JMIR Publications

ABSTRACT

Background:

Previous research has shown that in-lab motor skill learning (supervised by an experimenter) is sensitive to biomarkers of Alzheimer’s disease (AD). However, remote unsupervised screening of AD risk through a learning-based task via the internet has the potential to sample a wider and more diverse pool of individuals at scale.

Objective:

The purpose of the current study was to examine an online motor skill learning game (“Super G”) and its sensitivity to risk factors of AD (e.g., age, sex, APOE ε4 carrier status, and verbal learning deficits).

Methods:

E-mails were sent to 662 previous MindCrowd participants who had agreed to be contacted for future research, have their APOE ε4 carrier status recorded, and were at least 45 years of age or older. Participants who chose to participant were redirected to the Super G site where they completed 75 trials, each being 4.5 seconds in length, of the Super G task using the arrow keys of their personal computer keyboard. Once completed, different Super G variables were derived. Specifically, average time in target (TinT), average response time (RT), average time of reversal (TR), and average scaling ratio (SR) was calculated for each participant across all trials.

Results:

Fifty-four participants (~8% response rate) from the MindCrowd online cohort (mean age = 62.39 years; 42 Female; 23 APOE ε4 carriers) completed 75 trials of Super G. Results show that Super G performance was significantly associated with each of the targeted risk factors. Specifically, slower Super G RT was associated with being an APOE ε4 carrier, (OR = .12, 95% CI = [.02, .44], p=.006), greater Super G TinT was associated with being male (OR = 32.03, 95% CI = [3.74; 1192,61], p=.01), and lower Super G TinT was associated with greater age (β = -3.97, 95% CI = [-6.64; -1.30], p=.005). Further a sex-by-TinT interaction demonstrated a differential relationship between Super G TinT and verbal learning depending on sex (βMale:TinT = 6.77, 95% CI =[.34; 13.19], p=.04).

Conclusions:

This experiment demonstrated that this online game, Super G, has the potential to be a learning-based digital biomarker for screening of AD risk on a large scale with relatively limited resources.