The Gray Matters App Targeting Health Behaviors Associated with Alzheimer's Risk: Improvements in Intrinsic Motivation and Impact on Diet Quality and Physical Activity

Abstract

Objective

We examine if the Gray Matters intervention (education and behavioral self-monitoring app targeting lifestyle risks for Alzheimer's disease [AD]) affected participants' motivation for change and whether high motivation predicts improved diet and physical activity over 6 months.

Design

In this 6-month randomized controlled trial (treatment n=104; control n=42; assessed pre/post intervention) amotivation, external regulation, identified regulation, and intrinsic motivation were assessed via the Situational Motivation Scale (SIMS). Diet quality was assessed via adherence to the DASH diet, and physical activity was assessed in minutes.

Participants

Eligibility criteria included not having significant cognitive impairment. Participants were aged 39–64 (M = 54.17, SD = 6.9), 66% female, 81% married, 90% White, and 80% had a college degree.

Intervention

Treatment included an activity tracker, Gray Matters app, access to booster sessions, contact with a student health coach, and study website.

Setting

Participants were in the general community.

Results

Independent samples t-tests determined that intrinsic motivation (IM) increased significantly for the treatment group (M = 2.09 SD = 4.82), compared to the control group (M = 1.00 SD = 5.52; t (130) = -3.04, p =.003). Comparing subgroups of people with High vs Low IM we found that High IM males increased vigorous physical activity more than Lower IM males (F(1,42)=5.053, p=.030). Comparing persons aged 58–64 years with High vs Lower IM, High IM persons had less improvement in diet quality F(1,48)=4.538; p=0.038).

Conclusion

RCT results support that the Gray Matters AD-focused intervention increased IM, and IM was associated with improved physical activity and diet quality for some subgroups.

Introduction

Rates of Alzheimer's disease (AD) are expected to increase to nearly 14 million by 2050 (1). As a fatal condition with no known cure, prevention efforts are imperative. Alzheimer's risk is in part genetic (e.g. APOE4 allele, and other genes have known associations with AD) (2). However, Ridge and colleagues suggested that genetics accounts for approximately one-third of AD risk (3), meaning that much of the variance in risk for AD is attributed to non-genetic environmental factors.

Barnes and Yaffe (4) identified environmental/lifestyle factors posing the strongest AD risk, concluding that half of AD risk is explained by depression, stress, physical inactivity, diabetes, hypertension, obesity, and lower educational attainment (4). AD risk may be reduced by healthy behaviors including higher sleep quality (5), engaging in cognitively stimulating activities (6), and social engagement (7). Diet and physical activity also have established associations with AD risk. Specifically, the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets, dietary patterns high in vegetables, fruits, and legumes reduce the risk of heart disease, and reduce risk of AD and cognitive decline (8, 9, 10, 11). Physical activity is associated with reduced AD risk: a case-control twin study demonstrated that engagement in regular exercise in midlife was associated with 66% less risk of dementia 30 years later (OR = 0.34, 95% CI, [0.16–0.72]) (12). For older adults in both the highest tertiles for adherence to the Mediterranean diet and for physical activity, there was an observed 61% to 67% decrease in AD risk (13).

While genetic risk is currently not modifiable, lifestyle risk factors are modifiable and responsive to interventions. Health behavior interventions (targeting diet, physical activity, etc.) typically focus on education about the health-related beliefs and behaviors, and consequences of poor health behaviors related to obesity, heart disease, or general health problems (14). A few studies have been successful in modifying people's risk for dementia by framing lifestyle changes within the dementia-risk context, and not within the more generic improving health context (15, 16).

Motivation

A significant predictor of improved health behaviors of any behavioral intervention is the extent to which an individual is driven by internal versus external motivational factors. In Self Determination Theory (SDT) (17), individuals are described as being motivated along a continuum ranging from amotivation, external regulation, identified regulation, and to intrinsic motivation (IM). As they move from amotivation towards IM, actions and behaviors are more self-determined, rather than obligatory (17, 18). With amotivation, individuals are not motivated to behave in any particular fashion. Extrinsically motivated individuals are driven by external rewards. Identified regulation, which is externally motivated by benefits, is deemed personally important and viewed as a choice, rather than an obligation (19). The intrinsically motivated engage in a behavior because they perceive it to be enjoyable or interesting (17).

Motivation types influence engagement in interventions as well as behavioral changes. Both identified regulation and IM are associated with positive behavioral changes or outcomes, whereas external motivation and amotivation are associated with less change or negative outcomes (19). Environmental factors such as positive feedback and choice increase perceptions of competence and autonomy (17) such that autonomy and competence mediate the relationship between environmental factors and identified regulation and IM (18). Interventions can increase participants' self-determined motivation by including features offering choice, as well as positive feedback (19, 20). Finally, there is sufficient support that person-related characteristics, such as age and gender, differentially affect IM's influence on behavioral changes, particularly in the domains of physical activity (21) and diet quality (22).

Current Study

The Gray Matters intervention provided tailored education, not about improving health, per se, but about increasing self-determined motivation to reduce risk for AD. Gray Matters is a multi-domain lifestyle intervention, piloted as an RCT, which included an activity tracker, Gray Matters app (providing lifestyle-related AD risk education and daily behavioral self-monitoring), access to booster sessions, contact with a student health coach, and study website. It addressed six behavioral domains including: physical activity, diet quality, cognitive activity, social engagement, stress management, and sleep quality. Participants in the treatment group were encouraged to make healthy changes in all the domains, but were allowed to choose which ones to focus their efforts on throughout the intervention period (targeting autonomy). The smartphone app provided daily facts and weekly feedback that detailed each participant's progress (targeting competence). Additionally, each participant in the treatment group received a weekly contact with encouragement from their personal health coaches, who were college students trained in basic motivational interviewing (23). Participants in the control group were not given access to the educational activities, smartphone app, activity monitor, or student health coaches and were asked to continue their behavior as usual (a complete description of the study is provided elsewhere (23)).

In sum, Gray Matters allowed participants to select health domains to improve, and were given education and reminders using a “prevent-AD” approach, and not a generic “improve your health” approach. This is key in understanding the potential influence on motivation of its middle-aged participants. We hypothesized that participating in the Gray Matter's intervention, which included choice of behavioral domains to focus on, positive messaging, and personalized feedback, will result in increased identified regulation and IM.

Further, we hypothesized that individuals with higher baseline levels of IM will show greater improvements in physical activity and diet quality, which were the domains participants rated as the highest priority for change, excluding cognition which is not subject to short-term (6-month) changes. The current manuscript reports on secondary analyses of the data from the Gray Matters pilot study, with the goal to explore in a preliminary investigation, the extent to which motivation may be an important mediator between modern health technology approaches and actual behavior change in middle-aged persons, with the intention for this relationship to then be explored in greater detail in a subsequent study.

Methods

Study Design

Gray Matters was piloted as an RCT with pretest data collected at study entry and the same measures collected at six-month post-test (23).

Participants

Participant recruitment (N=146) was accomplished through email listservs, flyers, health fairs, and county health department contacts. Eligibility criteria included: 1) age 39 to 64 years, 2) body mass index at or below 41, 3) ownership of a smart phone or tablet, 4) residence in Cache County, Utah, 5) fluency in English, and 6) absence of the following medical conditions: dementia, pregnancy, unmanaged diabetes, or untreated major depression (BMI and other medical exclusions were selected, not for any hypothesized association with predictor or outcome variables but for participant safety). After completing the pre-test, the participants were randomly assigned to either the treatment (n=104) or control (n=42) group (23). Informed consent was obtained from all participants and Utah State University's Institutional Review Board approved the study.

Intervention program

The intervention is described in detail elsewhere (23). Briefly, the intervention targeted six behavioral domains: physical activity, diet quality, social engagement, cognitive stimulation, stress management, and sleep quality. The participants were given a wearable activity monitor (the Nike FuelBand SE), which tracked steps taken, calories burned, and reported a proprietary unit—Nike “FuelPoints;” the device acted as a visible reminder to engage in physical activity.

In addition, the participants downloaded the Gray Matters app (for Android OS and Apple iOS platforms). This app, developed specifically for this study by an expert app development team, prompted users to respond daily to 10 behavioral questions related to behaviors in the six domains targeted in the study. Responses took a total of 1–2 minutes to complete each day. The app provided evidence-based information on empirical findings regarding lifestyle factors and AD risk, pushing a randomly selected fact (“A diet rich in antioxidants is good for cognitive health”) and simple behavioral suggestion (“Try adding a few blueberries to your cereal this morning”) to the user's phone daily. The app provided the user with weekly feedback in the form of a histogram on participant progress in each of the behavioral domains. Finally, the treatment group was provided a study website, booster events (e.g. hikes, cooking classes), a social engagement workbook, and a personal student health coach trained in basic motivational interviewing who interacted weekly via email or text. A description of the app, including screen shots and user data, is provided in Hartin et al. (24). Control participants were given access to the app and educational materials at the end of the study period.

Measures

The Situational Motivation Scale (SIMS) (19) assessed participant motivation. This scale has four subscales: amotivation, external regulation, identified regulation, and IM. The original scale has four items on each subscale, however Gray Matters included only three items for amotivation and external regulation. Each item assesses why the respondent is engaged in a given activity using Likert scale response options ranging from 1 (not at all) to 7 (a lot). Sum scores for amotivation and external regulation ranged from 3–21 and for IM and identified regulation, ranged from 4–28 such that higher scores indicate higher use of that motivation subtype. Cronbach's a from the original scales19, are: amotivation α=.77, external regulation α=.86, identified regulation α=.80, and IM α=.95. For the current sample, amotivation α = .72, external regulation α = .78, identified regulation α = .77, and IM α = .78.

Physical activity assessment was derived from Centers for Disease Control guidelines (CDC) (25): “In a typical week, how many minutes do you engage in moderate physical activity, such as walking, biking, vacuuming, gardening, or anything else that causes small increases in breathing and heart rate?” and “In a typical week, how many minutes do you engage in vigorous physical activity, such as running, aerobics, heavy yard work or anything else that causes large increases in breathing or heart rate?”

Diet was measured using the Diet History Questionnaire (26), a semi-quantitative food frequency questionnaire (124 items). Responses were translated into a total DASH score ranging from 0 to 80, with a higher score indicating greater adherence to the DASH diet. Scores were based on the following dietary recommendations (13, 27).

• •Fruits 4.5 half cup portions
• •Vegetables 4.5 half cup portion
• •Low-fat Dairy 2.5 cup equivalent
• •6 oz. total grains, with half being whole grains (3 oz.)
• •1 serving nuts/seeds/legumes (1 oz. for nuts/seeds; 1/2 cup legumes)
• •Sodium limited to 1500mg.
• •Added sugar limited to 6 tsp. women, 9 tsp. men. This recommendation was adapted from the American Heart Association added sugar guidelines
• •Red and processed meats < or equal to 2 3-ounce servings per week

Scores within each category ranged from 1–10 points, with 10 points being given for those who consumed at or above the recommendations for the five healthier categories — fruit, vegetable, low-fat dairy, grains, nuts/seeds/legumes. In these same five categories, point value was assigned in direct proportion to the fraction of recommended level the participant consumed, multiplied by ten then rounded to the nearest integer (for example, 3 servings of vegetables resulted in a score of 7 for this category: 10*3/4.5=6.6 or 7). In each of the three less healthy categories- sodium, sugar, and meat -, participants were given 10 points for consuming less than or equal to the recommendations. In these same three categories, consumption in excess of recommended levels resulted in a score less than 10. Point value was assigned in inverse proportion to the fraction of recommended level the participant consumed, multiplied by ten then rounded to the nearest integer (for example, 4 servings of red meat resulted in a score of 10/(4/2)=5 for the meat category). Total DASH adherence score was the sum of the eight category scores, with diet quality ranging from 8–80.

Analysis

Our main research question queried the extent to which the Gray Matters intervention (random assignment to treatment group) predicted changes in motivation. Change scores were computed for the four SIMS (19) subscales (6-month post-scores minus pre-intervention baseline scores) such that positive scores indicate increased motivation over time for that subtype (note an increase in the amotivation scale reflects an increase in amotivation). Independent samples t-tests were computed to examine differences between the treatment and control groups' mean change over time in each SIMS outcome.

Second, we were interested in associations between high baseline levels of IM and changes in physical activity and diet quality outcomes. We created a categorical “high IM” variable based on a 75th percentile split. Scores 20+ in SIMS IM were labeled as high IM, and scores below 20 were labeled as lower IM. General linear models with repeated measures assessed if high or lower levels of IM at baseline predicted improvement in diet and physical activity outcomes (pre to post change), stratified by age and gender based on prior research suggesting possible age/gender differences (21, 22).

Results

Participants

The total sample size was n = 146 (104 in the treatment group and 42 in the control group). Participant age ranged from 39 years to 64 years (M = 54.17, SD = 6.9). There were 97 female participants and 49 male participants, 81% percent of the sample was married, and 80% had a college degree. Approximately 90% of the sample identified as White, not Hispanic or Latino. The rankings for all behavioral domains are reported in Table 2.

Table 2.

Independent samples t-test results for treatment and control groups on changes in motivation for SIMS subscales

			Change in Amotivation
	n	Mean		SD	df	t	P
Treatment	32	0.23		2.33	130	0.71	0.48
Control	100	0.56		2.29
			Change in External Regulation
	n	Mean		SD	df	t	P
Treatment	32	2.09		4.82	130	0.57	0.57
Control	100	−1.00		5.52
			Change in Identified Regulation
	n	Mean		SD	df	t	P
Treatment	32	0.18		4.12	131	−0.41	0.69
Control	101	−0.16		3.89
			Change in Intrinsic Motivation
	n	Mean		SD	df	t	P
Treatment	32	2.09	4.82	130		−3.04	0.003*
Control	101	−1.00	5.52

Baseline levels of the four motivation subscales did not differ significantly by control and treatment groups. Baseline amotivation was Mcontrol = 3.86, SD = 1.40 and Mtreatment = 4.16, SD = 2.18 (p = 0.445), and baseline external regulation was Mcontrol = 4.49, SD = 3.66 and Mtreatment = 4.92, SD = 3.06 (p = 0.492), indicating low levels of amotivation and external regulation for both treatment and control groups. Baseline identified regulation was Mcontrol = 23.40, SD = 3.79 and Mtreatment = 23.06, SD = 4.49 (p = 0.688), and baseline IM was Mcontrol = 14.97, SD = 4.63 and Mtreatment = 16.55, SD = 5.19 (p = 0.113), indicating that both groups had moderate-to-high identified regulation and moderate IM. Because having a family member with AD is related to fear of AD and motivation in this sample (28) we note that 40% of the total sample reported being a former or current dementia caregiver, and fear of AD was in the moderate range (23).

Baseline levels of DASH diet adherence (8–80 theoretical) ranged from 27–63. Mtreatment = 47.47, SD = 7.0 and Mcontrol =46.77, SD = 7.33, and average scores did not differ significantly by group (p = 0.518). Moderate physical activity ranged from 0–840 minutes per week. Mtreatment = 128.44, SD = 137.55, and Mcontrol = 115.79, SD = 107.36; these scores did not differ significantly by group (p = 0.719). Vigorous physical activity ranged from 1–1470 minutes per week. Mtreatment = 64.94, SD = 81.48 and Mcontrol = 55.77, SD = 77.62, and these scores also did not differ significantly by group (p = 0.619). In sum, the treatment and control groups were not statistically different from one another in terms of average motivation subtypes, diet, or physical activity (moderate or vigorous).

Gray Matters Impact on Motivation Change

Independent samples t-tests were used to assess treatment and control group differences in 6-month change across the four motivation subtypes. There were no significant treatment and control group differences in change over time for amotivation, external regulation, and identified regulation. However, the treatment group experienced a significantly greater increase in IM over time (Mtreatment = 2.09 SD = 4.82), compared to the control group (Mcontrol = 1.00 SD = 5.52), t (130) = -3.04, p = .003; see Table 1 and Figure 1.

Table 1.

Rankings for all behavioral domains, for participants in the treatment group Rank Order (n = 101, for treatment group)

	1st	2nd	3rd	4th	5th	6th
Physical Activity	33	24	18	8	10	9
Diet Quality	18	32	15	22	11	3
Cognition	20	14	22	19	11	15
Sleep	12	12	18	17	22	20
Social Engagement	3	9	7	15	27	41
Stress	15	12	22	20	20	13

Figure 1.

Changes in intrinsic motivation scores from baseline to post-test for treatment and control groups

High IM as a Predictor of Behavioral Change

High (top quartile) vs. lower IM at baseline was included in a general linear model to predict 6-month change in physical activity (moderate and vigorous — hours per week) and diet quality, as measured by the DASH adherence score. In the overall sample, baseline IM did not significantly predict behavior change in vigorous (p=.263) or moderate (p=.225) physical activity or in diet quality (p=.103). In follow-up models, the IM*Gender*Time parameter was nonsignificant: Vigorous Activity (p=.517), Moderate activity (p=.242), Diet quality (p=.715). For follow-up models with age interactions, IM*Agegroup*Time was nonsignificant for Vigorous Activity (p=.739) and Moderate activity (p=.892), and significant for Diet quality (p=.024).

In models stratified by gender (see Figure 2), among males, high vs. lower baseline IM was associated with significantly greater gains in vigorous physical activity level (MHighIM=83.5, SD=98.6; MLowerIM=13.7, SD=82.6; F(l,42)=5.053, p=.030). Males with high vs. lower baseline IM did not differ in changes in moderate physical activity level (MHighIM=−51.3, SD=301.1 vs. MLowerIM=82.7, SD=267.5, F(1,43)= 1.847, p=.181), nor in changes in diet quality (MHighIM=1.6, SD=6.1 vs. MLowerIM=3.1, SD=5.4; F(1,41)=.55, p=.462).

Figure 2.

Change in moderate and vigorous physical activity from baseline to end of study, by gender and level of motivation

Among females, high vs. lower baseline IM was not associated with change in any of the three outcomes: Vigorous physical activity level (MHighIM=16.3, SD=141.4 vs. MLowerIM= −8.5, SD=204.0; F(1,84)=.318, p=.574), Moderate physical activity level (MHighIM=-6.0, SD=227.7 vs. MLowerIM=10.1, SD=202.3; F(1,83)=.108, p=.743), and Diet quality (MHighIM=1.8, SD=8.4 vs. MLowerIM=4.3, SD=5.5; F(1,73)=2.263, p=.137).

In models stratified by age tertile (39–50, 51–57, 58–64; see Figure 3) among participants aged 39–50, high vs. lower baseline IM was not associated with change in vigorous physical activity level (MHighIM =−19.0, SD= 111.1 vs. MLowerIM =−32.6, SD=258.1; F(l,39)=.031, p=.862) nor moderate physical activity level (MHighIM =−87.3, SD=210.2 vs. MLowerIM =−1.5, SD=164.4; F(1,38)=1.937, p=.172). There was a trend toward less favorable improvement in diet quality for those with high IM in this age group (MHighIM =.82, SD=3.8 vs. MLowerIM =3.9, SD=5.1, F(1,35)=3.171, p=.084).

Figure 3.

Change is DASH adherence score from baseline to end of study as a function of age and level of intrinsic motivation

Among participants aged 51–57, high vs. lower baseline IM was not associated with change in any of the three outcomes: Vigorous physical activity level (MHighIM =34.2, SD=88.2 vs. MLowerIM =22.0, SD=78.5; F(1,29)=.111, p=.741); Moderate physical activity level (MHighIM =14.3, SD=171.8 vs. MLowerIM =57.6, SD=251.4; F(1,30)=.182, p=.673), nor diet quality (MHighIM =6.0, SD=6.4 vs. MLowerIM =2.0, SD=5.3; F(1,29)=2.788, p=.106).

Finally, among participants aged 58–64, high vs. lower baseline IM was not associated with change in vigorous physical activity level (MHighIM =71.3, SD=150.8 vs. MLowerIM =8.8, SD=127.3; F(1,56)=2.667, p=.108), nor moderate physical activity level (MHighIM =15.1, SD=290.2 vs. MLowerIM =52.0, SD=256.9; F(1,56)=.236, p=.629). High IM in this age group was a significant predictor of less favorable improvement in diet quality (MHighIM =.23, SD=9.9 vs. MLowerIM =5.0, SD=5.6; F(1,48)=4.538, p=.038).

Discussion

Because this study was an RCT, the results show that the increases in the treatment group's IM are likely the result of being exposed to the intervention, while the control group's IM decreased. Because of the RCT design, there is support that in part, these changes can be attributed to the treatment group's exposure to education about lifestyle behaviors and AD risk, choice in behavioral domains, positive messaging, and personalized feedback that were central parts of the Gray Matters intervention program.

Intervention conditions that support autonomy and create opportunities for competence can lead to increased IM and behavioral changes (29). While changes in identified regulation for the treatment group were not statistically different from the control group, it is possible that the IM scale alone could have captured increases in self-determined motivation. Additionally, baseline levels of identified regulation were moderately high for both the treatment and control groups, so there may have been a ceiling effect, limiting the ability to show significant improvements.

The results of the repeated measures general linear models indicate that certain gender and age subgroups with high levels of IM experienced changes over time in physical activity and diet quality. For males high in IM, even if they entered with lower baseline levels of vigorous physical activity than the participants with low IM, they made greater improvements in vigorous physical activity over the six months between the pre and post study measures. The effects of IM on DASH adherence were not directionally consistent. Although we expected higher IM to predict greater increases in DASH adherence overall, people both lower and high in IM made significant increases in DASH adherence. For certain age brackets, there were significant (age 58–64) and non-significant trends (age 39–50) suggesting less improvement in the high IM group. While these results did not support our own hypothesis, they are supported by prior research, which finds that dietary changes are influenced by both internal and external motivational factors (30). In a study about IM factors for exercise and dietary changes, less variance in dietary behavior was explained by IM than for exercise (31).

Some limitations of this study include the SIMS (19) missing two items, one for each the amotivation and external regulation subscales. These missing items prevent exact comparisons with other studies using the SIMS (19) subscales. Additionally, the sample was relatively homogenous in terms of racial, economic, and educational backgrounds and their overall levels of familial AD risk, fear of AD, and motivation to participate. Future studies should be replicated with samples that are more diverse. One additional limitation is that the intervention did not include substantial environmental components to increase participants' feelings of relatedness, which has been shown to increase IM and subsequent behavioral change (29, 32). Future studies should include components to increase participants' feelings of belonging and relatedness in order to increase self-determined types of motivation. Indeed, in post-study focus group interviews with treatment group participants, the suggestion for future improvement of the intervention most often mentioned was the facilitation of a supportive social group also engaged in health-related behavior change efforts (unpublished data).

Conclusion

The Gray Matters study was a unique multi-domain RCT, framing health behaviors within AD-prevention. Treatment participants exercised autonomy, and received positive messaging and personalized feedback, resulting in increased IM over time. Further, male participants with high IM improved their physical activity. Participants in different age brackets with low IM (somewhat unexpectedly) improved diet quality. Future research should examine how IM moderates behavioral change across different age groups, by gender, and how other types of motivation may be associated with behavioral change.

Funding

This project was funded by the Vice President for Research seed grant, Utah State University, and the Department for Employment and Learning, Northern Ireland.

Ethical Standards

This research was approved through Utah State University's Institutional Review Board for the protection of human subjects and complies with all laws governing research in the United States.

Conflicts of Interest

Alex Schiwal reports no conflicts of interest. Elizabeth Fauth reports no conflicts of interest. Heidi Wengreen Reports no conflicts of interest. Maria Norton Reports no conflicts of interest.