Effectiveness of a Countdown Timer in Reducing or Turnover Time

Effectiveness of a Countdown Timer in Reducing or Turnover Time

Majbah Uddin, MS¹, Robert Allen, PhD², Nathan Huynh, PhD¹, Jose M. Vidal, PhD³, Kevin M. Taaffe, PhD⁴, Lawrence D. Fredendall, PhD⁵, Joel S. Greenstein, PhD⁴

¹Department of Civil and Environmental Engineering, University of South Carolina; ²School of Health Research, Clemson University; ³Department of Computer Science and Engineering, University of South Carolina; ⁴Department of Industrial Engineering, Clemson University; ⁵Department of Management, Clemson University.

Corresponding Author: nathan.huynh@sc.edu

Journal MTM 6:3:25–33, 2017

doi:10.7309/jmtm.6.3.5

---

---

Read More

Clinical Application of a Smartphone-Based Ophthalmic Camera Adapter in Under-Resourced Settings in Nepal

Clinical Application of a Smartphone-Based Ophthalmic Camera Adapter in Under-Resourced Settings in Nepal

Carmel Mercado, MD¹, John Welling, MD^2,3,4, Matthew Oliva, MD^3,4,5, Jack Li, MD², Reeta Gurung, MD⁶, Sanduk Ruit, MD⁶, Geoff Tabin, MD^1,3,8, David Chang, MD⁷, Suman Thapa, MD, PhD⁶, David Myung, MD, PhD^1,8

¹Byers Eye Institute, Stanford University, Palo Alto, CA, USA; ²John A Moran Eye Center, University of Utah, Salt Lake City, Utah, USA; ³Himalayan Cataract Project, Waterbury, VT, USA; ⁴Medical Eye Center, Medford, OR, USA; ⁵Casey Eye Institute, Oregon Health Sciences University, Portland, OR, USA; ⁶Tilganga Institute of Ophthalmology, Kathmandu, Nepal; ⁷Los Altos Eye Physicians, Los Altos, CA, USA; ⁸VA Palo Alto Health Care System, Palo Alto, CA, USA

Correspondence Author: david.myung@stanford.edu

Journal MTM 6:3:34–42, 2017

doi:10.7309/jmtm.6.3.6

---

---

Read More

Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School

Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School

Kwee Choy Koh, MBBS, MMed, Pilane Liyanage Ariyananda, MD, FRCP, Esha Das Gupta, MRCP, FRCP, Rumi R. Khajotia, MD, MD, Sethuraman Nagappan, MRCP, FRCP, Vaani Valerie Visuvanathan, MBBS, MRCP, Mina Mustafa Mahmood, MD, MRCP, Siew Huoy Chua, MB BCh BAO, MRCP, Poh Sim Chung, Dipl. BA

Assoc. Prof. Kwee Choy Koh, Postal address: Head & Infectious Diseases Consultant, Department of Medicine, Clinical Campus, International Medical University, Jalan Rasah, 70400 Seremban, Negeri Sembilan, Malaysia

Corresponding Author: kweechoy_koh@imu.edu.my

Journal MTM 6:3:2–6, 2017

doi:10.7309/jmtm.6.3.2

---

---

Read More

Mobile Applications for Stroke Prevention: A Survey of Physicians’ Perspectives

Mobile Applications for Stroke Prevention: A Survey of Physicians’ Perspectives

Douglas Halket, BS¹, Jonathan Singer, MA², Clotilde Balucani, MD, PhD³, Dimitre Stefanov, PhD³, Steven R. Levine, MD, FAHA, FAAN, FANA⁴

¹SUNY Downstate College of Medicine, MSC, Brooklyn, NY; ²University of Nevada, Reno, Clinical Psychology, MSS, Reno, NV, ³SUNY Downstate Medical Center & Stroke Center, MSC, Brooklyn, NY; ⁴University Hospital of Brooklyn, Associate Dean for Clinical Research & Faculty Development, Downstate Medical Center & Stroke Center

Corresponding Author: douglas.halket@downstate.edu

Journal MTM 6:3:7–13, 2017

doi:10.7309/jmtm.6.3.3

---

---

Introduction

Stroke is the leading cause of preventable disability in the United States¹. Each year, approximately 795,000 Americans suffer a stroke¹. Many of these strokes are preventable. According to a case-control study of 6,000 individuals, 90% of strokes can be attributed to just 10 modifiable risk factors, and targeted interventions to reduce blood pressure and cigarette smoking as well as to promote physical activity and healthy diet could substantially reduce the risk of stroke². There are already many mobile applications that individually target each one of these interventions, although not necessarily in the specific context of stroke prevention³.

Healthcare related mobile technology has expanded rapidly over the last several years. In 2015, there were 165,000 medical and health related apps for sale in the Google and Apple store⁴, up from 40,000 in 2012⁵. However, little is known about the prevalence and nature of mobile application adoption in patient centered clinical practice, let alone about its effectiveness in changing healthcare outcomes. While there are many mobile applications in fields such as diabetes management, there are still usability and integration issues among almost all of them⁶. A 2014 study found 93 mobile application for iPhone and Android regarding general stroke information, although many of these were lacking scientifically valid information⁷.

There have been relatively few randomized controlled trials involving mobile applications, although some have shown promise. A systematic review of 9 studies assessing mobile application effectiveness in managing cardiovascular disease, lung disease, or diabetes mellitus found 3 studies where a mobile application intervention showed a statistically significant clinical improvement⁸. Additionally, there has been at least one quality improvement project studying mobile technology assistance in blood pressure control for stroke survivors⁹, although there have been no large scale randomized control trials specifically involving stroke prevention. Mobile applications have the potential to change the healthcare landscape, leading to improved outcomes while reducing cost. If mobile technology is going to be implemented in a meaningful way, it is imperative that it be grounded in clinical evidence.

The current study is the first step in examining the role that mobile technology plays in primary care physicians’ (PCPs’) practices. The purpose of this study is to examine the prevalence of PCPs using mobile applications in patient care. More specifically, to study if and how PCPs use mobile applications for stroke prevention. Also, we evaluated preferences and attitudes of PCPs toward the use of mobile applications in their practices with specific focus in stroke management, and how these preferences vary over physician and practice demographics. We hypothesize that younger physicians practicing in wealthier areas are more likely to use and recommend mobile applications.

Methods

Current PCPs in the New York City area specializing in Internal Medicine, Ob/Gyn, and Family Medicine were surveyed in person.

Participants: A convenience sample of 86 physicians (46 females) ranging from 25 to 68 years of age participated in this study, which was approved by the SUNY Downstate IRB. Participants were surveyed in person and were identified at SUNY Downstate grand rounds and the annual meeting of the New York Academy of Family Physicians. The mean age was 37 ± 12 SD. Forty one percent of the participants were attendings and the remaining 59% were residents. Sixty (70%) were Family Medicine specialists, 14 (16%) were in Ob/Gyn, and 12 (14%) were in Internal Medicine. These three specialties were selected because they provide the primary care lead in general preventative care, and the study focus is on actually managing stroke risk factors. Neurologists, despite managing patients after stroke, do not generally provide the ongoing lead in preventative care unless a patient has a separate neurological issue, and so were excluded from the study.

Measures: The standardized survey consisted of demographic questions and practice characteristics including gender, specialty, resident/attending, type of practice (private independent, private partnership, community or university hospital), years of experience (less than 3, 3 to 10, 11 to 20, greater than 20), patients seen per month (less than 30, 30 to 100, greater than 100), median income of practice zip code as determined by census data (less than $40,000 or greater than $40,000), and 11 questions on mobile application use (table 1).

Table 1: Demographic Summary of Study Subjects

Data analysis: Fisher’s exact test was used to test for association between demographic variables and whether or not the physicians used mobile applications in any patient care setting. Cochran–Armitage trend test was used to determine if higher levels of the ordinal predictors (age groups, experience and volume of patients) were associated with higher prevalence of mobile technology use. P values less than 0.05 were considered significant. All participants answered the primary question on mobile application use in patient care. Missing data in other, secondary questions were not factored into the analysis as we were not adequately powered for other specific analyses. A sample size of 87 physicians was determined to have 80% power to detect an odds ratio of 0.4 for the relationship between age and the use of mobile applications using a significance level of 0.05. The odds ratio of 0.4 corresponds to a hypothesized effect of physician age on mobile application use. This odds ratio is based on an expected 30% use of mobile applications by physicians at age 50, compared to 15% for physicians at age 63. The data were analyzed using SAS version 9.4 (SAS Institute Inc., Cary, N.C.)

Results

Of the 86 physicians surveyed, 74% reported using mobile applications in patient care, whether for their own use or in recommending to patients. Physicians reported using mobile applications to manage stroke risk factors 25% of the time, while 77% expressed interest in new apps to help their patients manage these risks. Forty one percent of physicians surveyed strongly agreed that mobile applications are useful in providing patient care, while 49% simply agreed and 0% disagreed.

Table 2 shows the most important determining factor, with 82% of physicians with less than 3 years practice experience using mobile apps, 78% of physicians with 3 to 10 years, 60% of physicians with 11 to 20 years, and 58% of physicians with greater than 20 years experience (p=0.045). Other demographic factors, including median income of practice zip code were not significant.

Table 2: Mobile Application Usage Responses

Age did not have an effect on whether or not physicians agree that mobile applications are useful in providing patient care (p=0.27; table 3). Position, type of practice, and volume of patients were not associated with higher use of mobile technology use (p=0.80, 0.49, 0.33, respectively).

Table 3: Usage of Mobile Applications in Patient Care by Demographics

Discussion

Previous studies have shown that there are a large number of mobile applications available to patients and physicians, particularly to manage stroke risk factors^3,4,6–8. This study builds on this knowledge in an attempt to determine how physicians view mobile application use in the primary care clinical setting, particularly in stroke prevention. Our data revealed that of a sampling of mostly urban PCPs, 74% are already using mobile applications in clinical practice and an even greater portion, 90%, believe that mobile applications belong in patient care. Although PCPs with more experience were less likely to use mobile applications, the findings suggest a wide acceptance across physician age groups and practice characteristics. Income of practice zip code was not a significant predictor in whether or not PCPs recommend mobile applications to patients, and neither was PCP age. Physician experience was potentially important, with less experienced PCPs more likely to recommend mobile applications than their more experienced colleagues. These findings suggest the adoption of mobile application use in clinical practice will likely continue to increase in the coming years.

Some physicians, 34% of the respondents, expressed concerns over mobile technology use in healthcare. These concerns included ease of use, confidentiality, determining which app is best, time limitations, and integration with Electric Health Records (EHR). Some of these concerns, such as determining which apps are best, can be solved with further study. Confidentiality and EHR integration issues are more complex but are also potentially manageable in the future⁵.

There was a major disparity between PCP-expressed interest in mobile applications (76%) compared with their actual use of mobile applications to manage stroke risk factors (25%). This gap between interest and use can be bridged by either the development of new mobile applications specifically tailored to manage stroke risk factors or the development of a tool to assist patients and physicians in selecting from already existing mobile applications. The development of such a tool would likely require a systematic method for determining which mobile applications are effective in the patient care setting. Mobile applications that can assist in managing chronic risk factors for stroke can then be identified, potentially leading to an inexpensive and effective method for reducing one of the largest causes of morbidity and mortality in the United States^1,2.

There were several limitations to the study. First, the sample size was one under the projected number, as the data were collected from physicians in discrete groups (grand rounds, etc.) rather than from individuals. Second the data were slightly skewed by specialty and position. We recruited a large percentage of Family Medicine attendings because of the ease of recruiting them at their annual meeting. Many of the Ob/Gyn and Internal Medicine respondents were residents because they were more likely to attend grand rounds and stay to fill out a survey. There were few respondents from high income zip codes. We acknowledge the potential lack of generalizability to other specialties, including neurologists who may participate in stroke prevention but generally do not lead and primarily manage this effort in their patients.

Conclusions

The majority of PCPs have either already adopted mobile applications in their clinical practice or believe that mobile applications belong in patient care. However, the implementation of mobile applications specifically for stroke prevention purposes is still limited. Concerns about mobile application use were limited to a small number of physicians and a significant number expressed interest in a new application to manage stroke risk factors. There is PCP interest in a mobile application specifically tailored to manage stroke risk factors that patients and physicians can use together and encompasses several aspects of stroke prevention. Since many physicians are already using mobile applications or support their use in patient care, it is likely that mobile applications will have a place in clinical practice in the future, and specifically in stroke prevention.

Acknowledgements

Supported by a SUNY Alumni fund medical student research scholarship to DH.

Disclosures

All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: DH received support from a SUNY Alumni grant, no other support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

References

1. Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart Disease and Stroke Statistics-2016 Update: A Report From the American Heart Association. Circulation. 2016;133(4):e38–360.

2. O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet (London, England). 2010;376(9735):112–23.

3. Mosa ASM, Yoo I, Sheets L. A Systematic Review of Healthcare Applications for Smartphones. BMC Medical Informatics and Decision Making. 2012;12:67.

4. Misra S. New report finds that more than 165,000 mobile health apps now available, takes close look at characteristics & use: iMedicalApps; 2015 [updated September 17, 2015. Available from: http://www.imedicalapps.com/2015/09/ims-health-apps-report/.

5. West D. How mobile devices are transforming healthcare. Issues in technology innovation. 2012;18(1):1–11.

6. El-Gayar O, Timsina P, Nawar N, Eid W. Mobile Applications for Diabetes Self-Management: Status and Potential. Journal of Diabetes Science and Technology. 2013;7(1):247–62.

7. Dubey D, Amritphale A, Sawhney A, Amritphale N, Dubey P, Pandey A. Smart phone applications as a source of information on stroke. Journal of stroke. 2014;16(2):86–90.

8. Whitehead L, Seaton P. The Effectiveness of Self-Management Mobile Phone and Tablet Apps in Long-term Condition Management: A Systematic Review. Journal of medical Internet research. 2016;18(5):e97.

9. Ovbiagele B, Jenkins C, Patel S, Brunner-Jackson B, Anderson A, Saulson R, et al. Mobile health medication adherence and blood pressure control in recent stroke patients. Journal of the neurological sciences. 2015;358(1–2):535–7.

Read More

Scope of Mobile Phones in Mental Health Care in Low Resource Settings

Scope of Mobile Phones in Mental Health Care in Low Resource Settings

M Sood¹, RK Chadda² K Sinha Deb³, R Bhad⁴, A Mahapatra⁴, R Verma³, AK Mishra³

¹Additional Professor; ²Professor; ³Assistant Professor; ⁴Senior Resident
Department of Psychiatry, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.

Corresponding Author: soodmamta@gmail.com

Journal MTM 6: 3:43–47, 2017

doi:10.7309/jmtm.6.3.7

---

---

Introduction

In the last few years, there has been a remarkable spread of mobile technology in low and middle-income (LAMI) countries, with its penetration being much higher than the general infrastructure. For example, in India, there are 943.9 million wireless telephones with a teledensity of ~75%, and the mobile phones constitute about 97% of the total telephones¹. Mobile technologies, with an easy and wider availability, portability, being self powered, increasingly better computational capacities and decreasing costs, user familiarity and internet connectivity², have opened up new alternatives for health care delivery that can be delivered through the existing infrastructure as people carry mobiles with them all the time.²

Recent studies from high-income (HI) countries have reported that a number of people use mobile phones to search for health related information. Most patients with mental illness own mobile phones, use it for activities other than spoken conversations like sending emails, web browsing and social networking, and mobile phones have been explored as a potential aid in mental health services³. Studies from HI countries have demonstrated use of mobile phones in delivering psychosocial interventions (crucial for recovery) like provision of health information, prompts for medications, reminders, self-monitoring, and practice of skills in real world situations⁴.

In India, mental health resources for psychosocial interventions are meagre and are rarely applied in the care of patients with mental disorders⁵ as there is a gross deficiency of mental health resources (0.2 psychiatrists, 0.03 clinical psychologists, 0.05 psychiatric nurses, and 0.03 social workers per 100,000 of the population^6,7. In this context, mobile technologies have a potential to become an important mental health care service link between the meagre mental health services and the unfulfilled mental health care needs of the vast majority of unreached patients and caregivers, and can be used for a range of indications in mental health like increasing awareness, training, linkages between services, clinical services and research.

We are planning to develop a mobile based intervention framework for imparting psychosocial interventions to the patients attending psychiatric services. Before making mobile based interventions suitable for the needs of the patients, it is important to know whether the patients with mental illness are using mobile phones as there is absence of research in this field from India. The present study was conducted to find out the frequency of mobile phone use among patients with mental illnesses, and to assess feasibility of using mobile phones in improving service delivery and delivering educational messages in them.

Methodology

The study was conducted in psychiatry outpatient services at the All India Institute of Medical Sciences, New Delhi, India. The service runs a walk-in clinic, where all the first contact patients are seen, and a follow up clinic where the old patients already on treatment from the service are seen. Every fifth patient aged 18-60 years, visiting the walk-in clinic, and every fifth patient visiting the follow up clinic over a period of three months (from February – April, 2015) were recruited for the study.

The subjects were explained the purpose of the study. A written informed consent was taken. In patients with psychotic disorders, who were unable to give consent, consent was taken from the accompanying relative. The data was collected using a semi-structured questionnaire (attached as Appendix), prepared for the study by the investigators. The study was approved by the Institute ethics committee.

The data was analysed using descriptive statistics, reported as means and standard deviations for continuous variables and percentages for discrete variables.

Results

A total of 350 subjects were recruited for the study, 205 (58.6%) males and 145 (41. 4 %) females. Two hundred and thirty five (77.1%) subjects were from the follow up clinic and 115(32.9%) were from the walk in clinic. Mean age of the subjects was 33.3 (±11.5) years. About 40% of the subjects had received 10 years of formal education, one fifth had studied upto 12^th standard, and about 30% were graduate. Mean duration of illness was 6.2 (± 7.6) years, and mean duration of treatment was 1.5 (± 3.5) years. Common diagnosis included neurotic, stress related and somatoform disorders (47.0%), mood disorders (32.0%), schizophrenia and related disorders (14.3%), and disorders due to psychoactive substance use (4.0%).

Three hundred and seven (87.7%) subjects reported using mobile phones regularly. Most of the subjects used mobile phone for making and receiving phone calls, and about two third also used it for sending and receiving short text messages. About half of them used clock and alarm functionalities, two fifth also used it for recreational activities, and around 30% used for accessing social network sites (Table 1)

Table 1: Mobile phone use and its utility as aid to psychiatric treatment Application of mobile phone in day-to-day life (n=307)

Most of the subjects reported that mobile phones could be used as an aid to treatment for psychiatric disorders. This included use as a reminder for appointments (90.4%), and to take medications (72.3%). About half of the subjects suggested that mobile phones could be used for recording and reporting of side effects. Forty two percent of the subjects reported that mobile phones could be used for receiving educational information related to their mental illness. Only 17% of the patients felt that these could be used for imparting psychological treatments (Table 1).

More than 70% of the mobile phone using subjects expressed that the phones could be used to receive educational messages regarding any precautions to be taken (70.5%), activities and exercises (55.4%), information about their mental illness (36.5%), dietary advice (26.9%) and information about stress reduction techniques (Table 1).

In a single open-ended question about any other use of mobile phone related to psychiatric treatment, only 31 (11.4%) subjects provided a suggestion. Sixteen subjects felt that mobile phones can be used to give feedback regarding treatment. Fifteen subjects felt that these can be used for sharing experience regarding treatment through common patient group on social networking sites.

Discussion

More than 85% of the patients with mental health problems in our study were using a mobile phone. The mobile phones was being used for a range of activities besides making phone calls. Most subjects opined that it could also be used as an aid in the treatment.

All of our subjects used mobile phone for making and receiving phone calls, but over two third also used it for sending and receiving short text messages. The phone was also used for other functions like clock and alarm by about half of the subjects, for recreation by about two fifths, and for accessing social network sites by 30% of the subjects. Earlier studies have also reported the most common uses of mobile phones in patients with severe mental illnesses as for making phone calls, texting, and internet³.

Regarding the potential usage of mobiles phones in treatment, most (90%) of the subjects reported that these can be used to remind them for appointments. Majority of the subjects indicated use of mobile phones in pharmacological treatment as reminder for taking medications and reporting side effects. However, only less than half of the subjects indicated that mobile phones can be used for receiving educational information related to their mental illness and even fewer subjects felt that these can be used for imparting psychological treatments. This finding is not unusual as majority of the patients with mental illnesses receive pharmacological treatment with minimal psychosocial interventions due to lack of resources for the latter⁵. However, when asked specifically about the kind of educational messages they wanted to receive, majority of them listed only psychosocial interventions. About three fourth of the subjects reported that they wanted to receive messages regarding any precautions to be taken regarding illness or medications, about half of them wanted messages regarding activities and exercises, and one third wanted information about their mental illness, and a quarter each wanted information regarding dietary advice and stress reduction techniques. This shows that the mobile phones appear to have a high potential of use in the mental health care settings.

Our results are in line with the studies from HI countries which have reported that n 72%-97% of patients with mental illnesses and substance use disorders own a mobile phone^{2, 8, 9}. The results are not surprising since compared to an average 2008–2012 growth rate of mobile subscriptions of just 10.15% in the HI countries, the growth rate was much higher (75.07%) in South Asia driven mainly by growth in India¹⁰.

The study had a limitation of being conducted in a tertiary care setting in a big city and hence the findings may not be generalizable to other settings. We did not screen specifically for the use of smart phones, since most of our subjects use ordinary phones.

The study concludes that most of the patients with psychiatric disorders attending outpatient services in India use mobile phones, and welcome its use as a treatment aid. The mobile phones offer a potential to be exploited in India with limited human resources in mental health and a potential use in psychosocial treatment.

References

1. ANNUAL REPORT (2014). Department of Telecommunications, Ministry of Communications and Information Technology, Government of India, New Delhi. Available at http://www.dot.gov.in/sites/default/files/Telecom%20Annual%20Report-2012-13%20%28English%29%20_For%20web%20%281%29.pdf (accessed on 12 Aug 2015)

2. Zhang MWB, Ho CSH, Cheok CCS, Ho RCM. Smartphone apps in mental healthcare: the state of the art and potential developments. BJ Psych Advances, 2015;21(5):354–8.

3. Ben-Zeev D, Davis KE, Kaiser S, Krzsos I, and Drake RE. Mobile technologies among people with serious mental illness: opportunities for future services. Adm and Policy Ment Health 2013;40(4):340–3.

4. Harrison V, Proudfoot J, Wee P P, Parker G, Pavlovic DH, Manicavasgar V Mobile mental health: Review of the emerging field and proof of concept study. J Ment Health 2011;20(6):509–24.

5. Sood M, Chadda RK. Psychosocial rehabilitation for severe mental illnesses in general hospital psychiatric settings in India. BJPsych International 2015;12:47–8.

6. Chadda RK, Prashanth R (2015) Allied Mental Health Professionals: clinical Psychologists, Psychiatric Nurses and Psychiatric Social Workers: Availability and competency. In Mental Health in South Asia: Ethics, Resources, Programs and Legislation (Eds JK Trivedi & A Tripathi) Springer (India) pp. 221–32.

7. The World Health Report: 2001: Mental in Low and Middle-Income (LAMI) Countries, World Health Organization, 2001.

8. Crankshaw T, Corless I B, Giddy J, Nicholas P K, Eichbaum Q, Butler L M. Exploring the patterns of use and the feasibility of using cellular phones for clinic appointment reminders and adherence messages in an antiretroviral treatment clinic, Durban, South Africa. AIDS Patient Care STDS 2010;24(11);729–734.

9. Milward J, Day E., Wadsworth E, Strang J, Lynskey M. Mobile phone ownership, usage and readiness to use by patients in drug treatment. Drug Alcohol Depend 2015;146(1):111–5.

10. Farrington C, Aristidou A, Ruggeri K. mHealth and global mental health: still waiting for the mH2wedding? Globalization and Health 2014;10:17.

Appendix

Feasibility of using mobile phones in improving service delivery and creating health awareness in patients with mental disorders

(Please tick mark the answers)

DateRegn No.Name

AgeGender Education

Address:

Mobile:E mail (if any):

Diagnosis

Duration of Illness:

Duration of treatment at AIIMS:

1. 1. Do you have a mobile phone?Yes/No

2. 2. For what all functions do you use mobile phone? (Kindly mark as many as applicable)

   • • Making & receiving calls

   • • Sending & receiving messages

   • • Clock & alarm

   • • Contact list

   • • Any other

3. 3. Do you think that it can be used as a helping aid in your treatment? Yes/No

4. 4. If yes, kindly tell, how? (Kindly mark as many as applicable)

5. • • Reminder for appointment

   • • Reminder to take medicines

   • • Recording & reporting of side effects

   • • Receiving education messages related to your illness

   • • Psychological treatment

   • • Any other

6. 5. What kind of educational messages you would like to receive?

   • • Any precautions to be taken

   • • Activities or exercises

   • • Dietary advice

   • • Any other

Any suggestions, you would like to make

Read More