Partnering a Compensatory Application with Activity-Aware Prompting to Improve Use in Individuals with Amnestic Mild Cognitive Impairment: A Randomized Controlled Pilot Clinical Trial

Abstract

Background: Compensatory aids can help mitigate the impact of progressive cognitive impairment on daily living.

Objective: We evaluate whether the learning and sustained use of an Electronic Memory and Management Aid (EMMA) application can be augmented through a partnership with real-time, activity-aware transition-based prompting delivered by a smart home.

Methods: Thirty-two adults who met criteria for amnestic mild cognitive impairment (aMCI) were randomized to learn to use the EMMA app on its own (N = 17) or when partnered with smart home prompting (N = 15). The four-week, five-session manualized EMMA training was conducted individually in participant homes by trained clinicians. Monthly questionnaires were completed by phone with trained personnel blind to study hypotheses. EMMA data metrics were collected continuously for four months. For the partnered condition, activity-aware prompting was on during training and post-training months 1 and 3, and off during post-training month 2.

Results: The analyzed aMCI sample included 15 EMMA-only and 14 partnered. Compared to the EMMA-only condition, by week four of training, participants in the partnered condition were engaging with EMMA more times daily and using more basic and advanced features. These advantages were maintained throughout the post-training phase with less loss of EMMA app use over time. There was little differential impact of the intervention on self-report primary (everyday functioning, quality of life) and secondary (coping, satisfaction with life) outcomes.

Conclusion: Activity-aware prompting technology enhanced acquisition, habit formation and long-term use of a digital device by individuals with aMCI. (ClinicalTrials.gov NCT03453554).

Keywords: Alzheimer’s disease; assistive technology; dementia; functional status; memory training; quality of life; real-time systems; reminder system; smart health aids; supervised machine learning.

Figures

Fig. 1.

CONSORT Flow Diagram.

Fig. 2.

Mean number of daily context-aware transition-based smart home prompts received and responded to by participants in the partnered condition plotted by week (error bars represent standard error). Training = weeks 1–4; post-training: month 1 = weeks 5–8 (prompts on); month 2 = 9–12 (prompts off); month 3 = 13–16 (prompts on).

Fig. 3.

Mean daily distinct uses of the EMMA app plotted as a function of weeks (x-axis) for participants in the partnered (solid) and EMMA-only (dotted) conditions (error bars represent standard error). Training = weeks 1–4; post-training: month 1 = weeks 5–8 (prompts on); month 2 = 9–12 (prompts off); month 3 = 13–16 (prompts on).

Fig. 4.

Mean daily for basic (top) and advanced (bottom) uses of the EMMA app plotted as a function of weeks (x-axis) for participants in the partnered (solid) and EMMA-only (dotted) conditions (error bars represent standard error). Training = weeks 1–4; post-training: month 1 = weeks 5–8 (prompts on); month 2 = 9–12 (prompts off); month 3 = 13–16 (prompts on).