Data on the prevalence and causes of hearing loss is lacking from many low and middle-income countries, in part, because all-age population-based surveys of hearing loss can be expensive and time consuming. Restricting samples to older adults would reduce the sample size required, as hearing loss is more prevalent in this group. Population-based surveys of hearing loss require clinicians to be involved in the data collection team and reducing the duration of the survey may help to minimise the impact on service delivery. The objective of this paper was to identify the optimal age-group for conduct of population-based surveys of hearing loss, balancing sample size efficiencies, and expected response rates with ability to make inferences to the all-age population. Methods: Between 2013–2014, two all aged population-based surveys of hearing loss were conducted in one district each of India and Cameroon. Secondary data analysis was conducted to determine the proportion of hearing loss (moderate or greater) in people aged 30+, 40+ and 50+. Poisson regression models were developed to predict the expected prevalence of hearing loss in the whole population, based on the prevalence in people aged 30+, 40+, and 50+, which was compared to the observed prevalence. The distribution of causes in these age groups was also compared to the all-age population. Sample sizes and response rates were estimated to assess which age cut-off is most rapid. Results: Of 160 people in India and 131 in Cameroon with moderate or greater hearing loss, over 70% were older than 50 in both settings. For people aged 30+ , 40+  and 50+  the proportions were higher. Prediction based on Poisson distributed observations the predicted prevalence based on those aged 30+, 40+, and 50+ fell within the confidence intervals of the observed prevalence. The distribution of probable causes of hearing loss in the older age groups was statistically similar to the total population. Sample size calculations and an analysis of response rates suggested that a focus on those aged 50+ would minimise costs the most by reducing the survey duration. Conclusion: Restricting the age group included in surveys of hearing loss, in particular to people aged 50+, would still allow inferences to be made to the total population, and would mean that the required sample size would be smaller, thus reducing the duration of the survey and costs. In 2018, the World Health Organization (WHO) estimated that 466 million people had “disabling” hearing loss globally  [The impact of hearing loss on affected individuals and their family is well-established. Childhood hearing loss can adversely affect speech and language development, social and cognitive development, and school performance ,6. PeoplReliable data on the prevalence and causes of hearing loss is lacking, particularly in LMICs, so global estimates are based on limited evidence. Stevens et al. (2013) conducted a systematic review of population-based surveys of hearing loss, identifying only 42 surveys in 29 countries worldwide . As a coA key driver of the lack of data on hearing loss is the significant methodological and economic challenges that exist in carrying surveys on this topic, ,13 and tAlternatives to all-age population-based survey approaches to measure the prevalence of other types of impairment exist. For instance, the Rapid Assessment of Avoidable Blindness (RAAB)  is a rapLike visual impairment and blindness, the prevalence of hearing loss dramatically increases with age, suggesting that a comparable “rapid” protocol could be used to estimate hearing loss in the population . HoweverThe aim of this paper was to consider the feasibility of a rapid population-based survey methodology for assessing the prevalence and causes of hearing loss to inform planning of ear and hearing services. Specifically, through analysis of data from previous all-age hearing surveys undertaken in India and Cameroon, we explored the data that would be captured by restricting the survey population to an older age group in terms of the proportion of hearing loss, the distribution of causes, expected response rates, and the required sample size. In 2013–2014, two all-age population-based surveys of hearing loss were conducted in India (Mahbubnagar District) and Cameroon . In the current paper, we analysed these data to estimate the prevalence and causes of hearing loss in people aged ≥30, ≥40 and ≥50 years, and compared findings to the total population. As the feasibility and cost of conducting a rapid survey also depends on the sample size and the response rate, we also computed sample size estimations and expected response rate for each of these age cut-offs.The methods and results of these surveys have been published previously. Nevertheless, the next section provides a summary ,25.The expected prevalence of “disabling” hearing loss  was conservatively estimated to be 4%. This required a sample of 4056, assuming a precision of 20%, 95% confidence, a design effect  of 1.5 and 20% non-response. A two-stage sampling procedure was used. Fifty-one clusters  of 80 people each were selected using probability-proportionate-to-size sampling, using the most recent census data  as the sampling frame. Census data is likely to include the vast majority of units of the population of interest , provided it is fairly recent. Within clusters, households were selected using compact segment sampling. All the people in the selected households were eligible.A modified version of the WHO Ear and Hearing Disorders survey protocol was used in both surveys. Screening for hearing loss was conducted by an audiologist in a central location in each cluster. Ambient noise was measured and recorded using a sound level meter prior to testing. A quiet environment was chosen for testing with the aim of keeping the ambient noise below 40dBA as per WHO recommendations . HoweverA two-stage screening protocol was used. Initial screening of all participants was through OAE (Otocheck LE or Otoport Lite) testing. Participants aged ≥4 years who failed OAEs in both ears underwent pure tone audiometry screening (Interacoustics screening audiometer model AS608) to assess the level of hearing loss. Hearing thresholds were determined at frequencies of 1 kHz, 2 kHz, 4 kHz, 0.5 kHz in each ear. Children <4 years underwent OAE testing only.Cases of hearing loss were defined as those with pure-tone average ≥41 dBHL in adults (age ≥ 18) and ≥31 dBHL in children (age 4–17) in the better ear, or children aged <4 who failed the OAE test in both ears. In addition, participants who could not undertake PTA, but failed OAE in both ears, were considered to have hearing loss.Probable conductive: impacted wax, foreign body, otitis externa, chronic suppurative otitis media, otitis media with effusion, acute otitis media, and dry perforation of the tympanic membrane.○Congenital: history indicative of congenital causes ○Infectious conditions: history of infectious conditions that cause hearing loss ○Noninfectious conditions: history of non-infectious conditions related to hearing loss ○Age-related undetermined: hearing loss present since old age (60 years +) (indicative of presbyacusis) .○Undetermined causes: where otoscopic examination was normal and clinical history did not provide sufficient information to classify hearing loss.Probable sensorineural:The exact causes of hearing loss are difficult to establish even in clinical settings. This survey used a pragmatic approach to determine the “probable” causes. Cases were examined by an Ear Nose and Throat nurse (Cameroon) or an audiologist (India) who indicated the main probable cause based on otoscopy and questions about clinical history of hearing loss, derived using the original WHO survey protocol. There are three types of hearing loss: sensorineural , conductive , and mixed . We categorised the causes broadly into probable sensorineural and probable conductive hearing loss according to the following:Data from the two surveys were re-analysed using STATA .Proportion of hearing loss in people aged 30+, 40+, 50+: the proportion of hearing loss (based on definitions above) in those aged 30+, 40+ and 50+ was calculated in both India and Cameroon by creating binary age group variables for each age cut-off. The prevalence of hearing loss in 10-year age bands was also calculated. To account for the clustering design, the “svy” command was used. Poisson regression models: In order to determine the impact of using data from older populations only to make inferences on hearing in the whole  population, we created Poisson regression models based on hearing loss data from 30+, 40+ and 50+ age groups only to predict all-age population prevalence. Poisson regression is used to model response variables that are counts (integers) which follow the Poisson distribution. The shape of the distribution of prevalence by age group in the two settings follows a Poisson distribution. The predicted prevalences were then compared to the observed prevalences. If the confidence intervals overlapped, we considered the model was a good predictor. Causes of hearing loss in people aged 30+, 40+, 50+ compared to the total population: the causes of hearing loss in those aged 30+, 40+, 50+ compared to the total population, using the binary variables created in the first analysis. n = 50) in RAAB relates to the number of people that can be feasibly examined in one day. A cluster size of 40 was chosen for the calculations because we assumed that fewer people can be feasibly examined in one day than in RAAB due to time-extensive clinical examinations. Sample sizes: The required sample sizes were calculated according to the prevalence of hearing loss in people aged 30+, 40+ and 50+. The purpose of this analysis was to demonstrate how the sample size changes with expected prevalence and to gain an estimate of how much the sample size reduces with different age cut-offs. The calculation was done using the expected prevalence for each age group and average DEFF for a cluster size of 40 (obtained from the datasets from each country), an expected response rate of 90%, required precision of 20% (around the estimate), and 95% confidence. The cluster size , the Public Health Foundation of India Institutional Ethics Committee (ref: 84/2012), the Indian Council of Medical Research (ref: 36/ADR/2013-NCD-1), the National Ethics Committee for Research in Human Health  (ref: 2013/03/084/CNERSH/SP), and the Cameroon Baptist Convention Health Board Institutional Review Board (ref IRB2013-07). In India, 4056 eligible people were enumerated, of whom 3573 were screened for hearing loss (response rate 87%). In Cameroon, 4104 people were enumerated, and 3567 screened for hearing loss (response rate 87%). Details of the non-responders and reasons for incomplete screening have been provided elsewhere ,25.The all-age prevalence of hearing loss in Cameroon was 3.7% . 76% of cases were older than 30 years, 75% were ≥40 years, 72% were ≥50 years and 66% ≥60 years. The prevalence of hearing loss for those ≥30 years was 8.9% , 11.5%  for those aged 40+, and 14.8%  for those aged 50+.In India, the expected prevalence was 3.8% (95%CI 3.0–4.5) based on a 50+ cut-point; 4.1% for 40+ ; and 4.3%  for 30+. These estimates fall within the confidence intervals of the observed prevalence . In Cameroon, the expected prevalence was 2.7%  based on a 50+ cut-point; 2.8 %  for 40+; and 2.8%  for 30+. These estimates are within the confidence intervals of the observed prevalence, with overlapping confidence intervals .To assess which age-cut off was most appropriate, consideration of the following factors was made: response rate, sample size required, proportion of hearing loss missed, predictive power (based on Poisson model), causes, and expected costs. This comparison is shown in These findings suggest that assessment of people aged 50+ in clusters of 40 may be the most appropriate in terms of survey duration and affordability. Restricting the population would result in a substantial reduction in the sample size required to accurately estimate the prevalence, whilst still capturing the majority of hearing loss and overall cause distribution and allowing the survey to be more affordable and efficient.In two population-based surveys of hearing loss in India and Cameroon, approximately 18% of the population were aged 50+, yet this age group accounted for over 70% of the prevalence of hearing loss. An age cut-off of 40+ increases this proportion to 75% and 81% in Cameroon and India respectively. People in older age groups are much more likely to have hearing loss than younger age groups, which suggests that focusing on older age groups is a valid public health approach for gathering epidemiological data on hearing loss prevalence for planning services and advocating for increased resources. Poisson regression models show that based on the 30+, 40+, and 50+ age groups, the prevalence in the total population be predicted and estimates fall within the 95% confidence interval range. The causes in the total population are comparable to the older age groups even in two settings where the proportion of causes attributed to conductive vs sensorineural hearing loss are quite different. The sample size reduces with increasing age cut-off, and the response rate increases. This may be because older people are less likely to be away from the home working when the survey team visits. Lower response rates can result in non-response bias and hence reduce the representativeness of the sample population. If lower response rates are expected, then the sample size is usually increased to account for this, increasing the resource burden for survey completion. These factors are important for reducing the time and costs of conducting population-based surveys and suggest that the 50+ age group would result in the most rapid data collection.This paper suggests that assessment of those aged 30+, 40+ or 50+ captures the majority of people with hearing loss in population-based surveys. The advantage of restricting to these older age groups is a reduction in the sample size required to accurately estimate prevalence. Taking into consideration the proportion of hearing loss, sample size, response rate for surveys including people aged 30+, 40+, or 50+ suggests that a focus on those aged 50+ would be the most rapid, lowest cost, and have lowest demand on human resources whilst still capturing the majority of hearing loss in the population. If surveys are lower cost and have less time demand on local clinicians, they are more likely to be conducted.A focus on adults also allows for a simplified examination, in comparison to the WHO protocol. In this age group, pure tone audiometry can be conducted without the need for more expensive tests such as ABR, and OAE testing required for children. Pure tone audiometry can now be conducted using validated smartphone-based tests which are automated . The advThis type of survey would take a pragmatic approach to acquiring hearing loss data in a targeted and low-cost manner. Rapid surveys are not intended to replace all-age surveys, however, are useful when resource or time-constraints exist for carrying out a full survey. The survey also takes a public health approach, focusing on the population who are most likely to experience hearing loss. This data is important for informing service provision, for example the need for hearing aids.Several challenges still remain in developing the methods for the rapid assessment of hearing loss, and these will require further research and testing.Carrying out surveys can take several weeks, and participation from specialist staff for this duration could have a detrimental impact on needed service provision. Ear and hearing care professionals are scarce in most LMICs. This highlights the importance of minimising the required sample size. In addition, recent research in Malawi has shown that non-specialist health workers  can accurately assess hearing levels, however, ear examination requires prior experience to assign causes accurately  . FurtherEvidence from South Africa suggests that trained community health workers can successfully carry out screening audiometry using a mobile-based test called hearScreen . This reWhilst otoscopy can help establish likely causes of conductive hearing loss, the exact causes of sensorineural or mixed hearing loss are difficult to establish even in clinical settings. This is one reason why a large proportion of causes of hearing loss are undetermined in many previous surveys ,24,25. TTympanometry is used to aid diagnosis of middle ear conditions such as middle ear effusion and to determine the type of hearing loss . Including tympanometry in the protocol could increase the costs of a rapid survey, both in terms of the equipment, personnel and time taken to complete the test. The added value of tympanometry needs to be explored and verified, for instance, by investigating whether tympanometry allows some of the undetermined causes to be otherwise classified. In addition, further research is required to determine whether less expensive tests and portable, such as tuning forks, are adequate for this purpose.The proposed rapid assessment method excludes the paediatric population. However, a population-based survey of hearing loss is not the ideal method for understanding childhood hearing loss. The prevalence of hearing loss in children is typically very low (<2% in India and Cameroon) which means that a large sample size  would be required to accurately obtain prevalence data for this age group via population-based surveys. Despite the low prevalence, the impact of hearing loss on children is likely to be greater due to the impact on speech and language development, and the greater number of years lived with disability. School or newborn screening, as well as clinic-based surveys or studies using the Key Informant Method (KIM) may be more appropriate study designs for understanding hearing loss epidemiology in children . RAHL doThe analysis presented here has some limitations that should be taken into account. The study used data from two all-age population-based surveys in distinct locations at one point in time. However, the results may not be generalisable to other settings. Further research is required to confirm the results from other settings at different time points to ensure that the cohort effect does not play a role in the findings. The models used in this paper to predict the overall prevalence in the population based on older age groups may not be applicable to other populations where age-specific prevalence is not known. This is because the distribution of the prevalence of hearing loss may not take the shape of a Poisson distribution. This warrants further attention.This paper presented a rationale for a rapid assessment of hearing loss and its causes in population-based surveys. The aim of a rapid assessment of hearing loss is to estimate the prevalence and causes of hearing loss and ear disease in a resource constrained setting, in order to plan and monitor services. This paper found that assessment of people aged 30+, 40+ and 50+ provides a good indication of the prevalence and causes of hearing loss in the total population. Restricting the sample to an older age group means that the required sample size was lower, reducing the time and expense of carrying out a survey of hearing loss. The costs and duration of carrying out the survey will be particularly reduced by focusing on those aged 50+ due to the higher prevalence and expected better response rates in this group. The examination protocol may be simplified using audiometry, alongside otoscopy, and a clearly defined questionnaire. Mobile-based automated audiometry could enable non-specialist staff to carry out hearing testing, reducing the cost of the survey and the impact on usual service delivery. Further research is required to refine the method before scaling-up data collection.


Participation in colorectal cancer screening is still low among Malaysians despite the increasing trend of incidence, with more than half of the new cases being detected in the advanced stages. Knowledge improvement might increase screening participation and thus improve the chances of disease detection. With the advancement of communication technology, people nowadays prefer to read from their mobile phone using a Web browser or mobile apps compared with the traditional printed material. Therefore, health education and promotion should adapt this behavior change in educating the community.This study aimed to document the process of designing and developing a mobile app for community education on colorectal cancer and assess the usability of the prototype.t test was used to assess the usability score with a cut-off value of 68 for the usable mobile app.The nominal group technique (NGT) was used for the content development of the mobile app. NGT involving community educationists and clinicians combined with community representatives as the target users identified relevant health information and communication strategies including features for a user-friendly mobile app. The prototype was developed using framework Ionic 1, based on the Apache Cordova and Angular JS (Google). It was published in the Google Play store. In total, 50 mobile phone users aged 50 years and above and who had never been diagnosed with any type of cancer were invited to download and use the app. They were asked to assess the usability of the app using the validated Malay version of System Usability Scale Questionnaire for the Assessment of Mobile Apps questionnaire. The One-sample ColorApp) was successfully developed in the local Malay language. The NGT discussion had suggested 6 main menus in the ColorApp prototype, which are Introduction, Sign and Symptoms, Risk Factors, Preventive Measures, Colorectal Cancer Screening Program, and immunochemical fecal occult blood test kit. A total of 2 additional artificial intelligence properties menus were added to allow user-ColorApp interaction: Analyze Your Status and ColorApp Calculator. The prototype has been published in the Google Play store. The mean usability score was 72 (SD 11.52), which indicates that ColorApp is a usable mobile app, and it can be used as a tool for community education on colorectal cancer.The Colorectal Cancer Awareness Application (ColorApp mobile app can be used as a user-friendly tool for community education on colorectal cancer. Colorectal cancer has become a prominent health problem in the twenty-first century worldwide . It is tColorectal cancer is one of the preventable diseases that are treatable with early detection and treatment. The 5-year survival rate is highly dependent on the stage at diagnosis. Its survival ranges from 95% if detected at stage 1 to 8% if detected at stage IV . In MalaHealth education and promotion in Malaysia had utilized a variety of communication strategies including health talk and forums as well as printed material such as pamphlets and health notices. Websites on healthy lifestyles had been created with interactive self-risk-assessment features. However, these might be able to reach those who seek health information either from community health activities or online. With the increasing number of registered mobile phone users, including people in the higher age group in Malaysia ,7, healtThe development of the mobile app prototype was conducted from November 2017 to February 2018. The developmental processes include content development, prototype development, and prototype usability assessment by the intended user. The details of the processes are provided below.The content of the mobile app prototype was developed based on the theory of Health Belief Model (HBM). Content development of the prototype involved a few stages. The first stage was conducting a literature review on colorectal cancer, sign and symptoms, associated factors, screening and prevention, and the features of the mobile app for health promotion and education. This evidence-based information served as important guiding points for the next stage in identifying content relevant to the intended users. For example, participants may not be familiar with the features of a usable mobile app and they may face difficulties in giving their opinion about the features of a user-friendly app. Therefore, researchers provided information on the expected features of a mobile app by the user from published researches.Nominal group technique (NGT) was applied to explore the following 3 elements:What is the information needed by the intended users?How to ensure that the information is self-explanatory?What are the features needed to make the mobile app a user-friendly app?NGT is a structured meeting that aims to provide an orderly procedure for obtaining qualitative information from target groups who are most closely related to the problem area . It is aThe NGT participants consist of the following:one public health physicianone gastroenterologistone family medicine physiciantwo medical officersone Assistant Environmental Health Officer (Noncommunicable Disease Control Unit)three individuals from intended userstwo researchers as moderatorParticipants were contacted by phone 2 weeks before the session. They were briefed on the topic and what is the expected outcome from the discussion.The session was conducted in the following sequence :Introduction to problem statement and explanation: Moderator briefed the participants on the flow of the session. They were reminded about the expected outcome from the discussion and emphasized on the importance of their contribution to the public benefit.Silent generation of ideas in writing: Each participant was asked to write down their own ideas on what information on colorectal cancer that the community would like to know and what are the mobile app features that will make the apps user friendly and attractive to be used. This session does not allow any discussion and ended after 20 min.Sharing ideas: The participants were invited to share their answers in the round robin manner. Each idea was written on the white board by the moderator. This session does not allow any discussion or argument of the ideas and ended after all participants had shared their answers.Discussion of ideas: The participants were invited to give a verbal explanation about their answers that had been written on the board. They were asked to justify the need of the information and the prototype features to be included in the mobile app. Other participants were encouraged to actively discuss the relevancy of each point.Voting and ranking: Participants were asked to vote their agreed answers based on the discussion. The votes will be tallied to produce the ranking of ideas from highest to lowest based on the research questions.Conclusion: The session was concluded by the moderators. The agreed points were written on the white board.ColorApp , sized 9.5 MB was successfully uploaded in early March 2018 onto the Google Developer Console  and published in the Google Play store as a beta option for assessment purposes. It will be released free of charge as a production option for public use as part of the public health contribution once this study is completed.The mobile app prototype development was conducted from December 2017 to March 2018. It was developed using the Ionic Framework that is based on the Apache Cordova and Angular JS. A total of 2 researchers who attended training of mobile app development had developed the prototype on the Android platform, which is the main platform for the majority of mobile phones in Malaysia . The NGTKomuniti Sihat Pembina Negara , in Kota Setar district in Kedah, which is in the northern state of Peninsular Malaysia. KOSPEN is one of the government initiatives for empowering the community for the prevention and control of non-communicable disease in Malaysia. There are a total of 153 localities in Kota Setar district, covering more than 20,000 people. This area was predetermined because of the distribution of KOSPEN localities in both urban and rural areas and good mobile broadband coverage.A cross-sectional study was conducted from February 2018 to March 2018 to assess the usability of the mobile app prototype. It was conducted along with the national-level community empowerment program, 0) was the mean usability score of ColorApp is equal to 68 ), and Human Research Ethics Committee USM, Malaysia (USM/JEPeM/17110601).All participants have agreed that the mobile app prototype must cover the following topics:Introduction to colorectal cancerSign and symptomsRisk factorsPreventionColorectal cancer screening programKanser Kolorektal instead of the general term of bowel cancer, which is Kanser Usus in Malay. This terminology of Kanser Kolorektal was agreed to be used to introduce this disease to the community as it is more precise in referring to the disease. The introduction on colorectal was agreed to be of importance to introduce the terminology. The intended user group representative had agreed that the terminology was acceptable and understandable to the community after reading the introduction section.Malay language was chosen to be the main language as it is the national language, with consideration to add other languages in the future. Colorectal cancer was agreed to be translated into the Malay language as The features of the mobile app prototype that were agreed upon are simple to operate, using easily understandable language, point-form information, infographic design, include a video, and interactive.ColorApp, the abbreviation of Colorectal Cancer Awareness Application. ColorApp.The mobile app prototype was developed for the Android platform. It is called Do You Know , totalling 72.9 (SD 11.52), higher than 68 on the Skala Kebolehgunaan Aplikasi Mudah Alih score which is the minimum cutoff point for a usable system. This indicates good usability of ColorApp as a mobile app, and, hence, a usable tool for colorectal cancer community education.The usability score for ColorApp prototype, addresses the perceived susceptibility, benefit, and health-seeking behavior of the user. According to HBM, the improvement of health-seeking behavior toward colorectal cancer prevention is related to the perception of an individual that he/she is susceptible to suffer from colorectal cancer, severity of colorectal cancer, and the benefit as well as barrier of preventive action including performing the colorectal cancer screening at the health clinic [The theory of HBM, which was adopted to develop the content of h clinic . All theColorApp was developed through NGT. NGT is a simplified semiqualitative method to look into preferences of the intended group [colorectal. As the introduction had included the simple explanation of what is colorectal, the term colorectal was agreed to be used. Limited health literacy can limit users’ understanding, and in a worse scenario, it can pose a risk to them from misunderstanding [ColorApp is delivered clearly, particularly the actionable messages.The content of ed group . NGT alled group . The comstanding . The comPeople aged 50 years and above are those who are at risk of getting colorectal cancer. This mobile app prototype, thus, is aimed to be used mainly by this group of people. Therefore, several issues that might be faced by the elderly when they use it must be considered. This included issues such as minimalist design to prevent cognitive overload in elderly population, large icons size that is easy to interpret for function and interaction logic, and simple navigation structure  . AlthougColorApp was developed as a tool for community education for colorectal cancer prevention. Thus, the usability of the prototype is therefore playing an important role in determining its effectiveness to improve health knowledge and awareness of the user. Usability is defined as the extent to which a product can be used by intended users to achieve specific goals effectively and efficiently as well as providing user satisfaction in a specified context of use [ColorApp is user friendly for the intended user.The t of use . The usaColorApp for community education on colorectal cancer will become the new method in disseminating the information about diseases using a mobile app. The cost of developing it is relatively cheaper, compared with producing printed materials. It is also easily updated when there are changes in information or management protocols. The features of ColorApp that are sharable through social media will help in disseminating this mobile app in the community. Furthermore, the usage of mobile apps is the current trend in information search that should be used to educate the public and improve their knowledge. This app had been registered under the intellectual property of Universiti Sains Malaysia with co-ownership with the Ministry of Health Malaysia and is already available in the Google Play store for all Malay speaking communities that use Android. The term and condition of the Google Play store will be applied with the use of this app. The limitation of this study is that the mobile app prototype is only being developed for the Android platform because of time and logistic reason.The development of ColorApp should be available in other platforms also, especially iOS. The language option also should be made available in English, Chinese, and Tamil as these languages are also used frequently in multiethnic communities in Malaysia. The use of various languages will benefit more users, especially those who prefer certain languages. This will enhance knowledge transfer and improve the user’s understanding. Future research also should look into the effectiveness of mobile apps usage in improving the knowledge on colorectal cancer and the attitude and practice toward prevention of this disease including the screening uptake.In the future, ColorApp, a mobile app for community education on colorectal cancer prevention, which had been developed through NGT and usability process, is a potentially useful tool in the modern technology era. It is available in the Google Play store for free and can be shared and distributed among the community. This study will be extended with the collaboration of the Ministry of Health Malaysia and Clinical Research Center, Hospital Sultanah Bahiyah, Alor Setar, Kedah, to assess the effectiveness of ColorApp as a community education and promotion tool in improving user knowledge on colorectal cancer and attitude toward colorectal cancer screening program.In conclusion, the newly developed


Task-evoked changes in pupil dilation have long been used as a physiological index of cognitive effort. Unlike this response, that is measured during or after an experimental trial, the baseline pupil dilation (BPD) is a measure taken prior to an experimental trial. As such, it is considered to reflect an individual’s arousal level in anticipation of an experimental trial. We report data for 68 participants, ages 18 to 89, whose hearing acuity ranged from normal hearing to a moderate hearing loss, tested over a series 160 trials on an auditory sentence comprehension task. Results showed that BPDs progressively declined over the course of the experimental trials, with participants with poorer pure tone detection thresholds showing a steeper rate of decline than those with better thresholds. Data showed this slope difference to be due to participants with poorer hearing having larger BPDs than those with better hearing at the start of the experiment, but with their BPDs approaching that of the better hearing participants by the end of the 160 trials. A finding of increasing response accuracy over trials was seen as inconsistent with a fatigue or reduced task engagement account of the diminishing BPDs. Rather, the present results imply BPD as reflecting a heightened arousal level in poorer-hearing participants in anticipation of a task that demands accurate speech perception, a concern that dissipates over trials with task success. These data taken with others suggest that the baseline pupillary response may not reflect a single construct. This use followed from the demonstration that a progressive increase in the difficulty or effort required to complete a cognitive task is accompanied by a progressive increase in pupil dilation  whether one will observe a progressive decrease in BPD over the course of a large number of listening trials, and, (b) to the extent this is so, whether participants with hearing impairment may exhibit a differentially steeper decrease in BPD over the course of the listening trials than those with better hearing as measured by pure tone detection thresholds. Our final question is whether decreasing performance accuracy is an invariant concomitant of decreasing BPD, and with it the presumption of a unified account of the meaning of BPD in behavioral experiments.M = 60.0, SD = 26.8). Participants were drawn from university students, staff, and volunteers from the local community.Participants were 68 adults  ranging in age from 18 to 89 years . Participants’ speech reception thresholds (SRT) in quiet . None of the participants were regular users of hearing aids and all testing was conducted unaided. All participants reported themselves to be native speakers of American English, with no history of stroke, Parkinson’s disease, or other neurologic involvement that might compromise their ability to perform the research task. Written informed consent was obtained from all participants according to a protocol approved by the Brandeis University Institutional Review Board.Audiometric evaluation was carried out for each participant using an AudioStar Pro clinical audiometer  by way of standard audiometric techniques in a sound-attenuated testing room. The participants’ better-ear pure tone average (PTA) over 0.5, 1, 2, and 4 kHz ranged from 1.3 to 50.0 dB HL  that digitized (16-bit) at a sampling rate of 44.1 kHz. Root-mean-square (RMS) amplitude was equated across stimuli.SL). After each sentence was presented, the participant was asked to indicate whether a given character was the agent of the action or the recipient of the action. A trial consisted of a 60 s baseline silent period followed by the presentation of a recorded sentence and then the participant’s response.Participants heard a total of 160 sentences presented binaurally via Eartone 3A insert earphones  at 20 dB above each participant’s better-ear SRT , using a standard 9-point calibration procedure. The Eye-Link acquired pupil size data at a rate of 1000 Hz, with data recorded via MATLAB software .Blinks were removed, and linear interpolation was performed starting 80 ms before and ending 160 ms after each blink e.g., . This prsenile miosis;SD = 19.8; values given in arbitrary units, AUs, standard to EyeLink trackers; e.g., SD = 6.4). Pupillary dynamic range was estimated by measuring each participant’s change in pupil dilation in response to light (199.8 cd/m2) and dark (0.4 cd/m2) screens presented for 60 s each. The mean dynamic range for participants below 60 years old was 26.1 AUs (SD = 7.4) and for participants above 60 years old was 14.9 AUs (SD = 6.3).Older adults generally exhibit an overall smaller pupil size and a smaller range of excursion when compared to younger adults (r(66) = −0.60, p < 0.001, with older adults showing a significantly smaller raw BPD than the younger adults. There was also a correlation between raw BPD and individuals’ PTA, r(66) = −0.56, p < 0.001. However, a partial correlation conducted between raw BPD and PTA, partialing out the effects of age, was not significant, r(66) = −0.13, p = 0.299, while the partial correlation between raw BPD and age, partialing out the effects of PTA, remained significant, r(66) = −0.29, p < 0.017. Also as would be expected (r(66) = −0.72, p < 0.001, with older adults showing a smaller dynamic range than younger adults. There was also a correlation between pupillary dynamic range and individuals’ PTA, r(66) = −0.62, p < 0.001. However, a partial correlation conducted between pupillary dynamic range and PTA, partialing out the effects of age, was not significant, r(66) = −0.03, p = 0.791, while the partial correlation between pupillary dynamic range and age, partialing out the effects of PTA, remained significant, r(66) = −0.48, p < 0.001. That is, for this sample, the differences in raw BPD and pupillary dynamic range were driven by age, not by hearing.As would be expected , particiexpected , particiTo adjust for this age difference, baseline pupil sizes were scaled by representing pupil dilation as a proportion of each participant’s dynamic range e.g., . It shouThe BPD was calculated as the mean pupil size measured over the last 30 s of the 60 s silent period prior to each sentence, represented as a proportion of the individual’s pupillary dynamic range.M = 10.9), and the poorer hearing group ranging from 22.5 to 50.0 dB HL (M = 31.4). The better hearing group thus fell within the range considered clinically normal hearing for speech  = 0.83, p < 0.001. For this reason, and to represent PTA and age as continuous variables, a linear mixed-effects model was run with BPD serving as the dependent variable. Participants and sentence items were included as random effects, such that by-participant and by-item random intercepts and random slopes were included for the continuous variables of PTA, age, and trial number  and the function lmer to fit the model. The effects of each variable on model fit were evaluated using model comparisons of the change in log-likelihood. Fixed effects and interactions are shown in the Predictor column and were added in the order they are listed in the table. Coefficients and X2 test results are shown for each step of the model.As would be expected from population demographics, the present sample reflected a general relationship between age and individuals’ PTA, l number . All varAs seen in If a decline in BPD over trials is to be considered as a reflection of a decrease in arousal concomitant to an increase in fatigue and a decline in task engagement, one is bound to ask whether there is a relationship between BPD and participants’ comprehension accuracy. Specifically, one might ask whether performance accuracy declines over the course of the task along with the observed decline in BPD. There is a general consensus in the literature that the BPD reflects an individual’s current state of arousal , with onIt might be tempting to similarly interpret the progressive decline in BPD over the course of the 160 comprehension trials in the present study as reflecting participant fatigue. It would further follow from this argument that, because participants with hearing impairment must engage more resource-demanding effort for front-end perception, a buildup of fatigue and consequent reduction in task engagement would lead to the observed steeper decline in BPD over the course of experimental trials relative to those with better hearing.Inconsistent with a fatigue argument, however, was the finding that, although a steeper decline in BPD appeared over trials for those with poorer hearing, accuracy in the sentence comprehension task increased over trials rather than decreased. It was also seen that the steeper rate of decline in BPD for those with poorer hearing relative to those with better hearing was in fact due to the poorer-hearing participants having larger BPDs at the beginning of the experiment, and then beginning to approximate those of the better-hearing participants after experience with the task.It has been over a 100 years since the articulation of the Yerkes–Dodson Law , yet it We assume, along with the extant literature, that BPD reflects the individuals’ level of arousal at that point at which it is being measured, in this case the time window prior to a sentence being presented. We suggest that, to the extent that BPD indexes the individual’s anticipatory mental state, individuals with poorer hearing would enter a task that relies on accurate speech perception with an increased level of anticipatory arousal than those with better hearing. One would thus expect to see this early level of anticipatory arousal to decrease progressively due to increasing success as the task of determining agents and recipients of actions in sentences improved with practice.It should be recognized that the poorer-hearing participants’ larger BPDs in the early experimental trials is open to at least two alternative interpretations, both of which were forecast by We do not intend to deny that a decline in pupil size over the course of an experiment can in some cases reflect a decline in task engagement linked to fatigue. As we have noted, such a finding has been reported for the especially taxing N-back task . Rather,The datasets generated for this study are available to any qualified researcher upon request to the authors.The studies involving human participants were reviewed and approved by Brandeis University Institutional Review Board (IRB). The participants provided their written informed consent to participate in this study.NA and AW collaborated on the experimental design, data analysis, data interpretation, and drafting of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The present study aims to compare the kinematic feeding variables of 3–4 days old broiler chickens using three different feed types: fine mash (F1), coarse mash (F2), and crumbled (F3); size was 476 µm, 638 µm, and 1243 µm, respectively. The head displacement and the maximum beak gape were automatically calculated by computational image analysis to find the feeding behavior of broilers. The results did not show strong correlations between birds’ weight, beak size (length and width), and the kinematic variables. The “catch-and-throw” movements in F1  generally occurred in the first mandibulation, while in F3 (the largest feed particle) occurred in the latest mandibulation. It can be suggested that the adoption of “catch-and-throw” in the latest mandibulations increases with larger particles.Past publications describe the various impact of feeding behavior of broilers on productivity and physiology. However, very few publications have considered the impact of biomechanics associated with the feeding process in birds. The present study aims at comparing the kinematic variables of young broiler chicks  while feeding them with three different feed types, such as fine mash (F1), coarse mash (F2), and crumbled feed (F3). The feeding behavior of the birds was recorded using a high-speed camera. Frames sequences of each mandibulation were selected manually and classified according to the temporal order that occurred . The head displacement and the maximum beak gape were automatically calculated by image analysis. The results did not indicate strong correlations between birds’ weight, beak size (length and width), and the kinematic variables of feeding. The differences between the tested feed were found mostly in the first and second mandibulations, probably explained by the higher incidence of “catch-and-throw” movements in F3 (33%) and F1 (26%) than F2 (20%). The “catch-and-throw” movements in F1  mostly occurred in the first mandibulation, as in F3 (the largest feed particle) also occurred in the latest mandibulations. It might be suggested that the adoption of “catch-and-throw” in the latest mandibulations increases with larger particles. The kinematic variables in the latest mandibulations (from the third one on) seem to be similar for all feed types, which represent the swallowing phase. It might be inferred that the temporal sequence of the mandibulations should be essential to describe the kinematics of a feeding scene of broiler chickens, and the first and second mandibulations are potentially the key factors for the differences accounted by the diverse feed particle sizes. Although genetics is mainly responsible for the exceptional growth rates of modern poultry species, advances in feed and nutrition are the main drivers of cost-effectiveness in commercial broiler farms.There is no actual chewing (mastication) in birds’ feeding behavior. The bird tongue is rigid, and the tactile sensibility is mostly perceived when the feed particles are touched and seized by the birds’ beak tip . It has Gallus gallus domesticus) is comparable to other birds, especially to pigeons (Columbia livia) [The feeding process of domestic chickens (a livia) ,7, and ca livia) .Previous studies have addressed the impact of feed granulometry on consumption , the phy®, Visario 1500, Nürnberg, Germany) during feeding at the age of 3 and 4 days (d). From the first week of rearing, the broiler chicks learn to eat at the trough [A total of 19 male broiler chicks were individually recorded with a high-speed camera , coarse mash (F2), and crumbled (F3) . The ordThe mandibulation consisted of one cycle of opening and closing of the chick’s beak in the stationing, transport, and swallow phases . The bea® software  without physical markers attached to the birds’ body. The technique consisted of the following steps: (1) initially eye detection was conducted (as a reference point) to determine the position of chick’s head; (2) then the head area was extracted to remove redundant background information (part of the picture that is of no interest). Such a procedure was followed by a (3) beak tips detection to analyze the maximum beak gape, and finally (4) the “removal” of the feed particle pixels was undertaken to inhibit possible errors during the detection of the beak tips in case of feed particles overlapping with the beak tips. A total of four kinematic variables were considered, (1) the maximum beak gape (mm); (2) the head displacement (mm); (3) the time of each mandibulations (ms) ; and (4) the head average speed (mm/s) during each mandibulation .The displacement of the birds’ head was analyzed automatically by tracking the bird’s eye position (2D) in the image, and then the total displacement during each mandibulation was calculated. The maximum beak gape in each mandibulation  was calculated automatically from the frame. A machine vision procedure  was used® software  was used to carry out the statistical analysis. Since the data were not normally distributed, non-parametrical tests were applied. At first, Spearman’s correlation test was used for the whole dataset considering the following variables, weight (g); beak length (mm); beak width (mm); head displacement (time); average speed (mm/s); and maximum beak gape (mm). After that, a correlation test was applied using the kinematic variables using the mandibulation order . The Mood’s median test was applied to verify the differences in the kinematic variables between feed types and mandibulation orders. The frequency of occurrence of “catch-and-throw” movements was identified, and the Chi-Square test was applied to establish the difference between feed types.A total of 57 × 2048 frames from 19 specimens (three feed types) resulted in a total of 1729 mandibulations analyzed, being 576 for F1, 602 for F2, and 551 for F3. Minitab 15The experiments were carried out at the Federal University of Grande Dourados-UFGD, Brazil, and the trial was approved by the University Ethics Committee (Protocol number 030/2013-CEUA/UFGD).The Spearman’s correlation test  did confp < 0.05). We found that when each mandibulation order per feed type was compared, they differed (p < 0.05), especially in the first and second mandibulations. The exception was for the head displacement at all mandibulations (indicated on the right side of The maximum beak gape a was higp < 0.05). p < 0.05) than other feed types  in the first mandibulation was higher than F1 and F2 d. The diThe positive correlation between the maximum beak gape and head displacement was expected due to the cranial kinesis presented in all species of birds ,13,14. TThe results in the present study  are simiOn the other hand, differences occurred in the first and second mandibulations (stationing or transporting phases). The results found in this study suggest that the temporal sequence of the mandibulations should be essential to describe the kinematics of a feeding scene of broiler chickens, and the first and second mandibulations present the main differences between feed types. It has been suggested that these birds can adapt specific movement patterns depending on the type of feed particles, but such behaviors are related to limitations of the morphological structure .p < 0.05) (According to  < 0.05) c, which The muscles involved with feeding can be considered as biological motors that consume chemical energy and perform mechanical work. Thus, the functions of muscles influence the birds’ metabolism, in addition to other processes (thermoregulation), which likewise consumes oxygen (and ATP) and generates heat. The metabolic power of muscles is almost impossible to measure directly in vivo. However, the rate at which muscles can perform the mechanical work of feeding is limited by three mechanical variables: the stress or force that the muscles can produce, their range of motion, and their contraction frequency or velocity. The maximal values of these three variables are determined by the hierarchical structure of muscles from molecules and sarcomeres to whole muscle architecture ,22. TherThe method used in the present study to measure the beak opening was the distance between upper and lower beak tips. However, it is believed that the angle of beak aperture (considering beak tips and its vertex) could represent a more realistic condition for beak motion analysis. Small differences in beak shapes and sizes might imply similar beak apertures with different angles. Moreover, the beak motion analysis could present more correlations in older specimens, since it has been suggested that preferences for different feed sizes are more evident in older chickens because of the development of both digestive and buccal apparatus ,23,24, wNo correlation was found between birds’ weight, beak size (length and width) with the studied kinematic variables. Moderate correlations were found for maximum beak gape with head displacement and speed, only in the first mandibulation right after the feed particle is grasped by the birds. Significant differences were found in the kinematic variables for all mandibulation orders at all feed types. Furthermore, differences between feed types were found mostly in the first and second mandibulations, probably explained by the higher incidence of “catch-and-throw” movements in F3 (33%) and F1 (26%) than in F2 (20%), but in F1 mostly in the first mandibulation. It can be suggested that the adoption of “catch-and-throw” movements increases with larger particles in further mandibulations until the final swallowing, as observed in the feed with the largest granulometry (F3). The latest mandibulations (from the third one on) seem to be similar for all feed types, which represent the swallowing phase. It can be suggested that the temporal sequence of the mandibulations should be essential to describe the kinematics of a feeding scene of broiler chickens, and the first and second mandibulations are potentially the key factors for the differences accounted by different feed particle sizes.