With the growing older adult population, the number of people with cognitive impairment or dementia is projected to increase significantly to approximately 115 million worldwide by 2050 (Alzheimer's Disease International, 2018). Dementia is a neurocognitive disorder that affects thinking, memory, behavior, activities of daily living (ADLs), judgment, and decision-making (Alzheimer's Disease International, n.d.; Christensen et al., 2013; World Health Organization [WHO], 2017). There are 50 million people worldwide with dementia, with 10 million new cases diagnosed each year (WHO, 2017); currently, approximately 6 million Americans have dementia (Alzheimer's Association, 2018). Alzheimer's disease, a subtype of dementia associated with multiple comorbidities and complications, is ranked in the top six chronic diseases listed as the cause of death in individuals age ≥65 (Alzheimer's Association, 2018).
Mild cognitive impairment (MCI) is a term used often to describe older adults' symptoms of memory impairment that cannot be attributed to other underlying issues (Smith et al., 2010). MCI is considered a precursor to dementia and Alzheimer's disease, and is defined as a condition in which cognitive changes are noted to be more extensive than expected with the normal aging process but less severe than changes associated with dementia (Blondell, Hammersley-Mather, & Veerman, 2014; Song, Yu, Li, & Lei, 2018). Dementia is progressive; the cognitive and motor decline are frequently associated with behavioral issues. This combination often results in debility in ADLs, including poor judgment, safety issues in the kitchen and bath, and poor hygiene. This gradual decline in physical and mental function during advanced stages of the disorder ultimately results in bedridden and uncommunicative individuals. To date, there is no cure for dementia or Alzheimer's disease; however, available pharmacological treatments may alleviate some symptoms (Groot et al., 2016) or help maintain cognitive function (Yiannopoulou & Papageorgiou, 2013). Pharmacological treatments, however, are expensive and may cause undesirable side effects (Galimberti & Scarpini, 2010). Interest in nonpharmacological interventions continues to increase because they are less costly, are associated with fewer side effects, and may provide more benefit than drug therapy.
Two nonpharmacological interventions that show promise for slowing cognitive decline in MCI and dementia are physical activity or exercise and cognitive training. Physical activity is defined by the WHO (2019) “as any bodily movement produced by skeletal muscles that requires energy expenditure” (para. 1). Encouraging physical activity in aging adults is critical because physical inactivity has been linked to frailty and debility or physical weakness in older adults, especially those with comorbidities. Physical activity has been shown to improve mobility and functional ability and improve health outcomes in older adults (de Vries et al., 2012; Kaushal, Langlois, Desjardins-Crepeau, Hagger, & Bherer, 2019). Dementia researchers have explored the use of specific interventions such as physical activity (e.g., aerobic exercise, walking, tai chi, video exercise [Wii®]) (Erikson & Kramer 2009; Gomes et al., 2018; Jain, Taylor, Sanzo, & Zerpa, 2017; Kahlberg, Sperandio, Carlson, & Hauselt, 2011) and cognitive training interventions including brain-training games, computerized games, and video game consoles (e.g., PlayStation®) (Baniqued et al., 2014; Corbett et al., 2015; Gates et al., 2019; Lu, Lin, & Yueh, 2017). Physical activity has been shown to improve hippocampal function (Erikson, Miller, & Roecklein, 2012), increase gray matter volume in the brain (Erikson, Leckie, & Weinstein, 2014), and improve neuroplasticity (Bamidis et al., 2014; Gregory, Parker, & Thompson, 2012; Lin, Tsai, & Kuo, 2018; Voelcker-Rehage & Niemann, 2013). Neuroplasticity is defined as the ability of the brain or the central nervous system to create new pathways or change in response to “intrinsic and extrinsic factors” (Shaffer, 2016, p. 1) within the environment (Sharma, Classen, & Cohen, 2013) throughout the lifespan. These changes may include altering cognitive strategies to manage new challenges, such as concentration (Sharma et al., 2013).
Cognitive training is used to improve cognitive skills or abilities. Cognitive training is based on the premise that the brain can change or improve with recurrent exercises that work on memory, attention, and problem-solving skills (Kueider, Bichay, & Rebok, 2014). This type of training can be done via computer or in person, either individually or in groups (Kueider et al., 2014), with computerized or online approaches being the most cost-effective (Corbett et al., 2015).
Studies combining physical activity with cognitive training interventions have found greater neuroplastic gains (Gheysen et al., 2018). Interventions combining physical activity and cognitive training in older adults and those with cognitive impairment to influence cognitive function are increasingly used due to the greater potential for improvement (Booth, Hood, & Kearney, 2016; Law, Barnett, Yau, & Gray, 2014). Integrating physical activity with cognitive training described in one program was thought to improve cognition by affecting neuroplasticity (Marmeleira, Godinho, & Fernandes, 2009). Importantly, multicomponent or combined interventions appear to be beneficial for not only cognitively intact older adults (Fabre, Chamari, Mucci, Massé-Biron, & Préfaut, 2002; Marmeleira et al., 2009), but also those who are cognitively impaired (Kounti et al., 2011; Suzuki et al., 2012). Although there have been studies using multimodal interventions that combined physical activity and computerized cognitive training, no studies were identified in the literature review that combined physical activity and cognitive training into one mobile/tablet-based application (app).
According to the PEW Research Center (Anderson & Perrin, 2017), 42% of older adults own and use smart-phones, whereas 67% use the internet. More older adults are accepting of the use of technology in their daily activities (Anderson & Perrin, 2017). Gerontechnology is the design and use of specific technologies for aging, allowing older adults to live independently and encourage supportive networks (Künemund & Tanshus, 2013). The goals of gerontechnology are based on goals from public health related to primary, secondary, and tertiary prevention, such as reducing or improving functional decline (Graafmans, 2017); however, few studies have been found that include feedback from older adults in developing gerontechnology. App interfaces should be developed that are usable by older adults and their family members (Rogers, Mayhorn, & Fisk, 2004) to ensure sustainability and consistency.
Consumer-based mobile apps, those that are already in app stores for download or purchase, can enhance quality of life (socialization and functional status) and improve mental and physical health. Some apps are free to use or have a nominal cost in the app store. Most are user-friendly, offering self-directed use, such as personal training, exercise routines, and dating. However, consumer-based apps may not be as effective, as apps developed involving those who will be using the soft ware (i.e., end users) are often not involved in the development and design (McCurdie et al., 2013). One reason that some technology interventions may not be successful is their non-specific designs that fail to meet users' unique needs or requirements. Including the end users in design and development is thus key in accomplishing user engagement in the app (McCurdie et al., 2013), ensuring sustainability.
Apps are frequently designed for younger people (Span et al., 2013) and without involvement from people with dementia or MCI (Bharucha et al., 2009), which questions whether people with MCI can use or accept an app (Meiland et al., 2014). According to Brown, Yen, Rojas, and Schnall (2013), app development requires that the needs of the intended users be appropriately considered so the apps are perceived beneficial and easy to use. Technology success depends heavily on end users' perspectives (Asghar, Cang, & Yu, 2019). User-centered design means utilizing the users at all stages of the design process (McCurdie et al., 2012). It is equally important that the needs of people with impaired cognition, including those with dementia, are met (Meiland et al., 2014). Thus, knowing the experiences and viewpoints of people with MCI regarding the use of existing apps is an important aspect (Scherer, 2005) in developing technological interventions to assist people with MCI. In cases where developers and participants, such as health care providers and patients, worked together on app development, satisfaction and app retainability were high (Chen & Allman-Farinelli, 2019; Liu-Ambrose et al., 2016).
The current authors primary aim in conducting this study was to identify key issues with utility and appeal of specific electronic mobile apps for people with MCI. The authors wanted to ensure that their app has the level of usability and functionality to promote autonomy with training and accessing information by the end user with MCI. Thus, by identifying issues with consumer-based apps through feedback with participants, their app can be developed for satisfaction and sustainability with the user. A secondary aim was to explore whether people with MCI could effectively use an electronic tablet device as evidenced by little to no assistance in operating the tablet and playing the apps through observation.
Consumer-based mobile apps for cognitive training and physical activity were viewed in the Google® Play store by the principal investigator (PI). With participants in mind for the current study (i.e., people with MCI), the Elevate® and Fit Brains Trainer® brain training apps were chosen for their apparent ease of use and the different types of games in the app (e.g., math and word recall, reading, writing) for memory training. (Since this study was conducted, the Fit Brains Trainer app is no longer available in the app/game store.) Physical activity applications, 7 Minute Workout® and Sworkit®, allowed the selection of personal workouts based on sections in the app; the PI chose one exercise for each section based on activity for an older adult who might have activity limitations, such as use of a cane or walker. These consumer-based physical activity apps required an exercise to be chosen from each section, and a section could not be bypassed without choosing one exercise from each section. However, some exercises required squatting from a standing position, getting on the floor for some exercises, and wall pushups, which resulted in modification of the exercises during the session. Modifications were accomplished by continuing to use the app as guidance; for example, knee crunches done on the floor were now conducted in the chair, and the video continued to play for the participant and to time the session before the next exercise/video. No exercise was included that might have placed participants at risk for injury.
The apps were downloaded to an ASUS® 10″ tablet to be used by participants. In collaboration with administrators from three assisted living facilities (ALFs) in northern Alabama, Institutional Review Board approval to conduct the study was granted. Eligibility criteria were age ≥65, MCI as evidenced by a Montreal Cognitive Assessment (MoCA) score between 19 and 25, and able to demonstrate understanding of the study with the MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR). Visual and auditory issues were not assessed and were not noted to be a problem; potential participants, when shown the tablet, would inform researchers they could not visualize or hear well and therefore did not think they would be a good candidate for the study.
A convenience sampling approach was used, and administrators or activity directors added the information session to their activity calendar or posted flyers announcing the session. Interested residents were directed toward a specific room at the ALF by either the administrator or activity director and recruitment sessions were held in a group setting. The study was explained to all attendees and attendees saw and held the tablet. They were also informed of the mobile apps involved in the study. Those attendees interested in participating were given the MoCA assessment. Other demographic information was obtained via the MoCA assessment, including age. Once a participant was noted to be eligible for recruitment into the study by age and MoCA score, ability to consent was assessed with the MacCAT-CR, which assesses understanding of the study. Based on eligibility criteria and participants' understanding of the study, 16 participants were recruited from the three facilities.
Two separate sessions were held with each participant. In each session, participants played one physical activity and one cognitive training app; the cognitive training app was played first in each session, which lasted approximately 10 minutes. The physical activity app was played during the workout, which averaged 7 to 10 minutes. Participants were observed, videotaped, and audiotaped during each session. Usability observation forms were used to obtain data from non-verbal (e.g., facial expressions, body language) and verbal (e.g., positive remarks, sounds of frustration, comments made) information during the sessions. Once the second game was finished, a survey was given to participants to complete regarding their thoughts and ideas about the apps and group sessions were held to obtain more thorough feedback.
The Likert-type survey was completed for each game at each session; scale items were scored based on strongly agree (5), agree (4), neutral (3), disagree (2), and strongly disagree (1). The scale included items such as examining ease of use and operation of the tablet, remembering commands from the games, learning to use the games, whether learning was complex, whether the applications were fun or frustrating, how often they might play these games, or recommend them to a friend. Group session interviews comprised questions regarding ease of use, which parts of the apps were more enjoyable, what they liked or did not like about the apps, their motivation for playing these apps, would they be more motivated if they knew the app improved their cognition/memory, and any suggestions about how they would improve the apps.
Data analysis included reviewing observation forms for verbal and nonverbal items during play of each app. Descriptive statistical analysis was completed for demographics and Likert scale means. The PI reviewed videotapes/audiotapes for data to provide additional information for observation and to ensure that no data were missed during observation of the sessions. Transcription of the group interviews were completed by two research team members to provide a more thorough analysis of feedback.
Although 16 participants were recruited for the study, only 14 participants completed the study; two participants did not show up for the final session and could not be located during the session. Participants' age ranged from 76 to 94 (mean = 87.43) and the MoCA score ranged from 19 to 25 (mean = 22.14). Most participants agreed (mean range = 4.07 to 4.64) that Sworkit, Elevate, and 7-Minute Workout were easy to use and fun, less frustrating, easy to learn to use, did not need a larger font, and they would recommend to a friend. Participants, based on their feedback, did not like Fit Brains Trainer and were neutral or disagreed with survey items related to this app (Table 1).
Comparison of Apps
Using the Usability Observation Tool to assess participants' responses resulted in varying behaviors and comments. One half of participants (57%) had questions regarding clarification about how to play the cognitive training and physical activity games. These questions were probably due to no training given before the session, as noted by the feedback in the group sessions. There were positive and negative comments about all the games (“fast & fun”; “like it”; “I've got the hang of it”; and “boring”; “goes too fast”; “this part is not so good”; respectively). Some participants (21%) stated confusion (“no instructions”; “didn't get that at all”; “am I doing this right?”) and showed frustration (30%) with themselves (e.g., shaking of the head, frustrated with performance) and with the games (e.g., app kept closing, participant kept hitting the home button, hitting buttons multiple times to accept responses). All (100%) participants smiled and appeared happy when playing the physical activity games, whereas only 35% smiled during the cognitive training games. Of participants, 78% to 100% showed concentration, such as a furrowed brow, during the cognitive training games, but only 43% to 50% of participants showed concentration during the physical activity games. Some participants were noted to possibly be bored, yawn, or seem unable to focus or concentrate, which may be due to the facility and a distracting environment. Two participants were noted to be slightly short of breath and wheezing when completing the 7-Minute Workout app, whereas one participant was noticeably short of breath upon completing the Sworkit app. Although most participants were able to complete the apps, there were some who completed the games with assistance from the research team, which included reading instructions on the screen, hitting buttons for participants, and assisting with getting ready for the next exercise.
Group session questions yielded more information about participants' thoughts of the consumer-based apps. Participants believed more time was needed between exercises for getting prepared for the next one. When asked the easiest part of the games, one participant replied, “the exercises,” whereas another participant stated, “it was too easy,” referring to the Elevate application. Participants' comments to the question, “What parts were frustrating?” included “trying to figure out what to do” and “had to press buttons a few times to get to the next screen.” Participants believed some instruction or training before using the apps was warranted. Upon asking about how often they would play these games, participants' responses included “every day” and “every couple of days.” When asked about motivation for playing more, participants' comments were “leveling up,” “being given praise,” “knowing it would improve memory,” “a reminder alarm to play it,” and “reminder on the activity board [at the facility].”
Results from this study are currently guiding the design and development of a mobile app that integrates physical and cognitive activities in one app (i.e., mPACT [mobile Physical Activity & Cognitive Training] app), which the authors propose may improve cognition or prevent further cognitive decline in people with MCI.
The current study explored whether people with MCI could effectively use an electronic tablet device to play mobile apps specifically directed at physical activity and cognitive training and to obtain feedback from participants on key issues, utility, and appeal on those mobile apps to guide the development of an app that would improve cognition. Other studies have shown that people with impaired cognition can use touchscreen tablets independently or with minimal assistance (Inoue, Jimbo, Taniguchi, & Urakami, 2011; Lim, Wallace, Luszcz, & Reynolds, 2013; Manera et al., 2015). As for whether the current participants could use tablets effectively, it was noted that although they could effectively play mobile apps on the tablet, they had to put the tablet down to adequately perform the physical activities. Satler, Belham, Garcia, Tomaz, and Tavares (2015) noted that tablets should be raised to a comfortable level to improve or alleviate muscle stress; the current research team concluded that stands should be provided to hold the tablets to improve participation in the physical activities.
Participants also provided valuable feedback to the research team on the consumer-based apps they played, which afforded the researchers key information for the design and build of the mPACT app. In a systematic review by Meiland et al. (2017), results noted a shift from obtaining technological and expert design to a user-driven approach, involving users in the design of the product or intervention. There have been questions raised in the past with inclusion of people with dementia in research studies; most of these questions surround ethical issues including capacity to consent and whether people with dementia are capable or competent of providing reliable reports or adding value to the study based on cognitive impairment (U.S. Department of Health & Human Services, Office of the Assistant Secretary for Planning and Evaluation, 2017). According to Holthe, Halvorsrud, Karterud, Hoel, and Lund (2018), usable and acceptable technology is technology that is simple and allows a person with impaired cognition to manage ADLs independently. Exploring the individual's view to understand usability and acceptability is crucial (Faucounau et al., 2009), and Robinson, Brittain, Lindsay, Jackson, and Olivier (2009) noted that people with MCI or dementia can give important feedback with the design process, which could mean the devices or soft ware are more suitable and appropriate for their needs and abilities. Span et al. (2018) found that people with dementia contributed beneficial input and “unique feedback” (p. 1415) on the design of their intervention.
Use of the consumer-based physical activity apps was challenging. The PI searched physical activity games in the app store but could not find any exercises specific to older adults. Exercises had to be modified for older adults unable to get down on the floor or perform strenuous exercise. The research team, to be consistent with modification, met and decided to include exercises that were different from the game, which made it more confusing for participants. Lack of training on the tablet before use may have led to increased frustration by participants. Although the researchers did not want to influence participants' use of the apps, some training on the tablets being used should be provided in future studies. Another limitation was the lack of WiFi within the tablet itself, which meant connecting to the facility WiFi; this process was challenging as well due to connectivity that slowed the game loading and connections, leading to participants' frustrations. Future research should have WiFi connectivity available within the tablets to increase loading times and decrease frustration or make the app non-WiFi dependent. In addition, physical fitness and medical history may need to be addressed, as some participants appeared to be short of breath with the small activities completed. Last, although attempts were made by the research team to identify and correct issues that may occur during the study, the team failed to realize that participants would have to put the tablet down to perform physical activities. In future studies, stands will be used to hold the tablets to allow for optimal participation.
The current study helps reveal that people with MCI can use technology and provide feedback regarding that technology. Although researchers hesitate to use individuals with cognitive impairment in research, there are many initiatives in the United States to encourage more people with MCI and dementia to participate in research efforts. The feedback given by participants was instrumental in creating the current authors' new mobile physical activity and cognitive training app, the mPACT app, which integrates cognitive training games with physical activities in the form of chair exercises. Future research includes using the mPACT app to examine whether using a mobile app integrating physical and cognitive training will improve cognition or slow its progression in people with MCI. This vulnerable population has much to offer researchers in the way of data, helping researchers understand and develop effective treatments for these older adults to remain as independent as possible and improve their quality of life.
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Comparison of Apps
|Item Statement||App (Mean, SD)|
|Sworkit®||Fit Brains Trainer®||7-Minute Workout®||Elevate®|
|Easy to use||4.06 (1.00)||3.63 (1.41)||4.57 (0.65)||4.57 (0.51)|
|Easy to operate||4.13 (0.50)||3.88 (1.26)||4.50 (0.52)||4.57 (0.51)|
|Difficult to remember commands||2.00 (1.46)||3.25 (1.44)||2.79 (1.48)||2.43 (1.16)|
|Learned to use quickly||4.31 (0.60)||4.13 (1.09)||4.64 (1.86)||4.36 (0.63)|
|Learning was complex||2.00 (1.32)||2.50 (1.55)||1.86 (1.03)||2.29 (1.38)|
|App was fun||4.33 (0.90)||3.94 (1.24)||3.36 (1.60)||4.14 (0.86)|
|App was frustrating||1.47 (0.74)||1.69 (0.87)||1.64 (1.08)||1.71 (1.07)|
|Font needs to be larger||1.88 (1.26)||1.75 (1.48)||2.36 (1.55)||2.57 (1.50)|
|Would play frequently||3.94 (1.53)||3.94 (1.39)||3.71 (1.27)||4.08 (1.04)|
|Would recommend to friend||4.19 (1.28)||3.75 (1.69)||3.69 (1.38)||3.86 (0.95)|