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 Table of Contents  
RESEARCH PROTOCOL
Year : 2021  |  Volume : 6  |  Issue : 2  |  Page : 91-94

Use of mHealth technology for modification of behavioural risk factors of noncommunicable diseases in primary health care settings: Effectiveness and Feasibility in South East Asian Region Countries


Department of Community Medicine, Maulana Azad Medical College, New Delhi, India

Date of Submission28-Feb-2021
Date of Acceptance14-May-2021
Date of Web Publication16-Jul-2021

Correspondence Address:
Dr. Bratati Banerjee
Department of Community Medicine, Maulana Azad Medical College, New Delhi
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jncd.jncd_8_21

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  Abstract 


Noncommunicable diseases (NCDs) kill 41 million people each year, of which more than three-quarters occur in low- and middle-income countries. Four groups of diseases, namely cardiovascular diseases, cancers, respiratory diseases, and diabetes, account for over 80% of all premature NCD deaths, and four common modifiable behaviors, namely tobacco use, physical inactivity, unhealthy diet, and the harmful use of alcohol, increase the risk of NCDs. The use of mHealth interventions for Behavior Change Communication has been shown to be effective in modifying the behavioral risk factors, through various studies. The present study proposes to conduct a systematic review and meta-analysis to assess the effectiveness and feasibility of the use of mHealth in improving diet and increasing physical activity, which are two of the major behavioral risk factors of NCDs. Research reported on this, from countries in the South-East Asian Region, during the past 20 years, will be included in this review.

Keywords: Behavioural risk factors, diet, physical activity, cancers, cardiovascular diseases, diabetes, meta-analysis, mHealth, noncommunicable disease, physical inactivity, respiratory diseases, systematic review, tobacco, unhealthy diet


How to cite this article:
Banerjee B. Use of mHealth technology for modification of behavioural risk factors of noncommunicable diseases in primary health care settings: Effectiveness and Feasibility in South East Asian Region Countries. Int J Non-Commun Dis 2021;6:91-4

How to cite this URL:
Banerjee B. Use of mHealth technology for modification of behavioural risk factors of noncommunicable diseases in primary health care settings: Effectiveness and Feasibility in South East Asian Region Countries. Int J Non-Commun Dis [serial online] 2021 [cited 2021 Aug 5];6:91-4. Available from: https://www.ijncd.org/text.asp?2021/6/2/91/321623




  Introduction Top


Noncommunicable diseases (NCDs) kill 41 million people each year, which is equivalent to 71% of all deaths globally. Of this enormous burden, most alarming is that 15 million people die from a NCD between the ages of 30 and 69 years. Over 85% of these premature deaths occur in low- and middle-income (LMI) countries. NCDs disproportionately affect people in LMI countries where more than three-quarters of global total NCD deaths, i.e., 32 million, occur.[1]

Four groups of diseases, namely cardiovascular diseases, cancers, respiratory diseases, and diabetes, account for over 80% of all premature NCD deaths. Rapid unplanned urbanization, globalization, unhealthy lifestyles, and population aging, which have led to change in lifestyle, are majorly responsible for increase in NCDs,[1] a phenomenon that is termed as epidemiological transition.[2]

Modifiable behaviors, such as tobacco use, physical inactivity, unhealthy diet, and the harmful use of alcohol, increase the risk of NCDs. Of all the NCD deaths, 4.1 million annual deaths have been attributed to excess salt/sodium intake and 1.6 million deaths annually can be attributed to insufficient physical activity. Tobacco and alcohol use are responsible for 7.2 and 3.3 million deaths, respectively, per year.[1] Hence, Behavior Change Communication (BCC) is considered as an important strategy for modification of behavioral or lifestyle-associated risk factors of NCDs, which need to be focused upon.

The World Health Organization (WHO) has defined mHealth or mobile health as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices.[3] There are more than 7 billion mobile telephone subscriptions across the world, over 70% of which are in LMI countries. To address the challenge of NCDs across the globe, the Be [email protected], Be Mobile initiative was set up by the WHO and the International Telecommunication Union in 2012, which works with governments to scale up mHealth services for NCDs and their risk factors.[4]

The use of mHealth interventions for BCC has been shown to be effective in improving diet and increasing physical activity, which are two of the major behavioral risk factors of NCDs, through various studies conducted in high-income[5],[6] and LMI countries[7] including India.[8] A systematic review for evaluating mHealth and eHealth interventions for promotion of healthy diets and physical activity suggested that eHealth and mHealth interventions could be effective in improving physical activity and diet quality in LMI countries. Overall, the review showed that 50% of the eHealth and mHealth interventions were effective in increasing physical activity, and 70% of the identified interventions were effective in improving diet quality,[7] though meta-analysis was not conducted by the authors.

However, great diversity has been encountered in interventions in the various studies in terms of geographic spread, intervention components evaluated, trial methods applied, and study quality.[7] Therefore, the present proposed review will be conducted for studies from similar settings, namely South-East Asian Region (SEAR) countries.

Risk factors for NCDs to be considered in this proposed review are unhealthy diet and physical inactivity. Although tobacco and alcohol use are also considered to be major behavioral risk factors, these will not be included, as substance abuse in the level of addiction may not be possible to be controlled by telephonic BCC intervention alone and will probably need to be managed at specialized de-addiction clinics. Hence, the effect size in the proposed review may be compromised. In terms of translation potential also, the use of mHealth may not be cost-effective for these risk factors.

History suggests the first use of telemedicine was in the late 1950s and early 1960s,[9] first use of short message service in 1992,[10] and commercial use of mHealth between 2008 and 2018.[9] Considering these timelines, the period of this proposed review will be the past 20 years, i.e., 2001–2020.

Key research questions

  1. Primary research question: How effective is mHealth technology in modification of behavioral risk factors of NCDs in individuals at risk or diagnosed with these diseases in comparison to conventional/routine methods/no method of health education in SEAR countries?
  2. Secondary research question: How feasible is it to implement mHealth methods in primary health-care settings in SEAR countries?


PICO components

  • Population – individuals at risk or diagnosed with NCDs
  • Intervention – use of mHealth technology
  • Comparator – conventional/routine methods of health education or no method
  • Outcome – effectiveness and feasibility.



  Materials and Methods Top


Study eligibility criteria

The following study selection criteria will be considered.

Types of studies

  • Randomized controlled trials
  • Nonrandomized controlled trials
  • Uncontrolled trials
  • Observational analytical studies of prospective or retrospective design.


Filter

  • Publications in English language
  • During the past 20 years, i.e., January 1, 2001–December 31, 2020.


Inclusion criteria

  • Use of mHealth technology
  • For modification of dietary practice and physical activity
  • In individuals at risk or suffering from NCDs
  • In SEAR countries.


Exclusion criteria

  • Other than original research
  • Gray literature.


Intervention

mHealth use for health education/awareness generation/BCC.

Outcome variables

  • Primary outcome


    • Improved diet
    • Increased activity.


  • Secondary outcome


    • Feasibility of mHealth use
    • Barriers to mHealth use.


Literature search

Source of data

Majority of relevant studies can be found within a limited number of databases.[11] A report by Halladay et al. for studies across all health-care areas showed that the majority of included studies could be identified via PubMed with a range of 75%–92%.[12] Hence, the present study is proposed to be focused mainly on PubMed. However, the following other major electronic bibliographical databases will also be searched.

  • PubMed
  • Embase
  • Cochrane Central.


Search strategy

Advanced search option on the major electronic bibliographical databases will be done applying filters for English language and time of the past 20 years, i.e., January 1, 2001–December 31, 2020. Search terms will include MeSH terms and other key words in various combinations, namely “mHealth” OR “mobile health technology” AND “unhealthy diet” OR “dietary practices” AND “physical inactivity” OR “sedentary” AND “non-communicable diseases” OR “NCD” OR “diabetes” OR “hypertension” OR “chronic disease” AND “prevention” AND “South East Asian region countries” OR “SEAR countries” OR “low and middle income countries.” However, this is only a proposed list, and the search terms will be further refined at the time of actually conducting the review.

Manual searching of other sites will also be done to locate publications that might not have been available on the databases searched. Cross-references quoted in articles selected for review will be searched for identifying additional publications. For unpublished literature, attempt will be made to search for reports of organizations working on NCDs and contact major universities of the SEAR countries.

Study selection

Titles and abstracts will be initially screened independently by two authors, and full texts of the relevant abstracts will be subsequently retrieved for possible inclusion in the review. Any disagreement between the two authors will be resolved by the third author who will act as arbitrator.

Assessment of methodological quality for individual studies

Data extraction

For extraction of relevant data, a common format will be prepared based on the Cochrane Data Extraction Form,[13],[14],[15] which will be pretested on randomly selected articles for modification. Data will be extracted from the articles meeting the eligibility criteria and hence to be selected for review using the final Data Extraction Form, which will include study identification details, general information, study eligibility based on PICOS components, and inclusion/exclusion criteria. Methodological quality of studies for the purpose of inclusion in the review will be judged on the basis of the Data Extraction Form. Accordingly, the studies will be included in or excluded from the review. For excluded studies, reasons for exclusion will be noted.

Quality assessment

The methodological rigor of the studies will be independently assessed by both the reviewers. STROBE and CONSORT statements will be used for assessing quality of report and adherence to guidelines for observational studies and randomized trials, respectively. The Effective Public Health Practice Project Quality Assessment Tool 2003[16] using component ratings will also be used for assessing the quality of the studies. Disagreements will be resolved with the help of the arbitrator.

Subsequently, at the time of analysis, quality of included studies, in terms of likely occurrence of bias, will be assessed and reported on the basis of risk of bias assessment.[16],[17],[18] Funnel plot will be prepared to assess publication bias.

Data abstraction, data management, data synthesis, analysis, and presentation

Data abstraction

Full electronic search strategy of major databases will be presented. PRISMA flow diagram will be prepared depicting process of selection of studies for review. All relevant details of studies included in a review such as study design, sample size, population groups, intervention, and outcome will be abstracted for all studies and presented as a table for summary of studies, as obtained from Data Extraction Form.

Statistical analysis

Data analysis will be conducted following the guideline provided in the Handbook of Cochrane Systematic Review.[15] Meta-analysis will be performed for the primary objective, using the RevMan v. 5.4 software,16 in the following manner.

  • Statistical heterogeneity of included studies will be analyzed with Chi-square test and I2 statistics. In case of statistical heterogeneity, random effect models will be used; otherwise, fixed effect models will be used
  • Dichotomous data will be summarized using odds ratio/risk ratio and 95% confidence interval
  • Overall pooled estimate will be calculated using random effect model with Mantel–Haenszel method of pooling the data
  • Forest plot will be prepared from randomized controlled trials and observational analytical studies in subgroup analysis
  • Funnel plot will be prepared for assessing reporting bias
  • Risk of bias will be assessed using Cochrane risk of bias tools.[16],[17],[18]


Translational value for implementation

Evidence-based decision may be taken for incorporating mHealth as a key component for health promotion under a national program for NCDs of the respective countries. Mobile apps can be formulated, and the same can be used for sending text/pictorial/audio/video messages to the target population at regular predefined intervals, strengthened by telephonic communication by a link worker/community worker/health worker at longer intervals. The app may also be designed to have a provision of obtaining feedback. Information technology platform with automated messages will be a good option though a human touch to it, by means of phone calls at regular intervals, will be a better choice.

Financial support and sponsorship

Applied to Centre of Excellence, World NCD Federation, for funds and support.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Noncommunicable Diseases Fact Sheet. World Health Organization. Available from: https://www.who.int/en/news-room/fact-sheets/detail/noncommunicable-diseases. [Last accessed on 2021 Feb 22].  Back to cited text no. 1
    
2.
Omran AR. The epidemiologic transition: A theory of the epidemiology of population change. 1971. Milbank Q 2005;83:731-57.  Back to cited text no. 2
    
3.
mHealth New Horizons for Health through Mobile Technologies. Based on the Findings of the Second Global Survey on eHealth Global Observatory for eHealth Series – Volume 3. World Health Organization; 2011. Available from: https://www.who.int/goe/publications/goe_mhealth_web.pdf. [Last accessed on 2021 Feb 22].  Back to cited text no. 3
    
4.
Addressing Mobile Health. World Health organization; 2021. Available from: https://www.who.int/activities/Addressing-mobile-health. [Last accessed on 2021 Feb 22].  Back to cited text no. 4
    
5.
Prestwich A, Perugini M, Hurling R. Can implementation intentions and text messages promote brisk walking? A randomized trial. Health Psychol 2010;29:40-9.  Back to cited text no. 5
    
6.
Haapala I, Barengo NC, Biggs S, Surakka L, Manninen P. Weight loss by mobile phone: A 1-year effectiveness study. Public Health Nutr 2009;12:2382-91.  Back to cited text no. 6
    
7.
Müller AM, Alley S, Schoeppe S, Vandelanotte C. The effectiveness of e-&mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review. Int J Behav Nutr Phys Act 2016;13:109.  Back to cited text no. 7
    
8.
Sharma M, Banerjee B, Ingle GK, Garg S. Effect of mHealth on modifying behavioural risk-factors of non-communicable diseases in an adult, rural population in Delhi, India. Mhealth 2017;3:42.  Back to cited text no. 8
    
9.
Iyengar S. Mobile Health (mHealth). In: Gogia S, editor. Fundamentals of Telemedicine and Telehealth. Massachusetts, US. Elsevier; 2019. Available from: https://doi.org/10.1016/B978-0-12-814309-4.00012-4. [Last accessed on 2020 Feb 22].  Back to cited text no. 9
    
10.
Eveleth R. The First Text Message, Sent Twenty Years Ago, Was 'Merry Christmas'. Smithsonian Magazine; 2012. Available from: http://www.smithsonianmag.com/smart-news/the-first-textmessage-sent-twenty-years-ago-was-merry-christmas-152311567. [Last accessed on 2020 Feb 23].  Back to cited text no. 10
    
11.
Hartling L, Featherstone R, Nuspl M, Shave K, Dryden DM, Vandermeer B. The contribution of databases to the results of systematic reviews: A cross-sectional study. BMC Med Res Methodol 2016;16:127.  Back to cited text no. 11
    
12.
Halladay CW, Trikalinos TA, Schmid IT, Schmid CH, Dahabreh I. Using data sources beyond PubMed has a modest impact on the results of systematic reviews of therapeutic interventions. J Clin Epidemiol 2015;68:1076-84.  Back to cited text no. 12
    
13.
Data Collection form for Intervention Review – RCTs and Non-RCTs. Cochrane Developmental, Psychosocial and Learning Problems. The Cochrane Collaboration; 2014. Available from: https://dplp.cochrane.org/data-extraction-forms. [Last accessed on 2020 Feb 22].  Back to cited text no. 13
    
14.
Data Extraction Form Adapted from the Cochrane Collaboration. BMJ. Available from: https:///bmjopen-2017-June-7-6--inline-supplementary-material-1.pdf. [Last accessed on 2021 Feb 22].  Back to cited text no. 14
    
15.
Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions Version. Vol. 6. Chichester (UK): The Cochrane Collaboration; 2019. Available from: https://training.cochrane.org/handbook/current. [Last accessed on 2021 Feb 22].  Back to cited text no. 15
    
16.
Review Manager Web (RevMan Web). The Cochrane Collaboration, London, England. 2019. [Cited 2020 July 04] Available at revman.cochrane.org.   Back to cited text no. 16
    
17.
Sterne JA, Savovic J, Page MJ, Elbers RG, Blencowe NS, Boutron I. RoB 2: A revised tool for assessing risk of bias in randomized trials. BMJ 2019;366:14898.  Back to cited text no. 17
    
18.
Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. BMJ 2016;355:i4919.  Back to cited text no. 18
    




 

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