Social behavior assessment in cognitively impaired older adults using a passive and remote smartphone application
Bibliographic record
Abstract
Abstract Background In Alzheimer’s Disease (AD), loss of social interactions has a major impact on well‐being. Therefore, AD patients would benefit from early detection of symptoms of social withdrawal. Current measurement techniques such as questionnaires are subjective and rely on recall, in contradiction to smartphone applications, which measure social behavior passively and objectively. Here, we examine social interactions through passive remote monitoring with the smartphone application BEHAPP in cognitively impaired participants. This study aims to investigate (1) the association between demographic characteristics and BEHAPP outcome variables in cognitively normal (CN) older adults, (2) if social behavior as measured using the passive smartphone app BEHAPP is impaired in cognitively impaired (CI) participants compared to subjects with subjective cognitive decline (SCD), and CN older adults. In addition, we explored in a subset of individuals the association between BEHAPP outcomes and neuropsychiatric symptoms. Method CN (n=209), SCD (n=55) and CI (n=22) participants, older than 45 years, installed the BEHAPP app on their own Android smartphone for 7‐42 days. CI participants had a clinical diagnosis of mild cognitive impairment or AD‐type dementia. The app continuously measured communication events, application usage and location. Neuropsychiatric Inventory (NPI) total scores were available from 20 SCD and 22 CI participants. Result We found that older cognitively healthy participants called less frequently and made less use of apps. No sex effects were found. Linear models corrected for age, sex and education showed that compared to the CN and SCD groups, CI participants called less unique contacts and contacted the same contacts relatively more often (Figure 1). They also made less use of apps, visited less unique places and traveled less far from home. Higher total NPI scores were associated with more unique stay points and further travelling. Similar behavior patterns were found when correcting for multiple comparisons. Conclusion Cognitively impaired individuals show reduced social activity, as measured by the smartphone application BEHAPP. Neuropsychiatric symptoms seemed only marginally associated with social behavior as measured with BEHAPP. This research shows that a passive and remote smartphone application is able to objectively and passively measure altered social behavior in a cognitively impaired 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".