1. Use Of Mobile Phones To Promote Physical Activity In A Rural Population Of Delhi, India
Dr. Malvika Sharma, Dr. B Banerjee, Dr. G.K Ingle, Dr. S. Garg
Department of Community Medicine,
Maulana Azad Medical College, New Delhi, India 1

2. BURDEN OF NON-COMMUNCABLE DISEASES (NCD)
NCDs are the leading cause of adult mortality and morbidity worldwide. (About 67% of all deaths)
82% of NCD deaths- CVD, Cancer, Diabetes, Chronic respiratory disease.
Nearly 75% of NCD deaths- LAMI countries
42% of all NCD deaths globally- before 70 years age (48% in LAMI countries)
India: NCDs account for 53% deaths and 44% DALYs (2004), estimated to cause 75% of all deaths in India by 2030.
Dual Burden
SDG Target 3.4

3. RISK FACTORS OF NON-COMMUNICABLE DISEASES
MODIFIABLE
Behavioural Risk Factors
Tobacco Use
Physical Inactivity
Unhealthy Diet
(Harmful) use of alcohol
Non-modifiable
Risk Factors
Age
Gender
Family History
Genetics
Physiological/Metabolic
Risk Factors
Raised Blood Pressure
Overweight/Obesity
Hyperglycemia
Hyperlipidemia

4. mHEALTH FOR HEALTH PROMOTION/LIFESTYLE MODIFICATION?
Most common NCDs share common pool of risk factors
High prevalence of these risk factors in the community- health promotion messages need to reach large population
mHealth  delivered to large number of individuals through one-to-one communication in cost-effective manner and shorter time.
Automated system-generated SMS- dissemination of health messages (also for surveillance, management and treatment compliance)

5. PROMISE OF mHEALTH IN INDIA
Second largest mobile-phone user base in the world million users (2016).
Low priced phones. Among the lowest mobile tariffs in the world.
Steady increase in geographical coverage of mobile networks.
Rural tele-density increased from 5.89% in 2007 to 52.3% in 2016 (Oct)
mHealth currently used in various health programs- Mother and Child Tracking System, Dengue outbreak control, Adherence to TB medication etc.

6. CAN mHEALTH BE EFFECTIVELY UTILIZED TO REDUCE BEHAVIOURAL RISK FACTORS OF NON-COMMUNICABLE DISEASES?

7. Literature Review for mHealth interventions for NCD risk-factor modification in Low and Middle Income countries
Systematic review by Muller et al, published in 2016 (Int J Behav Nutr Phys Act) reviewed 15 studies evaluating mHealth and eHealth interventions for promoting healthy diet and physical activity in LAMI countries:
50% of the interventions were effective in increasing physical activity and 70% were effective in improving diet quality
BUT, none of the included studies were conducted in general population.
Studies in India:
Pfammatter A et al (J Med Internet Res), 2016: parallel-group cohort study to determine effectiveness of mDiabetes, a text messaging programme to improve diabetes risk behaviours.
Shetty et al (J Assoc Physicians India), 2011: RCT to study feasibility of using SMS to ensure adherence to management prescriptions by diabetic patients.
Limited data on such interventions in apparently healthy, general population

8. Aim of the current study was to assess the effect of telephone-delivered communication on behavioural risk-factors of Non-Communicable Diseases.

9. MATERIALS AND METHODS
STUDY DESIGN: Community-based “Before and After” Intervention study with control.
Registered with Clinical Trials Registry of India: CTRI/2017/03/008264
STUDY AREA:
Barwala Village located in noth-west district of Delhi
The population of the village was 4500 with 639 households as per a demographic survey conducted on 2014.
The village is catered by the Rural Health Training Centre (RHTC), Department of Community Medicine, Maulana Azad Medical College.

10. INCLUSION CRITERIA: EXCLUSION CRITERIA: Adults aged 18-64 years
Resident of the village for at least 6 months
Having access to mobile phone with SMS facility in Hindi
Ability to read SMS
EXCLUSION CRITERIA:
Individuals with impaired hearing, inability to talk and severe mental retardation
Individuals who could not be contacted after 3 successive visits
Pregant women

11. Sample Size
The sample size was calculated using the formula: Sample size= 2 (Zα+Z1-β)2pq d2 Where, Zα is type 1 error, Z1-β is power p = (p1+p2)/2; p1= proportion before intervention, p2= proportion after intervention q is (1-p) d is absolute difference expected after intervention. Value of p was calculated as 0.1 using prevalence estimate from a study done by Garg et al in Delhi in With p=0.1, q=0.9, d=0.1, Zα =1.96 at 95% confidence, Z1-β = 0.84 at 80% power, calculated sample size was 144 in each group. Adding 20% for attrition and rounding up, our target sample size was 200 in each group.

12. Sampling Method Two stage sampling was done:
STAGE 1: Systematic random sampling at household level using household list available at RHTC as sampling frame.
STAGE 2: One adult fulfilling inclusion criteria was selected from each selected household using simple random sampling by lottery method.

13. Methodology
Study was conducted for a period of one year from January 2016 to December 2016, in 3 phases:
Pre-Intervention Phase:
2 months (January-February 2016)
Baseline data was collected from selected individuals after obtaining written, informed consent.
Pre-tested schedule (in Hindi) based on WHO STEPS approach was used to collect data.
Step 1: Questions on behavioural risk factors, history of raised BP, history of diabetes
Step 2: Physical examination: Height, Weight, Waist circumference, Hip circumference, Blood Pressure
Step 3: Investigations: Fasting blood sugar and Lipid Profile.

14. 3. Post-Intervention Phase:
8 months (March-October 2016)
mHealth package: weekly SMS (pre-prepared roster) + monthly phone call
Content: importance of modification of behavioural risk factors, addressing queries, positive reinforcement
No such intervention given in control group
3. Post-Intervention Phase:
2 months (November-December 2016)
Post-intervention data collected from both groups using the same schedule as in baseline.

15. RESULTS

16. Figure 1: Participant Flow
408 invited
8 excluded
6 unavailable
2 declined to participate
400 enrolled
200 control group
200 intervention group
8 lost to follow-up
2 became pregnant
3 moved out of study area
3 declined to continue
10 lost to follow-up
3 became pregnant
3 unavailable after 3 visits
2 moved out of study area
2 declined to continue
192 analysed
190 analysed

17. Baseline data

18. Intervention group, (n=190) Socio-economic status
Table 1: Socio-demographic characteristics of subjects in intervention and control group
Intervention group, (n=190)
Control group (n=192)
p-value
Age group
18-24
40 (21.1)
41 (21.4)
0.88
25-34
47 (24.7)
46 (24.0)
35-44
42 (22.1)
49 (25.5)
45-54
52 (27.4)
50 (26.0)
55-64
9 (4.7)
6 (3.1)
Sex
Male
94 (49.5)
91 (47.4)
0.69
Female
96 (50.5)
101 (52.6)
Education
Middle
71 (37.3)
68 (35.4)
0.76
Secondary
38 (19.8)
Higher Secondary
50 (26.3)
60 (31.3)
Graduate & above
29 (15.3)
26 (13.5)
Occupation
Clerical/Medium business
4 (2.1)
1 (0.5)
0.89
Agriculture
14 (7.4)
15 (7.8)
Domestic Work
55 (28.9)
61 (31.7)
Services
45 (23.7)
Sales
17 (8.9)
Manual Worker
22 (11.6)
23 (12.0)
Student
19 (10.0)
14 (7.3)
Unemployed
Socio-economic status
Class I
13 (6.8)
0.85
Class II
125 (65.8)
126 (65.6)
Class III
43 (22.6)
48 (25)
Class IV
5 (2.6)

19. Table 2: Baseline physical activity in intervention and control group.
Intervention group (n=190)
Control group (n=192)
p-value
Level of physical activity, Mean (SD)
Physical activity per week (METS-min/week)
(1347.2)
(1449.5)
0.97
Proportion of physical activity, n (%)
Insufficient (<600 MET-min/week)
79 (41.6%)
76 (39.6%)
0.75
Sufficient (>600 MET-min/week)
111 (58.4%)
116 (60.4%)

20. Table 3: Level of physical activity in different domains of activity at baseline
Intervention group (n=190),
Mean (SD)
Control group (n=192),
p value
Work-related Physical Activity
(MET-mins/week)
813.05
(1312.5)
(1403.5)
0.443
Travel-related Physical Activity
155.78
(259.9)
153.85
(260.7)
0.887
Recreation-related Physical Activity (MET-mins/week)
137.05
(402.7)
93.54
(314.04)
0.384

21. Change after intervention

22. Figure 1: Percentage change in low physical activity from baseline to Post-Intervention Phase

23. Table 4: Change in level of physical activity after mHealth intervention
Intervention group
(n=190)
Control group
(n=192)
Baseline,
Mean (SD)
Post-Intervention Phase, Mean (SD)
p-value
Physical activity
(in MET-min per week)
1105.8
(1347.2)
1166.5
(1332.5)
<0.001
(1449.5)
(1454.2)
0.66
Significant difference was found between the mean change in level of physical activity in intervention group (60.63 MET-min/week) and control group (0.54 MET-min/week) [p-value <0.001]

24. Table 5: Change in physiological and metabolic risk factors after mHealth intervention
Intervention group (n=190)
Control group (n=192)
Baseline, Mean (SD)
Post-intervention Mean (SD)
Mean difference
(p-value)
Baseline, Mean (SD)
Post-intervention Mean(SD)
Physiological risk factors
BMI (kg/m2)
23.8 (3.6)
23.7 (3.5)
-0.1 (0.005)
24.1 (3.8)
24.2 (3.8)
0.1 (0.003)
Systolic Blood Pressure (mmHg)
129.9 (11.9)
128.6 (10.6)
-1.3 (<0.001)
129.3 (12.1)
129.6 (11.9)
0.3 (0.08)
Diastolic Blood Pressure (mmHg)
80.5 (7.8)
80.8 (6.5)
0.3 (0.59)
80.6 (7.4)
80.7 (7.3)
0.1 (0.47)
Metabolic risk factors
Fasting blood glucose (mg/dL)
98.9 (15.9)
97.5 (14.7)
-1.4 (0.006)
98.2 (15.4)
98.4 (15.3)
0.2 (0.10)
Total cholesterol (mg/dL)
184.6 (15.0)
184.4 (14.2)
-0.2 (0.46)
185 (15.8)
184.7 (15.7)
-0.3 (0.07)
LDL cholesterol (mg/dL)
95.7 (9.1)
95.2 (7.9)
-0.5 (0.28)
96.1 (9.6)
96.2 (9.5)
0.1 (0.07)
HDL cholesterol (mg/dL)
55.5 (9.1)
55.7 (7.9)
0.2 (0.010)
56.3 (10.2)
56.2 (10.2)
-0.1 (0.32)

25. STRENGTHS OF THE STUDY
Evaluated behavioural, physiological as well as biochemical risk-factors: anthropometric measurement and measurement of BP, Blood sugar and Cholesterol provides objectivity.
mHealth package consisted of a monthly phone call that provided opportunity for two way communication.
Study conducted at community level in a randomly selected sample- enhances external validity.

26. LIMITATIONS OF THE STUDY
Allocation was non-random.
Blinding was not possible.
Self reported behavioural risk factors- prone to recall and self-desirability bias.
Sample size was calculated based on primary outcome (change in behavioural risk factor)- might be underpowered to detect changes in physiological and biochemical risk factors.
The intervention and control group were both served by the same health centre and were thus both exposed to ongoing health promotion activities and preventive services provided by the centre. Might lead to dilution of results.

27. CONCLUSION
At baseline, proportion of insufficient physical activity in intervention and control group was 41.6% and 39.6% respectively.
After mHealth intervention, insufficient physical activity reduced significantly by about 27% compared to baseline in intervention group. There was no significant change in control group.
Change observed only in the Recreation Domain.

28. CONCLUSION
Significant reduction in physiological risk factors- Mean BMI and Systolic BP in intervention group.
Among metabolic risk-factors: significant reduction was seen in fasting blood glucose and rise in HDL Cholesterol in intervention group.

29. RECOMMENDATIONS
Deployment of innovative measures such as mHealth to increase awareness of risk factors of NCDs- rapid, cheap, can reach large number of people with limited manpower.
mHealth for health promotion and lifestyle modification should be scaled up- incorporated in the national programme for NCDs.
Multipronged approach required for awareness creation and behaviour change- IEC activities through other media such as mass media and mid media, in addition to mHealth services.

30. THANK YOU