Archive | August, 2020

Tufts University study: Sweat science: Engineers detect health markers in thread-based, wearable sweat sensors

4 Aug

Helen Albert wrote in the Forbes article, Sweat Sensing Next Step For Health Tracking Devices:

Could chemicals in our sweat be the next thing activity trackers start to measure? Researchers are ramping up efforts to refine this technology so they can.

Many of us already wear some form of activity tracker or smartwatch, but these devices are still fairly limited as to how much they can really tell us about our health.

“The Fitbit, the wearable, the smartwatch have hit a roadblock,” says Sameer Sonkusale, a professor of electrical and computer engineering at Tufts University School of Engineering, who is developing a sweat sensor with his team.

“They can definitely give you a lot of information. But it does not provide you a real window on how you’re doing internally or biochemically.”

If researchers can get it to work effectively, this kind of technology has a lot of potential for monitoring athletes, or people in active careers such as the army, to optimize performance and make sure they stay healthy. It could also help assess how well medication, or diet and nutrition, is working to combat health issues in some people….      forbes.com/sites/helenalbert/2020/07/31/sweat-sensing-for-health-tracking/#7bf7e41f1b13

Resources:

The Health Benefits of Sweating                                                                                                   https://www.healthline.com/health/sweating-benefits

Wearable sweat sensor could monitor dehydration, fatigue                                                           https://www.medicalnewstoday.com/articles/305751

Utility of sweat patch testing for drug use monitoring in outpatient treatment for opiate dependence                                                                                                                          ncbi.nlm.nih.gov/pmc/articles/PMC3632440/

Science Daily reported in Sweat science: Engineers detect health markers in thread-based, wearable sweat sensors: Real-time measurement of electrolytes and metabolites could be used to diagnose and monitor disease or track performance:

Engineers at Tufts University have created a first-of-its-kind flexible electronic sensing patch that can be sewn into clothing to analyze your sweat for multiple markers. The patch could be used to to diagnose and monitor acute and chronic health conditions or to monitor health during athletic or workplace performance. The device, described today in the journal NPJ Flexible Electronics, consists of special sensing threads, electronic components and wireless connectivity for real time data acquisition, storage and processing.

Typical consumer health monitors can track heart rate, temperature, glucose, walking distance and other gross measurements. But a more detailed understanding of the health, stress and performance of an individual is required for medical data collection or high performance athletic or military applications. In particular, metabolic markers such as electrolytes and other biological molecules provide a more direct indicator of human health for accurate assessment of athletic performance, workplace safety, clinical diagnosis, and managing chronic health conditions.

The patch device created by the Tufts engineers performs real-time measurements of important biomarkers present in sweat including sodium and ammonium ions (electrolytes), lactate (a metabolite) and acidity (pH). The device platform is also versatile enough to incorporate a wide range of sensors cabable of tracking nearly every marker present in sweat. The measurements taken can have useful diagnostic applications. For example, sodium from sweat can indicate the hydration status and electrolyte imbalance in a body; lactate concentration can be an indicator of muscle fatigue; chloride ion levels can be used to diagnosis and monitor cystic fibrosis; and cortisol, a stress hormone, can be used to assess emotional stress as well as metabolic and immune functions.

Athletes could monitor a wide range of markers during physical exertion to aid in predicting performance peaks or declines during competition.

The ability to integrate the sensors into clothing is made possible by flexible threads coated with conductive inks. Different coatings alter the functionality of the threads; for example, lactate can be detected by coating a thread with an enzymatic sensing material incorporating the enzyme lactate oxidase. A pH sensing thread is coated with polyaniline that responds to acidity, and so on. The array of thread sensors is integrated into clothing or a patch and connected to a miniature circuit module and microprocessor, with wireless capability to communicate with a smartphone.

“Sweat is a useful fluid for heath monitoring since it is easily accessible and can be collected non-invasively,” said Trupti Terse-Thakoor, formerly a post-doctoral scholar at Tufts University School of Engineering and first author of the study. “The markers we can pick up in sweat also correlate well with blood plasma levels which makes it an excellent surrogate diagnostic fluid….”                                                                                                  https://www.sciencedaily.com/releases/2020/07/200728113558.htm

Citation:

Sweat science: Engineers detect health markers in thread-based, wearable sweat sensors:  Real-time measurement of electrolytes and metabolites could be used to diagnose and monitor disease or track performance

Date:        July 28, 2020

Source:    Tufts University

Summary:

Engineers have created a first-of-its-kind, flexible electronic sensing patch that can be sewn into clothing to analyze sweat for multiple markers. The patch could be used to to diagnose and monitor acute and chronic health conditions or to monitor athletic performance.

Journal Reference:

Trupti Terse-Thakoor, Meera Punjiya, Zimple Matharu, Boyang Lyu, Meraj Ahmad, Grace E. Giles, Rachel Owyeung, Francesco Alaimo, Maryam Shojaei Baghini, Tad T. Brunyé, Sameer Sonkusale. Thread-based multiplexed sensor patch for real-time sweat monitoringnpj Flexible Electronics, 2020; 4 (1) DOI: 10.1038/s41528-020-00081-w

 

Here is the press release from Tufts University:

Sweat science: engineers detect health markers in thread-based, wearable sweat sensors

Real-time measurement of electrolytes and metabolites could be used to diagnose and monitor disease or track performance

July 28, 2020

Mike Silver

Mike.Silver@tufts.edu

617.627.0545

MEDFORD/SOMERVILLE, Mass. (July 28, 2020)—Engineers at Tufts University have created a first-of-its-kind flexible electronic sensing patch that can be sewn into clothing to analyze your sweat for multiple markers. The patch could be used to to diagnose and monitor acute and chronic health conditions or to monitor health during athletic or workplace performance. The device, described today in the journal NPJ Flexible Electronics, consists of special sensing threads, flexible electronic components and wireless connectivity for real time data acquisition, storage and processing.

Typical consumer health monitors can track heart rate, temperature, glucose, walking distance and other gross measurements. But a more detailed understanding of the health, stress and performance of an individual is required for medical data collection or high performance athletic or military applications. In particular, metabolic markers such as electrolytes and other biological molecules provide a more direct indicator of human health for accurate assessment of athletic performance, workplace safety, clinical diagnosis, and managing chronic health conditions.

The patch device created by the Tufts engineers performs real-time measurements of important biomarkers present in sweat including sodium and ammonium ions (electrolytes), lactate (a metabolite) and acidity (pH).  The device platform is also versatile enough to incorporate a wide range of sensors cabable of tracking nearly every marker present in sweat. The measurements taken can have useful diagnostic applications. For example, sodium from sweat can indicate the hydration status and electrolyte imbalance in a body; lactate concentration can be an indicator of muscle fatigue; chloride ion levels can be used to diagnosis and monitor cystic fibrosis; and cortisol, a stress hormone, can be used to assess emotional stress as well as metabolic and immune functions.

Athletes could monitor a wide range of markers during physical exertion to aid in predicting performance peaks or declines during competition.

The ability to integrate the sensors into clothing is made possible by flexible threads coated with conductive inks. Different coatings alter the functionality of the threads; for example, lactate can be detected by coating a thread with an enzymatic sensing material incorporating the enzyme lactate oxidase. A pH sensing thread is coated with polyaniline that responds to acidity, and so on. The array of thread sensors is integrated into clothing or a patch and connected to a miniature circuit module and microprocessor, with wireless capability to communicate with a smartphone.

“Sweat is a useful fluid for heath monitoring since it is easily accessible and can be collected non-invasively,” said Trupti Terse-Thakoor, formerly a post-doctoral scholar at Tufts University School of Engineering and first author of the study. “The markers we can pick up in sweat also correlate well with blood plasma levels which makes it an excellent surrogate diagnostic fluid.”

Researchers tested the device on human subjects, monitoring their electrolyte and metabolite response during a maximum exertion exercise on stationary bikes. The sensors were able to detect variation in analyte levels as they moved up and down, within 5 to 30 second intervals – sufficient for most real-time tracking needs. The subjects included men and women with a range of physical conditioning, from physically active on a performance-tailored diet, to individuals who were not physically active and had no specific dietary restrictions. While the current study was not meant to determine a correlation between analyte readings and performance and conditioning, it did establish that the sensor was able to detect consistent patterns of analyte expression that could be used for future studies identifying these correlations.

“The sensor patch that we developed is part of a larger strategy to make completely flexible thread-based electronic devices,” said Sameer Sonkusale, professor of electrical and computer engineering at Tufts’ School of Engineering and corresponding author of the study. “Flexible devices woven into fabric and acting directly on the skin means that we can track health and performance not only non-invasively, but completely unobtrusively – the wearer may not even feel it or notice it.”

This work was supported by grants from the Center for Applied Brain and Cognitive Sciences (CABCS), a U.S. Army Combat Capabilities Development Command, Soldier Center (Cooperative Agreement W911QY-15-2-0001), the Office of Naval Research (N0014-16-1-2550), and the Government of India Department of Science and Technology, and Ministry of Human Resource Development (Scheme for the Promotion of Academic and Research Collaboration).

Terse-Thakoor, T., Punjiyam, M., Matharu, Z., Lyu, B., Ahmad, M., Giles, G.E.,  Owyeung, R., Alaimo, F., Baghini, M.S., Brunyé, T.T., and Sonkusale, S. “Thread-based multiplexed sensor patch for real-time sweat monitoring” NPJ Flexible Electronics 2020 July 28; DOI: 10.1038/s41528-020-00081-w

###

About Tufts University

Tufts University, located on campuses in Boston, Medford/Somerville and Grafton, Massachusetts, and in Talloires, France, is recognized among the premier research universities in the United States. Tufts enjoys a global reputation for academic excellence and for the preparation of students as leaders in a wide range of professions. A growing number of innovative teaching and research initiatives span all Tufts campuses, and collaboration among the faculty and students in the undergraduate, graduate and professional programs across the university’s schools is widely encouraged

Jason Heikenfeld wrote in the 2014 article, Sweat Sensors Will Change How Wearables Track Your Health: Your sweat may bring medical diagnostics to Fitbits and Fuelbands:

Using sweat to diagnose disease is not new. For decades, doctors have screened for cystic fibrosis in newborns by testing their sweat. And in the 1970s several studies tried using sweat to monitor drug levels inside the body. But in the early days of sweat diagnostics, the process of collecting it, transporting it, and measuring it was vastly more complicated than an ordinary blood test, so the technology didn’t catch on.

That’s about to change. Researchers have discovered that perspiration may carry far more information and may be easier to stimulate, gather, and analyze than previously thought.

My group at the University of Cincinnati, working with Joshua Hagen and other scientists at the U.S. Air Force Research Laboratory, at Wright-Patterson Air Force Base, in Ohio, began five years ago to look for a convenient way to monitor an airman’s response to disease, medication, diet, injury, stress, and other physical changes during both training and missions. In that quest, we developed patches that stimulate and measure sweat and then wirelessly relay data derived from it to a smartphone. In 2013 the Air Force expanded on my group’s work and that of our collaborators by sponsoring the Nano-Bio-Manufacturing Consortium, in San Jose, Calif., created to accelerate the commercialization of biomonitoring devices such as sweat sensors.

 Perspiration Detective: This patch, developed at the University of Cincinnati, uses paper microfluidics to wick sweat from the skin through a membrane that selects for a specific ion, such as sodium. Onboard circuitry calculates the ion concentration and sends the data to a smartphone. The electronics within the patch are externally powered, as in an RFID chip.

My colleagues and I started by looking for something sweat could reveal that would be useful to a large number of people. We settled on monitoring physical fatigue—in particular, alerting athletes if they were about to “crash” because of overexertion or dehydration. This problem may sound mundane, but it is hard to predict. Even million-dollar athletes regularly leave competitions because of cramping, and warning of an approaching imbalance in electrolytes could prompt an athlete to take in fluids to avoid such a mishap….

Ultimately, sweat-sensing patches will measure multiple electrolytes, metabolites, and other biomarkers at the same time. Their designers will no doubt have to devise some clever algorithms to account for differences in the way various electrolytes, metabolites, and biomarkers migrate into sweat. But it will be worth the effort. Being able to measure multiple biomarkers might allow physicians to conduct cardiac stress tests on a treadmill without drawing blood. They could also measure the impact of drugs on the body so that dosages could be determined more precisely, as opposed to the crude estimates we use now based merely on age and body weight.

Sweat analysis will offer minute-by-minute insight into what is happening in the body

There is still work to do on the digital signal processing and algorithms needed to analyze the raw electrical measurements of biomarkers in sweat. But a physical-exertion sensor patch is a near reality, about to be tried on hundreds of people. If all goes well, we could have sweat-sensing patches—at least sensors for athletics—on the market in low volume next year. These do not have to go through a lengthy approval process with the U.S. Food and Drug Administration because they are not meant to be used for diagnosis or treatment of disease….                                                        https://spectrum.ieee.org/biomedical/diagnostics/sweat-sensors-will-change-how-wearables-track-your-health

See, The future of remote ECG monitoring systems  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4987424/

Resources:

A self-healing sweat sensor                                                                                                       sciencedaily.com/releases/2019/12/191218153435.htm

Wearable sensors detect what’s in your sweat                                                                               https://news.berkeley.edu/2019/08/16/wearable-sensors-detect-whats-in-your-sweat/

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Ludwig-Maximilians-Universität München study: Predicting your personality from your smartphone data

1 Aug

A 2013 MIT Paper theorized that personality type could be predicted using cell phone data:

Predicting Personality Using Novel Mobile Phone-Based Metrics

Yves-Alexandre de Montjoye1,, Jordi Quoidbach2,∗, Florent Robic3,∗, and Alex (Sandy) Pentland1 1 Massachusetts Institute of Technology – The Media Laboratory, Cambridge, MA 2 Harvard University – Department of Psychology, Cambridge, MA 3 Ecole Normale Sup´erieure de Lyon, Lyon, France

Abstract. The present study provides the first evidence that personality can be reliably predicted from standard mobile phone logs. Using a set of novel psychology-informed indicators that can be computed from data available to all carriers, we were able to predict users’ personality with a mean accuracy across traits of 42% better than random, reaching up to 61% accuracy on a three-class problem. Given the fast growing number of mobile phone subscription and availability of phone logs to researchers, our new personality indicators open the door to exciting avenues for future research in social sciences. They potentially enable costeffective, questionnaire-free investigation of personality-related questions at a scale never seen before.

1 Introduction How much can one know about your personality just by looking at the way you use your phone? Determining the personality of a mobile phone user simply through standard carriers’ log has became a topic of tremendous interest. Mobile cellular subscriptions have hit 6 billion throughout the world [1] and carriers have increasingly made available phone logs to researchers [2] as well as to their commercial partners [3]. If predicted correctly, mobile phones datasets could thus provide a valuable unobtrusive and cost-effective alternative to surveybased measures of personality. For example, marketing and phone companies might seek to access dispositional information about their customers to design customized offers and advertisements [4]. Appraising users dispositions through automatically collected data could also benefit the field of human-computer interface where personality has become an important factor [5]. Finally, finding ways to extract personality and, more broadly, psycho-social variables from country scale datasets might lead to unprecedented discoveries in social sciences….                         http://web.media.mit.edu/~yva/papers/deMontjoye2013predicting.pdf

Resources:

What Your Phone Type Says About Your Personality                                                                        https://www.realsimple.com/health/mind-mood/smartphone-personality-differences

Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults – a prospective cohort study                                                                               https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3042390/

The Stress of Constantly Checking Your Phone                                                                                 https://www.verywellmind.com/constantly-checking-your-phone-4137954

Science Daily reported in Predicting your personality from your smartphone data

Everyone who uses a smartphone unavoidably generates masses of digital data that are accessible to others, and these data provide clues to the user’s personality. Psychologists at Ludwig-Maximilians-Universitaet in Munich (LMU) are studying how revealing these clues are.

For most people around the world, smartphones have become an integral and indispensable component of their daily lives. The digital data that these devices incessantly collect are a veritable goldmine — not only for the five largest American IT companies, who make use of them for advertising purposes. They are also of considerable interest in other contexts. For instance, computational social scientists utilize smartphone data in order to learn more about personality traits and social behavior. In a study that appears in the journal PNAS, a team of researchers led by LMU psychologist Markus Bühner set out to determine whether conventional data passively collected by smartphones (such as times or frequencies of use) provide insights into users’ personalities. The answer was clear cut. “Yes, automated analysis of these data does allow us to draw conclusions about the personalities of users, at least for most of the major dimensions of personality,” says Clemens Stachl, who used to work with Markus Bühner (Chair of Psychological Methodologies and Diagnostics at LMU) and is now a researcher at Stanford University in California.

The LMU team recruited 624 volunteers for their PhoneStudy project. The participants agreed to fill out an extensive questionnaire describing their personality traits, and to install an app that had been developed specially for the study on their phones for 30 days. The app was designed to collect coded information relating to the behavior of the user. The researchers were primarily interested in data pertaining to communication patterns, social behavior and mobility, together with users’ choice and consumption of music, the selection of apps used, and the temporal distribution of their phone usage over the course of the day. All the data on personality and smartphone use were then analyzed with the aid of machine-learning algorithms, which were trained to recognize and extract patterns from the behavioral data, and relate these patterns to the information obtained from the personality surveys. The ability of the algorithms to predict the personality traits of the users was then cross-validated on the basis of a new dataset. “By far the most difficult part of the project was the pre-processing of the huge amount of data collected and the training of the predictive algorithms,” says Stachl. “In fact, in order to perform the necessary calculations, we had to resort to the cluster of high-performance computers at the Leibniz Supercomputing Centre in Garching (LRZ).”

The researchers focused on the five most significant personality dimensions (the Big Five) identified by psychologists, which enable them to characterize personality differences between individuals in a comprehensive way. These dimensions relate to the self-assessed contribution of each of the following traits to a given individual’s personality: (1) openness (willingness to adopt new ideas, experiences and values), (2) conscientiousness (dependability, punctuality, ambitiousness and discipline), (3) extraversion (sociability, assertiveness, adventurousness, dynamism and friendliness), (4) agreeableness (willingness to trust others, good natured, outgoing, obliging, helpful) and (5) emotional stability (self-confidence, equanimity, positivity, self-control). The automated analysis revealed that the algorithm was indeed able to successfully derive most of these personality traits from combinations of the multifarious elements of their smartphone usage. Moreover, the results provide hints as to which types of digital behavior are most informative for specific self-assessments of personality. For example, data pertaining to communication patterns and social behavior (as reflected by smartphone use) correlated strongly with levels of self-reported extraversion, while information relating to patterns of day and night-time activity was significantly predictive of self-reported degrees of conscientiousness. Notably, links with the category ‘openness’ only became apparent when highly disparate types of data (e.g., app usage) were combined.

The results of the study are of great value to researchers, as studies have so far been almost exclusively based on self-assessments. The conventional method has proven to be sufficiently reliable in predicting levels of professional success, for instance…..                                                                                                                            https://www.sciencedaily.com/releases/2020/07/200717120152.htm

Citation:

Date:                July 17, 2020
Source:            Ludwig-Maximilians-Universität München
Summary:
Everyone who uses a smartphone unavoidably generates masses of digital data that are accessible to others, and these data provide clues to the user’s personality. Psychologists are now studying how revealing these clues are.

Journal Reference:

Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres Völkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, and Markus Bühner. Predicting personality from patterns of behavior collected with smartphonesPNAS, 2020 DOI: 10.1073/pnas.1920484117

Here is the press release from Ludwig-Maximilians-Universität München:

The most personal device

Munich, July 15th, 2020

Anyone who uses a smartphone leaves digital traces – in abundance. Such app data make it possible to draw conclusions about the personality of the user. LMU psychologists are researching their significance.

For many people, smartphones have long been personal companions in their daily lives. The digital traces that their owners leave around the clock are not only in great demand for the large American IT companies, for example for advertising purposes. They can also throw something off scientifically: social scientists, for example, use the data to find out more about people’s personality and social behavior. In a recent study published in the PNAS magazine, a team led by LMU psychologist Markus Bühner checkedthe question of whether there are already indications of the personality of the user from common behavior data of smartphones such as usage times or frequencies. The answer was clear: “Yes, we can automatically draw conclusions about the personality of the user, at least for most personality dimensions,” says Clemens Stachl, former member of the chair of Markus Bühner (psychological methodology and diagnostics) and now a researcher at Stanford University, USA.

As part of the PhoneStudy project, the LMU researchers asked a total of 624 volunteers to fill out an extensive personal questionnaire and to install the PhoneStudy research app developed at the LMU on their smartphones for 30 days. The app sent encrypted information about the behavior of the test participants to the server. The researchers primarily evaluated data on areas such as communication and social behavior, music consumption, app usage, mobility, general telephone activity and day and night activity. The scientists then fed both the data from the personality questionnaire and the behavior data from the smartphone into a machine learning algorithm. This algorithm, was then trained to recognize patterns in the behavioral data and then to associate them with higher or lower values ​​in the personality questionnaire. The algorithm’s ability to predict personality was then cross-validated using new data. “The most difficult part was the preprocessing of the enormous amount of data and the” training “of the algorithms,” says Stachl. “To do this, we had to access the LRZ high-performance computing cluster in Garching to make these calculations possible at all.” “The most difficult part was the preprocessing of the enormous amount of data and the” training “of the algorithms,” says Stachl. “To do this, we had to access the LRZ high-performance computing cluster in Garching to make these calculations possible at all.” “The most difficult part was the preprocessing of the enormous amount of data and the” training “of the algorithms,” says Stachl. “To do this, we had to access the LRZ high-performance computing cluster in Garching to make these calculations possible at all.”

The researchers focused on the five most important personality traits in psychology, the so-called Big Five. These five dimensions describe differences in human personality in a very global way. They include openness (how open a person describes new ideas, experiences and values), conscientiousness (how reliable, punctual, ambitious, and organized I assess myself), extraversion (gives hints on how sociable, assertive, adventurous, cheerful) someone describes), tolerance (how pleasant, accommodating, supportive and helpful a person is) and emotional stability (how confident, self-dominant and carefree a person assesses himself). The algorithm was able to automatically draw conclusions about most of the users’ personality traits from the combination of the behavioral data. The results also indicated which digital behaviors are informative for certain self-assessments of the personality. The communication and social behavior on the smartphone gave important clues as to how extravagant someone thinks, information about the day-night rhythm of users was particularly meaningful with regard to self-assessed conscientiousness. Openness could only be predicted through a variety of very different behavioral data. Which digital behaviors are informative for certain self-assessments of the personality. The communication and social behavior on the smartphone gave important clues as to how extravagant someone thinks, information about the day-night rhythm of users was particularly meaningful with regard to self-assessed conscientiousness. Openness could only be predicted through a variety of very different behavioral data. Which digital behaviors are informative for certain self-assessments of the personality. The communication and social behavior on the smartphone gave important clues as to how extravagant someone thinks, information about the day-night rhythm of users was particularly meaningful with regard to self-assessed conscientiousness. Openness could only be predicted through a variety of very different behavioral data.

The results are of great value to researchers, above all because personality diagnosis in psychology has so far been based almost exclusively on self-descriptions. These are useful for predicting professional success, for example. “Nevertheless, at the same time we know very little about how people actually behave in everyday life – apart from what they want to tell us in the questionnaire.” says Markus Bühner. “Smartphones are ubiquitous, widespread and have enormous technical capabilities, making them ideal research tools to see whether the self-descriptions also correspond to real behavior.”

Stachl is well aware that his research could also arouse desires in large IT companies. In addition to data protection and privacy protection, one has to work on taking a holistic view of the topic of artificial intelligence, says Stachl. “The focus of research must be on people, not machines. We must not use machine learning methods without thinking about it. ”The potential of possible applications is enormous, both in science and in business. “The possibilities of a data-driven society today can undoubtedly improve life for many people, but we also have to ensure that all participants in society can benefit from these developments.”
https://www.uni-muenchen.de/forschung/news/2020/stachl_smartphones.html

Technology Safety suggested 12 Tips on Cell Phone Safety and Privacy:

As cell phones become smarter, they’re more like mini computers that contain lots of personal information about us. Here are 12 easy steps to take to manage your privacy and safety when using your cell phone.

  1. Put a passcode on your phone.

The easiest thing for you to do is to put a passcode on your phone. Having a passcode will make it harder for someone to pick up your phone to scroll through, access your accounts, or install something malicious. In the event that your phone gets stolen or you lose it, it’ll make it a bit harder for others to get into your phone. Most phones just ask for a 4-digit passcode, but some phones will allow you to use a more complex passcode.

  1. Turn off location sharing.

Most phones have a GPS that can pinpoint your general or exact location. With this capability, many applications may collect and share your location information. However, many smartphones give you the option of managing your location sharing under the “settings.” You can pick and choose which applications may access your location or you can opt to turn off the location setting altogether.  Minimizing the location access can also help increase the battery life on your phone. If your phone doesn’t offer specific location-sharing settings, choose carefully when downloading new apps so you’re not sharing your location unknowingly.

  1. Turn off Bluetooth when not using.

Bluetooth allows your phone to communicate with other devices, such as the hands-free option in your car or your printer. If accessed by someone else though, they could misuse it to access your information or intercept your calls. Turn off the Bluetooth on your phone and turn it on only when you need to connect with other device. Many phones also allow users to set passcodes or additional security levels on their Bluetooth as well. Use all available options to increase your privacy.

  1. Check your privacy & security settings.

Most smartphones have settings that will help you manage your privacy and safety. You can find these controls through the settings on your phone or through the settings of a specific app. These settings may allow you to limit an application’s access to the data on your phone, including access to your location, pictures, contacts, notes, etc. You may even be able to block cookies and limit what data your mobile browser collects.

  1. What online accounts are you automatically logged into?

One of the convenient features of having a smartphone is to quickly access email or social media accounts with just a tap of a finger. However, this also means that you are always connected to accounts that may contain sensitive information. Consider logging out of certain accounts if you can so that others can’t access those accounts if they are using your phone. Keep in mind that depending on the type of phone you have, you might not be able to log out of some accounts, such as email accounts, but may have to remove the entire account from your phone. In this case, make your decision based on your own privacy and safety risk. While it may be inconvenient to access the account through the browser instead, it may be safer.

  1. Review the apps you download.

Know the apps that are on your phone, and if you have an unfamiliar app, delete it. Apps are easy to download and easy to forget, but depending on the app, it could be accessing private information or could be a monitoring program that someone surreptitiously installed.

  1. Put a password on your wireless carrier account to keep others from accessing your account.

If you’re worried that someone might be contacting your wireless carrier to obtain information about you and your account, you can ask your wireless carrier to put additional security on your account, such as a password. Only someone with this password will be allowed to make changes to your account.

  1. Lock down your online phone account.

Keep in mind that even if someone doesn’t have access to your phone, it might be possible for them to access your online account. Online accounts can include your wireless carrier account, call logs, your email or social media accounts, your Google Play/Apple AppStore, or iCloud account. Update the passwords and security questions for those accounts to ensure someone else can’t get access.

  1. Use virtual phone numbers (such as Google Voice) to keep your number private.

To further maximize your privacy, consider using a virtual number, such as Google Voice or a throw away number, so you don’t have to give out your actual phone number. A virtual phone number will also allow you to screen calls and make calls/send texts from the virtual number.

  1. Try not to store sensitive information on your phone.

Finally, although it may be tempting to store information such as passwords, account numbers, or personal information on your phone, the less sensitive information you have, the less likely someone else can access it. You might even want to consider deleting sensitive text messages or voicemails so they’re not stored on your phone.

  1. Use anti-virus and anti-spyware software on your phone.

After years of warnings, we are fairly used to ensuring we have anti-spyware, anti-malware, and anti-virus programs on our computers. This software should also be used on our smartphones as well. Search for programs in the app stores and discuss them with your wireless provider. Some phones come with built-in software that you won’t want to override.

  1. Take care when using safety apps.

There are many “personal safety apps” available for download that offer to increase the users’ personal safety – immediately connecting them with 911 or select trusted individuals. Several of these apps are designed and marketed specifically to survivors of violence. Before relying on any safety app in an emergency, be sure to test it out with friends and family to be sure that it works correctly for you. Your trusted friend may not receive your location with your emergency call or may not receive your call for help at all. Always know the quickest way to access 911 on your phone in case of an emergency. Many phones have a quick emergency call button that you can even dial without entering the phone’s passcode.                                                                                   https://www.techsafety.org/12tipscellphones

Resources:

CELL PHONE PRIVACY                                                                                                                aclu.org/issues/privacy-technology/location-tracking/cell-phone-privacy

Smartphone Privacy

Posted: Aug 01 2005  | Revised: Dec 19 2017                                                                                                                 https://privacyrights.org/consumer-guides/smartphone-privacy

Protecting Your Privacy: Phone and Cable Records                                                                          https://www.fcc.gov/consumers/guides/protecting-your-privacy

Personal Cellphone Privacy at Work                                                                                               https://www.shrm.org/ResourcesAndTools/hr-topics/technology/Pages/Personal-Cellphone-Privacy-at-Work.aspx

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