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User Engagement With and Perceived Impact of a Digital Cognitive Training App on Cognition, Daily Functioning, and Mental Fitness: Secondary Analysis of Cross-Sectional Survey Data

User Engagement With and Perceived Impact of a Digital Cognitive Training App on Cognition, Daily Functioning, and Mental Fitness: Secondary Analysis of Cross-Sectional Survey Data

User Engagement With and Perceived Impact of a Digital Cognitive Training App on Cognition, Daily Functioning, and Mental Fitness: Secondary Analysis of Cross-Sectional Survey Data

Authors of this article:

Kelsey McAlister1 Author Orcid Image ;   Lara Baez1 Author Orcid Image ;   Anna Braunsdorf2 Author Orcid Image ;   Yana Yang2 Author Orcid Image ;   Dan Kessler2 Author Orcid Image ;   Jennifer Huberty1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Fit Minded, Inc, Phoenix, AZ, United States

    2The Mind Company, San Francisco, CA, United States

    Corresponding Author:

    Kelsey McAlister, PhD

    Fit Minded, Inc

    2901 E Greenway Road

    PO Box 30271

    Phoenix, AZ, 85046

    United States

    Phone: 1 602 935 6986

    Email: kelsey@fit-minded.com


    Background: Cognitive difficulties are common and can interfere with daily functioning. While digital cognitive training apps are widely used, few studies have examined whether personalized tools support perceived improvements in cognitive functioning, daily functioning, and overall mental fitness among general adult users.

    Objective: The purpose of this secondary analysis was to explore the self-reported cognitive benefits of Elevate, a commercial, personalized cognitive training app developed to support cognitive functioning, as well as engagement with the app. We aimed to (1) describe demographics, engagement metrics, and self-reported improvements; (2) examine associations between app engagement and self-reported improvements in cognitive functioning skills directly targeted by the app; and (3) examine associations between app engagement and self-reported improvements in daily functioning and overall mental fitness as potential transfer effects of cognitive training.

    Methods: Adult Elevate users (aged ≥18 years) who used the app at least twice in the previous 30 days completed a brief web-based survey on perceived cognitive, functional, and mental fitness improvements. Responses were linked to objective app use data, including total active weeks, mean active days per week, and mean time per day. Ordinal logistic regressions tested associations between engagement metrics and self-reported outcomes controlling for demographic variables. A Bonferroni correction was applied to adjust for multiple comparisons.

    Results: A total of 3367 adult Elevate users were included. Participants were primarily middle-aged (mean 55, SD 16 y), White (2557/3336, 76.65%), and female (2184/3362, 64.96%), with 67.72% (2274/3358) holding at least a college degree. Using the app across more weeks was associated with a greater likelihood of reporting improvements in all cognitive skills (odds ratios [ORs] 1.0014-1.0027, 95% CI 1.0006-1.0036), several areas of daily functioning (eg, motivation and task efficiency; ORs 1.0014-1.0017, 95% CI 1.0006-1.0026), and overall mental fitness (OR 1.0021, 95% CI 1.0012-1.0031). More days of use per week were linked to improvement in math only (OR 1.15, 95% CI 1.09-1.22), whereas spending more time per day was associated with improvements in speaking, reading, math, motivation, personal progress, and mental fitness (ORs 1.02-1.04, 95% CI 1.01-1.06).

    Conclusions: Greater use of the Elevate app was linked to self-reported improvements in cognitive skills, daily functioning, and overall mental fitness. These findings suggest that personalized, adaptive cognitive training apps such as Elevate may serve as scalable tools for enhancing everyday cognitive and functional well-being. Future research should use rigorous, longitudinal methods to confirm these effects and clarify which app features drive meaningful outcomes.

    JMIR Form Res 2025;9:e80027

    doi:10.2196/80027

    Keywords

    cognitive functioning; mobile health; real-world evidence; digital health; app-based intervention; mental performance 


    Background

    Cognitive difficulties such as challenges with memory, attention, and information processing are increasingly common, with estimates suggesting that 2 out of 3 Americans will experience some level of cognitive difficulty [1]. Even minor declines in cognitive abilities (eg, slower processing and reduced working memory) can impair everyday tasks such as managing finances and organizing schedules [2-4]. When these difficulties persist over time, they are associated with more than twice the risk of developing mild cognitive impairment or dementia within the next decade [5]. This risk increases as individuals age as 75% of older adults with ongoing cognitive concerns go on to develop clinical symptoms within 10 years [5]. These everyday difficulties are often intensified by stressors such as chronic stress, frequent multitasking, and digital overload, which place ongoing strain on attention and executive function [6,7]. For example, multitasking between multiple forms of screen media (eg, texting, watching television, and engaging with social media) has been associated with greater cognitive difficulties, including poorer working memory, reduced attentional filtering, and increased vulnerability to distraction [7]. As such, there is a growing need for scalable interventions that prevent cognitive difficulties and promote cognitive functioning in everyday life.

    Mental fitness is a novel framework for assessing cognitive difficulties. Although it is an emerging concept that has not yet been empirically defined, we define mental fitness as a state of optimal cognitive and emotional functioning, supporting the ability to regulate emotions, think clearly, and apply knowledge effectively to navigate daily demands [8-13]. Rather than focusing solely on disease prevention or symptom reduction, mental fitness emphasizes the ongoing development of capacities that support real-world cognitive functioning and adaptability [1,11,13]. While related to constructs such as cognitive health and executive function, mental fitness is a dynamic, trainable construct that integrates both cognitive and emotional domains and is theorized to support everyday functioning. While mental fitness is gaining traction as an important construct, it lacks both a universally accepted definition and valid measurement approaches [8-11]. Nonetheless, this novel framing offers a useful theoretical foundation for evaluating how tools and practices may enhance cognitive functioning and prevent decline, especially in nonclinical or wellness-oriented populations.

    A variety of interventions have been developed to prevent cognitive difficulties and support cognitive functioning as a core component of mental fitness, including lifestyle interventions (eg, physical activity, mindfulness, and stress management) and compensatory strategies (eg, note-taking and time management techniques) [14-16]. Lifestyle interventions may promote general brain health but typically do not involve targeted practice of specific cognitive skills [15,16]. Compensatory strategies help individuals work around cognitive difficulties but also do not target underlying cognitive abilities [14]. In contrast, cognitive training (ie, the active use and challenge of mental skills) is a promising pathway for maintaining or enhancing cognitive function, with evidence linking intellectually stimulating activities to lower risk of cognitive difficulties and clinical impairments (eg, dementia) [17-19].

    A growing number of digital cognitive training tools aim to address cognitive difficulties and support functioning through brief exercises, often delivered via mobile apps targeting aspects of cognitive functioning such as attention, memory, and processing speed. While studies of research-based and commercial platforms have shown improvements in cognitive performance on specific trained tasks, research linking app-based cognitive training to related untrained tasks (ie, near-transfer effects) and real-world functioning (ie, far-transfer effects) is mixed [20,21]. Meta-analyses have shown that cognitive training tools may lead to improvements beyond the specific tasks trained. However, it remains unclear whether these gains translate into perceived improvements in real-world functioning or broader aspects of mental fitness, particularly among users outside of controlled research settings [20,22,23]. Furthermore, many studies evaluating the effectiveness of digital tools for cognitive decline have been conducted in specialized populations (eg, older adults and individuals with cognitive impairments), limiting generalizability to the broader adult population [24-27]. Although several commercial platforms have demonstrated effectiveness in real-world users [28-31], few studies have evaluated apps with more advanced technological features, such as a wide range of content coverage and adaptive personalization. These gaps point to the need for more research on commercial apps that incorporate more advanced features to determine their potential to support cognitive functioning in everyday life and overall mental fitness.

    Objectives

    The purpose of this secondary analysis was to explore app engagement and the self-reported cognitive benefits of Elevate, a commercial, personalized cognitive training app developed to support cognitive functioning. We aimed to (1) describe demographics, engagement metrics, and self-reported improvements; (2) examine associations between app engagement and self-reported improvements in cognitive functioning skills directly targeted by the app; and (3) examine associations between app engagement and self-reported improvements in daily functioning and overall mental fitness as potential transfer effects of cognitive training. This study offers novel insights by evaluating a personalized commercial cognitive training app in a real-world context, linking objective use data with self-reported outcomes, and exploring whether cognitive training supports broader mental fitness beyond task-specific performance. The findings will help clarify the potential of widely accessible platforms to serve as scalable tools for promoting everyday cognitive health in general adult populations.


    Study Design and Participants

    This study was a cross-sectional observational analysis involving secondary use of survey and app use data. Participants were adult users (aged ≥18 years) of Elevate, a cognitive training app, who met the following inclusion criteria: (1) ability to read and understand English, (2) being located within the United States, and (3) engagement with the app at least twice in the previous 30 days. Engagement was defined as completing any activity within the app, such as a game-based training exercise. Eligible users were identified via back-end data and invited via email (sent up to 2 times) to complete an optional survey assessing their perceptions of the Elevate app’s impact on cognitive skills and everyday functioning. Participants who completed the survey were entered into a drawing to win a US $50 gift card; this incentive was offered by the company and was independent of the research protocol. The survey was administered between June 10 and 23, 2025. User engagement data were extracted from Elevate’s back-end activity logs, spanning April 18, 2014, to June 23, 2025, and were linked to survey respondents to support exploratory analyses of perceived outcomes in relation to app engagement. Among 84,505 eligible users invited to participate via email, 4190 (4.96%) opened the survey, and 3367 (3.98%) completed it in full. This yielded an 80.36% (3367/4190) completion rate among users who accessed the survey, which, in addition to the response rate, is consistent with response patterns observed in large-scale, voluntary online surveys and still supports valid inferences given the sample size and analytic approach [32-34]. Only completed surveys were included in the analytic sample.

    Ethical Considerations

    The study protocol was reviewed by the Biomedical Research Alliance of New York Institutional Review Board (25-149-1708) and determined to be exempt under US federal guidelines for research involving human participants. Because the study met criteria for exemption, informed consent was not required. All data were deidentified before analysis to protect participant confidentiality.

    Cognitive Training App

    Elevate is a commercially available mobile app designed to enhance cognitive skills through personalized, curriculum-based training. Users find Elevate via app store searches (eg, Apple App Store and Google Play Store), digital advertisements, word of mouth, and media coverage. The app was developed by The Mind Company, with content created by an internal team of curriculum designers, cognitive scientists, and subject matter experts. Drawing on principles from cognitive science, Elevate delivers short, interactive exercises intended to strengthen real-world cognitive functioning rather than isolated task performance.

    Training is organized across 5 primary skill domains: writing, speaking, reading, math, and memory. Writing modules target clarity, grammar, and vocabulary to improve written communication. Speaking modules focus on diction, pronunciation, and word retrieval to support clear and confident verbal expression. Reading modules address comprehension, processing speed, and vocabulary development. Math modules emphasize practical numeracy skills, including estimation, proportions, and financial calculations. Memory modules include tasks designed to improve short-term recall, sequencing, and synthesis. An adaptive learning algorithm adjusts difficulty based on user performance, enabling tailored progression. Immediate feedback is provided, and users can track their progress and compare their performance to peer benchmarks.

    Self-Reported Improvements in Cognition, Daily Functioning, and Mental Fitness (Outcomes)

    Survey questions were created specifically for this study and were not taken from existing standardized tools. The survey was developed by 2 doctoral-level researchers with expertise in behavior change, complementary health approaches, and digital health. The survey was designed to evaluate users’ perceptions of how using the Elevate app affected their cognitive skills, daily functioning, and overall mental fitness. These topics were selected because they closely aligned with the app’s training goals and the anticipated real-world benefits of cognitive training. The survey was web based (delivered using SurveyMonkey [SurveyMonkey Inc]) and took most participants 5 to 10 minutes to complete. All responses were anonymous. Participants completed 27 quantitative items assessing (1) perceived changes in specific cognitive skills targeted by the app (14 items) and (2) perceived changes in overall mental fitness and daily functioning since using Elevate (13 items). All items were rated on a 5-point Likert scale ranging from “Much worse” to “Much better.” At the end of the survey, participants completed demographic questions, including age, gender, race and ethnicity, educational level, English-language fluency, and employment status. Although the survey included additional items, only a subset of items relevant to the aims of this study was analyzed and is reported in this paper. Table 1 summarizes the cognitive skills, daily functioning, and mental fitness domains examined in this study and the specific survey items used to assess each domain.

    Table 1. Cognitive skills, daily functioning, and mental fitness domains and their corresponding survey items.
    DomainSurvey item
    Cognitive skills

    		Writing“Writing clearly and effectively”

    		Speaking“Speaking clearly and confidently”

    		Reading“Understanding what I read”

    		Math“Doing mental math quickly and accurately”

    		Memory“Remembering things when I need to”
    Daily functioning

    		Task efficiency“Completing tasks efficiently”

    		Spending time intentionally“Spending my time in ways that feel intentional and worthwhile”

    		Keeping up with responsibilities“Keeping up with personal and work responsibilities”

    		Feeling motivated“Feeling motivated to grow or improve”

    		Making personal progress“Feeling a sense of making personal progress”
    Overall mental fitness

    		Mental fitness“Overall mental fitness (ie, how sharp, calm, and knowledgeable you feel)”

    App Engagement Data

    Objective engagement data were obtained from Elevate’s internal use tracking system starting from each user’s first recorded activity and linked to survey responses. We examined 3 app engagement metrics. Total active weeks was defined as the number of weeks in which a user engaged with the app at least once. Mean active days per week was calculated as the average number of days in which users used the app at least once during each active week. Mean time per day (in minutes) was defined as the average duration of app use on active days.

    Statistical Analysis

    Descriptive statistics, including mean, SD, total counts, and the corresponding percentages, were calculated for demographic variables, app engagement variables, and self-reported improvement variables. A series of ordinal logistic regressions (cumulative link models [35]) were used to test the effects of the number of total active weeks, mean active days per week, and mean time per day on self-reported improvements in cognitive skills (writing, speaking, reading, math, and memory), daily functioning (task efficiency, spending time intentionally, keeping up with responsibilities, feeling motivated, and making personal progress), and overall mental fitness. The proportional odds assumption was tested for predictors of interest in each model (Multimedia Appendix 1). Each ordinal dependent variable was modeled separately. All models included the effects of covariates, including age, gender, race, ethnicity, educational level, and time since downloading Elevate, to reduce confounding. All predictors were retained in all models regardless of statistical significance to maintain comparability across analyses. All models were conducted on complete cases (ie, no missing predictor, outcome, or covariate data). Odds ratios (ORs) were calculated from the model estimates. To adjust for multiple comparisons across 11 ordinal regression models with 3 main predictors, a Bonferroni correction was applied. The α level was adjusted to α=.05/33=.0015, and statistical significance was evaluated at the corrected threshold [36]. All analyses were conducted in R (version 4.3.1; R Foundation for Statistical Computing) [37]. This study is reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines, with the completed checklist provided in Multimedia Appendix 2.


    Descriptive Statistics of Demographic Variables

    Users (N=3367) were, on average, aged 55 (SD 16) years, mostly White or European (2557/3336, 76.65%), non-Hispanic (3113/3341, 93.18%), and native English speakers (3063/3360, 91.16%), and most identified as women (2184/3362, 64.96%). Users were also highly educated (2274/3358, 67.72% had at least a bachelor’s degree) and mostly employed (1481/3359, 44.09%) or retired (1153/3359, 34.33%; Table 2).

    Table 2. Demographic characteristics of Elevate users.
    CharacteristicValues
    Age (y), mean (SD)55 (16)
    Race (n=3336), n (%)

    		American Indian or Alaska Native17 (0.5)

    		Asian or Asian American202 (6.1)

    		Black or African American266 (8)

    		MENAa12 (0.4)

    		Multiracial194 (5.8)

    		Native Hawaiian or Pacific Islander5 (0.1)

    		White or European American2557 (76.6)

    		Other83 (2.5)
    Gender (n=3362), n (%)

    		Woman2184 (65)

    		Man1105 (32.9)

    		Gender diverse29 (0.9)

    		Other10 (0.3)

    		Declined to state34 (1)
    Hispanic ethnicity (n=3341), n (%)254 (7.6)
    English fluency (n=3360), n (%)

    		“I am a native speaker of English”3063 (91.2)

    		“I speak English fluently” (near native)213 (6.3)

    		“I speak English well” (intermediate)79 (2.4)

    		“I speak a little English” (basic or novice)3 (0.1)

    		“I do not speak English”2 (0.1)
    Educational level (n=3358), n (%)

    		11th grade or lower9 (0.3)

    		High school diploma or GEDb183 (5.4)

    		Some college465 (13.8)

    		2-y college or technical school degree379 (11.3)

    		Bachelor’s degree or equivalent (eg, BA or BS)1186 (35.3)

    		Graduate degree or equivalent (eg, MA, MD, or PhD)1088 (32.4)

    		Other48 (1.4)
    Employment status (n=3359), n (%)

    		Full-time employment1481 (44.1)

    		Part-time employment313 (9.3)

    		Student71 (2.1)

    		Retired1153 (34.3)

    		Unemployed172 (5.1)

    		Other169 (5)

    aMENA: Middle Eastern or North African.

    bGED: General Educational Development.

    Descriptive Statistics of Engagement and Self-Reported Improvement Variables

    Overall, users were highly engaged with the Elevate app as the average time since downloading was 1397 (SD 1281) days, which corresponds to 3.83 (SD 3.5) years. Users were active for an average of 120 (SD 130) weeks and an average of 5.42 (SD 1.42) days per week during active weeks and spent an average of 6.5 (SD 6.0) minutes per active day using the app (Table 3).

    Table 3. Descriptive statistics of engagement variables.
    VariableParticipants with data, nParticipants, mean (SD)
    Time since downloading Elevate (d)33671397 (1281)
    Number of total active weeks3361120 (130)
    Number of sessions per weeks33615.42 (1.42)
    Time spent per day (min)32696.5 (6.0)

    Most users reported improvement (ie, responding either “Better” or “Much better”) across all cognitive skills (range 1915/3365, 56.9%-2534/3366, 75.4%), most daily functioning items (range 1626/3361, 48.3%-2932/3364, 87.3%), and overall mental fitness (2480/3364, 84.4%). Reports of deterioration (ie, “Worse” or “Much worse”) were rare, ranging from 0.1% (3/3359) to 1.5% (53/3365) across all items (Table 4).

    Table 4. Distribution of responses to self-report questions about Elevate experience.
    DomainMuch worse, n (%)Worse, n (%)No change, n (%)Better, n (%)Much better, n (%)
    Cognitive skills

    		Writing (n=3362)1 (0)18 (0.5)1181 (35.1)1652 (49.1)510 (15.2)

    		Speaking (n=3353)2 (0.1)26 (0.8)1363 (40.7)1538 (45.9)424 (12.6)

    		Reading (n=3358)3 (0.1)9 (0.3)1275 (38)1618 (48.2)453 (13.5)

    		Math (n=3366)3 (0.1)23 (0.7)798 (23.8)1741 (51.8)793 (23.6)

    		Memory (n=3365)3 (0.1)50 (1.5)1397 (41.5)1559 (46.3)356 (10.6)
    Daily functioning

    		Task efficiency (n=3360)0 (0)11 (0.3)1398 (41.6)1565 (46.6)386 (11.5)

    		Spending time intentionally (n=3363)1 (0)34 (1)983 (29.2)1740 (51.7)605 (18)

    		Keeping up with responsibilities (n=3364)1 (0)24 (0.7)1713 (50.9)1290 (38.3)336 (10)

    		Feeling motivated (n=3361)0 (0)3 (0.1)476 (14.2)1962 (58.4)920 (27.4)

    		Making personal progress (n=3362)2 (0.1)5 (0.1)423 (12.6)2019 (60.1)913 (27.2)
    Overall mental fitness

    		Mental fitness (n=3364)0 (0)5 (0.1)519 (15.4)2265 (67.3)575 (17.1)

    Association Between Engagement Metrics and Improvements in Cognitive Skills

    A series of ordinal logistic regressions was used to test the effects of the number of total active weeks, mean active days per week, and mean time per day on self-reported improvements in each of the cognitive skills, adjusting for age, gender, race, ethnicity, educational level, and time since downloading Elevate. After Bonferroni correction (α=.0015), a greater number of total active weeks was associated with a greater likelihood of reporting improvements in writing (OR 1.0027, 95% CI 1.0019-1.0036), speaking (OR 1.0022, 95% CI 1.0013-1.0030), reading (OR 1.0023, 95% CI 1.0014-1.0031), math (OR 1.0016, 95% CI 1.0007-1.0025), and memory (OR 1.0014, 95% CI 1.0006-1.0023). A greater number of mean active days per week was associated with a greater likelihood of reporting improvements in math (OR 1.15, 95% CI 1.09-1.22). Greater mean time per day on the app was associated with a greater likelihood of reporting improvements in speaking (OR 1.02, 95% CI 1.01-1.03), reading (OR 1.03, 95% CI 1.01-1.04), and math (OR 1.03, 95% CI 1.02-1.04; Table 5).

    Table 5. Results from a series of ordinal logistic regressions conducted separately for each outcome (n=3160)a.
    OutcomePredictor

    		Number of active weeksMean active days per weekMean time per day

    		b (SE)ORb (95% CI)P valuecb (SE)OR (95% CI)P valuecb (SE)OR (95% CI)P valuec
    Cognitive skills

    		Writing0.0027 (0.00045)1.0027 (1.0019-1.0036)<.001−0.0011 (0.029)1.00 (0.94-1.06).970.018 (0.0057)1.02 (1.01-1.03).002

    		Speaking0.0022 (0.00044)1.0022 (1.0013-1.0030)<.0010.026 (0.029)1.03 (0.97-1.09).370.020 (0.0057)1.02 (1.01-1.03)<.001

    		Reading0.0023 (0.00044)1.0023 (1.0014-1.0031)<.0010.050 (0.029)1.05 (0.99-1.11).090.025 (0.0058)1.03 (1.01-1.04)<.001

    		Math0.0016 (0.00044)1.0016 (1.0007-1.0025)<.0010.14 (0.029)1.15 (1.09-1.22)<.0010.029 (0.0061)1.03 (1.02-1.04)<.001

    		Memory0.0014 (0.00044)1.0014 (1.0006-1.0023).0010.063 (0.029)1.07 (1.01-1.13).030.017 (0.0056)1.02 (1.01-1.03).002
    Daily functioning

    		Task efficiency0.0014 (0.00044)1.0014 (1.0006-1.0023).0010.055 (0.029)1.06 (1.00-1.12).060.015 (0.0063)1.02 (1.00-1.03).02

    		Spending time intentionally0.0016 (0.00044)1.0016 (1.0007-1.0025)<.001−0.016 (0.029)0.98 (0.93-1.04).580.017 (0.0063)1.02 (1.00-1.03).008

    		Keeping up with responsibilities0.0016 (0.00044)1.0016 (1.0007-1.0025)<.0010.063 (0.029)1.06 (1.01-1.13).030.0079 (0.0064)1.01 (1.00-1.02).22

    		Feeling motivated0.0017 (0.00046)1.0017 (1.0008-1.0026)<.001−0.0088 (0.030)0.99 (0.94-1.05).770.027 (0.0064)1.03 (1.01-1.04)<.001

    		Making personal progress0.0011 (0.00046)1.0011 (1.0002-1.0020).020.093 (0.030)1.10 (1.03-1.16).0020.027 (0.0065)1.03 (1.02-1.04)<.001
    Overall mental fitness

    		Mental fitness0.0021 (0.00048)1.0021 (1.0012-1.0031)<.0010.065 (0.032)1.07 (1.00-1.14).040.042 (0.0066)1.04 (1.03-1.06)<.001

    aAll models adjusted for time since downloading the Elevate app, age, gender, race, ethnicity, and educational level.

    bOR: odds ratio.

    cThe Bonferroni-corrected α on 33 tests was .0015.

    Association Between Engagement Metrics and Improvements in Daily Functioning and Mental Fitness

    A series of ordinal logistic regressions was used to test the effects of the number of total active weeks, mean active days per week, and mean time per day on self-reported improvements in each of the daily functioning and overall mental fitness outcomes, adjusting for age, gender, race, ethnicity, educational level, and time since downloading Elevate. After Bonferroni correction (α=.0015), a greater number of active weeks was associated with a greater likelihood of reporting improvements in task efficiency (OR 1.0014, 95% CI 1.0006-1.0023), spending time intentionally (OR 1.0016, 95% CI 1.0007-1.0025), keeping up with responsibilities (OR 1.0016, 95% CI 1.0007-1.0025), feeling motivated (OR 1.0017, 95% CI 1.0008-1.0026), and overall mental fitness (OR 1.0021, 95% CI 1.0012-1.0031). Mean active days per week did not predict any self-reported change in daily functioning outcomes or overall mental fitness. Greater mean time per day on the app was associated with a greater likelihood of reporting improvements in feeling motivated (OR 1.03, 95% CI 1.01-1.04), making personal progress (OR 1.03, 95% CI 1.02-1.04), and overall mental fitness (OR 1.04, 95% CI 1.03-1.06; Table 5).


    Principal Findings

    The purpose of this secondary analysis was to explore app engagement and self-reported cognitive benefits of Elevate, a commercial, personalized cognitive training app developed to support cognitive functioning. We aimed to (1) describe demographics, engagement metrics, and self-reported improvements; (2) examine associations between app engagement and self-reported improvements in cognitive functioning skills directly targeted by the app; and (3) examine associations between app engagement and self-reported improvements in daily functioning and overall mental fitness as potential transfer effects of cognitive training. Most Elevate users reported perceived improvements across cognitive skills, daily functioning, and overall mental fitness. Users who engaged with the app across more total active weeks (ie, used the app at least once during the week) were more likely to report improvements in all cognitive skill areas. More frequent use within active weeks (ie, using the app on more days) was linked to perceived improvement in math, whereas spending more time per day on the app was associated with improvements in speaking, reading, and math. Greater weekly engagement was also tied to perceived gains in aspects of daily functioning, including task efficiency, intentional time use, staying on top of responsibilities, and motivation. In addition, spending more time per day on the app was linked to feeling more motivated and making personal progress. Both more weeks of use and longer daily sessions were associated with a higher likelihood of users reporting improvements in overall mental fitness.

    Elevate users were primarily middle-aged (mean age 55, SD 16 years), with most identifying as women (2184/3362, 64.96%) and White individuals (2557/3336, 76.65%), and most reported a high level of education (2274/3358, 67.72% had a college degree or higher). These findings align with those of previous research indicating that middle-aged, well-educated adults, especially women, are frequent users of digital health and cognitive platforms [38,39]. Engagement data indicated consistent use across multiple weeks, and most users endorsed perceived improvements in cognitive skills, daily functioning, and overall mental fitness (range: 1626/3361, 48.3%–2932/3364, 87.2%). These findings could be attributed to the self-selected sample, which may have excluded dissatisfied or less engaged users, or to placebo or expectancy effects. Given that middle age may be an important time for cognitive health behaviors, the engagement patterns observed in our sample suggest that this demographic may be receptive to cognitive training interventions, although additional research is needed to determine the app’s objective cognitive health benefits [40,41].

    Elevate use was associated with perceived improvements in the core cognitive skills it was designed to target. More weeks of app use were associated with perceived improvements across all cognitive functioning domains, including writing, reading, speaking, math, and memory, which is in line with previous research showing that sustained engagement with cognitive training programs leads to improved cognitive functioning [42-44]. Engaging with the Elevate app on more days during an active week was associated with perceived improvement in math, whereas spending more time per day was linked to gains in speaking, reading, and math. These patterns may reflect differences in how cognitive skills are reinforced, with math potentially benefiting from frequent, distributed practice, whereas verbal skills may be more responsive to sustained, focused engagement that supports fluency and comprehension [45,46]. While previous studies of other commercial apps have reported gains in attention and processing speed [29,31], greater Elevate use was linked to self-reported improvements across a broad range of cognitive domains. Elevate’s personalized training structure, which tailors session content to individual skill levels and goals, may contribute to these patterns by aligning challenge and repetition to individual needs, thereby fostering engagement in targeted domains. While these associations were significant, the effect sizes were relatively small, limiting practical significance. Future research using validated outcome measures and objective cognitive assessments is critical to determine whether these small self-reported improvements reflect meaningful cognitive gains or are influenced by user expectations and recall bias.

    A greater number of weeks with any app use was associated with perceived improvements in daily functioning, including task efficiency, intentional time use, keeping up with responsibilities, and motivation. In addition, users who spent more daily time on the app were more likely to report feeling motivated and making personal progress. Engaging in core cognitive skill training has been linked to self-regulation skills, such as planning, sustained effort, and goal-directed behavior, that relate to everyday productivity and well-being [47,48]. Such perceived transfer effects are rarely examined in commercial cognitive training studies, which tend to focus more narrowly on task-specific performance metrics [29,31,49,50]. While one study of a commercial cognitive training app showed that using a cognitive training app more often was linked to better scores on related cognitive tests [51], this study extends this work by examining the effects of cognitive training on user perceptions of functional improvements. However, the practical significance of these small effects remains uncertain as the mechanisms underlying users’ perceived benefits are unclear. Prior work in digital health has shown that psychological empowerment and enjoyment of app use can drive behavior change, pointing to potential mechanisms underlying users’ perceived functional gains in this study [52]. Future research should use validated functional outcome measures and experimental designs to test these mechanisms and determine which app features most effectively promote meaningful improvements in daily functioning.

    To our knowledge, this is the first study to show that greater use of a commercial cognitive training app is linked to self-reported improvements in overall mental fitness. Much of the existing research on commercial cognitive training apps focuses on performance-based outcomes over the subjective abilities to regulate emotions, think clearly, and apply knowledge effectively (ie, capacities that define mental fitness [8-13,28,30,49,50]). This study builds on existing research by showing preliminary evidence that users may perceive psychological benefits from cognitive training. In doing so, it points to mental fitness as an outcome that merits scientific attention and the development of rigorous measurement tools. Future research should focus on creating and rigorously validating a standardized mental fitness assessment tool and also examine which aspects of Elevate’s training model (eg, domain variety, adaptive difficulty, and perceived personal relevance) are most effective in promoting sustained improvements in users’ mental fitness.

    Strengths and Limitations

    This study offers several notable strengths. First, it linked self-reported outcomes with objective, real-world app use data from a commercial cognitive training platform, enabling a more ecologically valid understanding of how such tools are experienced in naturalistic settings. Second, it moves beyond traditional performance-based outcomes to assess a broader set of perceived benefits, including daily functioning and overall mental fitness. To our knowledge, this is one of the first studies to capture user perceptions of mental fitness, a rising area of interest that encompasses clarity of thought, emotional regulation, motivation, and the ability to apply cognitive skills in everyday life. Third, by analyzing detailed engagement metrics, this study identified use patterns associated with perceived gains, helping inform hypotheses about dose-response relationships. Fourth, this study examined a general population sample of adults—rather than clinical or older adult populations—offering insights into how cognitive training may benefit individuals in everyday, nonclinical contexts. Finally, this study contributes novel insights into user engagement and perceived cognitive and functional benefits of a widely available, commercial cognitive training app, leveraging data from a large, real-world sample to explore how digital tools may support cognitive health and mental fitness in everyday life.

    There are also limitations. First, the cross-sectional study design without a control condition precludes us from determining whether our findings reflect genuine benefits of Elevate use, natural improvement over time, or user expectations about the app’s effectiveness. Future studies should use longitudinal experimental designs to better assess causal relationships between app engagement and outcomes. Second, this study relied entirely on self-reported outcomes, and the items used were developed specifically for this study rather than being drawn from validated instruments. Relatedly, the self-reported outcomes were based on the construct of mental fitness, which currently lacks an empirical definition and validated assessment tools. While we did assess perceived improvements in daily functioning domains and mental fitness relevant to everyday life, the absence of both objective outcome data and validated measurement tools limits the reliability and interpretability of these findings and could have led to measurement bias. Future research should incorporate validated cognitive and functional assessment tools, as well as behavioral or performance-based outcomes, to complement subjective reports. Fourth, this study did not include a control or comparison group, making it difficult to account for expectancy effects or alternative explanations for perceived change. Randomized controlled trials or matched comparison studies could help isolate the unique effects of the intervention. Fifth, the sample was predominantly composed of White, highly educated women, which limits the generalizability of the findings to more diverse populations. Finally, the self-selected sample was a relatively small proportion of the app’s total user base, which could have led to biased or inflated results because more engaged or satisfied users may have been more likely to volunteer to share their experiences in the survey. Future studies should include broader recruitment strategies to reach more diverse user profiles, including lower-engagement or disengaged users, and ensure representation across demographic groups.

    Conclusions

    This study found that greater use of the Elevate app was associated with perceived improvements in a range of cognitive skills, including reading, writing, speaking, math, and memory, as well as various outcomes related to perceived daily functioning, including task efficiency, spending time intentionally, keeping up with responsibilities, feeling motivated, and making personal progress. Users also reported perceived gains in mental fitness, a multidimensional construct encompassing the ability to regulate emotions, think clearly, and apply knowledge effectively. Despite limitations such as unvalidated self-report measures and small effect sizes, these findings provide preliminary evidence that users see cognitive training apps as helpful not only for domain-specific skills but also for broader benefits. Future research using rigorous designs (eg, controlled trials) is needed to establish whether these perceptions reflect actual improvements in cognitive skills and daily functioning and determine which app features best support lasting cognitive and functional improvements.

    Acknowledgments

    This work was supported by The Mind Company.

    Data Availability

    The datasets generated or analyzed during this study are not publicly available due to The Mind Company’s policy for user data but are available from the corresponding author on reasonable request.

    Authors' Contributions

    KM contributed to conceptualization, methodology, formal analysis, visualization, writing—original draft, writing—review and editing, and final approval. LB contributed to conceptualization, formal analysis, writing—original draft, writing—review and editing, and final approval. AB contributed to conceptualization, resources, writing—original draft, writing—review and editing, and final approval. YY contributed to conceptualization, data curation, writing—original draft, writing—review and editing, and final approval. DK contributed to conceptualization, resources, writing—original draft, writing—review and editing, and final approval. JH contributed to conceptualization, methodology, writing—original draft, writing—review and editing, final approval, and supervision.

    Conflicts of Interest

    AB, YY, and DK are employed by The Mind Company, and The Mind Company’s app (Elevate) was the cognitive training app used in this study. JH is a paid consultant of The Mind Company, and KM and LB are employed by JH. To mitigate potential conflicts of interest, data analysis and interpretation were led by authors not employed by The Mind Company. The analysis plan was prespecified, and the results were interpreted collaboratively to ensure objectivity. The conflict of interest has been fully disclosed. Authors’ employment status or salary are not dependent upon the results of their research.

    Multimedia Appendix 1

    Supplemental methods and results.

    DOCX File , 285 KB
    Multimedia Appendix 2

    STROBE checklist for cross-sectional studies.

    DOCX File , 22 KB
    1. Hale JM, Schneider DC, Mehta NK, Myrskylä M. Cognitive impairment in the U.S.: lifetime risk, age at onset, and years impaired. SSM Popul Health. Mar 31, 2020;11:100577. [FREE Full text] [CrossRef] [Medline]
    2. Corbo I, Favieri F, Forte G, Casagrande M. Decision-making under uncertainty in healthy and cognitively impaired aging: a systematic review and meta-analysis. Arch Gerontol Geriatr. Feb 2025;129:105643. [FREE Full text] [CrossRef] [Medline]
    3. Gray M, Gills JL, Glenn JM, Vincenzo JL, Walter CS, Madero EN, et al. Cognitive decline negatively impacts physical function. Exp Gerontol. Jan 2021;143:111164. [FREE Full text] [CrossRef] [Medline]
    4. Dubbelman MA, Jutten RJ, Tomaszewski Farias SE, Amariglio RE, Buckley RF, Visser PJ, et al. Decline in cognitively complex everyday activities accelerates along the Alzheimer's disease continuum. Alzheimers Res Ther. Oct 29, 2020;12(1):138. [FREE Full text] [CrossRef] [Medline]
    5. Liew TM. Trajectories of subjective cognitive decline, and the risk of mild cognitive impairment and dementia. Alzheimers Res Ther. Oct 27, 2020;12(1):135. [FREE Full text] [CrossRef] [Medline]
    6. Blum K. The impact of chronic stress on brain function and structure. Perspect Neurosci Psychiatry Open Access. 2024;7(5):262-264. [FREE Full text] [CrossRef]
    7. Parry DA, Le Roux D. “Cognitive control in media multitaskers” ten years on: a meta-analysis. Cyberpsychol J Psychosoc Res Cybersp. Apr 26, 2021;15(2). [CrossRef]
    8. Aidman E. Cognitive fitness framework: towards assessing, training and augmenting individual-difference factors underpinning high-performance cognition. Front Hum Neurosci. Jan 14, 2020;13:466. [FREE Full text] [CrossRef] [Medline]
    9. Robinson PL. Conceptualising and measuring mental fitness. University of Wollongong. 2014. URL: https:/​/ro.​uow.edu.au/​articles/​thesis/​Conceptualising_and_measuring_mental_fitness/​27661959?file=50371740 [accessed 2025-07-02]
    10. Bhat SG, Nagaraj M, Balentine C, Hogan T, Meier J, Prince H, et al. Assessing a structured mental fitness program for academic acute care surgeons: a pilot study. J Surg Res. Mar 2024;295:9-18. [FREE Full text] [CrossRef] [Medline]
    11. Iorfino F, Varidel M, Capon W, Richards M, Crouse JJ, LaMonica HM, et al. Quantifying the interrelationships between physical, social, and cognitive-emotional components of mental fitness using digital technology. Npj Ment Health Res. Jul 08, 2024;3(1):36. [FREE Full text] [CrossRef] [Medline]
    12. Prince M, Patel V, Saxena S, Maj M, Maselko J, Phillips MR, et al. No health without mental health. Lancet. Sep 08, 2007;370(9590):859-877. [CrossRef] [Medline]
    13. Keyes CL. Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health. Am Psychol. 2007;62(2):95-108. [CrossRef] [Medline]
    14. Kurtz MM. Compensatory strategies. In: Kreutzer JS, DeLuca J, Caplan B, editors. Encyclopedia of Clinical Neuropsychology. New York, NY. Springer; 2011.
    15. Moreau D, Wiebels K. A precision-mapping approach to physical exercise interventions targeting cognitive function. Prog Brain Res. 2024;283:231-253. [CrossRef] [Medline]
    16. Dhahbi W, Briki W, Heissel A, Schega L, Dergaa I, Guelmami N, et al. Physical activity to counter age-related cognitive decline: benefits of aerobic, resistance, and combined training-a narrative review. Sports Med Open. May 17, 2025;11(1):56. [CrossRef] [Medline]
    17. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. Aug 08, 2020;396(10248):413-446. [FREE Full text] [CrossRef] [Medline]
    18. Mayor S. Mentally stimulating activities reduce risk of mild cognitive impairment, study finds. BMJ. Jan 31, 2017;356:j526. [CrossRef]
    19. Yates LA, Ziser S, Spector A, Orrell M. Cognitive leisure activities and future risk of cognitive impairment and dementia: systematic review and meta-analysis. Int Psychogeriatr. Nov 2016;28(11):1791-1806. [CrossRef]
    20. Tetlow AM, Edwards JD. Systematic literature review and meta-analysis of commercially available computerized cognitive training among older adults. J Cogn Enhanc. Nov 9, 2017;1:559-575. [CrossRef]
    21. Melby-Lervåg M, Redick TS, Hulme C. Working memory training does not improve performance on measures of intelligence or other measures of "far transfer": evidence from a meta-analytic review. Perspect Psychol Sci. Jul 2016;11(4):512-534. [FREE Full text] [CrossRef] [Medline]
    22. Nguyen L, Murphy K, Andrews G. A game a day keeps cognitive decline away? A systematic review and meta-analysis of commercially-available brain training programs in healthy and cognitively impaired older adults. Neuropsychol Rev. Sep 2022;32(3):601-630. [CrossRef] [Medline]
    23. Bonnechère B, Langley C, Sahakian BJ. The use of commercial computerised cognitive games in older adults: a meta-analysis. Sci Rep. Sep 17, 2020;10(1):15276. [CrossRef] [Medline]
    24. Pedullà L, Brichetto G, Tacchino A, Vassallo C, Zaratin P, Battaglia MA, et al. Adaptive vs. non-adaptive cognitive training by means of a personalized app: a randomized trial in people with multiple sclerosis. J Neuroeng Rehabil. Oct 04, 2016;13(1):88. [FREE Full text] [CrossRef] [Medline]
    25. Bonnechère B, Klass M, Langley C, Sahakian BJ. Brain training using cognitive apps can improve cognitive performance and processing speed in older adults. Sci Rep. Jun 10, 2021;11(1):12313. [CrossRef] [Medline]
    26. Kirby ED, Beyst B, Beyst J, Brodie SM, D'Arcy RC. A retrospective, observational study of real-world clinical data from the Cognitive Function Development Therapy program. Front Hum Neurosci. Dec 18, 2024;18:1508815. [FREE Full text] [CrossRef] [Medline]
    27. Gary RA, Paul S, Corwin E, Butts B, Miller AH, Hepburn K, et al. Exercise and cognitive training intervention improves self-care, quality of life and functional capacity in persons with heart failure. J Appl Gerontol. Feb 2022;41(2):486-495. [FREE Full text] [CrossRef] [Medline]
    28. Hampshire A, Sandrone S, Hellyer PJ. A large-scale, cross-sectional investigation into the efficacy of brain training. Front Hum Neurosci. Jul 9, 2019;13:221. [FREE Full text] [CrossRef] [Medline]
    29. Al-Thaqib A, Al-Sultan F, Al-Zahrani A, Al-Kahtani F, Al-Regaiey K, Iqbal M, et al. Brain training games enhance cognitive function in healthy subjects. Med Sci Monit Basic Res. Apr 20, 2018;24:63-69. [FREE Full text] [CrossRef] [Medline]
    30. Hagen BI, Landrø NI, Lau B, Koster EH, Stubberud J. Predictors of long-term improvement following cognitive remediation in a sample with elevated depressive symptoms. Front Psychol. Sep 11, 2020;11:2232. [FREE Full text] [CrossRef] [Medline]
    31. Savulich G, Thorp E, Piercy T, Peterson KA, Pickard JD, Sahakian BJ. Improvements in attention following cognitive training with the novel "Decoder" game on an iPad. Front Behav Neurosci. Jan 21, 2019;13:2. [FREE Full text] [CrossRef] [Medline]
    32. Fosnacht K, Sarraf S, Howe E, Peck LK. How important are high response rates for college surveys? Rev High Educ. 2017;40(2):245-265. [CrossRef]
    33. Daikeler J, Bošnjak M, Manfreda KL. Web versus other survey modes: an updated and extended meta-analysis comparing response rates. J Surv Stat Method. Jun 2020;8(3):513-539. [CrossRef]
    34. Wu MJ, Zhao K, Fils-Aime F. Response rates of online surveys in published research: a meta-analysis. Comput Hum Behav Rep. Aug 2022;7:100206. [CrossRef]
    35. Christensen RH. ordinal: regression models for ordinal data. The Comprehensive R Archive Network. Aug 19, 2024. URL: https://cran.r-project.org/web/packages/ordinal/index.html [accessed 2025-07-02]
    36. Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. Jan 21, 1995;310(6973):170. [CrossRef] [Medline]
    37. R: the R project for statistical computing. The Comprehensive R Archive Network. URL: https://www.r-project.org/ [accessed 2025-03-26]
    38. Hegeman P, Vader D, Kamke K, El-Toukhy S. Patterns of digital health access and use among US adults: a latent class analysis. BMC Digit Health. 2024;2(1):42. [CrossRef] [Medline]
    39. Lee J, Lim JM. Factors associated with the experience of cognitive training apps for the prevention of dementia: cross-sectional study using an extended health belief model. J Med Internet Res. Jan 14, 2022;24(1):e31664. [FREE Full text] [CrossRef] [Medline]
    40. Antal BB, van Nieuwenhuizen H, Chesebro AG, Strey HH, Jones DT, Clarke K, et al. Brain aging shows nonlinear transitions, suggesting a midlife "critical window" for metabolic intervention. Proc Natl Acad Sci U S A. Mar 11, 2025;122(10):e2416433122. [FREE Full text] [CrossRef] [Medline]
    41. Gates NJ, Rutjes AW, Di Nisio M, Karim S, Chong LY, March E, et al. Computerised cognitive training for maintaining cognitive function in cognitively healthy people in midlife. Cochrane Database Syst Rev. Mar 13, 2019;3(3):CD012278. [FREE Full text] [CrossRef] [Medline]
    42. Kokubun K, Nemoto K, Yamakawa Y. Smartphone app for lifestyle improvement improves brain health and boosts the vitality and cognitive function of healthy middle-aged adults. Brain Behav. May 2024;14(5):e3500. [FREE Full text] [CrossRef] [Medline]
    43. Li Z, He H, Chen Y, Guan Q. Effects of engagement, persistence and adherence on cognitive training outcomes in older adults with and without cognitive impairment: a systematic review and meta-analysis of randomised controlled trials. Age Ageing. Jan 02, 2024;53(1):afad247. [CrossRef] [Medline]
    44. Belleville S, Cuesta M, Bier N, Brodeur C, Gauthier S, Gilbert B, et al. Five-year effects of cognitive training in individuals with mild cognitive impairment. Alzheimers Dement (Amst). Sep 06, 2024;16(3):e12626. [CrossRef] [Medline]
    45. Barzagar Nazari K, Ebersbach M. Distributed practice: rarely realized in self-regulated mathematical learning. Front Psychol. Nov 20, 2018;9:2170. [CrossRef] [Medline]
    46. Stine-Morrow EA, McCall GS, Manavbasi I, Ng S, Llano DA, Barbey AK. The effects of sustained literacy engagement on cognition and sentence processing among older adults. Front Psychol. Jul 11, 2022;13:923795. [FREE Full text] [CrossRef] [Medline]
    47. Seufert T. The interplay between self-regulation in learning and cognitive load. Educ Res Rev. Jun 2018;24:116-129. [CrossRef]
    48. Lee LY, Healy MP, Fischer NL, Tong K, Chen AS, Sahakian BJ, et al. Cognitive flexibility training for impact in real-world settings. Curr Opin Behav Sci. Oct 2024;59:101413. [CrossRef]
    49. Oki Y, Osaki T, Kumagai R, Murata S, Encho H, Ono R, et al. An 18-month multimodal intervention trial for preventing dementia: J-MINT PRIME Tamba. Alzheimers Dement. Oct 2024;20(10):6972-6983. [CrossRef] [Medline]
    50. Pressler SJ, Martineau A, Grossi J, Giordani B, Koelling TM, Ronis DL, et al. Healthcare resource use among heart failure patients in a randomized pilot study of a cognitive training intervention. Heart Lung. 2013;42(5):332-338. [FREE Full text] [CrossRef] [Medline]
    51. Osman AM, Jaffe PI, Ng NF, Kerlan KR, Schafer RJ. Transfer of learning: analysis of dose-response functions from a large-scale, online, cognitive training dataset. PLoS One. May 17, 2023;18(5):e0281095. [FREE Full text] [CrossRef] [Medline]
    52. Lin Y, Tudor-Sfetea C, Siddiqui S, Sherwani Y, Ahmed M, Eisingerich AB. Effective behavioral changes through a digital mHealth app: exploring the impact of hedonic well-being, psychological empowerment and inspiration. JMIR Mhealth Uhealth. Jun 15, 2018;6(6):e10024. [CrossRef] [Medline]

    ‎
    OR: odds ratio
    STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

    Edited by A Mavragani, J Sarvestan; submitted 02.Jul.2025; peer-reviewed by A Eisingerich; comments to author 28.Jul.2025; revised version received 01.Aug.2025; accepted 21.Sep.2025; published 10.Oct.2025.

    Copyright

    ©Kelsey McAlister, Lara Baez, Anna Braunsdorf, Yana Yang, Dan Kessler, Jennifer Huberty. Originally published in JMIR Formative Research (https://formative.jmir.org), 10.Oct.2025.

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