Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study

Abstract

Background Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. Objective The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. Methods We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Results Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study; 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Conclusions Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.

Symptom Monitoring based on Digital Data Collection During Inpatient Treatment of Schizophrenia Spectrum Disorders – a Feasibility Study

“…In conclusion, the diagnosis of a disorder in the schizophrenic spectrum does not seem to have an effect on the dropout rate, a patient might just need to be presented more assistance and patience while handling a digital device. We found the required support to be associated with patients’ age which is in line with studies on general non-clinical samples (Wildenbos et al, 2019) and PADs (Richter et al, 2020). As older adults in general are not as experienced in the use of digital devices this result does not come as a surprise (Mitzner et al, 2019).…”

“…We aimed to achieve a number of 100 participating patients and proceeded recruitment until we included 49 PSSDs and 51 PADs. The recruitment steps followed procedures described in our previous work (Richter et al, 2020). Patients with the appropriate diagnosis were identified through a patient recruitment system based on the diagnosis entered into the electronic health record by the attending physician at admission.…”

“…Touchscreen modules in particular are gaining increasing acceptance (Preuschoff et al, 2013). We have already established a patient-reported outcomes system at the University Hospital Münster and showed its validity in patients with an affective disorder (PADs; Richter et al, 2020).…”

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory

“… 43 In addition, participant-prioritized outcomes (eg, anxiety, pain) might differ from traditional clinical outcomes 44 and help with risk prediction. 45 Research demonstrates that integration of PROs into the EHR is possible in a number of different types of care settings, 46 , 47 and facilitating additional measures for research is becoming more of a priority. 48–50 If PROs have not been established before a PCT is being conducted, clinics could potentially use the trial to help identify domains to include in their EHR, although there is great variability in what patients think is important depending on the disease or condition.…”

Digital self-report instruments for repeated measurement of mental health in the general adult population: a protocol for a systematic review

“…The overarching aim of the present review is to advance understanding of the measurement of the multifaceted construct ‘mental health’ by conducting (to our knowledge) the first audit of instruments focusing on digital delivery and repeated measurement of mental health in the general adult population. Such instruments are becoming more common and more important across the landscape of mental health and well-being in digital monitoring,24 digital assessments,25 digital phenotyping,26 self-management27 and so on in research and practice. The outcomes from the present review will expand on and update previous reviews conducted on mental health instruments (eg, Breedvelt et al and Beidas et al 28 29) and ultimately be used to inform the development of a new digital mental health instrument for monitoring purposes in the general population.…”