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Supplemental material, sj-docx-1-dhj-10.1177_20552076231179031 for User-centered design of feedback regarding health-related behaviors derived from wearables: An approach targeting older adults and persons living with neurodegenerative disease by Karen Van Ooteghem, F Elizabeth Godkin, Vanessa Thai, Kit B Beyer, Benjamin F Cornish, Kyle S Weber, Hannah Bernstein, Soha O Kheiri, Richard H Swartz, Brian Tan, William E McIlroy and Angela C Roberts in DIGITAL HEALTH

Additional file 1: Supplementary File 1. Results of hyperparameter cross-validation on the training dataset for non-wear start (top) and non-wear end (bottom).

Additional file 2: Supplementary File 2. Results of decision tree analysis for non-wear start (top) and non-wear end (bottom).

Some studies have found that bilingualism promotes cognitive reserve. Objective: We aimed to determine whether bilingualism, defined as regularly (i.e. daily) using at least two languages at least since early adulthood, is associated with cognitive advantages in Parkinson’s disease (PD) or whether the possible benefits of bilingualism are lost in the context of PD, possibly affecting quality of life (QoL) and independence. Method: Participants with idiopathic PD (n = 140, mean age = 67.9 [SD = 6.4], 78% men) completed standard neuropsychological tasks evaluating attention/working memory, language, executive function, memory, and visuospatial ability, as well as measures of wellbeing and functional independence. Results: Bilinguals with PD (n = 21) performed worse than monolinguals with PD (n = 92) on attention/working memory and language measures. The between-group differences in attention/working memory were restricted to verbally-based measures. When measured along a continuum, a higher degree of bilingualism was correlated with lower scores on measures of attention/working memory and language. There were no group differences in self- or informant-reported cognitive decline, PD health-related QoL, or functional independence. Conclusions: Bilingualism in PD was not associated with better cognitive performance. Lower scores on language-based measures may reflect a distributed fund of linguistic information across more than one language, lower language proficiency in English, and/or other cultural artifacts. Furthermore, using normative data specific to the dominant language spoken or conducting neuropsychological testing in participants’ self-reported most proficient language may enhance additional studies addressing this topic. Future research may also examine the roles of bilingualism over time and across other neurodegenerative diseases with and without EF impairment to illuminate further the impact of bilingualism on cognition and QoL, and shape culturally and linguistically diverse research and clinical care.

Background Large and complex studies are now routine, and quality assurance and quality control (QC) procedures ensure reliable results and conclusions. Standard procedures may comprise manual verification and double entry, but these labour-intensive methods often leave errors undetected. Outlier detection uses a data-driven approach to identify patterns exhibited by the majority of the data and highlights data points that deviate from these patterns. Univariate methods consider each variable independently, so observations that appear odd only when two or more variables are considered simultaneously remain undetected. We propose a data quality evaluation process that emphasizes the use of multivariate outlier detection for identifying errors, and show that univariate approaches alone are insufficient. Further, we establish an iterative process that uses multiple multivariate approaches, communication between teams, and visualization for other large-scale projects to follow. Methods We illustrate this process with preliminary neuropsychology and gait data for the vascular cognitive impairment cohort from the Ontario Neurodegenerative Disease Research Initiative, a multi-cohort observational study that aims to characterize biomarkers within and between five neurodegenerative diseases. Each dataset was evaluated four times: with and without covariate adjustment using two validated multivariate methods – Minimum Covariance Determinant (MCD) and Candès’ Robust Principal Component Analysis (RPCA) – and results were assessed in relation to two univariate methods. Outlying participants identified by multiple multivariate analyses were compiled and communicated to the data teams for verification. Results Of 161 and 148 participants in the neuropsychology and gait data, 44 and 43 were flagged by one or both multivariate methods and errors were identified for 8 and 5 participants, respectively. MCD identified all participants with errors, while RPCA identified 6/8 and 3/5 for the neuropsychology and gait data, respectively. Both outperformed univariate approaches. Adjusting for covariates had a minor effect on the participants identified as outliers, though did affect error detection. Conclusions Manual QC procedures are insufficient for large studies as many errors remain undetected. In these data, the MCD outperforms the RPCA for identifying errors, and both are more successful than univariate approaches. Therefore, data-driven multivariate outlier techniques are essential tools for QC as data become more complex. Electronic supplementary material The online version of this article (10.1186/s12874-019-0737-5) contains supplementary material, which is available to authorized users.