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| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 7
| Issue : 4 | Page : 161-176 |
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Effectiveness of mHealth for modification of dietary habits and physical activity among individuals at risk or suffering from noncommunicable diseases in primary healthcare settings in South East Asian Region countries – A systematic review and meta-analysis
Bratati Banerjee1, Debashis Dutt2, Indranil Saha3, Bobby Paul4, Abhishek Shivanand Lachyan5
1 Department of Community Medicine, Maulana Azad Medical College, New Delhi, India
2 Department of Public Health Administration, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India
3 ICMR-Centre for Ageing and Mental Health, Indian Council of Medical Research, Kolkata, West Bengal, India
4 Department of Preventive and Social Medicine, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India
5 Department of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| Date of Submission | 27-Aug-2022 |
| Date of Decision | 22-Nov-2022 |
| Date of Acceptance | 01-Dec-2022 |
| Date of Web Publication | 07-Jan-2023 |
Correspondence Address:
Bobby Paul
Department of Preventive and Social Medicine, All India Institute of Hygiene and Public Health, Kolkata
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jncd.jncd_59_22
Background: Noncommunicable diseases (NCDs) are increasing, for which some behavioral risk factors are major concerns. mHealth has been found to be effective in changing these behavioral patterns.
Objective: To assess effectiveness of mHealth technology in modification of dietary habits and physical activity, among individuals having NCDs or their risk factors, in primary healthcare settings in South East Asian Region Countries.
Materials and Methods: A systematic review and meta-analysis was done with the primary outcome as effectiveness of mHealth for improving dietary practices and increasing physical activity. Articles were retrieved from PubMed, Cochrane Central, Google Scholar, and Pre-print servers followed by forward and backward searching. Quality and risk of bias of included studies were assessed. Meta-analysis was performed using RevMan v. 5.4.1 software. Heterogeneity was tested using χ2 test and measured using I2 statistic, with Forest plots as the final outcome.
Results: Nine publications from seven studies, of which seven were conducted in India and two in Bangladesh, qualified for the review. All studies used varied mHealth interventions. Most studies reported beneficial effects in reducing inadequate/improper diet and insufficient/improving physical activity, at community/workplace settings, except two studies reporting no apparent impact, both being from Bangladesh. Meta-analysis revealed statistically significant differences between intervention and control groups for pooled estimates of reduced dietary energy and increased fruits/vegetables. Although heterogeneity is absent between studies considered for fruits/vegetables, both studies were compromised in quality and bias. Studies on dietary energy intake had high statistical heterogeneity, in addition to having high risk of bias. Hence, the results need to be interpreted with caution. No effect was observed on increasing physical activity.
Conclusion: mHealth interventions have huge potential to facilitate behavior change. However, more research is needed before its potential scale-up.
Keywords: Behavioral risk factors, dietary habit, effectiveness, eHealth, mHealth, noncommunicable diseases, physical activity
How to cite this article:
Banerjee B, Dutt D, Saha I, Paul B, Lachyan AS. Effectiveness of mHealth for modification of dietary habits and physical activity among individuals at risk or suffering from noncommunicable diseases in primary healthcare settings in South East Asian Region countries – A systematic review and meta-analysis. Int J Non-Commun Dis 2022;7:161-76 |
How to cite this URL:
Banerjee B, Dutt D, Saha I, Paul B, Lachyan AS. Effectiveness of mHealth for modification of dietary habits and physical activity among individuals at risk or suffering from noncommunicable diseases in primary healthcare settings in South East Asian Region countries – A systematic review and meta-analysis. Int J Non-Commun Dis [serial online] 2022 [cited 2023 Mar 31];7:161-76. Available from: https://www.ijncd.org/text.asp?2022/7/4/161/367305 |
| Introduction | |  |
Noncommunicable diseases (NCD) are currently on the rise. Four major modifiable behaviors, namely unhealthy diet, physical inactivity, tobacco use and alcohol consumption, have been shown to increase risk of increase NCDs. NCDs account for four-fifth of global disease burden which will further increase, in low- and middle-income countries (LMIC) including WHO South-East Asian Region (SEAR) countries.[1]
Innovative interventions have been tried for the prevention and control of NCDs, including mHealth technology. Mobile health or mHealth was earlier defined as health-related uses of mobile telecommunication and multimedia technologies within health service delivery and public health systems. Subsequently, the definition was expanded to include public health and wellbeing.[2] The WHO has defined mHealth as “the use of mobile wireless technologies for public health,” which reflects its increasing importance for delivery of health services and public health.[3]
However, conclusive evidence regarding effectiveness of mHealth has not yet been established. Present systematic review and meta-analysis aimed to assess the effectiveness of mHealth technology in modification of selected behavioral risk factors namely dietary habits and physical activity.
| Materials and Methods | | |
Randomized controlled trials (RCT), nonrandomized trials (NRCT), uncontrolled trials, prospective/retrospective observational analytical studies, reported during 2001-2020, were included in this review. Inclusion criteria were use of mHealth technology for Behavior Change Communication; in primary healthcare setting; for modifying dietary practice and physical activity; among individuals at risk/suffering from NCDs; in SEAR countries. Exclusion criteria were studies other than original research/unpublished studies/grey literature. Primary outcome was effectiveness of mHealth for improving dietary practices and increasing physical activity.
Information sources were electronic bibliographical databases namely PubMed, Cochrane Central, Google Scholar and Pre-print servers (medRxiv, SSRN); forward and backward search of selected articles. Final search strategy is shown in [Box 1]. Titles and abstracts were screened and relevant articles selected. Full texts of abstracts likely to meet eligibility criteria were retrieved, which were distributed equally between two groups of two members each. Both members in a group independently reviewed the articles for inclusion. Any disagreement between the two members was resolved by the other group as arbitrator. Before final analysis, search was repeated for finding missed publications.
Data analysis was done following Cochrane guidelines,[4],[5] using RevMan v. 5.4.1 software (The Cochrane Collaboration, London, United Kingdom).[6] Inverse-variance method was used for continuous data and Mantel-Haenszel method for dichotomous data. Heterogeneity was tested using χ2 test and measured using I2 statistic. Assuming presence of statistical heterogeneity due to clinical and methodological diversity, initially random-effects model of meta-analysis was used to calculate pooled estimate, for both continuous and dichotomous data. In absence of heterogeneity fixed-effects model was used. Statistics used for effect measures were standardized mean difference (SMD) for continuous data and risk ratio (RR) for dichotomous data, with 95% confidence interval. Forest plots were prepared.
Effective Public Health Practice Project (EPHPP) Quality Assessment Tool 2010 was used for assessing quality of studies. Likely occurrence of bias was assessed using Cochrane Risk of Bias (ROB) Assessment Tools ROBINS-1[7] and RoB-2[8] for NRCT and RCT respectively. Our review followed PRISMA format.
| Results | | |
Selection of studies
[Figure 1] shows PRISMA flowchart for the study selection. Nine reports of seven individual studies qualified for the review, which were all done in India and Bangladesh.[9],[10],[11],[12],[13],[14],[15],[16],[17] All studies used mHealth interventions which were varied in nature, on subjects at risk/suffering from NCDs.
Out of the nine included publications, three reported outcomes in different phases of same study. A 2-year RCT was published by Ramachandran et al.,[9] while Ram et al. published results of same study with different end points.[10] Nanditha et al. published report of posttrial follow-up after 3 years of same primary study.[11]
Out of the seven studies, six were RCTs, amongst which Ramachandran et al. 2013 study was prospective, parallel-group, RCT while rest were cluster-RCT (CRCT). One study was NRCT as before-after intervention study with controls.[12] NCDs considered in these studies were metabolic syndrome, hypertension, diabetes, and various risk factors.[9],[10],[11],[12],[13],[14],[15],[16],[17]
PICO components of included studies
[Table 1] describes the characteristics of study population and study areas. Five studies were conducted in India and two in Bangladesh. Amongst Indian studies, one was multicentric in urban workplaces in two states of India,[9] four studies involved single geographical region (one in industrial setting,[13] one in urban community,[15] and two,[12],[17] in rural community). Both studies conducted in Bangladesh were in the rural community.[14],[16] | Table 1: Salient features of included articles in terms of study area and study population
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All RCTs used randomization for allocating study participants to intervention and control groups. The CRCTs randomized clusters, with random sampling for the selection of individuals within clusters. In the NRCT, two-stage sampling was done with systematic random sampling for household selection and simple random sampling for subject selection. Allocation ratio Intervention: Control was maintained at 1:1 in all studies. Sample size showed wide variation (Overall: 265–13,684; intervention group: 133–4561; control group: 132–4547) [Table 1].
Recruitment and data collection was done at workplaces in two studies (Ramachandran, Limaye), house-to-house visit done for both purposes in four studies, while recruitment from tertiary health facility and data collection at household level in one study (Jahan). In five studies, dropout rate in both intervention and control arm was <6%, while in two studies dropout rate was higher (Fottrell: 16% in both the arms; Limaye: 21% and 25.7% in intervention and control arms, respectively) [Table 1].
[Table 2] describes the characteristics of interventions and comparators used. In five studies, interventions were based on the theoretical models of behavior change. Sharma 2017 had designed intervention based on WHO-STEPS approach, while Limaye did not mention basis of intervention designing. Variations were observed in mHealth interventions namely text messages, audio clips, voice messages, telephone calls used in combination/alone. Kaur used social networking apps also. Six studies reported weekly messaging with variable frequency. Sharma 2017 tailored timing and frequency of messaging to participants' preferences. In most studies, control group received standard care. Due to the nature of intervention, blinding was not possible in all studies. Duration of interventions ranged from 5 to 24 months (median: 12 months; interquartile range [IQR]: 7–13 months). Median duration of follow-up was 12 months (IQR: 5.5–13 months). In two studies (Kaur, Jahan), health educational pamphlets were given to control participants. | Table 2: Salient features of the included articles in terms of intervention and comparator
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[Table 3] depicts outcome characteristics, showing wide variation with few studies reporting same parameters. Dietary energy intake (kcal) was estimated by two studies (Ramachandran, Kaur) while <5 servings of fruits/vegetables per day as cut off were reported by two studies (Sharma 2017, Sharma 2021). For physical activity, <150 min/week as cut off was reported by two studies (Fottrell, Limaye). Immediate postintervention outcome measurement was done in one study (Sharma 2017) while in Ramachandran study follow-up was done after 3 years of cessation of active intervention which was the maximum period of follow-up.
Behavior change following mHealth intervention
[Table 4] and [Table 5] show changes in dietary practice and physical activity at last postintervention follow-up. All studies documented changes in same group pre- and post-intervention as mean difference and postintervention between intervention and control group as net difference. | Table 4: Outcome reported inthe included articles in terms of overall dietary habits in the last follow up visit
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 | Table 5: Outcome reported in the included articles in terms of physical activity in the last follow up visit
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Ramachandran reported statistically significant reduction of dietary energy intake and Ram found significant decrease in consumption of carbohydrates, portion size and oil intake, in intervention compared to control group, while Nanditha found significant decrease in mean dietary energy intake both in intervention and control groups. Limayee showed decreased intake of calorie-dense foods in both intervention and control groups, though not significant. Kaur found significant mean differences in intake of fat, sugar, and salt pre- and post-intervention in both groups separately, and significant difference between net differences of intervention and control groups [Table 4].
Limayee found increased intake of fiber-rich foods in intervention group only, without any significant difference in mean and net differences. Sharma 2017 found decreased proportion of participants consuming inadequate diet in intervention group, compared to controls. Mean servings of fruits/vegetables consumed/day also increased significantly in both intervention and control groups. However, Fottrel found decrease in mean number of portions of fruits/vegetables consumed/day in intervention compared to control group but without any significant differences. Sharma 2021 found servings of ≥5 fruit and vegetable/day was significantly higher among intervention compared to control group. Kaur observed fruits/vegetables intake was significantly more in intervention than control group both for mean and net differences [Table 4].
Jahan 2020 found significant difference in adherence rate of salt intake between intervention and control groups, but observed almost no difference in adherence rate of fruits/vegetables intake between the two groups [Table 4].
Regarding physical activity, Ramachandran did not find any significant net difference between the two groups in physical activity score and percentage adherence of physical activity. Ram found regular physical activity among intervention and control groups but without any significant difference. Limaye found higher proportion of participants performing exercise ≥150 min/week in intervention compared to control group with significant net difference. Sharma 2017 found less proportion of participants in intervention group with insufficient physical activity of <600 MET-min/week with mean and net differences; but no change in control group. Nanditha reported higher physical activity score in intervention group with changes in both mean and net differences. Fottrell noticed higher proportion of control participants performing physical activity ≥150 min/week without significant difference. Jahan found increased adherence rate of physical activity of 30 min/day among control group, with significant difference. Sharma 2021 showed increased proportion of participants in nonoccupational physical activity among intervention group with significant mean and net differences [Table 5].
Results of meta-analysis
[Figure 2] shows Forest Plot for meta-analysis of mean daily dietary energy intake. Results show high statistical heterogeneity (I2 = 85%), Hence, inverse-variance method with random-effect model was used, with summary statistic for effect measure presented as SMD. As the outcome is reduction of dietary energy intake, results are located to left of line of no effect. Statistically significant differences (P = 0.002) were observed between intervention and control groups, in all three studies individually and in pooled estimate, indicating mHealth intervention to be effective in reducing dietary energy intake (Z = 3.17).
[Figure 3] shows Forest Plot for meta-analysis of percentage of population with consumption of ≥5 servings of fruits/vegetables per day. Heterogeneity was absent (I2 = 0%). Hence, Mantel-Haenszel method with fixed-effect model was used, with summary statistic for effect measure presented as RR. Since the outcome was shown as favourable i.e., proportion of subjects showing improvement of fruit and vegetable intake, the results are located to the right of the line of no effect, indicating risk is higher in controls. Both studies indicated the intervention to be effective (Sharma 2017 RR = 2.53, confidence interval [CI]: 0.81,7.92; Sharma 2021 RR = 1.95, CI: 1.20,3.19). Pooled estimate of fruits/vegetables ≥5 servings/day, shows statistically significant protective effect of the intervention (RR = 2.03, CI: 1.30, 3.19, P = 0.002). In absence of heterogeneity, the result may be considered conclusive.
[Figure 4] shows Forest Plot for meta-analysis of percentage of population with ≥150 min of physical activity/day. Statistical heterogeneity was high (I2 = 83%). Hence, Mantel–Haenszel method with random-effect model was used. Though Limaye observed statistically significant (RR = 1.95, CI: 1.34;2.84) difference between intervention and control groups, there were no statistical significance in Fottrel study (RR = 0.99, CI: 0.97,1.02) or in pooled estimate (RR = 1.36, CI: 0.70;2.64, P = 0.37), indicating mHealth intervention may not be effective in increasing physical activity.
Quality of studies
All included studies have followed CONSORT guidelines. [Table 6] shows assessment of study quality using EPHPP tool and risk of bias using Cochrane RoB tools.
Except one study (Limaye), all other studies had moderate/strong quality. Non-usage of validated tool for outcome measurement, lack of precise outcome reporting parameters, lackof blinding due to nature of intervention, inadequate handling of confounders, constrains generalisation and interpretation of results, in Limaye's study [Table 6].
All studies have serious/high risk of overall bias, due to awareness of those involved viz. study participants, those delivering intervention and those assessing outcome following intervention, which might have affected reporting and measurement. This flaw in methodology was probably unavoidable due to nature of the intervention i.e., delivering health messages via mHealth, it was not possible to follow blinding at any level. Most of the studies either did not have a protocol or available protocol did not detail plan of analysis. However, all studies were unbiased in terms of confounding, selection of participants, deviation from intended interventions and missing outcome data [Table 5] and [Figure 5], [Figure 6], [Figure 7]. | Figure 6: Risk of Bias assessment for CRCT. CRCT: cluster-randomised trials
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As Funnel Plot should not be prepared with few studies, conclusive comment cannot be made regarding publication bias in this review.
| Discussion | | |
The present systematic review and meta-analysis was conducted to synthesize the evidence available over the past twenty years, to assess effectiveness of mHealth interventions on selected behavioral risk factors for NCDs i.e., unhealthy diet and physical inactivity, in primary healthcare settings, amongst studies conducted in countries with similar geographical, cultural and health care settings viz. SEAR Countries, which are included in LMIC categories. Nine publications from seven studies qualified for the review, which were done in India and Bangladesh. All studies used varied mHealth interventions, on subjects at risk/having risk factors/suffering from NCDs.
Most studies reported beneficial effects of mHealth interventions in reducing inadequate diet and insufficient physical activity, at community/workplace settings, except two studies (Fottrel, Jahan) which reported no apparent impact, both of which were conducted in Bangladesh. However, comparability was difficult to establish because of variations in nature and duration of interventions, and tools for outcome assessment.
Overall results of meta-analysis indicate while there is no effect of mHealth intervention on increasing physical activity, statistically significant differences between intervention and control groups were observed for pooled estimates of reduced dietary energy and increased fruits/vegetables. However, considering heterogeneity, quality and bias, the results need to be interpreted with caution.
Earlier studies have shown effectiveness of mHealth interventions to combat the rising burden of chronic diseases,[18],[19],[20],[21],[22],[23],[24] though very few studies have been conducted in LMICs including SEA Region.
Various researchers have summarized the evidence from few studies conducted in developing countries, as systematic reviews[25],[26],[27],[28] or umbrella reviews.[29] The included studies were diverse in design, sample size, duration and characteristics of intervention.[26] Some reported significant positive effects of mHealth on physical activity and/or diet,[25],[26],[27] with interventions on physical activity being more successful than other health outcomes.[25] Contrary to this, our review revealed significant effect of intervention on dietary behavior, but not physical activity. The potential of mHealth as an effective intervention method for salt-reduction was also reported in one review.[28] However, some reviews showed diverse and even conflicting results.[29]
In studies that showed positive impact of mHealth, there were no significant differences between text-only or texting plus other components[25] and no clear association between design or characteristics and intervention effects.[26] SMS was observed to be the most widely used and successful intervention,[29] and personalized, tailored, individualized messages showed greater intervention efficacy.[25]
In self-reported behavior change, the outcome assessor i.e., study subject is aware of intervention received. Hence, outcome assessment is influenced by this knowledge, leading to compromised quality[30] due to presence of serious/high risk of bias in the concerned study.[31] Previous reviews conducted in developing countries reported the studies were of low/moderate quality or biased, often with small sample size, lack of control group, short follow-up, incomplete data, high risk of bias, no clarity on allocation concealment, lack of blinding of outcome assessors, incomplete/inadequate analysis using varied methods.[25],[26],[27],[28] All studies included in our review had serious/high overall risk of bias due to non-blinding, though global rating for quality showed only one study was weak while the others were moderate/strong.
Strengths and limitations of our review
Main strength of our review is that it included articles published over a long period of 20 years. All major databases and sources of information were systematically searched with pre-defined terms, and updated continuously. Forward and backward search was done to identify additional studies. Protocol for review was pre-registered in PROSPERO (CRD42021275416)[32] and published in an international journal.[33] Data Extraction Form was adapted from Cochrane Collaboration.[4],[5] Data were extracted in great detail despite the complex nature of some studies and large variations across studies. Meta-analysis was done using RevManv. 5.4.1 software.[6] Reporting was done following PRISMA guidelines. Methodological qualitywas assessed using EPHPP Tool. Risk of bias was assessed using Cochrane ROBINS-I tool NRCT[7] and Cochrane RoB-2 tool for RCT and CRCT.[8]
An important limitation is that only articles published in English were included. Studies published in local languages might have been missed. There is also possibility of publication bias as studies without beneficial effect may not have been published.[34],[35] Due to very few studies available for meta-analysis for selected outcomes, Funnel Plot did not have much relevance and hence publication bias could not be assessed. Due to differences in study designs, intervention components, outcome measures and tools for their measurements, varying lengths of studies, it was not possible to do meta-analysis for all outcomes. Also, included studies were only from India and Bangladesh. Therefore, conclusions from this review cannot be generalized.
| Conclusion | | |
mHealth interventions have huge potential to facilitate behavior change and reach large geographical distances, which will be useful for resource-constrained LMICs. Evidence-based decision may be taken for incorporating mHealth as a key component for health promotion under national programme for NCDs of respective countries. However, large scale RCTs and studies with mixed-methods approach conducted in varied populations need to be further explored through implementation research before its potential scale up.
Acknowledgment
Collaborating organization: Center of Excellence for Evidence-Based Research for NCDs in LMICs, World NCD Federation, Chandigarh.
Ethical approval statement
Since the article is a systematic review and metaanalysis, based on publicly accessible dataset; Institutional Ethics Committee approval is not applicable.
Financial support and sponsorship
Technical support from collaborating institution
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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