Abstract
Background:
Obesity is associated with early mortality and chronic disease among adults with intellectual disability (ID), yet there is a paucity of effective weight management interventions for this population.
Objective/Hypothesis:
This pilot study examined a tailored intervention on weight loss, waist circumference, A1c, and lipid profile among adults with ID.
Methods:
Obese adults (BMI ≥ 30 kg/m2) with mild to moderate ID were randomized to an intervention (n = 17) or comparison group (n = 18) for a 24-week trial. All participants completed health-related questionnaires and clinic visits. Participants in the intervention group received access to an online weight management platform that assisted them in monitoring their diet and physical activity along with weekly coaching calls (weeks 1–12) that were tapered off to calls every other week (weeks 12–24). The comparison group completed questionnaires and clinic visits, but did not receive access to the online platform or calls. Differences in weight, waist circumference, percent body fat, A1c, lipid profile were assessed at baseline and at week 24.
Results:
The intervention group reduced body weight by an average of 2.7% (−2.6 kg; p = 0.02) and waist circumference by 3.4% (−3.89 cm; p = 0.02) versus the comparison. There were no statistically significant group by time interactions observed among other variables.
Conclusion:
Adults with ID who received the intervention were able to maintain or slightly reduce their body weight and waist circumference after the 24-week intervention. Despite not achieving the targeted sample size, the pilot study findings serve as a basis for developing accessible weight management interventions for people with ID.
Keywords: Telehealth, Weight Loss, Intellectual disability, Obesity
Introduction
The prevalence of obesity among adults in the United States has consistently increased over the past two decades from 30% in 1999–2000 to 42% in 2017–2018.1 Obesity rates among adults with intellectual disability (ID) have been reported to be higher compared to the national average for adults without ID.2–4 In general, obesity is associated with early mortality, chronic disease (e.g., type 2 diabetes, cardiovascular disease, and hypertension), and socioeconomic burden.5–7 Despite the increased rates of obesity and health risks for individuals with ID, few studies have conducted comprehensive weight-loss/weight management interventions in this population.8
Adults with ID may possess unique conditions or barriers that require certain accommodations to assist with managing weight. For instance, adults with ID may experience transportation issues, increased reliance on caregivers, and financial constraints.9–13 In addition to these logistical barriers, adults with ID may express physiological differences and lifestyle factors that result in increased difficulty with physical activity or weight management behaviors.14Compared to people without ID, adults with ID experience much poorer continuity of care and health maintenance, including receiving fewer routine and preventive health services, such as blood pressure checks, cholesterol and cancer screenings.15Given the complexity of these barriers, efficacious weight management interventions may benefit from an individually tailored behavioral weight loss intervention initiated in a clinic and delivered remotely.
A blended onsite and remotely delivered intervention for adults with ID can utilize information and communication technology (ICT) to facilitate management of healthy behaviors by eliminating logistical barriers to healthcare services (e.g., transportation and cost).16ICT has been successfully used in the general population with positive effects on weight loss and weight management.17,18 ICT also provides a cost-effective way to deliver weight management services to adults with ID living in geographically diverse communities, and allows service providers to capture successful strategies that can be archived and used with other individuals with ID. While individuals with ID use internet and ICT devices at rates similar to the general population,19there is a paucity of studies that have implemented technology and remote health interventions for this population.
One intervention that did use ICT provided tablet computers to adolescents with ID for tracking their health behaviors. Participants in this study reported using the tablet on 83% of study days and experienced a 3–4% loss of body weight.20Though there are ongoing investigations comparing ICT weight-loss interventions with in-person care for adults with ID,16we are unaware of other randomized controlled trials that have reported the effectiveness of an ICT weight-loss intervention in comparison to usual care, such as regular clinic visits, for weight loss in adults with ID. The lack of mobile, ICT weight-loss interventions for adults with ID contrasts with the expanding number of remote interventions for the general population.
In addition to overall weight loss, other physiological biomarkers of metabolic health are likely to change along with body weight.21These biomarkers, such as A1c and cholesterol, are associated with metabolic diseases that are related to obesity (i.e., type 2 diabetes and cardiovascular disease).22,23 While weight loss is typically the primary outcome in weight loss/weight management studies, changes in biomarkers can confirm reduction of metabolic syndrome risk factors (e.g., high blood glucose, triglycerides, cholesterol). Although some previous weight loss interventions for adults with ID demonstrated efficacious weight loss,8,24 these studies did not report the effects of other biomarkers associated with obesity such as A1c and lipid profiles. Therefore, the purpose of this pilot study was to determine the effects of a tailored ICT health intervention on body weight and other metabolic risk factors in adults with ID. The health ICT intervention was referred to as the Personalized Online Weight and Exercise Response System for Individuals with Intellectual Disability, or POWERSforID. The POWERSforID intervention was based on a previously validated conceptual model that combined approaches from other well-established models, including the stages of change model, person-centered theory, socio-ecological model, and two previous studies that employed the POWERS system (additional details regarding the design of the system have been previously published).25Results from this pilot investigation of POWERSforID will be beneficial towards larger investigations into ICT based interventions for adults with ID.
Methods
Procedure
This study was conducted from 2016 to 2019 in Colorado Springs, Colorado. We recruited a convenience sample of adults (ages 18–50 years) with mild or moderate intellectual disability who received their healthcare in a clinic that specialized in providing services to adults with ID. Diagnosis of an intellectual disability was confirmed from either the participants’ medical record or through clinic staff. Ethical permission was obtained through the Institutional Review Board at a major university associated with the study design. All participants provided informed consent. In compliance with ethical guidelines, participants without a legally authorized representative were also included. Additional conceptual background and methodological guidelines for this study have been previously published.25
Eligible participants resided in either a group home, host home, family home, or lived independently. Individuals with ID who met the following criteria were eligible for the study: a) Body Mass Index ≥ 30 kg/m2; b) diagnosis of mild or moderate ID; c) 18 to 50 years of age; d) medical provider approval to participate in a weight loss program; e) has a regular caregiver willing to participate in a support role unless the participant serves as own legal guardian; and f) has access to a computer with internet throughout the week. Exclusion criteria included: a) already participating in a weight loss program; b) having a medical condition that prevents safe participation; and c) demonstrating insufficient capacity to consent.
Randomization Scheme
De-identified patient demographic data obtained from the intervention site was used to conduct simulation checks for balanced group assignment. The simulation checks accounted for age, race, and ethnicity. After simulation checks, a simple randomization scheme was utilized with each randomization assignment placed in a sealed envelope prior to any participants providing consent. Once a participant consented, a health coach opened the appropriate envelope to reveal group assignment. All other research staff, besides the health coach, were blinded to group assignment. A CONSORT diagram that displays recruitment, randomization and dropout is presented in Figure 1.
Figure 1.
CONSORT FLOW diagram displaying the screening, enrolment, randomization to treatment, and sample sizes at each stage of the project.
Intervention
All participants from the intervention and comparison group, and if applicable, their caregivers, were asked to complete pre/mid/post (weeks 1, 12, and 24) clinic visits for clinical measurements and in-person assessments by the health coach. At the clinic visits, participants randomized to the comparison group received laboratory measures, a consultation with a medical professional, and a discussion about overall health management strategies with the health coach. Participants also completed questionnaires regarding diet, physical activity, and perceived health.
Participants in the intervention group received the same treatment as participants in the comparison group but also received additional materials and weight management assistance through the POWERSforID platform and calls with the health coach. For participants in the intervention, answers to questionnaires completed at the clinic visits were used for populating the POWERSforID interface with computer-generated health strategies. Health strategies were pre-assigned to populate based on participants’ responses on the questionnaire. For example, if a participant indicated he or she did not enjoy consuming fruits and vegetables, POWERSforID would populate a suggestion for alternative methods to consume fruits and vegetables, such as a smoothie beverage. Or, if a participant indicated infrequent levels of physical activity, the system would populate physical activity suggestions. This also provided the health coach with individualized strategies to discuss with the participant. Additional details regarding the computer-generated health strategies were previously published.25Questionnaires were not analyzed for differences between groups.
Upon completion of the baseline questionnaire, participants in the intervention group received a portable scale to monitor their weight from home on a weekly basis and a pedometer to track weekly steps. The POWERSforID intervention was comprised of weekly (weeks 1–12) and biweekly (weeks 13–24) coaching calls via phone. During these calls, the coach used motivational interviewing techniques adapted for use with individuals with ID26 to help each participant set diet goals (e.g., reduce consumption of 1–3 ultra-processed carbohydrate target foods or beverages; consume lower calorie foods) and goals for increased physical activity (e.g., engage in exercise of choice 3 times per week for 30 minutes; during free-time engage in walking in place of sedentary behavior(s)). Barriers to meeting their goals were discussed and tailored strategies were offered to overcome these barriers. Calls were between the coach and participant, although a caregiver joined the call if the participant chose that option.
Participants were asked to use the POWERSforID online journal to track physical activity (frequency, duration, and type of activity) and target food consumption (frequency, quantity, and specific item), which the health coach monitored by using an independent login. Caregivers, if applicable, also had access to their participant’s POWERSforID account to provide support as needed and often participated in coaching calls. The POWERSforID platform contained links to educational resources and a discussion board for participants and caregivers to communicate directly with the coach in between coaching calls. The POWERSforID platform also used information from the diet and physical activity questionnaires to auto-generate customized health tips for each participant.
The comparison group did not receive the telehealth intervention (coaching sessions and POWERSforID website access). However, all participants received educational materials from the National Center on Health, Physical Activity and Disability (NCHPAD) upon completion of the 24-week study period. Participants also received NCHPAD’s Information Specialist’s contact information for advice on exercise and nutrition. See Neumeier et al. (2017)25for more detailed information about the intervention protocol.
Measures
Outcomes included adherence, anthropometric measurements (height, weight, BMI, waist circumference, body fat), vital signs (blood pressure, heart rate), and biomarkers (lipid profile, A1c). Participants were required to visit their medical provider at baseline, week 12, and week 24 for assessments. Biomarkers were monitored at baseline and week 24. Participant characteristics were described at baseline (age, sex, race, marital status, residence, staff support, and employment status). Adherence was defined as the participanťs level of engagement throughout the intervention, and variables included the frequency of participation in clinic visits, phone calls, and website use.
Physiological Measures
A qualified clinician used a physician’s scale to measure height and weight for the participants. Height and weight were used to calculate Body Mass Index (BMI) using the formula 703 × weight (lbs) / [height (in)]2. Imperial units were utilized for communication with the participant; units have been converted to the international system (SI) for this manuscript. Obesity was defined as a BMI equal to or greater than 30 kg/m2. Waist circumference was measured in inches using the gold standard clinical procedures (stand and place a tape measure around middle above hipbones and measure waist after breath expired) by a trained health professional. Body fat percentage was measured using a handheld bioelectrical impedance device (OMRON Fat Loss Monitor HBF-306C). Pre and post-intervention blood samples were drawn for assessing lipid profile including high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, and cholesterol.
Statistical Analysis
Sample size calculations were based on two previous studies that utilized a platform similar to POWERSforID but in populations with physical disability. Based on the previous studies and a moderate effect size of 0.62 and two-sided alpha of 0.05, the target sample for power of 0.8 was 35 participants per group.25Data analysis was performed by a statistician who was blinded to the intervention group assignment. Baseline characteristics were compared using t test (for continuous variables) or X2 test (for categorical data) to identify differences in demographics between the intervention and comparison groups. Descriptive statistics were used to assess participant attrition, session attendance, and instrument completion. Wilcoxon signed-rank tests were used to assess whether outcome variables differed between baseline and post-intervention in each group. Mixed-effect regression models with random effects were conducted to examine group, time, and group-by-time intervention effects on body weight, BMI, waist circumference, and lipid profile. The models included potential confounders such as participants’ age, sex, race, and caregiver or staff support (yes or no). Sample sizes varied based on outcome measure due to missed clinic visits, not arriving to the clinic in a fasted state, equipment error (i.e., body fat via BIA), or participant exercising the ability to forego a measurement (i.e., blood draw). Statistical significance was determined at .05 alpha level, and all analyses were conducted using SPSS version 24.0.
Results
Description of Participants
Table 1 displays baseline participant demographics. Thirty-five participants who enrolled in the program were randomly assigned either to the intervention (n = 17) or the comparison group (n = 18). The mean age of participants was 34.6 years (SD = 5.7, range = 24.6 – 48.9 years). There was a slightly higher percentage of males (54.3%) than females (45.7%). The mean BMI was 37.7 kg/m2 (SD = 6.8). Twenty-one participants (60%), 11 in the intervention and 10 in the comparison, had caregiver and/or staff support. There were no adverse events reported for any participants.
Table 1.
Sample demographics
| Characteristics | Total ( N = 35) | Intervention (n = 17) | Control ( n = 18) |
|---|---|---|---|
| Age, mean ± SD, yrs | 34.6 ± 5.7 | 34.4 ± 4.7 | 34.8 ± 6.7 |
| BMI, ± SD, kg/m2 | 37.7 ± 6.8 | 37.3± 6.9 | 38.0 ± 6.9 |
| Sex, n (%) | |||
| Men | 19 (54.3) | 9 (52.9) | 9 (55.6) |
| Women | 16 (45.7) | 8 (47.1) | 8 (44.4) |
| Race, n (%) | |||
| Non-white | 11 (31.4) | 5 (29.4) | 6 (33.3) |
| White | 24 (68.6) | 12 (70.6) | 12 (66.7) |
| Marital status, n (%) | |||
| Never married | 30 (85.7) | 16 (94.1) | 14 (77.8) |
| Residence, n (%) | |||
| Own home | 5 (14.3) | 3 (17.6) | 2 (11.1) |
| Family home | 17 (48.6) | 9 (52.9) | 8 (44.4) |
| Agency apt./ host home | 13 (37.1) | 5 (29.4) | 8 (44.4) |
| Have support staff, n (%) | |||
| Yes | 21 (60.0) | 10 (58.8) | 11 (61.1) |
| Employment, n (%) | |||
| Yes | 11 (31.5) | 6 (35.3) | 5 (27.8) |
Apt = apartment.
Adherence
All participants were asked to visit the clinic four times throughout the course of the study (weeks 1, 6, 12, and 24) to meet with the health coach and complete all the measures appropriate for the respective time point of the intervention. Overall, 71% of participants complied with four clinic visits; 11% visited three times; 14% visited two times; and 3% visited only one time. On average, the compliance rate of coaching calls for the intervention group was 61% with a range from 22% to 83%. The total number of coaching calls during weeks 1 to 12 ranged from 4 to 10 sessions per participant (M = 8.2, SD = 1.9), and the mean length of each call was 17.6 minutes (SD = 5.5) with a range from 2 to 48 minutes. The number of biweekly coaching calls during weeks 13 to 24 ranged from 0 to 6 sessions (M = 2.9, SD = 2.3). There were five participants (29%) who had no biweekly calls as they withdrew from the study after the first 12-week phase of the intervention. The mean length of the biweekly coaching calls was 11.7 minutes (SD = 6.5) with a range from 4 to 28 minutes. Three participants withdrew from the comparison group (two during weeks 1–12), and six from the intervention (three during weeks 1–12). The major reason why participants withdrew from the intervention group or missed calls included conflicting priorities with other obligations (e.g., school, work, and family). Other reasons included feeling overwhelmed by the requirements of the study (e.g., frequency of exercise, reducing target food consumption, tracking progress).
Anthropometric and Lipid Outcomes
Table 2 shows the results of anthropometric and lipid profile outcome measures by group based on Wilcoxon signed-rank tests within pre- and post-intervention. Sample size for each outcome measure is presented. Sample sizes vary due to missed clinic visits, not arriving to the clinic visit in a fasting state, equipment error (i.e., body fat via BIA), or participant exercising the right to refrain from a measurement (i.e., blood draw). At post-intervention, the intervention group had a statistically significant reduction with a medium effect size in body weight (-2.7%, 95% confidence interval [CI] -4.6 to -0.8, effect size r = 0.49, p = 0.009,), BMI (-2.9%, 95% CI -1.6 to -0.3, r = 0.49, p = 0.009) and waist circumference (-3.4%,95% CI -6.3to -1.5, r = 0.51, p = 0.012). The comparison group also exhibited reductions in body weight, BMI, and waist circumference but these differences were not statistically significant. Overall, 6 participants (3 from each group) reduced body weight by greater than 5%. Regarding lipid profile, the intervention group had a marginally significant reduction in LDL (12.8%, 95% CI -25.0 to 3.0, r = 0.41, p = 0.066) at post-intervention, but no other changes to metabolic markers were significant. The comparison group did not reduce LDL, but had a statistically significant reduction in A1c (-3.4%, p = 0.006,), and no other variables were significant.
Table 2.
Anthropometric and lipid profile outcome measures by group at pre-intervention and post-intervention
| Outcome Variables | Intervention | Control | ||||||
|---|---|---|---|---|---|---|---|---|
|
| ||||||||
| n | Pre | Post | Effect Size | n | Pre | Post | Effect Size | |
| Body weight, kg | 14 | 96.27 ±19.05 | 93.65 ±20.02 | 0.49** | 15 | 100.79 ±16.69 | 99.58 ±17.68 | 0.19 |
| BMI, kg/m2 | 14 | 35.42 ±4.80 | 34.46 ±5.45 | 0.49** | 15 | 37.59 ±6.61 | 37.20 ±7.36 | 0.18 |
| Waist circum., cm | 12 | 115.36 ±12.94 | 111.48 ±12.22 | 0.51* | 14 | 115.58 ±12.99 | 111.62 ±19.27 | 0.30 |
| Body fat, % | 11 | 35.36 ±8.03 | 34.72 ±8.75 | 0.31 | 10 | 35.67 ± 7.09 | 34.36 ± 7.72 | 0.21 |
| Systolic BP, mmHg | 13 | 121.77 ±10.33 | 122.23 ±10.37 | 0.05 | 15 | 121.53 ±14.39 | 119.73 ±11.53 | 0.05 |
| Diastolic BP, mmHg | 13 | 79.54 ±6.17 | 77.38 ±8.19 | 0.10 | 15 | 79.67 ±9.29 | 79.33 ±8.05 | 0.07 |
| A1C, (%) | 10 | 5.76 ±0.76 | 5.46 ±0.52 | 0.34 | 11 | 5.54 ±0.40 | 5.35 ±0.36 | 0.58* |
| Lipid profile | ||||||||
| HDL, mg/dL | 10 | 51.00 ±16.68 | 52.40 ±16.68 | 0.22 | 11 | 45.18 ±8.55 | 44.09 ±6.44 | 0.23 |
| LDL, mg/dL | 10 | 103.20 ±36.36 | 90.00 ±40.69 | 0.41¥ | 11 | 105.82 ±36.98 | 108.73 ±26.42 | 0.27 |
| Triglycerides, mg/dL | 10 | 193.20 ±208.62 | 150.90 ±104.92 | 0.03 | 11 | 131.73 ±90.51 | 132.27 ±70.07 | 0.05 |
| Cholesterol, mg/dL | 10 | 182.50 ±42.23 | 179.60 ±30.05 | 0.21 | 11 | 176.18 ±36.74 | 176.64 ±28.44 | 0.09 |
BMI = body mass index; BP = blood pressure; HDL = high density lipoprotein; LDL = low density lipoprotein. Values expressed as mean ± SD.
p < .05
P< .01.
p < .07
Intent-to-treat analyses were employed using mixed models and controlling for age, sex, race, and staff support on each outcome measures (Tables 3 & 4). There were statistically significant group by time interaction effects between the comparison and intervention groups on body weight and waist circumference. The intervention group had greater reductions in body weight (p = 0.02) and waist circumference (p = 0.02) than the comparison. There was also a marginally statistically significant reduction in BMI (p = 0.05) over time. There were no significant group by time interaction effects between the control and intervention group on lipid profile.
Table 3.
Mixed modelsa on anthropometric outcome measures by group, time and group by time interaction
| Outcome Variables | Week 12 Visitb | Week 24 Visit | Groupc | Week 12 × Groupc | Week 24 × Groupc | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| B | SE | P | B | SE | P | B | SE | P | B | SE | P | B | SE | P | |
| Anthropometrics | |||||||||||||||
| Body weight, kg | −0.40 | 1.13 | 0.73 | 2.28 | 1.85 | 0.23 | −16.39 | 15.89 | 0.31 | −3.04 | 1.64 | 0.07 | −6.48 | 2.69 | 0.02* |
| BMI, kg/m2 | −0.10 | 0.19 | 0.60 | 0.19 | 0.29 | 0.52 | −0.25 | 2.28 | 0.91 | −0.45 | 0. 27 | 0.11 | −0.84 | 0.41 | 0.05 |
| Waist circumference, cm | 0.45 | 0.34 | 0.19 | 2.26 | 0.55 | <0.01* | −0.33 | 1.68 | 0.85 | −0.45 | 0.48 | 0.35 | −2.69 | 0.78 | 0.02* |
| Body fat, % | −0.71 | 1.28 | 0.59 | −0.00 | 1.03 | 1.0 | −1.41 | 1.73 | 0.42 | −0.27 | 1.69 | 0.87 | −0.72 | 1.40 | 0.61 |
BMI = body mass index.
Models controlled for age, sex, ethnicity, and staff support (intent to treat)
Baseline as reference group
Control group as reference
indicates significant time and group effects or time × group interaction effects
Table 4.
Mixed modelsa on lipid profile outcome measures by group, time and group by time interaction
| Outcome Variables | Time | Groupb | Time × Groupb | ||||||
|---|---|---|---|---|---|---|---|---|---|
| B | SE | P | B | SE | P | B | SE | P | |
| Lipid profile | |||||||||
| HDL, mg/dL | −1.01 | 1.58 | 0.53 | −0.44 | 4.24 | 0.92 | 2.25 | 2.31 | 0.34 |
| LDL, mg/dL | 3.96 | 5.50 | 0.48 | 3.57 | 11.38 | 0.76 | −12.49 | 8.11 | 0.14 |
| Triglyceride, mg/dL | −15.00 | 39.94 | 0.71 | 48.39 | 45.30 | 0.29 | 37.47 | 58.61 | 0.53 |
| Cholesterol, mg/dL | 1.22 | 6.90 | 0.86 | 14.89 | 13.25 | 0.27 | −4.06 | 9.83 | 0.68 |
HDL = high density lipoprotein; LDL = low density lipoprotein.
Models controlled for age, sex, ethnicity, and staff support (intent to treat)
Control group as the reference group
Discussion
This pilot study examined changes in weight loss and biomarkers of cardiovascular health in response to an internet and phone-based weight management intervention which was remotely conducted with adults with ID. Results from this pilot randomized controlled trial demonstrated that a remote, telehealth intervention had a small, but statistically significant effect on reduction in body weight and BMI in adults with ID, though efficacy was not determined due to the small sample size.
Our primary aim was to determine if a remote, tailored, less intensive weight loss program (e.g., did not require extensive time to and from a facility, monitoring of daily caloric intake, having only a few health goals, etc.) could demonstrate changes in weight and lipids. Participants with access to the POWERSforID platform and coaching calls exhibited statistically greater weight loss compared to participants that did not. However, the overall reduction in weight loss for the intervention (2.7%) did not meet the clinically meaningful threshold of weight loss (5–10%) of initial body weight recommended for people with obesity.27–30
Another positive finding was that participants who completed the intervention had marginally significant improvements in their lipid profile (LDL decreased by 13%, 103.20 mg/dL at baseline to 90.00 mg/dL at week 24). This finding was encouraging for two reasons: 1) the magnitude of the finding was comparable with what might be expected from exercise in the general adult population (~10% reductions of LDL after exercise);37and 2) the magnitude was acceptable considering that the participants had a normal LDL level at baseline. Nevertheless, this positive finding requires further study to determine if other lipid measures could be improved (e.g., total cholesterol, HDL) with some combination of diet and exercise.
A recent meta-analysis identified a paucity of multi-component (i.e., diet, physical activity, and behavior change) weight management interventions for individuals with ID, and the interventions included in the analysis were not more effective than no treatment control conditions.8Due to the completion of questionnaires, randomization, and laboratory visits, the comparison group for this pilot study likely received additional care compared to traditional usual care settings, and weight loss has been demonstrated in minimal intervention control groups across multiple weight loss trials.31However, there are certainly clinical trials that have reported favorable changes in weight loss for adults with ID, with weight reductions ranging from 4.4% to 7.0%,16,32,33 which are larger than the reduction of 2.7% that was found in the present study. However, those studies were of longer duration and/or more intense in terms of the intervention requirements. While weight loss may vary with the intensity of an intervention with greater intensity potentially resulting in greater effect,34 the unique circumstances encountered by adults with ID must still be considered. Additional adaptations or newer technologies, such as videoconferencing, may provide an increased level of intervention while also assisting with barriers encountered by adults with ID.
Overall, weight loss interventions for adults with ID have not demonstrated results comparable to weight loss interventions for the general population. This is likely due to the unique barriers encountered by adults with ID rather than due to deficiencies in design of the relevant weight loss interventions. While weight loss results for this intervention may not be clinically significant, this study provides some support for a tailored, adaptable weight loss program for adults with ID. Though the POWERSforID platform attempted to fill this gap, additional tailoring of interventions may prove beneficial. For example, the current platform was only accessible through a website and was only offered remotely. Future interventions may be delivered by more accessible means, such as text messages, videoconferencing, or participants may be allowed to select the type of intervention best suited to their lifestyle.
Although the intervention included both a website platform and coaching calls, the primary driving force behind the intervention was likely the interactive coaching calls. Even though the website platform was tailored specifically for individuals with ID, compliance data for website platform use (24%), defined as at least one weekly logging of activity, was less than for the coaching calls (61%). A previous qualitative investigation of this same trial documented that 40% of participants did not access the website at all.36Those participants reported that they experienced usability issues such as frustration with the technology, slow internet speed, and the perceived need for support from caregivers.36Overall, additional emphasis on caregiver participation and interaction with the health coach may aid recruitment and retention in health interventions for adults with ID.
There are a few limitations to this study. First, the sample size was relatively small and did not achieve the sample size originally calculated based on a moderate effect size and threshold for clinically significant weight loss. Future weight loss interventions with this population should place emphasis upon recruitment methods. Though individuals with ID have higher rates of obesity compared to the general population, there is a lack of published weight loss interventions for this population. Though this pilot study made effort to alleviate burdens related to participation in a weight loss intervention, additional strategies for removal of barriers, increased recruitment, and enhanced retention are strongly suggested for future interventions. Second, the pilot study only included adults with mild to moderate level of ID, as confirmed by medical records or clinic staff, and all participants were recruited from a single location. Thus, it cannot be generalized to adults with severe ID. An increase in sample size, recruitment methods and geographic variation may help establish the generalizability of POWERSforID, which is currently limited for the aforementioned reasons. Third, the intervention period may have been too short to evaluate long-term weight maintenance and prevention of weight regain. However, we felt that it was necessary to keep the trial within a time frame that would permit greater adherence to the protocol and reduce the risk of dropout or non-compliance with a longer trial. Even across a period of 24 weeks, the dropout rate (35%) was substantial and participants’ engagement with the website was low.
Conclusion
Given the paucity of interventions resulting in clinically significant weight loss for individuals with ID and the increased rates of obesity in this population, this study could serve as a basis for future weight loss interventions, particularly where remote delivery may prove most effective. People with ID may experience enormous difficulty with transportation and finding appropriate health promotion programs that meet their specific needs. Therefore, remote-delivered interventions with computer-assisted guidance for trained health coaches, who understand the limitations associated with tailoring a weight management program for individuals with ID, hold promise for reaching this largely underserved population. Findings from this pilot study provide initial support for additional remote-delivered interventions, but inclusion of additional participants across multiple clinics is necessary prior to broad utilization of this intervention. Further study of the use of new e-Health mobile applications with tailored communication (text, video, voice) and social networking features hold promise for promoting health and weight maintenance for people with ID.
Acknowledgments:
The authors would like to thank all TRE Research Center staff, as well as the participants and their caregivers, and everyone that assisted with this project.
Funding:
The contents of this paper were developed under a grant #90RT5020–03-00 from the United States Department of Health and Human Services, Administration for Community Living (ACL), National Institute on Disability, Independent Living, and Rehabilitation Research (NIDLRR) to the University of Illinois at Chicago for the Rehabilitation Research and Training Center on Developmental Disabilities and Health.
In addition, multiyear private research funds have also been provided from the University of Colorado’s Coleman Institute for Cognitive Disabilities in the Office of University of Colorado President.
Footnotes
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