Decision Support on End-of-life Care Planning in Older Adults

I. Research Background: Chronic Obstructive Pulmonary Disease (COPD) is a progressive life-limiting condition and one of the leading causes of death globally and locally(MacPherson et al., 2013). Patients with COPD experience increasing symptom burden as the disease progresses, resulting in repeated episodes of exacerbation and hospital admissions(Sapey \& Stockley, 2006). However, they and their family members are generally unprepared for the health changes and the perceived "sudden" changes often lead to care incongruent with patients' preferences, compromised quality of life and mistrust towards the healthcare team. Despite the projected disease progression, interventions or mechanisms to discuss end-of-life (EOL) care have not been systematically introduced in the management of COPD (Momen et al., 2012). Advance care planning (ACP) aims to support people to plan for EOL and communicate their care wishes with family and the healthcare team before they lose mental capacity (Rietjens et al., 2017). ACP is gaining increased attention from the public. However, studies have shown that patients and their family members were unprepared for ACP due to unrealistic expectations towards medical treatments and cultural taboos of discussing death-related issues(Chan et al., 2018; Cheng et al., 2019). Conventional ACP interventions have positive but limited effects on empowering patients or EOL care decision-making. Studies reporting the effects of ACP on patients' readiness, decisional conflict, and the concordance between care preferences and the EOL care provided are mixed (Bravo et al., 2016; Cohen et al., 2019; Michael et al., 2022). Decision aids have been developed as tools to support patients in making informed and preference-sensitive treatment decisions, with some explicitly for ACP(Cardona-Morrell et al., 2017; Elwyn et al., 2006). A Cochrane review found that decision aids are effective in reducing decisional conflict, clarifying personal values, increasing decision-making behaviours and improving patient-doctor communication(Stacey et al., 2017). However, the effects of a locally-adapted disease-specific decision aid for COPD, on decision-making are lacking. II. Research Objectives: This study aims to assess the effectiveness of PDA in improving the understanding of ACP and EOL medical care among individuals diagnosed with Chronic COPD. The specific research objectives are as follows: 1. Assess the practicality of implementing PDAs for individuals with COPD. 2. Evaluate the thoroughness with which PDA address the needs of COPD patients. 3. Determine COPD patients' acceptance of PDA. 4. Assess the practicality of the research measurement tools. 5. Evaluate the effectiveness of strategies for recruiting participants. 6. Examine participant completion rates and analyse dropout causes. 7. Explore the experiences of participants using the PDA. III. Research Methodology: (I) Research Design: This 12-month study adopts a mixed-methods approach, incorporating the following procedures: 1. Quantitative Survey: We will conduct a single-blinded, randomised controlled trial (RCT) to assess the intervention's efficacy and impact systematically. 2. Qualitative Interviews: Semi-structured interviews will gather detailed insights into participants' experiences and perceptions regarding ACP, EOL treatment, and their interactions with the PDA. (II) Study Population, Location, and Sample Size: 1. Location: The study targets individuals diagnosed with Chronic Obstructive Pulmonary Disease (COPD) in the northern community of Taiwan. Recruitment will occur across various settings, including community care points, nursing homes, and neighbourhood activity centres. 2. Sample Size: We have determined the required sample size for this study based on prior research conducted by our team on severe disease patients and their families concerning ACP in community settings (Chan et al., 2018). The primary objective is to evaluate decisional conflicts regarding EOL care, measured through SURE test scores, with effect sizes (Cohen's d) of 0.26 and 0.47 at one and six months, respectively (Chan et al., 2018). Factoring in a 35% dropout rate, as observed in our previous ACP studies (Bell et al., 2018; Chan et al., 2018; Julious, 2005), and using the empirical rule, the study necessitates 120 participants, equating to 60 individuals per group. 3. Subject Recruitment: The research team will partner with community care points, nursing homes, and neighbourhood activity centres in the northern region of Taiwan. The Principal Investigator (PI) will obtain approval from the leaders of these facilities to conduct the study and to place recruitment posters. Social workers or nurses at these community centres will initially screen for eligibility and manage the registration process. (III) Informed consent, randomisation and blinding: 1. Informed Consent: Trained research personnel will evaluate participants' eligibility according to the inclusion criteria. Before any intervention, the research team will explain the informed consent form to the participants, ensuring they fully understand its contents. Following this, the team will ask participants to sign the consent form. 2. Randomisation and Blinding: This study will adopt a single-blind, random allocation approach. Before the commencement of the research activities, a computer-generated randomisation process will allocate participants to either the experimental or control group. A designated individual, Personnel C, who will have no involvement in the recruitment or the delivery of interventions, will manage this allocation process. The allocation outcomes will be sealed in envelopes, strictly accessible only to the research team. Trained research personnel A will administer the experimental interventions. Trained research personnel B will administer the control interventions. Trained Research Personnel who administer the interventions will not be blind to the participants' group assignments. Participants will be blinded regarding their group allocation to maintain the study's integrity. (IV) Data collection: 1. Demographic and past medical history at baseline: Demographic data, including age, sex, education, marital status, religious beliefs and living status, of the patients and their family carers will be collected. Charlson Comorbidity Index will be used to quantify comorbidity, and the Australia-modified Karnofsky Performance Scale will be used to assess the patient's functional ability (Barbetta et al., 2019). 2. The outcomes will be measured using Chinese validated instruments at baseline, 1 and 3 months post-allocation. The repeated-measure design will enable us to examine the short- and long-term intervention effects. (V) Data Analysis: 1. Quantitative data analysis will be conducted using will be conducted using SPSS 26.0 (IBM, Armonk, NY, USA) for statistical analysis. Descriptive statistical methods will summarise the characteristics of participants and study outcomes. One-way between-group ANOVA will be employed to compare the differences in continuous outcomes between the two study groups. The significance level will be set at 0.05. 2. Qualitative data analysis will be conducted using thematic and content analysis methods. Recordings from qualitative interviews will be transcribed verbatim, coded, and analysed. Qualitative findings will be compared and integrated with the quantitative survey results to provide a more comprehensive research conclusion.