## 2 Materials and Methods
### Analysis
First, the transcribed interviews were carefully read by the first analyst, and structural coding was conducted. Instances related to monitoring were highlighted and coded using the temporary code \"monitoring\". Then, the highlighted and coded sections were exported to a spreadsheet where a dual coding process, coding conducted separately by the two analysts, was initiated. In this stage, keywords related to monitoring were extracted from each coded section and sorted into categories separately by two analysts. Then, the second round of coding was conducted. All transcripts were searched using keywords related to monitoring; namely, \"monitoring\", \"alarm\", \"heart rate\", \"blood pressure\", \"follow-up\", \"smartphone\", and \"Bluetooth\". In the event of identifying instances that had not previously been coded, the section was highlighted and coded using the aforementioned analysis method. The keywords used for searching the transcripts were chosen because they had either been previously identified in relation to an ideal monitoring feature for a dialysis device [43], or during the first stage of the deductive analysis. Lastly, to ensure all instances had been correctly identified, all transcripts were carefully read by both analysts. During the coding and analysis of the interviews, both analysts had weekly meetings where the coding results were compared, and discrepancies were discussed until a coding agreement was reached. To represent our results, we placed each category based on our analysis in a table and displayed the total number of comments that fell under each category along with an exemplary quotation from either a patient or a care partner. To ensure the rigor of our qualitative analysis we considered three criteria of trustworthiness: validity, reliability, and generalizability [73]. To ensure validity, or the extent to which inferences made are accurate, the analysts had prolonged engagement with the project and prior experiences with interviewing and analyzing responses from patients and care partners to accurately capture their preferences guiding the designs of dialysis devices [63; 64; 65; 43; 67]. Our engagement is in line with human factors principles of accurately presenting users' information to guide design decisions. The validity of our analysis is further represented by the detailed presentations of participants' responses and our recruiting method of using a diversity matrix to capture demographic diversity and sample size to reach data saturation [74]. To ensure the reliability of our study or the extent to which the results of the study can be replicated, we aimed to meet two criteria: (i) to limit the possibility of subjective interpretations of participants' responses, we had two researchers code the interviews and preserved participants' responses, as codes; and (ii) for the purposes of replication, we provided detailed documentation of our process, from data collection and analysis. Lastly, to ensure the generalizability of our findings or the extent to which our results can be transferred to other patients and care partners, we made inferences based on the descriptive representation of our study population [75].