Introduction Current problems in psychological research Learning from biomedical and clinical research Device displacement Data/Information accessibility Promises of smartphone apps and games promise of microcontroller kits Potential Challenges Concluding remarks and future applications Competing interest Author contributions References


Editorial: The promises of Microcontroller kits and Smartphone apps for Psychological research

Authors: Samuel Ken-En Gan 1,2,#and Joshua Yi Yeo 1

1Antibody & Product Development (APD) Lab, A*STAR, Singapore 529757

2APD SKEG Pte Ltd, Singapore 529757

Received: 05 February 2020 |Published: 06 February 2020

Scientific Phone Apps and Mobile Devices (2019) 6:2



Traditional psychological research relying on the fixed location laboratories and surveys are fraught with limitations. To an extent, these limits contribute to the serious problem of both poor reproducibility and poor ecological validity by constraining the geographical sampling of participant, affecting convenience and willingness. While this has been alleviated with the Internet revolution bringing along on-line surveys, the more recent Smartphone and microcontroller kit revolutions promise to break down the limitations even further. Drawing from examples of these revolutions in related disciplines, microcontroller kit revolutions can improve convenience and administration of psychological research, both survey-based and experimental.

The impact of smartphones on the modern world is undeniably disruptive to convenience, daily life and research. Globally, the total number of smartphone users is predicted to reach 3.7 billion by 2021 (Newzoo, 2018), and mobile app downloads to reach 258.2 billion by 2022. (Statista, 2018). In certain areas, the smartphone revolution continues to penetrate various niche applications and research fields, albeit at varying rates. From previous reviews on how the smartphone revolution began disrupting biomedical research (Gan & Poon, 2016), clinical settings (Gan, Koshy, Nguyen, & Haw, 2016), and even towards a self-sustaining model of apps making apps (Wu, Budianto, & Gan, 2019), the disruption in social sciences, particularly psychology, is still small (Gan & Goh, 2016) with little change over the years. This article discusses how the microcontroller kits and smartphone apps can be used to revolutionize psychology research through more interdisciplinary teams and tech-savviness.


The "reproducibility crisis” (Earp & Trafimow, 2015; Lindsay, 2015; Maxwell, Lau, & Howard, 2015) demonstrating the poor reproducibility of merely 36 % of 100 studies from three journals (Open Science, 2015) cast a bad light on psychological research. However, there are many factors to this irreproducibility, contributed by biased self-assessment, mutable background factors and both geographical and time limitations.

Self-reported surveys in psychology often lead to flawed self-assessments, with people often overrating themselves (Dunning, Heath, & Suls, 2004), or demonstrating denial, mood and cognitive dissonance (Spector, 1994). Many psychological research today still rely on self-reports e.g. anxiety research based on Beck Anxiety Inventory (Beck, Epstein, Brown, & Steer, 1988), Hamilton Anxiety Scale (Maier, Buller, Philipp, & Heuser, 1988), GAD-7 (Spitzer, Kroenke, Williams, & Löwe, 2006), stress studies using Perceived Stress Scale or PSS (Cohen, Kamarck, & Mermelstein, 1983), and well-being using SF36 (McHorney, Ware, Lu, & Sherbourne, 1994), amongst many others. Objective methods borrowing from biomedical/clinical methods that include measuring physiological parameters, such as plasma cortisol levels (Van Cauter, Leproult, & Kupfer, 1996), both heart rate and blood pressure for stress (Yew, Lim, Haw, & Gan, 2015), body mass index (Sach et al., 2006), body fat percentage (Gallagher et al., 2000; Gan, Loh, & Seet, 2003; Zhu, Wang, Shen, Heymsfield, & Heshka, 2003) and blood work (Holt-Lunstad, Steffen, Sandberg, & Jensen, 2011) can provide additional objectivity and reliability to stress, anxiety, health states than self-reports.

The natural mutable situations of daily life can consistently affect the reproducibility of measured psychological parameters. In the volatile global environment, confounding factors can come in the least expected areas e.g. epidemics, political landscape and weather. The participant population demographics/responses may differ pre and post major such events e.g. in Brexit (Veltri, Redd, Mannarini, & Salvatore, 2019). In addition, weather changes have been reported to affect many psychological parameters. Adverse weather conditions led to compensatory behavior (Kilpeläinen & Summala, 2007), rainfall and temperature influenced shopping decisions (Parsons, 2001), cloudy and rainy days led to better memory and discrimination ability (Forgas, Goldenberg, & Unkelbach, 2009), sunshine led to willingness to assist and both sunshine and temperature was related to self-reports of mood (Cunningham, 1979) amongst others. This problem is aggravated by the location and time limitations for participant recruitment naturally imposed in most pen and paper studies that reduced the population diversity by limiting locality. These biases can compromise the research reproducibility when considering the mutable situations of daily life.

To overcome the above issues, psychological data collection needs to be large and diverse, recruiting participants from different continents, tested at multiple points or in varying times and situations. Such an ideal endeavour was virtually impossible until the Internet Revolution (O’Regan, 2016), and more recently, the smartphone and microcontroller kit revolutions.


Studying the disruption that the smartphone and microcontroller kit revolutions had on many sectors, one can identify the disruption to come as: 1) Device displacement and 2) Data/information accessibility.


In device displacement, clinical equipment displacement apps hold the greatest promise given the possibility for their immediate adoption for psychological research. However, they are slow to appear given the lengthy clinical regulations for approval in clinical use. Nonetheless, patient-focused clinical aid devices that bypass these long regulatory restrictions are increasingly available, e.g. APD Skin Monitoring (Wu, Yong, Federico, & Gan, 2019), Thyroid-SPOT (Sim, Zang, Nguyen, Leow, & Gan, 2017), smartwatches to monitor heart rate and ECG (Apple, 2019; Fitbit, 2019; Withings, 2019), and these can be easily adopted for psychological research.

Given that equipment displacement can be made faster for psychological research than clinical usage as gleaned from biomedical research, there seems to be opportunities for making psychology research tools such as those that measure eye tracking, reaction time, brain-waves, and touch, portable. Such a move would remove the restraints of such research financially and geographically.


Apps that allow for analysis or data collection on the go, are popular for the convenience brought to the participants e.g. PsychVey (Nguyen, Lim, Budianto, & Gan, 2015) used to gather clinical and research information, and in patient-clinician communication that can lead to better diagnosis (e,g, Thyroid-SPOT (Sim et al., 2017), OLR – Online Lab Report, see (OnlineLabReport, 2017), PathoGold Laboratory Software, (Birlamedisoft, 2019), which are used to analyze laboratory test reports).

The use of incentives in psychological research recruitment often results in selecting for specific participant types (Barnett, 2009; Brase, 2009; Hsieh & Kocielnik, 2016; Sharp, Pelletier, & Lévesque, 2006) A possible solution to this could be in the convenience to answer surveys on-the-go online/by apps to reduce deterrents as well as increasing accessibility across geographical borders, reducing biases (Pandya, 2012). Although there are qualms about the reliability and validity of the anonymous results, previous studies have shown that they are reliable even when reporting sensitive topics such as smoking and marijuana use (Ramo, Hall, & Prochaska, 2011; Ramo, Liu, & Prochaska, 2012). In fact, a recent study comparing online surveys with app-based surveys, found apps to be suitable for data collection, with general good test-retest reliability (Liu, Gervasio, & Reed, 2020)


There are several apps that allow for non-survey data to be collected. For example, the MindStrong app analyses clicks, taps and scrolls together with traditional neurocognitive assessments (Mindstrong, 2019), the YouHue classroom app (YouHue, 2019) and the Mood24/7 text-based app by Johns Hopkins University (RemedyHealthMedia, 2019) tracks mood, and the “Hack My Mood” program by Allison Nelson analyses social media posts (Shu, 2015). While there are also many game apps, they have yet to be validated through comparisons with established surveys.

With the borderless reach of smartphone apps/games, there is great potential for utilization of such apps to grow significantly in research. In addition, game apps have the added advantage over conventional apps to indirectly and covertly investigate the players’ psychological parameters. Imagine assessing psychological parameters like stress and intelligence through gameplay, bypassing the need to answer a battery of tests that is prone to self-biases or many confounding factors arising from mutable situations in daily life. Yet, for this to be achieved, there is a need to first validate the use of these game apps by studying the correlation of game results to established psychological batteries. When validated through psychological studies, smartphone game apps can indeed be the next frontier tool of psychological assessment and data collection.


Noting that game apps can involve devices such as EEG to control the game avatar to train focus and attention for better learning (Pei, Wang, Wang, & Li, 2013), there is potential to connect small portable add-on sensor devices to games and apps for psychological research. In recent years, the smartphone revolution is joined by the microcontroller kit revolution. Microcontroller kits (Arduino, Raspberry Pi, Micro:bit) now allow the easy building connectivity of small sensors by non-professional engineers. While the size of the smartphone limits the number of in-built sensors, microcontroller kit based sensors (e.g. EEGs in (CIBIM, 2019) could be added wirelessly via Bluetooth® or WiFi. With brain monitoring devices (a market estimated to be worth $12.2 billion by 2021 (MarketsAndMarkets, 2019)) now easily within reach as recently reviewed (Byrom, McCarthy, Schueler, & Muehlhausen, 2018), the technological advances lower the economic barrier to allow less affluent labs to carry out experiments with such custom-made equipment.

Being portable, easy to use, and cost-effective, the displacement of traditional fixed equipment is easy and inevitable. Already, many sensors have been incorporated as wearables with some connectivity to the smartphone, such as in smart shirts (HeartIn, 2019; Hexoskin, 2019) or smartwatches (Apple, 2019; Fitbit, 2019; Withings, 2019). By incorporating ambulatory physiological measurements (such as ECG), the collection of more objective physiological data (e.g. heart rate, blood pressure) in research areas of well-being, stress and lying can take place more easily and accurately. Such physiological data would be a source of reliable data over self-reports to better reflect the psychological/physiological state for better reproducibility by ruling out variations from self-biases. Aside from self-biases, ambulatory data collection of such physiological parameters or the use of longer-term markers (e.g. HbA1c over blood glucose for blood sugar measurements), allow the ruling out of temporary changes induced by transient psychological states. One clinically characterized example of such temporal physiological change due to psychological state is the “White Coat Hypertension” (Gan et al., 2003), where patients exhibit elevated blood pressure in a clinical setting, but reverted to normal ranges in a non-clinical environment. While limitations could exist where there is natural heart rate variability between the physically active and older people (Davy, Desouza, Jones, & Seals, 1998), and during physical activity (Freedson & Miller, 2000), these can be overcome with proper baselines. And these baselines can be established through wearable sensors for a better reflection of the basal state of the individual and the effects of interventions used in psychological studies.

Ambulatory monitoring enabled by such devices support continuous collection of physiological data, making it less error-prone. In a possible study to address current discrepancies of reports on the calming effects of sedative music, ambulatory modelling would allow the observation of the calming effects of music. Ambulatory monitoring of heart rate and blood pressure would also improve detection of effects from interventions, such as the calming effects of music on anxiety as previously reported (Gan, Lim, & Haw, 2016).

Beyond the use of ECG, there are now smartphone dependent EEGs (mBrainTrain, 2019; McKenzie et al., 2017) that now allow brain measurements to be incorporated in a “resource poor environment” (Williams et al., 2019). Incorporated into games, the borderless promises of microcontroller kit devices and games certainly go far to address the current problems in psychological research.


The fear of new technology, termed technophobia, has been shown to have psychological impacts on reluctance on the use of technology (Brosnan, 2002), with specific studies attempting to develop a technophobia scale (Sinkovics, Stöttinger, Schlegelmilch, & Ram, 2002). Age and gender were found to be correlated with technophobia in a 1995 study that found females to have less technology experience than males (Weil & Rosen, 1995). Specifically, technophobia may constrain the activity of and limit the benefits of app-based surveys. There is also a correlation between technophobia with education, perceived health and well-being, suggesting that technophobes are less satisfied with their lives (Nimrod, 2018) that can be reflected in app-based studies. Beyond possibly affecting participants, technophobia can also occur on the researcher side, causing inertia in utilizing new methods other than traditional paper-and-pen methods. However, given the smartphone and internet revolution (O’Regan, 2016), the wide-spread use of technology is inevitable, and the current inertia will gradually pass.

Aside from technophobia, smartphones by themselves are a source of distraction where texting and social media interfere in daily life, particularly studying (David, Kim, Brickman, Ran, & Curtis, 2015). This can often be aggravated by push notifications that include auditory and tactile alerts that have been shown to prompt task-irrelevant mind wandering thoughts, damaging task performance (Stothart, Mitchum, & Yehnert, 2015). Yet, with the possibility of using airplane modes, this can be sorted with a simple tap.


On the whole, psychological research have yet to fully take advantage of technological advances present in apps and microcontroller kits. To fully leverage on these advances, interdisciplinary teams and a lot of initial validation research involving these apps and devices would be required. When eventually established to be reliable through multiple studies, these apps/games and devices can recruit participants in greater numbers and demographic variety by simply leveraging on the distribution channels of Google and Apple App stores to carry out large pan-cultural, international, longitudinal studies.


There are no conflicting interests.


JYY drafted the manuscript. SKEG conceived the idea and co-wrote the manuscript. All authors have read and approve of the final manuscript


Apple. (2019). Apple Watch. Retrieved from

Barnett, J. E. (2009). Ask the ethicist: Incentives offered to research participants. Retrieved from

Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An inventory for measuring clinical anxiety: psychometric properties. Journal of consulting and clinical psychology, 56(6), 893.

Birlamedisoft (Producer). (2019). PathoGold Laboratory Software for Android (Version 1.9). Retrieved from

Brase, G. L. (2009). How different types of participant payments alter task performance. Judgment and Decision Making, 4(5), 419-428.

Brosnan, M. J. (2002). Technophobia: The psychological impact of information technology: Routledge.

Byrom, B., McCarthy, M., Schueler, P., & Muehlhausen, W. (2018). Brain Monitoring Devices in Neuroscience Clinical Research: The Potential of Remote Monitoring Using Sensors, Wearables, and Mobile Devices. Clinical pharmacology and therapeutics, 104(1), 59-71. doi:10.1002/cpt.1077

CIBIM. (2019). Truly wearable brain monitoring. Retrieved from

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, 24(4), 385-396. doi:10.2307/2136404

Cunningham, M. R. (1979). Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan. Journal of Personality and Social Psychology, 37(11), 1947-1956. doi:10.1037/0022-3514.37.11.1947

David, P., Kim, J.-H., Brickman, J. S., Ran, W., & Curtis, C. M. (2015). Mobile phone distraction while studying. New Media & Society, 17(10), 1661-1679. doi:10.1177/1461444814531692

Davy, K. P., Desouza, C. A., Jones, P. P., & Seals, D. R. (1998). Elevated Heart Rate Variability in Physically Active Young and Older Adult Women. Clinical Science, 94(6), 579-584. doi:10.1042/cs0940579

Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed Self-Assessment: Implications for Health, Education, and the Workplace. Psychol Sci Public Interest, 5(3), 69-106. doi:10.1111/j.1529-1006.2004.00018.x

Earp, B. D., & Trafimow, D. (2015). Replication, falsification, and the crisis of confidence in social psychology. Front Psychol, 6, 621. doi:10.3389/fpsyg.2015.00621

Fitbit. (2019). Fitbit Activity Trackers. Retrieved from

Forgas, J. P., Goldenberg, L., & Unkelbach, C. (2009). Can bad weather improve your memory? An unobtrusive field study of natural mood effects on real-life memory. Journal of Experimental Social Psychology, 45(1), 254-257. doi:10.1016/j.jesp.2008.08.014

Freedson, P. S., & Miller, K. (2000). Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate. Research Quarterly for Exercise and Sport, 71(sup2), 21-29. doi:10.1080/02701367.2000.11082782

Gallagher, D., Heymsfield, S. B., Heo, M., Jebb, S. A., Murgatroyd, P. R., & Sakamoto, Y. (2000). Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. The American Journal of Clinical Nutrition, 72(3), 694-701. doi:10.1093/ajcn/72.3.694

Gan, S. K.-E., & Goh, B. Y.-L. (2016). Editorial: A dearth of apps for psychology: the mind, the phone, and the battery. Scientific phone apps and mobile devices, 2(1), 1. doi:10.1186/s41070-016-0005-6

Gan, S. K.-E., Koshy, C., Nguyen, P.-V., & Haw, Y.-X. (2016). An overview of clinically and healthcare related apps in Google and Apple app stores: connecting patients, drugs, and clinicians. Scientific phone apps and mobile devices, 2(1), 8. doi:10.1186/s41070-016-0012-7

Gan, S. K.-E., Lim, K. M.-J., & Haw, Y.-X. (2016). The relaxation effects of stimulative and sedative music on mathematics anxiety: A perception to physiology model. <>Psychology of Music, 44(4), 730-741.

Gan, S. K.-E., Loh, C., & Seet, B. (2003). Hypertension in young adults-an under-estimated problem. Singapore medical journal, 44(9), 448-452.

Gan, S. K.-E., & Poon, J.-K. (2016). The world of biomedical apps: their uses, limitations, and potential. Scientific phone apps and mobile devices, 2(1), 6. doi:10.1186/s41070-016-0009-2

HeartIn. (2019). HeartIn Fit. Retrieved from

Hexoskin. (2019). Hexoskin. Retrieved from

Holt-Lunstad, J., Steffen, P. R., Sandberg, J., & Jensen, B. (2011). Understanding the connection between spiritual well-being and physical health: an examination of ambulatory blood pressure, inflammation, blood lipids and fasting glucose. Journal of Behavioral Medicine, 34(6), 477-488. doi:10.1007/s10865-011-9343-7

Hsieh, G., & Kocielnik, R. (2016). You Get Who You Pay for: The Impact of Incentives on Participation Bias. Paper presented at the Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, San Francisco, California, USA.

Kilpeläinen, M., & Summala, H. (2007). Effects of weather and weather forecasts on driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 10(4), 288-299. doi:10.1016/j.trf.2006.11.002

Lindsay, D. S. (2015). Replication in Psychological Science. Psychol Sci, 26(12), 1827-1832. doi:10.1177/0956797615616374

Liu, J. J. W., Gervasio, J., & Reed, M. (2020). Digitizing resilience: Feasibility of mobile applications for psychological research. Scientific phone apps and mobile devices, 6(1). doi:10.30943/2020/15012020

Maier, W., Buller, R., Philipp, M., & Heuser, I. (1988). The Hamilton Anxiety Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. Journal of affective disorders, 14(1), 61-68.

MarketsAndMarkets. (2019). Market Research Report (Report Code MD1369). Retrieved from

Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015). Is psychology suffering from a replication crisis? What does “failure to replicate” really mean? American Psychologist, 70(6), 487-498. doi:10.1037/a0039400

mBrainTrain. (2019). Smarting. Retrieved from

McHorney, C. A., Ware, J. E., Lu, J. F. R., & Sherbourne, C. D. (1994). The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Medical care, 32(1), 40-66. doi:10.1097/00005650-199401000-00004

McKenzie, E. D., Lim, A. S. P., Leung, E. C. W., Cole, A. J., Lam, A. D., Eloyan, A., . . . Mateen, F. J. (2017). Validation of a smartphone-based EEG among people with epilepsy: A prospective study. Scientific Reports, 7, 45567. doi:10.1038/srep45567

Mindstrong, H. (2019). Mindstrong App. Retrieved from

Newzoo. (2018). Global Mobile Market Report. 85.

Nguyen, P.-V., Lim, J. P.-H., Budianto, I.-H., & Gan, S. K.-E. (2015). PsychVeyApp: Research survey app. Scientific phone apps and mobile devices, 1(1), 3. doi:10.1186/s41070-015-0002-1

Nimrod, G. (2018). Technophobia among older Internet users. Educational Gerontology, 44(2-3), 148-162. doi:10.1080/03601277.2018.1428145

O’Regan, G. (2016). The Internet Revolution. In Introduction to the History of Computing: A Computing History Primer (pp. 163-178). Cham: Springer International Publishing.

OnlineLabReport (Producer). (2017). OLR - Online Lab Report for Android (Version 1.0.5) Retrieved from

Open Science, C. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. doi:10.1126/science.aac4716

Pandya, R. E. (2012). A framework for engaging diverse communities in citizen science in the US. Frontiers in Ecology and the Environment, 10(6), 314-317. doi:10.1890/120007

Parsons, A. G. (2001). The Association Between Daily Weather and Daily Shopping Patterns. Australasian Marketing Journal (AMJ), 9(2), 78-84. doi:10.1016/S1441-3582(01)70177-2

Pei, X. L., Wang, X., Wang, Y. F., & Li, M. K. (2013). Internet of Things Based Education: Definition, Benefits, and Challenges. Applied Mechanics and Materials, 411-414, 2947-2951. doi:10.4028/

Ramo, D. E., Hall, S. M., & Prochaska, J. J. (2011). Reliability and validity of self-reported smoking in an anonymous online survey with young adults. Health Psychology, 30(6), 693-701. doi:10.1037/a0023443

Ramo, D. E., Liu, H., & Prochaska, J. J. (2012). Reliability and validity of young adults' anonymous online reports of marijuana use and thoughts about use. Psychology of Addictive Behaviors, 26(4), 801-811. doi:10.1037/a0026201

RemedyHealthMedia. (2019). Mood 24/7. Retrieved from

Sach, T. H., Barton, G. R., Doherty, M., Muir, K. R., Jenkinson, C., & Avery, A. J. (2006). The relationship between body mass index and health-related quality of life: comparing the EQ-5D, EuroQol VAS and SF-6D. International Journal Of Obesity, 31, 189. doi:10.1038/sj.ijo.0803365

Sharp, E. C., Pelletier, L. G., & Lévesque, C. (2006). The double-edged sword of rewards for participation in psychology experiments. Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement, 38(3), 269-277. doi:10.1037/cjbs2006014

Shu, C. (2015). Hack My Mood Turns Social Media Postings Into A Mental Wellness Tool. Retrieved from

Sim, J.-Z., Zang, Y., Nguyen, P.-V., Leow, M. K.-S., & Gan, S. K.-E. (2017). Thyroid-SPOT for mobile devices: personalised thyroid treatment management app. Scientific phone apps and mobile devices, 3(1), 4. doi:10.1186/s41070-017-0016-y

Sinkovics, R. R., Stöttinger, B., Schlegelmilch, B. B., & Ram, S. (2002). Reluctance to use technology-related products: Development of a technophobia scale. Thunderbird International Business Review, 44(4), 477-494. doi:10.1002/tie.10033

Spector, P. E. (1994). Using self-report questionnaires in OB research: A comment on the use of a controversial method. Journal of organizational behavior, 15(5), 385-392. doi:10.1002/job.4030150503

Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of internal medicine, 166(10), 1092-1097.

Statista. (2018). Annual number of global mobile app downloads 2017-2022.

Stothart, C., Mitchum, A., & Yehnert, C. (2015). The attentional cost of receiving a cell phone notification. Journal of experimental psychology: human perception and performance, 41(4), 893.

Van Cauter, E., Leproult, R., & Kupfer, D. J. (1996). Effects of gender and age on the levels and circadian rhythmicity of plasma cortisol. J Clin Endocrinol Metab, 81(7), 2468-2473. doi:10.1210/jcem.81.7.8675562

Veltri, G. A., Redd, R., Mannarini, T., & Salvatore, S. (2019). The identity of Brexit: A cultural psychology analysis. Journal of Community & Applied Social Psychology, 29(1), 18-31. doi:10.1002/casp.2378

Weil, M. M., & Rosen, L. D. (1995). The psychological impact of technology from a global perspective: A study of technological sophistication and technophobia in university students from twenty-three countries. Computers in Human Behavior, 11(1), 95-133. doi:doi:10.1016/0747-5632(94)00026-E

Williams, J., Cisse, F. A., Schaekermann, M., Sakadi, F., Tassiou, N. R., BAH, A. K., . . . Mateen, F. (2019). Utilizing a wearable smartphone-based EEG for pediatric epilepsy patients in the resource poor environment of Guinea: A prospective study. (N5.001). Neurology, 92(15 Supplement), N5.001.

Withings. (2019). Move ECG. Retrieved from

Wu, W. L., Budianto, I. H. W., C.F., & Gan, S. K. E. (2019). A Review of Apps for Programming: programming languages and making apps with apps. Scientific Phone Apps and Mobile Devices 5(1).

Wu, W. L., Yong, K. Y. W., Federico, M. A. J., & Gan, S. K. E. (2019). The APD Skin Monitoring App for wound monitoring: Image processing, area plot, and colour histogram. Scientific phone apps and mobile devices, 5(3).

Yew, S.-H., Lim, K. M.-J., Haw, Y.-X., & Gan, S. K.-E. (2015). The association between perceived stress, life satisfaction, optimism, and physical health in the Singapore Asian context. Asian Journal of Humanities and Social Sciences (AJHSS), 3(1), 56-66.

YouHue. (2019). YouHue - Building Emotionally Healthy Classrooms Retrieved from

Zhu, S., Wang, Z., Shen, W., Heymsfield, S. B., & Heshka, S. (2003). Percentage body fat ranges associated with metabolic syndrome risk: results based on the third National Health and Nutrition Examination Survey (1988–1994). The American Journal of Clinical Nutrition, 78(2), 228-235. doi:10.1093/ajcn/78.2.228