Post Stroke, Stroke
Conditions
Keywords
Stroke, Self-management, Depression, Cognitive Function, Ecological Momentary Assessment
Brief summary
The goal of this feasibility study is to test whether a new approach that combines real-time symptom tracking (Ecological Momentary Assessment) with machine learning can help people recovering from a stroke to better manage depression, thinking difficulties, and daily functioning. The main questions it aims to answer are: Is this combined approach practical and acceptable for post-stroke survivors? Does the program improve mood, cognitive function, or functional ability to carry out daily activities? Participants will: Use a smartphone app called RehabCare Companion for a period of time Answer brief daily surveys about their mood, thoughts, and activities for one week Receive personalized self-care suggestions generated by machine learning based on their responses Complete assessments of depression, cognition, and functioning before and after the program Take part in a group interview to share their experiences
Interventions
BEHAVIORALEcological Momentary Assessment based Self-management Program
The research team collects participants' EMA data (ie., depression, cognitive function, daily activity, and contextual data) from participants for 7-day and analyze the data by machine learning in the first phase, while provide real-time, individualized, self-care messages to the participants via a personalized self-care AI app in the second phase.
Sponsors
The Hong Kong Polytechnic University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Intervention model description
Ecological Momentary Assessment based Self-Management Program
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* adults aged 18 years or older * having a confirmed diagnosis of stroke * having sufficient proficiency in Cantonese to understand and respond to the assessments and prompts * having a Hong Kong version of the Montreal Cognitive Assessment (HK-MoCA) score of 18 or higher * having a smartphone.
Exclusion criteria
* they have moderate to severe cognitive impairment (a HK-MoCA score of 17 or lower) * they have active psychiatric disorders other than depression (e.g., schizophrenia, bipolar disorder) * they have a terminal illness or prognosis that suggests a limited life expectancy * they have conditions that compromise their self-care ability (i.e., bedbound).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of the study: acceptance | Immediately after the intervention (T2). | The acceptance of participants to the program will be measured by recruitment rate, attrition rate, and retention rate. |
| Feasibility of the study: adherence | Immediately after the intervention (T2). | The adherence of the participants will be evaluated by using the percentage of completed EMA assessments and the frequency of app usage. |
| Feasibility of the study: experience of the participants in using the app | Immediately after the intervention (T2). | Participants' experience will be assessed through individual interviews. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Preliminary effectiveness: depression Levels | Pre-intervention (T1) and immediately after the intervention (T2). | Beck Depression Inventory (BDI) will be used to assess the depression levels of post-stroke patients. |
| Preliminary effectiveness: cognitive function | Pre-intervention (T1) and immediately after the intervention (T2). | Cognitive function will be screened using Montreal Cognitive Assessment Hong Kong version (HK-MoCA). |
| Preliminary effectiveness: disability and functioning | Pre-intervention (T1) and immediately after the intervention (T2). | The World Health Organization Disability Assessment Schedule (WHODAS 2.0) is a widely used instrument for assessing disability and functioning in individuals. |
Countries
Hong Kong
Contacts
CONTACTArkers KC Prof. Wong, PhD
CONTACTNuo Dr. Shi, DHSc
PRINCIPAL_INVESTIGATORArkers KC Wong, PhD
The Hong Kong Polytechnic University
Outcome results
None listed