Dhanam, Sreya

AbstractBackgroundFrontotemporal dementia (FTD) is a common young‐onset dementia. Challenges to in‐person FTD evaluations (e.g., behavioral symptoms, disease rarity), highlight the need to develop remote, low‐burden assessment techniques. A growing literature supports passive digital phenotyping for monitoring neurobehavioral change. Thus, we examined the utility of passively collected data from smartphones to detect prodromal or symptomatic FTD compared to that of the Montreal Cognitive Assessment (MoCA), a common cognitive screener.Method199 adults enrolled in the ALLFTD Mobile App study (mean age = 53.4 [SD = 15.2]; 58% women) completed up to 6 months of passive smartphone monitoring via the ALLFTD Mobile App. 55% were unimpaired [CDR®+NACC FTLD = 0], 22% had prodromal FTD [CDR®+NACC FTLD = 0.5], and 23% had symptomatic FTD [CDR®+NACC FTLD≥1] with a variety of syndromic presentations. Battery percentage was collected frequently (median interval = 45 min), and total daily battery usage was calculated as a proxy for smartphone use (higher battery usage = more smartphone use). Daily step counts were also passively collected. Logistic regression classified prodromal or symptomatic FTD vs. unimpaired as a function of: MoCA score [Model 1]; 7 passive features characterizing average, inter‐day variability, and changes over time in smartphone use and movement [Model 2]; or passive smartphone features and MoCA [Model 3]. Analyses were repeated excluding participants with symptomatic FTD to detect prodromal FTD vs. unimpaired.ResultThe area under the curves (AUCs) for detecting prodromal or symptomatic FTD (vs. unimpaired) from the MoCA alone [Model 1] and passive smartphone features alone [Model 2] were 0.77 (95%CI: 0.70‐0.85) and 0.77 (95%CI: 0.70‐0.84), respectively. Their combination [Model 3] resulted in a significantly improved AUC of 0.86 (95%CI: 0.80‐0.92) compared to Model 1 (p = 0.001). Significant Model 3 predictors included average daily battery usage (p = 0.02), slopes of change in step count (p = 0.04), and MoCA score (p<0.01). When distinguishing prodromal FTD from unimpaired, passive smartphone features alone (AUC = 0.75) were significantly better than the MoCA alone (AUC = 0.66; p<0.01), and their combination had the largest AUC (0.80).ConclusionResults support the utility of novel passively collected information from smartphones for detecting and monitoring FTD without contributing to assessment burden. With continued validation, passive digital monitoring methodologies have potential to increase access to dementia care.

ImportanceFrontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD.ObjectiveTo evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations.Design, Setting, and ParticipantsIn this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)–based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes.ExposureParticipants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests.Main Outcomes and MeasuresInternal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy.ResultsIn the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized β range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, −2.49 [95% CI, −0.19 to −0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back β = −0.49 [95% CI, −0.72 to −0.25]; P &amp;lt; .001) but not a composite of traditional neuropsychological measures (β = −0.14 [95% CI, −0.42 to 0.14]; P = .32).Conclusions and RelevanceThe findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.

AbstractBackgroundThere has been a rise in remote cognitive testing for neurodegenerative diseases. The ALLFTD Mobile App is designed to capture clinical features of frontotemporal dementia (FTD) through repeated remote assessment on smartphones. This is a departure from traditional neuropsychological practice in which a single examiner‐led assessment occurs in a controlled setting. When participants complete tasks on their own, those with greater disease severity may be more variable in their performance for a variety of reasons (e.g. greater distractibility, less engagement). Here we study whether intraindividual cognitive variability (IIV) is associated with older age and increasing disease severity.MethodParticipants were 279 (mean age = 54.9) healthy controls and participants with sporadic FTD or from familial FTD kindreds with a range of disease severities based on FTLD Clinical Dementia Rating scale (CDR®+NACC‐FTLD Global) scores: 0 (n = 132), 0.5 (n = 54), and ≥1 (n = 47). Participants completed three cognitive tasks at least two times across twelve days. Measures included an adaptive memory task and gamified versions of Flanker and Go/no‐go tasks. IIV was operationalized as the standard deviation in performance on each task. Regression analyses investigated the relationship of IIV with CDR®+NACC‐FTLD group, and with age in those without symptoms. Education and gender were included as covariates in all analyses.ResultGreater disease severity (CDR®+NACC‐FTLD) was associated with greater variability on Flanker (p<.001) and Go/No‐go (p = .02) tasks; a positive but non‐significant association was seen with memory testing (p = .16). Post‐hoc comparisons found that those with CDR®+NACC‐FTLD of 0.5 had significantly more IIV on the Flanker task (𝛃 = .39, p = .004) than those with CDR®+NACC‐FTLD’s of 0. Moreover, those with a CDR®+NACC‐FTLD ≥1 had significantly greater IIV on the Flanker (𝛃 = .85, p<.001) and Go/No‐go tasks (𝛃 = .51, p = .007) than those with a CDR®+NACC‐FTLD of 0. In those without symptoms (CDR®+NACC‐FTLD = 0), age was associated with increased IIV on the Flanker (𝛃 = .27, p<.001) and Go/no‐go (𝛃 = .23, p<.001) tasks.ConclusionIncreased IIV, particularly on processing speed/executive functioning tasks, was associated with greater age and greater functional impairment, even in the mildest stages. Future investigations exploring predictors of IIV and the prognostic value of IIV are warranted.

AbstractBackgroundFrontotemporal dementia (FTD) is a leading cause of dementia in individuals under age 65. Due to limitations in access to in‐person evaluations, there is an urgent need to examine remote, accessible, and low‐burden assessment techniques for FTD. Unobtrusive monitoring of at‐home computer use in older adults has shown that those with mild cognitive impairment (MCI) have significant declines in computer use over time. To extend these findings, we characterized daily trajectories in smartphone battery percentage, a proxy of smartphone use, and examined relationships with clinical indicators of disease severity in FTD.MethodParticipants included 250 adults enrolled in the ALLFTD Mobile App study (mean age = 53.9 [SD = 14.9]; 59% women; 94% White). To assess clinical disease severity, all participants completed the FTLD Clinical Dementia Rating scale (CDR®+NACC FTLD), a brief neuropsychological battery, the Neuropsychiatric Inventory Questionnaire (NPI‐Q), and brain MRI. The ALLFTD Mobile App was installed onto participants’ smartphones for remote, passive, continuous monitoring of smartphone use (average days of monitoring = 82 [range = 7‐541]). Battery percentage was collected every 15 minutes. Linear mixed effects models examined linear, quadratic, and cubic effects of time of day (i.e., hour; modeled as person‐specific random effects) and their interactions with each measure of disease severity on battery percentage. Models covaried for age.Result57% of participants were clinically normal, 21% had MCI, and 22% had dementia. CDR®+NACC FTLD Global score interacted with hour such that participants with MCI and dementia had flatter battery curves (i.e., less change in battery percentage throughout the day – a proxy of less smartphone use) than clinically normal participants (ps<0.001). Additional models showed that worse memory (hour3 x memory score: 𝛃 = 0.59, p<0.001) and executive functioning (hour3 x executive score: 𝛃 = 0.64, p<0.001), more neuropsychiatric symptoms (hour3 x NPI‐Q: 𝛃 = ‐0.22, p = 0.004), and smaller gray matter volumes (controlling for intracranial volume; hour3 x volume: 𝛃 = 0.49, p<0.001) were also statistically significantly related to less battery usage throughout the day.ConclusionThese findings are proof‐of‐concept that passively acquired smartphone data, specifically battery usage, can provide insights about disease progression in FTD. Results support future research focusing on the development of passively collected digital metrics sensitive to clinical declines in neurodegenerative disease.

AbstractBackgroundSmartphone‐based cognitive and motor tests enable frequent remote assessments in observational research studies and clinical trials, limiting travel burden and increasing access to research for underrepresented groups. One such smartphone tool is the ALLFTD Mobile App, which was designed to capture the clinical features of neurodegenerative diseases via a battery of cognitive, speech, and motor tasks. In this study, we focus on recruiting a demographically diverse normative sample. We have partnered with the UCSF Brain Health Registry (BHR) to recruit 1,000 cognitively healthy participants across the lifespan. Here we describe the study design and initial feasibility results.MethodsWe designed an automated workflow in which participants never interact with the study team except for troubleshooting. BHR participants without reported cognitive impairment were contacted by email to participate in this study (n = 668). Interested participants downloaded the app onto their smartphone and completed in‐app electronic consent. They were asked to complete three ∼30‐minute sessions of cognitive, motor and speech measures over two weeks. Adherence was determined by evaluating the completion rate of app tasks. In a subset who completed a user experience questionnaire (n = 116), responses were quantified using descriptive statistics.ResultsThe current enrollment rate is 23.20% (155/668) of contacted participants. Average age is 62 (SD = 8.2, range = 25‐84). Average adherence is 76%. Participants reported a range of perceived task difficulty: 38% indicated tasks were “very easy” or “somewhat easy”, 26% indicated they were “neutral,” 36% found the tasks “somewhat difficult,” and none reported them to be “very difficult.” The time commitment was reported to be acceptable by 97% of participants, and 20% were willing to complete additional tasks. Over half (59%) of participants indicated that they preferred using a smartphone to complete cognitive assessments over traditional pen‐and‐paper measures.ConclusionsOur preliminary results demonstrate the feasibility and acceptability of recruiting participants, including older adults, through a registry and conducting remote smartphone testing via a fully automated workflow that is highly scalable. Future analysis will study the effects of demographic factors on feasibility and investigate the reliability and validity of the app measures in this sample.