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NCT04695210COMPLETEDanonymous

A Virtual Peer-to-peer Support Programme for Family Caregivers of Individuals With Motor Neurone Disease at Risk of Becoming or Currently Technology-dependent: Randomised Controlled Trial

Sponsor

Source record

King's College London

Phase

Source record

NA

Modality

AI-normalized

behavioral intervention

Target

AI-normalized

Virtual peer-to-peer support

Indication / condition

AI-normalized

Motor Neuron Disease

Intervention

Source record

Virtual peer-to-peer support

Source & freshness

Source record

NCT ID

NCT04695210

Original source

ClinicalTrials.gov

Source last updated

Jun 01, 2026

Ingested at

Jun 16, 2026

Internal sync

Jun 16, 2026

Model version

trialsignal-ai-v1

Normalized confidence

96%

Validation status

validated

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NCT ID

NCT04695210

Title

A Virtual Peer-to-peer Support Programme for Family Caregivers of Individuals With Motor Neurone Disease at Risk of Becoming or Currently Technology-dependent: Randomised Controlled Trial

Sponsor

King's College London

Status

COMPLETED

Phase

NA

Condition raw

Motor Neuron Disease, Amyotrophic Lateral Sclerosis

Condition normalized

Motor Neuron Disease, Amyotrophic Lateral Sclerosis

Modality raw

behavioral intervention

Modality normalized

behavioral intervention

Target raw

Virtual peer-to-peer support

Target normalized

Virtual peer-to-peer support

Interventions

Virtual peer-to-peer support

Public preview

Source record

Background/scope There is growing recognition that family caregiving is a serious public health issue requiring supportive interventions. Family caregivers play an essential role in sustaining a stable environment enabling individuals with motor neurone disease (MND) that are technology dependent to live at home. The family caregivers can experi¬ence exceptional burden and significant decline in psychological wellbeing due to MND's rapid and pro¬gressive nature with profoundly debilitating effects and intensive support needs. Dependence on assistive technology adds an additional level of complexity to family caregiving due to the need to learn how to operate and troubleshoot medical devices, train other caregivers, and negotiate appointments with new specialties within the healthcare system.

Despite the recognized impact of caregiving for individuals with MND, data are scarce as to effective interventions that provide direct practical and psychosocial supports. Difficulty accessing support may increase psychological distress. As the burden of caring increases due to disease progression and increasing technology dependence, access to existing informal support networks may diminish. Online peer support using virtual modalities is a flexible and low cost form of support. Peers, people who have experienced the same health problem and have similar characteristics as support recipients, can be a key source of emotional, informational, and affirmational support. Peer support improves psychological well-being of caregivers of people with conditions such as dementia, cancer, and brain injury. Although peer support programmes for family caregivers of people with MND exist, data as to their efficacy are limited. Therefore, we have developed an online peer support programme, completed beta and usability testing and now propose to test the effect on caregiver psychological wellbeing and caregiver burden.

Aim/research question(s) Overall aim: to determine the efficacy of a 12-week online peer support programme on family caregiver psychological health and caregiver burden.

Primary research question:

What is the effect of the online peer support programme on psychological distress measured using the Hospital Anxiety and Depression Scale (HADS)?

Secondary research questions:

What is the effect on positive affect, caregiver burden, caregiving mastery, caregiving personal gain, and coping?

How do participants use the programme (fidelity and reach)?

What is the perceived usability and acceptability?

Methods The investigators will conduct a parallel group randomised controlled trial with participants allocated to 12-week access to the online peer support programme or a usual care control group. The investigators will enrol family caregivers of an individual with MND who is referred for consideration or receiving any of the following

assisted ventilation

cough assist

gastroscopy and enteral feeding

i.e., entering King's clinical staging Stage 4A: nutritional support; or Stage 4B: respiratory support \[51\]:

The 12-week peer-to-peer support programme entails:

audio, video, or text private messaging;

synchronous weekly chat;

asynchronous discussion forum; and

informational resources.

The investigators will collect demographic and caregiving data including the Caregiver Assistance Scale and Caregiving Impact Scale, and caregiver measures (HADS, Positive and Negative Affect Schedule, Zarit Burden Interview, Pearlin Mastery Scale, Personal Gain Scale, Brief COPE) at baseline and programme completion.

The investigators will download use of online peer support programme features, assess usability, and conduct semi-structured interviews to explore acceptability using the Theoretical Framework of Acceptability.

To test for a medium size effect (d=0.5), at 5% level of significance (2-sided) with power 80%, 64 participants are required in each arm (128 total). Adjusting for 20% attrition requires 154 participants.

Proposed findings The proposed study will demonstrate the effect of a online peer support programme on psychological distress, positive affect, caregiving burden, mastery, personal gain and coping. Data on programme fidelity will enable the investigators to objectively assess acceptability and interpret study results. Data on usability and acceptability will inform future scalability of the online peer support programme outside of the trial both nationally and internationally, and to other family caregiver populations.

AI-generated analysis supports research triage only. Verify source records, publications, sponsor disclosures and IP databases before making diligence decisions. Model: trialsignal-ai-v1.

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