New research: Can smartphone data be a digital marker for discriminating bipolar disorder from unipolar disorder?

New research: Can smartphone data be a digital marker for discriminating bipolar disorder from unipolar disorder?

by | Apr 13, 2022 | Bipolar, Blog, mHealth

Unipolar disorder (UD) refers to individuals suffering from depression without experiencing mania, whereas individuals suffering from bipolar disorder (BD) usually face episodes of mania in addition to their depression [1]. Clinicians often encounter difficulties identifying whether depressed patients suffer from BD or UD. Given that the course of illness and related treatments vary for patients with BD and UD, the discrimination between these two disorders is critical [2].

A new research paper has just been published on “Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder” in the Journal of Affective Disorders [2].

This new research investigated whether using the information on activity and mobility of patients with BD and UD as supplementary objective measure could assist in the discrimination between the two conditions [2]. Data for this study has been collected as part of the RADMIS trials, two similarly composed randomized controlled trials (RCTs) that investigated the effect of daily smartphone-based monitoring including a clinical feedback loop in individuals suffering from BD and UD. The Monsenso digital health solution was used for the collection of smartphone-based patient data in the intervention group of the trials [3].

The present study included gathering both passively collected smartphone-based location data and patient-reported smartphone-based data on mood from 65 patients with BD and 75 patients with UD [2]. Smartphone-based self-assessments of mood were completed by all patients, and smartphone data on location reflecting mobility patterns, routine and location entropy (chaos) was collected passively from all patients on a continuous basis over the course of six months [2]. The data collection was followed by an extensive data analysis, comparing differences between the two groups.

Results of the study show patients suffering from BD have significantly lower mobility in, e.g., their total time of daily movement during depressive periods (eB 0.74, 95% CI 0.57; 0.97, p = 0.027). Additionally, the area under the curve (AUC) of location data was rather high in classifying patients with BD compared with patients with UD, although results of the study may be limited by relatively low symptom severity of the participating patients contributing to the dimension of the AUC [2].

The study results suggest alterations in location data may be a promising digital diagnostic marker in patients with BD and UD, and smartphone data on mobility patterns could hence help in discriminating between the two disorders. 

Mads Frost, PhD, Co-Founder & Chief Information Security Officer at Monsenso, who has contributed to the research says: “The work on comparing mobility patterns between patients with bipolar disorder and patients with unipolar disorder has been highly interesting, and we look forward to further explore our data looking for potential digital diagnostic markers”.

 “We are excited that Monsenso is a part of promising new research on digital diagnostic markers, and contributes to the research in and the treatment of mental health and neurological disorders”, says Thomas Lethenborg, CEO at Monsenso.

About Monsenso:
Monsenso is an innovative technology company offering a medical grade digital health solution. Our mission is to help provide better mental health to more people at lower costs. Our solution helps optimise the treatment of mental disorders and gives a detailed overview of an individual’s mental health through the collection of outcome, adherence, and behavioural data. It connects individuals, carers, and health care providers to enable personalised treatment, remote care, and early intervention. Based on continuous research and development, our team is committed to developing solutions that fit seamlessly into the lives of individuals, increase their quality of life and improve the efficacy of mental health treatment. To learn more, visit www.monsenso.com

Research publication:
You can find the research publication in the Journal of Affective disorders here.

References:
[1] Quilty, L., Pelletier, M., DeYoung, C.G. & Bagby, M. (2013). Hierarchical personality traits and the distinction between unipolar and bipolar disorders. Journal of Affective Disorders 147(1-3): 247-254. https://www.sciencedirect.com/science/article/pii/S0165032712007604#bib2

[2] Faurholt-Jepsen, M., Busk, J., Rohani D.A., Frost, M. Tønning, M.L., Bardram, J.E. & Kessing, L.V. (2022). Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder. Journal of Affective Disorders 306: 246-253. https://www.sciencedirect.com/science/article/pii/S0165032722003019?dgcid=author

[3] Faurholt-Jepsen, M., Tønning, M.L., Frost, M., Martiny, K., Tuxen, N., Rosenberg, N., Busk, J., Winther, O., Thaysen-Petersen, D., Aamund, K.A. & Tolderlund, L., Bardram, J.E. & Kessing, L.V. (2020). Reducing the rate of psychiatric re-admissions in bipolar disorder using smartphones—The RADMIS trial. Acta Psychiatrica Scandinavia, 143(5): 453-465. https://onlinelibrary.wiley.com/doi/10.1111/acps.13274

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A smartphone app that can help psychiatrists diagnose mental illness

A smartphone app that can help psychiatrists diagnose mental illness

by | Jul 27, 2020 | Anxiety, Bipolar, Blog, Customer Stories, Depression, Mental Illness, mHealth

“A smartphone app that can help psychiatrists diagnose mental illness” –  Peter Hagelund, a Monsenso user, speaks about his experience using the Monsenso mobile health solution to support his treatment, and how it has helped  him improve the communication with his psychiatrist.

Prior to using the Monsenso smartphone app, Peter and his psychiatrist followed the typical therapeutic setting, they would schedule an appointment every two-three weeks, and they would have a conversation to discuss Peter’s previous weeks.  Peter would usually say that he had been doing fine for each appointment, but sometimes he forgot important details that he wanted to discuss.

“It can be pretty tricky to remember, two weeks later [between appointments] how you actually felt that day. With the app it’s really easy to go back and see if your mood has been pretty stable over the last two months, or if you had had some ups and downs over a period,” says Peter Hagelund.

Now, instead of relying on Peter’s memory during the appointment, his psychiatrist can access his data and see how he has been doing, as it happened. He can view how much he has been sleeping, how much he exercises, how much he drinks, how much anxiety he has, and other relevant aspects to his treatment and his disorder.

“My psychiatrist now says things like You say you’ve been doing fine, but I can actually see that you’ve had a few ups and downs. I think the app helps him get a real view of how I have been doing,” says Peter Hagelund.

In his Danish documentary series “Jeg savner min sygdom” (which translates to “I miss my illness”), Peter Hagelund talks about his experience of getting the wrong diagnosis and how finally after six years he got the right diagnosis and treatment. 

In 2014, he was diagnosed with Bipolar Disorder Type 2.

During his whole life, he knew there was something different about him. He always struggled with depression and anxiety, and what eventually turned out to be hypomania. When he was 22, he had his first big episode of depression and began taking antidepressants. However, it took six more years before he was officially diagnosed with Bipolar Disorder Type 2.

He says that one of the trickiest things about having this diagnosis is that he does not experience the typical manic episode where a person feels over-energetic and nearly psychotic. Instead, he feels hypomanic, which means that he is socially well functioning. He is not psychotic. He just feels really well; the problem with this, was that he didn’t feel the need to inform his psychiatrist the fact that he was feeling too well. 

His psychiatrist found out that he had Bipolar Disorder was because he couldn’t come out of his depression. At first, he was diagnosed with depression and ADD. But these diagnoses didn’t seem to fit because he still had strong mood swings and a lot of anxiety. When he was diagnosed with Bipolar Disorder Type 2, it actually made sense to him. Suddenly, he could see why he had felt the way he had most of his life.

“I really believe that the Monsenso smartphone app could have helped my psychiatrist give me the right diagnosis at an earlier stage because the app helps me to keep track of my mood and to become more aware of how I feel. With the app, I have to pause and take a moment to think about how I’ve actually been doing before entering my answers. Keeping track of all this information has helped my treatment. My psychiatrist and I can plan better on how to avoid my future depressive or hypomanic episodes because we can clearly identify when I am having mood swings”. says Peter Hagelund.  

In August 2018, his documentary about living with Bipolar Disorder aired on national Danish television and the response was overwhelming. Many people contacted him, to thank him for talking about his illness. He made the documentary so other people who have this illness, do not feel ashamed of it.

“My hope is that in the future people are diagnosed at an earlier stage than I was and I truly believe the Monsenso app is one of the things that can help. I really hope that other psychiatrists and their patients will start using the app,” he added.

Click here to read this story in Danish.

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A smartphone app that supports patient empowerment

A smartphone app that supports patient empowerment

by | May 1, 2020 | Bipolar, Blog, Customer Stories

“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

Using voice analysis to identify peaks on bipolar disorder

by | Aug 3, 2016 | Bipolar, Blog, mHealth

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:

  1. Voice features collected during phone calls, as objective markers of affective states in bipolar disorder
  2. 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.

Results

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.

Reference:
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

Improving the treatment of bipolar disorder with mobile health technology

Improving the treatment of bipolar disorder with mobile health technology

by | Dec 3, 2015 | Bipolar, mHealth

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. [1]

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. [2]

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. [2]

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. [3]

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 [3]

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. [2]

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 [2]

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 [4].

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.

Resources:

[1] What is bipolar disorder? National Institute of Mental Health. http://www.nimh.nih.gov/health/topics/bipolar-disorder/index.shtml

[2] Bipolar disorder. National Health Service (NHS) UK. http://www.nhs.uk/Conditions/Bipolar-disorder/Pages/Introduction.aspx

[3] Bipolar disorder treatment. HelpGuide.org http://www.helpguide.org/articles/bipolar-disorder/bipolar-disorder-treatment.htm