## 3 The Mutual Algorithm
## 5 Experiment Design

Twelve participants were randomly recruited from various locations (e.g., nursing home, hospital, park) to fill out a questionnaire in order to collect features data ([[https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) rvx-T9 kmsZ-ErRgl-wESieP2 qFnBfos/view?usp=sharing]([https://drive.google.com/file/d/1](https://drive.google.com/file/d/1) rvx-T9 kmsZ-ErRgl-wESieP2 qFnBfos/view?usp=sharing), accessed on 1 January 2022) of LCDA. To help participants understand LCDA, the prototype system (Figure 4) has been developed with mobile application software to assist them in completing the questionnaire successfully. Participants were over 65 years old. All participants were informed that any potentially identifying information learned and collected from this study would remain confidential and disclosed only upon receipt of permission from the participant. There are three levels for each factor. For the risk level factor, if older people consider that they do not require assistance from others most of the time, this is Level 1. If they estimate a nearly fifty-fifty chance that they need care from others, it is Level 2. The remaining risk level is Level 3. Older people can refer to their own Barthel index, as assessed by the government, to complete this part. Other features such as physiology, medical record, and demography are also classified according to three levels as shown in Table 1. The survey questionnaire about whether or not the proposed system can improve users' (a) perceived usefulness, (b) ease of use, (c) satisfaction, and (d) intention to use for long-term care. A five-point Likert scale [31] was used with 1 indicating \"strongly disagree,\", 3 indicating \"neutral,\" and 5 indicating \"strongly agree\". The system architecture (Figure 5) uses Android Studio software for designing Java programming and mobile application services (APPs). Figure 4: The Prototype System of Long-term Care. ## 6 Results

For the first hypothesis, [PERSON]'s alpha for user perception was 0.88, which revealed the consistency is reliable. A one-sample \(t\)-test is used for evaluating whether or not the average of user perception is equal to 3 (neutral) based on the middle point of Likert scale [32]. The perceived usefulness mean difference is 1.33 (\(p\) < 0.01), perceived ease of use is 1.29 (\(p\) < 0.01), user satisfaction is 0.96 (\(p\) < 0.01), and user intention mean difference is 1.17 (\(p\) < 0.01) are all significant. Therefore, the first hypothesis is supported. The data of all participants were collected from the questionnaire based on the proposed features shown in Table 2.

According to the MMC steps (1-3), including initial partition for older people, choosing seeds as temporary center members, and assigning older people to new groups based on the similarity computations, the dataset is represented in Tables 3-5, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Level** & **Risk Level** & **Physiology** & **Medical Record** & **Demography** \\ \hline
1 & Independence & Stand + Walk & No disease & Well-educated \\
2 & Independence + Help & Stand + Walk + Help & Minor disease & Basic education \\
3 & Help & Help & Major disease & No education \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Levels of Features of LCDA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Person** & **Risk Level** & **Physiology** & **Medical Record** & **Demography** \\ \hline P1 & 2 & 1 & 1 & 2 \\ P2 & 2 & 2 & 2 & 2 \\ P3 & 1 & 1 & 1 & 1 \\ P4 & 3 & 2 & 1 & 2 \\ P5 & 2 & 3 & 2 & 1 \\ P6 & 2 & 2 & 1 & 3 \\ P7 & 1 & 3 & 1 & 3 \\ P8 & 3 & 1 & 2 & 1 \\ P9 & 2 & 3 & 1 & 3 \\ P10 & 1 & 2 & 2 & 2 \\ P11 & 1 & 2 & 3 & 1 \\ P12 & 3 & 3 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset of Participant Features. Figure 5: The System Architecture of the Prototype System.