Aging, Control Locus, Sedentary Lifestyle, Self Efficacy
Conditions
Keywords
Physical Exercise, Behavior Change
Brief summary
The goal of this study is to develop a smart phone app to administer a behavior change program that helps adults to increase daily steps by planning where, when, and with whom to walk. The investigators tested the effectiveness of the walking program app for increasing the number of daily steps among sedentary older adults. The investigators examined the effects on self-efficacy and social integration/support.
Detailed description
Physical activity is broadly beneficial for physical, psychological, and cognitive aspects of health, yet only one in five U.S. adults meets the CDC physical activity guidelines. Making physical activity accessible and feasible throughout life is an important public health policy objective that is within reach with the right kind of behavioral and environmental supports. The project aims to provide such supports for an active lifestyle thereby contributing to healthy aging. The goal of this project is to increase physical activity (i.e., walking) in sedentary older adults by providing the environmental and behavioral resources to incorporate additional steps into their daily lives. The investigators used a behavioral approach that fosters a sense of control and facilitates planning by focusing on the what, when, where, and with whom aspects of their physical activity. The investigators proposed a user-friendly, practical way to increase steps. By providing people with specific, tailored information about the number of steps one can get by walking a certain distance or during a certain amount of time, participants can better plan when, where, and with whom they will be able to achieve the desired number of steps, break goals into manageable portions (at different times throughout the day or week), and thereby increase the likelihood of goal achievement. During the app development phase, the investigators demonstrated the app to 10 older adults to get their input. The goal was to get their feedback about the app features and to make sure it is user friendly. The investigators asked questions about the ease of using the app and their understanding of the app features. The interviewer recorded their answers to share with the research team and app developer. Modifications to the app were made based on the feedback. During the next phase of the study, the investigators tested whether the full app program was successful in increasing steps and whether it was more effective than the basic app that only includes step counting and goals, similar to a fitness tracker or pedometer. Sixty participants were randomly assigned to two conditions: the app with step counting and goals alone (control), or the full version of the app with the step counting and goals, schedule, maps, and social components (experimental). It was predicted that the intervention group would improve more on outcome measures than the control group.
Interventions
BEHAVIORALApp Experimental condition
This group were given the app to 1) count their steps, 2) add walks to their daily schedules, 3) create maps of their walking routes, and 4) text friends to invite them for a walk. Participants are asked to set a daily step goal and they can see how many steps they've taken each day since using the app. 2) There is an interface where participants can create maps based on walking routes. 3) They will also have the option to use a daily schedule to plan certain times in the day that they can walk. 4) The social feature gives participants the option to message friends, co-workers, or neighbors in one's contact list to invite them for a walk. They were also asked to respond to two questions twice a day about their mood and energy levels.
BEHAVIORALApp Condition Control
This group received the app with the first component, the ability to count steps and set daily step goals. This group will also be able to track walks to see the time, distance, and steps of each walk, but not see these walks displayed as a map. This group will monitor their daily steps over a one-month period, and will be asked to use the app as much as possible. They were also asked to respond to two questions twice a day about their mood and energy levels.
Sponsors
National Institute on Aging (NIA)
Brandeis University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
SINGLE (Subject)
Masking description
The participants are aware of the nature of the App features they are given. They are not aware of whether they are in an experimental or control condition.
Intervention model description
Both conditions will receive the App for use on an iphone. The control group will just have the accelerometer program to set step goals and to count steps. The experimental condition will have the accelerometer to set goals and count steps in addition to the schedule, map and social components.
Eligibility
Sex/Gender
ALL
Age
50 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Sedentary adults, who own an iphone with step-tracking capabilities (5s or later). * Participants must be fit enough to walk for at least 20 minutes at a time.
Exclusion criteria
* Cognitive impairment * A recent (within the past 6 months) cardiovascular event, or fall. * A doctor has advised them not to walk * Anyone who already exercises regularly: walks for exercise more than 30 minutes per day, or does other forms of exercise 150 minutes per week or more, will be excluded.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of Steps Walked | Daily for one month | Number of steps recorded daily on the phone app, weekly step averages |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Exercise Control Beliefs | Baseline and one month from the start of the intervention | Control over exercise was measured using the 6-item Exercise Control Beliefs Scale (Neupert, Lachman, & Whitbourne, 2009). Items assess the beliefs about one's control over exercise (e.g., I am confident in my ability to do an exercise routine), with answer choices ranging from strongly disagree (1) to strongly agree (5). The 6 items are averaged to create a mean exercise control score, with a higher score indicating greater control over exercise. |
| Social Contact Through the App | During the one month intervention | Number of participants who sent at least one text message via the app |
| Daily Mood and Energy Levels | Daily | Twice at random times, each day, mood and energy levels were assessed. A popup notification asked participants to rate their current mood (unhappy, neutral, happy) and energy (low, neutral, high) on a slider scale. Scores were converted by the StepMATE app to a 0-10 scale, with 0 indicating low mood/energy, and 10 indicating high mood/energy. If both mood and energy assessments were completed in one day, they were averaged to create daily average scores, one for mood and one for energy. Data presented below are the average of all daily scores across the month, while daily averages were used in the analyses. |
| Exercise Self-efficacy | Baseline and one month from the start of the intervention | A modified version of Bandura's Exercise Self-Efficacy scale (Bandura, 1997) was used in the current study. This 9-item scale assesses how sure one is that they would exercise under different conditions or constraints (e.g. How sure are you that you will exercise when you are feeling down or depressed?), with answer choices ranging from not sure at all (1) to very sure (4). The 9 items are averaged to create a composite score, where a higher score indicates greater exercise self-efficacy (Neupert et al., 2009). |
| Self-Reported Moderate Physical Activity | Baseline and one month from the start of the intervention | Moderate PA was measured with the question 'How often do you engage in moderate physical activity that is not physically exhausting, but it causes your heart rate to increase slightly and you typically work up a sweat?', with answer choices ranging from never (0) to several times a week (5). |
| Self-Reported Light Physical Activity | Baseline and one month from the start of the intervention | Light PA was measured using the question 'How often do you engage in light physical activity that requires little physical effort?', with answer choices ranging from never (0) to several times a week (5). |
| Self-Reported Vigorous Physical Activity | Baseline and one month from the start of the intervention | Vigorous PA was measured using the question 'How often do you engage in vigorous physical activity that causes your heart to beat so rapidly that you can feel it in your chest and you perform the activity long enough to work up a good sweat and are breathing heavily?', with answer choices ranging from never (0) to several times a week (5). |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| App Control Condition The control group will just have the App with the accelerometer program to set step goals and to count and record steps for 1 month
App Condition Control: This group received the app with the first component, the ability to count steps and set daily step goals. This group will also be able to track walks to see the time, distance, and steps of each walk, but not see these walks displayed as a map. This group will monitor their daily steps over a one-month period, and will be asked to use the app as much as possible. They were also asked to respond to two questions twice a day about their mood and energy levels. | 31 |
| App Experimental Condition The experimental condition will set step goals and have the schedule, map, and social components for 1 month.
App Experimental condition: This group were given the app to 1) count their steps, 2) add walks to their daily schedules, 3) create maps of their walking routes, and 4) text friends to invite them for a walk. Participants are asked to set a daily step goal and they can see how many steps they've taken each day since using the app. 2) There is an interface where participants can create maps based on walking routes. 3) They will also have the option to use a daily schedule to plan certain times in the day that they can walk. 4) The social feature gives participants the option to message friends, co-workers, or neighbors in one's contact list to invite them for a walk. They were also asked to respond to two questions twice a day about their mood and energy levels. | 32 |
| Total | 63 |
Withdrawals & dropouts
| Period | Reason | FG000 | FG001 |
|---|---|---|---|
| Overall Study | App Crash, Incomplete Data | 7 | 8 |
| Overall Study | Lost to Follow-up | 3 | 5 |
Baseline characteristics
| Characteristic | Total | App Control Condition | App Experimental Condition |
|---|---|---|---|
| Age, Continuous | 64.16 years STANDARD_DEVIATION 7.12 | 64.71 years STANDARD_DEVIATION 6.3 | 63.63 years STANDARD_DEVIATION 7.89 |
| Ethnicity (NIH/OMB) Hispanic or Latino | 1 Participants | 1 Participants | 0 Participants |
| Ethnicity (NIH/OMB) Not Hispanic or Latino | 62 Participants | 30 Participants | 32 Participants |
| Ethnicity (NIH/OMB) Unknown or Not Reported | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) American Indian or Alaska Native | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Asian | 1 Participants | 0 Participants | 1 Participants |
| Race (NIH/OMB) Black or African American | 6 Participants | 3 Participants | 3 Participants |
| Race (NIH/OMB) More than one race | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) White | 56 Participants | 28 Participants | 28 Participants |
| Region of Enrollment United States | 63 participants | 31 participants | 32 participants |
| Sex: Female, Male Female | 41 Participants | 21 Participants | 20 Participants |
| Sex: Female, Male Male | 22 Participants | 10 Participants | 12 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 41 | 0 / 45 |
| other Total, other adverse events | 0 / 41 | 0 / 45 |
| serious Total, serious adverse events | 0 / 41 | 0 / 45 |
Outcome results
Primary
Number of Steps Walked
Number of steps recorded daily on the phone app, weekly step averages
Time frame: Daily for one month
Population: Participants were excluded if they dropped out of the study or experienced the app crash. Analyses were conducted using an intent to treat population. Daily step averages less than 500 steps were classified as a missing day of data. Weekly step averages were only calculated if there were 4 or more days of valid data.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Number of Steps Walked | Week 1 Steps | 5744.00 Average Daily Steps | Standard Deviation 2398.89 |
| App Control Condition | Number of Steps Walked | Week 3 Steps | 5858.90 Average Daily Steps | Standard Deviation 2446.95 |
| App Control Condition | Number of Steps Walked | Week 2 Steps | 5317.08 Average Daily Steps | Standard Deviation 2249.66 |
| App Control Condition | Number of Steps Walked | Week 4 Steps | 5155.42 Average Daily Steps | Standard Deviation 2505.59 |
| App Control Condition | Number of Steps Walked | Baseline Steps | 3910.37 Average Daily Steps | Standard Deviation 2971.4 |
| App Experimental Condition | Number of Steps Walked | Week 4 Steps | 5285.26 Average Daily Steps | Standard Deviation 3493.65 |
| App Experimental Condition | Number of Steps Walked | Baseline Steps | 3477.66 Average Daily Steps | Standard Deviation 1749.29 |
| App Experimental Condition | Number of Steps Walked | Week 1 Steps | 5279.66 Average Daily Steps | Standard Deviation 2445.76 |
| App Experimental Condition | Number of Steps Walked | Week 2 Steps | 5116.88 Average Daily Steps | Standard Deviation 2983.89 |
| App Experimental Condition | Number of Steps Walked | Week 3 Steps | 5197.21 Average Daily Steps | Standard Deviation 2968.79 |
Comparison: Tested whether weekly steps increased from the baseline week in both conditions (main effect of time).p-value: <0.001Mixed Models Analysis
Comparison: Tested whether weekly step increases from the baseline week differed between the two conditions (time by condition interaction).p-value: 0.846Mixed Models Analysis
Secondary
Daily Mood and Energy Levels
Twice at random times, each day, mood and energy levels were assessed. A popup notification asked participants to rate their current mood (unhappy, neutral, happy) and energy (low, neutral, high) on a slider scale. Scores were converted by the StepMATE app to a 0-10 scale, with 0 indicating low mood/energy, and 10 indicating high mood/energy. If both mood and energy assessments were completed in one day, they were averaged to create daily average scores, one for mood and one for energy. Data presented below are the average of all daily scores across the month, while daily averages were used in the analyses.
Time frame: Daily
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Daily Mood and Energy Levels | Mood | 6.91 units on a scale | Standard Deviation 1.67 |
| App Control Condition | Daily Mood and Energy Levels | Energy | 5.58 units on a scale | Standard Deviation 1.35 |
| App Experimental Condition | Daily Mood and Energy Levels | Mood | 6.53 units on a scale | Standard Deviation 1.35 |
| App Experimental Condition | Daily Mood and Energy Levels | Energy | 5.80 units on a scale | Standard Deviation 1.61 |
Comparison: Tested whether daily walking was related to mood within-persons.p-value: <0.001Mixed Models Analysis
Comparison: Tested whether daily walking was related to energy within-persons.p-value: 0.003Mixed Models Analysis
Comparison: Tested whether the relationship between daily walking and mood differed between males and females (steps by sex interaction).p-value: <0.001Mixed Models Analysis
Comparison: Tested whether the relationship between daily walking and energy differed between males and females (steps by sex interaction).p-value: <0.001Mixed Models Analysis
Secondary
Exercise Control Beliefs
Control over exercise was measured using the 6-item Exercise Control Beliefs Scale (Neupert, Lachman, & Whitbourne, 2009). Items assess the beliefs about one's control over exercise (e.g., I am confident in my ability to do an exercise routine), with answer choices ranging from strongly disagree (1) to strongly agree (5). The 6 items are averaged to create a mean exercise control score, with a higher score indicating greater control over exercise.
Time frame: Baseline and one month from the start of the intervention
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Exercise Control Beliefs | Pre-test | 4.39 units on a scale | Standard Deviation 0.48 |
| App Control Condition | Exercise Control Beliefs | Post-test | 4.31 units on a scale | Standard Deviation 0.51 |
| App Experimental Condition | Exercise Control Beliefs | Pre-test | 4.23 units on a scale | Standard Deviation 0.53 |
| App Experimental Condition | Exercise Control Beliefs | Post-test | 4.25 units on a scale | Standard Deviation 0.62 |
Comparison: Tested whether exercise control increased from the baseline week to the end of the intervention in both conditions (main effect of time).p-value: 0.564Mixed Models Analysis
Comparison: Tested whether exercise control belief increases from the baseline to the end of the intervention differed between the two conditions (time by condition interaction).p-value: 0.641Mixed Models Analysis
Secondary
Exercise Self-efficacy
A modified version of Bandura's Exercise Self-Efficacy scale (Bandura, 1997) was used in the current study. This 9-item scale assesses how sure one is that they would exercise under different conditions or constraints (e.g. How sure are you that you will exercise when you are feeling down or depressed?), with answer choices ranging from not sure at all (1) to very sure (4). The 9 items are averaged to create a composite score, where a higher score indicates greater exercise self-efficacy (Neupert et al., 2009).
Time frame: Baseline and one month from the start of the intervention
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Exercise Self-efficacy | Pre-test | 2.61 units on a scale | Standard Deviation 0.67 |
| App Control Condition | Exercise Self-efficacy | Post-test | 2.43 units on a scale | Standard Deviation 0.83 |
| App Experimental Condition | Exercise Self-efficacy | Pre-test | 2.88 units on a scale | Standard Deviation 0.83 |
| App Experimental Condition | Exercise Self-efficacy | Post-test | 2.54 units on a scale | Standard Deviation 0.92 |
Comparison: Tested whether exercise self-efficacy increased from the baseline week to the end of the intervention in both conditions (main effect of time).p-value: 0.571Mixed Models Analysis
Comparison: Tested whether exercise self efficacy increases from the baseline to the end of the intervention differed between the two conditions (time by condition interaction).p-value: 0.361Mixed Models Analysis
Secondary
Self-Reported Light Physical Activity
Light PA was measured using the question 'How often do you engage in light physical activity that requires little physical effort?', with answer choices ranging from never (0) to several times a week (5).
Time frame: Baseline and one month from the start of the intervention
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Self-Reported Light Physical Activity | Pre-test | 4.77 units on a scale | Standard Deviation 0.56 |
| App Control Condition | Self-Reported Light Physical Activity | Post-test | 4.61 units on a scale | Standard Deviation 0.84 |
| App Experimental Condition | Self-Reported Light Physical Activity | Pre-test | 4.69 units on a scale | Standard Deviation 0.64 |
| App Experimental Condition | Self-Reported Light Physical Activity | Post-test | 4.78 units on a scale | Standard Deviation 0.61 |
Comparison: Tested whether self-reported light physical activity increased from the baseline week to the end of the intervention in both conditions (main effect of time).p-value: 0.199Mixed Models Analysis
Comparison: Tested whether self-reported light physical activity increases from the baseline to the end of the intervention differed between the two conditions (time by condition interaction).p-value: 0.203Mixed Models Analysis
Secondary
Self-Reported Moderate Physical Activity
Moderate PA was measured with the question 'How often do you engage in moderate physical activity that is not physically exhausting, but it causes your heart rate to increase slightly and you typically work up a sweat?', with answer choices ranging from never (0) to several times a week (5).
Time frame: Baseline and one month from the start of the intervention
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Self-Reported Moderate Physical Activity | Pre-Test | 3.94 units on a scale | Standard Deviation 1.44 |
| App Control Condition | Self-Reported Moderate Physical Activity | Post-test | 4.13 units on a scale | Standard Deviation 1.18 |
| App Experimental Condition | Self-Reported Moderate Physical Activity | Pre-Test | 3.50 units on a scale | Standard Deviation 1.67 |
| App Experimental Condition | Self-Reported Moderate Physical Activity | Post-test | 3.63 units on a scale | Standard Deviation 1.76 |
Comparison: Tested whether self-reported moderate physical activity increased from the baseline week to the end of the intervention in both conditions (main effect of time).p-value: 0.53Mixed Models Analysis
Comparison: Tested whether self-reported moderate physical activity increases from the baseline to the end of the intervention differed between the two conditions (time by condition interaction).p-value: 0.831Mixed Models Analysis
Secondary
Self-Reported Vigorous Physical Activity
Vigorous PA was measured using the question 'How often do you engage in vigorous physical activity that causes your heart to beat so rapidly that you can feel it in your chest and you perform the activity long enough to work up a good sweat and are breathing heavily?', with answer choices ranging from never (0) to several times a week (5).
Time frame: Baseline and one month from the start of the intervention
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| App Control Condition | Self-Reported Vigorous Physical Activity | Pre-test | 1.94 units on a scale | Standard Deviation 1.83 |
| App Control Condition | Self-Reported Vigorous Physical Activity | Post-test | 2.77 units on a scale | Standard Deviation 1.63 |
| App Experimental Condition | Self-Reported Vigorous Physical Activity | Pre-test | 2.13 units on a scale | Standard Deviation 1.83 |
| App Experimental Condition | Self-Reported Vigorous Physical Activity | Post-test | 2.53 units on a scale | Standard Deviation 1.83 |
Comparison: Tested whether self-reported vigorous physical activity increased from the baseline week to the end of the intervention in both conditions (main effect of time).p-value: 0.011Mixed Models Analysis
Comparison: Tested whether self-reported vigorous physical activity increases from the baseline to the end of the intervention differed between the two conditions (time by condition interaction).p-value: 0.232Mixed Models Analysis
Secondary
Social Contact Through the App
Number of participants who sent at least one text message via the app
Time frame: During the one month intervention
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| App Control Condition | Social Contact Through the App | 0 participants |
| App Experimental Condition | Social Contact Through the App | 1 participants |