“A smartphone app that supports patient empowerment” – Mads Trier-Blom, a Monsenso user, talks about his experience using the Monsenso app, and how it has helped him to become more aware of his mood and other parameters, such as sleep, levels of stress and anxiety and the influence these parameters have on his illness, Bipolar Disorder.
Mads Trier-Blom, who is using the Monsenso app as part of a clinical trial, says the app helps him to be more connected with his clinician, Bente who intervenes when she can see that Mads’s is not feeling well, to help him avoid having an episode.
To Mads, having bipolar disorder feels like walking on a tightrope, since he constantly needs to keep his balance, and avoid losing control to a depressive or a manic episode. He thinks the app helps him keep his balance since he needs to registers his mood every day, which makes him more aware of the way he is feeling. The app also helps him identify any mood fluctuations he has during the week.
During the clinical trial, Mads felt more connected to his clinician, Bente. He recalls an instance when he had missed completing his self-assessments for a couple of days, and Bente called him to see how he was doing. At the beginning, since he was not expecting the call, he was a bit confused. However, when she identified herself and the purpose of her call, he relaxed and told her that he had been a bit tense lately, but that he was overall feeling well. This made him feel more aware of his mood and his behaviour as well as more alert.
Using voice analysis to identify peaks on bipolar disorder is a summary of a recently-published research paper titled “Voice analysis as an objective state marker in bipolar disorder.” For additional information, please click on the link.
The human voice is composed of multiple different components, created through complex muscle movements, making each person’s voice individual, like ‘a fingerprint’. Studies analysing speech in affective disorders, date back as early as 1938. Several clinical observations suggest that changes in speech features have been suggested as valid measures, to identify periods of depression and mania in bipolar disorder. For instance, reduced speech activity may be considered a symptom of depression, and increased speech activity may predict a switch to hypomania.
A pilot study conducted by The Copenhagen Clinic for Affective Disorders, with patients with bipolar disorder, aimed to investigate the following:
- Voice features collected during phone calls, as objective markers of affective states in bipolar disorder
- If combining voice features with automatically generated objective smartphone data on behavioural activities (for example, number of text messages and phone calls per day) and electronic self-monitored data (mood) on illness activity, would increase the accuracy as a marker of affective states
The pilot study included the monitoring of 28 outpatients with bipolar disorder, in a natural environment from October 2013 to December 2014. During this period, patients were given a smartphone application developed by Monsenso, to collect voice features, electronic self-monitoring data, and collection of automatically generated data.
- Voice features
The voice features were extracted from the patients’ phone calls throughout the day, using the open-source Media Interpretation by Large feature-space Extraction (openSMILE) toolkit, which is a feature extractor for signal processing and machine learning applications.
- Electronic self-monitoring data
Patients were requested to provide daily electronic self-monitoring data. The parameters evaluated included: mood, sleep length, medication intake, activity level, alcohol consumption, mixed mood, irritability, cognitive problems, stress levels, and individualised early warning signs.
- Automatically generated data
Through the smartphones’ sensors, automated data tracking different aspects of behavioural activities was collected on a daily basis. The data compiled by the smartphones included the number and duration of phone calls and text messages, accelerometer data, and phone usage.
This innovative study revealed that changes in voice features could, in fact, detect individual changes in affective state. The accuracy of the prediction is increased, by combining voice features with automatically generated smartphone data on behavioural activities, and electronic self-monitoring. Therefore, according to the study, voice features collected by smartphones in a natural setting, could be used as an objective state marker in patients with bipolar disorder.
According to the researchers, the monitoring of symptoms in bipolar disorder and the accurate classification of affective states based exclusively on voice features has great potential.
Clinicians would be able to obtain accurate, objective data in real-time on the patients’ affective states based on collected voice features. The smartphone application could be used to monitor symptoms long-term, outside clinical settings, and enable early intervention between outpatient visits.
According to the researchers, at the time the study was conducted, it was the first study ever that investigated the combinations of voice features; automatically generated data, and electronic self-monitored data as state markers in patients with bipolar disorder. Using feature analysis collected in real-time from smartphones for classifying affective states in bipolar disorder reflects an innovative, objective and unobtrusive method for monitoring of illness activity (state) during long-term and in naturalistic settings.
Voice analysis as an objective state marker in bipolar disorder. M. Faurholt-Jepsen, J.Busk, M.Frost, M.Vinberg, E.M.Christensen, O.Winther, J.E.Bardram and L.V.Kessing. 5 May 2016. http://www.nature.com/tp/journal/v6/n7/pdf/tp2016123a.pdf
Bipolar disorder, also known as manic-depressive illness, is a brain disorder that causes unusual shifts in mood, energy, activity levels, and the ability to carry out day-to-day tasks. 
People suffering from bipolar disorder will have periods or episodes of depression – where they feel very low and lethargic mania – where they feel high and overactive. 
Unlike simple mood swings, each episode of bipolar disorder can last for several weeks and some people may not experience a “normal” mood very often. 
Getting an accurate diagnosis is the first step in bipolar disorder treatment. However, this isn’t always easy. The mood swings of bipolar disorder can be difficult to distinguish from other problems such as major depression, ADHD, and borderline personality disorder. For many people suffering from bipolar disorder, it takes years and numerous doctor visits before the problem is correctly identified and treated. 
Indicators of bipolar disorder:
- Repeated episodes of major depression
- First episode of major depression was experienced before age 25
- First-degree relative suffering from bipolar disorder
- Mood and energy levels are higher than most people’s when not depressed
- Oversleeping and overeating when depressed
- Episodes of major depression are shorter than 3 months
- Lost contact with reality while depressed
- Suffered from postpartum depression in the past
- Developed mania or hypomania while taking antidepressants
- Antidepressants stopped working after several months
- Tried three or more antidepressants without success 
If a person is not treated, episodes of bipolar-related mania can last for between three to six months. Episodes of depression tend to last longer, for between six and twelve months. However, with effective treatment, episodes usually improve within about three months. 
Most people with bipolar disorder can be treated using a combination of different treatments that can include:
- Medication such as mood stabilisers and antidepressants
- Learning to recognize triggers and early warning signs of an episode of depression or mania
- Psychotherapy to deal with depression and provide advice on how to improve relationships
- Lifestyle advice such as doing regular exercise, planning activities you enjoy that give you a sense of achievement, and advice on improving your diet and getting more sleep 
Mobile health technology
The Monsenso mHealth platform is based on The MONARCA Research Project, aimed at developing and validating a solution for multi-parametric, long-term monitoring of behavioral and physiological information relevant to bipolar disorder.
The Monsenso solution can help predict and prevent episodes by training patients to recognize their early warning signs, which are symptoms that indicate an oncoming episode .
In particular, during the research project, it was discovered that these three parameters are crucial in keeping a bipolar patient stable:
- Adherence to prescribed medication: Taking all medications on a daily basis, exactly as prescribed.
- Stable sleep patterns: Sleeping eight hours every night and maintaining a consistent routine of going to bed, waking up.
- Staying active both physically and socially: Getting out of the house every day, going to work, and engaging in social interaction.
Therefore, the Monsenso solution includes five core features that support a patient’s self-management:
- Self-assessments – Reminded by an alarm, patients enter subjective data directly into the system through their smartphones. This data includes mood, sleep, level of activity, and medication. Some items can be customized to accommodate a patient’s specific needs, while others are consistent to provide statistical analysis.
- Activity monitoring – Through a GPS and accelerometer, objective data is collected to monitor a patient’s level of engagement in daily activities. The system can also measure the amount of social activity based on phone calls and text messages.
- Historical overview of data – On the web portal, patients and clinicians can obtain a two-week snapshot of a patient’s basic data for immediate feedback. The portal also gives them access to a detailed historical overview of the data, enabling them to explore it in depth by going back in time, and focusing on specific variables.
- Coaching and self-treatment – The MONARCA systems supported psychotherapy in two ways. Firstly, through customizable triggers that notify the patient and clinician when the data potentially indicates a warning sign. Second, since the patients have access to their own Early Warning Signs, it empowers them to learn more about them.
- Data sharing – To strengthen the relationship between patients and clinicians, important information and treatment decisions are shared.
 What is bipolar disorder? National Institute of Mental Health. http://www.nimh.nih.gov/health/topics/bipolar-disorder/index.shtml
 Bipolar disorder. National Health Service (NHS) UK. http://www.nhs.uk/Conditions/Bipolar-disorder/Pages/Introduction.aspx
 Bipolar disorder treatment. HelpGuide.org http://www.helpguide.org/articles/bipolar-disorder/bipolar-disorder-treatment.htm