Feasibility and Effectiveness of Nutritional Telemonitoring for Home Care Clients: A Pilot Study

Feasibility and Effectiveness of Nutritional Telemonitoring for Home Care Clients: A Pilot Study Abstract Background and Objectives Undernutrition has unfavorable consequences for health and quality of life. This pilot study aimed to evaluate the feasibility of a telemonitoring intervention to improve the nutritional status of community-dwelling older adults. Research Design and Methods The study involved a one-group pretest post-test design, complemented by a qualitative study. The 3-month intervention included 20 Dutch home care clients aged >65 years and consisted of nutritional telemonitoring, television messages, and dietary advice. A process evaluation provided insight into intervention delivery and acceptability. Changes in behavioral determinants, diet quality, appetite, nutritional status, physical functioning, and quality of life were assessed. Results Researchers and health care professionals implemented the intervention as intended and health care professionals accepted the intervention well. However, 9 participants dropped out, and participants’ acceptance was low, mainly due to the low usability of the telemonitoring television channel. Adherence to the telemonitoring measurements was good, although participants needed more help from nurses than anticipated. Participants increased compliance to several Dutch dietary guidelines and no effects on nutritional status, physical functioning, and quality of life were found. Discussion and Implications Successful telemonitoring of nutritional parameters in community-dwelling older adults starts with optimal usability and acceptability by older adults and their health care professionals. This pilot study provides insight into how to optimize telemonitoring interventions for older adults for maximum impact on behavior and health. Nutrition and feeding issues, Information technology, Preventive medicine/care/services Undernutrition can be defined as “a state resulting from lack of intake or uptake of nutrition that leads to altered body composition (decreased fat-free mass) and body cell mass leading to diminished physical and mental function and impaired clinical outcome from disease” (Cederholm et al., 2017). It has unfavorable consequences for the health and quality of life of older adults including falls, fractures, infections, immune dysfunctions, prolonged hospitalization, and death (Morley, 2012; Rasheed & Woods, 2013). Furthermore, the estimated annual medical costs related to undernutrition among older adults are €1.5 billion in the Netherlands (Freijer et al., 2013). It is estimated that 11%–35% of Dutch community-dwelling older adults are undernourished, with the highest prevalence observed among home care clients (Schilp et al., 2012). Despite the serious consequences and high prevalence, older adults and health care professionals lack awareness of the problem, and it is still unclear how the nutritional status of community-dwelling older adults can be monitored (Ziylan, Haveman-Nies, van Dongen, Kremer, & de Groot, 2015). Feasible and effective approaches are necessary to signal undernutrition and diminish its risks. To monitor nutritional status among community-dwelling older adults, eHealth can be used. eHealth is defined as “health services and information delivered or enhanced through the internet and related technologies” (Eysenbach, 2001). EHealth may contribute to high-quality, efficient, and accessible health care (Bashshur, Shannon, Krupinski, & Grigsby, 2013; Eysenbach, 2001). The benefits of eHealth for older adults include preventing or delaying the onset of disability, improving communication, and enhancing self-management (Schulz et al., 2015). About 70% of Dutch older adults are willing to adopt eHealth if it enables them to live independently (Doekhie, de Veer, Rademakers, Schellevis, & Francke, 2014). Older adults are more willing to adopt eHealth if they are convinced of the benefits such as increased safety, perceived usefulness, or a reduced burden on family caregivers (Peek et al., 2014, 2016). Despite its potential, eHealth is still not widely implemented within health care (Bashshur et al., 2013). There is no evidence for the effective use of eHealth to prevent undernutrition among older adults, although Kraft and colleagues (2012) used a telemonitoring system to measure body weight and adherence to oral nutritional supplements among undernourished older adults. They found no significant effects, which was probably due to the small sample size. The study did not include a structured process evaluation, so insight into the feasibility of the telemonitoring intervention is lacking. More research is needed to investigate whether eHealth is a feasible and effective approach to prevent undernutrition in community-dwelling older adults. Before conducting a large-scale effectiveness study, we performed a pilot study in which we implemented the PhysioDom Home Dietary Intake Monitoring (HDIM) intervention among 20 home care clients. The intervention lasted 3 months and consisted of telemonitoring nutritional status, appetite, diet quality, and physical activity. The study included a process and effect evaluation. The process evaluation was guided by the theories of Saunders, Evans, and Joshi (2005) and Steckler and Linnan (Saunders et al., 2005; Steckler & Linnan, 2002). While Saunders provides a practical framework on how to develop a process evaluation plan, Steckler and Linnan present a framework of relevant process indicators. We chose to study the process indicators of reach, fidelity, and dose, as these are regarded as the minimum set of indicators to consider (Saunders et al., 2005). We also included the indicator acceptability, because this is important for understanding whether older adults will adopt eHealth and for how implementation might be scaled (Peek et al., 2014). We aimed to study the feasibility of this eHealth intervention, to test its potential impact on nutritional and health outcomes, and to further refine the intervention and/or study procedures. Research Design and Methods Study Design The 3-month pilot study ran from August 2015 until November 2015 and followed a one group pretest post-test design, complemented by a qualitative study. We measured process and effect outcomes, and telemonitoring measurements were carried out as part of the intervention. The study received approval from the Medical Ethical Committee of Wageningen University & Research and is registered at Clinical-Trials.gov (identifier NCT03211845). Participants The study included 20 home care clients. To qualify for participation, individuals needed to be 65 years or older, receive home care from care organization Zorggroep Noordwest-Veluwe (ZNWV), and live in the municipality of Nunspeet in the Netherlands. Individuals were excluded from participation if they were cognitively impaired (Mini-Mental State Examination (MMSE) < 20), received terminal care, would receive home care for less than 3 months, had a visual impairment which made them unable to watch the television screen, and/or had a physical impairment that prevented them to use the telemonitoring system properly. Three nurses from ZNWV handed out invitation brochures to eligible home care clients. Home care clients who were interested to participate were visited by researchers to receive more information, ask questions, sign the informed consent and be screened on eligibility criteria. Intervention Telemonitoring and feedback Participants measured their body weight weekly and kept track of their steps 1 week per month. Five participants also measured their blood pressure bi-weekly or monthly, depending on the advice of their nurse. For these telemonitoring measurements, participants received a weighing scale (A&D, type UC-411PBT-C), a pedometer (A&D, type UW-101), and a sphygmomanometer (A&D, type UA-767PBT-CI), respectively. Participants were instructed to weigh themselves without heavy clothes and shoes and after voiding. Participants had to measure their blood pressure at a fixed time during the day while being silent and sitting up straight and still in a chair with their left arm resting on the table. Participants also filled out the Dutch Healthy Diet Food Frequency Questionnaire (DHD-FFQ) about diet quality according to the Dutch dietary guidelines for a healthy diet (van Lee et al., 2016), the Simplified Nutritional Appetite Questionnaire (SNAQ) about appetite (Wilson et al., 2005), and the Mini Nutritional Assessment Short Form (MNA-SF) about nutritional status (Rubenstein, Harker, Salva, Guigoz, & Vellas, 2001). These questionnaires were filled out at the start of the intervention during an interview with a researcher and 2 months later. To improve the fit with the intervention, participants could choose before the start of the study how to fill out these questionnaires this second time: 10 participants chose to do this during a telephone interview with a researcher, 6 chose for a project tablet, and 4 chose to use their own PC. Participants could view the measurements results on their television. Their television contained an additional channel that included menus for an agenda, messages, measurement results, and dietary and physical activity advice. This channel was created through a set-top box connected to the participant’s television and the internet (either Ethernet or 3G connection). In this way, participants also received one to three nontailored and computer-tailored television messages per day about nutrition and physical activity. The nontailored messages were underpinned by behavior change techniques such as belief selection and consciousness raising (Eldredge, Parcel, Kok, Gottlieb, & Fernández, 2011). The computer-tailored messages contained the results of the DHD-FFQ and advice on how to improve diet quality and physical activity. The results of the telemonitoring measurements were also sent via the television set-top box to a website for health care professionals and were checked weekly by three nurses. Alerts were activated if a participant was undernourished or risked undernutrition, had lost more than 5% of baseline body weight, and/or had a body mass index (BMI) below 20 kg/m2 (21 kg/m2 for participants with chronic obstructive pulmonary disease). Alerts were also activated by a BMI above 30 kg/m2 and by a new blood pressure measurement if applicable. When the nurse received an alert, she contacted the participant to investigate the causes and to provide appropriate guidance. If the participant was at risk for undernutrition, she advised on how to improve protein and energy intake and gave a brochure with advice. If the participant was undernourished, she referred the participant to a GP or dietician. Nurses were aided in this decision making process by decision trees and could consult a dietician from the care organization about nutritional advice for participants. Implementation Researchers ensured optimal implementation of the intervention by 10 preparatory meetings with the involved health care professionals and/or a board member of ZNWV. In these meetings, the researchers discussed with the health care professionals how the intervention could connect to their needs, how it could fit within their working procedures, and which target group would benefit most from the intervention. The researchers also provided training sessions for health care professionals and participants. In a 4-hr training session, the researchers taught the health care professionals how to use the project website and the decision trees. The dietician gave a workshop for the nurses on how to provide nutritional advice to participants. The 45-min training for participants took place at their homes after the television channel and devices had been installed. The training followed the guided practice method (Eldredge et al., 2011), in which participants were prompted to rehearse with the intervention materials and received feedback from the researchers. Finally, a telephone helpdesk was available for the health care professionals and participants. If needed, researchers paid additional visits to participants to provide extra training. Measurements Process measures Reach was defined as “The proportion of intended target audience that participates in the intervention” (Saunders et al., 2005). Reach was studied by collecting sociodemographic characteristics of participants during a structured interview at the beginning of the study (see Effect measures section), by keeping a logbook of reasons for drop-out, and by keeping a logbook during the recruitment period. Fidelity was defined as “The extent to which the intervention was delivered as planned” (Steckler & Linnan, 2002), and was assessed by keeping a logbook of intervention activities. Dose received was defined as “The extent to which participants actively engage with, interact with, are receptive to, and/or use materials or recommended resources. It is a characteristic of the target audience and it assesses the extent of engagement of participants with the intervention” (Steckler & Linnan, 2002). Dose received was measured by a logbook of the data traffic of the television channel and a paper questionnaire including the question “How often do you watch the television channel (‘daily’, ‘often’, ‘sometimes’, ‘occasionally’, or ‘never’).” Acceptability was defined as “Participant’s satisfaction with the program and interactions with staff and/or investigators” (Saunders et al., 2005), and was measured with paper questionnaires for participants and health care professionals, in interviews with participants, and in an evaluation meeting with the nurses and board member. The questionnaire for participants contained statements about satisfaction (“I am satisfied with the project in general/with the nutrition component/with the physical activity component”), usability (“Weighing/Using the pedometer/Using the sphygmomanometer/Using the tablet is easy”), the television channel (“The TV channel is attractive,” “The TV channel is clear,” “It is easy to get an overview on the TV channel/to navigate on the TV channel”), the training (“The training was clear,” “The project has been explained sufficiently to me”), and the helpdesk (“The helpdesk was accessible,” “I am satisfied with the helpdesk”). The questionnaire for health care professionals contained the statements “I am satisfied with the project in general,” “I felt involved in the project,” “The project is useful to monitor nutritional status of clients/to monitor physical activity of clients,” “I am satisfied with the project website,” “I am satisfied with how the alerts worked,” “The project is a good addition to the care for clients,” and “The project fits well with my daily tasks.” The statements were answered on a 5-point Likert scale ranging from “totally disagree” to “totally agree.” Semi-structured interviews with participants were conducted face-to-face at the end of the study and guided by a topic list (Supplementary Appendix 1). The evaluative meeting with the nurses and board member took place at the end of the study. Effect measures Effect measurements included dietary and physical activity behavior, diet quality, appetite, nutritional status, body weight, physical functioning, and quality of life. They were performed at the beginning and at the end of the study unless stated otherwise. The baseline characteristics age, sex, body weight, current diagnoses, education level, living situation, civil status, cognitive function (measured by the MMSE (Folstein, Folstein, & McHugh, 1975) and type of received home care were recorded at the beginning of the study during an interview. Behavioral determinants of healthy eating and physical activity were measured with a self-developed paper questionnaire. The questionnaire contained statements about self-monitoring, goal-setting, social support, knowledge, awareness, outcome expectations, attitude, social norms, self-efficacy, and intention, to be answered on a 5-point Likert scale ranging from totally disagree to totally agree, except for the knowledge statements which were answered with true, false, or unsure. Statements were derived from validated questionnaires (Lorig et al., 1996; Nothwehr, Dennis, & Wu, 2007; Wójcicki, White, & McAuley, 2009) or were based on previous research (Duijzer et al., 2014; Hooft van Huysduynen, 2014). Diet quality was assessed with the DHD-FFQ (van Lee et al., 2016). This questionnaire contains 29 items and has an outcome score from 0 to 80, with a higher score meaning better compliance to the Dutch dietary guidelines (Gezondheidsraad, 2006). Eight subscores indicate compliance to the Dutch dietary guidelines for the intake of vegetables, fruit, fish, alcohol, saturated fat, trans-fat, salt, and dietary fiber. An additional score indicates compliance to the Dutch guidelines for physical activity. For this study, we added scores for compliance to guidelines for protein and vitamin D intake as well (Gezondheidsraad, 2006; Nordic Council of Ministers, 2014). The DHD-FFQ was administered at the beginning and 2 months after the start of the intervention. Appetite and nutritional status were measured with the SNAQ and Mini Nutritional Assessment (MNA), respectively, during a face-to-face interview at the beginning and end of the study (Vellas et al., 1999; Wilson et al., 2005). Body weight was measured by researchers to the nearest 0.1 kg, whereby participants were asked to take off their shoes and heavy clothes such as jackets. Level of independence of activities of daily living and physical functioning were assessed with the Katz-15 paper questionnaire and Short Physical Performance Battery test (SPPB), respectively (Guralnik et al., 1994; Laan et al., 2014). Finally, quality of life was measured with the Short Form 36 paper questionnaire (SF36; Aaronson et al., 1998; Ware & Sherbourne, 1992). Data Analysis Quantitative data were analyzed with SPSS version 22. Process outcomes were analyzed using descriptive statistics by showing percentages or frequency of the response categories. Effect outcomes were analyzed with paired t-tests or, in case of non-normality, a Wilcoxon signed-rank test. Qualitative data were analyzed with ATLAS.ti (version 7.0). Interview recordings were transcribed verbatim. Three researchers coded the first two interviews together to reach consensus on how to code the interviews consistently. The remaining interviews were coded separately by two researchers after which the assigned codes were checked for agreement. In case of disagreements in coding, the researchers discussed until an agreement about a final coding scheme was reached. Finally, codes were reviewed, and main themes were identified. Results Reach Thirty-six home care clients were invited to participate in the study, of whom 20 agreed to participate. Reasons to decline participation included being deterred by the intervention’s technology, perceiving the study as time-consuming or too burdensome, and not fitting the local culture. Reasons to participate included the extra attention for nutrition and health and the reassurance that nurses would be present for support during the study. Participants were, on average, 81 years old. The majority was female (75%), had an intermediate education level (65%), and lived alone (60%; Table 1). Nine participants dropped out of the study because of physical or mental health problems (n = 4), dislike of the project (n = 2), or difficulties with the television channel and measurement devices (n = 3). Four participants dropped out before the start of the intervention, two directly after the at-home training, and the remaining three after 3, 4, and 8 weeks of intervention, respectively. One drop-out was already very concerned about his health, expecting that the project would reinforce this in a negative way. Another drop-out felt burdened to watch the television channel daily, “did not want to feel bound by anything at her age,” and did not want to change dietary habits. Two other drop-outs mentioned becoming “nervous” of the technology, one indicating that she was also too impatient for it. Drop-outs were significantly lower educated and slightly, but not significantly, older than participants who completed the study (Table 1). Table 1. Baseline Characteristics of All Participants of the PhysioDom HDIM Pilot Study (N = 20), Participants Who Completed the Study (n = 11), and Participants Who Dropped Out (n = 9) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) Note: BMI = body mass index; MMSE = Mini-Mental State Examination; SD = standard deviation. aLow education level: primary school or less; intermediate level of education: secondary professional education or vocational school; High education level: higher vocational education, university. *p < .05 (significant difference). View Large Table 1. Baseline Characteristics of All Participants of the PhysioDom HDIM Pilot Study (N = 20), Participants Who Completed the Study (n = 11), and Participants Who Dropped Out (n = 9) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) Note: BMI = body mass index; MMSE = Mini-Mental State Examination; SD = standard deviation. aLow education level: primary school or less; intermediate level of education: secondary professional education or vocational school; High education level: higher vocational education, university. *p < .05 (significant difference). View Large Fidelity Researchers implemented the intervention as intended, although there were some small deviations from the intervention protocol. For example, participants should have done the telemonitoring measurements themselves, but nurses had to assist some of them as participants had difficulties using the television channel. As reasons for this participant mentioned having health or mental problems, being of old age, and having a lack of technical skills. Although the nurses implemented the intervention according to the protocol, they also mentioned implementation barriers such as a high workload, lack of support from colleagues, understaffing, and changes within the care organization. Dose Received Participants who completed the study adhered to 80% of the required weight measurements, 53% of the step count measurements, 84% of blood pressure measurements, and read 43% of the television messages. Two of the 11 respondents watched the television channel “daily,” four did this “often,” and the rest did this “sometimes,” “occasionally,” or “never.” Acceptability Half of the participants agreed that they were satisfied with the project and one-third was neutral about this statement (Figure 1). The interviews showed that participants were pleased with insight into their health status, the emphasis on the importance of a healthy lifestyle, and the attention that they received throughout the project (theme positive aspects of the intervention). On the other hand, some participants perceived the project as a heavy burden on their daily lives, were puzzled by the message that they risked undernutrition, and found the dietary advice not personal enough or sounding “unfriendly” (themes experience with dietary advice and risk of undernutrition). Furthermore, most participants agreed that they received sufficient explanation, training, and help throughout the project (Figure 1). The majority of participants found it easy to weigh themselves (89%), to use the pedometer (70%), to use the sphygmomanometer (100%), and to use the tablet (67%; Figure 1). The positive evaluation of the usability of these devices was also confirmed by the interviews, although some participants found the pedometer not sensitive enough and mentioned that the weighing scale did not connect well to the television channel (theme user-friendliness of devices). The usability of the television channel was rated lower in terms of attractiveness, clarity, ease to navigate, and ease to obtain overview (Figure 1). The interviews showed that participants experienced stress and frustration when the channel did not work properly and that some participants became insecure about their own capabilities (themes TV channel and stress and frustration). Figure 1. View largeDownload slide Acceptability aspects of the PhysioDom HDIM pilot study as rated by participants. Two participants filled out the questionnaire together and were considered as n = 1. Figure 1. View largeDownload slide Acceptability aspects of the PhysioDom HDIM pilot study as rated by participants. Two participants filled out the questionnaire together and were considered as n = 1. The nurses and dieticians were generally satisfied with the project and felt involved. They found the project useful to monitor physical activity and nutritional status, but they were less satisfied about the project website and how the alerts worked. Opinions were divided about whether the project was a good addition to care for their clients and about whether it fits with their daily tasks. Effect Outcomes Although the main goal of the effect measurements was to test the study procedures and not to show impact, we found significant improvements in scores for compliance to dietary guidelines for fish (M1 − M0 = 2.8 [1.3, 4.3]), dietary fiber (M1 − M0 = 1.2 [0.01, 2.4]), protein (M1 − M0 = 5.2 [2.6, 7.7]), and vitamin D (M1 − M0 = 0.6 [0.1, 1.1]). We found a significant decrease in the score for compliance to the guideline for saturated fatty acids (M1 − M0 = −3.3 [−5.9, −0.6]; Table 2). We did not find significant changes in most of the behavioral determinants, body weight, SNAQ score, MNA score, Katz-15 score, SPPB score, and SF-36 scores. In fact, significant negative changes were found for the following determinants of physical activity behavior: goal setting, expectations, and social norms (Supplementary Appendix 2). Table 2. Means at Baseline and at Follow-Up of Effect Outcomes of the PhysioDom HDIM Pilot Study (N = 11) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Note: BMI = body mass index; CI: confidence interval; DHD = Dutch Healthy Diet; IQR = interquartile range; SD = standard deviation; MCS = mental component score; Mdn = median; MNA = Mini Nutritional Assessment; PCS = physical component score; SF36 = Short Form 36; SNAQ = simplified nutritional assessment score; SPPB = short physical performance battery. aAnalyzed with paired t-test. bAnalyzed with Wilcoxon signed-rank test. cOnly a p-value is reported as this outcome was analyzed with McNemar’s test. d1 missing value. *p < 0.05. View Large Table 2. Means at Baseline and at Follow-Up of Effect Outcomes of the PhysioDom HDIM Pilot Study (N = 11) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Note: BMI = body mass index; CI: confidence interval; DHD = Dutch Healthy Diet; IQR = interquartile range; SD = standard deviation; MCS = mental component score; Mdn = median; MNA = Mini Nutritional Assessment; PCS = physical component score; SF36 = Short Form 36; SNAQ = simplified nutritional assessment score; SPPB = short physical performance battery. aAnalyzed with paired t-test. bAnalyzed with Wilcoxon signed-rank test. cOnly a p-value is reported as this outcome was analyzed with McNemar’s test. d1 missing value. *p < 0.05. View Large Discussion and Implications The main aim of this study was to evaluate the feasibility of a telemonitoring intervention that aimed to improve the nutritional status of community-dwelling older adults. Researchers and health care professionals implemented the intervention as intended. However, almost half of the participants dropped out of the study and participants were only moderately satisfied with the intervention. Participants significantly increased their compliance to the Dutch dietary guidelines for dietary fiber, fish, protein, and vitamin D, but these findings should be confirmed in a larger scale study given the study design and the high drop-out rate. We found one other pilot study that focused on telemonitoring of nutritional status of community-dwelling older adults (Kraft et al., 2012). The drop-out rate in this study was even higher than in our study. Drop-out in our study was due to poor usability of the intervention, dislike of the intervention, and mental and physical health problems. Furthermore, participants who completed the study were moderately satisfied with the intervention. Participants mentioned that the intervention put a burden on their daily lives, that the dietary advice was not sufficiently customized despite the computer-tailoring, and that the usability of the television channel was poor. Perceived system complexity and compatibility have previously been identified as determinants of adoption of eHealth (de Veer et al., 2015; Griebel, Sedlmayr, Prokosch, Criegee-Rieck, & Sedlmayr, 2013; Peeters, de Veer, van der Hoek, & Francke, 2012). Also, other determinants encountered in this study are known as barriers for eHealth adoption, such as a low education level, lack of technological skills, and old age (Hage, Roo, van Offenbeek, & Boonstra, 2013). This implies that intervention developers should take these determinants into account to ensure that their intervention fits with the needs and capabilities of the end-user. One way of doing this is participatory design in which the end-user and other stakeholders are involved, preferably in continuous evaluation cycles so that technology is shaped through its usage (Greenhalgh et al., 2015; van Gemert-Pijnen et al., 2011). Although time and resource consuming, this contributes to effective eHealth applications that are better adopted by their end-users (Devlin et al., 2016; Greenhalgh et al., 2015). Health care professionals were more positive about the intervention than participants and agreed that it was a good tool to monitor the nutritional status of home care clients. They felt involved and were enthusiastic about the project. Regionally based, motivated intervention staff is a facilitating factor for implementing eHealth (Hage et al., 2013). However, health care professionals also mentioned implementation barriers such as poor usability of the project website, poor fit with their daily tasks, a high workload, lack of support from colleagues, and understaffing. This is in line with literature (Fleuren, Wiefferink, & Paulussen, 2004; Hage et al., 2013) and shows the need for a well thought-out implementation strategy for telemonitoring interventions that takes these factors into account. This can be done by, for example, involving health care professionals in designing eHealth, a priori assessment of required and available resources, and integrating eHealth applications into usual care workflows (Ross, Stevenson, Lau, & Murray, 2016). The effect evaluation showed that participants significantly increased their compliance to guidelines for the intake of fish, dietary fiber, protein, and vitamin D. These results suggest that a telemonitoring intervention with computer-tailored dietary advice can possibly improve diet quality. This is confirmed by other studies in older populations (van den Berg, Schumann, Kraft, & Hoffmann, 2012) and in younger and/or chronically diseased populations (Broekhuizen, Kroeze, van Poppel, Oenema, & Brug, 2012; Kelly, Reidlinger, Hoffmann, & Campbell, 2016). Given the study design and the high drop-out rate, a larger-scale effectiveness study should confirm whether nutritional telemonitoring combined with tailored nutrition education can improve diet quality and nutritional status. However, this study indicates that the current intervention is not suitable for large-scale implementation and should first be improved to enhance acceptability by the intended end-users. Based on this study, we have suggestions for improvement of this intervention and for future research on this topic in general. Firstly, easy-to-use and appealing technology is a prerequisite, and even more important in case of older adults who may have little computer experience. In this study, participants could become anxious and frustrated when having difficulties with the technology, sometimes even blaming themselves. A friendly and reliable helpdesk is imperative to keep participants motivated and to report any issue to improve usability. Secondly, attention should be paid to proper communication before and during the study. Literature shows that providing sufficient information and discussing expectations contributes to the decision to participate in a study about telehealth (Sanders et al., 2012). For example, we learned to avoid emphasizing the “technology side” of the study during recruitment as this could deter individuals to participate. Instead, we emphasized the goal of the study, namely improving nutrition and physical activity, and we used easy terms for the intervention technology. Furthermore, in case of a prototype or a pilot intervention like in this study, communication that the technology may still need improvement helps to create the right expectations among participants. Thirdly, the telemonitoring intervention should be less intensive. Although we hypothesized that one to three television messages per day would keep participants engaged, they felt rather burdened to watch the television channel daily. It should be kept in mind that older adults without computer experience may feel more easily burdened by technology than older adults with computer experience or younger adults. Fourthly, drop-out mainly occurred before and shortly after the start of the intervention. The time between signing the informed consent and the start of the intervention could be up to 3 months. To minimize drop-out, the time between application and start of the study should preferably be short so that changing circumstances, such as health, do not interfere with a willingness to participate. Furthermore, it could be helpful to have additional helpdesk resources available at the start of the intervention to support participants in their learning curve to use the telemonitoring technology. Finally, it is necessary to improve communication about undernutrition. Some participants were distressed to hear that they risked undernutrition and were not able to deal with this message well. This may have to do with little awareness of this problem among older adults and requires communication that is sensitive to this (Beelen et al., 2017). In conclusion, successful telemonitoring of nutritional parameters in community-dwelling older adults starts with optimal acceptability by the intended users and their health care professionals. Considering the low acceptability and high drop-out rate, this telemonitoring intervention needs to be more user-friendly and less intensive to have an impact on behavior and health. Supplementary Material Supplementary data are available at The Gerontologist online. Funding This work was supported by the European Union (CIP-ICT-PSP-2013–7). Conflict of Interest The authors have no conflict of interest to declare. Acknowledgments The authors thank all participants, health care professionals, and the board of care organization Zorggroep Noordwest-Veluwe. We also thank Mirthe Groothuis for her contribution to the study coordination and data collection, Jan Harryvan for the technical support, and Marlot Kruisbrink for her contribution to the data collection and data analysis of this study. References Aaronson , N. K. , Muller , M. , Cohen , P. D. , Essink-Bot , M. L. , Fekkes , M. , Sanderman , R. , … Verrips , E . ( 1998 ). 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Dutch nutrition and care professionals’ experiences with undernutrition awareness, monitoring, and treatment among community-dwelling older adults: A qualitative study . BMC Nutrition , 1 , 38 . doi: 10.1186/s40795-015-0034-6 Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Gerontologist Oxford University Press

Feasibility and Effectiveness of Nutritional Telemonitoring for Home Care Clients: A Pilot Study

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© The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Abstract

Abstract Background and Objectives Undernutrition has unfavorable consequences for health and quality of life. This pilot study aimed to evaluate the feasibility of a telemonitoring intervention to improve the nutritional status of community-dwelling older adults. Research Design and Methods The study involved a one-group pretest post-test design, complemented by a qualitative study. The 3-month intervention included 20 Dutch home care clients aged >65 years and consisted of nutritional telemonitoring, television messages, and dietary advice. A process evaluation provided insight into intervention delivery and acceptability. Changes in behavioral determinants, diet quality, appetite, nutritional status, physical functioning, and quality of life were assessed. Results Researchers and health care professionals implemented the intervention as intended and health care professionals accepted the intervention well. However, 9 participants dropped out, and participants’ acceptance was low, mainly due to the low usability of the telemonitoring television channel. Adherence to the telemonitoring measurements was good, although participants needed more help from nurses than anticipated. Participants increased compliance to several Dutch dietary guidelines and no effects on nutritional status, physical functioning, and quality of life were found. Discussion and Implications Successful telemonitoring of nutritional parameters in community-dwelling older adults starts with optimal usability and acceptability by older adults and their health care professionals. This pilot study provides insight into how to optimize telemonitoring interventions for older adults for maximum impact on behavior and health. Nutrition and feeding issues, Information technology, Preventive medicine/care/services Undernutrition can be defined as “a state resulting from lack of intake or uptake of nutrition that leads to altered body composition (decreased fat-free mass) and body cell mass leading to diminished physical and mental function and impaired clinical outcome from disease” (Cederholm et al., 2017). It has unfavorable consequences for the health and quality of life of older adults including falls, fractures, infections, immune dysfunctions, prolonged hospitalization, and death (Morley, 2012; Rasheed & Woods, 2013). Furthermore, the estimated annual medical costs related to undernutrition among older adults are €1.5 billion in the Netherlands (Freijer et al., 2013). It is estimated that 11%–35% of Dutch community-dwelling older adults are undernourished, with the highest prevalence observed among home care clients (Schilp et al., 2012). Despite the serious consequences and high prevalence, older adults and health care professionals lack awareness of the problem, and it is still unclear how the nutritional status of community-dwelling older adults can be monitored (Ziylan, Haveman-Nies, van Dongen, Kremer, & de Groot, 2015). Feasible and effective approaches are necessary to signal undernutrition and diminish its risks. To monitor nutritional status among community-dwelling older adults, eHealth can be used. eHealth is defined as “health services and information delivered or enhanced through the internet and related technologies” (Eysenbach, 2001). EHealth may contribute to high-quality, efficient, and accessible health care (Bashshur, Shannon, Krupinski, & Grigsby, 2013; Eysenbach, 2001). The benefits of eHealth for older adults include preventing or delaying the onset of disability, improving communication, and enhancing self-management (Schulz et al., 2015). About 70% of Dutch older adults are willing to adopt eHealth if it enables them to live independently (Doekhie, de Veer, Rademakers, Schellevis, & Francke, 2014). Older adults are more willing to adopt eHealth if they are convinced of the benefits such as increased safety, perceived usefulness, or a reduced burden on family caregivers (Peek et al., 2014, 2016). Despite its potential, eHealth is still not widely implemented within health care (Bashshur et al., 2013). There is no evidence for the effective use of eHealth to prevent undernutrition among older adults, although Kraft and colleagues (2012) used a telemonitoring system to measure body weight and adherence to oral nutritional supplements among undernourished older adults. They found no significant effects, which was probably due to the small sample size. The study did not include a structured process evaluation, so insight into the feasibility of the telemonitoring intervention is lacking. More research is needed to investigate whether eHealth is a feasible and effective approach to prevent undernutrition in community-dwelling older adults. Before conducting a large-scale effectiveness study, we performed a pilot study in which we implemented the PhysioDom Home Dietary Intake Monitoring (HDIM) intervention among 20 home care clients. The intervention lasted 3 months and consisted of telemonitoring nutritional status, appetite, diet quality, and physical activity. The study included a process and effect evaluation. The process evaluation was guided by the theories of Saunders, Evans, and Joshi (2005) and Steckler and Linnan (Saunders et al., 2005; Steckler & Linnan, 2002). While Saunders provides a practical framework on how to develop a process evaluation plan, Steckler and Linnan present a framework of relevant process indicators. We chose to study the process indicators of reach, fidelity, and dose, as these are regarded as the minimum set of indicators to consider (Saunders et al., 2005). We also included the indicator acceptability, because this is important for understanding whether older adults will adopt eHealth and for how implementation might be scaled (Peek et al., 2014). We aimed to study the feasibility of this eHealth intervention, to test its potential impact on nutritional and health outcomes, and to further refine the intervention and/or study procedures. Research Design and Methods Study Design The 3-month pilot study ran from August 2015 until November 2015 and followed a one group pretest post-test design, complemented by a qualitative study. We measured process and effect outcomes, and telemonitoring measurements were carried out as part of the intervention. The study received approval from the Medical Ethical Committee of Wageningen University & Research and is registered at Clinical-Trials.gov (identifier NCT03211845). Participants The study included 20 home care clients. To qualify for participation, individuals needed to be 65 years or older, receive home care from care organization Zorggroep Noordwest-Veluwe (ZNWV), and live in the municipality of Nunspeet in the Netherlands. Individuals were excluded from participation if they were cognitively impaired (Mini-Mental State Examination (MMSE) < 20), received terminal care, would receive home care for less than 3 months, had a visual impairment which made them unable to watch the television screen, and/or had a physical impairment that prevented them to use the telemonitoring system properly. Three nurses from ZNWV handed out invitation brochures to eligible home care clients. Home care clients who were interested to participate were visited by researchers to receive more information, ask questions, sign the informed consent and be screened on eligibility criteria. Intervention Telemonitoring and feedback Participants measured their body weight weekly and kept track of their steps 1 week per month. Five participants also measured their blood pressure bi-weekly or monthly, depending on the advice of their nurse. For these telemonitoring measurements, participants received a weighing scale (A&D, type UC-411PBT-C), a pedometer (A&D, type UW-101), and a sphygmomanometer (A&D, type UA-767PBT-CI), respectively. Participants were instructed to weigh themselves without heavy clothes and shoes and after voiding. Participants had to measure their blood pressure at a fixed time during the day while being silent and sitting up straight and still in a chair with their left arm resting on the table. Participants also filled out the Dutch Healthy Diet Food Frequency Questionnaire (DHD-FFQ) about diet quality according to the Dutch dietary guidelines for a healthy diet (van Lee et al., 2016), the Simplified Nutritional Appetite Questionnaire (SNAQ) about appetite (Wilson et al., 2005), and the Mini Nutritional Assessment Short Form (MNA-SF) about nutritional status (Rubenstein, Harker, Salva, Guigoz, & Vellas, 2001). These questionnaires were filled out at the start of the intervention during an interview with a researcher and 2 months later. To improve the fit with the intervention, participants could choose before the start of the study how to fill out these questionnaires this second time: 10 participants chose to do this during a telephone interview with a researcher, 6 chose for a project tablet, and 4 chose to use their own PC. Participants could view the measurements results on their television. Their television contained an additional channel that included menus for an agenda, messages, measurement results, and dietary and physical activity advice. This channel was created through a set-top box connected to the participant’s television and the internet (either Ethernet or 3G connection). In this way, participants also received one to three nontailored and computer-tailored television messages per day about nutrition and physical activity. The nontailored messages were underpinned by behavior change techniques such as belief selection and consciousness raising (Eldredge, Parcel, Kok, Gottlieb, & Fernández, 2011). The computer-tailored messages contained the results of the DHD-FFQ and advice on how to improve diet quality and physical activity. The results of the telemonitoring measurements were also sent via the television set-top box to a website for health care professionals and were checked weekly by three nurses. Alerts were activated if a participant was undernourished or risked undernutrition, had lost more than 5% of baseline body weight, and/or had a body mass index (BMI) below 20 kg/m2 (21 kg/m2 for participants with chronic obstructive pulmonary disease). Alerts were also activated by a BMI above 30 kg/m2 and by a new blood pressure measurement if applicable. When the nurse received an alert, she contacted the participant to investigate the causes and to provide appropriate guidance. If the participant was at risk for undernutrition, she advised on how to improve protein and energy intake and gave a brochure with advice. If the participant was undernourished, she referred the participant to a GP or dietician. Nurses were aided in this decision making process by decision trees and could consult a dietician from the care organization about nutritional advice for participants. Implementation Researchers ensured optimal implementation of the intervention by 10 preparatory meetings with the involved health care professionals and/or a board member of ZNWV. In these meetings, the researchers discussed with the health care professionals how the intervention could connect to their needs, how it could fit within their working procedures, and which target group would benefit most from the intervention. The researchers also provided training sessions for health care professionals and participants. In a 4-hr training session, the researchers taught the health care professionals how to use the project website and the decision trees. The dietician gave a workshop for the nurses on how to provide nutritional advice to participants. The 45-min training for participants took place at their homes after the television channel and devices had been installed. The training followed the guided practice method (Eldredge et al., 2011), in which participants were prompted to rehearse with the intervention materials and received feedback from the researchers. Finally, a telephone helpdesk was available for the health care professionals and participants. If needed, researchers paid additional visits to participants to provide extra training. Measurements Process measures Reach was defined as “The proportion of intended target audience that participates in the intervention” (Saunders et al., 2005). Reach was studied by collecting sociodemographic characteristics of participants during a structured interview at the beginning of the study (see Effect measures section), by keeping a logbook of reasons for drop-out, and by keeping a logbook during the recruitment period. Fidelity was defined as “The extent to which the intervention was delivered as planned” (Steckler & Linnan, 2002), and was assessed by keeping a logbook of intervention activities. Dose received was defined as “The extent to which participants actively engage with, interact with, are receptive to, and/or use materials or recommended resources. It is a characteristic of the target audience and it assesses the extent of engagement of participants with the intervention” (Steckler & Linnan, 2002). Dose received was measured by a logbook of the data traffic of the television channel and a paper questionnaire including the question “How often do you watch the television channel (‘daily’, ‘often’, ‘sometimes’, ‘occasionally’, or ‘never’).” Acceptability was defined as “Participant’s satisfaction with the program and interactions with staff and/or investigators” (Saunders et al., 2005), and was measured with paper questionnaires for participants and health care professionals, in interviews with participants, and in an evaluation meeting with the nurses and board member. The questionnaire for participants contained statements about satisfaction (“I am satisfied with the project in general/with the nutrition component/with the physical activity component”), usability (“Weighing/Using the pedometer/Using the sphygmomanometer/Using the tablet is easy”), the television channel (“The TV channel is attractive,” “The TV channel is clear,” “It is easy to get an overview on the TV channel/to navigate on the TV channel”), the training (“The training was clear,” “The project has been explained sufficiently to me”), and the helpdesk (“The helpdesk was accessible,” “I am satisfied with the helpdesk”). The questionnaire for health care professionals contained the statements “I am satisfied with the project in general,” “I felt involved in the project,” “The project is useful to monitor nutritional status of clients/to monitor physical activity of clients,” “I am satisfied with the project website,” “I am satisfied with how the alerts worked,” “The project is a good addition to the care for clients,” and “The project fits well with my daily tasks.” The statements were answered on a 5-point Likert scale ranging from “totally disagree” to “totally agree.” Semi-structured interviews with participants were conducted face-to-face at the end of the study and guided by a topic list (Supplementary Appendix 1). The evaluative meeting with the nurses and board member took place at the end of the study. Effect measures Effect measurements included dietary and physical activity behavior, diet quality, appetite, nutritional status, body weight, physical functioning, and quality of life. They were performed at the beginning and at the end of the study unless stated otherwise. The baseline characteristics age, sex, body weight, current diagnoses, education level, living situation, civil status, cognitive function (measured by the MMSE (Folstein, Folstein, & McHugh, 1975) and type of received home care were recorded at the beginning of the study during an interview. Behavioral determinants of healthy eating and physical activity were measured with a self-developed paper questionnaire. The questionnaire contained statements about self-monitoring, goal-setting, social support, knowledge, awareness, outcome expectations, attitude, social norms, self-efficacy, and intention, to be answered on a 5-point Likert scale ranging from totally disagree to totally agree, except for the knowledge statements which were answered with true, false, or unsure. Statements were derived from validated questionnaires (Lorig et al., 1996; Nothwehr, Dennis, & Wu, 2007; Wójcicki, White, & McAuley, 2009) or were based on previous research (Duijzer et al., 2014; Hooft van Huysduynen, 2014). Diet quality was assessed with the DHD-FFQ (van Lee et al., 2016). This questionnaire contains 29 items and has an outcome score from 0 to 80, with a higher score meaning better compliance to the Dutch dietary guidelines (Gezondheidsraad, 2006). Eight subscores indicate compliance to the Dutch dietary guidelines for the intake of vegetables, fruit, fish, alcohol, saturated fat, trans-fat, salt, and dietary fiber. An additional score indicates compliance to the Dutch guidelines for physical activity. For this study, we added scores for compliance to guidelines for protein and vitamin D intake as well (Gezondheidsraad, 2006; Nordic Council of Ministers, 2014). The DHD-FFQ was administered at the beginning and 2 months after the start of the intervention. Appetite and nutritional status were measured with the SNAQ and Mini Nutritional Assessment (MNA), respectively, during a face-to-face interview at the beginning and end of the study (Vellas et al., 1999; Wilson et al., 2005). Body weight was measured by researchers to the nearest 0.1 kg, whereby participants were asked to take off their shoes and heavy clothes such as jackets. Level of independence of activities of daily living and physical functioning were assessed with the Katz-15 paper questionnaire and Short Physical Performance Battery test (SPPB), respectively (Guralnik et al., 1994; Laan et al., 2014). Finally, quality of life was measured with the Short Form 36 paper questionnaire (SF36; Aaronson et al., 1998; Ware & Sherbourne, 1992). Data Analysis Quantitative data were analyzed with SPSS version 22. Process outcomes were analyzed using descriptive statistics by showing percentages or frequency of the response categories. Effect outcomes were analyzed with paired t-tests or, in case of non-normality, a Wilcoxon signed-rank test. Qualitative data were analyzed with ATLAS.ti (version 7.0). Interview recordings were transcribed verbatim. Three researchers coded the first two interviews together to reach consensus on how to code the interviews consistently. The remaining interviews were coded separately by two researchers after which the assigned codes were checked for agreement. In case of disagreements in coding, the researchers discussed until an agreement about a final coding scheme was reached. Finally, codes were reviewed, and main themes were identified. Results Reach Thirty-six home care clients were invited to participate in the study, of whom 20 agreed to participate. Reasons to decline participation included being deterred by the intervention’s technology, perceiving the study as time-consuming or too burdensome, and not fitting the local culture. Reasons to participate included the extra attention for nutrition and health and the reassurance that nurses would be present for support during the study. Participants were, on average, 81 years old. The majority was female (75%), had an intermediate education level (65%), and lived alone (60%; Table 1). Nine participants dropped out of the study because of physical or mental health problems (n = 4), dislike of the project (n = 2), or difficulties with the television channel and measurement devices (n = 3). Four participants dropped out before the start of the intervention, two directly after the at-home training, and the remaining three after 3, 4, and 8 weeks of intervention, respectively. One drop-out was already very concerned about his health, expecting that the project would reinforce this in a negative way. Another drop-out felt burdened to watch the television channel daily, “did not want to feel bound by anything at her age,” and did not want to change dietary habits. Two other drop-outs mentioned becoming “nervous” of the technology, one indicating that she was also too impatient for it. Drop-outs were significantly lower educated and slightly, but not significantly, older than participants who completed the study (Table 1). Table 1. Baseline Characteristics of All Participants of the PhysioDom HDIM Pilot Study (N = 20), Participants Who Completed the Study (n = 11), and Participants Who Dropped Out (n = 9) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) Note: BMI = body mass index; MMSE = Mini-Mental State Examination; SD = standard deviation. aLow education level: primary school or less; intermediate level of education: secondary professional education or vocational school; High education level: higher vocational education, university. *p < .05 (significant difference). View Large Table 1. Baseline Characteristics of All Participants of the PhysioDom HDIM Pilot Study (N = 20), Participants Who Completed the Study (n = 11), and Participants Who Dropped Out (n = 9) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) All participants (N = 20) Completers (n = 11) Drop-outs (n = 9) Age (years), mean ± SD 80.6 ± 8.4 77.4 ± 9.3 80.9 ± 12.5 Gender (male), n (%) 5 (25.0) 3 (27.3) 2 (22.2) BMI (kg/m2), mean ± SD 28.1 ± 3.9 28.8 ± 4.4 27.1 ± 3.1 Number of illnesses, mean ± SD 2.2 ± 1.3 2.1 ± 1.4 2.2 ± 1.3 MMSE score, mean ± SD 28.2 ± 2.2 28.3 ± 2.6 28.1 ± 1.6 Education level, n (%)a  Low 2 (10.0) 0 (0.0) 2 (22.2)  Intermediate 13 (65.0) 6 (54.5) 7 (77.8)  High 5 (25.0) 5 (45.5) 0 (0.0)* Living alone, n (%) 12 (60.0) 5 (45.5) 7 (77.8) Civil status, n (%)  Married 8 (40.0) 6 (54.5) 2 (20.0)  Unmarried 1 (5.0) 0 (0.0) 1 (11.1)  Divorced 1 (5.0) 1 (9.1) 0 (0.0)  Widow(er) 10 (50.0) 4 (36.4) 6 (60.0) Care type, n (%)  Domestic care 16 (80.0) 9 (81.8) 7 (77.8)  Personal care 10 (50.0) 5 (45.5) 5 (55.6)  Nursing care 1 (5.0) 1 (9.1) 0 (0.0)  Individual support 1 (5.0) 1 (9.1) 0 (0.0) Note: BMI = body mass index; MMSE = Mini-Mental State Examination; SD = standard deviation. aLow education level: primary school or less; intermediate level of education: secondary professional education or vocational school; High education level: higher vocational education, university. *p < .05 (significant difference). View Large Fidelity Researchers implemented the intervention as intended, although there were some small deviations from the intervention protocol. For example, participants should have done the telemonitoring measurements themselves, but nurses had to assist some of them as participants had difficulties using the television channel. As reasons for this participant mentioned having health or mental problems, being of old age, and having a lack of technical skills. Although the nurses implemented the intervention according to the protocol, they also mentioned implementation barriers such as a high workload, lack of support from colleagues, understaffing, and changes within the care organization. Dose Received Participants who completed the study adhered to 80% of the required weight measurements, 53% of the step count measurements, 84% of blood pressure measurements, and read 43% of the television messages. Two of the 11 respondents watched the television channel “daily,” four did this “often,” and the rest did this “sometimes,” “occasionally,” or “never.” Acceptability Half of the participants agreed that they were satisfied with the project and one-third was neutral about this statement (Figure 1). The interviews showed that participants were pleased with insight into their health status, the emphasis on the importance of a healthy lifestyle, and the attention that they received throughout the project (theme positive aspects of the intervention). On the other hand, some participants perceived the project as a heavy burden on their daily lives, were puzzled by the message that they risked undernutrition, and found the dietary advice not personal enough or sounding “unfriendly” (themes experience with dietary advice and risk of undernutrition). Furthermore, most participants agreed that they received sufficient explanation, training, and help throughout the project (Figure 1). The majority of participants found it easy to weigh themselves (89%), to use the pedometer (70%), to use the sphygmomanometer (100%), and to use the tablet (67%; Figure 1). The positive evaluation of the usability of these devices was also confirmed by the interviews, although some participants found the pedometer not sensitive enough and mentioned that the weighing scale did not connect well to the television channel (theme user-friendliness of devices). The usability of the television channel was rated lower in terms of attractiveness, clarity, ease to navigate, and ease to obtain overview (Figure 1). The interviews showed that participants experienced stress and frustration when the channel did not work properly and that some participants became insecure about their own capabilities (themes TV channel and stress and frustration). Figure 1. View largeDownload slide Acceptability aspects of the PhysioDom HDIM pilot study as rated by participants. Two participants filled out the questionnaire together and were considered as n = 1. Figure 1. View largeDownload slide Acceptability aspects of the PhysioDom HDIM pilot study as rated by participants. Two participants filled out the questionnaire together and were considered as n = 1. The nurses and dieticians were generally satisfied with the project and felt involved. They found the project useful to monitor physical activity and nutritional status, but they were less satisfied about the project website and how the alerts worked. Opinions were divided about whether the project was a good addition to care for their clients and about whether it fits with their daily tasks. Effect Outcomes Although the main goal of the effect measurements was to test the study procedures and not to show impact, we found significant improvements in scores for compliance to dietary guidelines for fish (M1 − M0 = 2.8 [1.3, 4.3]), dietary fiber (M1 − M0 = 1.2 [0.01, 2.4]), protein (M1 − M0 = 5.2 [2.6, 7.7]), and vitamin D (M1 − M0 = 0.6 [0.1, 1.1]). We found a significant decrease in the score for compliance to the guideline for saturated fatty acids (M1 − M0 = −3.3 [−5.9, −0.6]; Table 2). We did not find significant changes in most of the behavioral determinants, body weight, SNAQ score, MNA score, Katz-15 score, SPPB score, and SF-36 scores. In fact, significant negative changes were found for the following determinants of physical activity behavior: goal setting, expectations, and social norms (Supplementary Appendix 2). Table 2. Means at Baseline and at Follow-Up of Effect Outcomes of the PhysioDom HDIM Pilot Study (N = 11) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Note: BMI = body mass index; CI: confidence interval; DHD = Dutch Healthy Diet; IQR = interquartile range; SD = standard deviation; MCS = mental component score; Mdn = median; MNA = Mini Nutritional Assessment; PCS = physical component score; SF36 = Short Form 36; SNAQ = simplified nutritional assessment score; SPPB = short physical performance battery. aAnalyzed with paired t-test. bAnalyzed with Wilcoxon signed-rank test. cOnly a p-value is reported as this outcome was analyzed with McNemar’s test. d1 missing value. *p < 0.05. View Large Table 2. Means at Baseline and at Follow-Up of Effect Outcomes of the PhysioDom HDIM Pilot Study (N = 11) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Mean M0 ± SD Mean M1 ± SD Mean change (CI)a Z (p-value)b Diet quality (DHD index) Total score (0–80) 62.0 ± 5.9 61.1 ± 8.9 -0.9 (-8.8, 7.0) Subscores (0–10)  Vegetables 5.7 ± 1.8 7.3 ± 2.5 1.5 (-0.1, 3.2)  Fruit 8.2 ± 2.3 8.3 ± 2.8 0.2 (-1.7, 2.1)  Fish* 5.6 ± 2.8 8.4 ± 2.2 2.8 (1.3, 4.3)  Alcoholic drinks 9.6 ± 1.5 9.6 ± 1.5 0  Dietary fiber* 6.8 ± 2.0 8.0 ± 2.1 1.2 (0.01, 2.4)  Saturated fatty acids* 8.0 ± 3.0 4.7 ± 4.3 -3.3 (-5.9, -0.6)  Trans-fatty acids 10.0 ± 0.0 7.3 ± 1.4 0.25 c  Sodium (Mdn ± IQR) 8.5 ± 0.7 8.5 ± 3.2 -0.3 (0.75) Additional scores (0–10)  Physical activity (Mdn ± IQR) 4.0 ± 8.0 6.7 ± 10.0 -0.7 (0.50)  Protein* 2.2 ± 2.3 7.4 ± 3.4 5.2 (2.6, 7.7)  Vitamin D* 1.8 ± 0.6 2.4 ± 0.8 0.6 (0.1, 1.1) Nutritional status  SNAQ score (0–20) (Mdn ± IQR) 16.0 ± 2.0 16.0 ± 1.0 -0.4 (0.71)  MNA score (0–30) 26.0 ± 2.4 26.3 ± 2.6 0.3 (-1.2, 1.8)  Body weight (kg) 79.6 ± 13.1 80.0 ± 13.5 0.4 (-0.6, 4.5) Physical functioning  Katz-15 sum score (0–15)d (Mdn ± IQR) 3.0 ± 5.0 2.0 (6.5) -1.0 (0.33)  SPPB score (0–10) (Mdn ± IQR) 6.0 ± 4.0 6.0 ± 5.0 -1.9 (0.06) Quality of life  SF36 PCS (0–100)d 37.4 ± 12.5 36.3 ± 12.3 -1.1 (-4.6, 2.5)  SF36 MCS (0–100)d (Mdn ± IQR) 41.6 ± 12.9 44.9 ± 29.9 -1.9 (0.06) Note: BMI = body mass index; CI: confidence interval; DHD = Dutch Healthy Diet; IQR = interquartile range; SD = standard deviation; MCS = mental component score; Mdn = median; MNA = Mini Nutritional Assessment; PCS = physical component score; SF36 = Short Form 36; SNAQ = simplified nutritional assessment score; SPPB = short physical performance battery. aAnalyzed with paired t-test. bAnalyzed with Wilcoxon signed-rank test. cOnly a p-value is reported as this outcome was analyzed with McNemar’s test. d1 missing value. *p < 0.05. View Large Discussion and Implications The main aim of this study was to evaluate the feasibility of a telemonitoring intervention that aimed to improve the nutritional status of community-dwelling older adults. Researchers and health care professionals implemented the intervention as intended. However, almost half of the participants dropped out of the study and participants were only moderately satisfied with the intervention. Participants significantly increased their compliance to the Dutch dietary guidelines for dietary fiber, fish, protein, and vitamin D, but these findings should be confirmed in a larger scale study given the study design and the high drop-out rate. We found one other pilot study that focused on telemonitoring of nutritional status of community-dwelling older adults (Kraft et al., 2012). The drop-out rate in this study was even higher than in our study. Drop-out in our study was due to poor usability of the intervention, dislike of the intervention, and mental and physical health problems. Furthermore, participants who completed the study were moderately satisfied with the intervention. Participants mentioned that the intervention put a burden on their daily lives, that the dietary advice was not sufficiently customized despite the computer-tailoring, and that the usability of the television channel was poor. Perceived system complexity and compatibility have previously been identified as determinants of adoption of eHealth (de Veer et al., 2015; Griebel, Sedlmayr, Prokosch, Criegee-Rieck, & Sedlmayr, 2013; Peeters, de Veer, van der Hoek, & Francke, 2012). Also, other determinants encountered in this study are known as barriers for eHealth adoption, such as a low education level, lack of technological skills, and old age (Hage, Roo, van Offenbeek, & Boonstra, 2013). This implies that intervention developers should take these determinants into account to ensure that their intervention fits with the needs and capabilities of the end-user. One way of doing this is participatory design in which the end-user and other stakeholders are involved, preferably in continuous evaluation cycles so that technology is shaped through its usage (Greenhalgh et al., 2015; van Gemert-Pijnen et al., 2011). Although time and resource consuming, this contributes to effective eHealth applications that are better adopted by their end-users (Devlin et al., 2016; Greenhalgh et al., 2015). Health care professionals were more positive about the intervention than participants and agreed that it was a good tool to monitor the nutritional status of home care clients. They felt involved and were enthusiastic about the project. Regionally based, motivated intervention staff is a facilitating factor for implementing eHealth (Hage et al., 2013). However, health care professionals also mentioned implementation barriers such as poor usability of the project website, poor fit with their daily tasks, a high workload, lack of support from colleagues, and understaffing. This is in line with literature (Fleuren, Wiefferink, & Paulussen, 2004; Hage et al., 2013) and shows the need for a well thought-out implementation strategy for telemonitoring interventions that takes these factors into account. This can be done by, for example, involving health care professionals in designing eHealth, a priori assessment of required and available resources, and integrating eHealth applications into usual care workflows (Ross, Stevenson, Lau, & Murray, 2016). The effect evaluation showed that participants significantly increased their compliance to guidelines for the intake of fish, dietary fiber, protein, and vitamin D. These results suggest that a telemonitoring intervention with computer-tailored dietary advice can possibly improve diet quality. This is confirmed by other studies in older populations (van den Berg, Schumann, Kraft, & Hoffmann, 2012) and in younger and/or chronically diseased populations (Broekhuizen, Kroeze, van Poppel, Oenema, & Brug, 2012; Kelly, Reidlinger, Hoffmann, & Campbell, 2016). Given the study design and the high drop-out rate, a larger-scale effectiveness study should confirm whether nutritional telemonitoring combined with tailored nutrition education can improve diet quality and nutritional status. However, this study indicates that the current intervention is not suitable for large-scale implementation and should first be improved to enhance acceptability by the intended end-users. Based on this study, we have suggestions for improvement of this intervention and for future research on this topic in general. Firstly, easy-to-use and appealing technology is a prerequisite, and even more important in case of older adults who may have little computer experience. In this study, participants could become anxious and frustrated when having difficulties with the technology, sometimes even blaming themselves. A friendly and reliable helpdesk is imperative to keep participants motivated and to report any issue to improve usability. Secondly, attention should be paid to proper communication before and during the study. Literature shows that providing sufficient information and discussing expectations contributes to the decision to participate in a study about telehealth (Sanders et al., 2012). For example, we learned to avoid emphasizing the “technology side” of the study during recruitment as this could deter individuals to participate. Instead, we emphasized the goal of the study, namely improving nutrition and physical activity, and we used easy terms for the intervention technology. Furthermore, in case of a prototype or a pilot intervention like in this study, communication that the technology may still need improvement helps to create the right expectations among participants. Thirdly, the telemonitoring intervention should be less intensive. Although we hypothesized that one to three television messages per day would keep participants engaged, they felt rather burdened to watch the television channel daily. It should be kept in mind that older adults without computer experience may feel more easily burdened by technology than older adults with computer experience or younger adults. Fourthly, drop-out mainly occurred before and shortly after the start of the intervention. The time between signing the informed consent and the start of the intervention could be up to 3 months. To minimize drop-out, the time between application and start of the study should preferably be short so that changing circumstances, such as health, do not interfere with a willingness to participate. Furthermore, it could be helpful to have additional helpdesk resources available at the start of the intervention to support participants in their learning curve to use the telemonitoring technology. Finally, it is necessary to improve communication about undernutrition. Some participants were distressed to hear that they risked undernutrition and were not able to deal with this message well. This may have to do with little awareness of this problem among older adults and requires communication that is sensitive to this (Beelen et al., 2017). In conclusion, successful telemonitoring of nutritional parameters in community-dwelling older adults starts with optimal acceptability by the intended users and their health care professionals. Considering the low acceptability and high drop-out rate, this telemonitoring intervention needs to be more user-friendly and less intensive to have an impact on behavior and health. Supplementary Material Supplementary data are available at The Gerontologist online. Funding This work was supported by the European Union (CIP-ICT-PSP-2013–7). Conflict of Interest The authors have no conflict of interest to declare. Acknowledgments The authors thank all participants, health care professionals, and the board of care organization Zorggroep Noordwest-Veluwe. We also thank Mirthe Groothuis for her contribution to the study coordination and data collection, Jan Harryvan for the technical support, and Marlot Kruisbrink for her contribution to the data collection and data analysis of this study. References Aaronson , N. K. , Muller , M. , Cohen , P. D. , Essink-Bot , M. L. , Fekkes , M. , Sanderman , R. , … Verrips , E . ( 1998 ). 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The GerontologistOxford University Press

Published: Jun 1, 2018

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