More than 50% of adults age 65 and older with osteoarthritis experience bothersome sleep symptoms such as disrupted sleep and insomnia (Foley, Ancoli-Israel, Britz, & Walsh, 2004; Kirwan et al., 2005). Arthritis pain has been implicated in sleep problems (Power, Perruccio, & Badley, 2005), and exercise interventions have been shown to improve arthritis pain and sleep quality (Regnaux et al., 2015). Unfortunately, older adults with osteoarthritis are mainly sedentary, and few achieve the recommended U.S. national guidelines of at least 150 minutes of moderate aerobic activity every week (Lee et al., 2015). In addition to physical activity, acceptance and self-management practices are other behavioral approaches that can improve sleep (Taibi & Vitiello, 2011; Vitiello, Rybarczyk, Von Korff, & Stepanski, 2009), but are underutilized in this population.
Commercially available activity tracking devices coupled with mobile health (mHealth) technologies are ubiquitous, functionally sophisticated, and have the capacity to provide timely feedback, personalization, and interactivity, all of which are important to support sustainable behavioral change. Digital reinforcers offered by mHealth technologies are especially pertinent for older populations because they target behaviors, bypass memory problems, and alleviate apprehension caused by in-person contacts. Previous studies have shown that wearable activity trackers are feasible and acceptable for use for behavioral activation in the general older adult population (Nguyen, Gill, Wolpin, Steele, & Benditt, 2009; Vidoni et al., 2016). However, little is known about the feasibility of this technology to elicit behavioral changes, promote self-management, and improve sleep in older adults with osteoarthritis and sleep disturbance. Comorbidities and functional limitations might interfere with older adults' willingness to adopt wearable technologies (Parker, Jessel, Richardson, & Reid, 2013), and thus affect their self-management behaviors. Another common methodological limitation in prior studies is that changes in behavioral measures were examined only at the end of treatment. Although conceptually sound, this approach focuses on short-term efficacy and might imperfectly predict longer-term behavioral maintenance.
To address these gaps, the current authors conducted a pilot study that evaluated the feasibility and preliminary efficacy of a mHealth self-management intervention aimed at improving sleep among older adults with osteoarthritis and disturbed sleep. Feasibility was determined from number of participants eligible, enrolled, and retained. Primary efficacy outcomes were Insomnia Severity Index (ISI) score and sleep actigraphy (i.e., total sleep time, sleep efficiency) measures. Overall step count, self-efficacy (SEff), and acceptance of sleep difficulties (ASD) were theoretical mechanistic variables of physical and psychological factors involved in self-management (Figure A, available in the online version of this article).
This was a prospective one-group pre-/posttest study to test the feasibility and efficacy of a multidimensional intervention, the components of which were wearable activity tracking devices, personalized texts, and motivational interviews. Participants received a Fitbit Charge 2™ and were given access to the Fitbit mobile application (app) to facilitate self-monitoring. The study occurred over 19 weeks. In the first 14 weeks, study staff maintained routine contact with participants, including personalized reminders to sync their activity device with the app. During the 5-week postintervention follow-up phase, participants used their activity device and mobile app without support from study staff. University of Washington Institutional Review Board approval of the study protocol was obtained.
Study participants were recruited between May 2017 and March 2018 through direct mailings, presentations, and flyers in retirement communities and other places frequented by older adults in Seattle, Washington (Figure 1). Eligibility criteria included: (a) age 65 or older, (b) a diagnosis of osteoarthritis, (c) having a smartphone, (d) having physical activity levels below the U.S. Department of Health and Human Services recommended guidelines evaluated using the Rapid Assessment of Physical Activity scale (Topolski et al., 2006), and (e) having an Insomnia Severity Index score >12 (Bastien, Vallières, & Morin, 2001). Exclusion criteria were: (a) an acute injury associated with hip or knee pain, (b) inability to stand up without assistance, (c) a Memory Impairment Screen for Telephone (Lipton et al., 2003) score <4, (d) severe hearing or visual impairment, and (e) an acute episode or change in the treatment of psychiatric problems within the past 3 months. Eligible participants were compensated up to $150.
Flow chart of participant recruitment.
Wearable Activity Tracking Devices. The Fitbit Charge 2 is a heart rate and fitness wristband used to deliver the intervention and measure daily step counts. This device was selected because (a) it can accurately sense step count in older adults (Burton et al., 2018); (b) it has an optical heart rate sensor that monitors and documents heart rate over time, thus allowing for collection of objective wear time; and (c) its technology permitted the retrieval of near real-time information exchange between the Fitbit app and iCardia research platform (described below), thus creating an optimal medium for a personalized intervention.
Personalized Texts. The adaptive personalized intervention was informed by the control theory framework (Carver & Scheier, 1982), which postulates that to initiate and maintain a desired behavior, such as increased physical activity, behavioral change techniques (e.g., specific goal setting, feedback on performance, self-monitoring of behavior, review of behavioral goals) should be provided.
Following a formative process that included an expert panel, end users, and literature review, targeted behavioral change text messages were developed that provided information and motivational reinforcement regarding participant's past and concurrent physical activity progress. Participants received weekly targeted and personalized text messages that provided motivational feedback according to their adaptive step count attainment. The step count attainment was determined at three time points and calculated as follows. Over the first week, average step count was calculated and constituted participants' baseline. Over the next 1 month, departure from that baseline was calculated each week and categorized according to the extent of percentage difference between baseline and that week's average step count. Categories included “decline of more than 5% from baseline,” “maintenance within 5%,” and “increase of more than 5% from baseline.” Weekly categories (i.e., decline, maintenance, increase) informed content of the weekly messages and provided reinforcement for those who increased or maintained, and encouragement for those who declined (Table A, available in the online version of this article). After 1 month, a new baseline count was calculated according to the average step count over the first 1 month. Accordingly, subsequent weekly text messages over the second month reacted to the percentage differences between weekly step counts and a recalibrated 1-month baseline. Over the third month, this procedure was repeated. Upon completion of the intervention, a total of 12 motivational text messages were sent to participants.
Text messages designed for the Mobile Interventional Physical Activity Targeted Intervention (MobMPATI) study
Motivational Interviews. In addition to text messages, three telephone calls at Weeks 1, 5, and 9 that were informed by motivational interviewing principles (Georgopoulou, Prothero, Lempp, Galloway, & Sturt, 2016) were scheduled with participants to discuss their goals and debrief strategies for facilitating behavioral change. These conversations focused on specific goal setting and action planning behavioral change techniques derived from the control theory framework. All telephone calls were scripted, recorded, and randomly sampled as to ensure the intervention fidelity.
Remote and Self-Monitoring. The iCardia research platform was used to retrieve data from the Fitbit cloud server, present these data on a user-friendly dashboard, and send personalized text messages to the end user smartphones (Kitsiou et al., 2017). In addition to motivational messages, reminders to sync the Fitbit and charge its battery were sent when participants failed to sync for more than 3 days and/or tracker battery was low. On average, five reminders were sent to participants over the first 14 weeks of the study. Participants were also encouraged to maintain sleep diaries (described below) over Weeks 1, 5, and 9 to target acceptance-based behaviors.
Training and Troubleshooting
Participants received a basic orientation when enrolled in the study, assistance with troubleshooting for the next 14 weeks, and limited assistance after that time. Approximately one half of enrolled individuals called research staff with questions and challenges solvable by telephone. On three occasions, an in-person visit was needed to address technical issues mainly related to failure to sync. In one instance, a tracker had to be replaced by the manufacturer due to technical failure. An interventionist, who delivered telephone calls, received training in motivational interviewing.
Data Collection Procedures
A study coordinator collected data during one-on-one interviews and remotely using standardized procedures at three time points. At the baseline in-person assessment, the study coordinator administered clinical and demographic questionnaires and provided actigraphs (described below). The second and third assessments at Weeks 14 and 19 occurred either in-person or remotely using a Research Electronic Data Capture (REDCap) online survey feature. During the second and third data collection points, participants were also provided with actigraphs, which they were instructed to wear for 1 week and mail back to the research office.
Questionnaires. Baseline questionnaires included the following demographic variables: self-reported age, sex, educational attainment, race, and ethnicity. Pain was measured using the Graded Chronic Pain Scale (GCPS), a seven-item scale that assesses two dimensions of overall chronic pain severity: pain intensity and pain-related disability. Higher GCPS scores indicate worse symptoms. The scale has high internal consistency (IC) from 0.81 to 0.89 for subscales and global scores in patients with chronic musculoskeletal pain (Salaffi, Stancati, & Grassi, 2006). SEff to manage chronic conditions was measured using the Self-Efficacy for Managing Chronic Disease 6-item scale (Lorig, Sobel, Ritter, Laurent, & Hobbs, 2001). The items cover several domains that are common across many chronic problems, including symptom control, role function, emotional functioning, and communicating with clinicians. A higher SEff score indicates better efficacy. The scale has IC of 0.88 to 0.95 (Lorig et al., 2001). The ISI is a seven-item questionnaire that assesses insomnia symptoms over the past 1 week. The ISI is scored from 0 to 28, with higher scores indicating more severe insomnia symptoms. Internal consistency of the ISI is 0.74 (Bastien et al., 2001). ASD was measured using the Sleep Problem Acceptance Questionnaire, an eight-item scale that assesses activity engagement and willingness to accept insomnia symptoms. The scale has IC of 0.73 to 0.89 across subscales (Bothelius, Jernelöv, Fredrikson, McCracken, & Kaldo, 2015). A higher ASD score indicates better acceptance.
Sleep Diary and Actigraphy. Participants were asked to complete sleep diaries and wear actigraphs at Weeks 1, 5, and 9. Sleep diaries collected time in bed (TIB), sleep onset latency (SOL), wake after sleep onset (WASO), and overall sleep quality (a 1 to 9 scale, where 1 = terrible and 9 = great), which were used to compute total sleep time (TST), sleep efficiency (SE), and overall sleep quality (SQual). TST was calculated as TIB – SOL – WASO – time between awakening and arising. SE was calculated as TST/TIB×100, with a higher value indicating better efficiency.
Participants wore a Phillips/Respironics AW64 actigraph on their non-dominant wrist, which was used to collect objective sleep measurements. The actigraphs recorded activity counts in 1-minute epochs, and each epoch was scored as being asleep or awake using the medium sensitivity threshold. Data were analyzed using Respironics Actiware version 6.0.9. Bedtime and rise-time were entered in the software based on participants' sleep diary entries. The calculated sleep variables from the Actiware software included TST and SE measures.
De-identified data from REDCap and iCardia were exported and analyzed using Stata 15.0 in two phases. First, univariate analyses were conducted to summarize participants' baseline characteristics and follow-up measures. Mixed effect models were used next because of the time repeated structure of the data. The advantages of the technique are that it is capable of handling nested observations (e.g., multiple observations for each individual) and missing values (Rabe-Hesketh & Skrondal, 2008). Average wear time and step counts over baseline (Week 1), intervention (Weeks 2 to 14), and postintervention (Weeks 15 to 19), and ISI, TST, SE, GCPS, SEff, ASD, and SQual scores at Weeks 1, 14, and 19 were entered as dependent variables in separate models adjusted for age and gender. Data collection occasions (1, 2, or 3) were also entered in the models to account for change over time. The adjusted main effects for time for each of the variables were tested for significance and reported as estimates and 95% confidence intervals (CI).
Of the 46 potential candidates screened, 24 were eligible, consented to the study, and provided baseline data. Of the 22 candidates who were ineligible, 50% were excluded because they were “very active” (Figure 1). One participant could not finish the intervention due to development of a major depressive episode, and one participant did not contribute Week 19 sleep data due to a scheduled surgical procedure, which left a total of 22 participants who completed the full protocol, resulting in a 96% retention rate. Table 1 summarizes the baseline data. Mean age was 71 years (SD = 4 years); the majority of participants were White (96%) and 70% were women. Over the first week of the study, participants wore the Fitbit on average 20.4 (SD = 2.2) hours per day and took 5,016 (SD = 2,524) steps. Average ISI score was 14.6 (SD = 5.7), which was consistent with moderate insomnia (Bastien et al., 2001). However, actigraphy measured an average TST of 7.1 (SD = 1.2) hours and mean SE of 84.2% (SD = 8.1%).
Participant Baseline Characteristics (N = 24)
Table 2 summarizes changes in self-reported and objective measures over time using descriptive statistics and mixed effect models. Overall, there was a significant longitudinal effect for time for one primary outcome, one mechanistic measure, and one secondary outcome. Specifically, ISI scores improved by an average of 1.2 points (95% CI [−2.43, −0.05]; p = 0.04), such that at Week 19, the average ISI measure was 11.8 (SD = 6.3), which was consistent with subthreshold insomnia (Bastien et al., 2001). Similarly, ASD improved by an average of 2.5 points (95% CI [0.9, 4.1]; p = 0.002) over 19 weeks. Self-reported SQual collected from sleep diaries also improved by an average of 0.3 points (95% CI [0.02, 0.58]; p = 0.04) over 19 weeks. Insignificant longitudinal changes were observed for the actigraphy measures, step count, SEff, pain, and other sleep diary variables. Fitbit wear time remained consistently high and did not decline even after cessation of structured memory cues (Table 2).
Descriptive and Mixed Effects Model Inferential Estimates of Longitudinal Changes in Behavioral and Health Outcomes (N = 24)
Overall, the current study showed that the mHealth intervention, designed to improve sleep in older adults with osteoarthritis, augmented through wearable devices and motivational interviews, was feasible and produced promising, albeit small, preliminary efficacy findings in terms of improved sleep. Enrollment and retention were successful, with the study experiencing only one drop-out during the intervention phase due to reasons unrelated to the study protocol. The current study showed retention rates similar or even better than other studies of mHealth behavioral interventions in adults. A recent review of 23 clinical trials that examined effectiveness of mHealth solutions to achieve behavioral change in adults showed an average 65% retention rate in intervention groups (Zhao, Freeman, & Li, 2016), compared to the current study's 96% retention rate. The authors hypothesize that participants valued receiving personalized communication from the research team in the form of weekly text messages and monthly telephone calls and that this contributed to the high retention rate.
Fitbit wear time, which was used as a proxy for adherence to wearing and syncing the Fitbit device with the mobile app, was high; participants wore the activity trackers most of the time without memory cues. Encouraging signals with respect to improvement in sleep measures and potential mechanisms that stimulated the change support further research on the mHealth intervention for sleep disturbance in older adults with osteoarthritis.
To the current authors' knowledge, only two prior studies used wearable technology and personalized texts to promote physical activity in community-dwelling individuals 65 and older. One study reported an increment of approximately 2,220 steps per day after an 8-week intervention that used daily actigraphy self-monitoring, goal setting, and biweekly counseling (Vidoni et al., 2016). Another study had a paradoxical decrease in approximately 1,000 steps per day after a 6-month intervention in patients with chronic obstructive pulmonary disease that used daily self-monitoring and weekly tailored reinforcement texts (Nguyen et al., 2009). The current study's modest, albeit insignificant, increase in step count falls between these two reports. One possible explanation is that the preprogrammed weekly text messages were insufficient to increase physical activity in this relatively sedentary population with bothersome pain symptoms because higher message intensity, personalization, and contextualization is needed. This conclusion is supported by exit interviews in which participants reflected on the intervention. Overall, participants believed texts should be “less canned,” more person-specific, and should be contextualized, in terms of frequency and content, in light of potential reasons for their setbacks in weekly progress.
Small, but statistically significant, improvements in ISI, SQual, and ASD scores echo growing research interest in acceptance-based processes for treating insomnia symptoms. In behavioral medicine, there is growing evidence that acceptance of symptoms while pursuing valued activities may be a more effective approach to chronic disease management than resistance or avoidance (McCracken, 2011). Change in ASD, observed in the current study, indicated that parts of the intervention, such as sleep self-monitoring, might have had positive effects on psychological factors that, in turn, improved insomnia symptoms and SQual. Indeed, in chronic pain research, evidence showed that low acceptance is correlated with psychological distress and worse functioning (Vowles, McCracken, & O'Brien, 2011). As such, one cautious interpretation is that improvement in acceptance might also improve sleep through reduced psychological distress, as shown in a recent study (Lau, Leung, Wing, & Lee, 2018).
The study has some limitations. The effects of the intervention, although promising, must be interpreted with caution as the study did not include a control group. Eligible participants needed to have a smartphone. Although nowadays almost four in 10 older adults own a smartphone (Anderson & Perrin, 2017), those who do not may respond differently to the intervention. In addition, this study was subject to the common limitations of research involving self-report. Although back-filling of sleep diaries was reduced by collecting these forms immediately after measurement occasions, there was still the potential for delayed completion of the forms.
It is feasible to use a technology-enhanced behavioral intervention for older adults with osteoarthritis to improve their sleep experience. This finding adds to a growing literature that suggests older adults might reap benefits from mHealth interventions. Results from this quasi-experimental study highlight a need for further research through a randomized control trial.
- Anderson, M. & Perrin, A. (2017, May17). Tech adoption climbs among older adults. Retrieved from http://www.pewinternet.org/2017/05/17/tech-adoption-climbs-among-older-adults
- Bastien, C.H., Vallières, A. & Morin, C.M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine, 2, 297–307. doi:10.1016/S1389-9457(00)00065-4 [CrossRef]
- Bothelius, K., Jernelöv, S., Fredrikson, M., McCracken, L.M. & Kaldo, V. (2015). Measuring acceptance of sleep difficulties: The development of the Sleep Problem Acceptance Questionnaire. Sleep, 38, 1815–1822. doi:10.5665/sleep.5170 [CrossRef]
- Burton, E., Hill, K.D., Lautenschlager, N.T., Thøgersen-Ntoumani, C., Lewin, G., Boyle, E. & Howie, E. (2018). Reliability and validity of two fitness tracker devices in the laboratory and home environment for older community-dwelling people. BMC Geriatrics, 18, 103. doi:10.1186/s12877-018-0793-4 [CrossRef]
- Carver, C.S. & Scheier, M.F. (1982). Control theory: A useful conceptual framework for personality-social, clinical, and health psychology. Psychology Bulletin, 92, 111–135. doi:10.1037/0033-2909.92.1.111 [CrossRef]
- Foley, S., Ancoli-Israel, S., Britz, P. & Walsh, J. (2004). Sleep disturbances and chronic disease in older adults: Results of the 2003 National Sleep Foundation Sleep in America Survey. Journal of Psychosomatic Research, 56, 495–502. doi:10.1016/j.jpsychores.2004.02.010 [CrossRef]
- Georgopoulou, S., Prothero, L., Lempp, H., Galloway, J. & Sturt, J. (2016). Motivational interviewing: Relevance in the treatment of rheumatoid arthritis?Rheumatology, 55, 1348–1356. doi:10.1093/rheumatology/kev379 [CrossRef]
- Kirwan, J.R., Hewlett, S.E., Heiberg, T., Hughes, R.A., Carr, M., Hehir, M. & Wale, J. (2005). Incorporating the patient perspective into outcomes assessment in rheumatoid arthritis–Progress at OMERACT 7. Journal of Rheumatology, 32, 2250–2256.
- Kitsiou, S., Thomas, M., Marai, G.E., Maglaveras, N., Kondos, G., Arena, R. & Gerber, B. (2017). Development of an innovative mHealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI) (pp. 133–136). New York, NY: IEEE. doi:10.1109/BHI.2017.7897223 [CrossRef]
- Lau, W.K.W., Leung, M.K., Wing, Y.K. & Lee, T.M.C. (2018). Potential mechanisms of mindfulness in improving sleep and distress. Mindfulness, 9, 547–555. doi:10.1007/s12671-017-0796-9 [CrossRef]
- Lee, J., Chang, R.W., Ehrlich-Jones, L., Kwoh, C.K., Nevitt, M., Semanik, P.A. & Dunlop, D.D. (2015). Sedentary behavior and physical function: Objective evidence from the Osteoarthritis Initiative. Arthritis Care & Research, 67, 366–373. doi:10.1002/acr.22432 [CrossRef]
- Lipton, R.B., Katz, M.J., Kuslansky, G., Sliwinski, M.J., Stewart, W.F., Verghese, J. & Buschke, H. (2003). Screening for dementia by telephone using the memory impairment screen. Journal of the American Geriatrics Society, 51, 1382–1390. doi:10.1046/j.1532-5415.2003.51455.x [CrossRef]
- Lorig, K.R., Sobel, D.S., Ritter, P.L., Laurent, D. & Hobbs, M. (2001). Effect of a self-management program on patients with chronic disease. Effective Clinical Practice, 4, 256–262.
- McCracken, L. (2011). Mindfulness and acceptance in behavioral medicine: Current theory and practice. Oakland, CA: New Harbinger Publications.
- Nguyen, H.Q., Gill, D.P., Wolpin, S., Steele, B.G. & Benditt, J.O. (2009). Pilot study of a cell phone-based exercise persistence intervention post-rehabilitation for COPD. International Journal of Chronic Obstructive Pulmonary Disease, 4, 301–313. doi:10.2147/COPD.S6643 [CrossRef]
- Parker, S.J., Jessel, S., Richardson, J.E. & Reid, M.C. (2013). Older adults are mobile too! Identifying the barriers and facilitators to older adults' use of mHealth for pain management. BMC Geriatrics, 13, 43. doi:10.1186/1471-2318-13-43 [CrossRef]
- Power, J.D., Perruccio, A.V. & Badley, E.M. (2005). Pain as a mediator of sleep problems in arthritis and other chronic conditions. Arthritis and Rheumatism, 53, 911–919. doi:10.1002/art.21584 [CrossRef]
- Rabe-Hesketh, S. & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata (2nd ed.). College Station, TX: Stata Press.
- Regnaux, J.P., Lefevre-Colau, M.M., Trinquart, L., Nguyen, C., Boutron, I., Brosseau, L. & Ravaud, P. (2015). High-intensity versus low-intensity physical activity or exercise in people with hip or knee osteoarthritis. Cochrane Database of Systematic Reviews, 29, CD010203. doi:10.1002/14651858.CD010203.pub2 [CrossRef]
- Salaffi, F., Stancati, A. & Grassi, W. (2006). Reliability and validity of the Italian version of the Chronic Pain Grade questionnaire in patients with musculoskeletal disorders. Clinical Rheumatology, 25, 619–631. doi:10.1007/s10067-005-0140-y [CrossRef]
- Taibi, D.M. & Vitiello, M.V. (2011). A pilot study of gentle yoga for sleep disturbance in women with osteoarthritis. Sleep Medicine, 12, 512–517. doi:10.1016/j.sleep.2010.09.016 [CrossRef]
- Topolski, T.D., LoGerfo, J., Patrick, D.L., Williams, B., Walwick, J. & Patrick, M.B. (2006). The Rapid Assessment of Physical Activity (RAPA) among older adults. Preventing Chronic Disease, 3, A118.
- Vidoni, E.D., Watts, A.S., Burns, J.M., Greer, C.S., Graves, R.S., Van Sciver, A. & Bieberle, N.A. (2016). Feasibility of a memory clinic-based physical activity prescription program. Journal of Alzheimer's Disease, 53, 161–170. doi:10.3233/JAD-160158 [CrossRef]
- Vitiello, M.V., Rybarczyk, B., Von Korff, M. & Stepanski, E.J. (2009). Cognitive behavioral therapy for insomnia improves sleep and decreases pain in older adults with co-morbid insomnia and osteoarthritis. Journal of Clinical Sleep Medicine, 5, 355–362.
- Vowles, K.E., McCracken, L.M. & O'Brien, J.Z. (2011). Acceptance and values-based action in chronic pain: A three-year follow-up analysis of treatment effectiveness and process. Behaviour Research and Therapy, 49, 748–755. doi:10.1016/j.brat.2011.08.002 [CrossRef]
- Zhao, J., Freeman, B. & Li, M. (2016). Can mobile phone apps influence people's health behavior change? An evidence review. Journal of Medical Internet Research, 18(11), e287. doi:10.2196/jmir.5692 [CrossRef]
Participant Baseline Characteristics (N = 24)
| Age (years)||71 (4)|
| Female (n [%])||17 (70)|
| White (n [%])||23 (96)|
| Post college education (n [%])||7 (30)|
| Wear time (hours per day)||20.4 (2.2)|
| Step count (steps per day)||5, 016 (2,524)|
| Insomnia Severity Indexa||14.6 (5.7)|
| Graded Chronic Pain Scaleb||4.7 (2)|
| Self-Efficacyc||6.7 (1.7)|
| Acceptance of Sleep Difficultiesd||24 (8.3)|
| Total sleep time (hours)||7.1 (1.2)|
| Sleep efficiency (%)||84.2 (8.1)|
| Total sleep time (hours)||6.8 (1.3)|
| Sleep efficiency (%)||78.3 (9.9)|
| Sleep qualitye||5.4 (1.2)|
Descriptive and Mixed Effects Model Inferential Estimatesa of Longitudinal Changes in Behavioral and Health Outcomes (N = 24)
|Measure||Mean (SD)||Estimate [95% CI]||p Value|
|Baseline (n = 24)||Week 14 (n = 23)||Week 19 (n = 22)|
| Wear time (hours per day)||20.4 (2.2)||20.9 (2.3)||20.9 (2.4)||0.4 [−0.4, 1.1]||0.3|
| Step count (steps per day)||5,016 (2,524)||5,286 (2,381)||5,262 (2,470)||45.6 [−311.6, 402.9]||0.8|
| Total sleep time (minutes)||422.7 (72.8)||418 (76.1)||414.2 (65.8)||−4.5 [−13.4, 4.4]||0.32|
| Sleep efficiency (%)||84.2 (8.1)||83.7 (7.8)||83.5 (7.4)||−0.3 [−1.0, 0.4]||0.4|
| Insomnia Severity Indexb||14.6 (5.7)||12.2 (6.7)||11.8 (6.3)||−1.24 [−2.43, −0.05]||0.04|
| Graded Chronic Pain Scalec||4.7 (2)||4.4 (2)||4.1 (1.8)||−0.13 [−0.47, 0.20]||0.4|
| Self-Efficacyd||6.7 (1.7)||7.1 (1.2)||7.3 (1.6)||0.28 [−0.07, 0.63]||0.1|
| Acceptance of Sleep Difficultiese||24 (8.3)||30.4 (7.7)||28.9 (6.8)||2.49 [0.91, 4.09]||0.002|
| Total sleep time (minutes)||410.1 (75.8)||426.5 (51.7)||425.9 (52)||8.9 [−2.9, 20.8]||0.14|
| Sleep efficiency (%)||78.3 (9.9)||81.6 (8.6)||81 (9.8)||1.7 [−0.06, 3.38]||0.06|
| Sleep qualityf||5.4 (1.2)||5.5 (1.3)||6 (1.2)||0.3 [0.02, 0.58]||0.04|
Text messages designed for the Mobile Interventional Physical Activity Targeted Intervention (MobMPATI) study
|>5% increase in step count ‘Increase’||within ± 5% step count ‘Maintenance’||>5% decreased in step count ‘Decline’|
|Congratulations <name> on making progress. You have increased by roughly <steps> steps per day over this week since the <month> count||Good job <name> on maintaining your step count. You are doing now <steps> steps per day!||Hi <name>! You decreased by about <steps> steps from the last contact. But not to worry-keep making small changes and you will soon be making progress again.|
|Congratulations <name> ! You have continued to increase your physical activity! That's fantastic progress ….. keep up the hard work!||Hi <name>! Well done for maintaining your steps. You are making fantastic progress. Keep it up!||Hi <name>! You decreased slightly from last week but keep making small changes and you will soon be back on track and making progress again.|
|And yet again <name>, you manage to keep increasing your activity :-) This is fantastic. Make sure you reward yourself by taking time out to do something fun this week!||And yet again you manage to maintain your activity. This is fantastic - make sure you reward yourself by taking time out to do something fun this week!||Hi <name>! We realize that increasing your activity can be really tough. We'll contact you shortly to try and provide some helpful suggestions…|