ConTEXT: Context-aware Adaptive SMS Client for Drivers to Reduce Risky Driving Behaviors

Text messaging while driving has been considered a dangerous activity that may lead to serious injuries and trac fatalities. Several assistive technologies and solutions have been developed to simplify texting activity. However, due to inconsistent and complex interface design, lack of logical navigational order, lack of context, complicated text-entry layouts, and laborious activities, the existing texting-related activities can lead to accidents. This paper recognized the risky driving patterns using the real-time AutoLog application. Based on this risky driving behavior, we have proposed ConTEXT, a usable SMS client, to overcome the issues pertaining to the usability of textual activities on smartphones while driving. ConTEXT application is evaluated both empirically as well as through real-time AutoLog application. We have collected data from 117 drivers through a questionnaire. The results show that the data is found reliable also alpha scores for all factors seem internally consistent as it ranges from 0.70 to 0.79 which is good. Similarly, we have reported Principal Component Factor Analysis (PCFA), which was found satised and appropriate as the Eigenvalue for all the factors is greater than zero. Furthermore, results obtained from the AutoLog dataset show an improved user experience, better control over the touch screen with minimum visual, physical, and mental load.


Introduction
Using a smartphone while driving is a global phenomenon, which has been acknowledged as a major source of accidents [1]. Using a smartphone while driving can potentially cause drivers to take their eyes and minds off the road and their hands off the steering wheel [2]. Generally, the usage of a smartphone while driving adversely affects driving performance in different ways [3], including (1) impairing driver ability to maintain the road lane positioning; (2) impairing to maintain the predictable speed; (3) resulting in missing tra c signals; (4) resulting into longer reaction time to an unexpected hazard or event; (5) minimizing the functional visual eld-of-view; (6) resulting in to not keeping minimum following distance to a vehicle ahead; and (7) increasing driver mental workload. Despite known catastrophes, people are habitual of using a smartphone while driving [1]. For example, 0.66 million drivers use smartphones at a particular instant while driving [4]. However, using a smartphone while driving is discouraged and banned in most countries and societies due to the leading distraction for accidents [5]. The National Safety Council has reported that annually 1.6 million accidents and 0.39 million injuries are caused due to using a smartphone while driving [6].
Text messaging has been the main distracting and dangerous activity among all smartphone activities while driving [7] and has attracted considerable public and media attention. The sources have explored that crashes resulting in injuries and possible drivers' deaths are mainly due to text messaging while driving [8]. For instance, one experimental study suggests that younger drivers spend more than an hour every day talking on phones compared to a global average of 27 minutes, with 49% using mobile phones for text messages weekly [2]. The reason for text messages increasing frequency might be as it is cheaper than talking on the phone. Investigating the effects of using a smartphone on driving performance has been the prime interest of research communities. It has been found that young drivers engaged with a smartphone for text messaging spent up to 400% more time not focusing on the road and being inconsistent in lane position up to 50% [9]. A comprehensive study of 348 drivers has shown that 70% of the drivers initiate text messages, 92% read text messages, and 81% reply to text messages [10]. An-other research [8] illustrated that typing and reading text messages seriously affect eye movements, reaction time, lane positioning, stimulus detection, speed, and headway. Typing and reading messages while driving can adequately affect the driver's ability to redirect attention outside the roadway.
Technically, drivers have limited freedom to move their hands, heads, eyes, and minds from the primary task while driving. Using a smartphone while driving can in-crease cognitive overload and seriously affect eye movement, head movement, hand movement, reaction time, lane positioning, stimulus detection, speed, and headway [8]. On-Road research [11] has observed driver activities using a camera and found that drivers using a smartphone while driving could have 23 times more accidents. Similarly, taking eyes off the road for 2 seconds increases accident chances 24 times [12]. Typing and reading messages while driving can affect a driver's capability to redirect the attention to the outside roadway adequately. For example, National Highway Tra c Safety Administration (NHTSA) 2013 Cell Phone Naturalistic Driving has reported that interacting with texting activity takes 36.4 seconds on average [13]. Moreover, using a smartphone for text messaging diverts focus off the road for 23 seconds on average, meaning that a text message sent or received will take a driver's eyes off the road for more than half of a kilometer if traveling with the speed of 90 km/h [13].
The already developed smartphone applications for performing these activities are complex and time-consuming, which could be limiting a driver's safe driving. For example, SMS contains several further sub-activities, including composing, replying, reading, forwarding, searching, and closing. Therefore, using these complex applications may lead to excessive head and hand movement, off the road visual engagement, loss of concentration, and may increase cognitive overload. Similarly, these limitations could vary due to different driving contexts (i.e., road type, bad weather, tra c density, and night driving.). The complex nature of SMS applications may produce frustrations, which are timeconsuming and lead to risky driving, leading to accidents.
The idea of universal design advocates the usage of technology accessible for all [14]. Technology should serve people with special needs according to their requirements and limitations. This can be accomplished through a process of adaptation in assistive technologies [15]. However, the available applications used by the drivers are designed with the perspective of normal people to be used in normal routine life, which are not suitable for drivers due to their limitations. Researchers have emphasized developing adaptive, context-aware user interfaces based on the Human-Computer Interaction (HCI) guidelines [16][17][18][19]. The organization of SMS-related activities such as reading, writing, deleting, and forwarding requires considerable visual, physical, and mental attention. Most of the available solutions, such as Android Auto and CarPlay, use text-to-speech and speech-to-text metaphors instead of visualmanual interactions [20]. However, the researchers have suggested drivers still face issues while driving as voice interfaces require a little bit visual-manual demands, interior glance time, and higher mental demand than baseline drive [1]. In addition, cognitive demands are high for tasks using voice interfaces [21]. This paper proposed a context-aware adaptive SMS client for drivers based on the DriverSense framework [22]. The proposed solution is implemented in the android platform, namely which means contextual texting. The proposed solution aims to accommodate the ConTEXT user interface requirements according to different driving contexts. For example, the proposed solution will e ciently use a smartphone and vehicular sensing technology to capture and identify different driving contexts, including vehicle speed, tra c status, noise level, driver's preferences, and road status to adapt user interfaces automatically. The context-dependent simpli ed interfaces can be generated using adaption rules to improve the drive's safety by minimizing visual, manual, and cognitive interactions. The proposed solution may help the drivers manage the SMS activities in a speci c order, resulting in quick memorization of shortcuts, perception clues, and minimal visual and physical engagement. The proposed ConTEXT solution has been evaluated by 117 drivers by performing different tasks. Results showed an improved user experience in terms of minimal visual, physical, and mental engagements, task completion accuracy, less navigational loss, and automatic adaptation of user interfaces. In addition, the proposed solution is compared with smartphone native SMS interfaces leveraging a signi cant correlation.

Related Work
Researchers indicated that using a smartphone for text messaging while maneuvering a vehicle can impair driving performance and lead to road accidents [23]. Increasing the usage of a smartphone while driving leads to an increase the tra c accidents [24]. For example, the research found that distraction-related fatalities increased from 10.9% in 1999 to 15.8% in 2008 due to increased texting while driving [7]. The risk postured by text messaging while driving has attracted considerable research attention among legislators, automakers, safety researchers, and developers to come up with distracted-free solutions [23]. On one side, text messaging is a dangerous activity while driving, and on the other side, it lacks context-awareness that automatically detects the context and responds accordingly.
The term context-aware computing was rst coined by Schilit et al. in 1964 [25]. Since then, it has been popular and used continuously by researchers. According to Zimmermann et al. [26], the term "context" can include the location, time, and temperature. An-other researcher [27] de nes the context as the location of the user's, who they are with, the time of the day, the season of the year, and the temperature. Schilit describes the con-text-sensitive systems that are aware of and adapt the location of use, detecting nearby objects and people according to time [28]. Similarly, Ryan et al. [29] de ne context as location, temperature, time, and user identity. Some researchers [28,29] also described the context as the computing environment or the environment that the computers know about [30,31]. In this regard, phone context was particularly used to allow users to de ne personal con-text rules (e.g., switching to meeting mode when the phone lies still or face down) [31].
Researchers have investigated several ways to sense contextual and emotional information in instant messaging applications [32]. Recently wearable and physiological sensors have been utilized to uplift the effect of contextawareness in text messaging. Researchers have used various smartphone, environmental, and on-body sensors to sense different contexts such as location, temperature, and activities [32][33][34]. Similarly, the researchers have used text analysis to sense the context and emotions in chatting applications. For example, [35][36][37], have used text analysis to surmise the mood in instant messaging applications. Some researchers [38][39][40] have used facial recognition in text messaging applications to communicate in-chat emotional states via avatars and images. Furthermore, for contexts (i.e., location, temperature, and activities) sensing and detecting, the researchers have used various smartphone sensors, environmental sensors, and a combination of on-body sensors. For example, ConChat [41], a contextsensitive chat application, captures and integrates information from the environment using embedded sensors, such as several people in the room, room temperature, and currently running application. Hong et al. [42] used four different sensors: accelerometer sensor, physiological sensors, GPS, and smartphone sensors to extract and analyzed emotions, location, stress, weather, movement, and time of the day using dynamic Bayesian network in the ConaMSN messenger. However, these solutions suffer from a lack of real-time contexts and adaptation to minimize the drivers physical, visual, and mental engagements. There is a dire need to make the interaction simpli ed and automatic using an adaptive user interface paradigm.

Adaptive User Interfaces
The adaptive user interfaces use a context-awareness approach and generate new interfaces according to changes in environment, user preferences, and device usage [43]. This approach will help drivers personalize their smartphone user interfaces irrespective of their visual, physical, and cognitive limitations. ICCS [44,45], an in-car communication system intended to minimize driver distraction when drivers are engaging with their cell phones with the help of speech input and output. However, this system is not widely adopted because of not using the vehicle contextual information for generating automatic UI. The researchers from different domains have emphasized adaptive user interfaces and have designed easy-to-use, user-friendly, and accessible interfaces according to the HCI guidelines to solve real-world problems in the different domains [46]. For example, a system called "Supple System" [47] generates user interfaces for the users based on their tasks, preferences, and cognitive abilities. The ndings have shown that novice users can complete a complex task in less than 20 minutes using the proposed user interface. A multipath user interface system is developed, using XML to generate user interfaces based on current contexts [48]. The Egoki system is a user interface generator system designed for people with disabilities [49]. The purpose of the system was to recommend appropriate user interfaces to select multimedia content to the users based on their needs. The MARIA system proposed a model-based user interface description language to automatically generate user interfaces and customize for the different devices in run time [50]. The ODESEeW system is a semantic web portal that automatically uses the WebODE platform and an ontology application to generate a knowledge portal of interests [51]. For example, it generates different menus based on the users' interests and adjusts the visibility of contents according to the users' needs.
A generic interface infrastructure has been presented in the MyUI system, aiming to increase accessibility through an adaptive user interface [52]. The MyUI provides a run-time adaptation to user preferences, device usages, and work conditions. An XML-based pervasive multimodal user interface framework is proposed, which helps the designer to design a wide range of platforms that support multi-languages [53]. The aim was mainly on how to change the monomodal web-oriented environment of a simpli ed interface for a variety of platforms. The ViMoS system has proposed a context-aware framework to provide information about adapted embedded in the user devices according to the environment [54]. The system is composed of a set of available widgets to render different data patterns on various visualization techniques to adapt and customize visual layouts in the available area. In this research work, we have used the context-awareness and the adaptation approach to simplify the interaction between driver and smartphone.

Assistive Technologies
Different assistive technologies (as shown in Table 1) have been used to reduce distractions. The main aim of these technologies is to simplify smartphone functionalities [1,20]. This approach is aimed to reduce visual interactions by simplifying driver interactions with smartphone applications. For example, a smartphone-based system, namely Safe Driving App: Drivemode, provides a simpli ed yet effective interface to smartphone usages and minimizes visual and motor demands by providing shortcuts to the apps and using voice commands for interactions. Following the idea, several smartphone systems are developed to support safe interaction between driver and mobile phones, such as Car assistant, is an external device linked with the smartphone via Bluetooth, providing the features of text messages, calls, and operate music without physical interaction by using voice commands or gestures. However, these solutions can result in excessive cognitive overload due to voice commands as discussed earlier, off-road visual engagement, and navigational complexity [68]. Furthermore, the latest study [20] found no empirical evidence regarding these applications in minimizing the risk of crashes. The existing user interfaces have numerous challenges due to heterogeneity issues. The heterogeneity can broadly be de ned as the multiplicity of the driver, input/out capabilities, environmental conditions, contextual variability, interaction modalities, and computing platforms. The multiplicity of the drivers is based on their physical, visual, and cognitive limitations.
These solutions are vital by considering the leading cause of deaths due to smartphone usages. However, these solutions still have certain limitations which might not be excluded. For example, operating the smartphone using voice commands still requires more visual-manual demands, interior glance time, and higher mental demand than baseline drive [1]. Similarly, this approach may fail to reduce cognitive overload in case of excessive tra c [44,45].
Privacy could also be an issue when using voice commands, especially when other passengers are in the vehicle.
Moreover, installing external hands-free systems is often a barrier due to usability, cost, and lack of practicality [20].

Context: A Usable Sms Client For Drivers
The ConTEXT SMS client has been named after the idea of contextual texting. Con-textual texting means texting based on various factors, including weather, location, vehicle speed, and time. The motivation behind the design of this solution was assisting the drivers by automatically managing their textual activities. We designed a driver-friendly SMS client to minimize distraction as texting using a handheld smartphone frequently requires physical, mental, and visual engagements. Usually, most people prefer to use the default messaging applications for text messaging, which are not according to the NHTSA guidelines. The default messaging applications consist of many activities that are either redundant, repetitive, complex, and require a longer route to follow. These applications are designed with the intention of being used by people in their daily routines. However, in driving scenarios, the usage of these applications is not suitable. The context-aware adaptive UI paradigm can potentially solve the distractions and increase the usability of a smartphone texting activity while driving. Figure 1 presents the schematic diagram of the proposed solution. The methodology mainly focuses on driver interaction, sensors data sources, creating the models, adaptation rule manager, and adaptive user interface generator.

Logging the Driver Interactions
In the beginning, the driver input could be captured through touches, gestures, or voice commands and stored in the user interactions-log for further operations. These interactions, i.e., a number of inputs or activities on a smartphone, could be extracted automatically based on driver preferences, the context of use, and the environment. Data could be collected in real-time through different sensors, i.e., gestures, vocals, and touches, while the driver interacts with the system and stored in the interaction log.

Sensors Data Sources
Sensory input could be captured from different devices, including the vehicle. For example, sensors can obtain information from various sensors, including Global Position System (GPS), accelerometer sensors, light, noise, and gyroscope. The GPS is used to nd the location, altitude, direction, speed of the car. Information from online sources, i.e., web services, are also used to obtained weather information, temperature, speed of the wind, and humidity. The vehicular data could be obtained from the Controller Area Network (CAN) using the standard Onboard Diagnostic (OBD-II) port [23,55]. This data will be further processed to obtain meaningful full context, and based on contextual values, a new mode of interaction or user interface will be generated for the driver while driving.

Generating Information Models
Different models (i.e., driver, vehicle, device, and context models) have been created, which could be considered the baseline for generating adaptive user interfaces. The driver model contains information related to driver demographics, experience, sensing power, and cognition. Similarly, the Vehicle model stores information related to vehicle data, i.e., type of vehicle, type of transmission, capacity, safety features, and types of telematics. Type of vehicle includes the company of the vehicle model, and transmission system involved automatic or manual gear system which will also affect the interaction with the system. Capacity can be modeled as the number of maximum passengers in the car. Safety features include brake assist, automatic emergency braking, and adaptive cruise control.
The device information could be stored in the device model, e.g., device type (i.e., smartphone, Smartwatch, or other infotainment systems), screen size, screen resolutions, display type, interaction mode, input/output capabilities, and connectivity. This information is essential for the e cient adaptation of the user interface. Furthermore, the user preferred mode of interaction also contributes to the better user interface adaption. Context model stores information about different contexts such as road condition, weather, noise, light, temperature, location, time, speed, and tra c condition. This contextual information could be collected using smartphone sensors, vehicle sensors, and other online sources. Once the contextual model is built, it will be passed to the adaptation rule manager.

Generating Adaptive UIs
The information obtained from the models can be input into the adaptation module, where the adaptation rules can be applied to generate new user interfaces. The adaptation rules can be speci ed in the form of events, conditions, and actions [56]. This approach has been used by many researchers, for example [57,58]. The event part of the rule should be composed of the associated event whose manifestation activates the evolution of the rules. The condition part comprises a Boolean condition, which needs to be satis ed to execute the action part. For contextual texting, we have proposed the following different adaptation rules. These adaptation rules have been used in a real-time android application called ConTEXT. The proposed application will handle the drives texting activity according to the different driving contexts. The interfaces and mode of interaction of the ConTEXT application will be automatically changed according to different vehicle speed variations, environmental conditions, and road conditions. For example, if low speed is detected, short messages will be visible with adjustable font size, whereas lengthy messages will be placed in the reading later queue. When medium speed is detected, short messages will be allowed through vocal modality. However, an auto-reply message will be generated for the lengthy and unknown contacts. The SMS reply will be divided into some categories in low and high speed, where the driver will choose an option through voice or touch. For example, an SMS reply could be shown in three parts: standard reply (I am driving), personal reply where you can write a short message or just press an auto-reply button, and fun reply (gossip type message from friends which may be skipped). Similarly, when high speed is detected, an auto-reply message will be generated for short and long messages and saved and unsaved contacts. The threshold values for the different contexts are presented in Table 2.

Rule 1
If the vehicle speed reaches 100km/h or more than 100km/h, then the mode of interaction for the SMS needs will be changed to auto-reply Rule 2 If the vehicle speed reaches low-speed (e.g., 30km/h), then short messages will be visible with adjustable font size, and lengthy messages will be placed at reading later queue. Rule 3 If the vehicle speed reaches medium-speed (e.g., 60km/h), short messages will be allowed to read through vocal modality, and lengthy messages will be placed at reading later queue Rule 4 If the vehicle speed is low, the driver will choose an option among the three parts (Standard Reply, Personal Reply, and Fun Reply) Rule 5 If the vehicle speed is high, then auto-reply message will be generated for short and long messages Rule 6 If the environmental noise is 25 decibels and speed is low, then the mode will be switched to graphical mode

ConTEXT Prototype
ConTEXT is implemented as an Android application to be installed on any smartphone operating system. ConTEXT is primarily tested on Android-based smartphones. However, it can be equally tested and installed on any Android-based infotainment system. Figure 2 shows the screenshots of the ConTEXT application. The ConTEXT application is developed with the intention to keep track of all the design considerations, including privacy and security, energy consumption, and speci cally the accessibility. ConTEXT application is exible to accommodate the new technologies related to accessibility. The ConTEXT application runs as a service in the background and is automatically launched when the driver starts driving. The application will only be activated when an SMS is received while driving. Reading and writing SMS is based on different driving contexts. For example, short messages with maximum adjustable font size will be displayed when the vehicle's speed is low. Similarly, the driver may also choose the text to speech option by simply clicking the speaker icon. It is to be noted that there is only minimum interaction of the driver with the ConTEXT application at low speed.
The ConTEXT application will intelligently have addressed the SMS reply activity as it diverts focus off the road for maximum time. For example, the auto-reply message will be initiated in case of high speed and medium speed.
Similarly, the SMS reply will be divided into three parts i.e., standard reply, personal reply, and fun reply for lower speed. In Standard reply, the driver may just click this option, and an auto SMS will be sent automatically to the receipt. In case of a personal reply, the driver may have an option to write a short message or reply using the voice option. Similarly, in a fun reply, the message may be skipped.

Recognizing Risky Driving Patterns
The AutoLog application has been used to log data about drivers' interactions with common smartphone applications to recognize drivers' risky driving behavior. The logged data containing information about different operations carried out by smartphone applications, such as the number of activities used to perform tasks, number of input taps, etc. The common smartphone applications include Calls, SMS, Email, WhatsApp, Navigation, Weather, etc. As discussed earlier, the applications and their interfaces are designed from the perspective of a normal user as the number of activities is redundant, repetitive, has a complex structure, long route to follow, etc. The logged data obtained from smartphone native interfaces are analyzed and compared with the data obtained from ConTEXT for using common smartphone activities.

Recognizing Steering Wheel Variations
The datasets also captured steering wheel control variation while driving. The steering wheel control has been analyzed while the driver performed smartphone activities both in smartphone native interfaces and ConTEXT. A comparatively high steering wheel variations have been observed when drivers performed common activities such as SMS using smartphone native SMS interfaces. However, signi cantly minimum steering wheel variations have been observed when the drivers performed the same activities on ConTEXT. A comparison of the steering wheel control variations while receiving voice calls is depicted in Fig. 3.

Recognizing Speed Variations
The speed variations data is also captured while performing activities like attending the call, reading, and replying to text messaging using both smartphone native interfaces and ConTEXT interfaces. The signi cant speed variations are observed when the drivers read and replied to text messages on smartphone native messenger. The speed is found degraded from approximately 80km/h to 50km/h. On the other hand, the data extracted from the DriverSense dataset have shown less speed variations than smartphone native interfaces. A comparison of the speed variations is depicted in Fig. 4.

Recognizing Throttle Position
The throttle position was captured and analyzed. Comparing with call logs we found that using the smartphone during the greater than average (22.54) throttle value have higher distractions and may be extremely dangerous. The reason is that more throttle value means, more engine Rotation Per Minute (RPM). The throttle values are depicted in Fig. 5.
The RPM values are depicted in Fig. 5. The average of the engine RPM is 1738.82. Figure 5 shows that the more throttle value means the more engine RPM. Hence, attending the calls or reading textual data at higher engine RPM or throttle value may increase the ratio of the incident with savior damages to driver and vehicles.

Results
The main theme of this contribution was to provide an adaptive and contextual prototype of an SMS client for the drives that can be effectively used on smartphones and Head-Up-Displays (HUD). The proposed solution may help drivers in managing their textual activities according to different driving contexts. The usability of the design has been evaluated using standards HCI usability and accessibility parameters. The proposed solution is also evaluated empirically using a questionnaire and a dataset created using the AutoLog [22,59,60] application. We have extended the HCI model in our proposed interfaces and tested different parameters, including the degree of accuracy, level of easiness, and user satisfaction.

Empirical Evaluation
The ConTEXT has been evaluated through an empirical study on drivers. while some (i.e., 20.51%, n = 24) use voice-based interfaces, e.g., Google Assistant. The participants daily traveling frequency was 66.67% (n = 78), and random or sometimes traveling frequency was 33.33% (n = 39). We have normally modeled the purpose of traveling as an employee (workplace) mostly, shopping mostly, and business mostly. According to traveling type, the participants, 57.26% (n = 67), were employees mostly, 25.64% (n = 30) were shopping mostly, and 17.10% (n = 20) were business mostly.

Evaluation Process
We have evaluated the proposed methodology using the real-world ConTEXT and AutoLog application. These two applications have been installed on participant's smartphones. We have educated the respondents that the AutoLog application will work as a service in the background and will collect and record the driver's smartphone activities, including activity duration time, activity completion time, changes in vehicle dynamics such as variations in speed, steering wheel, braking, and accelerator), and environmental data such as tra c status, location, weather, temperature, road condition, and light intensity. The respondents were assured that the data would be automatically anonymized before storing it in a database to avoid privacy disclosure. Similarly, it has further clari ed to the participants that the logged data will only be used for the research evaluation. After using the proposed solution for three months, the respondents were asked to ll a questionnaire to investigate the usefulness of the ConTEXT application.

Results And Discussion
We have used STATA, SPSS, and Excel to carry out different tests and analyzed the statistical data. In the initial phase, we used descriptive statistics to report the percentages and frequencies of the latent variables. We have also performed a cross-tabulation with cell percentages and cell Likelihood Ratio Chi2 tests. Similarly, a Cronbach alpha test has been carried out to check the reliability scales of the variables. We have also performed factor analysis as the

Empirical Observations
The participants' responses have been complied with and described each latent variable in the separated line graph. It should be noted that each latent variable contains several different questions, and participants have been educated to select one of the ve Likert Scale options ranging from 1 to 5. As shown in Fig. 6, the participants have a positive attitude towards using the ConTEXT application as most of the reported scales are higher than 3, which means that the idea is good and they have a positive attitude.
In terms of the System Usability Scale, the participants have been asked to answer seven questions. According to the analysis, the participants were con dent and happy to use the system frequently as they reported that the system is not too complex and components are well integrated, and they do not need a technical supporter to operate. The participants also showed their responses about the complexity and learnability of the systems. As shown in Fig. 7, maximum participants reported scales 1 and 2 (see bluish lines), which seems that the system is not too cumbersome, and they do not need other things to learn before operating the proposed systems.
One of the important aspects of safe driving is the cognitive load, as performing concurrent activities while driving may increase the cognitive load, leading to accidents. Most texting activities could be performed automatically so that it will not increase the drivers' cognitive load. As shown in Fig. 8, maximum participants have reported that the ConTEXT application has been designed to make every icon and description easy to interpret. Only a few participants have reported that it does not provide aids for entering hierarchic data.
Participants have reported that using ConTEXT as a client SMS application will minimize the visual interaction as most activities would be performed automatically in the background. As shown in Fig. 9 the scales of 4 to 5 (see. Figure 10). According to the participant's responses, the interaction has been minimized due to automatic responses and adaptation.

Factor Analysis
We have conducted the Cronbach alpha test to check the internal consistency and reliability of the measurement items. As shown in Table 3, the obtained alpha score of all factors seems to be consistent and reliable as it ranges from 0.70 to 0.79, which is considered good [65,66]. According to our results, the PCFA was found satis ed and appropriate compared to Iterated Principal Factor Analysis, Factor Analysis, and Maximum Likelihood. As shown in Table 4, the retained factors in PCFA express the contribution of variations by a speci c factor in total variations. The Eigenvalue of all factors is greater than 0, which have been considered as important factors. Total factors have been retained and contributed to total variation as no factor has a negative Eigenvalue.   These values indicate that the estimated covariance metrics of the observed model and the proposed model are found signi cant and good. The estimated structure model values and their recommended ranges are depicted in Table 5.

Observation through AutoLog Dataset
We have used the AutoLog application for evaluating the ConTEXT application. The AutoLog data about the ConTEXT during the normal use has been compared and analyzed with a dataset obtained from the Smartphone Native Messenger application. After evaluation, we have concluded that texting via ConTEXT requires comparatively less mental, physical, and visual attention than a native messenger. ConTEXT has shown comparatively minimum efforts, a minimum number of input taps, and touches. The main reason of driver's minimum engagements is that ConTEXT intelligently categorizes the SMS activity according to a different context. For example, composing an SMS by typing is not allowed in any context. However, the voice option will only be available for the drivers if they are driving at a very low speed. Similarly, this option will be disabled in case of medium or high speed. A voice interface will only be visible to drivers in case of low and medium speeds. However, at high speed, SMS will be stored at reading later queue. There are three categories in SMS reply (1) Standard reply, (2) Personal Reply, (3), and Fun Reply. In conclusion, the ConTEXT application requires no extra efforts, driver's engagements to perform a textual activity. On the other hand, as shown in Table 6, smartphone native interfaces are complex and require maximum input taps, touches, and efforts. Similarly, the ndings reveal that ConTEXT requires minimum physical, visual, and mental attention compared to Native Messenger.

Conclusions And Future Work
The available SMS applications are speci cally designed for ordinary people to use in their daily routine lives and do not explicitly meet drivers' requirements. While driving, drivers cannot afford to access the touchscreen for interaction as eyes off the road for two seconds increase the chances of accidents to twenty-four times. Due to this fact, the use of SMS applications is challenging for drivers. The proposed ConTEXT application has addressed these issues by adapting the user interfaces according to the different driving contexts. The proposed solution will e ciently use smartphone and vehicular sensing technology to automatically capture and identify different driving contexts, including vehicle speed, tra c status, noise level, driver's preferences, and road status, to adapt user interfaces automatically. The context-dependent simpli ed interfaces can be generated using adaption rules to improve safety by minimizing visual, manual, and cognitive interactions. The proposed solution is evaluated both empirically and using the AutoLog dataset. The results indicate that ConTEXT will help drivers manage textual activity with limited physical, visual, and mental interventions.
In the future, we intend to adapt the UI to the driving mode to include applications of machine learning. We are also planning to incorporate more adaptation rules to enhance the functionality of the proposed solution further.

Declarations
Funding Statement: There is no funding for this research Con icts of Interest: The authors declare that they have no con icts of interest Attitude of participants after using the ConTEXT Visual engagements while using the ConTEXT Figure 10 Physical engagements while using the ConTEXT