Factors Associated With the Experience of Cognitive Training Apps for the Prevention of Dementia: Cross-sectional Study Using an Extended Health Belief Model

Lee, Jaegyeong; Lim, Jung Min

doi:10.2196/31664

Cited by 11 publications

(4 citation statements)

References 51 publications

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“…The lifestyle behavior of focus was mental stimulation, large focus on mental stimulation in these apps is in line with the amount of apps that had contained game-based elements. This complements the observations of many studies that have observed dementia-prevention mHealth apps focused on mental stimulation in maintaining cognitive vitality in healthy individuals and those with mildly impaired cognition, including improved memory and enhanced quality of life [35].…”

Section: Lifestyle Element(s) Of Focussupporting

confidence: 75%

mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan

Ali,

Alizai,

Hagos

et al. 2024

JMIR Mhealth Uhealth

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Background: Lifestyle behaviors including exercise, sleep, diet, stress, mental stimulation, and social interaction have been shown to have a significant impact on the likelihood of developing dementia. mHealth apps have been valuable tools in addressing these lifestyle behaviors for general health and wellbeing, and there is growing recognition of their potential use as a tool focusing on brain-health and dementia prevention. In order to design effective apps that are both evidence-based and safeguard user data, app creators must address the gaps observed in the current state of dementia-related mHealth apps.Objective: Describe the scope of available apps intended for use by the general public relating to dementia prevention and risk factors, highlighting gaps and suggesting a path forward for future development.Methods: A systematic search of mobile app stores and environmental scan of peer-reviewed literature, dementia/Alzheimer's association/advocacy websites, and websites accessed using a browser search was conducted from October 19, 2022 to November 2, 2022. A total of 1044 mHealth apps were retrieved across this comprehensive array of sources. After screening, 152 apps met the inclusion criteria and were coded by two independent reviewers using an extraction framework. The extraction framework was adapted using the Silberg scale, other scoping reviews of mHealth apps for similar populations, and background research of modifiable dementia risk factors. Coded elements included: evidence-based and expert credibility, app features, lifestyle element(s) of focus, and privacy/security. Results:Of the final selection of apps that met final selection criteria, 88 (57.9%) addressed the modifiable lifestyle behaviors associated with reducing dementia risk. However, the majority of these apps only addressed one lifestyle behavior-with mental stimulation being the most frequently addressed of the lifestyle behaviors investigated in this study. The majority of apps scored 2 points out of 9 on the Silberg scale, with a mean score of 2.4 points. App information was not disclosed by the majority of the 152 apps, as 120 apps did not disclose expert consultation (78.9%), 125 did not disclose evidence-based information (82.2%), 146 did not disclose author credentials (96.1%), and 134 apps did not disclose their information sources (88.2%). Moreover, 105 apps (69.2%) did not disclose adherence to data privacy/security practices or lack thereof. Conclusions:There is an opportunity for mHealth apps to support individuals in regularly engaging in behaviors linked to reducing dementia risk. While there is a market for these products, there is a lack of dementia-related apps focused on multiple lifestyle behaviors. There are currently gaps in rigor of app development regarding evidence-base and credibility of apps, as well as adherence to data privacy/security standards. Addressing these observed gaps-such as through following established and validated guidelines-will be necessary for dementia-related apps to be effective and adva...

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Section: Lifestyle Element(s) Of Focussupporting

confidence: 75%

mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan

Ali,

Alizai,

Hagos

et al. 2024

JMIR Mhealth Uhealth

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“…10 AI has emerged as a promising tool in healthcare, particularly in the area of cognitive impairment diagnosis. 11,12 AI can provide a more accurate and standardized process for diagnosing and predicting disease. 13 Thabtah et al conducted a comprehensive analysis of mobile-based AD and MCI screening apps, highlighting the potential of AI applications for early detection and diagnosis of dementia and exploring AI to improve access to healthcare services.…”

Section: Introductionmentioning

confidence: 99%

Text dialogue analysis Based ChatGPT for Primary Screening of Mild Cognitive Impairment

Wang

Liu

et al. 2023

Preprint

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Background: AI models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone. Objectives: We would explore the effectiveness of ChatGPT for primary screening of mild cognitive impairment (MCI) and standardize the design steps and components of the prompt. Methods: We obtained 174 participants from the DementiaBank screening and classified 70% of them into the training set and 30% into the test set. Three dimensions of variables were incorporated, including: vocabulary, syntax and grammar, and semantics. These variables were generated from published studies and statistical analyses. We used R 4.3.0. for the analysis of variables and diagnostic indicators. Results: The final variables included by published studies included: word frequency and word ratio, phrase frequency and phrase ratio, lexical complexity, syntactic complexity, grammatical components, semantic density, and semantic coherence; variables included in the analysis included: tip-of-the-tongue phenomenon (P < 0.001), difficulty with complex ideas (P < 0.001), and memory issues (P < 0.001). The final GPT4 model achieved the sensitivity (SEN) of 0.8636, specificity (SPE) of 0.9487 and area under the curve (AUC) of 0.9062 on the training set; on the test set, the SEN, SPE and AUC reached 0.7727, 0.8333 and 0.8030, respectively. The prompt consisted of five main parts, including character setting, scoring system setting, indicator setting, output setting, and explanatory information setting. Conclusion: ChatGPT was effective in primary screening of participants with possible MCI. Improved standardization of prompts by professional clinicians would also improve the performance of the model. It is important to note that ChatGPT is not a substitute for a clinician making a diagnosis.

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“…AI has emerged as a promising tool in health care, particularly in cognitive impairment diagnosis [11,12]. AI can provide a more accurate and standardized process for diagnosing and predicting diseases [13].…”

Section: Introductionmentioning

confidence: 99%

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study

Wang,

Liu,

et al. 2023

J Med Internet Res

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Background Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone. Objective In this study, we explored the performance of ChatGPT for primary screening of mild cognitive impairment (MCI) and standardized the design steps and components of the prompts. Methods We gathered a total of 174 participants from the DementiaBank screening and classified 70% of them into the training set and 30% of them into the test set. Only text dialogues were kept. Sentences were cleaned using a macro code, followed by a manual check. The prompt consisted of 5 main parts, including character setting, scoring system setting, indicator setting, output setting, and explanatory information setting. Three dimensions of variables from published studies were included: vocabulary (ie, word frequency and word ratio, phrase frequency and phrase ratio, and lexical complexity), syntax and grammar (ie, syntactic complexity and grammatical components), and semantics (ie, semantic density and semantic coherence). We used R 4.3.0. for the analysis of variables and diagnostic indicators. Results Three additional indicators related to the severity of MCI were incorporated into the final prompt for the model. These indicators were effective in discriminating between MCI and cognitively normal participants: tip-of-the-tongue phenomenon (P<.001), difficulty with complex ideas (P<.001), and memory issues (P<.001). The final GPT-4 model achieved a sensitivity of 0.8636, a specificity of 0.9487, and an area under the curve of 0.9062 on the training set; on the test set, the sensitivity, specificity, and area under the curve reached 0.7727, 0.8333, and 0.8030, respectively. Conclusions ChatGPT was effective in the primary screening of participants with possible MCI. Improved standardization of prompts by clinicians would also improve the performance of the model. It is important to note that ChatGPT is not a substitute for a clinician making a diagnosis.

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Factors Associated With the Experience of Cognitive Training Apps for the Prevention of Dementia: Cross-sectional Study Using an Extended Health Belief Model

Cited by 11 publications

References 51 publications

mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan

mHealth Apps for Dementia, Alzheimer Disease, and Other Neurocognitive Disorders: Systematic Search and Environmental Scan

Text dialogue analysis Based ChatGPT for Primary Screening of Mild Cognitive Impairment

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study

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