Journal of Gerontological Nursing

CNE Article 

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

Marten Haesner, MA; Anika Steinert, MSc; Julie Lorraine O'Sullivan, Dipl. Psych; Markus Weichenberger, MSc

Abstract

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1.3 contact hours will be awarded by Villanova University College of Nursing upon successful completion of this activity. A contact hour is a unit of measurement that denotes 60 minutes of an organized learning activity. This is a learner-based activity. Villanova University College of Nursing does not require submission of your answers to the quiz. A contact hour certificate will be awarded once you register, pay the registration fee, and complete the evaluation form online at http://goo.gl/gMfXaf. To obtain contact hours you must:

Read the article “Evaluating an Online Cognitive Training Platform for Older Adults: User Experience and Implementation Requirements” on pages 22–31, carefully noting the tables and other illustrative materials that are included to enhance your knowledge and understanding of the content. Be sure to keep track of the amount of time (number of minutes) you spend reading the article and completing the quiz.

Read and answer each question on the quiz. After completing all of the questions, compare your answers to those provided within this issue. If you have incorrect answers, return to the article for further study.

Go to the Villanova website listed above to register for contact hour credit. You will be asked to provide your name; contact information; and a VISA, MasterCard, or Discover card number for payment of the $20.00 fee. Once you complete the online evaluation, a certificate will be automatically generated.

This activity is valid for continuing education credit until July 31, 2018.

Contact Hours

This activity is co-provided by Villanova University College of Nursing and SLACK Incorporated.

Villanova University College of Nursing is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation.

Activity Objectives

Review findings from training programs designed to improve cognitive functioning in older adults.

Discuss the results of an online platform for cognitive training in an older adult population.

Disclosure Statement

Neither the planners nor the authors have any conflicts of interest to disclose.

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. [Journal of Gerontological Nursing, 41(8), 22–31.]

Mr. Haesner is Working Group Head, Aging and Technology, Ms. Steinert is Researcher, and Ms. O’Sullivan is Researcher, Geriatrics Research Group of the Charité–Universitätsmedizin Berlin; and Mr. Weichenberger is Researcher, Max Planck Institute for Human Development, Berlin, Germany.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This article was produced as part of a project that was supported by the German Federal Ministry of Education and Research grant (16SV5638K). Responsibility for the contents of this publication lies with the authors. The authors thank partners German Research Center for Artificial Intelligence, Phoenix Software GmbH, Vitapublic GmbH, and Evangelisches Geriatriezentrum Berlin gGmbH.

Address correspondence to Marten Haesner, MA, Working Group Head, Aging and Technology, Geriatrics Research Group of the Charité–Universitätsmedizin Berlin, Reinickendorfer Straße 61, 13347 Berlin, Germany; e-mail: marten.haesner@charite.de.

Received: January 13, 2015
Accepted: May 22, 2015

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Abstract

How to Obtain Contact Hours by Reading this Article
Instructions

1.3 contact hours will be awarded by Villanova University College of Nursing upon successful completion of this activity. A contact hour is a unit of measurement that denotes 60 minutes of an organized learning activity. This is a learner-based activity. Villanova University College of Nursing does not require submission of your answers to the quiz. A contact hour certificate will be awarded once you register, pay the registration fee, and complete the evaluation form online at http://goo.gl/gMfXaf. To obtain contact hours you must:

Read the article “Evaluating an Online Cognitive Training Platform for Older Adults: User Experience and Implementation Requirements” on pages 22–31, carefully noting the tables and other illustrative materials that are included to enhance your knowledge and understanding of the content. Be sure to keep track of the amount of time (number of minutes) you spend reading the article and completing the quiz.

Read and answer each question on the quiz. After completing all of the questions, compare your answers to those provided within this issue. If you have incorrect answers, return to the article for further study.

Go to the Villanova website listed above to register for contact hour credit. You will be asked to provide your name; contact information; and a VISA, MasterCard, or Discover card number for payment of the $20.00 fee. Once you complete the online evaluation, a certificate will be automatically generated.

This activity is valid for continuing education credit until July 31, 2018.

Contact Hours

This activity is co-provided by Villanova University College of Nursing and SLACK Incorporated.

Villanova University College of Nursing is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation.

Activity Objectives

Review findings from training programs designed to improve cognitive functioning in older adults.

Discuss the results of an online platform for cognitive training in an older adult population.

Disclosure Statement

Neither the planners nor the authors have any conflicts of interest to disclose.

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. [Journal of Gerontological Nursing, 41(8), 22–31.]

Mr. Haesner is Working Group Head, Aging and Technology, Ms. Steinert is Researcher, and Ms. O’Sullivan is Researcher, Geriatrics Research Group of the Charité–Universitätsmedizin Berlin; and Mr. Weichenberger is Researcher, Max Planck Institute for Human Development, Berlin, Germany.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This article was produced as part of a project that was supported by the German Federal Ministry of Education and Research grant (16SV5638K). Responsibility for the contents of this publication lies with the authors. The authors thank partners German Research Center for Artificial Intelligence, Phoenix Software GmbH, Vitapublic GmbH, and Evangelisches Geriatriezentrum Berlin gGmbH.

Address correspondence to Marten Haesner, MA, Working Group Head, Aging and Technology, Geriatrics Research Group of the Charité–Universitätsmedizin Berlin, Reinickendorfer Straße 61, 13347 Berlin, Germany; e-mail: marten.haesner@charite.de.

Received: January 13, 2015
Accepted: May 22, 2015

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In the past decade, many studies have revealed the suitability of mental training to improve a variety of cognitive abilities. The largest intervention offering cognitive training was the randomized controlled trial ACTIVE, in which 2,832 healthy older adults took part in cognitive training sessions. The study showed a positive impact on the trained domains (Ball et al., 2002). Positive effects on cognitive functions were also found in the AKTIVA (Tesky, Thiel, Banzer, & Pantel, 2011), IMPACT (Smith et al., 2009), and COGITO (Schmiedek, Lovden, & Lindenberger, 2010) studies. However, these extensive studies found no or only small far transfer effects. The key question remains whether interventions with cognitive training programs can actually lead to an increased cognitive reserve and improved general cognitive function. The topic of transfer-of-training is still discussed controversially (Zelinski, 2009).

Institutions offering cognitive training, such as memory clinics, seniors groups, or adult education centers, are rarely found in rural areas. Older adults living in these areas are faced with unique transportation problems (Giarchi, 2006) and community mobility services are still limited or nonexistent (Dickerson et al., 2007). For older adults with mobility impairment, access to health care and prevention programs is also difficult. New online-based cognitive training programs could overcome these barriers and allow older adults to use these programs in their home environment or senior residence.

Over the past 10 years, online memory training programs have become increasingly popular. In 2005, American consumers spent an estimated $2 million on cognitive training and the worldwide industry generated more than $260 million selling such programs (Aamodt & Wang, 2007). Since 2010, many web-based cognitive training programs, such as Lumosity® (access http://www.lumosity.com), CogniFit (access https://www.cognifit.com), or Fit Brains (access http://www.fitbrains.com), have been entering the market, claiming to train the brain for better memory and increased cognitive performance. These new programs are being advertised as effective training tools, without providing evidence that the brain is being altered in any long-term way.

Intrinsic motivation seems to be crucial for a positive effect on cognition. Jaeggi, Buschkuehl, Shah, & Jonides (2014) showed that older adults reported relatively stable engagement levels throughout 4 weeks of training, but differed substantially with respect to motivation to engage in cognitive training. Unfortunately, older adults with mild cognitive impairment generally show less motivation and are less likely to adhere to training protocols (Gigler, Blomeke, Shatil, Weintraub, & Reber, 2013) even though they are the most important target group (Barnes et al., 2009; Zafeiropoulos, 2010).

Because cognitive improvements generally require several hours of training, a key challenge of developing cognitive training systems is to keep participants engaged (Stepankova et al., 2014). Little is known about long-term motivation in in-home cognitive training. Gamification elements of the training platform itself, such as scoring and feedback, may impact older adults’ intrinsic motivation. An important factor is the implementation of game-based motivational elements during training (Wang, 2008). However, it is still unclear which elements can increase patients’ motivation during the long process of rehabilitation. For older adults, some features, such as real-time scoring during training (Katz, Jaeggi, Buschkuehl, Stegman, & Shah, 2014), were found to negatively impact training improvements.

Most studies are performed in laboratory settings and focus on the effectiveness of cognitive training platforms; no larger studies focusing on the behavior of older adults using a cognitive training platform over a long period exist. Pilot studies with small sample sizes showed the feasibility of online cognitive training (Gigler et al., 2013) and demonstrated that older adults are able to use a training program independently and successfully after initial instruction (Mahncke et al., 2006). Yet, research of use behavior of cognitive training programs for older adults in nursing homes (Zhuang et al., 2013) or in their home environment (Gigler et al., 2013) is limited. It is crucial that older adults have support from technicians as well as their formal and informal caregivers for training motivation (Tappen & Hain, 2014).

Aim of the Study

To understand more about how older adults deal with cognitive training platforms, research focusing on specific factors inhibiting and facilitating successful use in older adults with and without cognitive impairment is needed. Therefore, the aim of the current study was to investigate which features participants were using and how often. Furthermore, the current authors wanted to get a first insight into which cognitive- and/or personality-related factors are associated with older adults’ long-term use and which requirements (a) are necessary for the use of a cognitive training system in a nursing home or at home and (b) can easily be used by older adults and formal and informal caregivers. The impact of the cognitive training on cognitive functions was not the main focus and will not be discussed.

Method

Participants

One hundred forty-three older adults were invited to participate in the study. Inclusion criteria were defined as age 60 or older, possession of a computer, no tablet experience, and no general contraindications to magnetic resonance imaging (MRI) (e.g., tinnitus, claustrophobia, metal parts in body).

After checking for inclusion and exclusion criteria and receiving informed written consent, 80 older adults participated in the study. Due to the focus on cognitive enhancement and rehabilitation, 40 participants who reported subjective memory impairment (SMI) and 40 cognitively healthy participants were recruited. Thereafter, participants were randomly assigned to a tablet, computer, active control (i.e., video game group), and passive control group to form subgroups of 20 participants, while ensuring an equivalent SMI-to-no-SMI ratio in each. Participants were recruited from classes at the Senior University, Berlin; announcements on the Internet; and former contacts of user studies of the geriatrics research group of the Charité. All study procedures were approved by the ethics review board and data protection office at the Charité.

Mean participant age was 70 (range = 61 to 90 years) and 55% (n = 44) were female. Most participants had a university degree (60%), 45.2% were married, and 49.6% were living alone. Thirty-nine participants had a Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975) score between 29 and 30; 41 could be classified as older adults with mild cognitive impairment (MMSE score = 25 to 28). Table 1 presents the baseline demographic and medical characteristics of all participants. No significant difference existed among the training and control groups at baseline in age, cognition, and education. One participant discontinued the study for health reasons.

Descriptive Statistics and Group Comparison

Table 1:

Descriptive Statistics and Group Comparison

Apparatus

The LeVer learning platform consists of four components: (a) cognitive exercises; (b) the possibility to communicate with other users; (c) a forum with specific information on healthy aging, nutrition, and cognition; and (d) general settings (Figure 1 and Figure 2). The system was developed for a standard computer and Samsung Galaxy II© tablet based on a style guide ensuring comparable graphic appearance and user interfaces. Aside from specification for text, colors, target sizes, and further interaction patterns (e.g., progress bar, three-step volume control), the style guide also defined the multimodal interactions, including gestures, speech interaction, and navigation widgets. In the past 3 years, several studies were conducted to ensure good usability of the platform and to diminish age-related barriers (Haesner, O’Sullivan, Gövercin, & Steinhagen-Thiessen, 2014; Haesner, Steinert, O’Sullivan, & Steinhagen-Thiessen, 2014; Steinert, Haesner, O’Sullivan, & Steinhagen-Thiessen, 2014).

 

Figure 1.

Main page of the LeVer platform, with four components: (a) exercises, (b) personal area, (c), information area, and (d) settings.


 

Figure 2.

LeVer exercise of memorizing pictures (i.e., put the photos in the correct order).

Measures and Procedures

After formal inclusion in the study, pretest assessments and training sessions were conducted in subgroups of eight to 10 participants. Because the current study is part of a larger study measuring the impact of the intervention on cognition, several clinical tests assessing fine motor skills and cognition were conducted, including MRI measurements, with a subgroup of participants. In addition, participants were asked several questions regarding sociodemographic data, computer and tablet use, health behavior, quality of life (Stewart & Ware, 1992), technology commitment (Neyer, Felber, & Gebhardt, 2012) and, in terms of personality-related characteristics, self-efficacy. Each session included a comprehensive introduction to all relevant features of the platform and video game. Intervention group participants (i.e., LeVer tablet, LeVer computer, and video game groups) received an older adult–friendly handbook and a technical support phone number, and were asked about their perceived use of the platform or video game.

After randomization, the active control group was required to use a tablet-based cognitive video game. Participants in the passive control group were instructed to maintain their current level of cognitive activity during the study period. In a second study visit that lasted approximately 2 hours, all participants completed three computer-based cognitive exercises and had to conduct a testing session with the LeVer platform consisting of five exercises with a moderate-to-high level of difficulty. MRI measurements were performed in a subgroup of patients comprising the LeVer tablet, video game, and passive control groups. Results of the cognitive tests and MRI measurements will be discussed in a future publication. The current focus is on the results of the user experience and individual influencing factors of use.

All cognitive training was performed in participants’ homes on study-provided tablets (LeVer tablet group) or personal computers (LeVer computer group). Participants were asked to use the platform as often as possible in addition to their normal daily life activities. One initial audio–video phone call was organized to ensure that the Internet connection was working properly and that participants were able to use the platform on their own. No other control mechanisms (e.g., phone calls) during the study were intended. During the study period, user interactions with the platform were automatically logged to be analyzed using log-data analysis. Participants were asked to repeat all tests of the first two study visits and also complete a questionnaire regarding concrete use behavior and subjective cognitive change.

Data were analyzed using IBM SPSS Statistics 21.0. Tests on normal distribution and variance homogeneity were conducted. Due to non-normal distribution, nonparametric Mann–Whitney U, Kruskal–Wallis, or Wilcoxon signed-rank tests were conducted (e.g., for number of clicks, vitality, mental health). In other cases (e.g., for age), t tests and analysis of variance were applied. Pearson correlation was used to find correlations between platform use and possible influencing intrinsic and extrinsic factors.

Results

Use of the Platform

For analysis of use data, the electronic logging data were evaluated. Three male participants were excluded from the analysis because they did not use the platform. To be able to compare the log data, 50 days of the 8-week use were evaluated. A large data set was created because each click or each time a button was pressed (e.g., opening a subpage) was logged. Use varied among participants. On average, approximately 21,700 subpages (range = 3,100 to 206,000) were opened by each participant.

Logging data shows an average use of the platform of 30.5 days; one female participant was active on each of the 50 use days. In many cases, participants used the platform several times per day, making the number of sessions accordingly higher, with an average of 43 sessions per participant. One participant even conducted 110 sessions. It became apparent that participants mainly focused on performing cognitive exercises, as 95% of all clicks targeted the area of exercises. On average, each participant completed 376 exercises during the study period, which corresponded to 7.5 exercises per day.

The logging data show no significant use changes over time (Figure 3). In the first week, a decrease in use was seen, but use was relatively stable throughout the study period. No influencing factors regarding platform use were found. A trend was found for older adults without cognitive impairment using the platform to a greater extent (28,571 clicks) than older adults with mild cognitive impairment (16,529 clicks) (Mann–Whitney U test = 111). A statistical correlation between platform use and gender, age, marital status, household size, or the technical device was not found. Only general Internet use correlated with platform use (r[33] = 0.04; p < 0.05).

 

Figure 3.

Chronological sequence of use.

When analyzing the subjective use of the platform, 51.3% of participants reported using it every day and 30% reported using it several times per week. Participants stated they used the platform mainly in the afternoon (46.9%). Large differences were found between the perceived and actual use of the platform (Table 2). Particularly, accessibility features (e.g., speech input; voice output; older adult–friendly, video-based tutorials) were not used by more than one half of participants.

Table 2:

Perceived and Actual Use of Platform Functions

Usability and Usefulness

At the end of the study, participants were asked about the design, usefulness, and usability of the platform. Highest acceptance rates were found for exercises (97.3% very good or good), whereas lowest acceptance rates were found for speech input (60% very good or good) and audio–video communication (58.4% very good or good). Overall, more than 90% of participants rated the graphical design and content as good or very good. Usability was also rated positively by 97.4% of participants. Subjective usefulness was viewed differently. Only 36.9% of participants considered the platform very useful or quite useful. Overall, 89.2% of participants stated they would continue using the LeVer platform.

Regarding subjective cognitive change, 71% of participants stated that due to their use of the platform, their cognitive abilities improved. More than one half of participants reported improvements in their memory skills, concentration, processing speed, and mental fitness. Although no significant improvements after the study duration were found (z = −1.28) with the vitality subscale of the quality of life questionnaire (SF36; Tarlov et al., 1989), participants showed a significant improvement in subjective mental health (z = −2.68, p < 0.01). A statistically significant improvement in individual health knowledge (z = −1.9, p < 0.05) was measured, but not in participants’ current state of health.

Support

The study required a lot of support. A total of 63 requests from participants of the LeVer tablet and computer groups were received by the study team. To contact the study team, 39 requests were sent via the platform messaging system and contact form. Subsequently, most issues were solved via telephone. Nevertheless, 21 home visits regarding problems with the LeVer platform were necessary. Most home visits (n = 11) were already planned at the beginning of the study because participants were afraid of not being able to connect the camera and/or open the platform for the first time on their own. More female than male participants needed home visits for assistance. Reasons for contacting the study team included difficulty understanding the exercises and Chrome™ browser and Internet connection problems. Internet connection problems existed for the video-based group training. Thus, before the training session, additional support was needed due to microphone, camera, or Internet connectivity issues.

Discussion

The aim of the current study was to understand more about how older adults are using cognitive training platforms. The feasibility of an online platform for cognitive training was demonstrated. Older adults are willing to use cognitive training over a long time period in their home environment. No significant decrease in use was found after 8 weeks. Almost all participants stated they would continue using the LeVer platform. In contrast to the findings of Gigler et al. (2013), the current authors did not see any lack of motivation in participants with cognitive impairment in cognitive training. On the contrary, the current target group used the platform more often than older adults without cognitive deficits.

In addition, the current data showed that participants used few features of the platform. Help functions were hardly used. Participants focused mainly on the basic function (i.e., the exercises) of the platform. It seems that older adults only use functions they benefit from the most. Exercising at home was not seen as a leisure activity; exercises were conducted with seriousness.

Good usability has a strong impact on acceptance and use of web platforms, so many pilot studies were conducted with the platform in advance. Usability was rated very positively by the target group and high acceptance rates were measured. After analyzing the differences between perceived and actual use, it was clear that computer-generated logging data can help in understanding the concrete use of older adults. A mixed-methods approach seems promising and realistic field tests are essential to measure long-term platform use. The current study showed the necessity of an older adult–specific training concept and, in accordance with other studies (Tappen & Hain, 2014), it is proposed that family and formal caregivers should participate in these training sessions.

Limitations

A limitation of the current study was the relatively short use period. Participants used the platform for 8 weeks, which is a large time period compared to other usability studies; however, no follow-up tests were planned. Therefore, it is not known if participants continued using the platform after the study. Moreover, an urban, well-educated sample was recruited. It is unclear how older adults with less education could be motivated for long-term use of such a platform.

There is a need for studies with a larger sample size and a more precise measurement for individual influencing factors for long-term motivation. Little is known about the ideal period of time for regular cognitive training for older adults. Therefore, more scientific results on statistical correlation of use behavior and cognitive improvements are needed. There is also a lack of research comparing different interventions regarding the impact on cognition and quality of life and the application in a nursing home setting over a long time period.

Due to the scientific evidence of the effects of cognitive training and acceptance of web-based cognitive training by the target group, the current authors propose an integration of these cognitive training programs in already existing programs in nursing homes. When organizing activities, nursing homes should provide a well-balanced distribution of physical and cognitive training.

Implications for Nurses

Increased independent cognitive training use by residents changes the role of nurses more toward being a tutor, helping in activity management. Hence, nurses should be trained in basic computer skills and independent cognitive training programs in an effort to serve as contacts for technical questions when older adults are conducting training on their own. Independent cognitive training could be complementary to recreational activities. In addition to cognitive group therapy (e.g., with pets, music), advances and difficulties with independent cognitive training could be a basis for group discussion. Online platforms with communication features and multiplayer cognitive games may help reach and integrate relatives not living nearby. Instructed access for older adults with low computer literacy with a comparable web-based platform could be a first step to use more technology and eventually lead to online communication with relatives and friends.

The authors’ recommendations for implementation in a home environment or nursing home include:

  • Assuring a broadband Internet connection (especially for using audio–video communication).
  • Choosing an intuitive platform focusing on cognitive exercises.
  • Taking into account general usability and accessibility requirements and attractive look-and-feel of the platform.
  • Developing exercises based on scientific psychological evidence.
  • Providing an older adult–friendly group training concept and written instructions.
  • Including formal and informal carers in training.
  • Providing extensive technical support (asynchronous communication is sufficient).

Conclusion

The current article provides concrete recommendations for the implementation of online cognitive training platforms. Long-term training is only successful when online platforms combine motivational aspects with good usability. It is hoped that new evidence-based programs will emerge on the market, taking into account the needs of older target groups. To guarantee suitability and aid optimization, it is crucial that these platforms are systematically evaluated and, in the future, modified as necessary.

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Descriptive Statistics and Group Comparison

Variable Total (N = 80) Mean (SD) Tablet Group (n = 20) Mean (SD) Computer Group (n = 20) Mean (SD) Video Games Group (n = 20) Mean (SD) Passive Control Group (n = 20) Mean (SD) p Value
Sociodemographic measures





  Age (years) 69.6 (5) 68.8 (4.4) 70.6 (6.1) 69.5 (5.7) 69.6 (3.6) ns
  Gender (% female) 55 75 35 50 65 ns
  Education (years) 16.1 (3.1) 15.1 (3.1) 15.1 (2.8) 16.8 (2.6) 17.3 (3.4) ns
Cognition and motoric measures





  9-Hole pegboard score (s) 21.7 (3.3) 23.2 (4.1) 21 (3.8) 21.5 (2.8) 21.1 (1.9) ns
  MMSE scorea 28.5 (1.2) 28.3 (1.4) 28.6 (1.2) 28.8 (1) 28.4 (1.1) ns
  Paper folding scorea 3.4 (1.6) 3.1 (1.2) 2.8 (1.7) 4 (1.8) 3.6 (1.7) <0.05
Personality-related measures





  Technology commitment scorea 15.1 (3.5) 15.4 (3.2) 14.4 (3.5) 14.7 (4.1) 16 (3.1) ns
  Self-efficacy scorea 31.1 (3.9) 32.8 (5.4) 30.4 (3.7) 29.8 (2.9) 31.4 (2.7) ns
  Subjective mental health scorea 24.6 25.7 23.3 24.8 24.4 ns

Perceived and Actual Use of Platform Functionsa

Function Every Day (%) >2 Times/Week (%) 1 to 2 Times/Week (%) Not Used (%)
Exercisesb



  Perceived use 32.5 42.5 25
  Actual use 50 32.5 10 7.5
Messages



  Perceived use 15.4*** 35.9*** 48.7***
  Actual use 2.5*** 10*** 65*** 22.5***
Settings



  Perceived use 5.3** 26.2** 63.2** 5.3**
  Actual use 12.8** 2.6** 33.3** 51.3**
Group training



  Perceived use 2.9* 11.4* 71.4* 14.3*
  Actual use 2.6* 56.4* 41*
Audio–video communication



  Perceived use 2.7** 10.8** 59.5** 27**
  Actual use 5.4** 35.1** 59.5**
Video tutorials



  Perceived use 17.9*** 61.6*** 20.5***
  Actual use 18.4*** 81.6***
Speech input



  Perceived use 15.4** 41** 43.6**
  Actual use 5.1** 10.3** 84.6**
Voice output



  Perceived use 7.5** 35** 57.5**
  Actual use 7.9** 92.1**

Keypoints

Haesner, M., Steinert, A., O’Sullivan, J.L. & Weichenberger, M. (2015). Evaluating an Online Cognitive Training Platform for Older Adults: User Experience and Implementation Requirements. Journal of Gerontological Nursing, 41(8), 22–31.

  1. A mixed-methods approach is necessary and realistic field tests are essential to measure long-term online training use of older adults.

  2. Older adults are willing to use cognitive training over a long time period in their home environment.

  3. More than two thirds of participants stated that due to the use of the platform, their cognitive abilities improved.

  4. Nurses should be trained in basic computer skills and independent cognitive training programs in an effort to serve as contacts for technical questions when offering online training.

10.3928/00989134-20150710-44

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