Digital Planning-Based Technologies to Support Memory-Related Functioning in Older Adults with Mild Cognitive Impairment: A Systematic Scoping Study
Bibliographic record
Abstract
Background: Mild cognitive impairment (MCI) is a precursor to dementia, with a prevalence of over 15% among community dwellers, with significant economic and social implications. Despite preserved autonomy in daily living, individuals with MCI face challenges in handling everyday technology and memory-related tasks, necessitating digital interventions, such as digital planning-based technologies to support cognitive functioning. Objective: The aim of this study is to identify and summarize available research literature on available digital planning-based technologies to support memory-related functioning in older adults with MCI. Specifically, this study seeks to (1) document the types and functions of these technologies, (2) report their effectiveness, and (3) identify gaps in the current evidence. Methods: A comprehensive literature search of four databases (Medline, Embase, APA PsycINFO, and CINAHL) was conducted in accordance with Arksey and O’Malley’s scoping review methodological framework from inception to February 6, 2024, without restrictions. Inclusion criteria focused on peer-reviewed studies involving adults aged 50 or older with diagnosed cognitive impairment, specifically using digital planning-based technologies for memory support. Results: From 1854 sources, 8 studies were included. Five categories of digital planning-based technologies were identified: (1) reminders and alarms, (2) electronic calendars, (3) digital memory notebooks, (4) digital visual mapping, and (5) smart-home integration. Several studies reported improved task performance, memory recall, and executive function, with digital memory notebooks and visual mapping software showing greatest impact on functional independence. While interventions were generally well-received, usability challenges and low adherence were common, often linked to technological familiarity and motivation. Conclusions: Digital planning-based technologies show promise in supporting memory-related functioning in older adults with MCI. While studies report positive outcomes, more research is needed to refine these tools, evaluate long-term effects, and ensure integration into daily life. Expanding the evidence base will be key to improving accessibility and effectiveness for this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".