Investigator(s)
| WHO Technical Officer |
Olivia Corazon Nieveras WHO Thailand Olivia Corazon Nieveras 
|
| Principal Investigator |
Hathairat Kosiyaporn International Health Policy Program Foundation Hathairat Kosiyaporn
|
| Co-Investigator(s) |
Rujira Adhibai International Health Policy Program Foundation Rujira Adhibai
|
| Co-Investigator(s) |
Pigunkaew Sinam International Health Policy Program Foundation Pigunkaew Sinam
|
Title(s) and abstract
| Scientific title | The Knowledge and Tool Development for Depressive Symptoms Screening in Thai Elderly from Passive Sensing Data of Smartphones or Smartwatches |
| Public title | The Knowledge and Tool Development for Depressive Symptoms Screening in Thai Elderly from Passive Sensing Data of Smartphones or Smartwatches |
| Background | Thailand has been rapidly transforming into an aging society. The elderly is one of the physically and some are mentally vulnerable to depression. However, geriatric depression is often overlooked, under-recognized, and undetected as a result of some complexities in diagnosis by using questionnaires from overlapping symptoms with their aging processes and cognitive impairment. Existing work on passive sensing data sheds light on how smartphones or wearables data (digital features) could play an important role as proxies of mental health status, which some could be mapped onto Major Depressive Disorder symptoms (symptom features). The findings suggested that there is a relationship between passive sensing and depression, nevertheless, the psychometric properties, and its feasibility for depressive symptoms screening has not been explored yet. Therefore, this highlights room for research to integrate and implement passive sensing data collection as another approach to screen for depressive symptoms in the elderly. |
| Objectives | This research aims to develop knowledge and tool using passive sensing data from smartphones or smartwatches for depressive symptoms screening in the Thai elderly. Specifically, this research aims to 1. Review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptoms screening for the elderly as followed; identify digital features, symptom features, data sources, methodology, psychometric properties and feasibility from published literature. 2. Test and evaluate the psychometric properties in terms of concurrent validity and internal reliability of the selected passive sensing data as a screening tool for depressive symptoms among the elderly in Thailand. 3. Explore the feasibility and develop policy implications for depressive symptom screening for Thai elderlies by using an appropriate and efficient digital technology tool. |
| Study Methods | This research will consist of two phases. In phase one, a systematic review of current literature on passive sensing data for elderlies’ depressive symptoms screening. Three international databases will be searched between January 2012 to September 2022 in accordance with the selection criteria. Among the selected literature, a data extraction process with quality assessment will be conducted. Then, the Delphi technique will be used to conduct three rounds of interviews with 30 digital technology or mental health experts from the public, private, and educational sectors. A thematic analysis will be used to categorize experts’ statements and descriptive statistics will be used to establish experts’ consensus. In phase two, a prospective cohort study of 183 Thai robust elderlies (aged 60 or above) recruited from NCD clinics and/or its network organizations across five regions of Thailand will be conducted. Participants with any mental condition, cognitive impairment, home-bounded or bed-bounded status will be excluded from the study. Passive sensing data from participants will be collected via an application for 8 consecutive weeks. Demographic variables such as comorbidity will be collected at the beginning of the study to identify any confounding variables. Participants will complete the Patient Health Questionnaire (PHQ) at baseline, every two weeks, and at follow-up and the System Usability Scale (SUS) at the end of the study. Associations between passive sensing data and the PHQ scores will be analyzed with Spearman correlation to examine their concurrent validity. Internal reliability will be evaluated with Cronbach’s alpha. Descriptive analysis of the SUS scores will be used to analyze the feasibility of passive sensing data. |
| Expected outcomes and use of results | It is expected that this research will yield a set of knowledge and appropriate tool on the use of passive sensing data for depressive symptom screening in the elderly population. This could be scaled up to screen depressive symptoms in parallel with existing screening systems; guiding dynamic health policies for depressive symptoms monitoring and uplifting the National Mental Health Developmental Plan in Thailand. The passive sensing tool could be further developed to facilitate the diagnosis of depression for the elderly in critical cases, where prompt access to care and treatment could be initiated while simultaneously raising the awareness of late-life mental health problems. Lastly, passive sensing data could be integrated into the Thai health system to enhance the coverage of data collection to be more comprehensive, which could be extended to use in other population groups and monitor diverse health conditions. |
| Keywords | elderly, passive sensing, digital technology, depression |
Research Details
| Student research | No |
| Start Date | 01-Nov-2022 |
| End Date | 31-Oct-2023 |
| Key Implementing Institution | International Health Policy Program |
| Multi-country research | No |
| Nationwide research | Yes |
| Research Domain(s) | Non-communicable diseases & Healthy Lifestyles |
| Research field(s) | |
| Involves human subjects | Yes |
| Data Collection | Primary and secondary data |
| Proposal reviewed by other Committee | Final decision available |