Abstract
Sedentary behavior has been associated with adverse health outcomes such as disturbed sleep in older adults. We conducted a single-group pretest and posttest study to evaluate the feasibility of a personalized behavioral intervention program using mobile health technology in improving physical activity and sleep in older adults. The four-week intervention included: personalized physical activity training, real-time physical activity self-monitoring, interactive prompts and feedback with a smartwatch, phone consultation with an exercise trainer and research team members, and weekly financial incentives for achieving weekly physical activity goals. Eight cognitively intact older adults were recruited and completed the study. Findings suggested that the intervention was feasible in this sample of older adults and provided favorable changes in levels of physical activity during the intervention and at post-intervention. Future studies will include a fully powered trial to evaluate the efficacy of this intervention in sedentary older adults.
Keywords: physical activity, behavioral intervention, mobile health, sleep
Introduction
Across age groups, older adults are the most sedentary population subgroup.[1] When measured objectively, older adults spend on average 9.4 hours a day on sedentary activities, which is equivalent to 65–80% of their wake time.[2] Recent studies suggest that sedentary activity in older adults is associated not only with metabolic syndrome and increased mortality but has also high risk for disturbed sleep, which is a geriatric syndrome that adversely impacts multiple health outcomes.[1,3,4] Studies have also shown that increased physical activity or regular exercise is associated with improved nocturnal sleep.[5,6]
A change in lifestyle by being more active or adding regular exercise is challenging for people in all age groups, especially for older adults. People are aging differently, usually with multi-comorbidity and various levels of physical and functional status. Compared with the general population, older adults usually receive less social support and have more depressive symptoms and fear of falling during physical activity.[7] These factors all contribute to the lower exercise adherence in older adults.[7,8] Individually tailored physical activity interventions that account for an individual’s preferences and functional limitations have been proven to improve adherence and study outcomes [9].
Recent developments in mobile health (mHealth) technology provide more innovative approaches to deliver individually tailored and more efficient behavioral interventions that can accommodate a person’s health status and can become embedded in the individual’s daily routine. Electronic activity monitors, such as wrist-worn accelerometers, can track physical activity and sleep to allow individuals to work towards their personal activity and sleep goals.[10] Some activity monitors, such as smartwatches, can also be paired with smartphones to enable functions of mHealth technology, such as activity reminders and interactive messages, that provide a more personalized physical activity intervention. Some studies have applied mHealth or wearable technology to promote physical activity.[11,12] Most of these studies were conducted in young or middle-aged adult populations. Behavioral interventions implemented with these techniques are limited in older adult populations.
Self-efficacy theory has been used to promote physical activity/exercise in older adults and has demonstrated positive effects on promoting physical activity and other health outcomes.[13,14] There are four approaches to increase a person’s self-efficacy: enactive mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective feedback.[15] We developed a comprehensive, personalized behavioral intervention program based on theoretical constructs from the self-efficacy theory. [13-15] This intervention was designed to enhance the participant’s self-efficacy by providing enactive mastery experiences and verbal persuasion. Challenging yet realistic and attainable goals, when coupled with incentives, serve as motivators to improve self-efficacy. Thus, our personalized behavioral intervention includes physical activity training, exercise prescription, goal setting, phone coaching, consultation and feedback, real-time physical activity self-monitoring, interactive prompts and feedback with a smartwatch, and weekly financial incentives that will contribute to the development of a stronger sense of self-efficacy in this older population. The intervention may engage participants and promote increased physical activity and facilitate positive behavioral changes, and possibly improve health outcomes, such as sleep quality.
A substantial body of research suggests that physical activity/exercise improves sleep outcomes.[16,17] For example, a meta-analysis study reviewed 66 exercise intervention studies and found beneficial effects of regular exercise on multiple sleep parameters including total sleep time, sleep efficiency, sleep latency, and overall sleep quality.[18] Similarly, a systematic review of 13 randomized controlled trials found chronic resistance exercise improved all aspects of sleep, especially sleep quality.[19]
The purpose of this study was to evaluate the personalized behavioral intervention for older adults on its feasibility and preliminary efficacy for outcomes including physical activity and sleep. We hypothesized that participants would have longer nocturnal sleep duration and higher sleep efficiency at post-intervention. We also hypothesized that participants would have increased levels of physical activity and less sedentary time during the intervention and at post-intervention.
Methods
Study design:
It was a pilot, feasibility study using a single-group pretest and posttest. Study duration and subject participation was approximately 6 weeks: a 1-week pretest, a 4-week intervention program, and a 1-week posttest. The protocol was approved by the XXX Review Board.
Recruitment and Participants
We recruited participants from a geriatric clinic at the XXX. Participants who presented for a wellness visit were screened for eligibility after providing consent. Eight eligible older adults were enrolled. Eligibility criteria included ages between 65 and 85, no prior diagnosis of cognitive impairment or dementia, sedentary lifestyle (self-reported more than 6 hours of sitting activities per day), poor sleep quality (Insomnia severity index ≥8), and no diagnosis of sleep apnea. The screening tools for these key inclusion criteria are described further in the Measures section.
Intervention
The personalized behavioral intervention program included the following: (A) technology learning session, (B) personalized physical activity education, (C) real-time physical activity self-monitoring, with interactive prompts, and feedback from a smartwatch, (D) phone consultation with an exercise trainer and research team members, and (E) weekly financial incentives for achieving the predetermined weekly physical activity goals. The participants came to the Exercise Medicine Unit for a technology learning session with a research team member and for a personalized physical activity educational session with a certified exercise trainer at the beginning of the intervention.
A. Technology learning session:
The participant received a Smartwatch (Moto 360) and a paired 7-inch Android tablet. The research team member demonstrated how to use the devices and involved technology, including how to charge the devices, open the apps (Motobody and Google Calendar) on the tablets, how to check steps, reminders, and notifications on the smartwatch and tablet. The participants received a booklet which provided information covered in the learning session.
B. A 2-hour Personalized physical activity educational session
with a certified exercise trainer. The educational session was designed using materials from the National Institute on Aging’s Go4Life program (https://go4life.nia.nih.gov). This training session included general rules of physical activity for older adults, an individualized physical activity prescription with training, and goal setting. The prescribed activities for the individual, which consisted of steps, strength, flexibility, and balance activities, were selected from the Go4Life program based on the participant’s needs and exercise capacity during the training session.
The initial step goal was determined by the participant’s baseline level of activity. The step goal was updated weekly during the 4-week intervention. If the step goal was met, the step goal for the following week would increase by 500 steps from the step goals of the prior week. If the step goal was not met, the step goal would remain the same for the following week. Participants were encouraged to walk as many steps as they could throughout the day. The overall goal of prescribed strength, balance, and flexibility activity was 20-30 minutes per session, 2 or 3 times per week. The participants’ most inactive periods were identified based on their profile of physical activity pattern measured by an Actiwatch 2 at the baseline. These activities were scheduled in these inactive time periods.
C. Physical activity self-monitoring:
The participants were encouraged to self-monitor their level of physical activity (steps & minutes of moderate activity) using the smartwatch (Motorola Moto 360 2nd generation) in the daytime. We developed an app (Elderfit) that was able to send sedentary alerts (>90 mins of inactivity during the day) to participants’ smartwatches that encouraged them toward their daily goals. In addition, we used the Google Calendar app to send reminders for scheduled activity, daily step goals, and inspiring messages to encourage more activity. The participants were asked to record their daily steps and other types of activities before bed every night.
D. Phone consultation:
Participants were scheduled for weekly phone consultation with the trainer to receive guidance and adjustment on activity plans. Research team members called the participants every other day during the intervention period to troubleshoot any issues related to technology, devices, and the intervention.
E. Financial incentives:
Participants who met their weekly exercise goals received $ 10/week. Meeting weekly exercise goals were defined as reaching the step goal for 4 or more days per week and completion of scheduled physical activities.
Measures:
Physical activity (PA)
was assessed objectively using Actiwatch 2 (Philips Respironics, Andover, MA), a small watch-like device worn on the non-dominant wrist to provide an objective estimate of the 24-hour physical movement and sleep-wake patterns using motion sensors. In this study, participants wore this device with the smartwatch concurrently from baseline through post-intervention to assess daytime physical activity and 24h sleep-wake patterns.
The following variables were extracted or calculated from participants’ actigraphy by the Respironics Actiware 6.0 computer algorithms: (1) physical activity: the mean level of physical activities during the daytime (counts/minute); (2) sedentary time: the mean duration of sedentary activity during the daytime (minutes/day), which was calculated based on minute-by-minute activity counts from the actigraphy and defined as < 100 counts/minute[20]; (3) sedentary activity: the mean percent of daytime that the participant spent on sedentary activity.
The Physical Activity Scale for the Elderly (PASE) was used as a subjective measure of physical activity at the baseline and post-intervention visits. The PASE is a structured questionnaire that measures the quantity and quality of an older adult’s physical activity, which include leisure-time exercise, household activity, and work-related activity in the past week.[21[ It measures the intensity, frequency, and duration of each type of activity. The total score of PASE is the sum scores of the three types of physical activity, with higher scores representing higher levels of physical activity.
We asked participants about how long they usually spent on sitting activities during the daytime before the informed consent process. More than 6 hours of sitting activities per day were considered as a sedentary lifestyle in this study.
Sleep Measures:
Sleep was our primary outcome of interest. Nocturnal sleep duration and sleep efficiency were measured using Actiwatch 2 at baseline, intervention, and postintervention. The sensitivity for wakefulness on Actiware 6.0 was set at low, which is 20 counts/min.[22] Sleep diaries were used to confirm actigraph-based nocturnal sleep.
The Insomnia Sleep Index (ISI) was used for participant screening and for subjectively assessing sleep quality at the baseline and post-intervention visits. The ISI is a 7-item instrument that was designed to assess the severity of both nighttime and daytime components of sleep. The ISI score ranges from 0 to 28, with a higher score representing worse sleep quality or more severe insomnia. Clinically, score 0–7 represented no clinically significant insomnia, score 8–14 represented subthreshold insomnia, score 15-21 represented moderate insomnia, and score 22-28 represented severe insomnia.[23] This study excluded people without clinically significant insomnia (ISI≤ 7).
Epworth Sleepiness Scale (ESS)
was used to assess the participant’s general level of daytime sleepiness and tendency to fall asleep or doze off at baseline. The ESS total score, ranging from 0 to 24, represented the sum scores of the eight individual items. Scores >10 were considered excessive daytime sleepiness (EDS).[24]
Screening for Cognitive Impairment:
The Montreal Cognitive Assessment (MoCA) was used for screening cognitive impairment before the baseline data collection. The MoCA was designed as a rapid screening instrument for mild cognitive dysfunction. It assesses different cognitive domains: attention and concentration, executive functions, memory, language, visuo-constructional skills, conceptual thinking, calculations, and orientation. The total possible score is 30 points; a score of 26 or above is considered normal[25]. Those who scored less than 26 were ineligible for participation.
Feasibility and Acceptability Measures
We collected data on the duration of the technology learning and personalized exercise training sessions, exercise prescriptions, days of wearing the smartwatch, days of correctly recording the steps from the smartwatch/tablet, weeks of achieving physical activity goals, numbers of calls with exercise trainer and research team members.
We conducted 10-20 minute semi-structured individual interviews with the 8 participants after the intervention. We asked open- and closed-ended questions on the appeal of the intervention, the acceptability and difficulty of using the involved technology, the perception of setting and meeting health goals, the perception of each intervention component, how likely they would participate again, and suggestions for improving the intervention (See Table 1 Post-Intervention Interview Guide). Interviews were audio-recorded, de-identified and transcribed for analysis.
Table 1:
Post-Intervention Interview Topic Guide
| Topic | Question | Follow Up/Probe |
|---|---|---|
| General Experience | Tell me what it is like to be in the study | What was it about the study was good or a bit challenging for you? |
| Smartwatch | Tell me about your experiences working with the smartwatch. | What did you like/dislike about the smartwatch? |
| Comfort level | Describe how comfortable you were operating the smartwatch in general. Describe your greatest challenge using the smartwatch. |
Describe how you dealt with <difficulty mentioned>. |
| Self-monitoring | How did you use the smartwatch to track your step/exercise How did checking steps affect your physical activity? |
|
| Notifications/messages | Describe how the notifications and messages affected you? What was your reaction to these notifications/messages? |
Describe how they affected your daily activity Describe <reaction>. |
| Personal activity goal | Describe your ability to reach your personal activity goals. | Describe <what was helpful>. Describe <what was challenging>. |
| Exercise training session | Describe how the exercise training session work for you? | |
| Phone calls | Describe how follow-up calls from the team worked for you? | Describe <what you like> Describe <what you dislike> |
| Financial incentives | How did the financial incentives affect you? |
Data collection:
Data were collected at baseline, during intervention, and at posttest.
After consent, participants were asked to wear the Actiwatch 2 and fill out the sleep diary for a consecutive 7 days to measure physical activity and sleep. Baseline visits were scheduled one week after the consent. At the baseline visit, the participants returned their Actiwatch and completed questionnaires for demographics, clinical characteristics, physical activity and sleep. We provided an Actiwatch 2 for participants at the end of the baseline visit to wear during the 4-week intervention to collect sleep and physical activity data during intervention. At the end of the 4-week intervention, we mailed another unit of Actiwatch 2 and a one-week sleep diary to the participant’s home and instructed the participant to take off the prior one and wear this one for 7 consecutive days. At the post-intervention visit, participants returned the two Actiwatch2 devices, filled out same questionnaires as completed at baseline, did the interview with a research team member.
Analysis:
The IBM-SPSS 24.0 was used to input and analyze the quantitative data. Descriptive analyses were used to describe the sample’s demographic and clinical characteristics. Means (standard deviations) and range are presented for continuous variables, and frequencies and percentages are presented for categorical variables. Differences in outcomes among pretest, intervention, and posttest were assessed using paired t-tests. Significance for all analyses was based on p<0.05.
The qualitative analysis software ATLAS.ti version 7.5.16. was used for data management and extraction of exemplar quotations. A content analysis method was used to analyze the data to identify patterns, consistencies, and differences throughout the interviews[26].
Results
Demographics and clinical characteristics
Eight cognitively intact (MoCA ≥26) community-dwelling older adults (mean [SD] age = 74.0 [5.4]) participated and completed the study. Among them, six were female, four were white, and four were African American. All participants had completed high school. Five participants were overweight or obese. On average, they had six chronic disease diagnoses and were taking six medications. Seven participants had subthreshold insomnia (ISI=9-14) and one had clinically moderate insomnia (ISI=19). Detailed characteristics of the sample are presented in Table 2.
Table 2.
Demographic and clinical characteristics at baseline (n=8)
| Variables | Mean (SD) | Range | N | |
|---|---|---|---|---|
| Age | 74 (5.42) | 67,83 | ||
| Female | 6 | |||
| Race | White | 4 | ||
| Black | 4 | |||
| Completed high school or some college | 8 | |||
| Body Mass Index | 28.68 (7.59) | 18,43.4 | ||
| Underweight (<18.5) | 1 | |||
| Normal (18.5-24.99) | 1 | |||
| Overweight (25-29.99) | 4 | |||
| Obese (>30) | 1 | |||
| Number of chronic conditions | 6.00 (2.14) | 3,10 | ||
| Number of Medication | 6.50 (2.98) | 2,10 | ||
| MoCA | 27.25 (1.28) | 26,29 | ||
| ESS | 4.00 (4.08) | 0,11 | ||
| EDS (>10) | 1 | |||
| ISI | 13.25 (3.06) | 9,19 | ||
| Send or receive text messages on a mobile device | 8 | |||
| Send or receive email occasionally or regularly | 6 | |||
| Used apps on a mobile device (e.g. weather, email, calendar etc.) | 5 | |||
Notes: Montreal Cognitive Assessment (MoCA), Excessive Daytime Sleepiness EDS), Epworth Sleepiness Scale (ESS)
Insomnia Sleep Index (ISI)
Evaluation of outcomes (Table 3)
Table 3.
Paired comparisons of evaluated outcomes at baseline with within intervention and post-intervention
| Baseline (1week) T1 |
Intervention (4weeks) T2 |
Post-intervention (1 week) T3 |
Intervention vs. Baseline | Post-intervention vs. Baseline | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | (SD) | Mean | (SD) | Mean | (SD) | D | 95%CI | P | D | 95%CI | P | |
| Daytime physical activity (PA) | ||||||||||||
| Sedentary activity (%) | 46.8 | (13.6) | 41.1 | (10.5) | 38.8 | (13.5) | −5.7 | −10.1,−1.3 | 0.019 | −8.0 | −12.1,−3.6 | 0.003 |
| Sedentary time (minutes/day) | 428.6 | (115.6) | 386.3 | (100.1) | 341.2 | (129.2) | −42.3 | −79.4,−5.2 | 0.032 | −87.4 | −133.5,−30.4 | 0.007 |
| Overall physical activity (count/min) | 245.5 | (74.4) | 263.8 | (68.9) | 287.0 | (72.5) | 18.3 | −53.8,84.1 | 0.610 | 41.5 | 8.8,74.4 | 0.020 |
| PASE | 73.8 | (45.5) | 169.9 | (109.4) | 96.2 | 15.8,176.5 | 0.025 | |||||
| Nocturnal sleep | ||||||||||||
| Sleep duration (minutes/night) | 393.3 | (74.8) | 390.1 | (47.2) | 406.3 | (58.7) | 3.2 | −39.1,51.4 | 0.749 | 13.0 | −45.6,71.6 | 0.616 |
| Sleep Efficiency (%) | 79.3 | (11.5) | 80.7 | (6.7) | 80.5 | (7.6) | 1.4 | −3.7,8.1 | 0.395 | 1.2 | xym4.9,7.2 | 0.669 |
| ISI | 13.3 | (3.1) | 12.9 | (2.8) | 0.4 | −0.4,1.1 | 0.285 | |||||
Notes: SD denotes standard deviation, D denotes mean differences, 95%CI denotes 95% confidence interval. Physical Activity Scale for the Elderly (PASE), Insomnia Sleep Index (ISI).
Increased physical activity:
As measured by actigraphy and compared with their baseline, the participants’ sedentary time decreased both during the intervention (D [mean difference] = −42.3 minutes, 95%CI[confidence interval] = [−79.4,−5.2], P=0.03) and at post-intervention (D =−87.4 minutes, 95%CI=[−133.5,−30.4], P=<0.01). The participants spent less of their waking time on sedentary activities both during the intervention (D=−5.7%, 95%CI=[−10.1,−1.3], P=0.019) and at posttest (D =−8.0%, 95%CI=[−12.1,−3.6], P=<0.01). On average, the participants’ level of daytime physical activity increased significantly at posttest (D = 41.5 counts/minutes, 95% CI = [8.8, 74.4], P=0.02). The self-reported physical activity (PASE score) increased at the posttest (D=96.2, 95% CI= [15.8,176.5], P=.025).
No change in sleep:
No significant differences in sleep measures (ActiGraph scored sleep duration, sleep efficiency, and ISI) were found between baseline, intervention, and post-intervention.
Feasibility
The delivery and receipt of the intervention
All participants attended the technology learning session (10-15 minutes), practiced what they learned, and showed the research team member how to use the devices and technology by the end of the session.
All participants received the personalized exercise prescription/plan and personalized exercise training during the educational session, which ranged from 2 to 3 hours. All participants were prescribed with steps, strength, flexibility, and balance movements. The initial step goal was determined by the participant’s baseline level of activity and ranged from 3,000 to 6,000 steps per day. Seven to twelve resistance, balance, and flexibility movements were prescribed in the initial exercise plans. The trainer taught the participants all prescribed movements. The participants demonstrated that they were able to perform these movement on their own during the educational session and understood the amounts (sets and reps) and frequency of exercise prescribed. A consultation phone call was scheduled during the second week of intervention at the end of the training session.
Enactment of the intervention:
Out of the 28 possible days (4 weeks) of wearing the smartwatch and correctly recording the steps from the smartwatch/tablet, the participants wore the smartwatch for an average of 25.2 days (SD=1.5) and correctly recorded their steps for 23.6 days (SD=1.7); 4, 1, and 3 participants had 2, 3, 4 phone consultations with the exercise trainer, respectively. The participants received 7-12 phone calls from the research team staff during the 4-week intervention; 6 and 7 older adults successfully achieved their weekly physical activity goals for 4 weeks and 2 weeks, respectively.
During the interview session, five participants reported difficulty in using the study-involved technology (such as, unable to sync the Moto 360 with the tablet) at the beginning of the intervention. For example, a 74-year old female participant stated:
I am not a tech-savvy person. I was a little depressed at first. I forgot to charge the watch and it ran out of battery. I charged it, but could not sync it with the tablet after I turned it on again. I know you provided information when we met. But you won’t know what the problem is until you have it.
All participants reported that setting personal goal was important for achieving their personal step goals. For example, one 78-year-old woman stated:
The goal kept me focused. I just kinda stick to it. I knew I had to take more steps.
I became addicted to checking my steps because I wanted to reach my goals.
All participants reported that the technology (self-monitoring, activity reminder, sedentary reminder, or inspiration quotes) was helpful in motivating them to participate in physical activity and to achieve their weekly physical activity goals. One 83-year-old men stated: “When I see it light up, and then it says, time to walk. I was laying down then, so then it said I had to get up and start walking. So I got up and walked.” Another 74-year-old women said: “I’d be looking during the day to see how many steps I had and if I could get my heart activity up. I felt good when I saw I was adding 1,500 to 2,000 steps after a walk…”. The phone calls with the research team staff helped the participants learn how to operate the device, use the technology, correctly record the steps and other activities, stay with the activity plan and weekly goals. “The phone calls were really helpful. I mean it is a whole lot of things to do. The phone calls helped me stay on track with what I need to do and what my goals are, etc.” Six out of 8 participants expressed that the weekly financial incentives did not motivate them to achieve the daily or weekly physical activity goals. For example, one 83-year man said: “I am delighted. But it did not make that much of an impact on me. I just want to be more active…“All participants expressed that they would continue this program if the team offers it again.
Discussion
This personalized behavioral intervention program for older adults was feasible and acceptable in a small sample of cognitively intact, sedentary older adults. It was also feasible for older adults to use mHealth technologies involved in the study. The intervention provided favorable changes in the levels of physical activity as characterized by decreased sedentary time during the intervention and at post-intervention and increased physical activity at postintervention. We did not find significant changes in sleep either during the intervention or at post-intervention.
Feasibility and acceptability findings showed that participants overall were highly engaged and reported enjoying the intervention. In our study, participants wore the Moto 360 on average for 25.2 out of the 28 intervention days (90%), which is comparable to findings from other studies using wearable activity trackers and mHealth technology. For example, 90% wearing times were found in middle-aged adults and 94% in older adults. [27,28] In addition, participants correctly recorded their steps from the Moto 360 tablets for 83% (23.6 days) of the intervention, compared with 91% in a group of middle-aged breast cancer survivors[29]. Considering the participants’ ages (mean age 73), we assumed that they would encounter more difficulties than younger people in using the mHeath technology involved in the study and carrying out the activity plan. Therefore, we designed the program to provide participants with more frequent counseling phone calls with the exercise trainer (each week) and with the research team members (every other day) during the intervention than other mHealth-supported physical interventions (weekly or biweekly phone calls). As a result, all participants completed phone consultations with the exercise trainer every other week and 37.5% (3 out of 8) completed weekly phone consultations with the exercise trainer. Almost all the participants had 2-3 phone calls with the research team each week. In addition, 75% of participants managed to achieve their weekly physical activity goals for 4 weeks.
Even though most of our participants had experience using smartphone apps, 5 out of 8 participants encountered some technological issues, especially during the first week of intervention, such as inability to connect to Wi-Fi and Bluetooth, to sync the watch and tablet, or to charge the devices. Most of these issues were resolved during phone calls with the research team. We only had one participant come back to fix a Bluetooth-related connection issue. After the one-week learning phase, participants reported that the Moto 360, tablet, and apps were user-friendly. To successfully deliver mHealth-supported interventions in older adult populations, we suggest that researchers add troubleshooting strategies for potentially encountered technology issues to the technology learning sessions, and to assist participants with technology use through frequent communications (e.g., coaching calls) during the technology learning phase ( e.g., first two weeks).
A few recent studies reported promising results using wearable or mHealth to promote physical activity in older adults.[27,30] For example, Lyons et al. (2017) pilot tested the preliminary effects of an intervention combining a wearable physical activity monitor, tablet device, and phone counseling among adults on physical activity in a group of adults mean aged 61 (younger than our sample) and reported more walking and less sitting time after the intervention. Jang et al. (2018) increased fitness levels in a sample of Korean older adults using a similar intervention.[12] Compared with these interventions, we implemented more mHealth strategies, such as goal setting, activity reminders, and motivation quotes, to promote physical activity. We also provided personalized physical activity training, exercise prescription, and financial incentives, which were not included in prior mHealth studies.
In our qualitative interview, participants expressed that self-monitoring using the smartwatch and goal setting helped them engage in physical activity and achieve their personal exercise/step goals. However, six participants denied that the financial incentives ($10/week) motivated them to achieve their daily or weekly activity goals. This seems contradictory with findings from prior studies. For example, a systematic review of financial incentives for physical activity reviewed twelve randomized trials and found physical–activity–related financial incentives had positive, short-term effects on participants’ physical activity and fitness.[31] Similarly, a systematic review and meta-analysis of 11 studies reported that financial incentives increased exercise intervention participation for up to 6 months.[32] In regard to the study methods, we used semi-structured in-person interviews, while the studies included in the two reviews used quantitative methods. It is possible that the participants might have felt uncomfortable and were reluctant to discuss during the interview how monetary incentive motivated their physical activity.
We did not find significantly improved sleep outcomes either during the intervention or at post-intervention. This may be due the relatively short intervention period and small sample size. The duration of exercise interventions that reported positive changes in sleep outcomes usually ranged from 10 to 24 weeks.[16,33] Due to the pilot nature of our study, our intervention was only 4 weeks, which may not be long enough to provide significant changes in sleep. Also the sample size may be relatively small to detect significant changes.
Although no significant findings on sleep were noted in our study, the intervention showed efficacy in promoting participants’ level of physical activity, as evidenced by reduced sedentary behaviors and increased physical activity during or after the intervention. These favorable results on physical activity were expected since the sample was sedentary (>6 hours of daily sitting time) older adults and the nature of the intervention was to promote physical activity. Physical activity provides multiple long-term health benefits in aging populations such as reduced risks for diabetes and cardiovascular diseases, sleep promotion, and protective effects on cognitive function.[5,6] If physical activity behavioral changes led by the intervention was sustained in older adults, then the intervention provided an innovative way to promote active and healthy aging.
The rigor of this study was limited by the pilot design, such as a small sample size, a lack of a control group, and a relatively short intervention period. We now have more competence to perform a study with a larger sample size and a control group. Maintenance of a behavioral change could take more than 6 months to assess.[34] Although favorable physical activity behavioral changes were noticed within and immediately after the 4-week intervention, we are unsure whether the behavioral changes could be sustained in a longer intervention period. We do not know whether a longer intervention would result in improvement in sleep either. Also, we did not access self-efficacy before and after the intervention and did not know whether the study intervention improved self-efficacy, which could lead to increased physical activity. Also, since it was a multi-component behavioral intervention, we could not determine the feasibility, acceptability, or efficacy of any individual intervention component. For example, determining the role of financial incentives in physical activity behavioral change is important since most of our participants denied the effects of financial incentives on physical activity behavioral change. However, due to the feature of this multi-component intervention and the lack of control groups, we lacked the capacity to know whether financial incentives made a difference in our outcome data.
In conclusion, our findings supported that a personalized behavioral intervention implemented with mHealth technology was feasible and acceptable in a sample of cognitively intact, sedentary older adults. This pilot study also demonstrated some positive physical activity behavioral changes. mHealth technology has not yet been commonly implemented to promote health in older adult populations. This pilot intervention provides insight into a potentially novel approach involving mHealth technology for older adults to achieve and maintain active lives. The use of technology has revolutionized the way in which nurses meet the needs of their older patients, their families, and communities. With the transition from healthcare being provided primarily in acute care settings to the community-based settings, nurses may not have access to reliable, evidence-based practices to promote health behaviors such as sleep and activity in older adults. This study addressed the need for information for nurses regarding the feasibility and efficacy of using mHealth technology to fill this gap. Future studies are needed to examine these interventions with a control group and a larger sample powered to detect significant changes in physical activity behavior and health outcomes such as sleep for a longer period (e.g., 24 weeks). Additionally, the cost and scalability of the intervention need to be explored in future research.
Highlights:
The personalized behavioral intervention implementing mHealth technologies improved physical activity in a sample of cognitively intact, sedentary older adults.
This study provides insight into a potentially novel approach involving mHealth technology for older adults to achieve and maintain active lives.
The cost and scalability of the intervention need to be explored in future research.
Acknowledgments
Funding sources: NIH/NHLBI (T32 HL07953); NINR (K99NR016484); University of Pennsylvania Junior Investigator Preliminary/Feasibility Grant
Footnotes
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