Can mHealth technology be used to save costs ?

Can mHealth technology be used to save costs ?

According to an article published on Harvard Business Review, digital health tools have the potential to provide effective, low-cost ways to prevent and treat chronic illnesses. The article states that these technology-based solutions that have a clinical impact on disease are comparable to the effectiveness of a drug, and they use consumer-grade technology such as mobile devices and big data analytics that can be deployed in real-time and at scale, which is critical for the management of chronic diseases.

From the chronic diseases, mental illnesses account for five of the ten leading causes of disability worldwide [1]. Therefore, prevention as well as the the early identification and treatment of mental illness represents a high priority since it promotes recovery, independence, self-sufficiency, as well as facilitating social activities and employment opportunities [2].

In fact, in the UK alone, depression and anxiety accounted for 11.7 million lost working days last year [3].  Moreover, according to the American Medical Association, stress accounts for 60% of all human illness and disease [4], which means that clinically-tested health apps can help government authorities, corporations and insurance companies reduce their costs by monitoring individuals remotely and intervening at an early stage.

Nowadays, the majority of large employers that offer health benefits also offer a wellness programme in an effort to promote employee health and productivity as well as to reduce health-related costs.

We live in an era where certified and clinically-tested health apps, or mobile health solutions (as they are widely known), are readily available, or are being developed for most chronic diseases.

Most of these digital tools are extremely easy to use, and can be obtained by downloading an application from iOS or Google Play, signing-up, and sharing your information with a coach. The clear advantages of this technology are scalability and low-cost, since an effective health app bundled up with a telehealth can provide affordable support to either 50 or 5 million users.

The Monsenso mHealth solution can be used by insurance companies and large corporations to reduce costs by offering a preventive mental wellness programme. Individuals can use the Monsenso smartphone app to enter their daily levels of stress, anxiety, irritability, physical activity and number of hours they slept. This information is gathered and stored electronically so it can be accessed by a healthcare professional anytime, anywhere. However, the coach only needs to take action when the web portal indicates that certain individuals present any triggers or warning signs. For example, the coach will be notified when anyone in the system indicates a high level of stress, anxiety and irritability for more than five consecutive days or when someone sleeps less than six hours for more than three consecutive days. These two actions would be considered indicators that the individual needs to be contacted for a “wellness check” and implement the necessary measures to prevent the person from going on stress-leave or from becoming affected by other physical conditions such as heart disease.

References:

[1] Prevention of Mental Disorders. Effective Interventions and policy options. World Health Organisation in collaboration with the Prevention Research Centre of the Universities of Nimegen and Maastricht. http://www.who.int/mental_health/evidence/en/prevention_of_mental_disorders_sr.pdf

[2] Early intervention and recovery for young people with early psychosis: consensus statement. J. Bertolote and P. McGorry. British Journal of Psychiatry (2005). http://bjp.rcpsych.org/content/bjprcpsych/187/48/s116.full.pdf

[3] Stress in the City: ‘At first, I thought my depression was a heart attack’. The Telegraph. Peter Stanford. (2017, January 7)
http://www.telegraph.co.uk/men/health/stress-city-first-thought-depression-heart-attack/

[4] How Stress Affects the Body (INFOGRAPHIC). Huffington Post. (2013, January 10)
http://www.huffingtonpost.com/heartmath-llc/how-stress-affects-the-body_b_2422522.html

Simple Digital Technologies Can Reduce Health Care Costs.
Harvard Business Review. Alexander L. Fogel, Joseph C. Kvedar. (2016, November 14).
https://hbr.org/2016/11/simple-digital-technologies-can-reduce-health-care-costs

Digital self-monitoring tools may promote positive behavioural changes, new study suggests

Digital self-monitoring tools may promote positive behavioural changes, new study suggests

Digital self-monitoring tools enables individuals to track meaningful data about themselves. This capability has encouraged healthcare providers to use these tools to personalise and scale treatment in a more cost-efficient way.

A recently published research paper titled “Self-monitoring utilisation patterns among individuals in an incentivized programme for healthy behaviours,” suggest that the use of digital self-monitoring tools could significantly improve a patient’s long-term health engagement.

According to the research paper, the 69% of Americans track regularly at least one indicator of health, including their weight, diet, exercise routine, or symptoms related to chronic disease.  Since there is a wide range of mHealth devices, there is a growing trend among the general population to measure, track, and make changes related to their health based on quantifiable data collected by oneself. Projections show that the number of everyday wearables, devices, and sensors will increase 5-fold by 2019 [1].

Although the effectiveness of these tools varies depending on the manufacturer, it is well-established that effective, digital self-monitoring tools can have profound health benefits. For example, among diabetics, blood glucose monitoring is a major component of disease management and provides individuals the ability to assess glycaemic targets and evaluate response to therapy. Additionally, blood pressure monitoring has been associated with improved short-term blood pressure control and medication adherence, and self-monitoring has also been shown to improve weight loss and short-term activity levels Importantly, monitoring programs, wearable devices, and other non-traditional healthcare resources can potentially facilitate healthy behaviour changes [1].

As non-traditional healthcare channels such as virtual care become more popular, there is a shift to value-based treatment. Together, these aspects have led to an interest in incorporating digital self-monitoring tools into chronic condition management, and the diagnosis of acute episodes. All these are important steps in incorporating digital technologies into routine patient care.

According to the research, after 20 weeks, 28.36% of registered users were still actively engaged in the program. Meanwhile, combined with the duration of program participation, the frequency of program participation over the first 20 weeks demonstrated some interesting trends. First, the average number of activities logged by users was 4.28 during the first week in the program. However, after excluding the roughly one-third of users who ceased recording activities after one week, the average number of activities logged by participating users increased to 7.53 by the second week. After four weeks, this number was 8.01 and remained relatively steady throughout the 20-week period examined [1].

Overall, the study demonstrated that while a large proportion of users stopped participating in the programme early on those that did continue to log activities did so at a fairly consistent level throughout their participation period [1].

Primary findings

Monitoring physiologic parameters, health activities, and health behaviours in a non-medical setting has the potential to enable alternative systems of health management that can be both more individualised and convenient for health consumers.

An understanding of home-based, self-tracking parameters can provide insights into optimising such programs in future health care models.

The results of the study suggest that incentives might work for connected and active participants in achieving healthy activities. The study showed consistent, extended results of how incentivised consumers track health behaviours and health data in a real-world setting with a large population.

 Long-term adherence to healthy behaviour programme and automated self-monitoring tool

According to the study 57% of all users that remained on the study after one month continued to participate for at least twenty weeks. However, it should be taken into consideration that engaging consumers initially and for prolonged lengths are important components of success.

There is still much to learn about long-term participation. However, the digital tools used, should be easy to use and they should incorporate proven behaviour change theories through the use of rewards or incentives. For example, useful tools to improve long-term, self-monitoring are mobile health-tracking technologies since they can collect, transmit, and aggregate health data.

In fact, a study looking for adherence to the protocol through mobile phone apps which compared website or paper diaries for weight loss also proved the advantage of mobile phone apps even when it was not a fully automated process [1].

 Web-based and mobile health self-monitoring is popular in the general population, and could play a critical role in the future of health management and wellness. Self-monitoring has been shown to improve health and management of chronic conditions. [1].

The Monsenso mHealth solution can be used as a digital self-monitoring tool by individuals with a mental illness. With the smartphone app, individuals can track their health and behavioural data, as well as their symptoms and medication compliance. Besides, all this information is also synchronised with the clinical web portal enabling healthcare professionals to offer a more personalised treatment.

Reference:

Self-Monitoring Utilization Patterns Among Individuals in an Incentivized Program for Healthy Behaviours. JMIR Publications. Ju Young Kim, MD, PhD; Nathan E Wineinger, PhD; Michael Taitel, PhD; Jennifer M Radin, MPH, PhD; Osayi Akinbosoye, PhD; Jenny Jiang, MS; Nima Nikzad, PhD; Gregory Orr, MBA; Eric Topol, MD; Steve Steinhubl, MD. 2016. http://www.jmir.org/2016/11/e292#Body