Results of the study selection process
A total of 1289 publications were retrieved for this review, including 1189 from the databases and 100 from Google search. After deleting duplicates, 1013 remained for screening the titles, of which 301 were selected for screening of abstracts. After reading abstracts, 82 full texts were considered potentially relevant, of which 32 met our criteria. In addition, 4 papers were identified from reference lists. As a result, 36 studies were included for the data extraction (see PRISMA flow diagram in Fig. 1; Moher et al., 2009). A complete list of all studies can be found in Additional file 1.
Descriptive results of geographic distribution and study designs
The studies included came from twenty different countries (see Fig. 2 Geographic distribution of mobile phone-based studies). Eighteen studies were conducted in the American region (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 Asia 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 were the countries with the highest number of publications (each one with six), however the studies identified in the United States were not performed under real-world conditions, but rather under controlled conditions, in particular laboratory facilities. 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.
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 services
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
Services
|
Definition and considerations
|
Number of hits
|
References
|
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
|
Ocampo et al., 2019; Abel Mangueira et al., 2019; Rodriguez-Valero et al., 2018; Hewavithana et al., 2018; Olso et al., 2017; Palmer et al., 2017; Lwin et al., 2017; Sanavria et al., 2017; Pepin et al. 2013; Eiras and Rensende, 2009; Leal Neto et al., 2017: Mukundarajan et al., 2017; Wu et al., 2016; Reddy et al., 2015; Patil et al., 2016; Thiha and Ibrahim, 2015; Lozano-Fuentes et al., 2013; Lozano-Fuentes et al., 2012
|
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
|
Kumoji and Sohail et al., 2019; Randriamiarana et al., 2018; Toda et al., 2017; Toda et al., 2016; Randrianasolo et al., 2010; Bhattarai et al., 2019; Dammert et al., 2014
|
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 image processing and visual readout.
|
6
|
Kaarj et al., 2018; Rong et al., 2018; Ganguli et al., 2017; Priye et al., 2017; Bhadra et al., 2018; Chan et al., 2016
|
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
|
Rajarethinam et al., 2019; Massaro et al., 2019; Mao et al., 2016; Wesolowski et al., 2015
|
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
|
Barde et al., 2018
|
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 focused on four major purposes: surveillance, prevention, diagnosis, and 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), thus those were assigned for both purposes in table 4, resulting in 39 studies. This review also identified specific aims in each purpose which are presented in table 4. Some mobile applications, were able to perform more than one aim in surveillance such as data collection, taking mosquitoes photos, 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, 6 for diagnosis and 1 for management. The mobile phone technology, mainly taking advantage of mobile applications, has been most frequently used for multiple aims in surveillance, followed by prevention and diagnosis. The use of simple voice communication (mobile phone number) focussing on communication management between health staff and patients was less explored. Short message services were used for surveillance (data collection and reporting) and prevention (health education and promoting behavioural change). Camera phones coupled to diagnostic platforms and/or assays were aimed at diagnosis of arboviruses and identification of mosquito species.
Table 4. Mobile phone-based studies by purpose and mobile technology category
Purpose
|
Specific aims in mobile phones
|
Mobile phone
Service
|
Application or system' names / Mobile phone projects
|
References
|
surveillance
(n = 25 studies)
|
Data collection and reporting mosquitoes, symptoms, socio-demographic factors on population
Tracking and monitor disease and outbreaks
Geolocation of users or breeding sites to visualizing hotspots
Estimation of human movements to predict outbreaks or possible risk areas
Capturing vector’ photos, images or sounds to identify mosquito species
|
mobile apps
(n=15)
|
VECTOS system; *OlympTRIP app; Google maps®app; Vigilant-e app; Mosquito Alert; *Mo-Buzz; ***MI-Dengue system, Healthy cup app; Abuzz project; Mobile device with OruxMaps, AutoNavi navigation and Baidu Map; *Monitoring app in Fiji; **Chaak system
|
Ocampo et al., 2019; Rodriguez-Valero et al., 2018; Hewavithana et al., 2018; Olso et al., 2017; Palmer et al., 2017; Lwin et al., 2017; Sanavria et al., 2017; Pepin et al. 2013; Eiras and Rensende, 2009; Leal Neto et al., 2017: Mukundarajan et al., 2017; Wu et al., 2016; Reddy et al., 2015; Lozano-Fuentes et al., 2013; Lozano-Fuentes et al., 2012
|
SMS
(n=5)
|
SMS survey in four countries; SMS for IDSR system in Madagascar; **mSOS project; SMS for sentinel surveillance
|
Kumoji and Sohail et al., 2019; Randriamiarana et al., 2018; Toda et al., 2017; Toda et al., 2016; Randrianasolo et al., 2010
|
Mobile phone tracking data
(n=4)
|
Two studies using CDR in Singapore; mobile phone signals (SS7) in China; CDR in Pakistan
|
Rajarethinam et al., 2019;
Massaro et al., 2019; Mao et al., 2016; Wesolowski et al., 2015
|
camera phone
(n=1)
|
Smartphone imaged LAMP-OSD assay
|
Bhadra et al., 2018
|
Prevention
(n = 7)
|
Health education
Promotion of behaviour change
m-learning approach
|
mobile apps
(n=5)
|
m-learning platform, *OlympTRIP; *Mo-buzz; Mobile social app in India; *Monitoring app in Fiji
|
Abel Mangueira et al., 2019; Rodriguez-Valero et al., 2018; Lwin et al. 2017; Patil et al., 2016; Reddy et al., 2015
|
SMS
(n=2)
|
SMS conducted in Nepal; SMS conducted in Perú
|
Bhattarai et al., 2019; Dammert et al., 2014
|
Diagnosis
(n = 6)
|
Point of care diagnosis for detecting viruses of dengue, Zika and chikungunya
|
camera phone
(n=5)
|
Four diagnostic studies using smartphone camera in USA and one in China
|
Kaarj et al., 2018; Rong et al., 2018; Ganguli et al., 2017; Priye et al., 2017; Chan et al., 2016
|
mobile app
(n=1)
|
Mobile app for image processing in Malaysia
|
Thiha and Ibrahim, 2015
|
Management
(n = 1)
|
Facilitating communication between health staff
and patients for timely diagnosis
|
simple mobile communication
(n = 1)
|
Contact using mobile phone number of patients in India
|
Barde et al., 2018
|
*Mobile phone projects addressing both surveillance and prevention; **The same mobile phone tool used in two studies; *** The same mobile phone tool used in three studies.
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 dimensions
This review assessed the following outcome dimensions: performance, acceptance, feasibility, usability, costs and effectiveness. A description is given in table 5 summarizing the scope of expected outcomes in the 36 studies. Although, the description was developed following prior definitions (Osorio et al, 2018; Proctor et al., 2011), some adjustments were developed deductively from the included articles.
Table 5. Description of outcome dimensions in 36 studies
Outcome
|
Description
|
Performance
|
Operational characteristics of the mobile phone technology in terms accuracy, completeness, quality data, timeliness, speed, and concordance with other medical reports
|
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 and user-friendly.
|
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
|
Positive effects of mobile phone implementation on public health or health-related behaviour changes.
|
The analysis of outcome dimensions (table 6) showed that a large number of studies assessed the performance of their mobile phone services (52%), particularly mobile applications, followed by studies that assessed feasibility (30%). It can be seen that few studies have provided information on acceptance, usability, and effectiveness. Costs analysis or at least estimated prices by mobile phone services were the least explored in this review. Mobile applications were the only service that assessed all outcome dimensions. Usability was only described by mobile apps-based studies. Table 6 summarizes the number of mobile phone services dealing with one or more outcome measurements.
Table 6 Number of studies by mobile phone category and outcome dimensions
Mobile phone services
|
Performance
|
Feasibility
|
Acceptance
|
Usability
|
Costs
|
Effectiveness
|
Mobile applications
|
9
|
6
|
3
|
5
|
2
|
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 (52%)
|
11 (30%)
|
5 (13%)
|
5 (13%)
|
4 (11%)
|
5 (13%)
|
n.a.= not available (No study provided information on that outcome).
The variability of mobile phone-based study designs makes it difficult to assess individual intervention 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
A variety of operational characteristics were assessed in performance studies. Mobile applications and simple voice communications (calls) reported improvements in terms of completeness, for example, reporting more houses to conduct vector control activities (Barde et al. 2018; Hewavithana et al. 2018; Ocampo et al., 2019). Familiarity health workers with the application and using well known apps (Google maps) and geographic information systems (GIS) helped in locating more houses in real-time. It was also demonstrated that mobile applications were more useful in ensuring data quality and timeliness rather than traditional capturing methods. For instance, Chaak app reported a 19% reduction in the time spent per survey, along with fewer errors in data transfer in comparation with the pen-and-paper data capturing methods (Lozano-Fuentes et al., 2012). The use of different modes of data transmission from mobile phones to the central server (transference with or without internet), good storing capacity of mobile phones, design of the app (white background and black lettering for better visibility), easy navigation (use of predefined terms, radio buttons and buttons in data entry fields instead of free text input) and trained health workers favoured the good performance of this mobile phone service (Lozano–Fuentes et al. 2013). The Mobile app, Vigilant-e, designed with question algorithms with simple terminology and visual aids demonstrated good agreement (concordance) between syndromic data reported by the general public and by nurses (Olso et al., 2017). However, the success of this intervention depends on the availability of mobile phones and internet connectivity in households as well as the willingness of people to use it. The use of smartphones has also led to the development of innovations to identify mosquito species using the acoustic sensor of mobile phones. For example, the Abuzz application was capable of sensitively identifying mosquito species at 10 to 50 mm distance, including Aedes aegypti (Mukundarajan et al., 2017).
SMS also demonstrated good performance in terms of completeness. Two studies conducted in Madagascar achieved to transmit more than 70% of patient’s data within 24 hours (Randrianasolo et al., 2010; Randriamiarana et al., 2018). However, one of them reported problems regarding timeliness and data quality, 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). Lack of guidelines and trainings to use mobile applications, high workload, and technical problems (poor telecommunication network, phone battery, energy cuts), were reported by health workers as main challenges of this SMS intervention.
Two papers also identified a promising strategy for tracking users through mobile phone data based on the Signalling System 7 (SS7) and Call Records Details (CRDs; Mao et al., 2016; Massaro et al. 2019). They showed a good performance in terms of predictive values allowing to identify areas with an increased transmission risk. The coordination with telecommunication companies was crucial to capture a large number of mobile phone data and thus to have a better representation of population.
Recently, the use of smartphone camera-based diagnostic platforms has been explored to acquire images and read assays such as ELISA tests (Thiha and Ibrahim), RT-LAMP reactions (Kaarj et al., 2018; priye et al., 2018, Gangulie et al., 2018), RT-PCR and RT-RPA assays (Chan et al., 2015) in arboviruses diseases. They demonstrated high accuracy in terms of sensitivity and specificity as well as a rapid detection of arbovirus (range between 10 to 20 minutes; Chan et al., 2016; Ganguli et al., 2017; Priye et al., 2017; Kaarj et al., 2018; Rong et al., 2018). Using a mobile application is an enabler for processing data and interpreting various tests. For example, Thiha and Ibrahim (2015), developed an ELISA reader for point-of-care dengue detection using the smartphone camera and mobile app. As a result, high performance was demonstrated, with 95% sensitivity and 100% specificity for dengue detection in comparison with standard ELISA microplate readers. These prototypes of smartphone-based diagnostic platforms require qualified personnel to take biological samples and further studies to validate its performance and impact in a real working environment (patient's home or clinic).
Feasibility
Mobile apps interventions have been shown to achieve their aims under real conditions. They were particularly used for collecting and transferring entomological information to assess the transmission risk of arboviral diseases. For example, the entomological data (collected by Vectos app, OruxMaps, AutoNavi Navigation and Baidu Map) were analysed in a web platform or central server that successfully identified the level of vector infestation (larval indices; Ocampo et al, 2019) as well as the most abundant breeding sites (Wu et al., 2016). Moreover, mobile phones together with traditional methods (ovitraps) have also implemented for tracking and monitoring mosquitoes. For example, a study used mobile phones for submitting ovitrap data to a web database that was able to estimate the index of female Aedes aegypti (Sanavria et al. 2017).
Mobile applications have also proved to be feasible for early detection of arboviral disease, using a participatory approach (Olson et al., 2017; Leal Neto et al., 2017; Rodriguez-Valero et al., 2018). These applications required medical staff to validate the data reported by users and checked their health status during the intervention. All these mobile applications were accompanied by a web-based application to facilitate the data management in real time.
Recent studies using mobile phone tracking data through CDR and mobile phone signals have been conducted in Asia region (Wesolowsiki et al., 2015; Mao et al., 2016; Rajarethinam et al., 2019). This strategy, 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
Mobile apps were generally well received in studies conducted in India, Fiji and Guatemala. User’s satisfaction with mobile interventions offered was commonly based on how they felt using the app, whether they found it helpful or useful, and whether they would recommend it to others. For example, users from Vigilant-e app in Guatemala showed high user satisfaction: 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). Another example was the M-learning app in India where 80% of students had a positive attitude and 76% acknowledged the importance of using it as a learning tool (Narayan Patil et al., 2016). On the other hand, a study in Fiji showed positive feedback on user-satisfaction, but its results depended more on connectivity to the internet (Reddy et al., 2015). Moreover, socio-economic factors of population might be related to people who did not accept the app (Olson et al., 2017).
The use of SMS was highly acceptable for the prevention and surveillance of arbovirus. The acceptability was assessed on how much participants enjoyed the service and whether they perceived it an informative and trustworthy strategy (Bhattarai et al., 2019). Another study also checked if their health workers could easily use the SMS (Toda et al., 2017). Health workers reported that the SMS was user-friendly service. The participation of stakeholders was key to promote SMS as media for the prevention of dengue and facilitate its acceptance amongst the community.
Usability
Most mobile applications showed a good proportion of active users out of all participants who downloaded the app (Rodrigo-Valero et al., 2018; Leal Neto et al., 2017; Patil et al., 2016), but some researchers thought more incentives, educational campaigns and constant communication between users and study personal were needed to keep them motivated (Olso et al. 2017, Lwin et al., 2017; Leal Neto et al., 2017). Some concerns related to additional expenses of mobile technology (e.g. mobile data plan), mobile phone features (less storage space, slow internet connection), lack of interest and ignorance regarding purpose of mobile phone intervention were associated with a proportion of users who did not use it (Patil et al., 2016). Fear and mistrust of adopting a new technology were other reasons for low usability, perceived by health staff in Mo-buzz (Lwin et al., 2017). Other external factors such as period of high staff turnover, cellular tower collapse and socio-politic events were associated with a decreased reporting (Olso et al. 2017).
Costs
Cost calculations were done in different ways. Description of market cost of mobile device (Bhadra et al., 2018), estimations of the mobile phone network including calculations of staff salary (Lozano-Fuentes et al., 2013), costs calculations on coverage of mobile phone intervention (Palmer et al., 2017) and description of each product or service for the whole intervention, identifying cost savings (Pepin et al., 2013).
Most studies on costs compared their mobile phone intervention with standards methods for vector surveillance. For example, Mosquito Alert app based on citizen-science initiatives demonstrated a reduction in the cost of coverage in comparation with ovitraps (Mosquito alert costed 1.23 Euros per km2 per month while ovitraps costed about 9.36 Euros per km2 per month). Vector surveillance with ovitraps required much effort to be installed and checked by qualified staff, while mobile application was mostly associated with community buildings and non-recurring investments in technology (Palmer et al., 2017). Similar economic benefits were briefly reported by Bhadra et al., 2018 in the discussion section, indicating a reduction of cost to capture fluorescent signals of treated mosquitoes with a phone camera in comparison with conventional laboratory equipment. In contrast, Chaak app, reported costs equal or slightly higher than traditional capturing methods (cost per household were U.S.$0.10 for the pen-and-paper method compared with a cheap mobile phone plan U.S.$0.10 and an expensive mobile phone plan U.S.$2.13 for Chaak system). Its cost was an issue associated with the type mobile phone network (Lozano-Fuentes et al., 2013). Additionally, a software developer or a person with technological skills is required to manage the central server which could add costs to the mobile phone intervention. On the other hand, one study analysed cost-effectiveness of the MI-Dengue system using multivariate models to estimate the median cost savings per case prevented which was median $58 (Pepin et al., 2013). This system based on the concept that vector control strategies should be applied in targeted areas with higher densities of gravid female mosquitoes, showed a better allocation of resources, saving hundreds of thousands of dollars in direct costs (health care and vector control) and approximately $7 million in lost wages (societal effect; Pepin et al., 2013). The cost analysis of this system not only included estimations on technological components (e.g., computers, mobile phones) but also costs associated with vector control inspections.
For the diagnosis of arboviral diseases, Chan et al., (2016) mentioned that smartphones are a more affordable alternative to collect fluorescent signals for point-of-care detection of arboviruses in comparison with other portable devices (ESEQuant Tubescanner, Chan et al., 2016). However, information regarding the cost of these diagnostic platforms was limited.
Effectiveness
Few studies showed effective m-health interventions in terms of reducing the 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 well-defined methodologies (randomized controlled trials or quasi-experimental designs), but some studies were conducted in specific setting with a short interventional period.
SMS-based studies were the only ones that reported effectiveness in term of improving knowledge and practices of arboviral disease. Preventive messages via mobile phone were able to produce positive changes in household behaviour improving dengue practices in population and consequently affecting vector densities in domestic settings. Dammert et al. (2014), showed that households exposed to repeated preventative messages in Peru reported an increase in dengue practices, (the use of window screening and/or mosquito bed nets increased 4.5%) and a reduction in infestation (e.g. vector water containers testing positive for dengue larvae was 1.44% in the exposed group with SMS vs 2.47% in non-exposed group). Additionally, SMS with conventional education methods were able to bring a major effect in the prevention of arboviral diseases. In Nepal, 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). Availability of mobile phones in households and shared responsibility of the community were identified as enablers of SMS interventions. In contrast, limited network access in remote areas, reaching private network users and lack of knowledge concerning the purpose of using mobile phones were the main obstacles perceived in the implementation of this mobile phone service.
For surveillance, the use of SMS has demonstrated to be effective for reporting immediately notifiable diseases. (Toda et al., 2016). Likewise, mobile applications plus traps were effective for monitoring of Aedes. Aegypti in real time (Pepin et al., 2013; Palmer et al., 2017). Their integration with geographic information systems enabled the development of early warning mechanisms. For example, GIS datasets obtained from mobile application provided early warning signals in low endemicity areas where traditional surveillance was limited (Palmer et al., 2017). Positive results were also observed in MI-Dengue system using a website platform, a mobile device (plus mosquito traps) and vector control inspections. Researchers showed that, in Brazil, the system was able to identify high risk areas which were then targeted for vector control and consequently prevented 27,191 cases of dengue fever (Pepin et al., 2013; Sanavria et al., 2017). Evidence suggests that using both approaches together (standard surveillance methods (traps) and mobile apps) are effective as entomological surveillance instruments for decision-making in the control of Aedes mosquitoes and subsequent action.