NIHR Global Health Research Centre for Multiple Long-Term Conditions Pilot Study to Assess the Acceptability, Feasibility, Fidelity and Usability of a Co-designed Intervention to Improve Management of Multiple Long-term Conditions
Conditions: Diseases of the Circulatory System, Diseases of the Respiratory System, Endocrine, Nutritional and Metabolic Diseases, Mental, Behavioral and Neurodevelopmental Disorders, Diseases of the Musculoskeletal System and Connective Tissue, Diseases of the Eye and Adnexa, Diseases of the Ear and Mastoid Process
Sponsor: Centre for Chronic Disease Control, India

1/27/2026 
 
 
 
NIHR Global Health Research Centre for Multiple 
Long-Term Conditions Pilot Study to Assess the 
Acceptability, Feasibility, Fidelity and Usability of 
a 
Co-designed 
Intervention 
to 
Improve 
Management of Multiple Long-term Conditions 
NCT number 
NOT AVAILBLE 

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Background: 
 
India is witnessing a rapid rise in the prevalence of multiple long-term conditions (MLTC), 
defined as the co-existence of two or more chronic diseases in an individual (1,2). Despite this 
growing burden, the current healthcare system remains largely oriented toward single-disease 
management, resulting in fragmented care pathways (3). This approach poses significant 
challenges for patients, caregivers, and healthcare providers alike, often leading to poor health 
outcomes and increased out-of-pocket expenditures (3). In response to this evolving 
landscape, the National Institute for Health and Care Research (NIHR) Global Health 
Research Centre (GHRC) for MLTC has been established. The Centre aims to co-design, 
implement, and evaluate integrated, technology-enabled interventions that strengthen MLTC 
care across diverse health system settings in India. Through collaborative research and 
innovation, GHRC seeks to support scalable solutions that align with national priorities and 
improve the quality of life for individuals living with chronic conditions. 
The Centre’s objectives span three phases. In the short term, we aim to conduct health system 
and case-mix assessments and identify challenges faced by patients with MLTC, their 
caregivers, and healthcare providers. The mid-term goals include co-designing an integrated, 
technology-enabled, patient-centered intervention comprising an Electronic Decision Support 
System (EDSS), assisted telemedicine models, patient-facing application and community 
champions. Three of these interventions will be pilot tested to assess the retention and 
recruitment rates, acceptability, feasibility, fidelity, and costs. Our long-term vision is to 
establish a fully functional Global Centre for improving MLTC-related health outcomes and 
research, closely aligned with the Government of India as a strategic partner. 
Between March 2024 and May 2025, we completed the formative phase to address our short- 
term objectives. We have estimated the prevalence and clustering of MLTC through a cross- 
sectional survey of 600 patients aged 40 years and above attending 20 primary health centers. 
Health system readiness was assessed at 20 PHCs and 40 sub-centers using the Indian 
Public Health Standards (IPHS, 2022) framework. Additionally, in-depth interviews with 60 
patients and caregivers, 40 healthcare professionals, and 10 state and district health officials 
revealed critical barriers, including fragmented care pathways, lack of provider training, 
insufficient digital support, and gaps in continuity of care. Additionally, out of 10 we have 
completed 4 co-design workshops to co-develop the integrated, technology enabled, health 
system interventions. 

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Technology enabled platforms for intervention delivery 
 
Expansion of DigiSetu EDSS module: DigiSetu, developed by the BRIDGE Centre for Digital 
Health at the Centre for Chronic Disease Control (CCDC), is a digital health and telemedicine 
platform designed to improve access to chronic disease care in underserved communities. 
Currently, it includes EDSS modules for hypertension, diabetes, and cardiovascular disease. 
Based on case-mix findings from the formative phase, the platform is being expanded to 
incorporate conditions such as asthma, depression, anxiety, substance use disorders, vision 
and hearing impairments, and osteoarthritis. These upgraded modules will deliver tailored, 
algorithm-2018driven decision support while systematically tracking clinical outcomes. 
Upgrade of Ai.m Healthy mobile application: The Ai.m Healthy application, developed and 
maintained by ClinAlly, will be deployed as the patient-facing tool to strengthen self- 
management and adherence. Core features include ABHA Health ID creation, secure storage 
of health records, and personalized health assessments through scorecards. The upgraded 
version integrates medication and visits reminders, symptom tracking, and multilingual 
educational content to promote awareness and lifestyle modification. Interactive functions are 
designed to enhance patient–provider communication and complement EDSS and 
telemedicine interventions. 
Objectives of the pilot study: 
1. To explore the acceptability of the intervention components among enrolled study 
participants and healthcare providers. 
2. To assess the usability, feasibility, and fidelity of integrating digital health interventions 
into routine service delivery at Primary Health. 
Methodology: 
 
1. Study design: We will conduct a cluster non-randomized pilot study to evaluate the 
feasibility, acceptability, usability, and fidelity of three digital health interventions in rural 
Primary Health Centres (PHCs). The study is designed to inform the development of a 
future large-scale trial. 
2. Study setting and duration: The pilot will be implemented across four purposively 
selected rural PHCs: two in Jodhpur, Rajasthan and two in Anakapalli, Andhra 
Pradesh. The pilot duration will be six months. 

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Figure 1: Study area for pilot 
3. Study Participants: Two participant groups will be recruited 
a. Intervention recipients: The recipients will include all individuals visiting the 
selected PHCs, will be screened for eligibility using a standardized screening 
tool. Eligible participants will include adults aged 40 years and above 
diagnosed with two or more of the following conditions: hypertension, diabetes 
mellitus, depression, anxiety, chronic obstructive pulmonary disease (COPD), 
asthma, vision impairment, hearing difficulties, osteoarthritis, and chronic back 
pain. 
b. Intervention implementers: Medical officer (MO) and staff nurse at each PHC 
who will implement the EDSS and assisted telemedicine components of the 
intervention. 
4. Intervention components: 
1. Electronic Decision Support System (EDSS): To integrate guideline-based 
decision making into PHC workflows. 
2. Assisted telemedicine models (full and backpack): To expand access to 
specialist input at both facility and community levels. 
3. Patient facing mobile application: To support self-management, adherence, 
and patient provider communication. 

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Table 1: Intervention components and core activities 
 
Setting 
Intervention 
Providers/Recipients 
Core activities 
PHC 
Electronic 
Decision 
Support 
System 
(EDSS) 
MO, staff nurse 
1. Capture symptoms, vitals, history, 
and lab results through structured 
digital forms. 
2. EDSS will develop generates 
individualized treatment plans and 
determine need for up referral and 
follow up. 
PHC 
Assisted 
telemedicine 
MO, staff nurse 
Expands specialist access through 
structured teleconsultations at both 
facility and community levels. 
Community 
Patient facing 
app 
Enrolled participants or 
care givers 
1. Support enrolled individuals with 
personalized reminders, education. 
2. PHC linkage-enhancing self- 
management, adherence, and 
continuity of care. 
 
5. Study procedure: A total of 30 participants will be enrolled per PHC. 
Training for intervention implementers: Medical officers and staff nurses of participating 
PHCs will undergo three days structured training on the use of the EDSS and assisted 
telemedicine protocols and procedures. Delivered through in person workshops the 
sessions will cover, standard operating procedures, system navigation, clinical 
workflows, patient documentation, and ethical data use. Facilitated by experts from 
NIHR GHRC, the training will include hands-on practice, case simulations, and 
pre/post assessments. 
PHC level implementation: 
Visit 1: Trained health personnel affiliated with the NIHR GHRC Centre will conduct 
preliminary screening of all individuals aged 40 years and above attending the primary 
health center, using a standardized screening tool. Individuals identified with two or 
more specified chronic conditions listed above will be considered eligible for study 
participation. Those meeting the eligibility criteria will be provided with a detailed 
participant information sheet and informed consent form. Health worker will explain the 
objectives, procedures, and expectations. Participants will be given up to seven days 
to review the materials and make an informed decision regarding their enrollment. 
Visit 2: Upon consent, health workers will enroll the participant and conduct baseline 
assessments. Data related to sociodemographic, quality of life [SF-12, EQ-5D, 
WHOQOL], and cost of illness will be collected in REDCap and generates unique ID 

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for enrolled participant. Subsequently, staff nurses will capture detailed patient history 
including past medical conditions, family history and perform clinical examinations. 
Patient records and clinical findings will be entered in a structured manner into the 
DigiSetu EDSS platform. The participant will be referred to the Medical Officer, who will 
review the recorded inputs, conduct additional assessments if require, and utilize the 
EDSS to generate a treatment plan. The MO may exercise clinical judgment to 
approve, modify, or reject the proposed treatment plan by EDSS. All decisions, along 
with the rationale for any modifications or disagreements, will be documented within 
the application. 
In cases requiring specialist input, assisted telemedicine services will be initiated by 
either the MO or the nurse. Two telemedicine models will be piloted: (i) a facility-based 
full model, providing comprehensive, integrated specialist consultations within the 
PHC, and (ii) a backpack model, a portable, outreach ready telemedicine kit for 
community setting. 
Following the consultation, the EDSS will be updated and the treatment plan revised 
to ensure continuity and clinical appropriateness. The nurse will then document the 
MO’s final decisions and deliver health education and counselling, covering both 
pharmacological and non-pharmacological recommendations generated by the EDSS. 
A structured follow-up plan will also be provided to guide subsequent clinic visits. 
 
Figure 2: Workflow at the Primary health care centres during the intervention 
Community level Implementation: 
A patient-facing mobile application will be deployed to support intervention recipients 
or their care givers. This application is intended to promote self-management, 

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strengthen treatment adherence, and facilitate continuity of care by providing 
personalized reminders, educational resources, and linkage with primary health care 
services. 
6. Pilot study outcomes: 
a. Acceptability: Perceived relevance, satisfaction, and willingness to adopt 
interventions among providers and participants. 
b.  Usability: Ease of use, navigation, and workflow integration of EDSS, 
telemedicine, and patient app. 
c. Feasibility: Practicality of implementation within PHC settings, including time, 
resources, and workflow fit. 
d. Fidelity: Delivery as intended, protocol adherence, and completeness of data 
entry. 
7. Pilot study evaluation: 
The EDSS and assisted telemedicine interventions will be evaluated for their 
acceptability, usability, and feasibility within Primary Health Centre (PHC) workflows. 
Acceptability will be explored among medical officers and staff nurses using the 
Theoretical Framework of Acceptability (TFA) frame work. This will include think-aloud 
sessions, where participants verbalize their thoughts while interacting with the digital 
tools. The evaluation will explore key TFA domains such as: 
 
Affective attitude – how providers feel about using the intervention 
 
Burden – perceived effort required to use the system 
 
Ethicality – alignment with professional values and norms 
 
Intervention coherence – understanding of how the intervention works 
 
Perceived effectiveness – belief in the intervention’s ability to improve care 
 
Self-efficacy – confidence in using the system effectively 
Usability will be evaluated among the same provider group using the System Usability 
Scale (SUS), it’s a validated 10-item questionnaire scored on a 0–100 scale, with 
scores ≥70 considered satisfactory and end of recruitment. 
Feasibility of the interventions will be assessed by the research team through a 
combination of backend analytics, direct monitoring, and structured checklists. Key 
operational indicators will include medical officer usage logs, number of log-ins, 
average screening time, consultation completion rates, system stability (e.g., app 
crashes), and portal update frequency, teleconsultation setup duration, tele-referral 
success rates. All metrics will be tracked continuously throughout implementation to 
guide iterative improvements and inform scalability. 

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The assisted telemedicine component will undergo a further evaluation to assess its 
operational feasibility and effectiveness in enhancing specialist access. 
Teleconsultation logs: Researchers will analyse provider–patient teleconsultation 
records to generate indicators including (i) success rate of completed sessions, (ii) 
average consultation duration, (iii) dropout or termination rates, (iv) frequency and 
nature of technical issues, and (v) proportion of consultations missed due to technical 
problems. These data will enable estimation of the percentage of successful 
teleconsultations relative to scheduled sessions. 
Table 2: Evaluation framework for EDSS and assisted telemedicine 
 
Outcome 
Method 
Study Tool 
Target Group 
Time Period 
Acceptability 
Qualitative 
(Think-aloud) 
TFA-based 
interviews & think- 
aloud sessions 
Medical Officers, 
Staff Nurses 
End of recruitment 
Usability 
Quantitative 
System Usability 
Scale (SUS), 
Validated 10-item 
Medical Officers, 
Staff Nurses 
End of recruitment 
Feasibility 
Qualitative 
(backend data + 
monitoring 
checklists) 
Usage logs, 
backend analytics, 
structured 
feasibility checklist 
Researchers 
(observing provider 
use) 
Continuous; 
summarized at (3M) 
& (6M) 
Operational 
feasibility 
(Telemedicine) 
Quantitative log 
analysis 
Teleconsultation 
logs 
Researchers 
(provider-patient 
sessions) 
Continuous; 
summarized at (3M) 
& (6M) 
 
The patient-facing app will be evaluated for their acceptability, usability, and feasibility with 
enrolled participants. 
Acceptability: In-Depth Interviews (IDIs) will be conducted with enrolled partcipants 
and/or caregivers at endline to explore user experiences, perceived benefits, and 
barriers/facilitators of app engagement. 
Usability: Will be quantitatively assessed using the mHealth App Usability 
Questionnaire (MAUQ), a validated 18-item tool specifically designed for mobile 
health applications. The MAUQ covers three subscales: Ease of Use and Satisfaction 
(7 items), System Information Arrangement (4 items), and Usefulness (7 items). Each 
item is scored on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree), 
with higher scores indicating better usability. A mean score of ≥5.0 is considered 
acceptable usability. 
Feasibility: Researchers will use backend data and usage logs to evaluate patient 
engagement patterns, including (i) number of log-ins per week, (ii) response rates to 
reminders, (iii) proportion of educational content accessed, and (iv) navigation 
patterns. 

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Table 3: Evaluation framework for patient facing application 
 
Outcome 
Method 
Study Tool 
Target Group 
Time Period 
Acceptability 
Qualitative 
In-Depth 
Interviews (IDIs) 
Participants 
and/or 
Caregivers 
End of recruitment 
Usability 
Quantitative 
mHealth App 
Usability 
Questionnaire 
(MAUQ) 
Participants 
and/or 
Caregivers 
End of recruitment 
Feasibility 
Quantitative 
(app 
analytics) 
Backend data 
and usage logs 
Researchers 
(based on patient 
use) 
Continuous; 
summarized at (3M) 
& (6M) 
 
The fidelity of all interventions (EDSS, assisted telemedicine, and patient-facing 
app) will be evaluated using the Carroll et al. (2007) conceptual framework for 
implementation fidelity, with refinements by Hasson (2010). This framework 
emphasizes four key domains of fidelity: adherence to protocol, dose/exposure, quality 
of delivery, and participant responsiveness. 
a. Adherence to protocol: Adherence refers to the extent to which intervention 
components are delivered as designed. For EDSS, this will include whether all 
required fields (symptoms, vitals, history, laboratory results) are completed and 
whether treatment plans are reviewed by medical officers. For telemedicine, 
adherence includes compliance with referral and consultation protocols. For the 
patient-facing app, adherence refers to consistent delivery of reminders and 
educational modules. 
 
Tools: Structured fidelity checklists, backend audit trails, supervisor 
observations.
 
Indicators: ≥80% of required steps completed per encounter.
b. Dose/Exposure: This domain assesses the amount of intervention actually 
received by participants. For EDSS, this will be the proportion of eligible 
participants managed using the system. For telemedicine, it includes the proportion 
of eligible cases successfully referred and completed. For the app, dose will be 
reflected in the proportion of participants actively engaging (log-ins, reminders 
responded to, content viewed). 
 
Tools: Usage logs, referral records, app analytics.
 
Indicators: ≥80% of eligible participants exposed to intervention as planned.
c. Quality of delivery: Quality refers to the competence and consistency of how 
interventions are delivered. For EDSS, this will assess whether medical officer 
modifications of treatment plans are appropriate and whether rationale is 

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documented. For telemedicine, it includes the clarity and completeness of referral 
documentation and the quality of specialist responses. For the app, it assesses 
whether educational content and reminders are delivered accurately and 
consistently with the protocol. 
 
Tools: Supervisor review checklists, random record audits, qualitative 
interviews.
 
Indicators: ≥75% of sampled encounters rated as high-quality delivery.
d. Participant response: 
Responsiveness captures the engagement and 
receptiveness of both providers and participants. Providers will be asked about tool 
relevance, usability, and integration into workflow. Participants will be assessed for 
engagement with the app (e.g., reminder responses, navigation patterns) and 
satisfaction with teleconsultations. 
 
Tools: SUS, MAUQ, Acceptability of Intervention Measure (AIM), in-depth 
interviews, focus groups.
 
Indicators: ≥70% of users reporting positive engagement (Likert ≥4/5).
 
 
Domain 
Key Indicators 
Adherence to 
protocol 
1. % of eligible participants who received consent and were 
registered in EDSS by staff nurses. 
2. % of EDSS-registered participants whose treatment plans were 
reviewed by Medical Officers. 
3. % EDSS treatment plans accepted by Medical Officers 
4. % of EDSS treatment plans rejected with documented 
rationale 
5. % of enrolled participants who returned for follow-up visits 
6. % EDSS encounters with all mandatory fields completed 
(symptoms, vitals, labs) 
7. % telemedicine referrals following protocol 
8. % patient facing app users receiving scheduled reminders 
9. % receipts installed patient facing app 
Exposure 
1. % of screened individuals found eligible for the study 
2. % of eligible participants who were consented 
3. % of consented participants who were initiated into EDSS 
workflow 
4. % eligible participants managed via EDSS 

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5. 
% eligible cases referred and completed via telemedicine 
6. % app users with ≥1 login and ≥1 module viewed 
Quality of 
delivery 
1. % EDSS treatment plans modified by MO 
2. % tele-referrals with complete documentation and specialist 
response 
3. % app messages delivered accurately and on time and 
screening time 
Participant 
responsiveness 
1. % providers rating tools as usable and relevant (SUS ≥70) 
2. % receipts responding to app reminders or engaging with 
content 
 
Data analysis: 
Quantitative 
The results of the pilot study i.e., usability, feasibility, and fidelity from provider’s and patient’s 
perspectives will be reported as number (percentages). Descriptive statistics about the 
baseline characteristics of the participants will also be reported as number (percentages). To 
assess the usability across all the three interventions, total score will be evaluated per 
intervention and categorized as number(percentages). Response obtained from SUS and 
MAUQ will be reported as median (IQR) under Likert scales. Spearman's rank correlation will 
be used to measure the relationships between ordinal variables used in the respective scales, 
and ordinal logistic regression can help us to model the relationship between the Likert scale 
response and other independent variables. 
Feasibility of the interventions will be assessed based on number of logins, average screening 
time, and estimate proportions namely for consultation completion, navigation pattern on app, 
response rate to reminders, and frequency to access educational content for the participants 
respectively. Further, feasibility of the application/patient app will be observed in terms of 
system stability by reporting the frequency of the participant visits to the portal, number of tele- 
referrals and average duration of teleconsultation. The operational feasibility will also be 
measured with respect to teleconsultation i.e., to measure the number of complete sessions, 
number of dropouts or termination due to technical issues. 
Fidelity across all the interventions will be assessed in terms of adherence, response and 
exposure by the participants. Adherence with respect to interventions such as EDSS and 
telemedicine will be measured in terms of proportions of participants with completion of 
registration, history and advised treatment plans which will be accepted or rejected by the 
providers. It will also measure the percentage of enrolled participants in terms of follow-ups, 
referrals and frequency of reminders from application/app. The exposure under fidelity will be 

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measured in terms of proportions of eligible participants, consented participants, under 
treatment via EDSS, cases referred and treatment via telemedicine. 
Response of the provider will be measured in terms of usability of the tool (SUS score ≥70) 
and participant response will be measured with respect to response to the reminders on app 
as well as pattern of navigation while using app. Further, fidelity will also be assessed in terms 
of consistency and competence of the intervention delivered to the participants. This will be 
measured under quality of delivery as proportions of treatment plans modified by providers; 
number of tele-referrals and proportion of apps messages delivered within screening time of 
the participants. 
Qualitative Analysis: 
The qualitative component of the pilot study will focus primarily on assessing the acceptability 
of the three intervention components i.e., Electronic Decision Support System (EDSS), 
assisted telemedicine (full and backpack models), and the patient-facing mobile application. 
The qualitative data collected from the Think-Aloud sessions with healthcare providers 
(medical officers and staff nurses) as they engage with EDSS and assisted telemedicine 
platforms. The participants will be encouraged to verbalize their thoughts, expectations, and 
decision-making processes in real time. These sessions will be audio recorded with participant 
consent, supplemented by researcher field notes documenting pauses, difficulties, and 
contextual factors. In-depth interviews (IDIs) with patients, their caregivers, and healthcare 
providers to explore reflective perceptions of intervention use, perceived benefits, and 
associated barriers/facilitators. Logbooks and process observations maintained by field 
researchers to record workflow integration, fidelity, user engagement, and contextual 
challenges documenting real-time observations during intervention delivery. 
This data in from of audio recordings with participant permission and observation notes will be 
transcribed verbatim and translated from local language to English and imported into NVivo 
software for analysis. The coding will follow a framework analysis approach using TFA 
domains as the primary deductive coding frame, while also allowing inductive codes to capture 
additional emergent themes using the thematic analysis framework by Braun & Clarke. This 
will be done by applying line-by-line coding allowing multiple codes per data segment and 
iteratively adding inductive codes as new findings emerge, the related codes will be grouped 
into a single theme and be reviewed for coherence, distinctiveness, and alignment with the 
data set. These themes will be then mapped to the acceptability framework by TFA. These 
themes will be compared and triangulated across intervention components and stakeholder 
groups (healthcare providers, patients, caregivers) and across sites using anonymized quotes 
to illustrate findings. To ensure coherence in data interpretation around 20% of transcripts will 
be independently double coded by two researchers, with differences resolved through 
discussions and eventually reviewed by a third researcher. Qualitative findings will be 

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integrated with the quantitative data to inform the acceptability outcome of the pilot study by 
using an explanatory approach that will guide refinement of intervention components prior to 
the full-scale cRCT. 
Data Management 
All participant data will be stored in secure, password-protected digital systems with role-
based access. A data dictionary defines all variables, coding structures, and permissible 
ranges. Data synchronization procedures ensure secure transfer from field devices to central 
servers. Identifiable information will be stored separately from analytical datasets.