## 2 Materials and Methods
### Analysis  
Our analysis followed guidelines given by [PERSON] and [PERSON] [61] and [PERSON] and colleagues [62] and our prior experience in analyzing textual data [43; 57; 63; 64; 65]. We conducted both inductive and deductive content analyses. We used an inductive content analysis approach to analyze patients' and care partners' awareness of indicators of ESRD before and after dialysis treatments (Section 3.1). A deductive content analysis approach was used to analyze patients' and care partners' design expectations of monitoring procedures related to their mobile hemodialysis treatments (Section 3.2). For the inductive content analysis, all the transcribed interviews were carefully read by two analysts and authors of this paper to gain knowledge of the interview data prior to starting the analysis process. This was an important step in achieving immersion in the data before the coding process begins [66; 67; 68; 69]. For the inductive content analysis, structural coding was conducted using the software ATLAS.ti (Version 9.1.2 (2087)) [70; 71]. In this stage, one analyst used ATLAS.ti to capture large segments of participants' full responses to the questions of interest to preserve the context of the interview data. Then, each segment was analyzed. Keywords and short phrases uttered by the participants were highlighted and coded using either the direct words as keywords or the direct short phrases as quotations and marked by the participants' study identification (ID) number. A word was identified as a keyword if it was in the form of an attribute related to the question of interest. For example, we asked the participants, \"How do you know if dialysis was performed well?\" One participant responded, \"my weight goes down and I have more energy\". This response had two keywords, \"weight\" and \"energy\", which were coded as \"weight goes down\" and \"more energy\". The former was later rephrased to \"weight loss\" to be documented with another participant's comment. The keywords and quotations were exported from ATLAS.ti to a spreadsheet to form an initial codebook. At this stage of the analysis, a second analyst joined in analyzing the interviews by confirming that all keywords and comments had been correctly captured and identified. In the event of discrepancies, adjustments were collaboratively made by the two analysts. Lastly, the codes were collaboratively organized into categories based on code commonalities. Code commonalities were identified when keywords or phrases were related to the same attribute. An overarching concept was chosen to describe each category where the repetition of keywords was the strategy most frequently used to identify a theme and to form and name  
Figure 1: Flowchart of the interview process. (**a**) interview preparation; (**b**) participant recruitment; (**c**) survey data collection; (**d**) interview data collection; (**e**) interview data handling and completion. each category [72]. However, during our analysis process, we noticed that by focusing only on repetitions, certain important comments made by our participants might have been overlooked. For this reason, we decided to include all responses that included a keyword in the form of an attribute of interest. To represent our results, we first created each category based on our analysis; we then displayed the number of keywords or short phrases, phrases that included a keyword, that fell under each category as a bulleted list connected to the category. For the deductive content analysis, we followed a more directed analysis approach where we systematically identified attributes in the interviews related to participants' expectations of monitoring procedures for mobile hemodialysis treatments. This was done to extend our understanding of patients' and care partners' needs for monitoring features as we had previously identified monitoring as an important feature of a dialysis device [43]. Our deductive content analysis strategy was performed in three stages.