Mobile applications as good intervention tools for individuals with depression
Mobilní aplikace jako vhodné intervenční nástroje pro nemocné trpící depresí
V současnosti trpí duševními poruchami přibližně 450 milionů lidí po celém světě. Deprese patří mezi jeden z nejzávažnějších typů chronického onemocnění, představuje zásadní hrozbu zátěže ekonomických a sociálních systémů vlád po celém světě. Jeden z aktuálních nefarmakologických přístupů, který je využíván i u osob trpících depresemi, je tzv. mHealth, tedy používání mobilních zařízení pro léčebné a podpůrné účely. Jeho účinnost je prokázána zejména v časných stadiích deprese.
Cílem tohoto článku je analyzovat nejnovější randomizované kontrolované studie, které ukazují účinnost využití mobilních aplikací v diagnostice nebo léčbě deprese. Cíle je dosaženo pomocí rešerše studií zaměřených na dopady jednotlivých aplikací pro lidi s depresí a na specifikaci kritérií hodnocení kvality těchto aplikací. Výsledky randomizovaných kontrolovaných studií (RCT) ukazují, že existuje velký potenciál mobilních aplikací v oblasti péče o nemocné trpící depresí, zejména v raných stadiích onemocnění.
Existuje naléhavá potřeba četnějších a dlouhodobějších RCT v této oblasti za účelem prokázání nezvratné účinnosti těchto mobilních aplikací v léčbě deprese. Příspěvek poukazuje i na silné a slabé stránky mobilních aplikací v oblasti detekce, diagnostiky a léčby deprese.
Klíčová slova:
mobilní aplikace • deprese • léčba
Authors:
Petra Marešová; Blanka Klímová; Kamil Kuča
Published in the journal:
Čes. slov. Farm., 2017; 66, 55-61
Category:
Původní práce
Summary
At present mental disorders affect approximately 450 million people around the world. Depressive disorder is probably one of the most serious disorders and as a type of chronic disease, it represents a global threat and burdens economic and social systems of both individuals and governments worldwide. One of these most recent non-pharmacological approaches is also the so-called mHealth (mobile health), the use of mobile devices for the practice of medicine and public health, which proves to be effective particularly in the early stages of depression.
The purpose of this article is to explore the most recent randomized controlled trial studies which indicate efficacy of the use of mobile applications in the detection, diagnostics or treatment of depression. The methods used in this study include a method of literature search of the studies focused on the impacts of individual applications for people with depression and on the specification of criteria evaluating quality of these applications.
The findings of the randomized controlled trials (RCT) show that there is a big potential of mobile applications in the detection, diagnostics, and treatment of depression, particularly in mild and moderate stages of the disease. They seem to be especially relevant for self-monitoring of depressive symptoms in the early stages of depression.
There is an urgent need of more longitudinal RCT in this field in order to prove conclusive efficacy of these mobile applications in the treatment of depression. The authors list the main strengths and weaknesses of mobile applications in the detection, diagnostics, and treatment of depression.
Key words:
mobile applications • depression • treatment
Introduction
At present mental disorders affect approximately 450 million people around the world1) Depressive disorder is one of the most serious disorders. It is a very complex psychic disorder which manifests itself in person’s depressed mood for a long period of time. This is caused by changes of chemical reactions in the brain, long-term stress or psychic shock. Depression is the fourth frequent cause of death. It can affect almost anybody, including children. However, most often it affects adults between the age of 25 and 40. Surprisingly, women incline to this disorder more than men. The statistics show that 25% of women suffer from depression in comparison with 12% of men. Generally, the prevalence of depression is estimated to be of about 5% in a general population, and a lifetime risk is of about 15%2). The main symptoms of depression include feelings of sadness and depression, which cannot be influenced by outer incentives; evident loss of interest and pleasure in activities, which are otherwise pleasant; disinterest in oneself, one’s job, family or friends; a lower ability of concentration, indecisiveness; a lack of emotions; lower confidence and selfesteem; feelings of hoplessness; thoughts of death; big fatigue; loss or gain of weight; insomnia or excessive sleep; or loss of sexual desire3).
Nowadays, depression as a type of chronic disease represents a global threat and burdens economic and social systems of both individuals and governments worldwide4, 5). This concerns also costs on pharmacological and non-pharmacological treatment. Nevertheless, in most cases non-pharmacological treatment is preferred since it is less invasive, has fewer side-effects and sometimes it is also less expensive. One of these non-pharmacological approaches is also the so-called mHealth (mobile health); the use of mobile devices for the practice of medicine and public health.
According to the World Health Organization report6–8) mHealth is a globally adopted technology. Employers, too, recognise that facilitating employees’ health maintenance is advantageous and reported successful trials for mental health issues. In addition, many current m-health initiatives focus on outdated, unidirectional models of patient communication (e.g., exclusively collecting data, providing information or sending reminders)6). The use of mobile technologies, in particular, is rapidly evolving within the field of telemental health. mHealth is conducted on “mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices”7) Furthermore, it is estimated that the mHealth applications market will grow to a substantial size of more than USD 26bn in 2017. In comparison with the global healthcare market that is estimated to have a gigantic size of USD 6 trillion1, mHealth represents only 0.5% of the whole pie7). This development will lead to an explosion of health and fitness data collected by an increasing number of app and sensor users. The present situation of mHealth applications is illustrated in Figure 1 below.
There are three different categories of vital parameters of mHealth applications: Health & fitness tracking data, patient monitoring data and medical examination data7).
As the prevalence of mental illnesses such as depression and anxiety continues to grow, clinicians have turned to mobile applications as tools for aiding and supporting their patients’ treatment. These applications can be especially helpful for teenagers and young adults suffering from mental illness due to their frequent use of technology as a means of communication. The applications can be helpful as a way to engage people who may be unwilling or unable to attend face-to-face therapy, and they can also provide support in between sessions. Experts believe that these applications will work best when used in conjunction with medication and/or in-person therapy10). At present there are over 200 mobile applications related to depression, fatigue, anxiety, or other disorders, but the efficacy of most of them has not been determined yet. Therefore it is very crucial to choose the right ones, which can meet certain criteria. According to11), mHealth applications must be safe, accurate, effective, secure, and protect privacy to be used by patients, recommended by health care professionals, and eventually reimbursed12).
In the study by11) these criteria were discussed in a more detail and the applications assessed according to three measures of effectiveness: perceived effectiveness, research evidence base for an app, and whether or not the app claimed that the effectiveness was tested11). The key criteria with respect to depressions seem to be as follows: password protection, number of consumer ratings, explicit privacy policy.
Another criteria also include: interactiveness/feedback, encryption, basis of research, software support, import/export capabilities, developer contactable, personalization, specificity of intervention, source of funding for research, discloses potential risks, effectiveness (perceived), continuous availability of data, effectiveness tested (claimed by app), ease of use, advertising policy stated and errors and performance issues11).
According to13), the smartphones should support builtin Bluetooth HDP for standard Bluetooth communication with medical devices. This will enable the smartphone applications to work with medical devices from different vendors. Other technical specifications which appear to be quite important are: long battery life, sufficiently large screen size, fast data input, virus-free computer, no magnetic interference with medical devices, efficient patient-physician interactions, avoidance of loss or theft, and data privacy and security13).
The privacy and security concerns of storing or communicating patient data with smartphones should be addressed cautiously. These security features of smartphones, while not available for all devices, may be useful: data backup, encryption of stored patient data, remote wiping to destroy all data on a device in case of loss or theft, and securely encrypted wireless data transmission over WiFi14–16) applications.
Finally, personal data must be considered when using mobile applications, which is also closely connected with the rules of handling these data. In many ways, these areas are not still legally specified. According to17), when using an application, the following criteria must be specified: compliance with privacy, security, accuracy of content, and safety. It warns the user against possible health dangers (e.g., side effects) related to the use of the app for different purposes or without following the suggested protocol.
The purpose of this article is to explore the most recent randomized controlled clinical trial studies which prove efficacy of the use of mobile applications in the diagnostics or treatment of depression. In conclusion, the authors list the main strengths and weaknesses of mobile applications in the diagnosis and treatment of depression.
Methods
The methods used in this study include a method of literature search of the studies focused on the impacts of individual applications for people with depression and on the specification of criteria evaluating quality of these applications. The focus was primarily on depression. Therefore, studies aimed primarily on anxiety or other psychiatric disorders were excluded, protocol design were also excluded. MEDLINE citations were searched in February 2016, using the PubMed search engine, for articles that discuss the quality and application area for smartphone software applications to be used by patients (12 clinical trials). In addition, articles found in the database ScienceDirect (404 articles) and Web of Science (97) were analyzed. The search keywords were “mobile application AND depression” and “criteria AND mobile application AND depression”.
The selection procedure of the final number of studies was performed as follows:
- detection of the available relevant sources on the basis of the key words in the period of 2010–2016
- duplication check
- assessment of relevancy on the basis of abstracts
- full text analysis
Figure 2 below demonstrates the selection procedure of the research studies.
Use of mobile applications in the treatment of depression – findings of clinical trials
Altogether six clinical trials describing the research issue were detected. The study was included if it was a randomized controlled trial, if it matched the corresponding period, i.e., from 2010 up to 2016; if it involved people with depression or depressive symptoms, if it focused on the use of mobile applications in the improvement, detection or assessment of depressive symptoms; and if it was written in English. Therefore other clinical studies exploring this issue were for the reasons described above excluded, e.g.
Khoja et al. (2016)18) describe e-Health solutions to address the four most common issues: depression, psychosis, post-traumatic stress disorder, and substance abuse. Preliminary evaluation of the intervention shows enhanced access to care for remote communities, decreased stigma, and improved quality of health services. Maulik et al. (2016)19) discuss the development and testing of the electronic decision support systems (EDSS), for common mental disorders. Kim et al (2016)20) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening.
Table 1 below provides an overview and description of mobile applications that can help improve, detect and assess depressive symptoms. The studies are presented in alphabetical order of their first author.
Discussion
It is estimated that 75% of mental health problems begin in adolescence. Therefore, their early detection and monitoring is essential. The findings in Table 1 show that mobile apps could be one of the solutions since young people nowadays use them naturally on a daily basis. The findings of RCT21, 22, 24, 26) described above also indicate efficacy of technology-enhance self-monitoring, particularly in the early stages of depression. Mobile apps are thus ideally suited for the first-step intervention programs for treating depression through increasing self-awareness of patients, which can bring rapid improvements for patient´s state of health. For example, in the study by Kobak et al. (2015)22) the results showed a considerable decrease in depression found in both groups [t(34) = 8.453, p < 0.001 and t(29) = 6.67, p < 0.001 for CBT and TAU, respectively). The intervention group in the study by Proudfoot et al. (2013)24) also showed significantly greater improvement in symptoms of depression, anxiety and stress and in work and social functioning relative to both control conditions at the end of the 7-week intervention phase (between-group effect sizes ranged from d = .22 to d = .55 based on the observed means). Furthermore, Topolovec-Vranic et al.27) argue that self-monitoring treatment approaches for depression seem to be more accessible for patients since they can exploit them from anywhere and at any time. In addition, they are more economical. This argument has been also supported by Winslow et al. (2016)28) whose findings indicate that mHealth approaches have the potential to provide or augment treatment at low cost in the absence of in-person care.
Ly et al. (2014)29) state that mobile applications intervention programs have especially an impact on patients with mild-to-moderate depression when both patients and their caregivers can still profit from their intervention, specifically derived from CBT, which can solve current problems and change unhelpful thinking and behavior (cf. 18). However, the study by Watts et al. (2013)25) suggest that delivering a CBT program using a mobile application may also have significantly positive effect on outcomes for patients with major depression.
The results also point out at some publically available self-guided psychological treatment delivered via mobile phone and computer such as myCompass (Proudfoot et al., 2013)24)designed to reduce mild-to-moderate depression, anxiety and stress, and improve work and social functioning. Similarly effective and supporting mobile application seems to be COMPANION-SMS, which is a software system that sends text messages to monitor the emotional state of individuals. This information, such as feelings of sadness or loneliness, decreased energy, difficulty concentrating, and disinterest in activities, gets sent to clinicians who are able to respond. The model behind this intervention is based on how genuine and immediate support through the mobile phone can improve the way someone feels and can encourage that individual to interact with trained clinicians9). As Andersson and Titov30) state, the Internet-based programs supported by an experienced therapist can monitor and support patients before a crisis starts to develop. However, these interventions must be of good quality and sufficiently stimulating to engage patients with depression. In addition, their privacy data should be protected.
Generally, more promotion of the benefits of mobile health applications for the treatment and diagnosis of depression is needed. East and Harvard31) propose several ways of improving this:
- raise awareness of evidence-based applications
- infuse mental health mobile applications into graduate counselor education
- disseminate information about mobile health applications during clinical staff meetings
- integrate mobile health applications into therapy
- publish research in this filed and present it at conferences
Table 2 below summarizes the main strengths and weaknesses of using mobile health applications for the treatment, detection, and diagnosis of depression.
Thus, the findings of the studies described above indicate that there is a big potential of mobile applications in the treatment of depression, particularly in mild and moderate stages of the disease.
Conclusion
As the findings of this study indicate, the number of mobile health applications is rapidly growing thanks to the rapid development of these technologies worldwide. As far as the treatment and diagnosis of depressive disorders are concerned, there is a general support for their use32). Since it is quite a new field of research, more clinical trials are needed to prove efficacy of mobile health applications for the treatment and diagnosis of depression33, 34).
Overall, the use of mobile health applications appear to be beneficial for the treatment and diagnosis of depressive disorders despite some of the barriers mentioned above. However, researchers should assess what kind of intervention with the help of mobile applications is the most effective for patients suffering from depressive disorders and conduct more randomized controlled clinical trials in this field, which have appeared to be just a few so far.
Effort to take advantage of using other approaches with technologies in the treatment of diseases is worldwide supported in healthcare in many directions because it is one of the possibilities how to use the limited financial means effectively35–37).
Acknowledgement
This study was supported by the research project The Czech Science Foundation (GACR) 2017 No. 15330/16/AGR Investment evaluation of medical device development run at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic.
Conflicts of interest: none.
Received January 16, 2017
Accepted March 12, 2017
doc. Ing. Mgr. Petra Marešová, Ph.D.
University of Hradec Kralove
Faculty of Informatics and Management,
Department of Economics
Rokitanskeho 62,
500 03 Hradec Králové 3,
Czech Republic
e-mail: petra.maresova@uhk.cz
B. Klímová
University of Hradec Kralove,
Faculty of Informatics and Management
Czech Republic
K. Kuča
University of Hradec Kralove,
Center for Basic and Applied Research
University Hospital Hradec Kralove,
Biomedical Research Center,
Czech Republic
Zdroje
1. mHealth Alliance. mHealth solutions for improving mental health and illnesses in the aging process, White Paper Series on mHealth and Aging, http://www.mhealthknowledge.org/sites/default/files/7_mHA-Aging-Paper3_092713.pdf (accessed 27 October 2016).
2. Lönnqvist J. Major psychiatric disorders in suicide and suicide attempters. In: D. Wasserman and C. Wasserman (eds.), Oxford Textbook of Suicidology and Suicide Prevention: A Global Approach (pp. 275–286). Oxford: Oxford University Press 2009.
3. Richards C. S., O’Hara M. The Oxford Handbook of Depression and Comorbidity. OUP 2014.
4. Klimova B., Maresova P., Valis M., Hort J., Kuca K. Alzheimer’s disease and language impairments: social intervention and medical treatment. Clin. Interv. Aging 2015; 10, 1401–1408.
5. Maresova P., Mohelska H., Dolejs J., Kuca K. Socio-economic aspects of Alzheimer’s disease. Current Alzheimer Research 2015; 12(9), 903–911.
6. Evans W. D., Abroms L. C., Poropatich R., Nielsen P. E., Wallace J. L. Mobile health evaluation methods: the Text4baby case study. J. Health Commun. 2012; 17(1), 22–29.
7. Research2guidance, mHealth App Developer Economics 2015, Free Report, p. 35.
8. WHO, mHealth New Horizonts for health through mobile technologies. http://www.who.int/goe/publications/goe_mhealth_web.pdf (accessed 27 October 2016).
9. Kohn R., Saxena S., Levav I., Saraceno B. The treatment gap in mental health care, Bulletin of the World Health Organization, vol. 82. 2004.
10. Dellabella H. Top 10 mental health apps, http://www.psychiatryadvisor.com/top-10-mental-health-apps/slideshow/2608/ (accessed 27 October 2016).
11. Powell A. C., Torous J., Chan S., Raynor G. S., Shwarts E., Shanahan M., Landman A. B. Interrater reliability of mHealth app rating measures: analysis of top depression and smoking cessation apps. JMIR 2016; 4(1), e15.
12. Powell A. C., Landman A. B., Bates D. W. In search of a few good apps. JAMA 2014; 311(18), 1851–1852.
13. Haller G., Haller D. M., Courvoisier D. S., Lovis C. Handheld vs. laptop computers for electronic data collection in clinical research: a crossover randomized trial, Journal of the American Medical Informatics Association 2009; 16, 651.
14. iPhone in Business. Security Overview, http://images.apple.com/iphone/business/docs/iPhone_Security.pdf (accessed 27 October 2016).
15. Palm webOS Security Overview for Enterprise, http://www.hpwebos.com/us/assets/pdfs/business/Palm_WhitePaper_Security.pdf (accessed 27 October 2016).
16. Device Administration, http://developer.android.com/guide/topics/admin/device-admin.html (accessed 27 October 2016).
17. Ozdalga E., Ozdalga A., Ahuja N. The smarphone in Medicine: a review of current and potential use among physicians and students. J. Med. Internet. Res. 2012; 14(5), e128.
18. Khoja S., Scott R., Husyin N., et al. Impact of simple conventional and Telehealth solutions on improving mental health in Afghanistan. J. Telemed. Telecare 2016; 22(8), 495–498.
19. Maulik P. K., Tewari A., Devarapalli S., Kallakuri S., Patel A. The Systematic Medical Appraisal, Referral and Treatment (SMART) Mental Health Project: Development and Testing of Electronic Decision Support System and Formative Research to Understand Perceptions about Mental Health in Rural India. PLoS ONE 2016; 11(10), e0164404. doi:10.1371/journal.pone.0164404
20. Kim J., Lim S., Min Y. H., Shin Y. W., Lee B., Sohn G., Jung K. H., Lee J. H., Son B. H., Ahn S. H., Shin S. Y., Lee J. W. Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients. J. Med. Internet. Res. 2016; 18(8), e216.
21. Kauer S. D., Reid S. C., Crooke A. H. D., Khor A., Hearps S. J. C., Jorm A. F., Sanci L., Patton G. Self-monitoring using mobile phones in the early stages of adolescent depression: randomized controlled trial. J. Med. Internet. Res. 2012; 14(3), e67.
22. Kobak K. A., Mundt J. C., Kennard B. Integrating technology into cognitive behavior therapy for adolescent depression: a pilot study. Ann. Gen. Psychiatry 2015; 14, 37.
23. Ly K. H., Janni E., Wiede R., Sedem M., Donker T., Carlberg P., Andersson G. Experiences of a guided smart-based behavioral activation therapy for depression: a qualitative study. Internet Interventions 2015; 2(1), 60–68.
24. Proudfoot J., Clarke J., Birch M. R., Whitton A. E., Parker G., Manicavasagar V., Harrison V., Christensen H., Hadzi-Pavlovic D. Impact of a mobile phone and web program on symptom and functional outcomes for people with mild-to-moderate depression, anxiety and stress: a randomised controlled trial. BMC Psychiatry 2013; 13, 132.
25. Watts S., Mackenzie A., Thomas C. H., et al. CBT for depression: a pilot RCT comparing mobile phone vs. Computer. BMC Psychiatry 2013; 13, 49.
26. Whittaker R. A., Merrz S., McDowell H., Stasiak K., Shepherd M., Dohertz I., Ameratunga S. A multimedia mobile phone based programme to prevent depression in adolescents, https://researchspace.auckland.ac.nz/handle/2292/18935 (accessed 27 October 2016).
27. Topolovec-Vranic J., Zhang S., Wong H., et al. Canadian Brain Injury and Violence Research Team. PLoS One 2015; 10(11), e0141699. doi:10.1371/journal.pone.0141699.
28. Winslow B. D., Chadderdon G. L., Dechmerowski S. J., et al. Development and Clinical Evaluation of an mHealth Application for Stress Management. Frontiers in Psychiatry 2016; 7, 130.
29. Ly K. H., Janni E., Wiede, R., Sedem, M., Donker, T., Carlberg, P., Andersson, G. Experiences of a guided smart-based behavioral activation therapy for depression: a qualitative study. Internet Interventions 2015; 2(1), 60–68.
30. Andersson G., Titov N. Advantages and limitations of Internet-based interventions for common mental disorders. World Psychiatry 2014; 13(1), 4–11.
31. East M. L., Harvard B. C. Mental health mobile apps: from infusion to diffusion in the mental health social system. JMIR 2016; 2(1).
32. McCann E. HealthcareNews, http://www.healthcareitnews.com/news/novel-mhealth-app-detects-depression (accessed 27 October 2016).
33. Hedman E, Ljótsson B, Lindefors N. Cognitive behavior therapy via the Internet: a systematic review of applications, clinical efficacy and cost-effectiveness. Expert. Rev. Pharmacoecon Outcomes Res. 2012; 12745–12764.
34. Donker T., Petrie K., Proudfoot J., Clarke J., Birch M. R., Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J. Med. Internet Res. 2013; 15, 11.
35. Marešová P., Klímová B., Kuča K. Alzheimer disease: Cost cuts call for novel drugs development and national stratégy. Čes. slov. Farm. 2015; 64, 25–30.
36. Marešová P., Mohleská H., Kuča K. Drugs and Health Care Expenditure on the Aging Population. Čes. slov. Farm. 2015; 64, 173–177.
37. Marešová P., Klímová B., Krejcar O., Kuča K. Legislative aspects of the development of medical devices. Čes. slov. Farm. 2015; 64, 133–138.
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