Cancer and cancer treatments have been shown to accelerate cognitive decline [22, 32–36]. While the prevalence of subjective cognitive decline in adults older than 45 years of age is ~ 11% , up to 75% of cancer survivors report experiencing subjective cognitive decline symptoms [22, 32, 34, 38, 39]. The use of an ambulatory cognitive assessment approach can sharpen detection of subjective and objective cognitive decline symptoms, which are often momentary and periodic. Understanding the circumstances surrounding episodes of impairment is crucial to understanding the drivers and etiology of cancer- and cancer treatment-associated cognitive impairments. Therefore, we conducted a signal-finding study in women with breast cancer receiving ET to: 1) determine acceptability of such longitudinal mobile assessments, 2) assess characteristics of patients who completed the study versus those who did not, and 3) contextualize our experience to guide future studies that incorporate similar methods. Overall, we observed 1) study eligibility and consent rates of 28% and 36%, respectively, 2) no clinical or demographic differences between participants who completed the study and those who did not, but differences might have been difficult to detect given the sample size, 3) the majority of patients who did not complete the study withdrew early and were significantly less compliant to data collection at baseline, and 4) participants who did not complete the study did not appear to have differences in sleep or physical activity habits, nor were differences in cognition observed but, again, sample size makes it difficult to detect differences that are not large.
The rationale for the study design was to dissociate potential drivers of cognitive impairments experienced by newly diagnosed breast cancer patients over the course of their daily lives. Hypothesized drivers included ET effects themselves, factors found to correlate with patient reported cognitive outcomes in previous studies (psychosocial factors such as changes in stress, pain, and affect); behavioral factors known to influence cognition in studies of middle-aged and older adults (sleep and physical activity), and performance-based indicators of cognitive health (assessments of processing speed, working memory, and associative long-term memory). Our methodology involved the use of three different study devices simultaneously (smartphones, watches and hip actigraphy devices) for 7 days each month for several months. While this could have created a burden for a number of women, the most common reason for withdrawal was a global sense of stress and feeling overwhelmed. Indeed, stress from a cancer diagnosis is common for stage I-III cancer patients . Stress levels are typically moderate to severe at the initial stages of diagnosis and remain high for approximately 6-months . This suggests that the time-period chosen for this study was at the height of women being stressed over their disease.
No significant differences were observed in demographic or clinical characteristics between participants who completed the study versus those who did not complete. Participants who did complete the study were more compliant (i.e., used the devices the full 7 days, wore the devices longer, completed more cognitive surveys); these early data thus provide a predictor of who will complete the study. To our knowledge, this is the first study to assess the practicality of recruiting newly diagnosed breast cancer patients for a research study that utilizes a combination of technological devices (smartphones and multiple actigraphy devices). Breast cancer studies utilizing EMA approaches are limited. Existing studies have primarily focused on fatigue, physical activity and affect [16, 41, 42].
Other elements of our study design and methodology are worth noting. As indicated in Fig. 1 and Table 1, we recruited women who would be receiving ET. However, at the time of study recruitment it was clinically ambiguous if a patient would move forward with chemotherapy, or radiation therapy. Given the flow of clinical care, it was impossible to restrict our enrollment to women who would be receiving ET as their only adjuvant therapy. Therefore, we designed the study to include a second baseline measurement after chemotherapy and before ET for women who received adjuvant chemotherapy. In practicality, only 8% of our study population received adjuvant chemotherapy, which is about half of the national average (19%) .
In line with observations related to clinical care, 6% of women in our study refused ET even though it was initially recommended to them . Future studies focused on recruiting women before starting ET may need to accommodate both the use of adjuvant therapy and ET refusal into sample size projections. Finally, our initial goal to recruit a homogenous group of cancer patients had to be revised to include women with a psychiatric diagnosis or DCIS, given their relatively high incidence in this population (Table 2).
In conclusion, it was difficult to identify demographic or clinical characteristics in women with breast cancer that could predict study completion, although initial compliance rate was indicative of study completion rate. Our experience suggests that retention might be improved by distributing data collection periods, developing more regular check-ins with patients, and using a “washout” period between study consent and baseline device use. Accounting for attrition between consent and enrollment or consent and study completion is an important aspect of sample size determination. Further, investigators should be aware of the clinical flow of treatment decisions in relationship to the research time line and account for these clinical decisions in their study design. Thus, our observations may inform future trials.