Prospective feasibility study of a telehealth platform for community-based NCD risk assessment by community health leaders in rural northern Thailand

Abstract

Introduction

Limited access to preventive healthcare in rural and remote areas poses a persistent challenge for early identification of non-communicable disease (NCD) risk. We evaluated the feasibility and acceptability of HealthD, a telehealth-supported platform that enables Community Health Leaders (CHLs) to conduct community-based NCD risk assessment and facilitate follow-up in rural northern Thailand.

Methods

We conducted a prospective, single-arm feasibility and acceptability study from June to November 2023 in Chiang Mai Province, Thailand. A total of 120 adults aged 30-70 years without a prior recorded diagnosis of the target NCDs were enrolled. Trained CHLs used HealthD to perform community-based assessment for diabetes mellitus (DM), cardiovascular disease (CVD), and respiratory symptom/functional limitation screening using guideline-informed algorithms and field-based measures, followed by risk-tailored counseling or teleconsultation at two- and four-month follow-up. Feasibility was assessed through recruitment, retention, CHL competency, workflow delivery, and acceptability among CHLs and participants.

Results

Of 126 individuals approached, 120 were enrolled (recruitment rate 95.2%), and 119 completed follow-up (retention rate 99.2%). CHL competency improved after training, with mean assessment scores increasing from 8.4 to 9.3 (p= 0.010). Acceptability was high among CHLs (92%) and participants (97%). The HealthD workflow, including in-person assessment, point-of-care testing, digital data entry, algorithm-based risk-output generation, and teleconsultation follow-up, was implemented in the study setting. NCD-related risk outputs are presented as descriptive, exploratory findings. At baseline, 73% of participants were classified for DM-related risk, 3% for CVD-related risk, and 9% for respiratory symptom/functional limitation category. Across follow-up visits, DM-related risk categories changed, whereas CVD- and respiratory symptom/functional limitation categories remained relatively stable.

Conclusion

This prospective study suggests that HealthD is feasible and acceptable for CHL-led, telehealth-supported community-based NCD risk assessment and follow-up in a rural Thai setting. These findings are preliminary and are intended to inform workflow refinement and the design of larger studies evaluating effectiveness, implementation outcomes, and sustainability.

Keywords

telehealth feasibility study non-communicable diseases community health leaders rural Thailand

1. Introduction

Non-communicable diseases (NCDs) are a major global health challenge and account for most deaths worldwide, with cardiovascular diseases (CVDs), diabetes mellitus (DM), chronic respiratory diseases, and cancer contributing substantially to premature mortality.¹ In Thailand, NCDs account for a large proportion of overall mortality, with cancer, CVD, DM, and chronic respiratory diseases among the leading causes.^2,3 Beyond their clinical burden, NCDs place sustained pressure on the health system by increasing service demand, contributing to overcrowding and longer waiting times, and generating substantial long-term economic and productivity costs.⁴ Although Thailand’s Universal Coverage Scheme has improved financial access to care, disparities in access to preventive services and early detection persist, particularly in rural and remote areas where distance, transport barriers, and limited local resources can delay screening and referral.⁵ Moreover, a substantial proportion of people at risk of or living with NCDs remain unaware of their health status.⁶ Although NCD screening is an important tool for identifying individuals at risk and monitoring those living with NCDs, gaps remain in extending risk assessment and referral support beyond conventional health facilities.

Telehealth-supported NCD screening may help address these service-delivery gaps.^7–9 Several studies indicate that telehealth is feasible and acceptable across diverse health domains, including rural and remote videoconferencing care, chronic disease monitoring, and hybrid models that combine in-person and remote components.^10–12 In low- and middle-income countries, telehealth interventions have shown potential to expand access to care and strengthen NCD services.^13–15 However, important implementation uncertainties remain before such an approach can be evaluated at scale.

Community Health Leaders (CHLs), also known as Village Health Volunteers (VHVs) in Thailand, are trusted community-based actors who link households with the formal health system.¹⁶ They commonly support health promotion, disease prevention, basic assessment, and follow-up care within their communities. Because they are locally accessible and familiar with community contexts, CHLs may be well positioned to support task-shifted screening workflows in settings with limited professional health personnel. Their role may be particularly valuable in rural areas for convening participants, facilitating initial assessments, and supporting referral adherence.

However, it remains unclear whether CHLs can consistently perform standardized assessments and use digital tools after brief training, whether CHL-led workflows can be delivered with acceptable fidelity under routine village conditions, and whether teleconsultation-supported follow-up is practical and acceptable for rural community members. In addition, the operational feasibility of the full workflow-community recruitment, point-of-care assessment, digital data entry, algorithm-based risk stratification, and teleconsultation referral has not been well established in real-world rural settings. A feasibility study is therefore needed to identify implementation barriers, assess workflow performance, and inform refinement prior to broader deployment.

In Thailand, telehealth applications for proactive, community-based NCD risk assessment remain limited, particularly integrated workflows that extend beyond single-condition tools or facility-based models. Many existing approaches have focused on disease-specific management or clinic-centered delivery rather than community-led, multi-condition early risk identification. HealthD, a telehealth platform, was developed to address this gap by supporting a CHL-delivered, Thai guideline-concordant workflow for community-based assessment of risk related to DM, CVD, and respiratory symptom/functional limitation categories. The platform integrates digital data capture, algorithm-based risk stratification, and teleconsultation-supported referral. In this model, CHLs conduct point-of-care assessments, enter data into the platform, and facilitate follow-up and referral when indicated, thereby supporting the early identification of at-risk community members outside traditional healthcare facilities.

Accordingly, this prospective feasibility study aimed to assess the feasibility and acceptability of implementing the end-to-end HealthD workflow in a rural community in Thailand. Feasibility was evaluated in relation to participant recruitment and retention, CHL training competency, completion of key workflow steps, data completeness, and the practicality of teleconsultation-supported follow-up. Acceptability was evaluated through satisfaction surveys administered to both CHLs and study participants. A secondary exploratory objective was to descriptively characterize NCD-related risk profiles within the study population. This study was designed to evaluate operational feasibility and acceptability rather than diagnostic validation or clinical effectiveness.

2. Materials and methods

2.1. Overview of the HealthD system

HealthD is a telehealth-supported platform developed to facilitate CHL-led community-based NCD risk assessment and remote consultation in underserved areas. Its core field implementation tool is the HealthD backpack, a mobile kit containing vital sign monitoring devices, physical examination tools, point-of-care testing (POCT) equipment, sanitary supplies, and a tablet computer for digital data entry and teleconsultation.

HealthD incorporates the T-logic algorithm, a web-based clinical decision-support tool that classifies individuals into four NCD-related risk levels-green (normal), yellow (low risk), orange (moderate risk), and red (high risk) based on Thai and international clinical guidance adapted for Thai populations. Trained CHLs conducted in-person community assessments, including sociodemographic data collection, vital signs measurement, physical examination, and NCD-related risk assessment excluding cancer screening. Screening data were uploaded to a secure online database, and the T-logic algorithm generated risk outputs and triage recommendations. Participants then received risk-tailored teleconsultation or counseling, followed by repeat assessments at two and four months (Figure 1). The HealthD workflow was therefore evaluated both as an integrated intervention and through component-level feasibility indicators.

Figure 1.

Framework of HealthD system.

This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for observational studies.

2.1.1. Composition of the HealthD backpack

The HealthD backpack contained Thai Food and Drug Administration-approved devices. Vital-sign monitoring equipment included a digital thermometer (Omron MC-246), fingertip pulse oximeter (Yuwell YX102), and electronic blood pressure monitor (Yuwell YE670D); respiratory rate was measured manually. Anthropometric assessment included a digital weighing scale (Xiaomi Smart Scale 2), a height tape, and a waist measurement tape. Respiratory symptom and functional limitation assessment included the Six-Minute Walk Test (6MWT), which was used as a field-based measure of aerobic capacity and functional endurance. POCT equipment included a blood glucose meter (ACCU-CHEK® Instant), lipid analyzer (Accutrend® Plus), and urine dipstick kit (Combur3 Test®). The backpack also contained sterile lancing devices, sanitation materials, biohazard disposal supplies, and a tablet computer used for real-time data entry and teleconsultation (Figure 2).

Figure 2.

HealthD backpack and medical equipment.

2.1.2. T-logic algorithm and digital platform

The T-logic algorithm for NCD-related risk categorization was developed using JavaScript in Visual Studio Code v1.87 and was based on Thai and international clinical practice guidance.^17–19 A web-based interface using HTML5 and CSS enabled access from tablets, desktop computers, and smartphones. Data were managed using phpMyAdmin v4.9.5 and the platform was simulated locally using XAMPP v8.2.12. Before field implementation, the platform underwent functionality testing using predefined test cases and workflow simulations to verify data capture, risk-output generation, and secure data handling. The algorithm was rule-based and functionally tested before deployment; it was not independently validated as a diagnostic algorithm before this feasibility study.

For transparency, separate outputs were generated for diabetes-related risk, cardiovascular disease (CVD)-related risk, and respiratory symptom/functional limitation categories. The overall integrated NCD risk category was determined according to the highest severity level identified across the disease-specific outputs. Risk levels were classified as high, moderate, low, or normal and represented by red, orange, yellow, and green indicators, respectively. These outputs were intended to support triage and follow-up decision-making rather than provide definitive disease diagnoses (Figure 3).

Figure 3.

Framework of T-logic algorithm. BP; Blood pressure, BMI; Body Mass Index, CAT score; COPD Assessment Test score, DRS; Diabetes Risk Score, FPG; Fasting plasma glucose, FDM; Family history of Diabetes mellitus, Thai CV risk score; Thai Cardiovascular Risk Score, WC; Waist circumference.

2.1.3. Data security, privacy, and teleconsultation governance

Participants were assigned unique study identification codes, and identifiable information was stored separately from research datasets whenever feasible. Access to online case report forms and the HealthD database was restricted to authorized study personnel through password-protected accounts. Teleconsultations were conducted via mobile devices using Line® because the platform is widely used in Thailand and is familiar to local participants and CHLs. Participants were informed that teleconsultations would occur through a mobile communication platform, and they provided consent prior to participation.

CHLs assisted participants in initiating teleconsultations when needed, and healthcare professionals documented consultation recommendations within study records. Participants requiring further evaluation were referred according to their assigned risk category and local clinical referral pathways. These measures were implemented to support data confidentiality, operational feasibility, and continuity of care during the feasibility phase of the study.

2.1.4. HealthD Teleconsultation Framework

Teleconsultations were delivered using mobile messaging applications (Line®) and digital devices (tablet or smartphone) to enable remote interaction between participants and healthcare professionals. The intensity of teleconsultation was determined by the risk level generated by the T-logic algorithm. Participants classified as green, indicating normal risk, received lifestyle counseling from CHLs. Those classified as yellow, indicating low risk, or orange, indicating moderate risk, received teleconsultation from medical technologists or registered nurses. Participants classified as red, indicating high risk, were referred to family physicians for further assessment, counseling, and management recommendations. Teleconsultation content was informed by the Transtheoretical Model of behavior change.²⁰ For participants in precontemplation or contemplation stages, counseling emphasized awareness of risk and the potential benefits of change. For those in preparation or action stages, providers discussed practical goal setting and behavior-change planning, including diet, physical activity, medication-seeking or referral behavior, and follow-up planning. Maintenance-oriented messages reinforced sustained preventive practices and repeat monitoring. This stage-informed approach was used to tailor counseling to participants’ readiness for change.²¹

2.2. Study implementation

This was a prospective, single-arm feasibility and acceptability study conducted from June to November 2023. The study was designed to evaluate the operational feasibility and user acceptability of the integrated HealthD workflow.

2.2.1. Sample size justification

This study was designed as a feasibility and acceptability evaluation rather than a population-representative survey or an effectiveness trial. Accordingly, the sample size was determined pragmatically on the basis of implementation capacity and the need to estimate key feasibility parameters with reasonable descriptive precision. We targeted 120 participants, corresponding to delivery capacity across 12 CHLs with approximately 10 participants per CHL. This sample size allowed each CHL to complete repeated assessment and follow-up procedures while keeping the field workload manageable for a supervised feasibility phase. This sample size was considered sufficient to assess the feasibility of the end-to-end workflow across multiple CHLs and participating villages. The study was not powered to detect changes in clinical outcomes, and NCD-related risk outputs were analyzed as secondary, exploratory findings.

2.2.2. Study locations and ethical considerations

This prospective feasibility study was conducted in Chiang Mai Province, Thailand, a mountainous region where access to healthcare remains challenging. The study took place in Doi Lo District, approximately 45 km from Chiang Mai city, an area characterized by plateau and upland terrain at an average elevation of approximately 300 meters.²² Field implementation was conducted from June to November 2023 across five rural villages: Don Chuen, Lao Pao, Lang Mon, Wang Kham Pom, and Huay Pao Yong.

Ethical approval was obtained from the Research Ethics Committee of the Faculty of Associated Medical Sciences, Chiang Mai University (Approval No. AMSEC-66EX-011). Written informed consent was obtained from all participants prior to enrollment.

2.2.3. CHL training

Eligible CHLs were required to have at least one month of POCT experience, be able to use tablet computers, and provide written informed consent. No exclusion criteria were specified. CHLs completed an intensive 8-hour standardized training program delivered by licensed healthcare professionals, including medical technologists, registered nurses, and physical therapists with experience in community health and NCD screening or management. Training content was aligned with the nationally standardized training curriculum for Village Health Volunteers in Thailand.²³

The training program included: (1) CHL registration and orientation to the HealthD workflow and digital platform; (2) a pre-training NCD knowledge assessment; (3) standardized instruction on measurement procedures, POCT operation, basic quality control, risk-category interpretation, teleconsultation procedures, documentation, privacy and confidentiality, and hands-on practice with POCT devices and the HealthD software; and (4) a post-training NCD knowledge assessment. Competency and practical skills were evaluated by healthcare professionals using structured observation checklists during simulation-based practice sessions. CHLs were required to achieve a minimum score of 80% to be certified for field implementation. CHLs also completed a post-training assessment of knowledge and practical skills covering all steps of the HealthD workflow. In addition, CHLs completed a satisfaction survey evaluating the training program (Supplementary Figure S1).

2.2.4. Participant recruitment and retention procedures

Eligible participants were community-dwelling adults aged 30–70 years who resided in the participating villages, had no prior diagnosis of the target NCDs, and were able to provide written informed consent. Exclusion criteria included: (1) known diagnosis of DM, CVD, COPD, or other target NCDs already managed within formal care pathways; (2) physical disability or functional limitation preventing safe completion of field assessments; (3) severe mental disorder; and (4) history of drug abuse. Individuals with prior NCD diagnoses were excluded because this feasibility phase was designed to evaluate a screening workflow for risk identification among adults who were unaware of their NCD status and not already managed within formal NCD care pathways. Participants were recruited by trained CHLs using community-based mobilization and individual contact within their assigned villages. CHLs informed potentially eligible adults about the study through local community networks and direct invitation, screened them for initial eligibility, and referred interested individuals to the study team for informed-consent procedures. Retention strategies included CHL support, flexible scheduling of follow-up visits, reminder contacts before scheduled teleconsultations, assistance with teleconsultation initiation, and rescheduling when participants were unavailable or connectivity problems occurred.

2.2.5. HealthD implementation by CHLs

After training, each CHL recruited participants over a one-week period, with a target of approximately 10 participants per CHL. After informed consent was obtained, eligible participants were assigned unique study identification codes to maintain confidentiality. The intervention consisted of the integrated end-to-end HealthD workflow, including CHL-led recruitment, in-person point-of-care assessment, functional assessment, digital data capture, algorithm-supported risk stratification, teleconsultation-supported counseling or referral, and scheduled follow-up. Component-level feasibility indicators were documented to inform refinement before larger-scale evaluation. These indicators included completion of individual workflow steps, completeness of core data fields, successful generation of risk outputs, completion of teleconsultations, user acceptability, and operational barriers such as connectivity disruption or device-related issues.

CHLs collected socio-demographic information, medical history, vital signs, and physical examination data. NCD-related assessment components included the following:

2.2.5.1. Diabetes-related risk assessment

Diabetes-related risk was assessed using the Diabetes Risk Score (DRS) of the Diabetes Association of Thailand, incorporating age, body mass index (BMI), waist-to-height ratio, blood pressure (BP), family history of diabetes (FDM), and fasting plasma glucose (FPG).¹⁷ (Supplementary Table S1).

2.2.5.2. Cardiovascular risk assessment

CVD-related risk was assessed using the Thai Cardiovascular Risk Score, also known as the Thai CV Risk Score, which was derived from Thai population data and adopted by Thailand’s Ministry of Public Health.^18,24 This score incorporates diabetes status, sex, smoking status, age, blood cholesterol, and systolic blood pressure to generate risk outputs and color-coded triage categories (Supplementary Figure S2).

2.2.5.3. Respiratory symptom and functional limitation risk assessment

This component used the CAT together with the 6MWT. The CAT comprises eight items assessing cough, sputum, chest tightness, breathlessness, activity limitation, confidence, sleep, and energy,¹⁹ while the 6MWT was used as a field-based functional measure.²⁵ This component was intended to support exploratory stratification of respiratory symptoms and functional limitation in the community setting rather than diagnostic confirmation (Supplementary Table S2).

Data were recorded on paper forms and in online case report forms (CRFs). Teleconsultations were conducted at two-month and four-month follow-up points according to participant risk level. Paper CRFs were checked against online entries by the study team to identify incomplete fields or inconsistencies. At study completion, participants completed satisfaction surveys to assess the acceptability of the HealthD system.

2.2.6. Feasibility and acceptability indicators

The primary objective of this study was to evaluate the feasibility and acceptability of implementing the integrated HealthD workflow. Feasibility was assessed using predefined operational indicators, including recruitment of at least 80% of approached eligible individuals, retention of at least 80% at final follow-up, completion and certification of at least 80% of CHLs following training, successful completion of key workflow components (including POCT completion, digital data entry, and teleconsultation-supported follow-up) in at least 80% of participants, successful generation of risk outputs for at least 80% of attended visits with complete required data, completeness of core data fields, and the occurrence of operational barriers during implementation, such as connectivity disruptions or device-related issues. Acceptability was evaluated using structured satisfaction surveys completed by CHLs and participants at the end of the study. Satisfaction or acceptability ratings of at least 80% were considered indicative of acceptable implementation feasibility. The satisfaction surveys included items on perceived usefulness, ease of use, clarity of information, confidence in CHL-supported procedures, convenience, teleconsultation experience, willingness to use HealthD again, and perceived suitability for community integration. (Supplementary Tables S3 and S4). A secondary, exploratory objective was to descriptively characterize NCD-related risk profiles in the study population. These findings were not intended to establish effectiveness, diagnostic performance, or comparative benefit.

2.3. Statistical analysis

Data were analyzed primarily using descriptive statistics because this was a feasibility and acceptability study. Baseline participant characteristics, including sex, age, occupation, income, education, vital signs, height, weight, BMI, waist-to-hip ratio, and NCD-related risk scores for diabetes, CVD, and respiratory symptom and functional assessment, were summarized descriptively. Categorical variables are presented as numbers and percentages. Continuous variables are presented as medians with interquartile ranges or means with standard deviations depending on distribution. For key feasibility proportions, percentages were calculated using the number of eligible, enrolled, attended, or responding participants as the denominator, as appropriate. CHL pre- and post-training knowledge scores were compared using the Wilcoxon signed-rank test. The association between CAT score and 6MWT distance was explored using Pearson correlation analysis. A p-value of ≤0.05 was considered statistically significant.

Satisfaction survey responses were evaluated using a 5-point Likert-type scale ranging from 1, indicating very dissatisfied, to 5, indicating very satisfied. Mean scores were categorized as follows: 1.00-1.80 = very dissatisfied; 1.81-2.60 = dissatisfied; 2.61-3.40 = neutral; 3.41-4.20 = satisfied; and 4.21-5.00 = very satisfied.²⁶ Because this was a feasibility study, analyses of NCD-related risk outputs were descriptive and exploratory. All analyses were performed using Stata version 16.0 (StataCorp, College Station, TX, USA).

3. Results

3.1. Participant recruitment and retention (feasibility)

Of the 126 individuals approached, 120 were enrolled, corresponding to a recruitment rate of 95.2%. One participant was lost to follow-up before the third visit, resulting in an overall retention rate of 99.2% among enrolled participants (Supplementary Figure S3). A practical recruitment challenge was that some individuals with known NCD diagnoses expressed interest in enrollment because they wished to access telehealth follow-up care instead of attending routine hospital visits. As the study was designed to assess the feasibility of a screening workflow among adults without prior recorded diagnoses of the target NCDs, these individuals were not eligible for participation.

3.2. CHL training completion and competency (feasibility)

Twelve CHLs completed the standardized training program and underwent pre- and post-training knowledge assessments and practical competency assessments. Before training, 11 of 12 CHLs achieved the predefined certification threshold of at least 80%, with a mean pre-test score of 8.4 out of 10 (SD 0.9). After training, all 12 CHLs achieved scores of at least 80%, and the mean post-test score increased to 9.3 out of 10 (SD 0.6). This improvement was statistically significant (Wilcoxon signed-rank test, p= 0.010), suggesting that the training program was adequate for preparing CHLs to implement the workflow.

3.3. Workflow delivery and implementation outcomes (feasibility)

The planned HealthD workflow was implemented during the study period and included CHL-led participant recruitment, in-person assessment, POCT procedures, digital data entry, algorithm-based risk-output generation, and risk-tailored teleconsultation follow-up. Data entry and risk-output generation were completed for 100% across all phases, while data completeness ranged from 97.5% to 100.0%, exceeding the predefined feasibility criteria of 80%. However, data verification accuracy ranged from 70.8% to 75.8%, which did not meet the predefined feasibility threshold. Teleconsultation-supported follow-up demonstrated high completion across all risk groups. Among participants in the normal-risk group, CHL-led follow-up completion was 100% at the first and second visits and 95.8% at the third visit. Medical technologists and nurses achieved 100% follow-up completion for all participants in the low-to moderate-risk group across all study visits. Likewise, family physicians achieved 100% follow-up completion among participants in the high-risk group across all visits. Operational disruptions were infrequent throughout the study period. Internet connectivity issues were reported for 8.3% (10/120) of participants at the first visit, decreasing to 5.8% (7/120) at the second visit and 5.9% (7/119) at the final visit. Missed scheduled telehealth appointments were rare, with no cases reported at the first or second visit and only one case reported at the final visit (0.8%) (Table 1).

Table 1.

Operational feasibility indicators.

Feasibility indicators	1^st visit n/N (%)	2^nd visit n/N (%)	3^rd visit n/N (%)	Accepted criteria	Outcome
Recruitment rate	120/126 (95.2%)	Not applicable	Not applicable	≥80%	Achieved
Retention rate	120/120 (100.0%)	120/120 (100.0%)	119/120 (99.2%)	≥80%	Achieved
Data entry	120/120 (100.0%)	120/120 (100.0%)	119/119 (100.0%)	≥80%	Achieved
Data verification	91/120 (75.8%)	85/120 (70.8%)	85/119 (71.4%)	≥80%	Not achieved
Data completeness	120/120 (100.0%)	117/120 (97.5%)	116/119 (97.5%)	≥80%	Achieved
Risk-output generation	120/120 (100.0%)	120/120 (100.0%)	119/119 (100.0%)	≥80%	Achieved
Teleconsultation-supported follow-up
Community health leaders (Normal group)	26/26 (100%)	41/41 (100%)	23/24 (95.8%)	N/A	N/A
Medical technologists/Nurses (Low/Moderate risk group)	69/69 (100%)	60/60 (100%)	65/65 (100%)	N/A	N/A
Family physician (High risk group)	25/25 (100%)	19/19 (100%)	31/31 (100%)	N/A	N/A
Operational disruptions
Internet connectivity issues	10/120 (8.3%)	7/120 (5.8%)	7/119 (5.9%)	N/A	N/A
Missed scheduled telehealth appointment	0/120 (0%)	0/120 (0%)	1/119 (0.8%)	N/A	N/A

3.4. Participant acceptability

3.4.1. CHL satisfaction with training and HealthD implementation

All CHLs reported satisfaction with the training program, with mean session ratings of at least 4.7 out of 5 across training components. Most CHLs reported high satisfaction with modules covering vital signs assessment, physical examination, POCT demonstration, respiratory symptom/functional limitation assessment, presentation materials, software usability, training duration, and the overall implementation process (Figure 4(A)). By study completion, 11 of 12 CHLs (91.7%) reported satisfaction with HealthD implementation overall, with a mean rating of 4.6 out of 5. One CHL provided neutral feedback regarding teleconsultation, whereas the remaining CHLs reported positive perceptions of system usability and implementation processes (Figure 4(B)).

Figure 4.

The proportion of HealthD satisfaction surveys obtained from stakeholders. A) The proportion of CHLs training satisfaction surveys, n=12. B) The proportion of the HealthD system satisfaction surveys obtained from the CHLs, n=12. C) The proportion of the Health D system satisfaction surveys obtained from the participants, n=119.

3.4.2. Participant satisfactions

Among participants who completed the satisfaction survey (n = 119), most reported being satisfied or very satisfied with the HealthD program, with mean rating of at least 4.5 out of 5. The majority reported being very satisfied, and only one participant expressed dissatisfaction with the monitoring process. Overall, nearly all respondents indicated that HealthD could be integrated into their community (Figure 4(C)).

3.5. Participant characteristics

A total of 120 participants were enrolled, with a median age of 51 years (IQR, 43–59). The sample included 42 males (35%) and 78 females (65%). Most participants were freelance workers (60%). Monthly income was 5,001-10,000 THB for 57% of participants, while 31% reported income below 5,000 THB. Regarding education, 54% had completed primary education and 35% had completed secondary education (Table 2).

Table 2.

Socio-demographic characteristics of participants.

Characteristics	All participants n=120 (%)
Sex
Male; n (%)	42 (35)
Female; n (%)	78 (65)
Age median (IQR)	51 (43-59)
30-40 years old; n (%)	26 (22)
41-50; n (%)	34 (28)
51-60; n (%)	34 (28)
61-70; n (%)	26 (22)
Occupation
Agriculturist; n (%)	14 (12)
Merchant; n (%)	24 (20)
Company employee; n (%)	3 (2)
Government officials & State enterprise employee; n (%)	1 (1)
Freelance; n (%)	72 (60)
Others; n (%)	6 (5)
Income (THB)
≤ 5,000; n (%)	37 (31)
5,001 - 10,000; n (%)	68 (57)
10,001 - 20,000; n (%)	10 (8)
20,001 - 30,000; n (%)	3 (2)
> 30,000; n (%)	2 (2)
Education levels
< Primary school; n (%)	4 (3)
Primary school; n (%)	65 (54)
Secondary school; n (%)	42 (35)
Bachelor’s degree or equal; n (%)	9 (8)

3.6. Exploratory NCD-related risk outputs

Risk outputs related to DM, CVD, and respiratory symptom/functional limitation assessment are summarized in Table 3, Figure 5, and Supplementary Figures S4-S6. These findings are presented as secondary descriptive and exploratory outcomes. At baseline, 73% of participants were classified above the normal category for diabetes-related risk, 3% for CVD-related risk, and 9% for respiratory symptom and functional limitation risk. Across visits, vital sign-based categories and anthropometric indicators showed modest variation. Median BMI remained in the overweight range, and the prevalence of abdominal obesity remained high throughout follow-up. Diabetes-related risk category distributions varied across visits, with a higher observed proportion of participants classified in moderate- or high-risk categories at the final visit than at baseline. CVD-related risk outputs remained predominantly in the normal category, with small observed shifts toward low risk by the final visit. Most participants remained in the normal respiratory symptom/functional limitation categories. CAT scores showed a very weak negative correlation with 6MWT distance (Pearson’s r =−0.111, p= 0.037; Supplementary Figure S7). Although statistically significant, the magnitude of the association was negligible, suggesting limited clinical relevance in this feasibility sample. These findings should therefore be interpreted with caution given the exploratory nature of the analysis.

Table 3.

Participant risk stratification based on T-Logic classification within the HealthD system.

Parameters	1^st visit (n=120) n (%) or median (IQR)	2^nd visit (n=120) n (%) or median (IQR)	3^rd visit (n=119) n (%) or median (IQR)
Systolic blood pressure	127 (116-138)	128 (118-138)	127 (118-140)
Diastolic blood pressure	80 (76-88)	81 (74-88)	84 (75-90)
Risk of hypertension
Normal (110-139/80-89 mmHg)	82 (68)	85 (71)	78 (66)
Low (140-159/90-99 mmHg)	29 (24)	28 (23)	22 (18)
Moderate (160-179/100-109 mmHg)	8 (7)	6 (5)	16 (13)
High (≥180/≥110 mmHg)	1 (1)	1 (1)	3 (3)
Body mass index (BMI), kg/m²	24.2 (21.9-27.5)	24.1 (22.0-27.5)	24.3 (21.9-27.4)
BMI groups
Normal (18.5-22.9 kg/m²)	41 (34)	47 (39)	48 (40)
Low (23-24.9 kg/m²)	29 (24)	22 (18)	18 (15)
Moderate (25-29.9 kg/m²)	32 (27)	32 (27)	34 (29)
High (≥30.0 kg/m²)	18 (15)	19 (16)	19 (16)
Waist-hip ratio (WHR)	0.88 (0.84-0.92)	0.88 (0.84-0.92)	0.89 (0.84-0.91)
Male	0.91 (0.86-0.94)	0.91 (0.88-0.94)	0.89 (0.87-0.95)
Female	0.86 (0.8-0.92)	0.86 (0.81-0.91)	0.88 (0.83-0.91)
Risk of obesity
Normal	54 (45)	50 (42)	52 (44)
Male (<0.90)	18 (43)	20 (48)	23 (55)
Female (<0.85)	36 (46)	30 (38)	29 (38)
Low	33 (28)	43 (36)	32 (27)
Male (0.90-<0.95)	17 (40)	16 (38)	9 (21)
Female (0.85-<0.90)	16 (21)	27 (35)	23 (30)
Moderate	23 (19)	20 (17)	24 (20)
Male (0.95-<1.00)	7 (17)	6 (14)	6 (14)
Female (0.90-<0.95)	16 (21)	14 (18)	18 (23)
High	10 (8)	7 (6)	11 (9)
Male (≥1.00)	0 (0)	0 (0)	4 (14)
Female (≥0.95)	10 (13)	7 (9)	7 (10)
Diabetes risk (DMR score)	10 (7-14)	10 (6-13)	11 (8-15)
Normal (≤7)	32 (27)	44 (37)	29 (24)
Low (8-10)	34 (28)	26 (21)	19 (16)
Moderate (11-14)	29 (24)	31 (26)	40 (34)
High (≥15)	25 (21)	19 (16)	31 (26)
Cardiovascular risk (Thai CV risk)
Normal	117 (97)	115 (96)	112 (94)
Low	2 (2)	5 (4)	7 (6)
Moderate	1 (1)	0 (0)	0 (0)
High	0 (0)	0 (0)	0 (0)
Total cholesterol	192 (173-218)	193 (170-223)	204 (177-229)
Respiratory symptom/Functional limitation risk (CAT score)
Normal (≤5)	109 (90)	113 (94)	110 (92)
Low (6-10)	9 (7)	3 (3)	9 (8)
Moderate (11-20)	2 (2)	4 (3)	0 (0)
High (≥21)	0 (0)	0 (0)	0 (0)
6-minute walk test
distance (m)	455 (405-492)	448 (420-480)*	476 (440-507)**
NCD risk groups
Normal	26 (22)	41 (34)	24 (20)
Low	37 (31)	26 (22)	24 (20)
Moderate	32 (26)	34 (28)	40 (34)
High	25 (21)	19 (16)	31 (26)

*Remained participants (n=117) for 6MWT.

**Remained participants (n=116) for 6MWT.

Figure 5.

The number of participants being monitored for NCDs across three visits A) normal group. B) low-risk group. C) moderate-risk group. D) high-risk group.

4. Discussion

This prospective feasibility study evaluated the feasibility and acceptability of HealthD, a CHL-delivered, telehealth-supported workflow for community-based NCD risk assessment and follow-up in a rural district of Chiang Mai, Thailand. The HealthD platform focuses on NCD risk identification to support early detection before complications develop, particularly among individuals unaware of their NCD status, thereby facilitate timely intervention and management. HealthD differs from fully remote telehealth assessment models²⁷ by incorporating CHL-assisted delivery, enabling participation among individuals with varying levels of digital literacy and limited access to digital devices or reliable internet connectivity. Overall, the findings support the operational feasibility of this approach in the study setting. Recruitment and retention were high, CHL competency improved after standardized training, the core workflow was delivered in the field, and acceptability among both CHLs and participants was high. However, this study was not designed or powered to evaluate clinical effectiveness, diagnostic performance, or comparative benefit. Accordingly, the NCD-related risk outputs should be interpreted as descriptive and exploratory findings intended to inform workflow refinement and the design of a larger study.

The high recruitment and retention rates observed in this study suggest that community-based enrollment and follow-up were achievable within the planned implementation period. This is consistent with prior telehealth and community-based delivery studies, which have shown that engagement may improve when services are brought closer to participants and supported by trusted intermediaries.^7,8 Participants were recruited through CHL-led community mobilization and direct invitation rather than random household sampling. Therefore, the recruitment and retention rates should be interpreted as indicators of implementation feasibility within a CHL-supported model rather than as estimates of population-wide uptake. In our setting, interest from community members with pre-existing NCD diagnoses, although outside the eligibility criteria, suggested unmet demand for alternatives to routine hospital-based follow-up. This observation is important for future implementation planning because it indicates that clear eligibility definitions and referral pathways will be needed if the platform is expanded beyond a screening-focused feasibility phase.

Data entry, data completeness, and automated risk-output generation consistently exceeded the predefined feasibility threshold. These findings suggest that CHLs were able to reliably collect, document, and process participant data using the HealthD platform. In contrast, data verification did not meet the predefined acceptance criterion. This finding suggests that, although data collection was feasible, maintaining data accuracy and consistency remained a challenge. Potential contributing factors may include variation in CHL experience, the complexity of data-entry procedures, and errors during measurement or documentation. These results highlight the importance of ongoing supervision and refresher training to improve data quality in large-scale implementations.

A key finding was that CHLs were able to implement the HealthD workflow after structured training, with improvement in post-training competency scores and generally positive usability feedback. This finding supports the feasibility of a task-shifted model for community-based NCD risk assessment in rural settings. Previous studies have likewise shown that trained community health workers can support screening, data collection, and follow-up for chronic disease care in resource-constrained environments.^28–30 In the present study, the need for refresher support for some procedures, particularly POCT-related tasks, indicates that one-time training alone may be insufficient for longer implementation periods. Practical reinforcement strategies for future implementation may include periodic booster training every three to six months, remote case-review consultations, observation-based recertification processes, refresher video modules integrated into the tablet interface, device-specific procedural checklists, periodic blinded reassessment of selected measurements, and peer-support networks among CHLs.

HealthD was designed as an integrated workflow. Its practical contribution lies in combining community recruitment, in-person assessment, POCT, digital data capture, algorithm-based risk stratification, and teleconsultation-supported follow-up into a single field-deployable model. Comparable telehealth-supported approaches in low- and middle-income countries have shown that digitally assisted workflows can extend NCD-related services beyond conventional health facilities.^13–15,31 In this study, the web-based platform and multi-device interface appeared operationally workable in the rural community setting. However, workflow success remained dependent on local infrastructure and implementation conditions, particularly internet stability, scheduling reliability, and user support.

Although teleconsultation was generally acceptable, several implementation barriers were observed. Occasional connectivity disruptions affected real-time teleconsultations, and some follow-up sessions required rescheduling or were not completed. In addition, variation in participant’s digital literacy increased the need for CHL support during teleconsultation initiation and follow-up. These observations are consistent with prior reports that telehealth delivery in rural settings may be constrained not only by connectivity but also by practical scheduling and user-readiness factors.^32–34 These barriers did not prevent overall implementation, but they indicate that scale-up should not rely on ideal conditions. Future refinement of HealthD should therefore include connectivity-resilient options such as offline-capable data capture with later synchronization, clearer reminder and rescheduling procedures, standardized participant orientation, and structured troubleshooting support for CHLs. Site selection and collaboration with local network providers may also improve reliability for synchronous consultations.

These findings indicate preliminary acceptability, particularly in relation to affective attitude, perceived usefulness, and perceived fit with community-based delivery. The high satisfaction scores should be interpreted with caution. Although the HealthD workflow was flexible in supporting follow-up and teleconsultation, satisfaction surveys in close community settings may be influenced by courtesy bias, social desirability, trust in CHLs, or gratitude for receiving attention from health personnel. The survey provided useful preliminary evidence of acceptability but did not fully capture all seven constructs of the Theoretical Framework of Acceptability.³⁵ Future studies should incorporate qualitative interviews or focus groups to understand affective attitude, burden, ethicality, perceived effectiveness, intervention coherence, opportunity costs, and self-efficacy in greater depth. Therefore, the absence of qualitative interviews limited the ability to explore privacy concerns, trust in teleconsultation, perceived burden, and reasons for dissatisfaction in depth.

The descriptive risk outputs suggest that this community had a substantial cardiometabolic risk burden, including overweight and elevated blood pressure, broadly consistent with national patterns and known socioeconomic determinants of NCD risk in Thailand.^36–38 However, because this was a feasibility study with short follow-up and no comparator group, changes in risk categories across visits should not be interpreted as intervention effects or clinical improvement. Rather, these findings illustrate the types of risk profiles that the workflow identified in the field and may inform sample selection and outcome planning for future studies.

The respiratory component warrants particularly cautious interpretation. In this study, respiratory symptom and functional limitation screening was based on the CAT together with the 6MWT and was intended to support exploratory symptom and functional risk stratification in a field setting, not diagnostic confirmation. The generally low respiratory symptom/functional burden observed in this study may reflect the composition of the study population rather than the absence of respiratory health concerns in the wider region. Northern Thailand remains an area where air pollution and smoke exposure are relevant contextual factors for respiratory health.^39,40 The weak association between CAT score and 6MWT distance may reflect the low respiratory symptom/functional limitation burden of the study population, heterogeneity in age and physical fitness, and variability in test performance conditions.²⁵ Although these tools are cost-effective and require minimal equipment, the 6MWT requires adequate space and well-trained supervision for standardized administration. Smartphone-based physiological monitoring technologies may offer more objective and reproducible assessments while remaining feasible for remote use.⁴¹ Future studies should consider whether spirometry, smartphone-based monitoring tools, or other objective respiratory assessments can be integrated into the HealthD platform when diagnostic evaluation is required.

This study provides several practical implementation lessons. Key successful components included the ability of trained CHLs to recruit community participants, conduct standardized in-person assessment, use POCT devices, enter data into the digital platform, and facilitate teleconsultation-supported follow-up with high reported acceptability. Less optimal components included workflow elements dependent on stable connectivity, scheduling reliability, and participant digital readiness. These challenges required additional CHL support and identified clear areas for redesign before broader implementation. Accordingly, the next phase of development should focus on refining connectivity-resilient workflows, strengthening teleconsultation logistics, standardizing user-support materials, incorporating prespecified progression criteria and prospectively collecting fidelity and cost-effectiveness data.

This study has several limitations. First, as a single-arm feasibility study conducted in a single rural district, the findings should be interpreted within the context of implementation feasibility rather than clinical effectiveness. The sample size was pragmatic and appropriate for feasibility assessment but was not powered to support confirmatory inferences regarding clinical outcomes. In addition, households were not randomly sampled; therefore, the study sample should not be considered representative of the broader target population. The study population also included a higher proportion of women than men, which may reflect differences in community availability, health-seeking behavior, or engagement through CHL networks and may limit transferability to men. Second, although the T-logic algorithm was based on published guidance and was functionally tested before implementation, it did not undergo independent external clinical validation in this feasibility study. Accordingly, its outputs should be interpreted as decision-support results rather than diagnostic classifications. Third, although CHL competency improved after training, inter-operator variability and implementation fidelity across all workflow components were not systematically evaluated. Furthermore, competency assessments were conducted under supervised training conditions and may not fully reflect CHL performance in routine field settings where they operate independently. Fourth, the respiratory symptom/functional assessment component relied on symptom and functional measures rather than spirometry and therefore should not be interpreted as diagnostic validation for COPD. Fifth, teleconsultation-related barriers, such as missed encounters, time burden, and workflow costs, were not systematically quantified in sufficient detail to support a full implementation or economic evaluation. Sixth, acceptability was assessed primarily through structured satisfaction measures and was not supplemented by qualitative interviews or focus groups that could have provided deeper insight into usability, barriers, and contextual implementation factors. Finally, the workflow did not include cancer screening, and future studies should examine whether broader multi-condition screening can be integrated without compromising feasibility.

Taken together, these findings suggest that HealthD has operational promise as a CHL-delivered telehealth-supported workflow for community-based NCD risk assessment in rural settings. Before scale-up, a randomized controlled trial should be conducted to evaluate the platform’s effectiveness. Future studies should use larger multisite designs with longer follow-up, stronger fidelity assessment, qualitative methods, and formal costing and cost-effectiveness analyses to determine whether the model is effective, sustainable, and scalable within the Thai health system.

5. Conclusion

This prospective feasibility study suggests that HealthD can be delivered by trained CHLs to support telehealth-enabled, community-based NCD risk assessment and follow-up in a rural setting in Thailand. Feasibility was supported by high recruitment and retention, improved CHL competency after training, successful delivery of the core workflow, and high reported acceptability among CHLs and participants. However, these findings are preliminary and should not be interpreted as evidence of diagnostic validation or clinical effectiveness. Before wider implementation, the workflow should be refined to improve connectivity resilience, strengthen scheduling and follow-up procedures, support participants with varying levels of digital literacy, and incorporate formal fidelity and cost evaluations. Larger multi-site studies are needed to evaluate effectiveness, implementation outcomes, sustainability, and scalability.

Supplemental material

Supplemental material - Prospective feasibility study of a telehealth platform for community-based NCD risk assessment by community health leaders in rural northern Thailand

Supplemental material for Prospective feasibility study of a telehealth platform for community-based NCD risk assessment by community health leaders in rural northern Thailand by Sujittra Kaewkart, Pitaya Suebtam, Sawittree Thatong, Nisachon Kaewkart, Aphirak Pinasu, Premmarin Inmonthian, Napatsakorn Kohklang, Kittichai Wantanajittikul, Sakorn Pornprasert, Faiz Shah, Tanawan Samleerat Carraway, Woottichai Khamduang in Sage Open Medicine.

Footnotes

Acknowledgments

We extend our gratitude to the Community Health Leaders and all participants for their valuable contributions to this study. We also sincerely thank the Chief Executive of the Doi Lo Subdistrict Administrative Organization for facilitating and supporting the study.

ORCID iD

Sujittra Kaewkart

Ethical considerations

The study was approved by the Research Ethics Committee (REC) of the Faculty of Associated Medical Sciences, Chiang Mai University (approval number: AMSEC-66EX-011) on June 01, 2023. All participants provided written informed consent prior to participating.

Author contributions

Sujittra Kaewkart: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project Administration Resources, Software, Validation, Visualization, Writing–Original Draft Preparation, Writing–Review & Editing. Pitaya Suebtam: Data curation, Formal analysis, Software, Visualization. Sawittree Thatong: Investigation, Methodology. Nisachon Kaewkart: Investigation, Methodology. Aphirak Pinasu: Methodology, Resources. Premmarin Inmonthian: Methodology, Resources. Napatsakorn Kohklang: Methodology, Resources. Kittichai Wantanajittikul: Funding acquisition. Sakorn Pornprasert: Funding acquisition. Faiz Shah: Funding acquisition. Tanawan Samleerat Carraway: Funding acquisition, Project administration, Resources, Supervision. Woottichai Khamduang: Funding acquisition, Project administration, Resources, Supervision, Validation, Visualization, Writing–Original Draft Preparation, Writing–Review & Editing.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Faculty of Associated Medical Sciences, Chiang Mai University and Yunus Center, Asian Institute of technology, Thailand.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.*

Supplemental material

Supplemental material for this article is available online.

Appendix

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Supplementary Material

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