of the use of a mobile technology in a specific context |
Effectiveness | Effectiveness comprises results related to reduce vector densities or disease burden, to predict early outbreaks and improve disease prevention and health behaviours. |
The analysis of outcome dimensions (Table 5) showed that nineteen (52%) of all included studies analysed aspects of performance, eleven (30%) of feasibility, five (13%) of acceptance, five (13%) of usability, five (13%) of effectiveness; one of these evaluated cost-effectiveness. Only three (8%) analysed costs or at least estimated prices by mobile phone services (some studies covered several outcomes). Studies on performance of mobile technologies were evaluated in all mobile phone categories particularly when using applications (n = 9). Studies on effectiveness and acceptance were conducted only through mobile applications and short message services (SMS). Studies on feasibility were most frequently carried out through mobile apps (n = 6), followed by mobile phone tracking data (n = 3) and SMS (n = 2). Mobile application technologies represent the only category that analysed usability/user-friendliness (n = 5). Costs have been assessed very rarely in mobile phone programmes. |
Table 6 summarizes the number of studies dealing with one or the other dimension. It can be seen that few studies have provided information on acceptance, costs and effectiveness and only a small number on usability/user-friendliness studies were identified. Mobile applications were the only category that included all outcome dimensions.
Table 6
Number of studies by mobile phone category and outcome dimensions
Mobile phone category | Performance | Feasibility | Acceptance | Usability | Costs | Effectiveness |
Mobile applications | 9 | 6 | 3 | 5 | 1 | 2 |
Short message service (SMS) | 2 | 2 | 2 | n.a. | 1 | 3 |
Mobile phone tracking data | 2 | 3 | n.a. | n.a. | n.a. | n.a. |
Camera phone | 5 | n.a. | n.a. | n.a. | 1 | n.a. |
Simple mobile communication | 1 | n.a. | n.a. | n.a. | n.a. | n.a. |
| 19 | 11 | 5 | 5 | 3 | 5 |
n.a.=not available |
The variability of mobile phone-based study designs makes it difficult to assess individual interventions or to identify the most effective and socially accepted mobile phone service. In the following the six outcome dimensions for mobile phone programmes will be described in more detail.
Performance
The 19 performance studies provide valuable information on how mobile phone interventions perform in terms of completeness, timeliness, data quality, concordance and predictive value of data collection. Mobile applications such as Vectos apps; Mosquito social apps; Google maps® app; Chaak app, MID system, Vigilant-e app demonstrated good performance for data collection. Regarding completeness of data sets, VECTOS in Colombia achieved to locate 84% of dengue cases (Ocampo et al., 2019) and Google maps® app in Sri Lanka located 93% of dengue patients (Hewavithana et al. 2018). Chaak in Mexico achieved with a mobile application to increase the speed of data transmission reducing the delay time by 19% compared to the pen-and-paper capture method (Lozano-Fuentes et al., 2012). The Chaak application also showed improvements in data quality in terms of reducing errors (the proportion of errors for the pen-and-paper method was 0.23 while the proportion of errors for the mobile method was 0.17; Lozano-Fuentes et al., 2012). Another example is Vigilant-e, a syndromic diary app in Guatemala showing good agreement (concordance) between the collected data by participants and by nurses during home visits (Olson et al., 2017). The Abuzz app developed in the United States for capturing wingbeat sounds highlighted a high sensitivity to identify mosquito species at 10 to 50 mm distance (Mukundarajan et al., 2017). Mobile applications have also demonstrated their potential for the management of arbovirus diseases. Two studies developed point-of-care platforms for detecting arbovirus diseases using a smartphone application and image sensor (Thiha and Ibrahim, 2015; Priye et al. 2017). They showed good performance in terms of speed to acquire images and visualize the tests. With respect to short message service (SMS), few studies were identified to analyse the performance aspect. A study in Madagascar showed that SMS improved the surveillance data in terms of completeness: 73% completeness in 2014-15 compared to 20% in 2008-9; however, timeliness and data quality remained a problem as 90% of health workers had more than 4 errors during data transmission and only 43% of SMS were received in time (Randriamiarana et al., 2018). In contrast, another study in the same country had better results regarding timeliness and data quality, showing that patient data transmission with SMS was improved by 89% within 24 hours (Randrianasolo et al., 2010). The use of mobile phones and internet were able to facilitate the communication between health workers and patients for the timely diagnosis. In India, vector control staff used the contact mobile numbers for tracking dengue positive patients and consequently, conducting vector control activities within 24 hours plus completing 82% of house visits to these patients (Barde et al. 2018). This paper also identified a promotion strategy for tracking users through mobile phone data based on the Signalling System 7 (SS7) in combination with different datasets and observations of mosquito activities (Mao et al., 2016). The study showed a strong performance identifying three clusters with increased transmission risk in a city of China.
In summary, mobile applications have enhanced diagnostic capabilities and improved data collection and transfer in terms of data quality, completeness and timeliness, becoming a promising tool for surveillance and the management of arbovirus diseases. On the other hand, short message services (SMS) showed a good completeness of disease data transmission, but timeliness and data quality were yet an issue depending on the surveillance procedure and capacities of health workers to use SMS. Mobile phone tracking data (mobile signal SS7) to estimate human movements showed a good performance in terms of predictive values. However, additional studies on mobile signals are needed to validate its prediction capacities for the movement of arbovirus diseases and outbreaks.
Feasibility
Mobile apps interventions have been shown to achieve their aims under real conditions. They were particularly used for recording and transferring entomological information. For example, the VECTOS system aimed to monitor the transmission risk of arboviral diseases in three cities in Colombia. The entomological data (collected by Vectos app) and epidemiological data were analysed in a web platform that successfully identified the level of vector infestation (larval indices), the epidemiological risk and the distribution of disease displayed on maps (Ocampo et al, 2019). Similar results were achieved in China in which android mobile devices using mobile applications (OruxMaps, AutoNavi Navigation and Baidu Map) were able to identify the level of larval/pupal infestation and the most abundant breeding sites (Tai-Ping et al., 2016) demonstrating its usefulness for the surveillance of mosquito habitats. Another example is the Monitoring Intelligent dengue system, a mobile software for submitting ovitrap data which are then analysed in a web database. The system achieved to assess the transmission risk (index of female Aedes aegypti) in 2015 showing that mobile apps together with other tools are able to monitor entomological indices (Sanavria et al. 2017). For the early detection of arboviral disease, using a symptomatic approach. OlympTRIP was used. This is an application developed to monitor the health status of users during the Olympic games in Brazil. Although no participants reported Zika, the app proved to be useful for monitoring symptoms in real time such as headache, cough and conjunctivitis (Rodriguez-Valero et al., 2018).
Recent studies using mobile phone tracking data through CDR and mobile phone signals have been conducted in Singapore (Rajarethinam et al., 2019), China (Mao et al., 2016) and Pakistan (Wesolowsiki et al., 2015), demonstrating to be a feasible strategy to assess population movements, map changing population densities and estimate dengue spread. Evidence suggests that mobile applications are feasible and useful for monitoring and tracking mosquitoes and/or disease outbreaks since this mobile phone service can involve other technologies (e.g. web platform, internet, GPS). Additionally, mobile phone tracking data -when integrated with disease surveillance data and environmental data- has the potential to estimate human mobility in order to predict the spread of arbovirus diseases and outbreaks.
Acceptance
The acceptance or user satisfaction of the mobile phone technology for arboviral disease has been assessed in studies conducted in India, Fiji, Guatemala and Nepal. The users from e-Vigilant in Guatemala showed high user satisfaction with mobile apps: 98.8% of families reported that the application was beneficial to them and 96,6% that it was beneficial to the community (Olson et al., 2017). The M-learning app in India showed that 80% of students had a positive attitude and 76% perceived the importance of the tool for learning (Narayan Patil et al., 2016). Although, a study in Fiji showed positive feedback on user-satisfaction, its results depended more on connectivity to the internet. With respect to the acceptability of SMS interventions, dengue preventive messages were assessed only in one study in Nepal where messages were well accepted by the community and stakeholders (acceptability of SMS of 4.4 on a 5-point scale; Bhattarai et al., 2019). In summary, we identified in our review that mobile apps and short message services were well accepted by the general public, but no information on acceptance in health workers was given, although most mobile phone technologies were addressed to this user group. Further studies should analyse the acceptance of mobile phone technologies in health workers.
Usability/user friendliness
Five mobile applications were identified to provide information on the proportion of participants who downloaded the app and subsequently used it. OlympTRIP and Healthy Cup, developed in Brazil showed 75% and 65,7% respectively of active users out of participants who downloaded the app. The participants also reported that OlympTRIP app was user- friendly (Rodrigo-Valero et al., 2018). Similar results were obtained in India where a social application (M-learning) for students reported 73% of active users who accessed the device more than twice a week (Olson et al., 2017). For another application in Guatemala (vigilant- e) it was shown that 78% of targeted users reported weekly their symptoms. In contrast, Mo-Buzz in Sri. Lanka, a mobile-based platform with two versions, one for health staff and the other for the general public, had a low initial use by health staff (only 10%) but then this increased gradually to 76% while it remained low for the general public (Lwin et al., 2017). The authors suggested to increase the uptake of the app by further training of health staff, incentives for the use and strong communication with the general public. In summary, this review evidenced that most mobile applications demonstrated good usability, but that the uptake of this service can require additional promotional and educational efforts.
Costs
Cost calculations were done in different ways. One study described the market costs of a mobile device (Bhadra et al., 2018), others presented the costs of data transfer (Lozano-Fuentes et al., 2013), others included calculations on staff salary and/or coverage of mobile services (Palmer et al., 2017) and one analysed direct and indirect costs of mobile services (Pepin et al., 2013).
Three studies compared their mobile interventions with traditional methods (Lozano-Fuentes et al., 2013; Palmer et al., 2017; Bhadra et al., 2018). Researchers from Mexico compared the Chaak system demonstrating that the mobile phone-based system did not substantially increase costs compared with the traditional data collection method (cost per household were U.S.$0.10 for the pen-and-paper method compared to U.S.$0.10 to U.S.$2.13 for the Chaak system). Mosquito Alert (mobile application and website) in Spain estimated the costs as 1.23 Euros per km2 per month while ovitraps costed about 9.36 Euros per km2 per month (almost eight times the cost of Mosquito Alert). The authors mentioned that vector surveillance with ovitraps required much effort to be installed and checked by experts so that staff costs were the highest cost components, in contrast, the mobile application costs were mostly associated with community buildings and non-recurring investments in technology (Palmer et al., 2017). Similar economic benefits were demonstrated in a smartphone-read LAMP-OSD assay platform for detecting mosquitoes species indicating in low cost to capture fluorescence signals with a camera phone (<$200) compared with a lab-based qPCR testing (~$30,000 in start-up investment and ~$700 in annual maintenance; Bhadra et al., 2018).
One study analysed cost-effectiveness of the MI-Dengue system in Brazil (Pepin et al., 2013) using multivariate models to estimate the median cost savings per case prevented which was median $58; this saved annually around $364,000 in direct costs (health care and vector control) and approximately $7 million in lost wages (societal effect; Pepin et al., 2013). In summary, although, there is limited evidence on costs associated with the mobile phone technology, examples from Mexico and Spain showed that the use of mobile apps cost less or the same as traditional methods being therefore affordable for disease and vector surveillance. Furthermore, investing efforts in a system that integrates mobile devices, website and a vector surveillance tools can help to reduce case load and thus medical costs.
Effectiveness
Few studies showed effective m-health interventions in terms of reducing the burden of the disease and vector densities through improved dengue prevention and behaviour change and/or, performing as an early warning indicator for outbreaks. The analysis of effectiveness was based on randomized controlled trials which were conducted at large scale. Examples are the Mosquito Alert in Spain (Palmer et al., 2017) and Monitoring Intelligent dengue system (MI-Dengue) in Brazil (Pepin et al., 2013); they used mobile devices and other tools which were effective for dengue surveillance and subsequent action. Mosquito Alert is an app that encourages the public to report mosquitoes through taxonomic surveys and photos taken of the vector to be analysed by experts. Their results showed high detection rates by the app (64% out of 274 municipalities in which tiger mosquitoes were present according to ovitrap plus Mosquito Alert data). However, the system failed to detect Aedes mosquitoes in some areas that had positive ovitraps and vice versa. The authors suggest using both approaches together –ovitraps and Mosquito Alert. This study highlighted that this mobile technology provides particularly early warning signals in low endemicity areas where traditional surveillance is limited. MI-Dengue for counting mosquitoes in real time was the only study that analysed effectiveness of reducing vector density and dengue incidence. Positives results were also observed in MI-Dengue system,-using a website platform-, a mobile device plus traps for mosquitoes and vector control inspections (Pepin et al., 2013; Sanavria et al., 2017)- showed that the system prevented in Brazil 27,191 cases of dengue fever. The authors highlighted that MI-Dengue system was more cost-effective in cities with high levels of mosquito infestation. In summary, the mobile phone technology may be a potential supplement for the control and surveillance of arboviruses diseases.
Short message service (SMS) was the only mobile phone service shown to enhance prevention practices of people related to arbovirus transmission and control. Households exposed to repeated preventative messages in Peru reported an increase in the use of window screening and/or mosquito bednets by 4.5% and a reduction of vector densities (Dammert et al. 2014). A positive effect was also demonstrated in Nepal, where SMS together with a prevention leaflet were sent to the community, which increased knowledge and practice of people towards dengue prevention (Bhattarai et al, 2019). The combination of mobile phone messages with conventional education methods can produce an improved effect in the prevention of arboviral diseases in terms of reducing vector densities in domestic settings (Bhattarai et al, 2019).