Effectiveness of a mobile application for independent computerized cognitive training in patients with mild cognitive impairment: study protocol for the NeNaE Study, a randomized controlled trial.

BACKGROUND: Mild cognitive impairment (MCI) involves cognitive decline beyond typical age-related changes, but without significant daily activity disruption. It can encompass various cognitive domains as the causes of MCI are diverse. MCI as well as frequent comorbid neuropsychiatric conditions like depression and anxiety affect individuals' quality of life. Early interventions are essential, and computerized cognitive training (cCT) is an established treatment method. This paper presents the protocol for the NeuroNation MED Effectiveness Study, evaluating the self-administered mobile cCT intervention ("NeuroNation MED") in individuals with MCI to assess training effects on cognitive domains, health competence, neuropsychiatric symptoms, psychological well-being, and the general application usability. METHODS: This study protocol presents a single-blinded multicenter randomized controlled trial that will be carried out in six study centers in Germany and Luxembourg. We included adults with MCI (existing F06.7 ICD-10-GM diagnosis and TICS ≥ 21 and ≤ 32). The intervention group will use a mobile, multi-domain cCT ("NeuroNation MED") for 12 weeks. Meanwhile, the wait list control group will receive standard medical care or no care. The eligibility of volunteers will be determined through a telephone screening. After completion of the baseline examination, patients will be randomly assigned to one of the experimental conditions in a 2:1 ratio. In total, 286 participants will be included in this study. The primary outcome is the change of cognitive performance measured by the index score of the screening module of the Neuropsychological Assessment Battery. Secondary outcomes are changes in the Cognitive Failures Questionnaire, Hospital Anxiety and Depression Scale, Health-49, Health Literacy Questionnaire, among others. All of the primary and secondary outcomes will be assessed at baseline and after the 12-week post-allocation period. Furthermore, the intervention group will undergo an assessment of the System Usability Scale, and the training data of the NeuroNation MED application will be analyzed. DISCUSSION: This study aims to assess the effectiveness of a mobile self-administered cCT in enhancing cognitive abilities among individuals diagnosed with MCI. Should the findings confirm the effectiveness of the NeuroNation MED app, it may confer possible benefits for the care management of patients with MCI, owing to the accessibility, cost-effectiveness, and home-based setting it provides. Specifically, the cCT program could provide patients with personalized cognitive training, educational resources, and relaxation techniques, enabling participants to independently engage in cognitive training sessions at home without further supervision. TRIAL REGISTRATION: German Clinical Trials Register DRKS00025133. Registered on November 5, 2021.

Older adults' communication with an interactive humanoid robot : Expectations and experiences of older adults in verbal and nonverbal communication with a socially interactive humanoid robot: a mixed methods design in Germany.

BACKGROUND AND OBJECTIVE: One possible approach to counter singularization and loneliness of older adults is the development and implementation of socially interactive robots. Little is known about the expectations and experiences of older adults with socially interactive humanoid robots. MATERIAL AND METHODS: In a mixed-methods design study, user expectations before interaction and the experience and evaluation of verbal and non-verbal communication after interaction with a robot were assessed. Semi-structured interviews were conducted after the interaction. RESULTS: The majority of older adults expected verbal communication. After the interaction the evaluation of the quality of verbal communication differed. Participants did not expect any form of nonverbal communication. Nonverbal communication was highlighted as particularly positive. Gestures, facial expressions, and body movements were described as confidence building. CONCLUSION: The robot's ability to communicate nonverbally might positively influence older adults' experience of communication with the robot. In the development of socially interactive robots non-verbal communication should be given more consideration in order to contribute to successful human-robot interaction.

[Use of technology in old age: associations between acceptance, competence, control, interest and social indicators in individuals over 60 years old]. / Technologienutzung im Alter: Zusammenhänge zwischen Akzeptanz, Kompetenz, Kontrolle, Interesse und sozialen Indikatoren bei Personen über 60 Jahre.

BACKGROUND: In addition to sociodemographic factors, action-theoretical constructs, such as technology acceptance and competence play an important role in technology use. OBJECTIVE: This study aimed to examine the associations between technology use, sociodemographic factors, action-theoretical constructs, and technology interest. MATERIAL AND METHODS: Data were collected from 585 study participants aged over 60 years from 14 surveys conducted between 2014 and 2020. A structural equation model was used to explain the relationships. RESULTS: The structural equation model with covariates of survey year, age, gender, and education (n = 585) yielded the following fit indices: comparative fit index (CFI) = 0.918, Tucker-Lewis index (TLI) = 0.894, Root Mean Square Error of Approximation (RMSEA) = 0.056 [95 % confidence interval: 0.050-0.063], Standardized Root Mean Square Residual (SRMR) = 0.079, χ2 = 3051.936 (p < 0.001), χ2/degrees of freedom (df) = 18.499. The strongest associations with technology use were found for technology acceptance and competence. Additionally, technology competence showed a significant association with technology interest. Gender and technology interest were not related to technology use but it was observed that men had higher levels of technology acceptance, control, competence, and interest. DISCUSSION: Taking technology competence beliefs into account plays a crucial role in understanding the technology usage and interest of older individuals. Additionally, gender-specific differences in the theoretical constructs of action and interest in technology have been revealed in the context of the digital divide.

App-Based Evaluation of Older People's Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study.

BACKGROUND: Falls and the risk of falling in older people pose a high risk for losing independence. As the risk of falling progresses over time, it is often not adequately diagnosed due to the long intervals between contacts with health care professionals. This leads to the risk of falling being not properly detected until the first fall. App-based software able to screen fall risks of older adults and to monitor the progress and presence of fall risk factors could detect a developing fall risk at an early stage prior to the first fall. As smartphones become more common in the elderly population, this approach is easily available and feasible. OBJECTIVE: The aim of the study is to evaluate the app Lindera Mobility Analysis (LIN). The reference standards determined the risk of falling and validated functional assessments of mobility. METHODS: The LIN app was utilized in home- and community-dwelling older adults aged 65 years or more. The Berg Balance Scale (BBS), the Tinetti Test (TIN), and the Timed Up & Go Test (TUG) were used as reference standards. In addition to descriptive statistics, data correlation and the comparison of the mean difference of analog measures (reference standards) and digital measures were tested. Spearman rank correlation analysis was performed and Bland-Altman (B-A) plots drawn. RESULTS: Data of 42 participants could be obtained (n=25, 59.5%, women). There was a significant correlation between the LIN app and the BBS (r=-0.587, P<.001), TUG (r=0.474, P=.002), and TIN (r=-0.464, P=.002). B-A plots showed only few data points outside the predefined limits of agreement (LOA) when combining functional tests and results of LIN. CONCLUSIONS: The digital app LIN has the potential to detect the risk of falling in older people. Further steps in establishing the validity of the LIN app should include its clinical applicability. TRIAL REGISTRATION: German Clinical Trials Register DRKS00025352; https://tinyurl.com/65awrd6a.

Algorithm based on one monocular video delivers highly valid and reliable gait parameters.

Despite its paramount importance for manifold use cases (e.g., in the health care industry, sports, rehabilitation and fitness assessment), sufficiently valid and reliable gait parameter measurement is still limited to high-tech gait laboratories mostly. Here, we demonstrate the excellent validity and test-retest repeatability of a novel gait assessment system which is built upon modern convolutional neural networks to extract three-dimensional skeleton joints from monocular frontal-view videos of walking humans. The validity study is based on a comparison to the GAITRite pressure-sensitive walkway system. All measured gait parameters (gait speed, cadence, step length and step time) showed excellent concurrent validity for multiple walk trials at normal and fast gait speeds. The test-retest-repeatability is on the same level as the GAITRite system. In conclusion, we are convinced that our results can pave the way for cost, space and operationally effective gait analysis in broad mainstream applications. Most sensor-based systems are costly, must be operated by extensively trained personnel (e.g., motion capture systems) or-even if not quite as costly-still possess considerable complexity (e.g., wearable sensors). In contrast, a video sufficient for the assessment method presented here can be obtained by anyone, without much training, via a smartphone camera.

Digitization of neuropsychological diagnostics: a pilot study to compare three paper-based and digitized cognitive assessments.

BACKGROUND AND OBJECTIVE: The number of people suffering from dementia is increasing worldwide and so is the need for reliable and economical diagnostic instruments. Therefore, the aim of this study was to compare the processing times of the neuropsychological tests Trail Making Tests A and B (TMT-A/B) and Color-Word Interference Test (CWIT), which were performed in both digital and paper versions. METHODS: The pilot study was conducted among 50 healthy participants (age 65-83 years) using a randomized crossover design. The correlations and differences in the individual processing times of the two test versions were statistically analyzed. Further research questions concerned the influence of the individual usage of technology and the technology commitment of participants as well as the influence of the assessed usability on participants' performance. RESULTS: Between the two versions (paper-based vs. digital) statistically significant correlations were found in all tests, e.g., TMT-A r(48) = 0.63, p < 0.01; TMT-B rs(48) = 0.77, p < 0.001). The mean value comparison showed statistically significant differences, e.g., interference table (CWIT) t(49) = 11.24, p < 0.01). Correlations with medium effect were found between the differences in processing times and the individual usage of computer (e.g., rs(48) = - 0.31) and smartphone (rs(48) = - 0.29) and between the processing times of the TMT-B and the usability (rs(48) = 0.29). CONCLUSIONS: The high correlations between the test procedures appear promising. However, the differences found in the processing times of the two test versions require validation and standardization of digitized test procedures before they can be used in practice.

Accuracy of Monocular Two-Dimensional Pose Estimation Compared With a Reference Standard for Kinematic Multiview Analysis: Validation Study.

BACKGROUND: Expensive optoelectronic systems, considered the gold standard, require a laboratory environment and the attachment of markers, and they are therefore rarely used in everyday clinical practice. Two-dimensional (2D) human pose estimations for clinical purposes allow kinematic analyses to be carried out via a camera-based smartphone app. Since clinical specialists highly depend on the validity of information, there is a need to evaluate the accuracy of 2D pose estimation apps. OBJECTIVE: The aim of the study was to investigate the accuracy of the 2D pose estimation of a mobility analysis app (Lindera-v2), using the PanopticStudio Toolbox data set as a reference standard. The study aimed to assess the differences in joint angles obtained by 2D video information generated with the Lindera-v2 algorithm and the reference standard. The results can provide an important assessment of the adequacy of the app for clinical use. METHODS: To evaluate the accuracy of the Lindera-v2 algorithm, 10 video sequences were analyzed. Accuracy was evaluated by assessing a total of 30,000 data pairs for each joint (10 joints in total), comparing the angle data obtained from the Lindera-v2 algorithm with those of the reference standard. The mean differences of the angles were calculated for each joint, and a comparison was made between the estimated values and the reference standard values. Furthermore, the mean absolute error (MAE), root mean square error, and symmetric mean absolute percentage error of the 2D angles were calculated. Agreement between the 2 measurement methods was calculated using the intraclass correlation coefficient (ICC[A,2]). A cross-correlation was calculated for the time series to verify whether there was a temporal shift in the data. RESULTS: The mean difference of the Lindera-v2 data in the right hip was the closest to the reference standard, with a mean value difference of -0.05° (SD 6.06°). The greatest difference in comparison with the baseline was found in the neck, with a measurement of -3.07° (SD 6.43°). The MAE of the angle measurement closest to the baseline was observed in the pelvis (1.40°, SD 1.48°). In contrast, the largest MAE was observed in the right shoulder (6.48°, SD 8.43°). The medians of all acquired joints ranged in difference from 0.19° to 3.17° compared with the reference standard. The ICC values ranged from 0.951 (95% CI 0.914-0.969) in the neck to 0.997 (95% CI 0.997-0.997) in the left elbow joint. The cross-correlation showed that the Lindera-v2 algorithm had no temporal lag. CONCLUSIONS: The results of the study indicate that a 2D pose estimation by means of a smartphone app can have excellent agreement compared with a validated reference standard. An assessment of kinematic variables can be performed with the analyzed algorithm, showing only minimal deviations compared with data from a massive multiview system.

Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study.

BACKGROUND: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants. OBJECTIVE: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app's discriminative ability based on real-world data. METHODS: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score's ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model. RESULTS: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%. CONCLUSIONS: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score's prospective accuracy will be further validated in a prospective trial.

Effects of a long-term smartphone-based self-monitoring intervention in patients with lipid metabolism disorders.

The majority of lipid metabolism disorders can be managed well if patients adhere to their therapies. Self-monitoring can drive adherence with regards to medication intake, physical activities, and nutrition. Technical devices like smartphones can further support its users to achieve health-related goals. In a clinical trial, 100 patients with lipid metabolism disorders were asked to use a smartphone application over a duration of 12 months. Users of this app could set reminders to keep track of their medication and other disease-related variables, such as weight and cholesterol. More than half of all patients that started to use the app continued to use the app over the full 12 months. However, 43% of the patients that were asked to use the app stated that they never started to use the app. The reasons cited were lack of time, health problems, lack of motivation, and technical problems. The number of patients with high medication adherence increased significantly due to the use of the app. Health apps might benefit patients by enabling them to better manage chronic diseases, but successful digital health concepts will need to address efficient onboarding as well as long-term motivation.

Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System.

Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis.

An eye movement analysis of web usability: Differences between older adults with and without mild cognitive impairment.

Older adults are exposed to computer-based applications daily. However, most websites and applications are not specifically developed for older adults. Studies have shown that older adults with mild cognitive impairment (MCI) behave differently from older adults without MCI in website usage. Eye tracking is a valuable tool to assess users' eye movement behavior in relation to website usability. Understanding the differences in web navigational behavior between older adults with and without MCI would be helpful for developing websites for this target group. This article presents eye tracking data from several tasks while using a cognitive training application. Overall results revealed that older adults with MCI required significantly longer to complete the tasks (U = 116.0, p < 0.05) and were significantly less successful in completing the tasks than those without MCI (U = 101.5, p < 0.05). However, there were no significant differences in eye movement patterns for any of the individual tasks, except one that required participants to use several pathways in order to successfully complete it. These findings demonstrate that eye tracking is an effective method for accessing users' eye movement patterns and the usability of a platform. However, the method was not successful in differentiating eye movement behavior between older adults with and without MCI.

Taking control! Structural and behavioural plasticity in response to game-based inhibition training in older adults.

While previous attempts to train self-control in humans have frequently failed, we set out to train response inhibition using computer-game elements. We trained older adults with a newly developed game-based inhibition training on a tablet for two months and compared them to an active and passive control group. Behavioural effects reflected in shorter stop signal response times that were observed only in the inhibition-training group. This was accompanied by structural growth in cortical thickness of right inferior frontal gyrus (rIFG) triangularis, a brain region that has been associated with response inhibition. The structural plasticity effect was positively associated with time spent on the training-task and predicted the final percentage of successful inhibition trials in the stop task. The data provide evidence for successful trainability of inhibition when game-based training is employed. The results extend our knowledge on game-based cognitive training effects in older age and may foster treatment research in psychiatric diseases related to impulse control.

Evaluating an Online Cognitive Training Platform for Older Adults: User Experience and Implementation Requirements.

Decline of cognitive function is a part of aging. However, intensive cognitive training can improve important cognitive functions, such as attention and working memory. Because existing systems are not older adult-friendly and are usually not based on scientific evidence, an online platform was developed for cognitive training with information and communication features and evaluated in an 8-week field test. In a randomized clinical trial with 80 older adults, findings from log data analysis and questionnaires revealed a good use of the online platform. Communication or assistive features were not used often. Good usability ratings were given to the cognitive exercises. Subjective improvements of cognitive functions due to the training were reported. The current article presents concrete requirements and recommendations for deploying cognitive training software in older adult residential homes.

[Analysis of how elderly internet users react to unexpected situations]. / Analyse des Umgangs älterer Internetnutzer mit unerwarteten Situationen.

BACKGROUND: Although internet usage among older adults is steadily increasing, there is still a digital divide between generations. Younger internet users seem to be more open towards new media. Recent studies showed the negative influence of computer anxiety on internet usage. It is not known how older adults deal with computer and internet issues in their home environment and which problem-solving strategies they apply. AIM: The behavior of elderly people in unexpected situations when using an internet portal was analyzed to establish whether older users can solve the problems without assistance and what individual reactions (e.g. facial expressions and gesticulations) they show during the interaction. MATERIAL AND METHODS: In a clinical trial with 50 older adults aged 60 years and older various typical problems which may occur while using web platforms were simulated and user behavior was analyzed using logging data, videography and with questionnaires to measure the subjective opinion of the study participants. RESULTS: The study participants had severe problems in solving the tasks on their own and many of them could not find a suitable solution at all. Overall, the videography data indicated an increased concentration of the participants during the whole session, which is in contrast to the low levels of perceived mental workload reported by the participants. Regarding task completion, no differences were found between seniors with and without cognitive impairment. CONCLUSION: The results showed the serious difficulties of older adults when dealing with unexpected events while using a web platform. For developers of internet platforms for inexperienced seniors, it seems to be crucial to incorporate a simple integration of all available features within the platform, without including features requiring high multi-tasking skills.