Leveraging Software Solutions to Mental Health Problems in Resource-low Settings.

As the WHO expects societal mental health costs to significantly increase in the coming years - especially in regards to fast-growing emerging economies in Africa, Asia and South America - the world is increasingly looking for scalable tools for solving these issues.

Nadinga et al (2019) conducted a literature review regarding effects of digitalisation on mental health and wellbeing in resource-limited settings. Resource-limitation was defined as the study being located at a country that meets the criteria of low- and middle income-classifications and/or describing the population as living in “rural”, “low-income” or “marginalized” settings. 67 studies met the inclusion criterias, and investigated themes such as adherence, ecological momentary assessment, well-being promotion, telemedicine, machine learning and games. Roughly a third of all studies were focused on some form of service care delivery, whilst roughly the same amount was focused on behavioral change communication. Overall, the author, although stating the limitations of the generalisability of the study with limited long-term follow up of included studies and several pilot projects included, identified several promising trends in regards to the effects of digitalisation on mental health interventions in low-resource settings. Robotics, predictive analytics and VR-solutions are stated as examples of potential cost-effective solutions, although there is a need to build the infrastructure in place to host these types of solutions.

Adherence to treatment

The author mentions several promising implementations of phone calls or SMS-systems in order to increase adherence to mental health treatment. A Nigerian study found SMS-based appointment reminders to lead to twice the baseline rate of adherence in regards to attending upcoming appointments (Thomas et al, 2017). Another Indian study found similar results - reporting an increase in adherence to their first appointments through SMS-reminders (Sing et al., 2017). A South African RCT using SMS-prompts mixed with an assigned “treatment partner” for the patient in order to increase adherence to medical treatment, showed acceptable but not feasible results. Although the majority of health-care providers showed preliminary support for the efficacy of text-based reminders to increase adherence to treatment, results showed that implementation of software and tele-health solutions in this environment to be significantly limited due to patients changing phones, and difficulty in local IT-system integration - pointing to the need for a careful analysis of the digital infrastructure where similar solutions are implemented (Mall et al., 2013; Sibeko et al., 2017).

Interestingly one pilot-study using a SMS-based approach to modulised versions of CBT assignments in a low-income public healthcare setting, reported a 65% response rate to messages and overall positive support from patients. However, the study lacked a control group - limiting the generalisability of the study (Aguillera et al.,2011). Two studies in the Allianchu Project in Peru also utilised a similar solution tailored towards screening of mental health disorders. Of those screened - 74% sought treatment(Diez-Canseco et al., 2018, Toyama et al., 2017).

Improving data validity

Ecological momentary assessment studies were also included - these types of solutions might potentially report more valid data of symptoms, as live-data reporting should theoretically mean reporting of more accurate emotional states. Two Chinese studies provided interesting results in this regard - a preliminary one-month RCT pointed to a non-significant but measurable reduction in screening for urine-detection of drugs (50% in control group and 26,2% in intervention showed positive urine samples after one month of treatment), after usage of a CBT-based application with daily monitoring of symptoms. However, both control and intervention groups used the same application - with the control group only receiving educational text-messages prompted by the application whilst the intervention population received both text-messages and daily tracking of symptoms and drug usage (Lian et al.,2018). However, at a larger follow up-study of the aforementioned pilot study, there was low concordance of in-app reporting and real-life screening of drug use, and a response rate of only 49% (Han et al., 2018). However, concordance seemed to consistently increase each week, lending to the possibility of an increase of accuracy as patients get used to the technology over time. The authors however concluded that there were several limitations in regards to the feasibility of implementation in the associated healthcare setting. A South African study using a similar SMS-based approach to monitoring depression risks in a refugee population, reported similar reliability to IRL-screening, and no significant differences in patient preference (Tomita et al.,2016). Another US-based rural study of daily digital screening of mood in order to prospectively predict attendance to treatment. Interestingly, even after controlling for patient-history of attendance, a simple mood tracking application could significantly predict patient adherence to treatment (Bruehlman et Senecal.,2017). Similar positive results for a post-treatment automated tele-response software was found in a rural american setting - with a significantly higher abstinence rate after undergoing alcohol disorder treatment (Rose et al.,2010). Two studies also showed promising results regarding training of local healthcare personnel to cheaply implement solutions in a feasible way (Tsai et al., 2014) (Tewari et al.,2017).

In summary, there are potentially very promising applications to leveraging technology, where digital systems can cheaply enhance treatment adherence and improve ecological validity of screening. Software systems with these properties have the potential to address the growing needs of effective mental health treatment in low-resource settings.

A solution such as Zeeds Insights, has the potential to both increase adherence and data validity in these types of settings. Want to know more? Contact Info@zeedsapp.com.


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