Implementation of WHO Package of Essential Noncommunicable-Based Brief Behavior-Change Interventions in Primary Care: A Quasi-Experimental Study from Himachal Pradesh, India

Abstract

Background:

Over 86% of premature deaths from noncommunicable diseases (NCDs) occur in low- and middle-income countries. This study aimed to assess the impact of integrating components of the WHO’s Package of Essential Noncommunicable Disease (PEN) protocols on blood pressure control. Secondary objectives included evaluating changes in biological and behavioral NCD risk factors, 10-year cardiovascular disease (CVD) risk, and treatment adherence.

Material and Methods:

A quasi-experimental study was conducted in four health centers—two intervention (urban and rural) and two control sites. WHO-PEN protocols 1 and 2 were implemented in the intervention arm, where 302 patients received behavior-change interventions by trained nonphysician health workers using “Brief Advice.” The control arm (305 patients) received usual care. The intervention lasted six months, with follow-ups at two, four, and six months.

Results:

After six months, blood pressure control did not differ significantly between the intervention and control groups after adjustment for baseline blood pressure control, income, and family type (adjusted odds ratio [AOR] 0.71, P = 0.188). In the unadjusted analysis, 88.5% of patients in the intervention arm had controlled blood pressure, with a risk difference of − 7.03 (95% confidence interval [CI]: −12.8 to − 1.27), an odds ratio (OR) of 0.57 (95% CI: 0.36–0.91), and a statistically significant association (P = 0.018). Significant improvements were observed in physical activity (≥150 minutes/week) with a risk difference of − 12.43 (95% CI: −22.25 to − 6.60), P < 0.001; vegetable consumption (−31.06, 95% CI: −34.65 to − 27.47), P < 0.001; and fruit intake (−41.46, 95% CI: −48.48 to − 34.44), P < 0.001.

Conclusions:

Behavior-change communication by nonphysician health workers, focusing on modifiable risk factors, improved healthy behaviors among NCD patients, while blood pressure control did not improve significantly after adjustment.

BACKGROUND

Noncommunicable diseases (NCDs) are growing toward epidemic proportions worldwide.[1] The data show that 17 million people die from an NCD before the age of 70 each year; 86% of these premature deaths occur in low- and middle-income countries. People of all age groups are vulnerable to risk factors (i.e. unhealthy diets, physical inactivity, exposure to tobacco, or the harmful use of alcohol or air pollution), contributing to NCDs.[2] The leading metabolic risk factor globally attributing to deaths is elevated blood pressure (BP) (to which 19% of global deaths are attributed), followed by raised blood glucose and overweight.[3] NCDs impede progress toward the Sustainable Development Goals 2030, which include a target to reduce premature death from the four main NCDs (cardiovascular diseases [CVDs], cancers, chronic respiratory diseases, and diabetes) by one-third by 2030. To control NCDs, a focus on reducing the risk factors associated with these diseases is required. Low-cost solutions are required for governments to reduce the common modifiable risk factors.[2]

There is a significant research gap in the area of secondary prevention in NCDs (CVDs and diabetes mellitus), particularly in the management of risk factors or early disease to prevent disease progression. It is imperative to conduct research on strengthening health systems, including primary care levels, to provide optimal care for NCDs. This research is of utmost importance and can significantly contribute to the field of NCD prevention and management.

While the primary healthcare (PHC) approach has proven effective in addressing public health challenges,[4] its full potential for NCD prevention in the WHO Southeast Asia Region remains untapped. PHC services often provide fragmented, reactive care, lacking coordination, and patient-centeredness.[5]

To address this, WHO developed the Package of Essential Noncommunicable Disease Interventions (WHO-PEN), a cost-effective, evidence-based tool for strengthening NCD services in low-resource primary care settings. The protocols support both clinical management and behavioral counseling, enabling both physicians and nonphysician health workers to deliver NCD care effectively.[4]

In India, CVDs are the most prevalent NCDs, followed by chronic respiratory diseases and diabetes.[6] Due to high patient loads, physicians often lack time for individualized behavioral counseling. Studies suggest that involving nonphysician health workers in NCD care can improve outcomes.[7,8,9] A North India community trial demonstrated that their involvement led to reductions in systolic blood pressure (SBP) and better medication adherence.[9]

The WHO-PEN protocols have been adopted in Bhutan[10] and the Democratic Republic of Korea,[11] where nonphysician health workers and household doctors, respectively, have led to clinically significant improvement in BP and blood glucose controls, a decrease in 10-year CVD risk factors, and improvements in other behavioral risk factors.

This study evaluates the integration of WHO-PEN protocols 1 and 2 into primary healthcare in Shimla district, Himachal Pradesh. Protocol 1 focuses on managing diabetes and hypertension, while Protocol 2 addresses health education and behavioral counseling. The primary research question was whether adopting these protocols improves BP control in the intervention group compared to controls. The primary outcome of the study was improvement in BP control among patients in the intervention group compared with those in the control group. Secondary outcomes included changes in selected biological risk factors (such as raised blood glucose and overweight/obesity) and behavioral risk factors for major noncommunicable diseases, 10-year CVD risk, and treatment adherence.

METHODS

Study design

This is a quasi-experimental study with a pre-test and post-test control group design.

Study area

The study is conducted in the Shimla district of Himachal Pradesh, a north Indian mountainous state that has both urban and rural areas divided over eight block headquarters. One block has been purposively selected where two health centers (urban and rural) in the intervention arm conducted behavioral interventions for NCDs using a brief advice strategy for six months. Two health centers (one urban and one rural) in the control arm of the same block provided the usual care. No randomization has been applied in the selection of health centers, as we required full voluntary cooperation of healthcare staff at the health centers for the implementation of interventions.

Sample size

As hypertension is the most common CVD, the sample size is calculated assuming the proportion of the hypertensive population with high BP decreased from 42.3% to 21.5% over three months of the WHO-PEN intervention, as shown previously in Bhutan.[10] With an alpha error level of 5% and 90% power, 117 participants in each arm using Fleiss continuity correction are required for the study.[12] Considering 20% loss to follow-up by three follow-up visits, the total sample size required for this study was 147 in each intervention and control arm, i.e., a total of 294 participants. The study was done in both urban and rural health centers, so to estimate the rural-urban difference, 294 individuals had to be enrolled in both urban and rural health centers. The study enrolled 607 participants over a period of 6 months.

Study population

Patients diagnosed with CVDs (coronary heart disease, stroke, hypertension) and/or diabetes mellitus who attended the outpatient clinics of the selected health institutions during the study period formed the study population. The patients were asked about diagnosed heart disease, stroke, transient ischemic attack, diabetes mellitus, kidney disease, and other symptoms, medicine intake, and behavioral risk factors at enrollment, as mentioned in WHO-PEN protocol 1. The patients who were permanent residents or likely to stay in the study area for at least six months and provided written informed consent to take part in the study were enrolled. Patients with other NCDs, like carcinomas, and psychiatric disorders were excluded from enrollment in the study.

Study tools

WHO-PEN protocols were used for the study.[4] Components of protocols 1 and 2 were included in the current study. Protocol 1 is “Prevention of heart attacks, strokes and kidney disease through integrated management of diabetes mellitus and hypertension.” Protocol 2 pertains to “Health Education and Counselling on Healthy Behaviors.”

Intervention description

The intervention included health education and counseling on behavioral risk factors (protocol 2), delivered by trained nonphysician health workers (female health worker/public health nurse) using “Brief Advice” based on the 5As (Ask, Advise, Assess, Assist, Arrange) and 5Rs (Relevance, Risks, Rewards, Roadblocks, Repetition) models.[4] A primary care provider can help with behavior change within 3−5 minutes with the 5As approach for a person ready to change in a primary care setting, and the 5Rs approach addresses a motivational counseling intervention to help those not ready to quit. Counseling was provided at enrollment and follow-up visits (2, 4, and 6 months) in the intervention arm, while the control arm received usual care. Flip charts and handouts in English and Hindi were used. Materials followed the WHO-PEN guidelines. Intervention and control centers were geographically separated across urban and rural areas to prevent contamination of the intervention.

Data collection and follow-up

A questionnaire based on the WHO-PEN clinical form was used to assess patients at enrollment for diagnosed conditions (e.g., heart disease, stroke, diabetes, kidney disease), medication use, and behavioral risk factors as outlined in protocol 1. Modifiable risk factors such as tobacco use, alcohol intake, diet, and physical activity were evaluated in both intervention and control arms. BP, body mass index (BMI), and blood sugar were measured using standard techniques. The 10-year CVD risk was assessed using WHO/ISH risk charts for the SEAR D region and classified as low risk (<20%) and high risk (≥20%) for 10-year CVD risk.[13] Standard treatment protocols were followed, and referrals to a cardiologist were made when necessary. Assessments were conducted at baseline and follow-up visits at two, four, and six months. Each center was equipped with measurement tools and glucometers. Each center was staffed with two trained nonphysician health workers: one was responsible for delivering the behavioral counseling (intervention arm), while the other conducted data collection and outcome assessments to minimize potential bias.

The study protocol received approval from the Institutional Ethics Committee. This study is reported in accordance with the TREND (Transparent Reporting of Evaluations with Nonrandomized Designs) statement for nonrandomized evaluations of behavioral and public health interventions. The completed TREND checklist is provided as supplementary material. (SupplementaryFile_https://www.equator-network.org/reporting-guidelines/improving-the-reporting-quality-of-nonrandomized-evaluations-of-behavioral-and-public-health-interventions-the-trend-statement/).

Statistical analysis

Frequency and proportion were reported for categorical variables, while continuous variables—SBP, diastolic blood pressure (DBP), weight, height, and BMI—were summarized as mean ± standard deviation (SD).

Independent t-tests were used to compare changes in continuous variables between control and intervention groups. To adjust for baseline values and potential confounders, analysis of covariance (ANCOVA) was applied, and adjusted mean differences with 95% confidence intervals (CI) were reported.

For categorical outcomes, logistic regression analysis was performed to estimate odds ratios (OR) with 95% CI. In addition, adjusted risk ratios and risk differences were estimated using the adjrr package in Stata using Stata Statistical Software Release 17 (Stata Corp. 2023). Descriptive statistics and initial data management were performed using SPSS version 27.0. A sensitivity analysis restricted to participants with hypertension at baseline was conducted to assess the robustness of findings. All statistical tests were assessed at a 5% level of significance.

RESULTS

The study results are presented for the intervention and control groups at baseline and the final follow-up visit (six months). The findings are also presented separately in rural and urban areas for a comprehensive comparison.

Sociodemographic profile and health parameters

The study included 607 participants (mean age: 59.3 ± 12.2 years), with 302 in the intervention group and 305 in the control group. Most were female (56.3%) and married (84.5%). A third belonged to nuclear families (37.2%), the majority were Hindu (96.4%), and 55.9% had attained secondary education. Most (94.6%) of participants were employed [Table 1].

Table 1.

Distribution of participants based on sociodemographic details, disease/condition presentation

Variable		Control group (n=305) n (%)		Intervention group (n=302) n (%)		Total (n=607) n (%)		P
Age (mean±SD) Years		59.3±12.2		59.3± 12.1		59.3±12.2		0.863
Gender
Male		122 (40.0)		143 (47.4)		265 (43.7)		0.068
Female		183 (60.0)		159 (52.6)		342 (56.3)
Marital status
Unmarried		11 (3.6)		3 (1.0)		14 (2.3)		0.117
Married		259 (84.9)		254 (84.1)		513 (84.5)
Widowed		33 (10.8)		42 (13.9)		75 (12.4)
Separated		2 (0.7)		3 (1.0)		5 (0.8)
Type of family
Nuclear		97 (31.8)		129 (42.7)		226 (37.2)		<0.001
Joint		195 (63.9)		91 (30.1)		286 (47.1)
Three generation		13 (4.3)		82 (27.2)		95 (15.7)
Religion
Hindu		294 (96.4)		291 (96.4)		585 (96.4)		0.587
Muslim		3 (1.0)		6 (2.0)		9 (1.5)
Christian		7 (2.3)		5 (1.6)		12 (2.0)
Others		1 (0.3)		0		1 (0.1)
Education
No formal Education		64 (21.0)		65 (21.5)		129 (21.2)		0.916
Senior Secondary		169 (55.4)		170 (56.3)		339 (55.9)
Graduation and above		72 (23.6)		67 (22.2)		139 (22.9)
Occupation
Unemployed		18 (5.9)		15 (5.0)		33 (5.4)		0.612
Employed/Housewives/Farmers		287 (94.1)		287 (95.0)		574 (94.6)
Income (mean±SD) in Rupees		47750±33056		34716±28477		41266±31520		<0.001
Disease/condition distribution
Myocardial Infarction		2 (0.7)		4 (1.3)		6 (1.0)		0.405
Ischemic Heart Disease		4 (1.3)		16 (5.3)		20 (3.3)		0.006
Transient Ischemic Attack		0		7 (2.3)		7 (1.2)		0.007
Post MI		2 (0.7)		7 (2.3)		9 (1.5)		0.105
Post Stroke		1 (0.3)		8 (2.6)		9 (1.5)		0.020
Diabetes Mellitus		97 (31.8)		88 (29.1)		185 (30.5)		0.476
COPD		15 (4.9)		18 (6.0)		33 (5.4)		0.571
Hypertension		277 (90.8)		261 (86.4)		538 (88.6)		0.088

SD=Standard deviation, MI=Myocardial infarction, COPD=Chronic obstructive pulmonary disease

Hypertension was most prevalent (88.6%), followed by diabetes (30.5%). CVD and stroke were more common in the intervention group. Sociodemographic characteristics were similar between groups except for family type and income [Table 1].

A total of 305 participants were recruited from rural health centers (153 control, 152 intervention) and 302 from urban health centers (152 control, 150 intervention). Rural intervention and control groups differed in income, gender, and family type, while urban groups differed only in income [Table 2].

Table 2.

Distribution of urban and rural participants in control and intervention groups based on socioeconomic parameters

Parameter				Rural (n=305) n (%)				P (within group)		Urban (n=302) n (%)				P (within group)
				Control group (n=153)		Intervention group (n=152)				Control group (n=152)		Intervention group (n=150)
Gender		Male		44 (28.8)		66 (43.4)		0.008		78 (51.3)		77 (51.3)		0.998
		Female		109 (71.2)		86 (56.6)				74 (48.7)		73 (48.7)
Type of family		Nuclear		25 (16.3)		48 (31.6)		0.002		72 (47.4)		81 (54.0)		0.249
		Others		128 (83.7)		104 (68.4)				80 (52.6)		69 (46.0)
Religion		Hindu		152 (99.4)		150 (98.6)		0.748		142 (93.4)		140 (94.0)		0.706
		Muslim		0		1 (0.7)				3 (2.0)		5 (3.3)
		Christian		1 (0.6)		1 (0.7)				6 (3.9)		4 (2.7)
		Others		0		0				1 (0.7)		0
Occupation		Job		50 (32.7)		62 (40.8)		0.469		96 (63.2)		94 (62.7)		0.160
		Housewives		74 (48.4)		63 (41.4)				52 (34.2)		56 (37.3)
		Farmer		15 (9.8)		12 (7.9)				-		-
		Unemployed		14 (9.1)		15 (9.9)				4 (2.6)		0
Education		No formal education		54 (35.3)		52 (34.2)		0.980		10 (6.6)		13 (8.7)		0.738
		Senior Secondary		84 (54.9)		85 (55.9)				85 (55.9)		85 (56.7)
		Graduation & above		15 (9.8)		15 (9.9)				57 (37.5)		52 (34.6)
Income		Mean±SD in Rupees		43241±23872		27036±21337		<0.001		52289±39812		42500±32490		0.020

BP control

The proportion of patients with controlled BP levels (<140/90 mmHg) was significantly higher in the intervention group than in the control group at the last follow-up visit (88.5% vs. 81.4%) (OR 0.57; P = 0.018) [Figure 1 and Table 3]. Although the unadjusted analysis suggested a difference between groups, multivariable logistic regression, adjusting for baseline BP control, income, and family type showed that the intervention was not independently associated with improved BP control at the last follow-up (AOR = 0.71; P = 0.188).

Figure 1.

Comparison of risk factors between control and intervention groups at the baseline and last follow-up visit

Table 3.

Changes in hypertension and other parameters at final follow-up in control and intervention arms, overall and depending on place of residence

Parameter		Visit		Control				Intervention				Risk Difference in % with 95% CI		OR with 95% CI		P
				n		%		n		%
Overall
Controlled Blood Pressure (BP <140/90 mmHg)		Baseline		159		52.1		205		67.8		-15.75 (-23.44, -8.06)		0.51 (0.37, 0.72)*		<0.001
		Last Follow-up		237		81.4		261		88.5		-7.03 (-12.8, -1.27)		0.57 (0.36, 0.91)		0.018
10 year CVD Risk (<20%)		Baseline		243		79.7		231		76.7		3.18 (-3.39, 9.76)		1.19 (0.81, 1.75)		0.383
		Last Follow-up		258		88.5		266		90.2		-1.51 (-6.49, 3.47)		0.85 (0.50, 1.44)		0.553
Regular Medication intake (self-reported)		Baseline		263		86.2		212		70.2		16.03 (9.58, 22.48)		2.66 (1.77, 4.00)		<0.001
		Last Follow-up		254		87.3		259		87.8		-0.51 (-5.86, 4.84)		0.95 (0.58, 1.56)		0.851
Follow-up		Baseline		305		100.0		302		100.0		N/A
		Last Follow-up		291		95.4		295		97.7		-2.27 (-5.17,0.63)		2.03 (0.81, 5.10)		0.190
Urban
Controlled BP (BP <140/90 mmHg)		Baseline		83		54.6		98		65.3		-10.73 (-21.71, 0.25)		0.64 (0.40,1.02)		0.057
		Last Follow-up		104		72.2		138		92.0		-19.78 (-28.28, -11.27)		0.23 (0.11,0.45)		<0.001
10-year CVD Risk (< 20%)		Baseline		126		82.9		124		82.7		0.58 (-15.63, 14.47)		0.98 (0.54,1.78)		0.94
		Last Follow-up		125		86.8		140		93.3		-6.53 (-13.35, 0.29)		0.47 (0.21,1.05)		0.06
Regular Medication intake (self-reported)		Baseline		128		84.2		103		68.7		15.54 (6.13, 24.96)		2.43 (1.39, 4.24)		<0.001
		Last Follow-up		119		82.6		137		91.3		-8.69 (-16.35, -1.04)		0.45 (0.22,0.92)		0.03
Follow-up		Baseline		152		100.0		150		100.0		N/A
		Last Follow-up		144		94.7		150		100.0		-5.22 (-8.97,-1.48)		17.71 (1.01,309.57)		0.019
Rural
Controlled BP (BP <140/90 mmHg)		Baseline		76		49.7		108		70.6		-21.81 (-32.94,-10.68)		0.41 (0.26,0.66)		<0.001
		Last Follow-up		133		90.5		124		84.9		4.96 (-2.48, 12.4)		1.61 (0.78,3.30)		0.19
10-year CVD Risk (<20%)		Baseline		117		76.5		108		70.6		5.42 (-4.44, 15.27)		1.35 (0.81,2.26)		0.244
		Last Follow-up		133		90.5		127		87.0		2.89 (-4.27, 10.05)		1.35 (0.64, 2.80)		0. 43
Regular Medication intake (self-reported)		Baseline		135		88.2		109		71.2		26.30 (13.39,39.2)		3.028 (1.66,5.54)		<0.001
		Last Follow-up		135		91.8		122		83.6		16.52 (7.73, 25.32)		2.96 (1.61,5.42)		0.003
Follow-up		Baseline		153		100.0		152		100.0
		Last Follow-up		147		96.1		145		95.4		0.68 (-3.85, 5.22)		0.85 (0.28, 2.58)		0.990

CVD=Cardiovascular disease, CI=Confidence interval *(AOR=0.71; P=0.188) with analysis of covariance-equivalent multivariable logistic regression analysis

In urban areas, BP control was 92.0% in the intervention group compared to 72.2% in the control group (risk difference − 19.78%; 95% CI: −28.28 to − 11.27, OR: 0.23; P < 0.001) [Table 3].

BP outcomes were assessed in all 607 enrolled participants, including those without hypertension at baseline, consistent with the integrated WHO-PEN approach to comprehensive cardiovascular risk management. To address potential dilution of effect from including normotensive individuals, a sensitivity analysis was conducted, restricting the analysis to participants with baseline hypertension. Among participants with hypertension at baseline, the adjusted mean difference in SBP between intervention and control groups was − 1.1 mmHg (95% CI: −2.7 to 0.6; P = 0.22). For DBP, the adjusted mean difference was + 1.8 mmHg (95% CI: 0.5 to 3.2; P = 0.01). Interaction analyses between group and baseline hypertension status in the full cohort showed no significant interaction for SBP (P = 0.79) or DBP (P = 0.69). These findings suggest that inclusion of normotensive participants did not influence the primary results and did not mask a significant intervention benefit.

The proportion of participants with 10-year CVD risk < 20% in the intervention arm increased from 76.7% to 90.2%, reflecting a reduction in high CVD risk (≥20%) from 23.3% to 9.8% [Table 3].

Medication adherence

In urban areas, regular medication intake at final follow-up was significantly higher in the intervention arm than in the control arm (91.3% vs. 82.6%; OR 0.45, 95% CI: 0.22–0.92; P = 0.03). In rural areas, regular medication adherence at final follow-up was significantly higher in the control arm compared to the intervention arm at the last follow-up visit (91.8% vs. 83.6%; OR 2.96, 95% CI: 1.61 to 5.42; P = 0.003) [Table 3].

Of the 607 NCD patients enrolled, 14 (4.6%) were lost to follow-up in the control group and seven (2.3%) in the intervention group over six months [Table 3].

Behavioral risk factors

Physical activity increased significantly in the intervention group (80.0% vs. 68.4%, OR: 0.54, risk difference: −12.43%; 95% CI: −22.25 to − 6.60; P = 0.001) [Table 4].

Table 4.

Changes in behavioral risk factors during the first and final visits between the control and intervention groups

Parameter		Visit		Control				Intervention				Risk Difference in % with 95% CI		OR with 95% CI		P
				n		%		n		%
Physical activity (at least 150 minutes per week, 5 days a week)		Baseline		178		58.36		88		29.14		29.22 (21.68, 36.76)		3.41 (2.43, 4.77)		<0.001
		Last Follow-up		199		68.38		236		80.00		-12.43 (-22.25, -6.60)		0.54 (0.37, 0.79)		0.001
Vegetable consumption (≥5 times a week)		Baseline		179		58.69		203		67.22		-8.94 (-16.57, -1.30)		0.69 (0.50, 0.96)		0.030
		Last Follow-up		172		59.11		270		91.53		-31.06 (-34.65, -27.47)		0.13 (0.08, 0.21)		<0.001
Fruit consumption (≥5 times a week)		Baseline		96		31.48		143		47.35		-14.64 (-19.97, -9.30)		0.51 (0.37, 0.71)		<0.001
		Last Follow-up		123		42.27		247		83.73		-41.46 (-48.48, -34.44)		0.14 (0.09, 0.21)		<0.001
Current tobacco smoking (yes, if tobacco use during the last 12 months)		Baseline		27		8.85		42		13.91		-5.06 (-19.88, 2.63)		0.60 (0.36, 1.00)		0.052
		Last Follow-up		20		6.87		33		11.91		-5.04 (-20.58, 2.48)		0.59 (0.33, 1.05)		0.071
Alcohol Use (yes, if consumed in the past 30 days)		Baseline		39		12.79		38		12.58		0.46 (-11.48, 12.40)		1.02 (0.63, 1.64)		0.940
		Last Follow-up		38		13.06		28		9.49		3.57 (-2.24, 19.15)		1.43 (0.85, 2.40)		0.174
Salt restriction		Baseline		283		92.79		198		65.56		27.38 (21.13, 33.32)		6.76 (4.12, 11.08)		<0.001
		Last Follow-up		262		90.03		271		91.86		-1.86 (-19.62, 8.50)		0.80 (0.45, 1.41)		0.441
Smokeless tobacco		Baseline		16		5.25		17		5.63		-0.38 (-19.27, 15.55)		0.93 (0.46, 1.87)		0.835
		Last Follow-up		23		7.90		13		4.41		3.49 (3.71, 21.93)		1.86 (0.92, 3.75)		0.082
Smoke Second-hand exposure		Baseline		64		20.98		110		36.48		-15.54 (-20.01, -11.07)		0.46 (0.32, 0.67)		<0.001
		Last Follow-up		64		21.99		82		27.80		-7.47 (-15.76, 0.83)		0.73 (0.50, 1.07)		0.105
Household Exposure to biomass fuel*		Baseline		129		42.30		92		30.46		11.74 (5.43, 18.05)		1.67 (1.20, 2.34)		0.003
		Last Follow-up		127		43.64		76		25.76		16.29 (11.89, 20.69)		2.23 (1.57, 3.16)		<0.001

*Exposure to biomass fuel for cooking/ heating

In urban areas, 92.7% of the intervention group met the physical activity target compared to 77.8% of controls (P = 0.003). Fruit consumption also improved substantially in the intervention group as compared to the control group (93.3% vs. 74.3%; P < 0.001). Salt restriction improved in the intervention group at the last follow-up (risk difference: −29.53%; 95% CI: −47.30 to − 11.76; P = 0.005). Smokeless tobacco use was higher in the control group at the last follow-up (risk difference: 27.05%; 95% CI: 8.14 to 45.95; P = 0.013). Household air pollution exposure was significantly lower in the intervention group as compared to the control group at the last follow-up (12.7% vs. 35.4%, P < 0.001) [Table 5].

Table 5.

Changes in risk factors during the first and final visit between the control and intervention groups in urban and rural areas

Parameter		Visit		Control				Intervention				Risk Difference in % with 95% CI		OR with 95% CI		P
				n		%		n		%
Urban
Physical activity (at least 150 minutes per week, 5 days a week)		Baseline		109		71.7		42		28.0		43.71 (33.75,53.85)		6.52 (3.95,10.76)		<0.001
		Last Follow-up		112		77.8		139		92.7		-29.80 (-44.21,-15.38)		0.28 (0.13,0.57)		0.003
Vegetable Consumption (≥ 5 times a week)		Baseline		145		95.4		123		82.0		13.52 (18.67,48.36)		4.547 (1.19,10.80)		0.001
		Last Follow-up		136		94.4		144		96.0		-8.57 (-35.15, 18.0)		0.70 (0.24,2.09)		0.531
Fruit Consumption (≥5 times a week)		Baseline		82		53.9		101		67.3		-14.01% (-25.42,-2.61)		0.57 (0.36,0.91)		0.02
		Last Follow-up		107		74.3		140		93.3		-35.4% (-48.63, -22.17)		0.21 (0.10,0.43)		<0.001
Current tobacco smoking (yes, if tobacco use during the last 12 months)		Baseline		17		11.2		14		9.3		5.02 (-13.48,23.52)		1.22 (0.58,2.58)		0.596
		Last Follow-up		12		8.3		11		7.3		3.46 (-17.8, 24.73)		1.15 (0.49,2.69)		0.750
Alcohol Use (yes, if consumed in the past 30 days)		Baseline		27		17.8		21		14.0		7.04% (-8.283, 22.36)		1.33 (0.71,2.47)		0.372
		Last Follow-up		31		21.5		21		14.0		12.92 (-1.82, 27.66)		1.69 (0.92,3.10)		0.09
Salt restriction		Baseline		140		92.1		86		57.3		46.16 (35.85, 56.51)		8.68 (4.43,17.01)		<0.001
		Last Follow-up		125		86.8		144		96.0		-29.53 (-47.3,-11.76)		0.27 (0.16,0.71)		0.005
Smokeless tobacco		Baseline		9		5.9		7		4.7		1.25 (-18.74,31.24)		1.29 (0.46,3.55)		0.627
		Last Follow-up		17		11.8		6		4.0		27.05 (8.14,45.95)		3.20 (1.26,9.10)		0.013
Smoke Second-hand exposure		Baseline		19		12.5		20		13.3		-1.85 (-18.66,14.96)		0.93 (0.47, 1.82)		0.829
		Last Follow-up		19		13.2		16		10.7		6.02 (-11.57,23.61)		1.27 (0.63,2.59)		0.504
Household Exposure to biomass fuel*		Baseline		46		30.3		24		16.0		20.02 (7.19,32.86)		2.28 (1.30,3.98)		0.003
		Last Follow-up		51		35.4		19		12.7		22.74 (10.09,43.59)		3.78 (2.10,6.82)		<0.001
Rural
Parameter		Visit		Control				Intervention				Risk Difference in % with 95% CI		OR with 95% CI		P
				n		%		n		%
Physical activity (at least 150 minutes per week, 5 days a week)		Baseline		69		45.1		46		30.1		16.02 (4.63,27.41)		1.91 (1.19,3.06)		0.007
		Last Follow-up		87		59.2		97		66.4		-7.763 (-19.56,4.04)		0.73 (0.45,1.18)		0.199
Vegetable Consumption (≥5 times a week)		Baseline		34		22.2		81		52.9		-32.74 (-43.55,-21.93)		0.25 (0.15,0.41)		<0.001
		Last Follow-up		36		24.5		127		87.0		-63.3 (-72.1,-54.5)		0.05 (0.03,0.09)		<0.001
Fruit Consumption (≥5 times a week)		Baseline		14		9.2		43		28.1		-31.26 (-44.03,-18.50)		0.26 (0.13,0.50)		<0.001
		Last Follow-up		16		10.9		108		74.0		-64.61 (-73.24,-55.98)		0.04 (0.02,0.08)		<0.001
Current tobacco smoking (yes, tobacco using during the last 12 months)		Baseline		10		6.5		29		19.0		-12.72 (-42.87,-12.97)		0.30 (0.14,0.64)		0.001
		Last Follow-up		8		5.4		23		15.8		-10.25 (-17.2,-3.6)		0.31 (0.13,0.71)		0.004
Alcohol Use (yes, if consumed in past 30 days)		Baseline		12		7.8		18		11.8		-4.09 (-29.58,7.41)		0.64 (0.30,1.38)		0.250
		Last Follow-up		7		4.8		8		5.5		-3.69 (-29.61,22.23)		0.87 (0.29,2.5)		0.780
Salt restriction		Baseline		143		93.5		113		73.9		35.86 (23.21,48.50)		5.06 (2.43,10.56)		<0.001
		Last Follow-up		137		93.2		128		87.7		15.98 (-2.75,34.72)		1.93 (0.86,4.33)		0.108
Smokeless tobacco		Baseline		7		4.6		10		6.5		-9.34 (-33.44,14.75)		0.68 (0.18, 0.25)		0.454
		Last Follow-up		6		4.1		7		4.8		-4.20 (-31.93,23.52)		0.84 (0.28,2.58)		0.767
Smoke Second-hand exposure		Baseline		45		29.4		91		59.5		-30.44 (-41.16,-19.72)		0.28 (0.18,0.46)		<0.001
		Last Follow-up		45		30.6		67		45.9		-16.18 (-27.78,-4.57)		0.52 (0.32,0.84)		0.007
Household Exposure to biomass fuel*		Baseline		83		54.2%		68		44.4		9.81 (-1.34,20.96)		1.48 (0.94,2.32)		0.086
		Last Follow-up		76		51.7%		57		39.0		12.77 (1.37,24.17)		1.67 (1.05,2.66)		0.029

*Exposure to biomass fuel for cooking/ heating

In rural areas, vegetable and fruit consumption improved significantly at the end of intervention (OR: 0.05; 95% CI: 0.03–0.09, and OR: 0.04; 95% CI: 0.02–0.08, respectively; P < 0.001). Household pollution exposure was significantly reduced in the intervention group (39.0%) than the control arm (51.7%) (OR 1.67, 95% CI: 1.05–2.66; P = 0.029), suggesting a favorable intervention effect in reducing household air pollution exposure [Table 5].

Metabolic parameters

At the last follow-up, the intervention group had significantly lower SBP (adjusted mean difference = 3.33 mmHg; 95% CI: 1.47–5.19; P < 0.001), and after adjustment with ANCOVA for baseline SBP, no statistically significant difference was found in SBP control between the intervention and control groups. The intervention group had significantly lower BMI (adjusted mean difference = −1.68 kg/m²; 95% CI: −2.42 to − 0.94; P < 0.001) than the control group after adjustment for family type and income. No statistically significant differences were observed for DBP, blood sugar, or total cholesterol after adjusting for family type and income.

In rural areas, the intervention group showed significant reductions in adjusted mean difference in BMI (−1.65 kg/m²; 95% CI: −2.83, −0.47; P = 0.006) and random blood sugar (−10.68 mg/dL; 95% CI: −18.07−3.29; P = 0.005) compared to the control group. Total blood cholesterol was also significantly lower in the intervention group (19.63 mg/dL mean difference; 95% CI: 10.47 to 28.78; P < 0.001), while no significant differences were observed in systolic or DBP after adjusting for monthly family income, gender, and family type.

In urban areas, significant improvements were observed in adjusted mean differences in SBP in the control group as compared to the intervention group (6.71 mmHg; 95% CI: 4.19 to 9.24; P < 0.001). Significant improvements were observed in adjusted mean differences in intervention group at the last follow-up visit in BMI (−2.06 kg/m²; 95% CI: −2.95 to − 1.17; P < 0.001), random blood sugar (−19.67 mg/dL; 95% CI: −30.83 to − 8.50; P = 0.001), and total cholesterol (−11.09 mg/dL; 95% CI: −18.98 to − 3.20; P = 0.006), with no significant change in DBP after adjusting for monthly family income.

DISCUSSION

This study implemented WHO-PEN protocols for integrated management of hypertension and diabetes (protocol 1)[14] alongside health education and counseling on healthy behaviors (protocol 2).[15] Nonphysician health workers provided brief behavior-change interventions, emphasizing physical activity, healthy diets, tobacco cessation, and regular follow-up.

The quasi-experimental study demonstrated improvements in BP control and behavioral risk factors. Patients in the intervention group showed better treatment adherence and healthier lifestyle choices at the end of the intervention. The 10-year CVD risk (>20%) declined.

The study highlighted the feasibility and potential of incorporating WHO-PEN protocols for diagnosing and managing CVD and diabetes mellitus at the primary healthcare level. Findings from various studies have consistently shown that health systems that prioritize the management of NCDs and address modifiable risk factors are better positioned to promote and maintain health.[16]

A systematic review of implementation research on NCDs found that fewer than 10% of the interventions evaluated focused on hypertension management.[5] Primary care services such as hypertension management and glucose control play a significant role in reducing mortality. Recent work shows that treatment and control rates for hypertension were below 25% and 10%, respectively, in many countries in sub-Saharan Africa and South Asia.[17]

The proportion of participants with controlled BP was significantly higher in the intervention group. Implementation of the PEN intervention in the primary healthcare setting in the Southeast Asia region has led to improvement in BP control and diabetes control and a reduction in CVD risk.[10,11,18] The attenuation of the intervention effect after adjustment indicates that baseline BP control was a strong determinant of follow-up BP status. The observed difference could be due to the control group starting with higher BP rather than a true effect of the intervention.

The study observed a higher rate of BP control in the rural control arm at final follow-up (90.5% vs. 84.9%). One possible explanation is that baseline BP control was substantially higher in the rural intervention group (70.6%) compared to controls (49.7%), potentially limiting further improvement (ceiling effect), while the control group had greater scope for change. In addition, variations in routine service delivery, patient engagement, and healthcare worker practices across facilities may have influenced outcomes. The Government of India has formulated a population-based screening program for NCDs in which patients receive counseling regarding behavioral risk factors at the community level.[6] Such programs are likely to influence both the intervention and control arms equally, minimizing their impact on observed differences. Control participants who were asked to attend follow-ups for assessments may have become self-motivated after enrollment. The improvement observed in the control group may partly reflect a Hawthorne effect, where participants modified their behavior due to awareness of being observed during follow-up visits, potentially influencing the study outcomes.

The proportion of participants with 10-year CVD risk ≥ 20% decreased in both groups over 6 months. However, the between-group difference at final follow-up was not statistically significant (OR 0.85; P = 0.553). Implementing WHO-PEN interventions has led to a significant reduction in the number of people with a 10-year risk of CVD ≥20% over periods ranging from 3 months to 1 year in Bhutan and the Democratic People’s Republic of Korea.[10,11]

Our study has shown significant improvement in biological and behavioral parameters in the intervention group at follow-up visits 6 months after enrollment, compared with the control group. There was a significant improvement in the proportion of participants with adequate physical activity in the intervention arm, with 80.0% participants compared to 68.4% in the control group. Similarly, vegetable and fruit consumption at least five times a week increased significantly (P < 0.001). The most effective preventive strategy is the one that leads to changes in lifestyle, including diet, physical activity, and control of metabolic disorders.[19] Lifestyle activities include healthy eating and limiting salt, sugar, and saturated fats.[20] Most global discussions focus on the role of individual responsibility in managing the NCD risk factors of self-management (tobacco and alcohol consumption, physical activity, and diet), though the Government of India has formulated a population-based screening program for NCDs.[6] Complementing population-wide interventions with individual interventions is essential, as population-level interventions alone will not adequately protect high-risk individuals.[21]

In the current study, participants in the intervention group in the urban area were significantly more likely to adhere to regular medication at the last follow-up visit than the control group. Medication intake has been associated with lower SBP and DBP in primary healthcare centers situated in urban and suburban communities in Sweden.[22]

Brief interventions using the “5As” and “5Rs” model for behavioral risk factors have been provided by trained nonphysician health workers. Several studies have shown the utility and high acceptability rate of motivational interviewing using “5As” among health providers to address behavioral risk factors.[23,24,25,26,27]

Various studies have demonstrated that nonphysician primary healthcare workers can extend screening, diagnostic, treatment, and counseling services to NCD patients from a health facility close to their homes.[8,28] It has been seen that through enhanced training and community engagement, community health workers can contribute to preventing and controlling NCDs, including health education and community mobilization.[29,30] The evaluation of a program indicated that a combination of printed material and face-to-face intervention was a suitable approach to support lifestyle behavioral change in the Vietnamese population throughout the six-month intervention.[31] Other studies have highlighted the potential of PEN training in primary healthcare settings for improved NCD management.[32]

A significant reduction in household exposure to biomass fuel was observed in the intervention group across both urban and rural settings. At the final follow-up, exposure levels remained considerably higher in the control group (overall OR 2.23; P < 0.001). While the intervention did not directly facilitate access to clean fuels, counseling delivered under WHO-PEN protocol 2 provided education on the health risks of indoor air pollution and guidance on improved ventilation and safer cooking practices. In Himachal Pradesh, where biomass fuel use is prevalent due to geographic and climatic conditions, these results indicate that even brief behavioral counseling can mitigate environmentally linked health risks.

The intervention improved overall health outcomes, with better BP control, greater gains in physical activity, medication adherence in urban areas, and more substantial dietary and tobacco-related changes in rural settings. This suggests that the overall effect may be site-specific and not fully generalizable, highlighting the need for tailored interventions.

Strengths and limitations

This quasi-experimental study, conducted in both urban and rural settings, used a pretest-posttest comparison group design without recruiting additional staff, highlighting its feasibility and scalability. However, the lack of random assignment may introduce selection bias. The inclusion of patients with different but related noncommunicable diseases may have introduced clinical heterogeneity, and the absence of disease-specific subgroup analyses limits the interpretation of intervention effects. As multiple secondary outcomes were evaluated, there is potential for inflation of type I error. Secondary outcomes were not prespecified with adjustment for multiple testing; therefore, these analyses were exploratory and should be interpreted cautiously. Data on the number of participants screened or found ineligible were not systematically recorded during recruitment. Self-reported data on behavior change (e.g., tobacco, alcohol, salt restriction, and medication adherence) is another limitation that, in this unblinded study, may lead to social desirability bias. It may be recommended for future studies to use objective verification methods, e.g., cotinine for tobacco, pill counts for adherence, and urinary sodium for salt. Substantial baseline differences in fruit and vegetable consumption between intervention and control groups may have influenced the observed improvements and limited attribution solely to the intervention. As the intervention was allocated at the facility level but analyzed at the individual level without adjusting for clustering, the reported P-values may be underestimated and should be interpreted with caution. The six-month follow-up period limits insights into the sustainability of behavior changes, necessitating longer-term studies.

CONCLUSION

The study indicates that integrating WHO-PEN protocols, especially the components focusing on integrated hypertension and diabetes management (protocol 1) and health education and counseling on healthy behaviors (protocol 2), has led to improved behavioral outcomes among patients at the primary healthcare level, while BP control did not improve significantly after adjustment. We propose involving nonphysician health workers in managing NCD patients to provide individualized counseling to initiate and sustain behavior change. This study reinforces the importance of integrating WHO PEN guidelines within national health programs to strengthen primary healthcare services for NCD management.

List of abbreviations

WHO World Health Organization, PEN Package of essential noncommunicable disease, NCD Noncommunicable disease, CVDs Cardiovascular diseases, DM Diabetes mellitus, OPD Outpatient department, BCC Behavior-change communication, ISH International Society of Hypertension, SEAR Southeast Asia region, BMI Body Mass Index, BP Blood pressure, SBP Systolic blood pressure, DBP Diastolic blood pressure.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

The author has received an intramural grant from the host institution for conducting the research. The funding source has no involvement in study design, data collection, or the decision to submit the article for publication.